# US 12,009,101 B2 · Wellness monitoring by measurement of incidental routine > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,009,101 B2 - Document ID: US12009101B2 - Status: Granted - Kind: B2 - Application number: 16/169,592 - Filed: 2018-10-24 - Published: 2019-05-02 - Granted: 2024-06-11 - Earliest claimed: 2017-10-24 - Assignees: The Quantum Group, Inc.; Noel Guillama - Applicants: The Quantum Group, Inc. - Inventors: Noel J. Guillama; Chester A. Heath - Web page: https://patentgrep.com/patent/US12009101B2 ## CPC - G16H 50/30: for calculating health indices; for individual health risk assessment - G06V 40/20: Movements or behaviour, e.g. gesture recognition (recognition of facial expressions G06V40/16) - G06V 40/25: {Recognition of walking or running movements, e.g. gait recognition} - G16H 10/60: for patient-specific data, e.g. for electronic patient records - G16H 20/70: relating to mental therapies, e.g. psychological therapy or autogenous training - G16H 40/67: for remote operation - H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks - H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)} - G06V 40/15: {Biometric patterns based on physiological signals, e.g. heartbeat, blood flow} - G06V 40/193: {Preprocessing; Feature extraction} - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract The present disclosure provides for the automatic measurement of measurable incidental routine parameters (MIRP). MIRP can include every movement of the body and a user's routine. This data can be incorporated into an accurate and comprehensive evaluation of a user's routine. The present disclosure functions by identifying normalcy for an individual in the consistency of habitual movement and behavior. This normalcy can become an indicator of wellness. Therefore, the present disclosure provides systems or methods which can objectively analyze a user's behavior to provide holistic predictors and analysis of a user's health. ## Claims ### Claim 1 A method comprising: receiving, via a plurality of sensors, first activity data for a first time period associated with an individual, wherein the first activity data includes event data corresponding to a plurality of events; based on the first activity data, determining a routine associated with the individual during the first time period; receiving, via the plurality of sensors, second activity data for a second time period associated with the individual, wherein the second activity data includes event data corresponding to the plurality of events, wherein the event data associated with the first activity data comprises a first order in which each of the events in the plurality of events occurred with respect to each other during the first time period, wherein the event data associated with the second activity data comprises a second order in which each of the events in the plurality of events occurred with respect to each other during the second time period; based on the second activity data, identifying whether at least one abnormal deviation exists between the first order and the second order during the second time period by: determining whether the second order is different than the first order; and in response to identifying the at least one abnormal deviation exists between the first order and the second order during the second time period, transmitting a notification indicating the presence of the abnormal deviation, wherein the event data comprises: a timing of each event in the plurality of events; a first sequence of each event in the plurality of events with respect to remaining events in the plurality of events; and a completion indicator of each event in the plurality of events. ### Claim 2 (depends on 1) The method of claim 1, wherein determining the routine comprises: detecting the timing of each event and the completion indicator of each event in the plurality of events; based on the detected timing of each event and the completion indicator of each event, determining correlations between a portion of events in the plurality of events; determining a plurality of time windows corresponding to the plurality of events, wherein each time window in the plurality of time windows indicates when the corresponding event in the plurality of events occurs during the first time period; and determining the routine based on the determined correlations and the determined time windows. ### Claim 3 (depends on 2) The method of claim 2, wherein identifying whether the at least one abnormal deviation exists between the first order and the second order during the second time period comprises determining whether at least one event in the plurality of events occurred outside of the determined time window for the at least one event. ### Claim 4 (depends on 2) The method of claim 2, wherein identifying whether the at least one abnormal deviation exists between the first order and the second order during the second time period further includes: determining a wellness metric based on the second activity data and the determined routine; and determining that the determined wellness metric is below a threshold. ### Claim 5 (depends on 1) The method of claim 1, wherein the plurality of sensors comprises wearable sensors. ### Claim 6 (depends on 1) The method of claim 1, wherein the plurality of sensors comprises sensors in communication with a plurality of mechanical and electrical objects associated with the individual, wherein the first and/or second activity data does not include measurable medical parameters. ### Claim 7 A non-transitory computer-readable medium, in which is embedded computer-readable code, that, when loaded on a computer, causes the computer to perform the steps of: receiving, via a plurality of sensors, first activity data for a first time period associated with an individual, wherein the first activity data includes event data corresponding to a plurality of events; based on the first activity data, determining a routine associated with the individual during the first time period; receiving, via the plurality of sensors, second activity data for a second time period associated with the individual, wherein the second activity data includes event data corresponding to the plurality of events, wherein the event data associated with the first activity data comprises a first order in which each of the events-in the plurality of events occurred with respect to each other during the first time period, wherein the event data associated with the second activity data comprises a second order in which each of the events in the plurality of events occurred with respect to each other during the second time period; based on the second activity data, identifying whether at least one abnormal deviation exists between the first order and the second order during the second time period by: determining whether the second order is different than the first order; and in response to identifying the at least one abnormal deviation exists between the first order and the second order during the second time period, transmitting a notification indicating the presence of the abnormal deviation. ### Claim 8 (depends on 7) The non-transitory computer-readable medium of claim 7, wherein both the first activity data and the second activity data comprise event data corresponding to a plurality of events, wherein the event data comprises: a timing of each event in the plurality of events; and a completion indicator of each event in the plurality of events. ### Claim 9 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein determining the routine comprises: detecting the timing of each event and the completion indicator of each event in the plurality of events; based on the detected timing of each event and the completion indicator of each event, determining correlations between a portion of events in the plurality of events; determining a plurality of time windows corresponding to the plurality of events, wherein each time window in the plurality of time windows indicates when the corresponding event in the plurality of events occurs during the first time period; and determining the routine based on the determined correlations and the determined time windows. ### Claim 10 (depends on 9) The non-transitory computer-readable medium of claim 9, wherein identifying whether the at least one abnormal deviation exists between the first order and the second order during the second time period comprises: determining whether the at least one event in the plurality of events occurred outside of the determined time window for the at least one event. ### Claim 11 (depends on 9) The non-transitory computer-readable medium of claim 9, wherein identifying whether the at least one abnormal deviation exists between the first order and the second order during the second time period further includes: determining a wellness metric based on the second activity data and the determined routine; and determining that the determined wellness metric is below a threshold. ### Claim 12 (depends on 7) The non-transitory computer-readable medium of claim 7, wherein the plurality of sensors comprises wearable sensors. ### Claim 13 (depends on 7) The non-transitory computer-readable medium of claim 7, wherein the event data comprises a relative importance for each of the plurality of events, wherein the first time period includes the same time entries as the second time period, wherein the first and second time periods correspond to different days. ### Claim 14 A system, comprising: a plurality of sensors; a processor, communicatively coupled to the plurality of sensors; and a memory having stored therein a plurality of code sections executable by the processor, the plurality of code comprising instructions for: receiving, via a plurality of sensors, first activity data for a first time period associated with an individual, wherein the first activity data includes event data corresponding to a plurality of events; based on the first activity data, determining a routine associated with the individual during the first time period; receiving, via the plurality of sensors, second activity data for a second time period associated with the individual, wherein the second activity data includes event data corresponding to the plurality of events, wherein the event data associated with the first activity data comprises a first order in which each of the events in the plurality of events occurred with respect to each other during the first time period, wherein the event data associated with the second activity data comprises a second order in which each of the events in the plurality of events occurred with respect to each other during the second time period, wherein the first time period and second time period do not overlap, wherein the event data comprises a relative importance for each of the plurality of events; based on the second activity data, identifying whether at least one abnormal deviation exists between the first order and the second order during the second time period by: determining whether the second order is different than the first order; and in response to identifying the at least one abnormal deviation exists between the first order and the second order during the second time period, transmitting a notification indicating the presence of the abnormal deviation. ### Claim 15 (depends on 14) The system of claim 14, wherein both the first activity data and the second activity data comprise event data corresponding to a plurality of events, wherein the event data comprises: a timing of each event in the plurality of events; and a completion indicator of each event in the plurality of events. ### Claim 16 (depends on 15) The system of claim 15, wherein determining the routine comprises: detecting the timing of each event and the completion indicator of each event in the plurality of events; based on the detected timing of each event and the completion indicator of each event, determining correlations between a portion of events in the plurality of events; determining a plurality of time windows corresponding to the plurality of events, wherein each time window in the plurality of time windows indicates when the corresponding event in the plurality of events occurs during the first time period; and determining the routine based on the determined correlations and the determined time windows. ### Claim 17 (depends on 16) The system of claim 16, wherein identifying whether the at least one abnormal deviation exists between the first order and the second order during the second time period comprises: determining whether the at least one event in the plurality of events occurred outside of the determined time window for the at least one event. ### Claim 18 (depends on 16) The system of claim 16, wherein identifying whether the at least one abnormal deviation exists between the first order and the second order during the second time period further includes: determining a wellness metric based on the second activity data and the determined routine, the wellness metric being positively correlated with how closely the second activity data matches the determined routine; and determining whether the determined wellness metric is below a threshold, wherein the first and second time periods correspond to different days. ### Claim 19 (depends on 14) The system of claim 14, wherein the plurality of sensors comprises wearable sensors. ### Claim 20 (depends on 14) The system of claim 14, wherein the plurality of sensors comprises sensors in communication with a plurality of mechanical and electrical objects associated with the individual, wherein the first and/or second activity data does not include measurable medical parameters.