# US 12,192,406 B2 · Enhanced digital messaging > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,192,406 B2 - Document ID: US12192406B2 - Status: Granted - Kind: B2 - Application number: 17/878,316 - Filed: 2022-08-01 - Published: 2022-11-17 - Granted: 2025-01-07 - Earliest claimed: 2019-07-26 - Assignees: Avaya Management L.P. - Applicants: Avaya Management L.P. - Inventors: George Erhart; Reinhard Klemm; Wen-Hua Ju; Michael Sisselman; Atsushi Hirano - Web page: https://patentgrep.com/patent/US12192406B2 ## CPC - H04L 51/046: Interoperability with other network applications or services - H04M 3/5183: {Call or contact centers with computer-telephony arrangements} - H04L 51/02: using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages - H04L 51/043: using or handling presence information - H04L 51/216: Handling conversation history, e.g. grouping of messages in sessions or threads - H04M 3/5166: {in combination with interactive voice response systems or voice portals, e.g. as front-ends} - H04M 3/5175: {Call or contact centers supervision arrangements} - G06Q 30/0281: {Customer communication at a business location, e.g. providing product or service information, consulting} - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - H04M: TELEPHONIC COMMUNICATION (circuits for controlling other apparatus via a telephone cable and not involving telephone switching apparatus G08) - 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 of the disclosure provide a method of processing messages received in an asynchronous communication system. In some embodiments, the method includes analyzing interactions on a digital communication channel, determining that the interactions have paused for an amount of time, analyzing content of the interactions to determine an estimate of the amount of time, updating a state of the agent to release the agent for the estimated amount of time, and setting a timer that will automatically change the state of the agent back to an occupied state for the interactions at a future time that aligns with an expiration of the timer. ## Claims ### Claim 1 A method, comprising: analyzing, by a processor, message content of one or more messages exchanged during a conversation on a digital communication channel; generating, by the processor and based on the analyzing, a score reflecting a likelihood of a person disengaging from the conversation on the digital communication channel in the future; comparing, by the processor, the score to a predetermined threshold value; triggering, by the processor, at least one first action in response to the score meeting or exceeding the predetermined threshold value, the at least one first action comprising sending over the digital communication channel and to an agent communication device a message regarding a detection of a natural disengagement from the conversation; determining, by the processor and based on the analyzing, a break in the conversation over the digital communication channel has occurred; and adjusting, by the processor and in response to the determining that the break in the conversation over the digital communication channel has occurred, the score to reflect a new likelihood of the person disengaging from the conversation on the digital communication channel in the future. ### Claim 2 (depends on 1) The method of claim 1, wherein the at least one first action includes: updating a state of an agent to release the agent for a first amount of time. ### Claim 3 (depends on 1) The method of claim 1, wherein the score reflecting the likelihood of the person disengaging from the conversation in the future is determined by at least one machine learning model processing the message content of the one or more messages exchanged during the conversation. ### Claim 4 (depends on 1) The method of claim 1, further comprising: analyzing, by the processor, historical communications over the digital communication channel; detecting, by the processor, at least one pattern in the historical communications; and performing, by the processor and based on the at least one pattern, at least one second action. ### Claim 5 (depends on 4) The method of claim 4, wherein the detecting the at least one pattern includes: applying, by the processor, a predictive communication model to the historical communications, wherein the predictive communication model includes an artificial intelligence model. ### Claim 6 (depends on 5) The method of claim 5, wherein performing the at least one second action includes: updating a state of an agent to release the agent for a first amount of time. ### Claim 7 (depends on 1) The method of claim 1, wherein analyzing the message content comprises: determining a conversational velocity of the conversation on the digital communication channel. ### Claim 8 A system, comprising: a processor; and memory storing data thereon that enable the processor to: analyze message content of one or more messages exchanged during a conversation on a digital communication channel; generate, based on the analyzing, a score reflecting a likelihood of a person disengaging from the conversation on the digital communication channel in the future; compare the score to a predetermined threshold value; trigger, based on the comparing, at least one first action in response to the score meeting or exceeding the predetermined threshold value, the at least one first action comprising sending, over the digital communication channel and to an agent communication device, a message regarding a detection of a natural disengagement; determine, based on the analyzing, a break in the conversation over the digital communication channel has occurred; and adjust, in response to the determining that the break in the conversation over the digital communication channel has occurred, the predetermined threshold value to reflect a new likelihood of the person disengaging from the conversation on the digital communication channel in the future. ### Claim 9 (depends on 8) The system of claim 8, wherein the at least one first action includes: updating a state of an agent to release the agent for a first amount of time. ### Claim 10 (depends on 8) The system of claim 8, wherein the score reflecting the likelihood of the person disengaging from the conversation in the future is determined by at least one machine learning model. ### Claim 11 (depends on 8) The system of claim 8, wherein the data further enable the processor to: analyze historical communications over the digital communication channel; detect at least one pattern in the historical communications; and perform, based on the at least one pattern, at least one second action. ### Claim 12 (depends on 11) The system of claim 11, wherein the detecting the at least one pattern includes: applying a predictive communication model to the historical communications, wherein the predictive communication model includes at least one of a machine learning model and an artificial intelligence model. ### Claim 13 (depends on 12) The system of claim 12, further comprising: a user interface in communication with the processor that presents a message regarding the detection of the natural disengagement from the conversation. ### Claim 14 (depends on 8) The system of claim 8, wherein analyzing the message content further comprises the processor being enabled to: determine a conversational velocity of the conversation on the digital communication channel. ### Claim 15 A method, comprising: receiving information related to one or more historical communications exchanged over a digital communication channel; providing the information related to one or more historical communications exchanged over the digital communication channel to a model as part of training the model to predict a person disengaging from a communication session over the digital communication channel in the future; after training the model, providing the trained model with message content of one or more messages of a first conversation exchanged over the digital communication channel; predicting, using an output of the trained model, a likelihood of a break in the first conversation occurring by comparing the output to a predetermined threshold value; determining, based on the message content, the break in the first conversation has occurred; sending, over the digital communication channel and to an agent communication device, a message regarding a detection of a natural disengagement, and adjusting, based on the determining that the break in the first conversation has occurred, the predetermined threshold value to reflect a new likelihood of the person disengaging from the first conversation on the digital communication channel in the future. ### Claim 16 (depends on 15) The method of claim 15, wherein information related to the one or more historical communications includes information about one or more conversational features, and wherein the one or more conversational features comprises at least one of an intent of the first conversation and a sentiment of a user. ### Claim 17 (depends on 15) The method of claim 15, wherein the training of the model further comprises: labeling one or more conversations of the one or more historical communications with a conversation topic. ### Claim 18 (depends on 15) The method of claim 15, wherein the training of the model includes providing the one or more historical communications recursively.