PERSONALIZED MEDICINE SYSTEM

• US 20090271342A1
• Filed: 07/04/2009
• Published: 10/29/2009
• Est. Priority Date: 12/10/2002
• Status: Abandoned Application

• Current Assignee: Square Halt Solutions Limited Liability Company (Intellectual Ventures LLC)
• Original Assignee: Square Halt Solutions Limited Liability Company (Intellectual Ventures LLC)
• Inventors: Eder, Jeffrey Scott
• Application Number:
  US12/497,656
• Publication Number:
  US 20090271342A1
• Time in Patent Office: Days
• US Class Current: 706/20
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing

Abstract

A method, program storage device and system for developing a Personalized Medicine System (100) for an individual or group of individuals that automates the operation, customization and coordination of computer systems, software, products, services, data and/or devices.

Claims

1. A personalized medicine method, comprising:

preparing data from a plurality of subject related systems for use in processing,

defining a subject using at least a portion of said data and a plurality of user input,

analyzing said data as required to define and store a causal predictive model for one or more health function measures of said subject, and

using one or more automated analyses of said causal predictive model to complete activities selected from the group consisting of customizing a treatment, customizing a test, ordering a treatment, ordering a test, forecasting a sustainable longevity, analyzing the impact of user specified changes on a subject performance, customizing information for the subject, customizing a product or a service for the subject, displaying a graph about subject performance, exchanging an element with one or more subjects or entities in an automated fashion, forecasting one or more future values of subject related variables, identifying one or more metrics and rules for monitoring subject performance, identifying one or more changes that will optimize subject performance on one or more function measures, quantifying one or more risks to a subject function measure performance, quantifying an impact of surprises on subject performance, simulating a subject performance, establishing one or more priorities for subject actions and commitments, establishing an expected performance level for the subject, reviewing a subject performance using user defined measures, identifying a data and information combination that is most relevant to the subject, identifying one or more subject preferences, loading a data and information combination that is most relevant to the subject into a cache, and creating a summary of subject context

2. The method of claim 1, wherein a causal predictive model for one or more health measures of a subject comprises two or more aspects of a complete context for a health of said subject selected from the group consisting of a reference frame context, a resource context, an element context, an environment context, a measure context, a lexical context, a relationship context, a transaction context and combinations thereof.

3. The method of claim 2, wherein a causal predictive model for one or more health measures of a subject and the two or more aspects of a complete context comprise a context frame.

4. The method of claim 1, wherein a subject is a patient, two or more patients or a patient-entity system.

5. The method of claim 1, wherein a set of useful activities further comprise developing one or more programs for subject related devices, creating a true natural language interface for the subject and developing one or more subject related software programs.

6. The method of claim 1, wherein a causal predictive model for one or more health measures of a subject comprises an impact of one or more functions of one or more biological entities associated with said subject, and where a biological entity is selected from the group consisting of macromolecular complex, protein, rna, dna, methylation, gene, organelle, cell, monomer, dimer, large oligomer, aggregate and particle.

7. Means for completing the method of claim 1 and a plurality of narrow systems.

10. The method of claim 2, wherein a causal predictive model for one or more health measures of a subject and the two or more aspects of a complete context comprise a context frame.

13. The method of claim 1, wherein a causal predictive model for one or more health measures of a subject comprises an impact of one or more functions of one or more biological entities associated with said subject, and where a biological entity is selected from the group consisting of macromolecular complex, protein, rna, dna, methylation, gene, organelle, cell, monomer, dimer, large oligomer, aggregate and particle.

8. A program storage device readable by a computer, tangibly embodying a program of instructions executable by a computer to perform method steps for performing a personalized medicine method, comprising:

preparing data from a plurality of subject related systems for use in processing,

defining a subject using at least a portion of said data and a plurality of user input,

analyzing said data as required to define and store a causal predictive model for one or more health function measures of said subject, and

using one or more automated analyses of said causal predictive model to complete activities selected from the group consisting of customizing a treatment, customizing a test, ordering a treatment, ordering a test, forecasting a sustainable longevity, analyzing the impact of user specified changes on a subject performance, capturing a subject related knowledge from one or more subject matter experts, customizing information for the subject, customizing a product or a service for the subject, displaying a graph about subject performance, exchanging an element with one or more subjects or entities in an automated fashion, forecasting one or more future values of subject related variables, identifying one or more metrics and rules for monitoring subject performance, identifying one or more changes that will optimize subject performance on one or more function measures, quantifying one or more risks to a subject function measure performance, quantifying an impact of surprises on subject performance, simulating a subject performance, establishing one or more priorities for subject actions and commitments, establishing an expected performance level for the subject, reviewing a subject performance using user defined measures, identifying a data and information combination that is most relevant to the subject, identifying one or more subject preferences, loading a data and information combination that is most relevant to the subject into a cache, and creating a summary of subject context.

9. The program storage device of claim 8, wherein a causal predictive model for one or more health measures of a subject comprises two or more aspects of a complete context for a health of said subject selected from the group consisting of a reference frame context, a resource context, an element context, an environment context, a measure context, a lexical context, a relationship context, a transaction context and combinations thereof.

11. The program storage device of claim 8, wherein a subject is a patient, two or more patients or a patient-entity system.

12. The program storage device of claim 8, wherein a set of useful activities further comprise developing one or more programs for subject related devices, creating a true natural language interface for the subject and developing one or more subject related software programs.

14. The program storage device of claim 8, wherein the computer readable medium further comprises a plurality of bots or intelligent agents.

15. A system for personalized medicine, comprising:

a computer with a processor having circuitry to execute instructions;

a storage device available to said processor with sequences of instructions stored therein, which when executed cause the processor to;

prepare data from a plurality of subject related systems for use in processing,

define a subject using at least a portion of said data and a plurality of user input,

analyze at least a portion of said data as required to define and store a causal predictive model for one or more health function measures of said subject, and

using one or more automated analyses of said causal predictive model to complete useful activities selected from the group consisting of customizing a treatment, customizing a test, ordering a treatment, ordering a test, forecasting a sustainable longevity, analyzing the impact of user specified changes on a subject performance, capturing a subject related knowledge from one or more subject matter experts, customizing information for the subject, customizing a product or a service for the subject, displaying a graph about subject performance, exchanging an element with one or more subjects or entities in an automated fashion, forecasting one or more future values of subject related variables, identifying one or more metrics and rules for monitoring subject performance, identifying one or more changes that will optimize subject performance on one or more function measures, quantifying one or more risks to a subject function measure performance, quantifying an impact of surprises on subject performance, simulating a subject performance, establishing one or more priorities for subject actions and commitments, establishing an expected performance level for the subject, reviewing a subject performance using user defined measures, identifying a data and information combination that is most relevant to the subject, identifying one or more subject preferences, loading a data and information combination that is most relevant to the subject into a cache, and creating a summary of subject context.

16. The system of claim 15, wherein a causal predictive model for one or more health measures of a subject comprises three or more aspects of a complete context for a health of said subject selected from the group consisting of a reference frame context, a resource context, an element context, an environment context, a measure context, a lexical context, a relationship context, a transaction context and combinations thereof.

17. The system of claim 15, wherein a causal predictive model for one or more health measures of a subject and the two or more aspects of a complete context comprise a context frame.

18. The system of claim 15, wherein a subject is a patient, two or more patients or a patient-entity system.

19. The system of claim 15, wherein a set of useful activities further comprise developing one or more programs for subject related devices, creating a true natural language interface for the subject and developing one or more subject related software programs.

20. The system of claim 15, wherein a causal predictive model for one or more health measures of a subject comprises an impact of one or more functions of one or more biological entities associated with said subject, and where a biological entity is selected from the group consisting of macromolecular complex, protein, rna, dna, methylation, gene, organelle, cell, monomer, dimer, large oligomer, aggregate and particle.