# US 2026/0088024 A1 · Caller intent recognition > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0088024 A1 - Document ID: US20260088024A1 - Status: Published application - Kind: A1 - Application number: 19/197,135 - Filed: 2025-05-02 - Published: 2026-03-26 - Earliest claimed: 2024-09-24 - Assignees: Transaction Network Services, Inc. - Applicants: Transaction Network Services, Inc. - Inventors: Sanjay Saini; Bryan Douglas Burns - Web page: https://patentgrep.com/patent/US20260088024A1 ## CPC - G10L 15/1815: {Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning} - G06F 40/30: Semantic analysis - G06F 40/35: Discourse or dialogue representation - G06F 40/56: Natural language generation - G10L 13/027: Concept to speech synthesisers; Generation of natural phrases from machine-based concepts (generation of parameters for speech synthesis out of text G10L13/08) - G10L 15/183: using context dependencies, e.g. language models - G10L 15/30: Distributed recognition, e.g. in client-server systems, for mobile phones or network applications - H04M 3/42059: {Making use of the calling party identifier} - H04M 3/436: Arrangements for screening incoming calls {, i.e. evaluating the characteristics of a call before deciding whether to answer it (based on the calling party profile H04M3/42059; based on location H04M3/42348; based on presence H04M3/42365; diversion H04M3/54)} - H04M 3/4365: {based on information specified by the calling party, e.g. priority or subject} - G10L 13/00: Speech synthesis; Text to speech systems - G10L 15/26: Speech to text systems (G10L15/08 takes precedence) - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H04M: TELEPHONIC COMMUNICATION (circuits for controlling other apparatus via a telephone cable and not involving telephone switching apparatus G08) ## Abstract Disclosed are various embodiments for performing caller intent recognition. In one embodiment, a call is answered on a honeypot phone number previously used by a customer. The system communicates with a caller on the call using human-level synthetic speech generated based at least in part on a language model, where the human-level synthetic speech is generated to elicit information from the caller regarding an intent of the caller. The intent of the caller is determined based at least in part on the information from the caller provided in one or more responses of the caller to the human-level synthetic speech. ## Claims ### Claim 1 A computer-implemented method, comprising: answering a call on a honeypot phone number previously used by a customer; communicating with a caller on the call using human-level synthetic speech generated based at least in part on a language model, the human-level synthetic speech being generated to elicit information from the caller regarding an intent of the caller; and determining the intent of the caller based at least in part on the information from the caller provided in one or more responses of the caller to the human-level synthetic speech. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein communicating with the caller further comprises: converting speech of the caller into first text using a speech-to-text engine; providing the first text to the language model; receiving second text from the language model; and generating the human-level synthetic speech from the second text using a text-to-speech engine. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, further comprising ending the call in response to the intent of the caller being determined with at least a threshold confidence level. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, further comprising continuing the call in response to the intent of the caller not being determined with at least a threshold confidence level. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein determining the intent of the caller further comprises using a machine learning model to determine the intent of the caller based at least in part on the information from the caller. ### Claim 6 (depends on 5) The computer-implemented method of claim 5, further comprising training the machine learning model based at least in part on the call originating from a phone number associated with a known intent. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, further comprising storing an association between a phone number originating the call and the intent of the caller. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, further comprising storing metadata about the call. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, further comprising randomly selecting the honeypot phone number to be used as a honeypot from a pool of phone numbers previously used by customers. ### Claim 10 A system, comprising: at least one computing device; and instructions executable by the at least one computing device that cause the at least one computing device to at least: answer a call on a honeypot phone number previously used by a customer; communicate with a caller on the call using human-level synthetic speech generated based at least in part on a language model, the human-level synthetic speech being generated to elicit information from the caller regarding an intent of the caller; and determine the intent of the caller based at least in part on the information from the caller provided in one or more responses of the caller to the human-level synthetic speech. ### Claim 11 (depends on 10) The system of claim 10, wherein the instructions further cause the at least one computing device to at least: convert speech of the caller into first text using a speech-to-text engine; provide the first text to the language model; receive second text from the language model; and generate the human-level synthetic speech from the second text using a text-to-speech engine. ### Claim 12 (depends on 10) The system of claim 10, wherein the instructions further cause the at least one computing device to at least end the call in response to the intent of the caller being determined with at least a threshold confidence level. ### Claim 13 (depends on 10) The system of claim 10, wherein the instructions further cause the at least one computing device to at least continue the call in response to the intent of the caller not being determined with at least a threshold confidence level. ### Claim 14 (depends on 10) The system of claim 10, wherein the instructions further cause the at least one computing device to at least use a machine learning model to determine the intent of the caller based at least in part on the information from the caller. ### Claim 15 (depends on 10) The system of claim 10, wherein the instructions further cause the at least one computing device to at least train a machine learning model to determine the intent of the caller based at least in part on the call originating from a phone number associated with a known intent. ### Claim 16 (depends on 10) The system of claim 10, wherein the instructions further cause the at least one computing device to at least store an association between a phone number originating the call and the intent of the caller. ### Claim 17 (depends on 10) The system of claim 10, wherein the instructions further cause the at least one computing device to at least randomly select the honeypot phone number to be used as a honeypot from a pool of phone numbers previously used by customers. ### Claim 18 A non-transitory computer-readable medium storing instructions that when executed cause at least one computing device to at least: answer a call on a honeypot phone number previously used by a customer; communicate with a caller on the call using human-level synthetic speech generated based at least in part on a language model, the human-level synthetic speech being generated to elicit information from the caller regarding an intent of the caller; and determine the intent of the caller based at least in part on the information from the caller provided in one or more responses of the caller to the human-level synthetic speech. ### Claim 19 (depends on 18) The non-transitory computer-readable medium of claim 18, wherein the instructions further cause the at least one computing device to at least end the call in response to the intent of the caller being determined with at least a threshold confidence level. ### Claim 20 (depends on 18) The non-transitory computer-readable medium of claim 18, wherein the instructions further cause the at least one computing device to at least continue the call in response to the intent of the caller not being determined with at least a threshold confidence level.