# US 2025/0093170 A1 · Device and method for providing information based on speech recognition > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0093170 A1 - Document ID: US20250093170A1 - Status: Published application - Kind: A1 - Application number: 18/671,500 - Filed: 2024-05-22 - Published: 2025-03-20 - Earliest claimed: 2023-09-19 - Assignees: Hyundai Motor Company; Kia Corporation - Applicants: Hyundai Motor Company; Kia Corporation - Inventors: Chang Woo Chun - Web page: https://patentgrep.com/patent/US20250093170A1 ## CPC - G01C 21/3608: {using speech input, e.g. using speech recognition} - G01C 21/3644: {Landmark guidance, e.g. using POIs or conspicuous other objects} - G10L 15/08: Speech classification or search - G10L 15/16: using artificial neural networks - G10L 15/1822: {Parsing for meaning understanding} - G10L 2015/088: {Word spotting} - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract A device and a method are configured to provide information based on speech recognition. The device includes a memory storing computer-executable instructions and a processor. The processor is configured to execute the computer-executable instructions to classify an utterance intent of a speech utterance of a vehicle occupant, extract at least one keyword corresponding to a slot of the utterance intent from the speech utterance, obtain location information corresponding to the at least one keyword by applying a first deep learning model to the at least one keyword when the utterance intent is route setting, and provide a navigation route from a current location of the vehicle occupant to the location information. ## Claims ### Claim 1 A device for providing information based on speech recognition, the device comprising: at least one memory storing computer-executable instructions; and at least one processor, wherein the at least one processor is configured to execute the computer-executable instructions to: classify an utterance intent of a speech utterance of an occupant of a vehicle, extract at least one keyword corresponding to a slot of the utterance intent from the speech utterance, obtain location information corresponding to the at least one keyword by applying a first deep learning model to the at least one keyword when the utterance intent is route setting, and provide the occupant with a navigation route from a current location of the vehicle occupant to the location information. ### Claim 2 (depends on 1) The device of claim 1, when the utterance intent is any one of a point of interest (POI) guidance, a route description, an accident information guidance, or a congested section check, wherein the at least one processor is configured to: identify location coordinates based on the at least one keyword, obtain a POI name by applying a second deep learning model to the location coordinates, and provide the POI name. ### Claim 3 (depends on 2) The device of claim 2, wherein the first deep learning model includes: a text encoder trained to encode a training input keyword into a first vector representation; and a location decoder trained to output a training output location corresponding to the training input keyword from the first vector representation. ### Claim 4 (depends on 3) The device of claim 3, wherein the second deep learning model includes: a location encoder trained to encode a training input location into a second vector representation; and a text decoder trained to output a training output keyword corresponding to the training input location from the second vector representation. ### Claim 5 (depends on 4) The device of claim 4, wherein the text encoder and the location encoder have trained to reduce a difference between the first vector representation and the second vector representation when the training input keyword corresponds to the training input location. ### Claim 6 (depends on 2) The device of claim 2, wherein when the utterance intent is the POI guidance, the at least one processor is configured to: obtain first location coordinates around a target location according to the at least one keyword, obtain first POI names by applying the second deep learning model to the first location coordinates, and provide the first POI names. ### Claim 7 (depends on 2) The device of claim 2, wherein when the utterance intent is the route description, the at least one processor is configured to: obtain second location coordinates within the navigation route according to the at least one keyword, obtain second POI names by applying the second deep learning model to the second location coordinates, and provide the second POI names. ### Claim 8 (depends on 2) The device of claim 2, wherein when the utterance intent is the accident information guidance, the at least one processor is configured to: identify third location coordinates for an accident point within a spatial range according to the at least one keyword based on accident information, obtain a third POI name by applying the second deep learning model to the third location coordinates, and provide the third POI name. ### Claim 9 (depends on 2) The device of claim 2, wherein when the utterance intent is the congested section check, the at least one processor is configured to: identify fourth location coordinates for a congested section within a spatial range according to the at least one keyword based on traffic information, obtain a fourth POI name by applying the second deep learning model to the fourth location coordinates, and provide the fourth POI name. ### Claim 10 (depends on 1) A vehicle comprising the device of claim 1. ### Claim 11 A computer implemented method for providing information based on speech information, the method comprising: classifying an utterance intent of a speech utterance of an occupant of a vehicle; extracting at least one keyword corresponding to a slot of the utterance intent from the speech utterance; obtaining location information corresponding to the at least one keyword by applying a first deep learning model to the at least one keyword when the utterance intent is route setting; and providing the occupant with a navigation route from a current location of the vehicle occupant to the location information. ### Claim 12 (depends on 11) The method of claim 11, further comprising: when the utterance intent is any one of POI guidance, route description, accident information guidance, or congested section check, identifying location coordinates based on the at least one keyword; obtaining a POI name by applying a second deep learning model to the location coordinates; and providing the POI name. ### Claim 13 (depends on 12) The method of claim 12, wherein the first deep learning model includes: a text encoder trained to encode a training input keyword into a first vector representation; and a location decoder trained to output a training output location corresponding to the training input keyword from the first vector representation. ### Claim 14 (depends on 13) The method of claim 13, wherein the second deep learning model includes: a location encoder trained to encode a training input location into a second vector representation; and a text decoder trained to output a training output keyword corresponding to the training input location from the second vector representation. ### Claim 15 (depends on 14) The method of claim 14, wherein the text encoder and the location encoder have trained to reduce a difference between the first vector representation and the second vector representation when the training input keyword corresponds to the training input location. ### Claim 16 (depends on 12) The method of claim 12, wherein when the utterance intent is the POI guidance, the identifying of the location coordinates includes: obtaining first location coordinates around a target location according to the at least one keyword; and obtaining first POI names by applying the second deep learning model to the first location coordinates. ### Claim 17 (depends on 12) The method of claim 12, wherein when the utterance intent is the route description, the identifying of the location coordinates includes: obtaining second location coordinates within the navigation route according to the at least one keyword; and obtaining second POI names by applying the second deep learning model to the second location coordinates. ### Claim 18 (depends on 12) The method of claim 12, wherein when the utterance intent is the accident information guidance, the identifying of the location coordinates includes: identifying third location coordinates for an accident point within a spatial range according to the at least one keyword based on accident information; and obtaining a third POI name by applying the second deep learning model to the third location coordinates. ### Claim 19 (depends on 12) The method of claim 12, wherein when the utterance intent is the congested section check, the identifying of the location coordinates includes: identifying fourth location coordinates for a congested section within a spatial range according to the at least one keyword based on traffic information; and obtaining a fourth POI name by applying the second deep learning model to the fourth location coordinates.