# US 2019/0171977 A1 · Using Machine Learning System to Dynamically Process Events > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0171977 A1 - Document ID: US20190171977A1 - Status: Published application - Kind: A1 - Application number: 15/833,049 - Filed: 2017-12-06 - Published: 2019-06-06 - Earliest claimed: 2017-12-06 - Assignees: Bank of America Corporation - Applicants: Bank of America Corporation - Inventors: Manu Kurian; Matthew E. Carroll - Web page: https://patentgrep.com/patent/US20190171977A1 ## CPC - G06N 20/00: Machine learning - G06Q 20/202: {Interconnection or interaction of plural electronic cash registers [ECR] or to host computer, e.g. network details, transfer of information from host to ECR or from ECR to ECR} - G06Q 20/3278: {RFID or NFC payments by means of M-devices} - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - 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 for dynamically processing events are provided. In some examples, a system may receive a request to process an event. The request may be received from, for example, a mobile device of a user. In some arrangements, location data associated with the mobile device may be received. Additional data, such as event details, may also be received. In some arrangements, one or more machine learning datasets may be used to identify one or more sources from which to process the requested event based on the received request, location data, additional data, and the like. In some examples, the event may be processed from the identified source automatically. A notification may also be generated and transmitted to the mobile device including details of the event, source from which the event was processed, and the like. ## Claims ### Claim 1 A dynamic event processing computing platform, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the dynamic event processing computing platform to: receive a request to process an event from a mobile device of a user; responsive to receiving the request to process the event, enable dynamic event processing functions; receive first data associated with a location of the mobile device of the user from which the request to process the event was received; receive second data including event details; identify, based on the first data, the second data, and one or more machine learning datasets, a first source from which to process the event; and process the event using the identified first source. ### Claim 2 (depends on 1) The dynamic event processing computing platform of claim 1, wherein the first data associated with the location of the mobile device of the user includes global positioning system (GPS) data from the mobile device. ### Claim 3 (depends on 1) The dynamic event processing computing platform of claim 1, wherein the mobile device is a wearable device. ### Claim 4 (depends on 1) The dynamic event processing computing platform of claim 1, wherein the request to process the event is received from a mobile event processing application executing on the mobile device. ### Claim 5 (depends on 1) The dynamic event processing computing platform of claim 1, wherein the identified first source is one of: a business source associated with an entity and a personal source associated with the user. ### Claim 6 (depends on 1) The dynamic event processing computing platform of claim 1, wherein the request to process the event is generated via near-field communication between the mobile device and a point-of-sale system of an entity. ### Claim 7 (depends on 1) The dynamic event processing computing platform of claim 1, further including instructions that, when executed, cause the dynamic event processing computing platform to: receive third data including calendar data associated with the user, and wherein identifying the first source from which to process the event is further based on the third data. ### Claim 8 (depends on 1) The dynamic event processing computing platform of claim 1, further including instructions that, when executed, cause the dynamic event processing computing platform to: determine, based on the one or more machine learning datasets, whether one or more criteria are met; responsive to determining that the one or more criteria are met, identifying, based on the first data, the second data, and one or more machine learning datasets, at least a second source from which to process the event; and process the event using the first source and the at least a second source. ### Claim 9 A method, comprising: at a computing platform comprising at least one processor, memory, and a communication interface: receiving, by the at least one processor and via the communication interface, a request to process an event from a mobile device of a user; responsive to receiving the request to process the event, enabling, by the at least one processor, dynamic event processing functions; receiving, by the at least one processor and via the communication interface, first data associated with a location of the mobile device of the user from which the request to process the event was received; receiving, by the at least one processor and via the communication interface, second data including event details; identifying, by the at least one processor and based on the first data, the second data, and one or more machine learning datasets, a first source from which to process the event; and processing, by the at least one processor, the event using the identified first source. ### Claim 10 (depends on 9) The method of claim 9, wherein the first data associated with the location of the mobile device of the user includes global positioning system (GPS) data from the mobile device. ### Claim 11 (depends on 9) The method of claim 9, wherein the mobile device is a wearable device. ### Claim 12 (depends on 9) The method of claim 9, wherein the request to process the event is received from a mobile event processing application executing on the mobile device. ### Claim 13 (depends on 9) The method of claim 9, wherein the identified first source is one of: a business source associated with an entity and a personal source associated with the user. ### Claim 14 (depends on 9) The method of claim 9, wherein the request to process the event is generated via near-field communication between the mobile device and a point-of-sale system of an entity. ### Claim 15 (depends on 9) The method of claim 9, further including: receiving, by the at least one processor and via the communication interface, third data including calendar data associated with the user, and wherein identifying the first source from which to process the event is further based on the third data. ### Claim 16 (depends on 9) The method of claim 9, further including: determining, by the at least one processor and based on the one or more machine learning datasets, whether one or more criteria are met; responsive to determining that the one or more criteria are met, identifying, by the at least one processor and based on the first data, the second data, and one or more machine learning datasets, at least a second source from which to process the event; and processing, by the at least one processor, the event using the first source and the at least a second source. ### Claim 17 One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to: receive a request to process an event from a mobile device of a user; responsive to receiving the request to process the event, enable dynamic event processing functions; receive first data associated with a location of the mobile device of the user from which the request to process the event was received; receive second data including event details; identify, based on the first data, the second data, and one or more machine learning datasets, a first source from which to process the event; and processing the event using the identified first source. ### Claim 18 (depends on 17) The one or more non-transitory computer-readable media of claim 17, wherein the first data associated with the location of the mobile device of the user includes global positioning system (GPS) data from the mobile device. ### Claim 19 (depends on 17) The one or more non-transitory computer-readable media of claim 17, wherein the mobile device is a wearable device. ### Claim 20 (depends on 17) The one or more non-transitory computer-readable media of claim 17, wherein the request to process the event is received from a mobile event processing application executing on the mobile device. ### Claim 21 (depends on 17) The one or more non-transitory computer-readable media of claim 17, wherein the identified first source is one of: a business source associated with an entity and a personal source associated with the user. ### Claim 22 (depends on 17) The one or more non-transitory computer-readable media of claim 17, wherein the request to process the event is generated via near-field communication between the mobile device and a point-of-sale system of an entity. ### Claim 23 (depends on 17) The one or more non-transitory computer-readable media of claim 17, further including instructions that, when executed, cause the computing platform to: receive third data including calendar data associated with the user, and wherein identifying the first source from which to process the event is further based on the third data. ### Claim 24 (depends on 17) The one or more non-transitory computer-readable media of claim 17, further including instructions that, when executed, cause the computing platform to: determine, based on the one or more machine learning datasets, whether one or more criteria are met; responsive to determining that the one or more criteria are met, identifying, based on the first data, the second data, and one or more machine learning datasets, at least a second source from which to process the event; and process the event using the first source and the at least a second source.