# US 11,451,656 B2 · Intelligent notification mode switching in user equipment > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,451,656 B2 - Document ID: US11451656B2 - Status: Granted - Kind: B2 - Application number: 16/054,741 - Filed: 2018-08-03 - Published: 2020-02-06 - Granted: 2022-09-20 - Earliest claimed: 2018-08-03 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Shikhar Kwatra; Sarbajit K. Rakshit; Komminist Weldemariam; Paul Krystek - Web page: https://patentgrep.com/patent/US11451656B2 ## CPC - H04W 4/021: Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences - G06N 20/00: Machine learning - H04M 1/72463: to restrict the functionality of the device - H04W 4/02: Services making use of location information - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04M: TELEPHONIC COMMUNICATION (circuits for controlling other apparatus via a telephone cable and not involving telephone switching apparatus G08) ## Abstract Embodiments for intelligent notification mode switching in user equipment (UE) by a processor. Data may be received from one or more UEs associated with one or more users. Electronic communication switching factors associated with the one or more users may be identified. An implication of a switching notification mode on the one or more UEs based on the electronic communication switching factors may be learned. An electronic communication modes of the one or more UEs associated with the one or more users may be cognitively switched according to the learned implication. ## Claims ### Claim 1 A method for intelligent notification mode switching in user equipment (UE) by a processor, comprising: receiving data on one or more UEs associated with one or more users; identifying electronic communication switching factors associated with the one or more users; learning an implication of a switching notification mode on the one or more UEs based on the electronic communication switching factors, wherein the identifying of the electronic communication switching factors includes analyzing facial gestures of the one or more users and contextual conversations between the one or more users and an alternative user obtained from a device external to the one or more UEs, and wherein learning the implication includes identifying an indirect sub-factor influencing the implication of the switching notification mode, and determining that the switching notification mode of the one or more UEs conflicts with the indirect sub-factor; and dynamically switching electronic communication modes of the one or more UEs associated with the one or more users according to the learned implication, wherein, in conjunction with dynamically switching the electronic communication modes, a notification is generated by the one or more UEs indicative of an explanation of the learned implication. ### Claim 2 (depends on 1) The method of claim 1, wherein identifying the electronic communication switching factors further includes defining one or more parameters, events, and contextual information to include calendar data, audio communications, video communications, text data, monitored communications of the one or more users, historical data, user preferences, location-based dependencies, alerts, notifications, or a combination thereof. ### Claim 3 (depends on 1) The method of claim 1, wherein identifying the electronic communication switching factors further includes determining one or more scheduled meetings at a selected time, one or more locations associated with the one or more scheduled meetings, a physical presence or absence of the one or more users in the one or more locations, a virtual presence or absence of the one or more users in the one or more locations, one or more contextual communications associated with the one or more UEs associated with the one or more users, one or more policies or regulatory information affecting prioritization of the one or more parameters, events, and contextual information, or a combination thereof. ### Claim 4 (depends on 1) The method of claim 1, further including monitoring the electronic communication switching factors associated with the one or more users and the one or more UEs. ### Claim 5 (depends on 1) The method of claim 1, further including initializing a machine learning component to learn or define the electronic communication switching factors. ### Claim 6 (depends on 1) The method of claim 1, further including: determining the switching notification mode for the one or more UEs according to the electronic communication switching factors, wherein the switching notification mode includes a silent notification mode, an audible notification mode, a vibration mode, an airplane mode; and cognitively switching the electronic communication modes of the one or more UEs linked to the user according to the determined switching notification mode. ### Claim 7 (depends on 1) The method of claim 1, further including: cognitively switching the electronic communication modes of the one or more UEs associated with the one or more users upon commencement of the one or more events; or cognitively switching the electronic communication modes of the one or more UEs associated with the one or more users upon termination of the one or more events. ### Claim 8 A system for intelligent notification mode switching in user equipment (UE), comprising: one or more computers with executable instructions that when executed cause the system to: receive data on one or more UEs associated with one or more users; identify electronic communication switching factors associated with the one or more users; learn an implication of a switching notification mode on the one or more UEs based on the electronic communication switching factors, wherein the identifying of the electronic communication switching factors includes analyzing facial gestures of the one or more users and contextual conversations between the one or more users and an alternative user obtained from a device external to the one or more UEs, and wherein learning the implication includes identifying an indirect sub-factor influencing the implication of the switching notification mode, and determining that the switching notification mode of the one or more UEs conflicts with the indirect sub-factor; and dynamically switch electronic communication modes of the one or more UEs associated with the one or more users according to the learned implication, wherein, in conjunction with dynamically switching the electronic communication modes, a notification is generated by the one or more UEs indicative of an explanation of the learned implication. ### Claim 9 (depends on 8) The system of claim 8, wherein identifying the electronic communication switching factors further define one or more parameters, events, and contextual information to include calendar data, audio communications, video communications, text data, monitored communications of the one or more users, historical data, user preferences, location-based dependencies, alerts, notifications, or a combination thereof. ### Claim 10 (depends on 8) The system of claim 8, wherein identifying the electronic communication switching factors further determine one or more scheduled meetings at a selected time, one or more locations associated with the one or more scheduled meetings, a physical presence or absence of the one or more users in the one or more locations, a virtual presence or absence of the one or more users in the one or more locations, one or more contextual communications associated with the one or more UEs associated with the one or more users, one or more policies or regulatory information affecting prioritization of the one or more parameters, events, and contextual information, or a combination thereof. ### Claim 11 (depends on 8) The system of claim 8, wherein the executable instructions further monitor the electronic communication switching factors associated with the one or more users and the one or more UEs. ### Claim 12 (depends on 8) The system of claim 8, wherein the executable instructions further initialize a machine learning component to learn or define the electronic communication switching factors. ### Claim 13 (depends on 8) The system of claim 8, wherein the executable instructions further: determine the switching notification mode for the one or more UEs according to the electronic communication switching factors, wherein the switching notification mode includes a silent notification mode, an audible notification mode, a vibration mode, an airplane mode; and cognitively switch the electronic communication modes of the one or more UEs linked to the one or more users according to the determined switching notification mode. ### Claim 14 (depends on 8) The system of claim 8, wherein the executable instructions further: cognitively switch the electronic communication modes of the one or more UEs associated with the one or more users upon commencement of the one or more events; or cognitively switch the electronic communication modes of the one or more UEs associated with the one or more users upon termination of the one or more events. ### Claim 15 A computer program product for intelligent notification mode switching in one or more users equipment (UE) 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 receives data on one or more UEs associated with one or more users; an executable portion that identifies electronic communication switching factors associated with the one or more users; an executable portion that learns an implication of a switching notification mode on the one or more UEs based on the electronic communication switching factors, wherein the identifying of the electronic communication switching factors includes analyzing facial gestures of the one or more users and contextual conversations between the one or more users and an alternative user obtained from a device external to the one or more UEs, and wherein learning the implication includes identifying an indirect sub-factor influencing the implication of the switching notification mode, and determining that the switching notification mode of the one or more UEs conflicts with the indirect sub-factor; and an executable portion that dynamically switches electronic communication modes of the one or more UEs associated with the one or more users according to the learned implication, wherein, in conjunction with dynamically switching the electronic communication modes, a notification is generated by the one or more UEs indicative of an explanation of the learned implication. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein the executable portion that identifies the electronic communication switching factors further defines one or more parameters, events, and contextual information to include calendar data, audio communications, video communications, text data, monitored communications of the one or more users, historical data, user preferences, location-based dependencies, alerts, notifications, or a combination thereof. ### Claim 17 (depends on 15) The computer program product of claim 15, wherein the executable portion that identifies the electronic communication switching factors further determines one or more scheduled meetings at a selected time, one or more locations associated with the one or more scheduled meetings, a physical presence or absence of the one or more users in the one or more locations, a virtual presence or absence of the one or more users in the one or more locations, one or more contextual communications associated with the one or more UEs associated with the one or more users, one or more policies or regulatory information affecting prioritization of the one or more parameters, events, and contextual information, or a combination thereof. ### Claim 18 (depends on 15) The computer program product of claim 15, further including an executable portion that: monitors the electronic communication switching factors associated with the one or more users and the one or more UEs; and initializes a machine learning component to learn or define the one or more parameters, events, contextual information. ### Claim 19 (depends on 15) The computer program product of claim 15, further including an executable portion that: determines the switching notification mode for the one or more UEs according to the electronic communication switching factors, wherein the switching notification mode includes a silent notification mode, an audible notification mode, a vibration mode, an airplane mode; and cognitively switches the electronic communication modes of the one or more UEs linked to the one or more users according to the determined switching notification mode. ### Claim 20 (depends on 15) The computer program product of claim 15, further including an executable portion that: cognitively switches notification modes of the one or more UEs associated with the one or more users upon commencement of the one or more events; or cognitively switches notification modes of the one or more UEs associated with the one or more users upon termination of the one or more events.