# US 2019/0378348 A1 · Method and system for collecting activity-data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0378348 A1 - Document ID: US20190378348A1 - Status: Published application - Kind: A1 - Application number: 16/001,526 - Filed: 2018-06-06 - Published: 2019-12-12 - Earliest claimed: 2018-06-06 - Assignees: XSELIS, Inc. - Applicants: XSELIS, Inc. - Inventors: Tyler Logan Fox; Kevin Tyler Gardenhire - Web page: https://patentgrep.com/patent/US20190378348A1 ## CPC - G07C 1/02: not involving the registering, indicating or recording of other data - G07C 1/10: together with the recording, indicating or registering of other data, e.g. of signs of identity - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 3/098: Distributed learning, e.g. federated learning - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 20/00: Machine learning - G06N 20/20: Ensemble learning - H04L 67/306: User profiles - H04L 67/52: specially adapted for the location of the user terminal - H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)} - H04W 4/02: Services making use of location information - G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE - 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) - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract A method for collecting activity-data of a user executing a task including at least one activity includes tracking a user-device to determine a location-data of the user-device associated with the user; retrieving, at a server-arrangement from a database-server, a client-data; determining a change in the location-data of the user-device; and generating the activity-data, based on the change in the location-data, by one of correlating the location-data with the client-data by employing machine learning techniques, or processing a prompt-response generated by a manual-input at the user-device, wherein the prompt-response is generated based on a prompt provided by the server-arrangement to the user-device when the machine learning techniques fail to correlate the location data with the client-data. ## Claims ### Claim 1 A method for collecting activity-data of a user executing a task including at least one activity, the method comprising: tracking a user-device to determine a location-data of the user-device associated with the user; retrieving, at a server-arrangement from a database-server, a client-data; determining a change in the location-data of the user-device; and generating the activity-data, based on the change in the location-data, by one of correlating the location-data with the client-data by employing machine learning techniques, or processing a prompt-response generated by a manual-input at the user-device, wherein the prompt-response is generated based on a prompt provided by the server-arrangement to the user-device when the machine learning techniques fail to correlate the location data with the client-data. ### Claim 2 (depends on 1) The method according to claim 1, further comprising retrieving, at the server-arrangement from the database-server, a schedule-data of the at least one activity defining the task, wherein generating the activity-data further comprises one of correlating the location-data with at least one of the client-data and the schedule-data by employing machine learning techniques, or processing the prompt-response generated by the manual-input at the user-device, wherein the prompt-response is generated based on the prompt provided by the server-arrangement to the user-device when the machine learning techniques fail to correlate the location data with at least one of the client-data and the schedule-data. ### Claim 3 (depends on 1) The method according to claim 1, further comprising retrieving, at the server-arrangement from the database-server, a historical data of activities performed by the user, wherein generating the activity-data further comprises one of: correlating the location-data with at least one of the client-data, the schedule-data and the historical data by employing machine learning techniques, or processing the prompt-response generated by the manual-input at the user-device, wherein the prompt-response is generated based on the prompt provided by the server-arrangement to the user-device when the machine learning techniques fail to correlate the location data with at least one of the client-data, the schedule-data and the historical data. ### Claim 4 (depends on 1) The method according to claim 1, wherein the tracking of the user-device is initiated based on one of: a pre-defined time; a user-defined time; and the historical data. ### Claim 5 (depends on 1) The method according to claim 1, further comprising terminating the tracking of the user-device based on one of: a pre-defined time; a user-defined time; and the historical data. ### Claim 6 (depends on 1) The method according to claim 1, wherein correlating the location-data with the client-data, the schedule-data and the historical data comprises identifying a pattern for activities of the task based on the correlation of the location data with at least one of the client-data, the schedule-data and the historical data. ### Claim 7 (depends on 1) The method according to claim 1, further comprising presenting the generated activity-data on the user-device. ### Claim 8 (depends on 1) The method according to claim 1, wherein the user-device comprises a first user-device and a second user-device, and wherein the generated activity-data is presented at the first user-device in a different manner than the second user-device. ### Claim 9 (depends on 1) The method according to claim 1, wherein the client-data comprises name and location information of existing or potential clients. ### Claim 10 (depends on 1) The method according to claim 1, wherein the historical data comprises at least one of schedule data of the at least one previous activity, previous sales information, previous locations visited, time associated with visit of the previous locations. ### Claim 11 (depends on 1) The method according to claim 1, wherein the schedule data comprises at least one of calendar data associated activities with the task assigned to the user and sales information. ### Claim 12 (depends on 1) The method according to claim 1, wherein the machine learning techniques are implemented using artificial intelligence algorithms. ### Claim 13 (depends on 1) The method according to claim 1, wherein the activity-data of an activity comprises a start time of the activity, an end time of the activity, a completion time of the activity, and a nature of the activity. ### Claim 14 (depends on 1) The method according claim 1, wherein the location-data comprises location of the user-device; and accelerometer data of the user-device; ### Claim 15 A system for collecting activity-data of a user executing a task including at least one activity, the system comprising: a user-device having one or more sensing modules to track a location-data; a database-server for storing a client-data; and a server arrangement communicably coupled with the user-device and the database-server, wherein the server arrangement is configured to: track the user-device to determine the location-data of the user-device associated with the user; retrieve the client-data from the database-server; determine a change in the location-data of the user-device; and generate the activity-data, based on the change in the location-data, by one of correlating the location-data with the client-data by employing machine learning techniques, or processing a prompt-response generated by a manual-input at the user-device, wherein the prompt-response is generated based on a prompt provided by the server-arrangement to the user-device when the machine learning techniques fail to correlate the location-data with the client-data. ### Claim 16 (depends on 15) The system according to claim 15, wherein the database-server is further configured to store a schedule-data of the at least one activity defining the task, and wherein the server arrangement is further configured to: retrieve the schedule-data from the database-server; and generate the activity-data, based on the change in the location-data, by one of correlating the location-data with the schedule-data by employing machine learning techniques, or processing the prompt-response generated by the manual-input at the user-device, wherein the prompt-response is generated based on the prompt provided by the server-arrangement to the user-device when the machine learning techniques fail to correlate the location data with at least one of the client-data and the schedule-data. ### Claim 17 (depends on 15) The system according to claim 15, wherein the database-server is further configured to store a historical data of activities performed by the user; and wherein the server arrangement is further configured to: retrieve the historical data from the database-server; and generate the activity-data, based on the change in the location-data, by one of correlating the location-data with the historical data by employing machine learning techniques, or processing the prompt-response generated by the manual-input at the user-device, wherein the prompt-response is generated based on the prompt provided by the server-arrangement to the user-device when the machine learning techniques fail to correlate the location data with at least one of the client-data, the schedule-data and the historical data. ### Claim 18 (depends on 15) The system according to claim 15, wherein the one or more sensing module comprises one or more of an accelerometer sensor, a location sensor. ### Claim 19 (depends on 17) The system according to claim 17, wherein the server arrangement is configured to initiate the tracking of the user-device based on one of: a pre-defined time; a user-defined time; and the historical data. ### Claim 20 (depends on 17) The system according to claim 17, wherein the tracking of the user-device is terminated based on one of: a pre-defined time; a user-defined time; and the historical data. ### Claim 21 (depends on 17) The system according to claim 17, wherein correlating the location-data with the client-data, the schedule-data and the historical data comprises identifying a pattern for activities of the task based on the correlation of the location data with at least one of the client-data, the schedule-data and the historical data. ### Claim 22 (depends on 15) The system according to claim 15, wherein the server arrangement is further configured to present the generated activity-data on the user-device. ### Claim 23 (depends on 15) The system according to claim 15, wherein the user-device comprises a first user-device and a second user-device, and wherein the generated activity-data is presented at the first user-device in a different manner than the second user-device. ### Claim 24 (depends on 15) The system according to claim 15, wherein the client-data comprises name and location information of existing or potential clients. ### Claim 25 (depends on 17) The system according to claim 17, wherein the historical data comprises at least one of schedule data of the at least one previous activity, previous sales information, previous locations visited, time associated with visit of the previous locations. ### Claim 26 (depends on 16) The system according to claim 16, wherein the schedule data comprises at least one of calendar data associated with the task assigned to the user, sales information. ### Claim 27 (depends on 15) The system according to claim 15, wherein the machine learning techniques is implemented using artificial intelligence algorithms. ### Claim 28 (depends on 15) The system according to claim 15, wherein the activity-data of an activity comprises a start time of the activity, an end time of the activity, a completion time of the activity, and a nature of the activity.