# US 11,443,215 B2 · Intelligent recommendation of convenient event opportunities > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,443,215 B2 - Document ID: US11443215B2 - Status: Granted - Kind: B2 - Application number: 16/180,699 - Filed: 2018-11-05 - Published: 2020-05-07 - Granted: 2022-09-13 - Earliest claimed: 2018-11-05 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Joao H. Bettencourt-Silva; Theodora Brisimi; Marco Luca Sbodio; Natalia Mulligan - Web page: https://patentgrep.com/patent/US11443215B2 ## CPC - G06N 5/048: Fuzzy inferencing - G06N 5/04: Inference or reasoning models - G06N 20/00: Machine learning - G06Q 30/02: Marketing; Price estimation or determination; Fundraising - 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 Embodiments for implementing intelligent recommendations of convenient event opportunities by a processor. A group of entities may be identified for one or more event opportunities or the one or more event opportunities may be identified for the group of entities according to one or more entity selection criteria and one or more event criteria. The one or more event opportunities and the group of entities may be matched according to a level of convenience for attending the one or more event opportunities of the group of entities. The one or more matching event opportunities may be ranked and suggested to the group of entities. ## Claims ### Claim 1 A method for implementing intelligent recommendations of convenient event opportunities by a processor, comprising: identifying a group of entities for one or more event opportunities or identifying the one or more event opportunities for the group of entities according to one or more entity selection criteria and one or more event criteria; executing machine learning logic to train, in a first stage, a suggested opportunity model using observed behavior, inclusive of activities of daily living (ADL), of each entity in the group of entities as input; matching, based on the suggested opportunity model, the one or more event opportunities and the group of entities according to a level of convenience for attending the one or more event opportunities of the group of entities; ranking the one or more matching event opportunities according to the level of convenience; suggesting the ranked one or more matching event opportunities to the group of entities, wherein the one or more matching event opportunities each require at least one of the group of entities to perform and complete a plurality of actions in a predefined sequence to participate in a final event, and wherein suggesting the ranked one or more matching event opportunities includes suggesting scheduling arrangements for the at least one of the group of entities to perform the plurality of actions in the predefined sequence based on the level of convenience determined for each of the plurality of actions; and executing the machine learning logic to train, in a second stage, the suggested opportunity model using feedback data collected subsequent to suggesting the ranked one or more matching event opportunities, wherein the suggested opportunity model is iteratively updated using the feedback data to enhance an accuracy of future suggested event opportunities. ### Claim 2 (depends on 1) The method of claim 1, further including defining the one or more entity selection criteria to include at least an entity profile, features and characteristics common among a plurality of entities, same interests, a type of connection, historical data of each of the plurality of entities, calendar data, frequency of attended historical events, a selected amount of time between events, or a combination thereof. ### Claim 3 (depends on 1) The method of claim 1, further including defining the one or more event criteria to include at least a location, venue, travel options, time, financial costs, common interests, topics, meeting history, entity preferences, activity type, or a combination thereof. ### Claim 4 (depends on 1) The method of claim 1, further including suggesting those of the ranked one or more matching event opportunities having the level of convenience greater than a defined threshold. ### Claim 5 (depends on 1) The method of claim 1, further including initializing a machine learning mechanism to learn, identify, or recommend the one or more one or more matching event opportunities, the one or more entity selection criteria, and the group of entities. ### Claim 6 (depends on 1) The method of claim 1, further including parsing data gathered from one or more internet of things (IoT) devices for identifying one or more event opportunities and the selected group of entities. ### Claim 7 A system for implementing intelligent recommendations of convenient event opportunities, comprising: one or more computers with executable instructions that when executed cause the system to: identify a group of entities for one or more event opportunities or identifying the one or more event opportunities for the group of entities according to one or more entity selection criteria and one or more event criteria; execute machine learning logic to train, in a first stage, a suggested opportunity model using observed behavior, inclusive of activities of daily living (ADL), of each entity in the group of entities as input; match, based on the suggested opportunity model, the one or more event opportunities and the group of entities according to a level of convenience for attending the one or more event opportunities of the group of entities; rank the one or more matching event opportunities according to the level of convenience; suggest the ranked one or more matching event opportunities to the group of entities, wherein the one or more matching event opportunities each require at least one of the group of entities to perform and complete a plurality of actions in a predefined sequence to participate in a final event, and wherein suggesting the ranked one or more matching event opportunities includes suggesting scheduling arrangements for the at least one of the group of entities to perform the plurality of actions in the predefined sequence based on the level of convenience determined for each of the plurality of actions; and execute the machine learning logic to train, in a second stage, the suggested opportunity model using feedback data collected subsequent to suggesting the ranked one or more matching event opportunities, wherein the suggested opportunity model is iteratively updated using the feedback data to enhance an accuracy of future suggested event opportunities. ### Claim 8 (depends on 7) The system of claim 7, wherein the executable instructions further define the one or more entity selection criteria to include at least an entity profile, features and characteristics common among a plurality of entities, same interests, a type of connection, historical data of each of the plurality of entities, calendar data, frequency of attended historical events, a selected amount of time between events, or a combination thereof. ### Claim 9 (depends on 7) The system of claim 7, wherein the executable instructions further define the one or more event criteria to include at least a location, venue, travel options, time, financial costs, common interests, topics, meeting history, entity preferences, activity type, or a combination thereof. ### Claim 10 (depends on 7) The system of claim 7, wherein the executable instructions further suggest of the ranked one or more matching event opportunities having the level of convenience greater than a defined threshold. ### Claim 11 (depends on 7) The system of claim 7, wherein the executable instructions further initialize a machine learning mechanism to learn, identify, or recommend the one or more one or more matching event opportunities, the one or more entity selection criteria, and the group of entities. ### Claim 12 (depends on 7) The system of claim 7, wherein the executable instructions further parse data gathered from one or more internet of things (IoT) devices for identifying one or more event opportunities and the selected group of entities. ### Claim 13 A computer program product for implementing intelligent recommendations of convenient event opportunities by a processor, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising: an executable portion that identifies a group of entities for one or more event opportunities or identifying the one or more event opportunities for the group of entities according to one or more entity selection criteria and one or more event criteria; an executable portion that executes machine learning logic to train, in a first stage, a suggested opportunity model using observed behavior, inclusive of activities of daily living (ADL), of each entity in the group of entities as input; an executable portion that matches, based on the suggested opportunity model, the one or more event opportunities and the group of entities according to a level of convenience for attending the one or more event opportunities of the group of entities; an executable portion that ranks the one or more matching event opportunities according to the level of convenience; an executable portion that suggests the ranked one or more matching event opportunities to the group of entities, wherein the one or more matching event opportunities each require at least one of the group of entities to perform and complete a plurality of actions in a predefined sequence to participate in a final event, and wherein suggesting the ranked one or more matching event opportunities includes suggesting scheduling arrangements for the at least one of the group of entities to perform the plurality of actions in the predefined sequence based on the level of convenience determined for each of the plurality of actions; and an executable portion that executes the machine learning logic to train, in a second stage, the suggested opportunity model using feedback data collected subsequent to suggesting the ranked one or more matching event opportunities, wherein the suggested opportunity model is iteratively updated using the feedback data to enhance an accuracy of future suggested event opportunities. ### Claim 14 (depends on 13) The computer program product of claim 13, further including an executable portion that: define the one or more entity selection criteria to include at least an entity profile, features and characteristics common among a plurality of entities, same interests, a type of connection, historical data of each of the plurality of entities, calendar data, frequency of attended historical events, a selected amount of time between events, or a combination thereof; and define the one or more selected criteria according to location, venue, travel options, time, financial costs, common interests, topics, meeting history, entity preferences, activity type, or a combination thereof. ### Claim 15 (depends on 13) The computer program product of claim 13, further including an executable portion that suggests those of the ranked one or more event opportunities having the level of convenience greater than a defined threshold. ### Claim 16 (depends on 13) The computer program product of claim 13, further including an executable portion that initializes a machine learning mechanism to learn, identify, or recommend the one or more one or more event opportunities, the one or more entity selection criteria, and the selected group of entities. ### Claim 17 (depends on 13) The computer program product of claim 13, further including an executable portion that parses data gathered from one or more internet of things (IoT) devices for identifying one or more event opportunities and the selected group of entities.