FARMINGTON HILLS, Mich. and NASHVILLE, Tenn. – January 06, 2009 Robert Bosch North America, Inc. (RBNA) and Healthways Inc. (NASDAQ: HWAY) today announced the immediate availability of a single patent license under their joint patent portfolios related to remote health monitoring, automated diagnostics and health and disease management.

Both companies collectively hold extensive catalogs of patents in the U.S. and other countries in the area of remote health monitoring, automated diagnostics and disease management. The combined portfolio will give licensees the right to practice over 105 U.S. patents, 154 U.S. patent applications and 78 corresponding non-U.S patents.

The joint licensing program announced today will be managed by Health Hero Network, a wholly owned subsidiary of Robert Bosch North America. Health Hero Network will be able to offer licensees a single license that, depending on the needs of the licensee, may include patents owned by Healthways and Health Hero Network. A licensee may elect to take a license from Health Hero Network under the Healthways patents – alone or in combination with the Health Hero Network patents.

“In recent years, health care costs in the United States have relentlessly increased,” said Derek Newell, president of Health Hero Network. “With this increase in costs, employers, payers, and providers will continue to turn to technology as a critical part of their solutions both for their disease management and wellness programs.”

“Under this single license available from Health Hero Network, manufacturers who develop products, and companies that provide health management services can conveniently obtain rights to both the Health Hero Network and Healthways patents,” Newell continued. “This agreement will make it easier for the marketplace to utilize the innovations owned by Health Hero Network and Healthways to extend the range of health management solutions available in our industry.”

“The market demand for comprehensive, integrated health, wellness, prevention and care management solutions, including automated patient diagnostic activity and the accurate retrieval of healthcare information by consumers and all of their providers, will require defined technologies that automate the proven processes the industry currently provides to its customers and to the millions of individuals who participate in our programs,” said Ben R. Leedle, Jr., CEO of Healthways. “We are excited to share with Health Hero Network, and make available through this single license opportunity, the extensive intellectual property portfolios we both own and maintain in order to better serve our customers and business partners.”

The joint patent license contains a provision that waives royalties for practice of the technologies for the period prior to Dec. 31, 2008 for companies who become licensees on or before June 1, 2009.

Joint licensing programs provide the market with a convenient and cost-effective way to obtain licenses to the patents essential to practice health management technologies. The programs encourage greater adoption of the critical technologies in a cost effective manner.

Companies that sell, offer, make or use products and services that practice remote health management or disease management should contact Sandeep Jaggi, Sr. Vice President of Intellectual Property at Health Hero Network (sandeep.jaggi@us.bosch.com), to learn more about the joint patent license and to request a sample agreement for review.

Specific inquiries regarding Healthways participation in the joint patent licensing program should be directed to Healthways Executive Vice President and Chief Financial Officer Mary Chaput (mary.chaput@healthways.com).

About the Bosch Group and Health Hero Network
The Bosch Group is a leading global supplier of technology and services. In the areas of automotive and industrial technology, consumer goods, and building technology, some 271,000 associates generated sales of 46.1 billion euros ($63.2 billion) in fiscal 2007. The Bosch Group comprises Robert Bosch GmbH and its roughly 300 subsidiary and regional companies in over 50 countries. This worldwide development, manufacturing, and sales network is the foundation for further growth. Bosch spends more than three billion euros each year for research and development, and in 2006 applied for over 3,000 patents worldwide. The company was set up in Stuttgart in 1886 by Robert Bosch (1861-1942) as “Workshop for Precision Mechanics and Electrical Engineering.”

In North America, the Bosch Group manufactures and markets automotive original equipment and aftermarket products, industrial automation and mobile products, power tools and accessories, security technology, thermo-technology, packaging equipment and household appliances. Bosch employs approximately 25,000 associates in more than 80 locations throughout the U.S., Canada and Mexico, with reported sales of $9.5 billion in fiscal 2007. For more information on the company, visit www.boschusa.com.

Based in Palo Alto, Health Hero Network develops and markets the Health Buddy System for health improvement. The Health Buddy System serves as the interface between patients at home and care providers, facilitating monitoring of and self-management support for patients with chronic conditions. The System includes monitoring technologies, clinical information databases, Internet-enabled decision support tools, health management programs and content development tools. Through increased communication, behavior modification, and prevention, the Health Buddy System improves the quality of patient care. Health Hero Network’s systems are protected by 57 issued US patents. For more information, visit www.healthhero.com.

About Healthways
Healthways is the leading provider of specialized, comprehensive Health and Care Support(SM) solutions to help millions of people maintain or improve their health and, as a result, reduce overall healthcare costs. Healthways’ solutions are designed to help healthy individuals stay healthy, mitigate and slow the progression of disease associated with family or lifestyle risk factors and promote the best possible health for those already affected by disease. Our proven, evidence-based programs provide highly specific and personalized interventions for each individual in a population, irrespective of age or health status, and are delivered to consumers by phone, mail, internet and face-to-face interactions, both domestically and internationally. Healthways also provides a national, fully accredited complementary and alternative Health Provider Network, offering convenient access to individuals who seek health services outside of, and in conjunction with, the traditional healthcare system. For more information, please visit www.healthways.com.

Yesterday, I had the good fortune to hear a thought provoking presentation on innovation and design thinking by the CEO of IDEO, Tim Brown. Tim described the lengths to which IDEO designers go to understand the point of view of their users and then to generate a stream of prototypes as they experiment and try out ideas. Prototyping is part of the learning process. Insights are more likely to come spending time with extreme users, the youngest and the oldest, and the most challenged.

Here is a presentation by IDEO from the First Conference and Intensive Training on User-Centered Design in May 2008 which conveys the IDEO design process and basic principles of design thinking:

Nowhere is the IDEO approach to human centered design more necessary than in health care, where we spend more resources than any other sector of our economy and yet we still have the greatest unmet needs. While I was CEO of Health Hero Network, we partnered with IDEO to design the first Health Buddy device for home health monitoring. Here is a sketch from the Health Buddy design patent that we received on the in-home appliance that served as the front end for a home health monitoring service:

Health Buddy Design Patent Sketch

Our goal with Health Buddy was to enable people with chronic conditions to effortlessly record health status information at home and share it with remote care providers over the Internet. We hoped to enable caregivers to identify problems early and do a better job of educating and supporting patients at home to prevent more serious problems that would lead to hospitalization.

The first design challenge that I gave IDEO was to enable my grandmother to communicate meaningful information with her nurse over the Internet using just one trembling knuckle. The second challenge was to use design to deliver a friendly, supportive and compassionate interface to remote caregivers so that patients would feel comfortable in sharing information daily about health issues that most people would rather not think about.

The collaboration with IDEO was tremendously successful in creating an better interface to chronic care from the home. The most common response from our users was that they “felt like someone was there for them.” Hospitalizations were reduced, patients adhered to treatment, and caregiver productivity improved. Now if only the design of the economic models of health care could catch up to advances in designing a better chronic care model!

A patent recently issued to Health Hero Network describes a simple but powerful idea that addresses the pandemic challenge by enabling near-real-time syndromic surveillance that can be adapted on the fly. Easy-to-navigate survey devices collect data from hospital waiting rooms, school nurses, and other points of care. The survey script can be changed and updated remotely by public health authorities based on the latest information. The devices report data to central computers that look for any unusual patterns and then alert public health authorities immediately so that they can investigate further.

Disease outbreaks that look like the flu at the beginning can be hard to detect early because flu-like symptoms are common and are not always reported. The first cases of an outbreak may be spread out over many different clinics, hospitals, and schools in a metropolitan area. Unusual patterns might emerge only when looking at a broader cross section of a region. The other challenge is that we may not know what data is relevant and important at the beginning stages of an outbreak. Where it might have been fever, rash, and working in a mail room for one threat, it might be diarrhea and travel to a specific region or eating a particular food in another threat.

While many efforts have been discussed and may even be underway to facilitate early detection of outbreaks by sifting through electronic medical records and pharmacy data, the most important information might be missed because no one knew to ask the right question. When we do figure out what question to ask, we won’t have time to add fields to medical records or change forms. Our public health authorities need the ability to change the script as soon as they learn new information.

Despite the simplicity of the approach, it is not easy to organize health systems around new ways of doing things. On the other hand, maybe we won’t need to. Public health surveys could be pushed to iPhone users, for example. There just might be enough iPhones out there by now to provide a statistically significant sample size enabling highly sensitive early detection of potential public health emergencies.

More information on Health Hero Network patents.

Health Hero Network Patent 7,320,030, Issued January 15, 2008

Remote health monitoring apparatus using scripted communications

Abstract

A system for remotely monitoring an individual. The system includes a server system for generating a script program from a set of queries. The script program is executable by a remote apparatus that displays information and/or a set of queries to the individual through a user interface. Responses to the queries that are entered through the user interface together with individual identification information are sent from the remote apparatus to the server system across a communication network. The server system also includes an automated answering service for providing a series of questions from a stored set of questions for an individual at the remote apparatus to respond to, storing responses to each provided question in the series of questions and providing a service based on the individual’s response to the questions.

Inventor: Brown; Stephen J.

Assignee: Health Hero Network, Inc. (Palo Alto, CA)

Remotely monitoring an individual using scripted communications in the home health and remote health management field.

Abstract

A system for remotely monitoring an individual. The system includes a server system for generating a script program from a set of queries. The script program is executable by a remote apparatus that displays information and/or a set of queries to the individual through a user interface. Responses to the queries that are entered through the user interface together with individual identification information are sent from the remote apparatus to the server system across a communication network. The server system also includes an automated answering service for providing a series of questions from a stored set of questions for an individual at the remote apparatus to respond to, storing responses to each provided question in the series of questions and providing a service based on the individual’s response to the questions.

Inventors: Brown; Stephen J.

Assignee: Health Hero Network, Inc. (Palo Alto, CA)

United States Patent: 7,277,867
Issued: October 2, 2007

Abstract

The invention presents a method for conducting an on-line bidding session to accumulate a collective bid for a property. The bidding session is conducted over a computer network that includes a central computer, a number of remote computers, and communication lines connecting the remote computers to the central computer. According to the method, at least one bidding group is registered in the central computer. The bidding group can be an association, institution, or group of investors formed for the purpose of bidding together for the property. The bidding group has a total bid for the property which is tracked in the central computer. The central computer receives bids entered from the remote computers by members of the bidding group. Each bid includes an individual bid amount which is contributed to the total bid of the group to accumulate the collective bid for the property.

FIELD OF THE INVENTION

The present invention relates generally to on-line auctions, and in particular to a method for conducting an on-line bidding session that allows individual bidders to pool their bids in real-time during the bidding session.

BACKGROUND OF THE INVENTION

Auctions provide a popular and exciting marketplace for the buying and selling of property. In particular, auctions are often used to sell highly valued properties such as fine art, collectibles, real estate, and luxury items. Traditionally, participation in these high stakes auctions has been exclusively reserved for the extremely wealthy. Many ordinary individuals who would like to participate in the excitement of a high stakes auction are denied access for two reasons.

The first reason is that individual bidders are usually required to attend an auction in person to place a bid on an item for sale. This requirement limits participation in the auction to those people who live near the auction site or those people who can afford the time and expense to travel to the auction site. The second reason is that bidders are required to have sufficient funds to pay for a sale item should they place the winning bid. Because very few people can afford the price of an expensive property, bidding is limited to extremely wealthy individuals or to organizations who have raised sufficient funds to pay for a winning bid before the bidding session has begun. Both of these restrictions must be overcome before participation in high stakes auctions can become more widespread.

Many attempts have been made to solve the first problem, gaining bid access to an auction without having to be physically present at an auction site. For example, U.S. Pat. No. 4,789,928 issued to Fujisaki on Dec. 6, 1988 describes an auction information processing system which enables individuals spread over a wide area to participate in an on-line auction. The system includes a host computer connected via communication lines to remote terminals. Individual bidders enter bids from the remote terminals and the current highest bid and eventual winning bid are displayed in real-time on the remote terminals.

While this system has the advantage of allowing a large number of bidders to participate in an on-line auction, it has the disadvantage of requiring each bidder to have sufficient funds to cover a winning bid. Thus, this system solves the first problem of gaining remote access to auctions, but it still limits participation to those individuals who can personally afford the entire purchase price of the property for sale.

Another computerized bidding system is disclosed in U.S. Pat. No. 4,903,201 issued to Wagner on Feb. 20, 1990. Wagner describes an automated futures trading exchange in which bids to purchase or offers to sell a particular commodity contract are made by exchange members through remote terminals connected to an exchange computer. The exchange computer matches offer prices and bid prices to complete trading transactions. This system described by Wagner suffers from the same disadvantage of requiring each individual bidder to have sufficient funds to cover a winning bid. Additionally, bidders are limited to exchange members, so that the ordinary public is excluded from participating.

A teleprocessing system used by QVC Incorporated is described in an article entitled “Fashion Re-Evaluates Flickering Fortunes of TV Home Shopping”, WWD, Nov. 8, 1995 V170 N87. Shoppers call from their home phones to order items advertised on their television screens. As the orders are received, QVC tallies how many people have bought each particular sale item. QVC then displays the tally for each item on the viewers’ television screens in real-time. This interactive television method of buying an item provides easy remote access to a sale and real-time feedback to buyers. Unfortunately, it also requires each home shopper to pay for items individually, so that the sale is limited to relatively low cost items.

Another use of television to sell items is described in an article entitled “Auctions Become High Tech”, Dealer Business, March 1995 V29 N7. The article describes an auction system in which an auction company sends a signal via satellite to the televisions of individual car dealers. The dealers view a car for sale on their televisions and bid on the car using a telephone or a remote terminal. Like the previous on-line auction systems, this system requires each bidder to have sufficient money to cover the entire cost of the property for sale.

In addition to the auctions mentioned above, several on-line auctions are now being conducted over the Internet. One such auction is described in an article entitled “Cathay Pacific Airways-USA to Hold First Ever Internet CyberAuction” Business Wire, Sep. 26, 1995 p9261084. The article states that Cathay Pacific is auctioning off fifty business class seats from Los Angeles to Hong Kong. Registered bidders submit concealed bids by electronic mail over a two week bidding session. The fifty highest bidders at the close of the bidding session receive an electronic mail message instructing them on how to purchase tickets. This auction system suffers from the same disadvantage of requiring each bidder to have sufficient funds to pay for a winning bid. Moreover, bid information is not displayed to bidders in real-time.

Similarly, Save the Earth Foundation has an Artrock Auction that is described at their world-wide web site http://www.conmerce.com/save_earth. To participate in the auction, bidders register and submit bids for auction items through the Internet. Bidders are notified by electronic mail when a bid higher than their own is placed on an item. The winning bidder is also contacted by electronic mail at the close of the bidding session. As in the Cathay Pacific auction, the Artrock Auction requires each bidder to have sufficient funds to pay for a winning bid, and bid information is not displayed to the bidders in real-time.

Auction Web also has on-line auctions, as described at their world-wide web site http://www.ebay.com. In this auction system, bidders also register and submit bids through the Internet. Items for sale are graphically displayed on the bidders’ screens, in addition to the bid information for each item. Bid information is updated hourly throughout each two week bidding session. Similarly, Christie’s International describes “Results of the World’s First On-Line Auction” at their world-wide web site http://www.christies.com. In Christie’s auction, bidders register and submit bids in the same manner as the Auction Web auction. Unfortunately, like the previously mentioned on-line auctions, both Auction Web and Christie’s require each bidder to have sufficient funds to pay for a winning bid.

Thus, existing on-line auctions require each bidder to have sufficient funds to cover a winning bid, severely restricting the number of people who can participate in a high stakes auction. Additionally, not all of the on-line auctions enable bidders to view bid information in real-time, further limiting the excitement of the auction for those few who can afford to participate.

OBJECTS AND ADVANTAGES OF THE INVENTION

Accordingly, it is an object of the present invention to provide a method for conducting an on-line bidding session that permits individual bidders to pool their bids in order to accumulate a collective bid for a property. It is another object of the invention to greatly increase the number of participants in the bidding session by permitting bidders to contribute amounts of any size to the collective bid. It is a further object of the invention to display updated bid information in real-time for bidders to view from remote computers or terminals.

These and other objects and advantages of the present invention will become more apparent after consideration of the ensuing description and the accompanying drawings.

SUMMARY

The invention presents a method for conducting an on-line bidding session to accumulate a collective bid for a property. The bidding session is conducted over a computer network that includes a central computer, a number of remote computers, and communication lines connecting the remote computers to the central computer. In a preferred embodiment, the central computer is a world-wide web server and the communication lines are Internet lines that connect bidders at their remote computers to the world-wide web server.

The method includes the step of registering at least one bidding group in the central computer. The bidding group can be an association, institution, museum, or group of investors formed purely for the purpose of bidding together for the property. The bidding group has a total bid for the property which is tracked in the central computer. The method also includes the step of receiving in the central computer bids entered from the remote computers by members of the bidding group. Each of the bids includes an individual bid amount to be added to the total bid. The method further includes the step of contributing the individual bid amounts to the total bid to accumulate the collective bid for the property. The total bid is preferably displayed to the bidders on the remote computers and updated in real-time after each bid amount is added to the total bid.

In a particularly advantageous embodiment, the method also includes the step of creating a bidder account for each bidder in an account creation computer networked to the central computer. Each bidder account includes a bidder name, a bidder identification number, and a financial account number, such as the bidder’s credit card, checking account, or savings account number. In this embodiment, each bid received in the central computer also includes the name and identification number of the person contributing the bid. At the close of a bidding session, if the bidding group accumulates a total bid sufficient to acquire the property, each individual bid contributed to the total bid is matched by bidder identification number to a corresponding bidder account. The corresponding bidder account is then charged the bid amount contributed, facilitating collection of the total bid of the group.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a computer network according to a preferred embodiment of the invention.

FIG. 2 is a block diagram illustrating the creation of a bidder account according to the preferred embodiment of the invention.

FIG. 3 is a sample account creation form as it appears on the screen of a remote computer of FIG. 1.

FIG. 4 is a sample confirmation message as it appears on the screen of a remote computer of FIG. 1.

FIG. 5 is a block diagram illustrating the processing of a bid according to the preferred embodiment of the invention.

FIG. 6 is a sample bid entry form as it appears on the screen of a remote computer of FIG. 1.

FIG. 7 is a flow chart illustrating a method for conducting an on-line bidding session according to the preferred embodiment of the invention.

FIG. 8 is a flow chart illustrating the creation of a bidder account (step 120 in FIG. 7).

FIG. 9 is a flow chart illustrating the execution of a bidding session (step 130 in FIG. 7).

FIG. 10 is a flow chart illustrating the processing of a winning group (step 150 in FIG. 7).

DETAILED DESCRIPTION

FIGS. 1 6 illustrate a preferred computer network for conducting an on-line bidding session according to the present invention. Referring to FIG. 1, the computer network 10 includes a central computer 18 of an on-line auction company 12. Central computer 18 is connected to a database server 16 serving a bidder database 14 such that database 14 is accessible to central computer 18. Central computer 18 is further networked to a router and a modem M1. for connecting central computer 18 to communication lines 24. In the preferred embodiment, central computer 18 is a world-wide web server machine and communication lines 24 are Internet lines.

Network 10 further includes an account creation computer 28 of an account company 26. Account company 26 is preferably of the type that provides Internet users with a secure account for performing on-line commerce, such as First Virtual Holdings Incorporated located at 11975 El Camino Real, Suite 300, San Diego, Calif. Account creation computer 28 has a modem M2 for connecting account creation computer 28 to communications lines 24.

Network 10 also includes a number of remote computers 30 each having a modem M3 for connecting the remote computers to communication lines 24. For simplicity of illustration, only three remote computers are illustrated in FIG. 1. It is to be understood, however, that any number of remote computers can be included in the computer network of the present invention. Further, the preferred embodiment utilizes modems and the Internet to network central computer 18, account creation computer 28, and remote computers 30. It will be apparent to one skilled in the art that any type of connections may be used to network the computers. Specific techniques for networking computers are well known.

Each remote computer 30 preferably has an Internet browser 29 for displaying web content in the form of Hyper Text Markup Language (HTML). A suitable browser is Netscape 3.01 available from Netscape Corporation of Mountain View, Calif. Browser 29 allows remote computers 30 to access and display the content of an HTML template 22 residing on central computer 18. HTML template 22 contains the main web pages displayed to on-line bidders on remote computers 30. Similarly, account creation computer 28 has an on-line account HTML template 27 accessible from remote computers 30. In a particularly advantageous embodiment, templates 22 and 27 are Secure HTML templates, and Internet browser 29 is a Secure HTML compliant browser. Using Secure HTML ensures confidentiality for the account and bid transactions that will be described below.

FIG. 2 illustrates the main components of central computer 18, account creation computer 28, and remote computer 30 used in the creation of a bidder account 40 and a registration record 42. A bidder 38 at remote computer 30 accesses on-line account HTML template 27 residing on account creation computer 28. Template 27 contains an account creation form 32 which is displayed to bidder 38 on the screen of remote computer 30.

FIG. 3 shows a sample account creation form 32. Account creation form 32 has three fields 54 corresponding to a bidder name 44, a financial account number 46, and a financial account type 48. In the preferred embodiment, financial account number 46 is a credit card number corresponding to a credit card account of the bidder. In alternative embodiments, financial account number 46 is the number of a checking account, savings account, or any other account in which the bidder has available cash or credit. Account creation form 32 also contains a note 56 advising the bidder that a bidder identification number will be assigned in a new account confirmation message, as will be described below. Additionally, form 32 contains a button 52 for the bidder to press to send form 32 to account creation computer 28 after completing the form.

Referring again to FIG. 2, account creation computer 28 has an electronic mail server E2 for sending a new account confirmation message 34 to bidder 38 at remote computer 30. Remote computer 30 has an electronic mail client E3 for receiving and confirming new account confirmation message 34. FIG. 4 illustrates a sample account confirmation message 34. Message 34 includes four fields 55 corresponding to the bidder name 44, financial account number 46, financial account type 48, and bidder identification number 50. Bidder identification number 50 is generated by account creation computer 28 upon receipt of a valid account creation form. Message 34 further contains a note 58 advising the bidder to confirm the new account information with the account company.

Referring again to FIG. 2, account creation computer 28 has storage capability for storing a bidder account 40 that includes the bidder name 44, financial account number 46, and bidder identification number 50. Additionally, mail server E2 is capable of generating a bidder registration message 36 upon receipt of account confirmation from remote computer 30. Registration message 36 includes the bidder name 44 and bidder identification number 50 from the corresponding bidder account. Central computer 18 has an electronic mail server E1 for receiving the registration message 36 from mail server E2. Mail server E1 is linked to bidder database 14 such that a corresponding registration record 42 is created in bidder database 14 upon receipt of registration message 36. Registration record 42 includes the bidder name 44 and bidder identification number 50.

FIG. 5 illustrates the main components of central computer 18, bidder database 14, and remote computer 30 used to submit and record bids in the on-line bidding session. The HTML template 22 residing on central computer 18 contains a bid entry form 76. Bid entry form 76 is displayed on the screen of remote computer 30 when bidder 38 accesses HTML template 22 through communication lines 24.

FIG. 6 illustrates a sample bid entry form 76. Form 76 preferably includes a block 70 showing a graphical picture 72 and/or a description 74 of a property for sale. Bid entry form 76 also lists one or more bidding groups 66 with each bidding group having a total bid 68 for the property. Although two bidding groups are illustrated in FIG. 6, it is to be understood that the method for conducting an on-line bidding session according to the present invention may be practiced with only one bidding group or with any number of bidding groups. In any case, each bidding group has a total bid for the property.

Form 76 further includes four fields 57 corresponding to the bidder name 44, bidder identification number 50, bid amount 62, and bid designation 64. Bid designation 64 indicates the bidding group for which the bid is intended, and bid amount 62 is the amount that the bidder desires to contribute to the total bid of the designated group. Form 76 additionally includes an enter bid button 78 and a create account button 80. Button 78 is for bidder 38 to press to submit bid entry form 76 to central computer 18. Create account button 80 is preferably a link button for bidder 38 to press to connect remote computer 30 to on-line account HTML template 27 so that the bidder can create a bidder account.

Referring again to FIG. 5, central computer 18 is linked to database 14 such that central computer 18 can query registration records 42 stored in database 14. This ensures that when central computer 18 receives a bid entry form, the central computer can verify that the bidder name 44 and/or bidder identification number 50 match an existing registration record 42 in database 14. Central computer 18 and database 14 are further linked such that central computer 18 can store a bid record 60 in database 14. Bid record 60 includes the bidder name 44, bidder identification number 50, bid amount 62, and bid designation 64 received in the bid entry form.

The operation of the preferred embodiment is illustrated in FIGS. 1 10. FIG. 7 is a flow chart illustrating the overall flow a preferred method for conducting an on-line bidding session according to the present invention. The bidding session is conducted to accumulate a collective bid for a property. For example, and not by way of limitation, the bidding session may be conducted by an investment bank during a corporate takeover to accumulate a collective bid from small investors in order to purchase a company. Of course, a company is just one example of a property for which a collective bid may be accumulated. It will be apparent from this description that the method of the present invention may be used to accumulate a collective bid for any type of property. Moreover, there may be only one bidding group accumulating a single collective bid for the property or multiple bidding groups, with each bidding group accumulating a respective collective bid.

To start the bidding session, company 12 preferably receives a graphical picture and/or description of the property, as well as the names of bidding group(s) wishing to participate. Next, in step 110, company 12 registers at least one bidding group in central computer 18. To register the bidding group, company 12 inserts the name of the bidding group into bid entry form 76 of HTML template 22. Company 12 also preferably inserts into bid entry form 76 the graphical picture and/or description of the property. Next, bidders connect their remote computers 30 to central computer 18 to access bid entry form 76. When the bidders access bid entry form 76, the bidding groups 66 and total bids 68 are displayed on their remote computers, as shown in FIG. 6.

Next, each bidder creates his or her own bidder account 40 in account creation computer 28. The creation of bidder accounts is generally indicated as step 120 in FIG. 7 and detailed in steps 120 127 of FIG. 8. To start the creation of a bidder account, step 120 in FIG. 8, a bidder presses create account button 80 of bid entry form 76. Pressing create account button 80 causes browser 29 to connect remote computer 30 to account creation form 32 contained in HTML template 27. The bidder fills in three fields 54 of account creation form 32 with their name 44, financial account number 46, and financial account type 48, as shown in FIG. 3. The bidder then presses button 52 to send the new account information to computer 28, step 121.

Upon receiving the new account information, account company 26 verifies the bidder name 44, financial account number 46, and financial account type 48 to ensure that they correspond to a valid financial account of the bidder. After successful verification, account creation computer 28 generates identification number 50 for the bidder. Referring again to FIG. 2, account creation computer 28 then generates new account confirmation message 34 on mail server E2 and sends message 34 to mail client E3 of remote computer 30. Bidder 38 then receives new account confirmation message 34 on mail client E3, step 122. Bidder 38 confirms the new account information by pressing button 58 of new account confirmation message 34, step 123.

Upon receiving successful confirmation of the new account information, account creation computer 28 stores bidder account 40 for bidder 38. Account creation computer 28 also generates bidder registration message 36 on mail server E2. Registration message 36 is then sent to mail server E1 of central computer 18, step 124. When central computer 18 receives message 36, central computer 18 creates registration record 42 in database 14, step 125. Registration record 42 is then secured in database 14 from access from remote computers 30, step 126. Securing registration record 42 ensures confidentiality of account information for bidder 38. This ends the account creation process, step 127.

After registering at least one bidding group in central computer 18, company 12 starts an on-line bidding session for the property, which is generally indicated as step 130 in FIG. 7 and detailed in steps 130 138 in FIG. 9. To submit bids, each bidder completes the four fields 57 of bid entry form 76 with the bidder name 44, identification number 50, bid amount 62, and bid designation 64, as shown in FIG. 6. After completing the bid entry form 76, the bidder presses enter bid button 78 to send the bid information to central computer 18.

Central computer 18 receives bid entry form 76 entered from remote computer 30, step 131. Central computer 18 then queries database 14 to verify that the bidder name 44 and bidder identification number 50 received on the bid entry form match an existing registration record. The query results indicate if the bidder has a valid bidder account, step 132. If the answer is NO, central computer 18 executes step 134, notifying the bidder that he or she does not have a valid bidder account. After notifying the bidder, central computer 18 proceeds to step 137, which will be described below.

If the answer is YES, central computer 18 proceeds to step 133, recording bid record 60 in database 14. Bid record 60 includes the bidder name 44, identification number 50, bid amount 62, and bid designation 64, as shown in FIG. 5. Next, central computer 18 contributes bid amount 62 to the total bid of the designated group indicated by bid designation 64, step 135. After contributing bid amount 62, central computer 18 updates total bids 68 in real-time, step 136. Changes in total bids 68 are dynamically displayed to the bidders by sending commands from HTML template 22 to browsers 29. The commands cause browsers 29 to update themselves so that the bidders may view the updated total bids after each bid amount is contributed. Specific techniques of updating browsers in this manner are well known in the art.

Next, central computer 18 determines if the bidding session is completed, step 137. If the answer is NO, central computer 18 returns to step 131, receiving another bid entry form 76. If the answer is YES, the bidding session ends, step 138. In the preferred embodiment, the bidding session runs for a pre-defined period of time, such as two weeks, so that central computer 18 can determine if the bidding session is completed by using a chronometer (not shown). A time period of two weeks is used for illustrative purposes only. It is to be understood that any duration could be set for the bidding session. In an alternative embodiment, the bidding session runs until bidding activity slows below a pre-defined threshold, such as receiving fewer than three bids per day. Again, this is only an example of a suitable threshold. Any other thresholds for minimum bidding activity could be set.

After the bidding session is completed, it is determined if the total bid of any bidding group exceeds a minimum price or threshold value for the property, step 140 in FIG. 7. For example, if the property is a public company, the threshold value may be set as the stock market price of the company. Alternatively, the owner(s) of the property may set any desired minimum price for which he or she is willing to sell the property. If the total bid of any bidding group exceeds the threshold value, the method preferably includes the additional step of processing a winning group, generally indicated as step 150 in FIG. 7 and detailed as steps 150 156 in FIG. 10.

Referring to FIG. 10, company 12 declares the one bidding group having the largest total bid which exceeds the threshold value the winning group, step 151. Next, the winning group is displayed on remote computers 30 for the bidders to view, step 152. Central computer 18 then retrieves winning bid records from database 14, step 153. The winning bid records are those bid records whose bid designation 64 indicates the winning group.

Central computer 18 then sends the winning bid records from mail server E1 to mail server E2 of account company 26. Account company 26 uses bidder-name 44 and/or bidder identification number 50 of each winning bid record to match each winning bid record to a corresponding bidder account, step 154. Next, for each winning bid record, account company 26 charges the bid amount 62 to the financial account number 46 of the corresponding bidder account, step 155. After charging the bid amount for each winning bid record, account company 26 transfers the finds generated to company 12, ending the winning group processing, step 156.

SUMMARY, RAMIFICATIONS, AND SCOPE

Although the above description contains many specificities, these should not be construed as limiting the scope of the invention, but merely as illustrating the presently preferred embodiment. Many other embodiments of the invention are possible. For example, the method for conducting an on-line bidding session according to the present invention need not be used exclusively for accumulating a collective bid for a publicly traded company. The method of the present invention is effective for accumulating a collective bid for any type of property.

In addition, the method described need not be conducted over the Internet using a world-wide web server. The method is effective using any network that allows the transmission of data between bidders at remote locations and a central processor. Similarly, modems are not necessary to network the central computer, account creation computer, and remote computers. Many other methods of connection are possible, as is well known in the art of computer networking.

For simplicity of understanding, the steps of registering bidding groups and creating bidder accounts are presented before the step of commencing a bidding session. This is for illustrative purposes only, as bidding groups may be registered in the central computer after the commencement of a bidding session. For example, bidders wishing to participate but not wishing to bid with any of the current bidding groups may form a new bidding group during the bidding session. In this case, the bid entry form is updated during the bidding session with the name of the new bidding group. Similarly, bidders can create bidder accounts either before or during the bidding session if they desire to contribute to the total bid of a bidding group.

Therefore, the scope of the invention should be determined by the appended claims and their legal equivalents.

* * * * *

Related U.S. Patent Documents

Application Number Filing Date Patent Number Issue Date
09304446 May., 1999 6167386
09092604 Jun., 1998 6023686
08603131 Feb., 1996 5794219
09653664
09625080 Jul., 2000
09160970 Sep., 1998 6240393

Current U.S. Class: 705/37 ; 705/26
Current International Class: G06Q 40/00 (20060101)
Field of Search: 705/1,26,27,35-44 709/217,219,227 340/825.26,825.27,825.3
References Cited [Referenced By]
U.S. Patent Documents

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Primary Examiner: Laneau; Ronald
Parent Case Text

This application is a continuation of U.S. application Ser. No. 09/304,446, filed May 3, 1999, which is now U.S. Pat. No. 6,167,386 which is a continuation of U.S. application Ser. No. 09/092,604, filed Jun. 5, 1998, which is now U.S. Pat. No. 6,023,686, which is a continuation in part of U.S. application Ser. No. 08/603,131, filed Feb. 20, 1996, which is now U.S. Pat. No. 5,794,219. This application is also a continuation in part of U.S. application Ser. No. 09/625,080, filed Jul. 25, 2000, now abandoned, which is a continuation of U.S. application Ser. No. 09/160,970, filed Sep. 25, 1998, which is now U.S. Pat. No. 6,240,393.
Claims

What is claimed is:

1. A method of conducting an on-line bidding session, comprising: storing registered bidding groups in a computer accessible database coupled to a processor in an auction computer, each of the registered bidding groups including a list of associated bidders; receiving bids from bidders on a plurality of remote computers; determining a respective bidding group’s total bid by accumulating the bids received from bidders included in the respective bidding group’s list of associated bidders; and storing the determined total bid in the database.

2. The method of claim 1 further comprising creating registration records in the database, each registration record corresponding to a bidder account and including a bidder name, a bidder identification and a financial account number.

3. The method of claim 1, wherein receiving bids from bidders includes receiving the respective bidder name and respective bidder identification from the bidders and the method further comprises: verifying, using the processor, whether the respective bidder name and respective identification match a registration record stored in the database; and recording a bid record in the database if the respective bidder name and the respective identification have a verified match.

4. The method of claim 3, further comprising: determining if the respective bidding group’s total bid exceeds a threshold, and if so declaring the bidding group associated with the respective total bid a winning group; retrieving the recorded bid records associated with the respective total bid; determining at least one bidder account that corresponds to the retrieved bid records; and charging the at least one bidder account at least one of the bid amounts included in the bid records.

5. The method of claim 1, further comprising, after determining the total bid associated with the respective bidding group, sending the determined total bid over a computer network to at least one remote computer of at least one bidder associated with the respective bidding group.

6. A method of conducting an auction comprising registering a plurality of bidding groups; receiving bids from bidders, each bid including a respective bid designation corresponding to one of the plurality of bidding groups; associating each bid with the one of the plurality of bidding groups corresponding to the respective bid designation; declaring a winning bidding group for the auction from the plurality of bidding groups based upon the bids associated with each bidding group; and displaying the winning bidding group for bidders to view.

7. The method of claim 6, wherein each bidding group has a corresponding total bid amount and each bid further includes a respective bid amount that is contributed to the total bid amount for the corresponding bidding group.

8. The method of claim 7, wherein the winning bidding group has the largest total bid amount at the end of a bidding session of the auction.

9. The method of claim 7, further comprising displaying the total bid amount for one or more of the plurality of bidding groups.

10. The method of claim 6, wherein registering the plurality of bidding groups uses at least one central computer coupled to a network.

11. The method of claim 10, wherein the network comprises the Internet.

12. The method of claim 10, wherein the network comprises one or more communication lines.

13. The method of claim 10, further comprising using one or more remote computers coupled to the network to receive bids.

14. The method of claim 6, wherein the auction is on-line.

15. The method of claim 6, further comprising creating one or more bidder accounts.

16. The method of claim 6, wherein associating each bid with at least one of the plurality of bidding groups comprises bid pooling.

17. The method of claim 6, wherein the auction continues until a pre-defined threshold occurs.

18. The method of claim 17, wherein the pre-defined threshold comprises a period of time.

19. The method of claim 17, wherein the pre-defined threshold comprises a minimum bidding activity.

20. A method of conducting an auction, comprising: registering a plurality of bidding groups; receiving bids from bidders, each bid including a bid designation corresponding to one of the plurality bidding groups; contributing a respective bid amount in each bid to at least one total bid amount corresponding to at least one of the plurality of bidding groups; declaring a winning bidding group from the plurality of bidding groups; and notifying the bidders that the winning bidding group has been declared.

21. The method of claim 20, wherein the winning bidding group has the largest total bid amount at the end of the bidding session.

22. The method of claim 20, further comprising using at least one central computer coupled to a network to register the plurality of bidding groups.

23. The method of claim 22, wherein the network comprises the Internet.

24. The method of claim 22, wherein the network comprises one or more communication lines.

25. The method of claim 22, further comprising using one or more remote computers coupled to the network to receive bids.

26. The method of claim 20, wherein the auction is on-line.

27. The method of claim 20, further comprising creating one or more bidder accounts.

28. The method of claim 20, wherein contributing the respective bid amount in each bid to at least one total bid amount corresponding to at least one of the plurality of bidding groups comprises bid pooling.

29. The method of claim 20, further comprising displaying the total bid amount for one or more of the plurality of bidding groups.

30. The method of claim 20, wherein the auction continues until a pre-defined threshold occurs.

31. The methods of claim 30, wherein the pre-defined threshold is a period of time.

32. The method of claim 30, wherein the pre-defined threshold is a minimum bidding activity.

33. The method of claim 20, wherein the winning bidding group is declared based upon a total bid associated with the winning bidding group exceeding a pre-defined threshold.

34. The method of claim 33, wherein the pre-defined threshold comprises a minimum price for a property being auctioned.
Description

Inventor: Brown

Issued: August 21, 2007

Title: System and methods for monitoring a patient’s heart condition

Abstract: A patient’s heart condition is monitored by a patient-location based unit. The patient-location based unit includes a receptacle for receiving a cartridge that includes heart condition monitoring information. The patient-location based unit receives information from the patient, monitors the patient’s heart condition, and communicates with the patient regarding the patient’s heart condition. The patient-location based unit also communicates with a central server. Both the patient-location based unit and central server communicate with a health care professional computer by transferring, to the health care professional computer, information related to the monitored patient’s heart condition.

United States Patent: 7,167,818
Issued: January 23, 2007
Assignee: Health Hero Network Inc.

Abstract

A system and method for predicting the effect of patient self-care actions on a disease control parameter. A future disease control parameter value X(t.sub.j) at time t.sub.j is determined from a prior disease control parameter value X(t.sub.i) at time t.sub.i based on an optimal control parameter value R(t.sub.j) at time t.sub.j, the difference between the prior disease control parameter value X(t.sub.i) and an optimal control parameter value R(t.sub.i) at time t.sub.i, and a set of differentials between patient self-care parameters having patient self-care values S.sub.M(t.sub.i) at time t.sub.i and optimal self-care parameters having optimal self-care values O.sub.M(t.sub.i) at time t.sub.i. The differentials are multiplied by corresponding scaling factors K.sub.M. The system includes an input device for entering the patient self-care values S.sub.M(t.sub.i). A memory stores the optimal control parameter values R(t.sub.i) and R(t.sub.j), the prior disease control parameter value X(t.sub.i), the optimal self-care values O.sub.M(t.sub.i), and the scaling factors K.sub.M. A processor in communication with the input device and memory calculates the future disease control parameter value X(t.sub.j). A display is connected to the processor to display the future disease control parameter value X(t.sub.j) to a patient.

BACKGROUND

1. Field of the Invention

The present invention relates generally to disease simulation systems, and in particular to a system and method for simulating a disease control parameter and for predicting the effect of patient self-care actions on the disease control parameter.

2. Description of Prior Art

Managing a chronic disease or ongoing health condition requires the monitoring and controlling of a physical or mental parameter of the disease. Examples of these disease control parameters include blood glucose in diabetes, respiratory flow in asthma, blood pressure in hypertension, cholesterol in cardiovascular disease, weight in eating disorders, T-cell or viral count in HIV, and frequency or timing of episodes in mental health disorders. Because of the continuous nature of these diseases, their corresponding control parameters must be monitored and controlled on a regular basis by the patients themselves outside of a medical clinic.

Typically, the patients monitor and control these parameters in clinician assisted self-care or outpatient treatment programs. In these treatment programs, patients are responsible for performing self-care actions which impact the control parameter. Patients are also responsible for measuring the control parameter to determine the success of the self-care actions and the need for further adjustments. The successful implementation of such a treatment program requires a high degree of motivation, training, and understanding on the part of the patients to select and perform the appropriate self-care actions.

One method of training patients involves demonstrating the effect of various self-care actions on the disease control parameter through computerized simulations. Several computer simulation programs have been developed specifically for diabetes patients. Examples of such simulation programs include BG Pilot.TM. commercially available from Raya Systems, Inc. of 2570 El Camino Real, Suite 520, Mountain View, Calif. 94040 and AIDA freely available on the World Wide Web at the Diabetes UK website http://www.pcug.co.uk/diabetes/aida.htm.

Both BG Pilot.TM. N and AIDA use mathematical compartmental models of metabolism to attempt to mimic various processes of a patient’s physiology. For example, insulin absorption through a patient’s fatty tissue into the patient’s blood is represented as a flow through several compartments with each compartment having a different flow constant. Food absorption from mouth to stomach and gut is modeled in a similar manner. Each mathematical compartmental model uses partial differential equations and calculus to simulate a physiological process.

This compartmental modeling approach to disease simulation has several disadvantages. First, understanding the compartmental models requires advanced mathematical knowledge of partial differential equations and calculus which is far beyond the comprehension level of a typical patient. Consequently, each model is an unfathomable “black box” to the patient who must nevertheless trust the model and rely upon it to learn critical health issues.

A second disadvantage of the compartmental modeling approach is that a new model is needed for each new disease to be simulated. Many diseases involve physiological processes for which accurate models have not been developed. Consequently, the mathematical modeling approach used in BG Pilot and AIDA is not sufficiently general to extend simulations to diseases other than diabetes.

A further disadvantage of the modeling approach used in BG Pilot.TM. and AIDA is that the mathematical models are not easily customized to an individual patient. As a result, BG Pilot.TM. and AIDA are limited to simulating the effect of changes in insulin and diet on the blood glucose profile of a typical patient. Neither of these simulation programs may be customized to predict the effect of changes in insulin and diet on the blood glucose profile of an individual patient.

OBJECTS AND ADVANTAGES OF THE INVENTION

In view of the above, it is an object of the present invention to provide a disease simulation system which is sufficiently accurate to teach a patient appropriate self-care actions and sufficiently simple to be understood by the average patient. It is another object of the invention to provide a disease simulation system which may be used to simulate many different types of diseases. A further object of the invention is to provide a disease simulation system which may be easily customized to an individual patient.

These and other objects and advantages will become more apparent after consideration of the ensuing description and the accompanying drawings.

SUMMARY OF THE INVENTION

The invention presents a system and method for simulating a disease control parameter and for predicting the effect of patient self-care actions on the disease control parameter. According to the method, a future disease control parameter value X(t.sub.j) at time t.sub.j is determined from a prior disease control parameter value X(t.sub.i) at time t.sub.i based on an optimal control parameter value R(t.sub.j) at time t.sub.j, the difference between the prior disease control parameter value X(t.sub.i) and an optimal control parameter value R(t.sub.i) at time t.sub.i, and a set of differentials between patient self-care parameters having patient self-care values S.sub.M(t.sub.i) at time t.sub.i and optimal self-care-parameters having optimal self-care values O.sub.M(t.sub.i) at time t.sub.i. In the preferred embodiment, the differentials are multiplied by corresponding scaling factors K.sub.M and the future disease control parameter value X(t.sub.j) is calculated according to the equation:

.function..function..function..function..SIGMA..times..times..function..fu- nction..function. ##EQU00001##

A preferred system for implementing the method includes an input device for entering the patient self-care values S.sub.M(t.sub.i). The system also includes a memory for storing the optimal control parameter values R(t.sub.i) and R(t.sub.j), the prior disease control parameter value X(t.sub.i), the optimal self-care values O.sub.M(t.sub.i), and the scaling factors K.sub.M. A processor in communication with the input device and memory calculates the future disease control parameter value X(t.sub.j). A display is connected to the processor to display the future disease control parameter value X(t.sub.j) to a patient.

In the preferred embodiment, the system further includes a recording device in communication with the processor for recording an actual control parameter value A(t.sub.i) at time t.sub.i, an actual control parameter value A(t.sub.j) at time t.sub.j, and actual self-care parameters having actual self-care values C.sub.M(t.sub.i) at time t.sub.i. The processor adjusts the scaling factors K.sub.M based on the difference between the actual control parameter value A(t.sub.j) and the optimal control parameter value R(t.sub.j), the difference between the actual control parameter value A(t.sub.i) and the optimal control parameter value R(t.sub.i), and the difference between the actual self-care values C.sub.M(t.sub.i) and the optimal self-care values O.sub.M(t.sub.i). Thus, the scaling factors K.sub.M are customized to an individual patient to predict the effect on the disease control parameter of self-care actions performed by the individual patient.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a simulation system according to the invention.

FIG. 2 is a sample physiological parameter entry screen according to the invention.

FIG. 3 is a sample self-care parameter entry screen according to the invention.

FIG. 4 is a table of values according to the invention.

FIG. 5 is a sample graph of disease control parameter values created from the table of FIG. 4.

FIG. 6 is another table of values according to the invention.

FIG. 7 is a sample graph of disease control parameter values created from the table of FIG. 6.

FIG. 8 is another table of values according to the invention.

FIG. 9 is a sample graph of disease control parameter values created from the table of FIG. 8.

FIG. 10 is a schematic illustration of the entry of actual parameter values in a recording device of the system of FIG. 1.

FIG. 11 is a schematic diagram of another simulation system according to the invention.

FIG. 12 is a schematic block diagram illustrating the components of the system of FIG. 11.

FIG. 13 is a flow chart illustrating steps included in a method of the invention.

FIG. 14 is a flow chart illustrating steps included in another method of the invention.

DESCRIPTION

The present invention is a system and method for simulating a disease control parameter and for predicting an effect of patient self-care actions on the disease control parameter. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details need not be used to practice the invention. In other instances, well known structures, interfaces, and processes are not shown in detail to avoid unnecessarily obscuring the present invention.

FIGS. 1 10 illustrate a preferred embodiment of a simulation system according to the invention. The following table illustrates a representative sampling of the types of diseases, patient self-care parameters, and disease control parameters which may be simulated using the system and method of the invention.

TABLE-US-00001 Disease Self-Care Parameters Control Parameter Diabetes insulin, diet, exercise blood glucose level Asthma allergens, exercise, inhaled peak flow rate bronchial dilators, anti- inflammatory medications Obesity diet, exercise, metabolism weight altering medications Hypertension diet, exercise, stress reduction, blood pressure blood pressure medications Coronary Artery diet, exercise, stress reduction, cholesterol Disease lipid lowering medications Panic Disorder stress reduction, anti-depressant number of episodes medications Nicotine cigarettes smoked, coping urges to smoke Addiction behaviors

The above table is not intended as an exhaustive list, but merely as a representative sampling of the types of diseases and disease control parameters which may be simulated. For simplicity, the preferred embodiment is described with reference to a single disease, diabetes, having a single disease control parameter, a blood glucose level. However, it is to be understood that the system and method of the invention are sufficiently flexible to simulate any disease which has a measurable control parameter and which requires patient self-care actions.

Referring to FIG. 1, a simulation system generally indicated at 10 includes a server 12 having a processor and memory for executing a simulation program which will be described in detail below. Server 12 is in communication with a healthcare provider computer 14 through a network link 48. Healthcare provider computer 14 is preferably a personal computer located at a healthcare provider site, such as a doctor’s office.

Server 12 is also in communication with a patient multi-media processor 24 through a network link 50. Patient multi-media processor 24 is located at a patient site, typically the patient’s home. In the preferred embodiment, server 12 is a world wide web server, multi-media processor 24 is a web TV processor for accessing the simulation program on server 12, and links 48 and 50 are Internet links. Specific techniques for establishing client/server computer systems in this manner are well known in the art.

Healthcare provider computer 14 includes a processor and memory, a standard display 16, and a keyboard 20. Computer 14 further includes a card slot 18 for receiving a data storage card, such as a smart card 22. Computer 14 is designed to read data from card 22 and write data to card 22. Patient multi-media processor 24 includes a corresponding card slot 26 for receiving card 22. Processor 24 is designed to read data from card 22 and write data to card 22. Thus, healthcare provider computer 14 communicates with patient multi-media processor 24 via smart card 22. Such smart card data communication systems are also well known in the art.

Multi-media processor 24 is connected to a display unit 28, such as a television, by a standard connection cord 32. Display unit 28 has a screen 30 for displaying simulations to the patient. An input device 34, preferably a conventional hand-held remote control unit or keyboard, is in signal communication with processor 24. Device 34 has buttons or keys 36 for entering data in processor 24.

System 10 also includes an electronic recording device 38 for recording actual control parameter values and patient self-care data indicating actual self-care actions performed by the patient. Recording device 38 includes a measuring device 40 for producing measurements of the disease control parameter, a keypad 44 for entering the self-care data, and a display 42 for displaying the control parameter values and self-care data to the patient.

Recording device 38 is preferably portable so that the patient may carry device 38 and enter the self-care data at regular monitoring intervals. Device 38 is further connectable to healthcare provider computer 14 via a standard connection cord 46 so that the control parameter values and patient self-care data may be uploaded from device 38 to computer 14. Such recording devices for producing measurements of a disease control parameter and for recording self-care data are well known in the art. For example, U.S. Pat. No. 5,019,974 issued to Beckers on May 28, 1991 discloses such a recording device.

In the example of the preferred embodiment, the disease control parameter is the patient’s blood glucose level and recording device 38 is a blood glucose meter, as shown in FIG. 10. In this embodiment, measuring device 40 is a blood glucose test strip designed to test blood received from a patient’s finger 54. Device 38 is also designed to record values of the patient’s diet, medications, and exercise durations entered by the patient through keypad 44. Of course, in alternative embodiments, the recording device may be a peak flow meter for recording a peak flow rate, a cholesterol meter for recording a cholesterol level, etc.

The simulation system of the present invention includes a simulation program which uses a mathematical model to calculate disease control parameter values. The following variables used in the mathematical model are defined as follows: N=Normal time interval in which patient self-care actions are employed to make a measurable difference in the disease control parameter or a natural rhythm occurs in the disease control parameter. For diabetes and asthma, time interval N is preferably twenty-four hours. For obesity or coronary artery disease, time interval N is typically three to seven days. t.sub.1, t.sub.2, . . . t.sub.i, t.sub.j . . . t.sub.N=Time points at which the disease control parameter is measured by a patient. For a daily rhythm control parameter such as a blood glucose level, the time points are preferably before and after meals. For weight or cholesterol control parameters, the time points are preferably once a day or once every second day. X(t)=Simulated disease control parameter value at time t determined by the simulation program. R(t)=Optimal control parameter value at time t expected as a normal rhythm value of the disease control parameter at time t if the patient performs optimal self-care actions in perfect compliance from time t.sub.1 to the time point immediately preceding time t. A(t)=actual control parameter value at time t measured by the patient. O.sub.M(t.sub.i)=Optimal self-care parameter values O.sub.1(t.sub.i), O.sub.2(t.sub.i), . . . O.sub.m(t.sub.i) at time t.sub.i expected to produce optimal control parameter value R(t.sub.j) at time t.sub.j. For example, a diabetes patient’s optimal self-care parameter values include a prescribed dose of insulin, a prescribed intake of carbohydrates, and a prescribed exercise duration. S.sub.M(t.sub.i)=Patient self-care parameter values S.sub.1(t.sub.i), S.sub.2(t.sub.i), . . . S.sub.m(t.sub.i) at time t.sub.i entered in the simulation system by the patient to simulate self-care actions. C.sub.M(t.sub.i)=Actual self-care parameter values C.sub.1(t.sub.i), C.sub.2(t.sub.i), . . . C.sub.m(t.sub.i) at time t.sub.i indicating actual self-care actions performed by the patient at time t.sub.i. K.sub.M=Corresponding scaling factors K.sub.1(t.sub.i), K.sub.2(t.sub.i), . . . K.sub.m for weighting the impact on a future disease control parameter value X(t.sub.j) at time t.sub.j which results from differentials between patient self-care values S.sub.M(t.sub.i) and corresponding optimal self-care values O.sub.M(t.sub.i).

With these definitions, future disease control parameter value X(t.sub.j) is calculated according to the equation:

.function..function..function..function..SIGMA..times..times..function..fu- nction..function. ##EQU00002##

Future disease control parameter value X(t.sub.j) at time t.sub.j is determined from a prior disease control parameter value X(t.sub.i) at time t.sub.i based on an optimal control parameter value R(t.sub.j) at time t.sub.j, the difference between prior disease control parameter value X(t.sub.i) and an optimal control parameter value R(t.sub.i) at time t.sub.j, and a set of differentials between patient self-care values S.sub.M(t.sub.i) and optimal self-care values O.sub.M(t.sub.i). The differentials are multiplied by corresponding scaling factors K.sub.M.

Thus, as patient self-care parameter values S.sub.M(t.sub.i) deviate from optimal self-care parameter values O.sub.M(t.sub.i), future disease control parameter value X(t.sub.j) deviates from optimal control parameter value R(t.sub.j) by an amount proportional to scaling factors K.sub.M. This mathematical model follows the patient’s own intuition and understanding that if the patient performs optimal self-care actions in perfect compliance, the patient will achieve the optimal control parameter value at the next measurement time. However, if the patient deviates from the optimal self-care actions, the disease control parameter value will deviate from the optimal value at the next measurement time.

The simulation program is also designed to generate an entry screen for entry of the patient self-care parameter values. FIG. 3 shows a sample patient self-care parameters entry screen 52 as it appears on display unit 28. The patient self-care parameters include a food exchange parameter expressed in grams of carbohydrates consumed, an insulin dose parameter expressed in units of insulin injected, and an exercise duration parameter expressed in fifteen minute units of exercise performed.

These self-care parameters are illustrative of the preferred embodiment and are not intended to limit the scope of the invention. It is obvious that many different self-care parameters may be used in alternative embodiments. Screen 52 contains data fields 53 for entering a food exchange parameter value S.sub.1(t), an insulin dose parameter value S.sub.2(t), and an exercise duration parameter value S.sub.3(t). Each data field 53 has a corresponding time field 51 for entering a time point corresponding to the patient self-care parameter value. Screen 52 also includes an OK button 55 and a cancel button 57 for confirming and canceling, respectively, the values entered in screen 52.

FIG. 4 shows a sample table of values 56 created by the simulation program using the data entered by the patient through the self-care parameters entry screen. Table 56 includes a column of simulated disease control parameter values calculated by the simulation program, as will be explained in the operation section below. The simulation program is further designed to generate graphs of simulated disease control parameter values. FIG. 5 illustrates a sample graph 58 generated from table 56 as it appears on screen 30 of the display unit. Specific techniques for writing a simulation program to produce such a graph are well known in the art.

In the preferred embodiment, healthcare provider computer 14 is programmed to determine scaling factors K.sub.M from values of physiological parameters of the patient. FIG. 2 shows a sample physiological parameter entry screen 41 as it appears on the healthcare provider computer. The physiological parameters of the patient include a body mass, a metabolism rate, a fitness level, and hepatic and peripheral insulin sensitivities. These physiological parameters are illustrative of the preferred embodiment and are not intended to limit the scope of the invention. It is obvious that many different physiological parameters may be used in alternative embodiments. Screen 41 includes data fields 43 for entering physiological parameter values, an OK button 45 for confirming the values, and a cancel button 47 for canceling the values.

Healthcare provider computer 14 stores indexes for determining the scaling factors from the physiological parameters entered. For example, FIG. 4 shows an insulin sensitivity scaling factor K.sub.2 corresponding to insulin dose parameter value S.sub.2(t). Computer 14 is programmed to determine from a stored insulin index a value of scaling factor K.sub.2 based on the entered values of the patient’s body mass and insulin sensitivities. In this example, computer 14 determines a value of -40 for scaling factor K.sub.2, indicating that for this patient, one unit of insulin is expected to lower the patient’s blood glucose level by 40 mg/dL. Computer 14 is programmed to determine the remaining scaling factors in a similar manner. The specific indexes required to determine the scaling factors from values of a patient’s physiological parameters are well known in the art.

In the preferred embodiment, healthcare provider computer 14 is also programmed to adjust scaling factors K.sub.M based on the difference between an actual control parameter value A(t.sub.j) measured at time t.sub.j and optimal control parameter value R(t.sub.j), the difference between an actual control parameter value A(t.sub.i) measured at time t.sub.i and optimal control parameter value R(t.sub.i), and the difference between actual self-care values C.sub.M(t.sub.i) performed by the patient at time t.sub.i and optimal self-care values O.sub.M(t.sub.i).

Scaling factors K.sub.M are adjusted to fit the mathematical model presented above, preferably using a least squares, chi-squares, or similar regressive fitting technique. Specific techniques for adjusting coefficients in a mathematical model are well known in the art. For example, a discussion of these techniques is found in “Numerical Recipes in C: The Art of Scientific Computing”, Cambridge University Press, 1988.

The operation of the preferred embodiment is illustrated in FIG. 13. FIG. 13 is a flow chart illustrating a preferred method of using system 10 to simulate the disease control parameter. In step 200, optimal self-care values and optimal control parameter values for each time point are determined for the patient, preferably by the patient’s healthcare provider. The optimal self-care values and optimal control parameter values are then entered and stored in provider computer 14.

In the preferred embodiment, the optimal self-care values include an optimal food exchange parameter value O.sub.1(t) expressed in grams of carbohydrates, an optimal insulin dose parameter value O.sub.2(t) expressed in units of insulin, and an optimal exercise duration parameter value O.sub.3(t) expressed in fifteen minute units of exercise. Specific techniques for prescribing optimal self-care values and optimal control parameter values for a patient are well known in the medical field.

In step 202, the healthcare provider determines the physiological parameter values of the patient and enters the physiological parameter values in computer 14 through entry screen 41. As shown in FIG. 2, the physiological parameter values include a body mass, a metabolism rate, a fitness level, and hepatic and peripheral insulin sensitivities. Specific techniques for testing a patient to determine these physiological parameter values are also well known in the medical field.

Following entry of the physiological parameter values, computer 14 determines scaling factors K.sub.M from the stored indexes, step 204. For example, FIG. 4 shows a food exchange scaling factor K.sub.1 corresponding to food exchange parameter value S.sub.1(t), an insulin sensitivity scaling factor K.sub.2 corresponding to insulin dose parameter value S.sub.2(t), and an exercise duration scaling factor K.sub.3 corresponding to exercise duration parameter value S.sub.3(t).

In this example, computer 14 determines a value of 4 for scaling factor K.sub.1, a value of -40 for scaling factor K.sub.2, and a value of -5 for scaling factor K.sub.3. These values indicate that one gram of carbohydrate is expected to raise the patient’s blood glucose level by 4 mg/dL, one unit of insulin is expected to lower the patient’s blood glucose level by 40 mg/dL, and fifteen minutes of exercise is expected to lower the patient’s blood glucose level by 5 mg/dL. Of course, these values are just examples of possible scaling factors for one particular patient. The values of the scaling factors vary between patients in dependence upon the physiological parameter values determined for the patient.

The determined optimal self-care values, optimal control parameter values, and scaling factors are then stored on smart card 22, step 206. Typically, the values are stored on smart card 22 during a patient visit to the healthcare provider. The patient then takes home smart card 22 and inserts smart card 22 in patient multi-media processor 24, step 208. Next, the patient accesses the simulation program on server 12 through multi-media processor 24, step 210.

The simulation program generates self-care parameters entry screen 52, which is displayed to the patient on screen 30 of display unit-28. In step 212, the patient enters patient self-care values S.sub.M(t) and corresponding time points in data fields 53 and 51, respectively, using input device 34. The optimal self-care values, optimal control parameter values, scaling factors, and patient self-care values are transmitted from multi-media processor 24 to server 12 through link 50. In step 214, the simulation program calculates simulated disease control parameter values at each time point according to the equation:

.function..function..function..function..SIGMA..times..times..function..fu- nction..function. ##EQU00003##

Thus, each future disease control parameter value X(t.sub.j) is calculated from optimal control parameter value R(t.sub.j), the difference between prior disease control parameter value X(t.sub.i) and optimal control parameter value R(t.sub.i), and the set of differentials between patient self-care values S.sub.M(t.sub.i) and optimal self-care values O.sub.M(t.sub.i). The differentials are multiplied by corresponding scaling factors K.sub.M. In the preferred embodiment, first simulated disease control parameter value X(t.sub.1) at time t, is set equal to first optimal control parameter value R(t.sub.1) at time t.sub.1. In an alternative embodiment, first simulated disease control parameter value X(t.sub.1) is determined from the last disease control parameter value calculated in a prior simulation.

FIGS. 4 5 illustrate a first example of simulated disease control parameter values calculated by the simulation program. Referring to FIG. 4, the simulation program creates table of values 56 having a time column, an optimal control parameter value column, a simulated control parameter value column, three self-care value differential columns indicating differentials between patient self-care parameter values and optimal self-care parameter values, and three corresponding scaling factor columns for weighting the corresponding self-care value differentials.

Table 56 illustrates the simplest simulation, in which the patient follows the optimal self-care actions in perfect compliance at each time point. In this simulation, each patient self-care parameter value equals its corresponding optimal self-care parameter value, so that the simulated disease control parameter value at each time point is simply equal to the optimal control parameter value at each time point. Referring to FIG. 5, the simulation program generates graph 58 of the simulated disease control parameter values. Graph 58 is displayed to the patient on screen 30 of display unit 28, step 216.

FIGS. 6 7 illustrate a second example of simulated disease control parameter values calculated by the simulation program. FIG. 6 shows a table of values 59 having identical structure to table 56. Table 59 illustrates a simulation in which the patient consumes 10 extra grams of carbohydrates at 8:00 and exercises for 60 extra minutes at 15:00. In this simulation, the differential S.sub.1(t)-O.sub.1(t) is equal to 10 at 8:00 due to the 10 extra grams of carbohydrates consumed by the patient. Because scaling factor X.sub.1 equals 4, the simulation program calculates simulated disease control parameter value X(t.sub.2) at time point 10:00 as 40 mg/dL higher than optimal control parameter value R(t.sub.2) at 10:00.

Similarly, the differential S.sub.3(t)-O.sub.3(t) is equal to 4 at time point 15:00 due to the 60 extra minutes of exercise performed by the patient. With simulated disease control parameter value X(t.sub.4) exceeding optimal control parameter value R(t.sub.4) by 40 mg/dL at 15:00 and with scaling factor K.sub.3 equal to -5, the simulation program calculates simulated disease control parameter value X(t.sub.5) at time point 18:00 as 20 mg/dL higher than optimal control parameter value R(t.sub.5). FIG. 7 shows a graph 60 of the simulated disease control parameter values determined in table 59. Graph 60 is displayed to the patient on screen 30 of the display unit.

FIGS. 8 9 illustrate a third example of simulated disease control parameter values calculated by the simulation program. Referring to FIG. 8, a table of values 61 illustrates a simulation in which the patient consumes 10 extra grams of carbohydrates at 8:00, injects 1 extra unit of insulin at 10:00, and exercises for 60 extra minutes at 15:00. The differential S.sub.2(t)-O.sub.2(t) is equal to 1 at 10:00 due to the 1 extra unit of insulin injected by the patient. With simulated disease control parameter value X(t.sub.2) exceeding optimal control parameter value R(t.sub.2) by 40 mg/dL at 10:00, and with scaling factor K.sub.2 equal to -40, the simulation program calculates simulated disease control parameter value X(t.sub.3) at time point 12:00 as equal to optimal control parameter value R(t.sub.3). FIG. 8 shows a graph 62 of the simulated disease control parameter values determined in table 61.

In addition to performing simulations with the simulation program, the patient records actual control parameter values and actual self-care values indicating actual self-care actions performed by the patient at each time point, step 218. These values are preferably recorded in recording device 38. Upon the patient’s next visit to the healthcare provider, the actual control parameter values and actual self-care values are uploaded to provider computer 14, step 220. Those skilled in the art will appreciate that recording device 38 may also be networked to provider computer 14 through a modem and telephone lines or similar network connection. In this alternative embodiment, the actual control parameter values and actual self-care values are transmitted directly from the patient’s home to provider computer 14.

In step 222, provider computer 14 adjusts scaling factors K.sub.M based on the difference between the actual control parameter values and the optimal control parameter values at each time point and the difference between the actual self-care values and the optimal self-care values at each time point. Scaling factors T.sub.M are adjusted to fit them to the actual patient data recorded. In this manner, the scaling factors are customized to the individual patient to enable the patient to run customized simulations. The new values of the scaling factors are stored on smart card 22 which the patient takes home and inserts in processor 24 to run new simulations.

FIGS. 11 12 illustrate a second embodiment of the invention. The second embodiment differs from the preferred embodiment in that the components of the simulation system are contained in a single stand-alone computing device 64. The second embodiment also differs from the preferred embodiment in that the system predicts each future disease control parameter value from an actual measured disease control parameter value rather than from a prior simulated disease control parameter value.

Referring to FIG. 11, computing device 64 includes a housing 66 for holding the components of device 64. Housing 66 is sufficiently compact to enable device 64 to be hand-held and carried by a patient. Device 64 also includes measuring device 40 for producing measurements of actual control parameters values and a display 70 for displaying data to the patient. Device 64 further includes a keypad 68 for entering in device 64 the optimal control parameter values, the optimal self-care values, the patient self-care parameter values, the actual self-care parameter values, and the patient’s physiological parameter values.

FIG. 12 shows a schematic block diagram of the components of device 64 and their interconnections. Device 64 has a microprocessor 72 and a memory 74 operably connected to microprocessor 72. Measuring device 40 and display 70 are also connected to microprocessor 72. Keypad 68 is connected to microprocessor 72 through a standard keypad decoder 78. Microprocessor 72 is connected to an input/output port 76 for entering in device 64 a simulation program to be executed by microprocessor 72 which will be explained in detail below.

Memory 74 stores the optimal control parameter values, the optimal self-care values, the patient self-care parameter values, the actual self-care parameter values CH(t), the scaling factors, and the patient’s physiological parameter values. Memory 74 also stores the simulation program to be executed by microprocessor 72 and the indexes for calculating the scaling factors from the patient’s physiological parameter values.

In the second embodiment, microprocessor 72 is programmed to perform the functions performed by the healthcare provider computer of the preferred embodiment. The functions include determining scaling factors K.sub.M from the patient’s physiological parameter values. The functions also include adjusting scaling factors K.sub.M based on the difference between actual control parameter value A(t.sub.j) and optimal control parameter value R(t.sub.j), the difference between actual control parameter value A(t.sub.i) and optimal control parameter value R(t.sub.i), and the difference between actual self-care values C.sub.M(t.sub.i) and optimal self-care values O.sub.M(t.sub.i).

The operation of the second embodiment is shown in FIG. 14. FIG. 14 is a flow chart illustrating a preferred method of using the system of the second embodiment to predict an effect of patient self-care actions on a disease control parameter. In step 300, the optimal control parameter values and optimal self-care values are entered in device 64 and stored in memory 74. The optimal control parameter values and optimal self-care values may be entered in device 64 either through keypad 68 or through input/output port 76.

In step 302, the patient or healthcare provider determines the patient’s physiological parameter values. The physiological parameter values are then entered in device 64 through keypad 68 and stored in memory 74. Following entry of the physiological parameter values, microprocessor 72 determines scaling factors K.sub.M from the indexes stored in memory 74, step 304. Scaling factors K.sub.M are then stored in memory 74. In an alternative method of determining and storing scaling factors K.sub.M in memory 74, scaling factors K.sub.M are determined in a healthcare provider computer, as previously described in the preferred embodiment. Scaling factors K.sub.M are then entered in device 64 through keypad 68 or port 76 and stored in memory 74.

In step 306, the patient enters in microprocessor 72 actual disease control parameter A(t.sub.i). To enter actual disease control parameter A(t.sub.i), the patient places his or her finger on measurement device 40 at time t.sub.i. Measurement device 40 produces a measurement of actual disease control parameter A(t.sub.i) which is stored in memory 74. In step 308, the patient enters in microprocessor 72 patient self-care values S.sub.M(ti) using keypad 68. In step 310, microprocessor 72 executes the simulation program stored in memory 74 to calculate future disease control parameter value X(t.sub.j).

The simulation program of the second embodiment differs from the simulation program of the preferred embodiment in that future disease control parameter value X(t.sub.j) is calculated from actual disease control parameter A(t.sub.i) rather than from a prior simulated disease control parameter value. In the second embodiment, future disease control parameter value X(t.sub.j) is calculated according to the equation:

.function..function..function..function..SIGMA..times..times..function..fu- nction..function. ##EQU00004##

Thus, future disease control parameter value X(t.sub.j) is determined from optimal control parameter value R(t.sub.j), the difference between actual disease control parameter A(t.sub.i) and optimal control parameter value R(t.sub.i), and the set of differentials between patient self-care values S.sub.M(t.sub.i) and optimal self-care values O.sub.M(t.sub.i). The differentials are multiplied by corresponding scaling factors K.sub.M. Future disease control parameter value X(t.sub.j) is displayed to the patient on display 70, step 312.

Once future disease control parameter value X(t.sub.j) is displayed to the patient, the patient uses the value to select appropriate actual self-care actions to perform at time t.sub.i. Alternatively, the patient may perform several more simulations of future disease control parameter value X(t.sub.j) to decide appropriate self-care actions to perform at time t.sub.i. Once the patient has performed the self-care actions, the patient enters in microprocessor 72 actual self-care values C.sub.M(t.sub.i) indicating the self-care actions performed, step 314. The actual self-care values are then stored in memory 74.

The patient also enters in microprocessor 72 actual disease control parameter A(t.sub.j) measured at time t.sub.j. To enter actual disease control parameter A(t.sub.j), the patient places his or her finger on measurement device 40 at time t.sub.j. Measurement device 40 produces a measurement of actual disease control parameter A(t.sub.j) which is stored in memory 74, step 316. In step 318, microprocessor 72 adjusts scaling factors K.sub.M based on the difference between actual control parameter value A(t.sub.j) and optimal control parameter value R(t.sub.j), the difference between actual control parameter value A(t.sub.i) and optimal control parameter value R(t.sub.i), and the difference between actual self-care values C.sub.M(t.sub.i) and optimal self-care values O.sub.M(t.sub.i). In this manner, the scaling factors are customized to the individual patient to enable the patient to run customized simulations. The new values of the scaling factors are stored in memory 74 and used by microprocessor 72 in subsequent simulations.

SUMMARY, RAMIFICATIONS, AND SCOPE

Although the above description contains many specificities, these should not be construed as limitations on the scope of the invention but merely as illustrations of some of the presently preferred embodiments. Many other embodiments of the invention are possible. For example, the preferred embodiment is described in relation to diabetes. However, it is obvious that the system and method of the invention may be used for simulating any disease which has a measurable control parameter and which requires patient self-care actions. Similarly, the self-care parameters, corresponding scaling factors, and physiological parameters described are exemplary of just one possible embodiment. Many different self-care parameters, scaling factors, and physiological parameters may be used in alternative embodiments.

The preferred embodiment also presents a simulation system that includes a server, a healthcare provider computer, and patient multi-media processor communicating with the provider computer via a smart card. This configuration of system components is presently preferred for ease of setting, storing, and adjusting the model parameters and scaling factors under the supervision of a healthcare provider. However, those skilled in the art will recognize that many other system configurations are possible. For example, in one alternative embodiment, the system is configured as a single stand-alone computing device for executing simulations.

In another embodiment, the smart card is eliminated from the simulation system. In this embodiment, the model parameter values and scaling factors are transmitted directly to the server from the healthcare provider computer. In a further embodiment, the provider computer is also eliminated and the recording device is networked directly to the server. In this embodiment, the server is programmed to set, store, and adjust the model parameters and scaling factors based on patient data received through the recording device and patient multi-media processor.

In yet another embodiment, the server is eliminated and the simulation program is run on the patient multi-media processor. In this embodiment, the recording device and multi-media processor may also be networked directly to the provider computer, eliminating the need for a smart card. Specific techniques for networking computers and recording devices in these alternative system configurations are well known in the art.

Further, the first embodiment is described as a system for simulating a disease control parameter from simulated data and the second embodiment is described as a system for predicting a future value of a disease control parameter from actual patient data. These systems are presented in separate embodiments for clarity of illustration and ease of understanding. However, it is anticipated that both embodiments could be combined into a single simulation system for simulating disease control parameter values from simulated data, actual patient data, or a combination of simulated and actual data.

Therefore, the scope of the invention should be determined not by the examples given but by the appended claims and their legal equivalents.

* * * * *

Related U.S. Patent Documents

Application Number Filing Date Patent Number Issue Date
09399122 Sep., 1999 6233539
08781278 Jan., 1997 5956501

Current U.S. Class: 703/11 ; 600/300; 607/28
Current International Class: G06F 19/00 (20060101); G01N 33/487 (20060101)
Field of Search: 703/11,2 705/1,2,3 700/54,55,67 600/300,309,301 702/19 607/28
References Cited [Referenced By]
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Primary Examiner: Phan; Thai
Attorney, Agent or Firm: Maiorana, PC; Christopher P.
Parent Case Text

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 09/399,122, filed Sep. 20, 1999, now U.S. Pat. No. 6,233,539 which is a continuation of U.S. patent application Ser. No. 08/781,278 filed Jan. 10, 1997, now U.S. Pat. No. 5,956,501, which is herein incorporated by reference.

This application is also related to U.S. Pat. Nos. 6,379,301; 5,822,715; and 6,167,362, all of which claim priority from U.S. Pat. No. 5,956,501.
Claims

What is claimed is:

1. An electronic device for outputting a signal configured according to at least one control parameter of a patient, comprising: (a) an electronic data recording device configured for receiving a prior blood glucose value; (b) a memory comprising one or more optimal blood glucose values, self-care values of a patient, optimal self-care values, and one or more scaling factors for weighting the impact on a future blood glucose value and that are customized to an individual patient to predict the effect on the blood glucose of self-care actions performed by the individual patient; (c) a microprocessor, in communication with said electronic data recording device and said memory, programmed to calculate a further value, said further value being based on said self-care values said optimal blood glucose values, and said scaling factors, and (d) a display configured to display information according to said further value, thereby enabling the patient to select appropriate self care actions.

2. The device of claim 1, further comprising a housing, wherein said memory and said microprocessor are housed within said housing, and further comprising an output port, said output port is integral with said housing, thereby providing a hand-held, readily transportable device.

3. The device of claim 2, wherein said electronic data recording device is arranged within said housing.

4. The device of claim 1, wherein said processor is programmed to calculate a plurality of future blood glucose values representative of a corresponding plurality of expected blood glucose concentrations of the patient.

5. The device of claim 1, further comprising an output port coupled to said processor for establishing a communication link between said device and a healthcare provider computer and for transmitting and receiving data therebetween.

6. The device of claim 5, wherein said device further comprises a modem for establishing said communication link through a communication network.

7. The device of claim 5, wherein said output port comprises an input/output port for establishing said communication link through a connection cord.

8. The device of claim 1, wherein said display is configured for displaying future blood glucose values in graphical form.

9. The device of claim 1, wherein the electronic data recording device further comprises a glucose measurement device for measuring the prior blood glucose value, and wherein the measured blood glucose value is stored in the memory.

10. The device of claim 9, wherein the glucose measuring device comprises a blood glucose meter.
Description