# US 12,008,600 B2 · Sentiments based transaction systems and methods > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,008,600 B2 - Document ID: US12008600B2 - Status: Granted - Kind: B2 - Application number: 18/072,671 - Filed: 2022-11-30 - Published: 2023-03-23 - Granted: 2024-06-11 - Earliest claimed: 2014-01-13 - Assignees: Nant Holdings IP, LLC - Applicants: Nant Holdings IP, LLC - Inventors: Patrick Soon-Shiong - Web page: https://patentgrep.com/patent/US12008600B2 ## CPC - G06Q 30/0269: {based on user profile or attribute} - 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 ## Abstract Systems and methods of facilitating transactions related to targeted or customized commercial offerings based on derived sentiment states are provided. The sentiment states are derived from digital representations such as images, videos and sound recordings. ## Claims ### Claim 1 A digital sentiment analysis system comprising: at least one computer readable memory storing software instructions; and at least one processor coupled with the computer readable memory and wherein the processor, upon execution of the software instructions, performs operations to: obtain, via a digital sensor, sensor data of an environment, wherein the sensor data comprises at least one digital data modality and at least some data derived by executing a recognition algorithm, wherein the recognition algorithm comprises at least one of the following recognition algorithms: a scale-invariant feature transform (SIFT) algorithm, a Fast Retina Key-point (FREAK) algorithm, a DAISY descriptor algorithm, or a Features from Accelerated Segment Test (FAST) algorithm; associate one or more sentiment characteristics with at least some of the sensor data, wherein the associating the one or more sentiment characteristics comprises mapping a threshold number or percentage of items present in the sensor data to a sentiment characteristic; generate one or more digital models of sentiment states in the environment based on at least some of the sensor data and the one or more sentiment characteristics; determine a set of sentiment states relevant to the environment from the one or more digital models of sentiment states; and facilitate a mobile device to take action based on the set of sentiment states relevant to the environment. ### Claim 2 (depends on 1) The system of claim 1, wherein the action is facilitated in real time. ### Claim 3 (depends on 1) The system of claim 1, wherein the at least one digital data modality comprises location data. ### Claim 4 (depends on 1) The system of claim 1, wherein the at least one digital data modality comprises one or more of: tactile data, temperature data, time data, biometric data, heart rate data, or blood pressure data. ### Claim 5 (depends on 1) The system of claim 1, wherein the sensor data comprises at least one of: image data, video data, or audio data. ### Claim 6 (depends on 1) The system of claim 1, wherein the sensor data comprises a representation of at least one of: a portion of a person, an identity, a face, a fingerprint, an eye, a mouth, a stance, or a pose. ### Claim 7 (depends on 1) The system of claim 1, wherein the set of sentiment states relevant to the environment comprises at least two sentiment states. ### Claim 8 (depends on 7) The system of claim 7, wherein the set of sentiment states relevant to the environment comprises a ranked set of sentiment states relevant to the environment. ### Claim 9 (depends on 8) The system of claim 8, wherein sentiment states in the ranked set of sentiment states relevant to the environment are ranked based on a fitness measure. ### Claim 10 (depends on 1) The system of claim 1, wherein the set of sentiment states relevant to the environment comprises conflicting sentiment states. ### Claim 11 (depends on 1) The system of claim 1, wherein the operations further comprise deriving sentiment characteristics from the sensor data. ### Claim 12 (depends on 11) The system of claim 11, wherein the operation of generating the one or more digital models includes using the sentiment characteristics to derive sentiment states of the digital models. ### Claim 13 (depends on 12) The system of claim 12, wherein the one or more digital models are generated based on a query submitted to a database, wherein the query is based on the sentiment characteristics. ### Claim 14 (depends on 1) The system of claim 1, wherein the action comprises a transaction. ### Claim 15 (depends on 14) The system of claim 14, wherein the transaction comprises a healthcare transaction. ### Claim 16 (depends on 14) The system of claim 14, wherein the transaction comprises a game transaction. ### Claim 17 (depends on 14) The system of claim 14, wherein the transaction relates to at least one commercial offering. ### Claim 18 (depends on 17) The system of claim 17, wherein the operations further include mapping the set of sentiment states relevant to the environment to the at least one commercial offering. ### Claim 19 (depends on 18) The system of claim 18, wherein the at least one commercial offering is from at least one vendor. ### Claim 20 (depends on 18) The system of claim 18, wherein the at least one commercial offering is ranked according to a for-fee schedule. ### Claim 21 A non-transitory computer readable medium containing program instructions which, when executed by at least one processor and memory, cause the at least one processor to perform one or more steps comprising: obtaining, via a digital sensor, sensor data of an environment, wherein the sensor data comprises at least one digital data modality and at least some data derived by executing a recognition algorithm, wherein the recognition algorithm comprises at least one of the following recognition algorithms: a scale-invariant feature transform (SIFT) algorithm, a Fast Retina Key-point (FREAK) algorithm, a DAISY descriptor algorithm, or a Features from Accelerated Segment Test (FAST) algorithm; associate one or more sentiment characteristics with at least some of the sensor data, wherein the associating the one or more sentiment characteristics comprises mapping a threshold number or percentage of items present in the sensor data to a sentiment characteristic; generating one or more digital models of sentiment states in the environment based on at least some of the sensor data and the one or more sentiment characteristics; determining a set of sentiment states relevant to the environment from the one or more digital models of sentiment states; and facilitating a mobile device to take action based on the set of sentiment states relevant to the environment. ### Claim 22 A computer-implemented method for digital sentiment analysis, the method comprising: obtaining, via a digital sensor, sensor data of an environment, wherein the sensor data comprises at least one digital data modality and at least some data derived by executing a recognition algorithm, wherein the recognition algorithm comprises at least one of the following recognition algorithms: a scale-invariant feature transform (SIFT) algorithm, a Fast Retina Key-point (FREAK) algorithm, a DAISY descriptor algorithm, or a Features from Accelerated Segment Test (FAST) algorithm; associate one or more sentiment characteristics with at least some of the sensor data, wherein the associating the one or more sentiment characteristics comprises mapping a threshold number or percentage of items present in the sensor data to a sentiment characteristic; generating one or more digital models of sentiment states in the environment based on at least some of the sensor data and the one or more sentiment characteristics; determining a set of sentiment states relevant to the environment from the one or more digital models of sentiment states; and facilitating a mobile device to take action based on the set of sentiment states relevant to the environment.