# US 2023/0056983 A1 · Method and wearable device for enhancing quality of experience index for user in iot network > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0056983 A1 - Document ID: US20230056983A1 - Status: Published application - Kind: A1 - Application number: 17/889,669 - Filed: 2022-08-17 - Published: 2023-02-23 - Earliest claimed: 2021-08-05 - Assignees: Samsung Electronics Co., Ltd. - Applicants: Samsung Electronics Co., Ltd. - Inventors: Satyajit ANAND; Mayank SINGH; Ajit S BOPARDIKAR - Web page: https://patentgrep.com/patent/US20230056983A1 ## CPC - H04L 12/2803: {Home automation networks} - H04L 65/1046: Call controllers; Call servers - H04L 65/80: Responding to QoS - H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks - H04L 67/125: involving control of end-device applications over a network - H04W 24/02: Arrangements for optimising operational condition - 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 enhancing a Quality of Experience (QoE) index, by a wearable device, for a user in an Internet of things (IoT) network comprising a plurality of IoT devices is provided. The method includes monitoring the QoE index of the user in the IoT network, detecting a drop in the QoE index of the user, determining whether the dropped QoE index is less than a QoE threshold, in response to determining that the dropped QoE index is less than the QoE threshold, determining at least one IoT device from the plurality of IoT devices that is responsible for the drop in the QoE index of the user using an Artificial intelligence (AI) model, and controlling the IoT device(s) to raise the QoE index of the user above the QoE threshold. ## Claims ### Claim 1 A method for enhancing a Quality of Experience (QoE) index for a user in an Internet of things (IoT) network comprising a plurality of IoT devices, the method comprising: monitoring, by a wearable device, the QoE index of the user in the IoT network; detecting, by the wearable device, a drop in the QoE index of the user; determining, by the wearable device, whether the dropped QoE index of the user is lower than a QoE threshold; in response to determining that the dropped QoE index of the user is lower than the QoE threshold, determining, by the wearable device, at least one IoT device of the plurality of IoT devices that is responsible for the drop in the QoE index of the user using at least one Artificial intelligence (AI) model; and controlling, by the wearable device, the at least one IoT device to raise the QoE index of the user above the QoE threshold. ### Claim 2 (depends on 1) The method of claim 1, wherein the detecting of the drop in the QoE index of the user comprises: determining, by the wearable device, the QoE index of the user based on a calculation of at least one of changes in an emotional state of the user or changes in a body-comfort parameter of the user; and storing, by the wearable device, the QoE index of the user in database. ### Claim 3 (depends on 2) The method of claim 2, wherein changes in the emotional state of the user comprises at least change from a calm state to an agitated state, or a sudden irritation state, and wherein the body-comfort parameter of the user comprises at least sweating, or shortness of breath. ### Claim 4 (depends on 1) The method of claim 1, wherein the determining the at least one IoT device of the plurality of IoT devices that is responsible for the drop in the QoE index of the user using the at least one AI model comprises: determining, by the wearable device, a current context of each IoT device of the plurality of IoT devices, and current operational data of each IoT device of the plurality of IoT devices; correlating, by the wearable device, the current context and the current operational data of each IoT device with the detected drop in the QoE index of the user using the at least one AI model; and determining, by the wearable device, the at least one IoT device of the plurality of IoT devices that is responsible for the drop in the QoE index based on the correlation. ### Claim 5 (depends on 4) The method of claim 4, wherein the determining of the current context of each IoT device of the plurality of IoT devices comprises: determining, by the wearable device, at least one activity of the user; and determining, by the wearable device, environmental context associated with the user. ### Claim 6 (depends on 4) The method of claim 4, wherein the determining of the current operational data of each IoT device of the plurality of IoT devices comprises: determining, by the wearable device, current status of the at least one IoT device of the plurality of IoT devices; and determining, by the wearable device, recent changes in the current status of the at least one IoT device of the plurality of IoT devices. ### Claim 7 (depends on 1) The method of claim 1, wherein the controlling of the at least one IoT device to raise the QoE index of the user above the QoE threshold comprises: retrieving, by the wearable device, new operational data for the at least one IoT device to raise the QoE index of the user above the QoE threshold by applying the at least one AI model on the detected drop in the QoE index of the user, and a current context of each of the IoT device and the at least one IoT device of the plurality of IoT devices that is responsible for the drop in the QoE index based on a correlation; and modifying, by the wearable device, a current operational data of the at least one IoT device using the retrieved new operational data to raise the QoE index of the user above the QoE threshold. ### Claim 8 (depends on 1) The method of claim 1, wherein the at least one AI model is trained by: detecting, by the wearable device, a raise in the QoE index of the user in the IoT network by modifying a current operational data of the at least one IoT device using a retrieved new operational data; comparing, by the wearable device, the modified current operational data of the at least one IoT device with the current operational data to derive arbitration data; and storing, by the wearable device, the derived arbitration data in the at least one AI model for future prediction to raise the QoE index of the user above the QoE threshold. ### Claim 9 (depends on 1) The method of claim 1, further comprising: when the at least one AI model has none or inaccurate prediction for the drop in the QoE index, learning, by the at least one AI model, from the user's manually invoked action in the IoT network to raise the QoE index of the user above the QoE threshold. ### Claim 10 (depends on 1) The method of claim 1, further comprising: applying the at least one AI model either partially or fully to different IoT devices or environment. ### Claim 11 A wearable device enhancing a Quality of Experience (QoE) index for a user in an Internet of things (IoT) network comprising a plurality of IoT devices, the wearable device comprising: a memory; at least one processor; and a QoE controller, operably connected to the memory and the at least one processor, and configured to: monitor the QoE index of the user in the IoT network, detect a drop in the QoE index of the user, determine whether the dropped QoE index of the user is lower than a QoE threshold, in response to the determination that the dropped QoE index of the user is lower than the QoE threshold, determine at least one IoT device of the plurality of IoT devices that is responsible for the drop in the QoE index of the user, using at least one Artificial intelligence (AI) model, and control the at least one IoT device to raise the QoE index of the user above the QoE threshold. ### Claim 12 (depends on 11) The wearable device of claim 11, wherein the at least one processor is further configured to: determine the QoE index of the user based on a calculation of at least one of changes in an emotional state of the user or changes in a body-comfort parameter of the user; and store the QoE index of the user in database. ### Claim 13 (depends on 11) The wearable device of claim 11, wherein the at least one processor is further configured to: determine a current context of each IoT device of the plurality of IoT devices, and current operational data of each IoT device of the plurality of IoT devices; correlate the current context and the current operational data of each IoT device with the detected drop in the QoE index of the user using the at least one AI model; and determine the at least one IoT device of the plurality of IoT devices that is responsible for the drop in the QoE index based on the correlation. ### Claim 14 (depends on 13) The wearable device of claim 13, wherein the at least one processor is further configured to: determine current status of the at least one IoT device of the plurality of IoT devices; and determine recent changes in the current status of the at least one IoT device of the plurality of IoT devices. ### Claim 15 (depends on 12) The wearable device of claim 12, wherein the emotional state of the user comprises at least a calm state to an agitate state or a sudden irritation state, and wherein the body-comfort parameter of the user comprises at least sweating, or shortness of breath. ### Claim 16 (depends on 13) The wearable device of claim 13, wherein the processor is further configured to: determine at least one activity of the user; and determine environmental context associated with the user. ### Claim 17 (depends on 11) The wearable device of claim 11, wherein the processor is further configured to: retrieve new operational data for the at least one IoT device to raise the QoE index of the user above the QoE threshold by applying the at least one AI model on the detected drop in the QoE index of the user, and a current context of each of the IoT device and the at least one IoT device of the plurality of IoT devices that is responsible for the drop in the QoE index based on a correlation; and modify a current operational state of the at least one IoT device using the retrieved new operational data to raise the QoE index of the user above the QoE threshold. ### Claim 18 (depends on 11) The wearable device of claim 11, wherein, to train the at least one AI model, the processor is further configured to: detect a raise in the QoE index of the user in the IoT network by modifying a current operational data of the at least one IoT device using a retrieved new operational data; compare the modified current operational data of the at least one IoT device with the current operational data to derive arbitration data; and store the derived arbitration data in the at least one AI model for future prediction to raise the QoE index of the user above the QoE threshold. ### Claim 19 (depends on 11) The wearable device of claim 11, wherein the processor is further configured to: when the at least one AI model has none or inaccurate prediction for the drop in the QoE index, learn, by the at least one AI model, from the user's manually invoked action in the IoT network to raise the QoE index of the user above the QoE threshold. ### Claim 20 A computer-readable recording medium having recorded thereon a program for executing, the method of the following: monitoring, by a wearable device, the QoE index of the user in the IoT network; detecting, by the wearable device, a drop in the QoE index of the user; determining, by the wearable device, whether the dropped QoE index of the user is lower than a QoE threshold; in response to determining that the dropped QoE index of the user is lower than the QoE threshold, determining, by the wearable device, at least one IoT device of the plurality of IoT devices that is responsible for the drop in the QoE index of the user using at least one Artificial intelligence (AI) model; and controlling, by the wearable device, the at least one IoT device to raise the QoE index of the user above the QoE threshold.