# US 2014/0156698 A1 · Using aggregated sensed data of individuals to predict the mental state of an individual

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- Number: US 2014/0156698 A1
- Document ID: US20140156698A1
- Status: Published application
- Kind: A1
- Application number: 14/060,072
- Filed: 2013-10-22
- Published: 2014-06-05
- Earliest claimed: 2007-02-16
- Assignees: BodyMedia, Inc.
- Applicants: BodyMedia, Inc.
- Inventors: John M. Stivoric; Eric Teller; David Andre; John A. Monocello, III
- Web page: https://patentgrep.com/patent/US20140156698A1

## CPC

- G06Q 30/0251: Targeted advertisements
- A61B 5/021: Measuring pressure in heart or blood vessels
- A61B 5/0826: {Detecting or evaluating apnoea events (sleep apnoea A61B5/4818)}
- A61B 5/1118: {Determining activity level}
- A61B 5/1123: {Discriminating type of movement, e.g. walking or running (A61B5/1116, A61B5/112 take precedence)}
- A61B 5/168: {Evaluating attention deficit, hyperactivity}
- A61B 5/4806: {Sleep evaluation (A61B5/4821 takes precedence; devices for inducing sleep A61M21/02)}
- A61B 5/4812: {Detecting sleep stages or cycles}
- A61B 5/4815: {Sleep quality}
- A61B 5/4818: {Sleep apnoea}
- A61B 5/4833: {Assessment of subject's compliance to treatment}
- A61B 5/4836: {Diagnosis combined with treatment in closed-loop systems or methods (A61B5/0036 takes precedence)}
- A61B 5/6801: {specially adapted to be attached to or worn on the body surface}
- A61B 5/6802: {Sensor mounted on worn items}
- A61B 5/72: {Signal processing specially adapted for physiological signals or for diagnostic purposes}
- 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}
- A61B 5/7278: {Artificial waveform generation or derivation, e.g. synthesizing signals from measured signals}
- A61B 5/7282: {Event detection, e.g. detecting unique waveforms indicative of a medical condition (cough events A61B5/0823; seizures A61B5/4094; sleep apnoea A61B5/4818)}
- A61B 5/742: {using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339)}
- A61B 5/746: {Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms}
- A61B 5/747: {in case of emergency, i.e. alerting emergency services}
- A61B 5/7475: {User input or interface means, e.g. keyboard, pointing device, joystick}
- 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
- 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)
- G06F 16/24575: {using context}
- G06F 16/248: Presentation of query results
- G06F 16/25: Integrating or interfacing systems involving database management systems
- G06F 16/285: {Clustering or classification}
- G06F 16/38: Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F 16/90344: {by using string matching techniques}
- G06N 5/04: Inference or reasoning models
- G06N 20/00: Machine learning
- G06Q 30/0242: Determining effectiveness of advertisements
- G06Q 30/0269: {based on user profile or attribute}
- G06Q 30/0271: {Personalized advertisement}
- G06Q 30/0277: {Online advertisement}
- G06Q 40/08: Insurance
- G09B 5/00: Electrically-operated educational appliances (working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F)
- 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)
- 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
- G16B 50/00: ICT programming tools or database systems specially adapted for bioinformatics
- G16B 99/00: Subject matter not provided for in other groups of this subclass
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 40/63: for local operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- A61M 2021/0027: {by the hearing sense}
- A61M 2021/005: {images, e.g. video}
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16H 50/50: for simulation or modelling of medical disorders
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- A61M: DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY (introducing media into or onto the bodies of animals A61D7/00; means for inserting tampons A61F13/26; devices for administering food or medicines orally A61J; containers for collecting, storing or administering blood or medical fluids A61J1/05); DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY (surgery A61B; chemical aspects of surgical articles A61L); DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR {(Electrotherapy, e.g. producing anaesthesia by the use of alternating or intermittent currents A61N1/36021)}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B)
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

The methods and systems described herein may involve determining at least one lifeotype of at least one individual, analyzing the at least one lifeotype, and delivering content to at least one individual based on the analysis. The methods and systems described herein may involve providing a game, determining at least one lifeotype of at least one player of the game, analyzing the at least one lifeotype, and affecting the game play based on the analysis. The methods and systems described herein may involve providing an interactive space, determining at least one lifeotype of at least one individual in the space, analyzing the at least one lifeotype, and modifying at least one attribute of the space based on the analysis.

## Claims

### Claim 1

A computer-system-implemented method of predicting the mental state for an individual based on the individual's traits, the computer system having at least one programmed processor to implement the method, the method comprising:
continuously collecting data components of an individual from a wearable sensor device;
collecting another set of data components of the individual from a source separate from the wearable device;
accessing a database by said computer system comprising data structures of other individuals, wherein said data structures of other individuals are related to known human traits;
the computer system—
(i) assembling a data structure for the individual that includes at least one component from the collected data components from the wearable sensor device and at least one of said another set of data components;
(ii) accessing said database to determine a similarity of the data structure for the individual with data structures of other individuals;
(iii) based on the determination, assigning a human trait to the individual; and
(iv) based on the trait of the individual, predicting the mental state of the individual based on the assigned trait.

### Claim 2 (depends on 1)

The method of claim 1, wherein the mental state is a healthy state.

### Claim 3 (depends on 1)

The method of claim 1, wherein the mental state is an unhealthy state.

### Claim 4 (depends on 3)

The method of claim 3, wherein the mental state is a disease state.

### Claim 5 (depends on 1)

The method of claim 1, wherein the mental state is a mood of the individual.

### Claim 6 (depends on 1)

The method of claim 1, wherein said another set of data components 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, continuous data and real-time data.

### Claim 7 (depends on 1)

The method of claim 1, wherein said set of data components from a wearable sensor device 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, continuous data and real-time data.

### Claim 8 (depends on 1)

The method of claim 1, wherein at least one data component collected from a wearable sensor device is a data component that is derived from a plurality of sensors that is distinct from the output of any single sensor.

### Claim 9 (depends on 1)

The method of claim 1, wherein the assembled data structure that includes at least one component from the collected data components from the wearable sensor device includes at least one data component collected from a wearable sensor device is a data component that is derived from a plurality of sensors that is distinct from the output of any single sensor.