# US 12,217,298 B2 · Systems and methods for integration of calendar applications with task facilitation services > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,217,298 B2 - Document ID: US12217298B2 - Status: Granted - Kind: B2 - Application number: 17/930,302 - Filed: 2022-09-07 - Published: 2023-03-09 - Granted: 2025-02-04 - Earliest claimed: 2021-09-07 - Assignees: Yohana LLC - Applicants: Yohana LLC - Inventors: Yoky Matsuoka; Nitin Viswanathan - Web page: https://patentgrep.com/patent/US12217298B2 ## CPC - G06Q 30/0631: {Recommending goods or services} - G06F 9/4831: {with variable priority} - G06F 9/54: Interprogram communication - G06N 20/00: Machine learning - G06Q 10/063114: {Status monitoring or status determination for a person or group} - G06Q 10/06313: {Resource planning in a project environment} - G06Q 10/06316: {Sequencing of tasks or work} - G06Q 10/109: Time management, e.g. calendars, reminders, meetings or time accounting - G06Q 10/1097: {using calendar-based scheduling for task assignment} - H04L 67/306: User profiles - H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)} - G06F 40/284: Lexical analysis, e.g. tokenisation or collocates - G06F 40/35: Discourse or dialogue representation - G06Q 10/20: Administration of product repair or maintenance - 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 - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract Integration of an external calendar application with a task facilitation service includes mechanisms for creating tasks within the task facilitation service based on calendar data of the calendar application received by the task facilitation service and processed using various dynamic models and algorithms. Further examples of integration include the task facilitation service generating recommendations for new calendar items and modifications to existing calendar items by leveraging the data and models available to the task facilitation service. ## Claims ### Claim 1 A computer-implemented method comprising: receiving calendar data for a particular user of a task facilitation service through an external application programming interface (API), wherein the calendar data is associated with a calendar of a calendar application; accessing a user model corresponding to the particular user, wherein the user model is updated based on historic activity of the particular user; processing the calendar data and the user model using a natural-language processing (NLP) model to generate a task recommendation, wherein the task recommendation indicates one or more recommended tasks for delegation by the particular user, wherein the NLP model was initially trained with a training dataset using unsupervised training and without user supervision, and wherein the training dataset includes training data associated with other users; transmitting an indication corresponding to the task recommendation, wherein, when the indication is received by a computing device, the computing device is enabled to approve the task recommendation; receiving an approval to proceed with performing the one or more recommended tasks; accessing task-execution data associated with the one or more recommended tasks, wherein the task-execution data identifies performance statuses associated with the one or more recommended tasks; and updating the NLP model based on the task-execution data, wherein updating includes adjusting one or more weights of the NLP model using the unsupervised training and without user supervision, and wherein the one or more weights of the NLP model are adjusted until a corresponding logarithmic loss exceeds a predetermined threshold. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the computing device is a user computing device corresponding to the particular user. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the computing device is a representative computing device different than a user computing device corresponding to the particular user, and wherein the representative computing device corresponds to a representative assigned to the particular user to facilitate task completion for the particular user. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein the calendar data includes details for a calendar item of the calendar. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the calendar data includes details for a calendar item of the calendar, and wherein receiving the approval further includes: transmitting an update for application data of the calendar application to indicate that the one or more recommended tasks have been generated for the calendar item. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein receiving the approval further includes: transmitting an update for application data of the calendar application to (i) create a calendar item corresponding to the task recommendation, and (ii) indicate that the one or more recommended tasks have been generated for the calendar item. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein receiving the approval further includes: transmitting a first update for updating first application data to indicate that the one or more recommended tasks have been generated for a calendar item of the calendar; and transmitting a second update for updating second application data to create a new calendar item in a second calendar. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, further comprising: transmitting a calendar item modification recommendation, wherein, when the calendar item modification recommendation is received by the computing device, the computing device is enabled to approve the calendar item modification recommendation to modify a calendar item of the calendar; receiving approval of the calendar item modification recommendation; and transmitting an update for application data of the calendar application to modify the calendar item according to the calendar item modification recommendation. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the task-execution data includes sensor data that identify the performance statuses, and wherein the NLP model is updated further based on the sensor data. ### Claim 10 A system comprising: one or more processors; and a non-transitory computer-readable storage medium that stores instructions that, when executed by the one or more processors, cause the one or more processors to perform operations of: receiving calendar data for a particular user of a task facilitation service through an external application programming interface (API), wherein the calendar data is associated with a calendar of a calendar application; accessing a user model corresponding to the particular user, wherein the user model is updated based on historic activity of the particular user; processing the calendar data and the user model using a natural-language processing (NLP) model to generate a task recommendation, wherein the task recommendation indicates one or more recommended tasks for delegation by the particular user, wherein the NLP model was initially trained with a training dataset using unsupervised training and without user supervision, and wherein the training dataset includes training data associated with other users; transmitting an indication corresponding to the task recommendation, wherein, when the indication is received by a computing device, the computing device is enabled to approve the task recommendation; receiving an approval to proceed with performing the one or more recommended tasks; accessing task-execution data associated with the one or more recommended tasks, wherein the task-execution data identifies performance statuses associated with the one or more recommended tasks; and updating the NLP model based on the task-execution data, wherein updating includes adjusting one or more weights of the NLP model using the unsupervised training and without user supervision, and wherein the one or more weights of the NLP model are adjusted until a corresponding logarithmic loss exceeds a predetermined threshold. ### Claim 11 (depends on 10) The system of claim 10, wherein receiving the approval further includes: transmitting an update for application data of the calendar application to (i) create a calendar item corresponding to the task recommendation if the calendar item does not yet exist, and (ii) indicate that the one or more recommended tasks have been generated for the calendar item. ### Claim 12 (depends on 10) The system of claim 10, wherein the instructions further cause the one or more processors to perform the operations of: transmitting a calendar item modification recommendation, wherein, when the calendar item modification recommendation is received by the computing device, the computing device is enabled to approve the calendar item modification recommendation to modify a calendar item of the calendar; receiving approval of the calendar item modification recommendation; and transmitting an update for application data of the calendar application to modify the calendar item according to the calendar item modification recommendation. ### Claim 13 (depends on 10) The system of claim 10, wherein the computing device is a representative computing device different than a user computing device corresponding to the particular user, and wherein the representative computing device corresponds to a representative assigned to the particular user to facilitate task completion for the particular user. ### Claim 14 (depends on 10) The system of claim 10, wherein the task-execution data includes sensor data that identify the performance statuses, and wherein the NLP model is updated further based on the sensor data. ### Claim 15 (depends on 10) The system of claim 10, wherein receiving the approval further includes: transmitting a first update for updating first application data to indicate that the one or more recommended tasks have been generated for a calendar item of the calendar; and transmitting a second update for updating second application data to create a new calendar item in a second calendar. ### Claim 16 A non-transitory computer-readable storage medium that stores instructions that, when executed by one or more processors, cause the one or more processors to perform operations of: receiving calendar data for a particular user of a task facilitation service through an external application programming interface (API), wherein the calendar data is associated with a calendar of a calendar application; accessing a user model corresponding to the particular user, wherein the user model is updated based on historic activity of the particular user; processing the calendar data and the user model using a natural-language processing (NLP) model to generate a task recommendation, wherein the task recommendation indicates one or more recommended tasks for delegation by the particular user, wherein the NLP model was initially trained with a training dataset using unsupervised training and without user supervision, and wherein the training dataset includes training data associated with other users; transmitting an indication corresponding to the task recommendation, wherein, when the indication is received by a computing device, the computing device is enabled to approve the task recommendation; receiving an approval to proceed with performing the one or more recommended tasks; accessing task-execution data associated with the one or more recommended tasks, wherein the task-execution data identifies performance statuses associated with the one or more recommended tasks; and updating the NLP model based on the task-execution data, wherein updating includes adjusting one or more weights of the NLP model using the unsupervised training and without user supervision, and wherein the one or more weights of the NLP model are adjusted until a corresponding logarithmic loss exceeds a predetermined threshold. ### Claim 17 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein receiving the approval further includes: transmitting an update for application data of the calendar application to (i) create a calendar item corresponding to the task recommendation if the calendar item does not yet exist, and (ii) indicate that the one or more recommended tasks have been generated at the task facilitation service for the calendar item. ### Claim 18 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein the instructions further cause the one or more processors to perform the operations of: transmitting a calendar item modification recommendation, wherein, when the calendar item modification recommendation is received by the computing device, the computing device is enabled to approve the calendar item modification recommendation to modify a calendar item of the calendar; receiving approval of the calendar item modification recommendation; and transmitting an update for application data of the calendar application to modify the calendar item according to the calendar item modification recommendation. ### Claim 19 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein the computing device is a representative computing device different than a user computing device corresponding to the particular user, and wherein the representative computing device corresponds to a representative assigned to the particular user to facilitate task completion for the particular user. ### Claim 20 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein the task-execution data includes sensor data that identify the performance statuses, and wherein the NLP model is updated further based on the sensor data. ### Claim 21 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein receiving the approval further includes: transmitting a first update for updating first application data to indicate that the one or more recommended tasks have been generated for a calendar item of the calendar; and transmitting a second update for updating second application data to create a new calendar item in a second calendar.