# US 10,482,124 B2 · Music recommendation based on biometric and motion sensors on mobile device > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,482,124 B2 - Document ID: US10482124B2 - Status: Granted - Kind: B2 - Application number: 15/250,354 - Filed: 2016-08-29 - Published: 2016-12-22 - Granted: 2019-11-19 - Earliest claimed: 2013-03-15 - Assignees: Futurewei Technologies, Inc. - Applicants: Futurewei Technologies, Inc. - Inventors: Chia-Chin Chong; Jianyu Zhang - Web page: https://patentgrep.com/patent/US10482124B2 ## CPC - G06F 16/636: {by using biological or physiological data} - A61B 5/1118: {Determining activity level} - G06F 3/16: Sound input; Sound output (speech processing G10L) - G06F 3/165: {Management of the audio stream, e.g. setting of volume, audio stream path} - G06F 16/635: Filtering based on additional data, e.g. user or group profiles - G06F 16/639: {using playlists} - G06F 16/686: {using information manually generated, e.g. tags, keywords, comments, title or artist information, time, location or usage information, user ratings} - G06F 16/687: using geographical or spatial information, e.g. location - G06F 16/90324: {using system suggestions} - G06N 20/00: Machine learning - H04H 60/33: Arrangements for monitoring the users' behaviour or opinions - H04H 60/46: for recognising users' preferences - H04H 60/49: for identifying locations - H04H 60/65: for using the result on users' side - A61B 5/01: Measuring temperature of body parts {; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue} (clinical contact thermometers G01K13/20) - A61B 5/02055: {Simultaneously evaluating both cardiovascular condition and temperature} - A61B 5/4806: {Sleep evaluation (A61B5/4821 takes precedence; devices for inducing sleep A61M21/02)} - A63B 24/0062: {Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance} - G06F 16/68: Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04H: BROADCAST COMMUNICATION (multiplex communication H04J; pictorial communication aspects of broadcast systems H04N) - A63B: APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT (apparatus for passive exercising, massage A61H) ## Abstract A method implemented by a mobile device for music recommendation to a user of the mobile device, the method comprising acquiring one or more measurements from at least one biometric sensor to obtain biometric information of the user, determining a music based at least in part on the biometric information, and recommending the music to the user for playing. ## Claims ### Claim 1 A method implemented by a user-portable mobile device for recommending audio to a user of the user-portable mobile device, the method comprising: acquiring one or more measurements from at least one biometric sensor to obtain biometric information of the user; determining, based on the biometric information, a biometric identifier as a classification of a biometric state of the user; mapping the biometric identifier to a mood classifier to generate an audio selection, wherein the mood classifier identifies a particular mood from a plurality of moods based on the biometric identifier, and wherein the audio selection is associated with the particular mood; modeling the audio selection based on an activity identifier, the biometric identifier, the mood classifier, user feedback, a location, time patterns, a motion status, and a current activity; and producing the audio selection associated with the particular mood to the user through an output of the user-portable mobile device, wherein the output can be in the form of a signal or sound from a speaker. ### Claim 2 (depends on 1) The method of claim 1, further comprising: updating, using a machine-learning algorithm, the mapping of the biometric identifier to the mood classifier based on user feedback, wherein the user feedback indicates a preference of the user regarding previous audio selections for the user; and playing future audio selections to the user based on the previous audio selections via the updated mapping. ### Claim 3 (depends on 2) The method of claim 2, further comprising: acquiring physical motion data of the user from at least one motion sensor; and determining, based on the physical motion data, the activity identifier as a classification of physical motion of the user, wherein the activity identifier and the biometric identifier are mapped to the mood classifier. ### Claim 4 (depends on 3) The method of claim 3, wherein the user-portable mobile device stores a mapping of each of a plurality of activity identifiers to a corresponding one of a plurality of biometric identifiers. ### Claim 5 (depends on 3) The method of claim 3, wherein the classification of the physical motion is one of a plurality of physical activities. ### Claim 6 (depends on 5) The method of claim 5, wherein the plurality of physical activities comprises one or more of standing, walking, running, biking, dancing, and skating. ### Claim 7 (depends on 1) The method of claim 1, wherein the biometric information of the user includes at least one of heart rate, skin temperature, perspiration level, oxygen level, brain wave, actigraphy sleep pattern, and electrocardiography (ECG). ### Claim 8 A user-portable mobile device comprising: a memory configured to store executable instructions; a processor coupled to the memory and at least one biometric sensor, wherein the processor is configured to: acquire biometric information of a user from the at least one biometric sensor; determine, based on the biometric information, a biometric identifier as a classification of a biometric state of the user; map the biometric identifier to a mood classifier to generate an audio selection, wherein the mood classifier identifies a particular mood from a plurality of moods, and wherein the audio selection is associated with the particular mood; model the audio selection based on an activity identifier, the biometric identifier, the mood classifier, user feedback, a location, time patterns, a motion status, and a current activity; and play the audio selection associated with the particular mood to the user through an output of the user-portable mobile device. ### Claim 9 (depends on 8) The user-portable mobile device of claim 8, wherein the processor is further configured to: update, using a machine-learning algorithm, the mapping of the biometric identifier to the mood classifier based on user feedback, wherein the user feedback indicates a preference of the user regarding previous audio selections for the user; and play future audio selections to the user based on the previous audio selections via the updated mapping. ### Claim 10 (depends on 9) The user-portable mobile device of claim 9, wherein the processor is further coupled to at least one motion sensor, and wherein the processor is further configured to: acquire physical motion data of the user from at least one motion sensor; and determine, based on the physical motion data, the activity identifier as a classification of physical motion of the user, wherein the activity identifier and the biometric identifier are mapped to the mood classifier. ### Claim 11 (depends on 10) The user-portable mobile device of claim 10, further comprising a storage configured to store the mapping of the activity identifier and the biometric identifier to the mood classifier. ### Claim 12 (depends on 10) The user-portable mobile device of claim 10, wherein the classification of the physical motion is one of a plurality of physical activities, and wherein the plurality of physical activities comprises one or more of standing, walking, running, biking, dancing, and skating. ### Claim 13 A computer program product comprising computer executable instructions stored on a non-transitory computer readable medium such that when executed by a processor cause a user-portable mobile device to: acquire one or more measurements from at least one sensor to obtain physical condition data of a user using the user-portable mobile device, wherein the physical condition data includes physical motion data and biometric information of the user; determine, based on the biometric information, a biometric identifier as a classification of a biometric state of the user; determine, based on the physical motion data, an activity identifier as a classification of physical motion of the user; map the activity identifier and the biometric identifier to a mood classifier to generate an audio selection, wherein the mood classifier identifies a particular mood from a plurality of moods, and wherein the audio selection is associated with the particular mood; model the audio selection based on the activity identifier, the biometric identifier, the mood classifier, user feedback, a location, time patterns, a motion status, and a current activity; and play the audio selection associated with the particular mood to the user through an output of the user-portable mobile device. ### Claim 14 (depends on 13) The computer program product of claim 13, wherein the processor further causes the user-portable mobile device to: update, using a machine-learning algorithm, the mapping of the activity identifier and the biometric identifier to the mood classifier based on user feedback, wherein the user feedback indicates a preference of the user regarding previous audio selections to the user; and play future audio selections to the user based on the previous audio selections via the updated mapping. ### Claim 15 (depends on 13) The computer program product of claim 13, wherein the physical condition data includes at least one of the physical motion data and the biometric information of the user. ### Claim 16 (depends on 13) The computer program product of claim 13, wherein the physical condition data is a combination of the physical motion data and the biometric information of the user, wherein the physical motion data is acquired from at least one motion sensor, and wherein the biometric information is acquired from at least one biometric sensor. ### Claim 17 (depends on 13) The computer program product of claim 13, wherein the classification of the physical motion is one of a plurality of physical activities, and wherein the plurality of physical activities comprises one or more of standing, walking, running, biking, dancing, and skating.