LIFEOTYPES

• US 20090006457A1
• Filed: 02/19/2008
• Published: 01/01/2009
• Est. Priority Date: 02/16/2007
• Status: Abandoned Application

• Current Assignee: JB IP Acquisitions LLC
• Original Assignee: JB IP Acquisitions LLC
• Inventors: Monocello, John A., Andre, David, Stivoric, John M., Teller, Eric
• Application Number:
  US12/033,722
• Publication Number:
  US 20090006457A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/0826 Detecting or evaluating apnoea events A61B5/4818 takes precedence
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/168 Evaluating attention deficit, hyperactivity
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/4833 Assessment of subject's compliance to treatment
• CPC Class Codes: A61B 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/6802 Sensor mounted on worn items
• CPC Class Codes: A61B 5/72 Signal processing specially adapted for physiological signals or for diagnostic purposes character recognition, recognising digital ink, document-oriented image-based pattern recognition G06V30/00; recognition or understanding of postal images G06V30/424
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 5/747 in case of emergency, i.e. alerting emergency services
• CPC Class Codes: A61B 5/7475 : User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: A61M 2021/0027 : by the hearing sense
• CPC Class Codes: A61M 2021/005 : images, e.g. video
• CPC Class Codes: A61M 21/00 : Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
• CPC Class Codes: A61M 21/02 : for inducing sleep or relaxation, e.g. by direct nerve stimulation, hypnosis, analgesia for massage A61H; electrotherapy A61N, e.g. applying alternating or intermittent electric currents for producing anaesthesia A61N1/36021
• CPC Class Codes: G06F 16/24575 : using context
• CPC Class Codes: G06F 16/248 : Presentation of query results
• CPC Class Codes: G06F 16/25 : Integrating or interfacing systems involving database management systems
• CPC Class Codes: G06F 16/285 : Clustering or classification
• CPC Class Codes: G06F 16/38 : Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/90344 : by using string matching techniques
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 5/04 : Inference or reasoning models
• CPC Class Codes: G06Q 30/0242 : Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0251 : Targeted advertisements
• CPC Class Codes: G06Q 30/0269 : based on user profile or attribute
• CPC Class Codes: G06Q 30/0271 : Personalized advertisement
• CPC Class Codes: G06Q 30/0277 : Online advertisement
• CPC Class Codes: G06Q 40/08 : Insurance
• CPC Class Codes: G09B 19/00 : Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26
• CPC Class Codes: G09B 5/00 : Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F
• CPC Class Codes: G16B 40/00 : ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 50/00 : ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 99/00 : Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G16H 10/60 : for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients

Abstract

The present invention provides a method and a system for assembling data from at least one data source into at least one life bit, assembling the at least one life bit into at least one life byte, and defining a lifeotype based on the attributes of at least one life byte.

Claims

1. A method, comprising:

assembling data from at least one data source into at least one life bit;

assembling the at least one life bit into at least one life byte; and

defining a lifeotype based on the attributes of at least one life byte.

2. The method of claim 1, wherein the data sources include sensed health data, residence data, medical records and survey data.

3. The method of claim 1, wherein the life byte consists of four life bits, with one bit consisting of each of sensed health data, residence data, medical records and survey data.

6. The method of claim 1, wherein the lifeotype is defined based on analysis of correlation between attributes of a life byte and other information about a population of individuals.

15. The method of claim 1, wherein more than one life byte is organized into a life byte sequence.

16. The method of claim 1, wherein the at least one data source is a body monitor including at least one sensor.

17. The method of claim 1, wherein the data is selected from the group consisting of:

physiological data, contextual data and environmental data.

18. The method of claim 1, wherein the data is selected from the group consisting of:

derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, real-time date, feedback loop data, relative level data and changes in levels data.

19. The method of claim 1, wherein the data consists of at least two types of data selected from the group consisting of:

derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, real-time date, feedback loop data, relative level data and changes in levels data.

456. The method of claim 1, wherein the data is derived data, wherein said derived data is generated from both a first parameter and a second parameter of an individual, said both first and second parameters being generated by a sensor device and being at least one of a physiological, contextual and environmental parameter of said individual.

458. The method of claim 1, wherein the data is derived data generated by a processor from a first wearable sensor generating data indicative of at least one of a first physiological, first contextual and first environmental parameter of an individual and a second wearable sensor generating data indicative of at least one of a second physiological, second contextual and second environmental parameter of said individual, wherein the derived data is a status parameter that cannot be directly measured with any of said first and second sensors alone.

4-5. -5. (canceled)

7-14. -14. (canceled)

20-455. -455. (canceled)

457. (canceled)

459-464. -464. (canceled)

465. A method, comprising:

classifying a population of individuals into lifeotypes that correspond to defined sequences of status data that are collected with respect to specified aspects of the human lifestyle.

466. The method of claim 465, wherein the status data is selected from the group consisting of human status data, human condition data, and human lifestyle data.

468. The method of claim 465, wherein the at least a subset of the status data was obtained using a body monitor including at least one sensor.

469. The method of claim 465, wherein the status data is selected from the group consisting of:

physiological data, contextual data and environmental data.

470. The method of claim 465, wherein the status data is selected from the group consisting of:

derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, real-time date, feedback loop data, relative level data and changes in levels data.

471. The method of claim 465, wherein the status data consists of at least two types of data selected from the group consisting of:

derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, real-time date, feedback loop data, relative level data and changes in levels data.

908. The method of claim 465, wherein the status data is derived data, wherein said derived data is generated from both a first parameter and a second parameter of an individual, said both first and second parameters being generated by a sensor device and being at least one of a physiological, contextual and environmental parameter of said individual.

910. The method of claim 465, wherein the status data is derived data generated by a processor from a first wearable sensor generating data indicative of at least one of a first physiological, first contextual and first environmental parameter of an individual and a second wearable sensor generating data indicative of at least one of a second physiological, second contextual and second environmental parameter of said individual, wherein the derived data is a status parameter that cannot be directly measured with any of said first and second sensors alone.

467. (canceled)

472-907. -907. (canceled)

909. (canceled)

911-915. -915. (canceled)

916. A method, comprising:

classifying a population of individuals into lifeotypes that correspond to defined sequences of status data, the status data including data obtained by at least one sensor associated with the individual.

917. The method of claim 916, wherein the status data is selected from the group consisting of human status data, human condition data, and human lifestyle data.

918. The method of claim 916, further comprising:

analyzing patterns within and across lifeotypes to draw conclusions about individuals sharing a certain lifeotype.

919. The method of claim 916, wherein the at least one sensor is associated with a body monitor.

920. The method of claim 916, wherein the status data is selected from the group consisting of:

physiological data, contextual data and environmental data.

921. The method of claim 916, wherein the status data is selected from the group consisting of:

derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, real-time date, feedback loop data, relative level data and changes in levels data.

922. The method of claim 916, wherein the status data consists of at least two types of data selected from the group consisting of:

derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, real-time date, feedback loop data, relative level data and changes in levels data.

923-1366. -1366. (canceled)

1367. A method, comprising:

classifying a population of individuals into lifeotypes that correspond to defined sequences of status data that are collected with respect to specified aspects of the human condition.

1368. The method of claim 1367, wherein the status data is selected from the group consisting of human status data, human condition data, and human lifestyle data.

1369. The method of claim 1367, further comprising:

analyzing patterns within and across lifeotypes to draw conclusions about individuals sharing a certain lifeotype.

1370. The method of claim 1367, wherein the at least a subset of the status data was obtained using a body monitor including at least one sensor.

1371. The method of claim 1367, wherein the status data is selected from the group consisting of:

physiological data, contextual data and environmental data.

1372. The method of claim 1367, wherein the status data is selected from the group consisting of:

derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, real-time date, feedback loop data, relative level data and changes in levels data.

1373. The method of claim 1367, wherein the status data consists of at least two types of data selected from the group consisting of:

derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, real-time date, feedback loop data, relative level data and changes in levels data.

1812. The method of claim 1367, wherein the status data is derived data generated by a processor from a first wearable sensor generating data indicative of at least one of a first physiological, first contextual and first environmental parameter of an individual and a second wearable sensor generating data indicative of at least one of a second physiological, second contextual and second environmental parameter of said individual, wherein the derived data is a status parameter that cannot be directly measured with any of said first and second sensors alone.

1374-1811. -1811. (canceled)

1813-1817. -1817. (canceled)

1818. In a computer system, a computer-readable storage media storing:

at least one computer program that operates to automatically generate a data structure uniquely corresponding to an individual, wherein said data structure comprises;

a plurality of discrete data bits, said data bits comprising at least one of information that is automatically sensed regarding the individual and information that is input into said system; and

a plurality of bytes assembled from sequences of predefined types of said discrete data bits, wherein said bytes are assembled into a data structure unique to said individual.

1819. The method of claim 1818, wherein the data bits include sensed health data, residence data, medical records and survey data.

1820. The method of claim 1818, wherein a byte consists of four bits, with one bit consisting of each of sensed health data, residence data, medical records and survey data.

1824. The method of claim 1818, wherein a data bit is selected from the group consisting of:

derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, real-time date, feedback loop data, relative level data and changes in levels data.

1825. The method of claim 1818, wherein a data bit consists of at least two types of data selected from the group consisting of:

derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, real-time date, feedback loop data, relative level data and changes in levels data.

1821-1823. -1823. (canceled)

1826-2270. -2270. (canceled)