# US 2026/0097656 A1 · Context-aware functionality for vehicles > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0097656 A1 - Document ID: US20260097656A1 - Status: Published application - Kind: A1 - Application number: 19/352,362 - Filed: 2025-10-07 - Published: 2026-04-09 - Earliest claimed: 2024-10-09 - Assignees: QUALCOMM Incorporated - Applicants: QUALCOMM Incorporated - Inventors: Sunvir GUJRAL; Jonathan KIES; Stanislaus KADUGAMPARAMBIL - Web page: https://patentgrep.com/patent/US20260097656A1 ## CPC - B60K 35/29: Instruments characterised by the way in which information is handled, e.g. showing information on plural displays or prioritising information according to driving conditions - B60K 35/10: Input arrangements, i.e. from user to vehicle, associated with vehicle functions or specially adapted therefor - B60K 35/22: Display screens - B60K 35/265: {Voice} - B60K 35/28: characterised by the type of the output information, e.g. video entertainment or vehicle dynamics information; characterised by the purpose of the output information, e.g. for attracting the attention of the driver - B60W 40/09: Driving style or behaviour - B60W 50/14: Means for informing the driver, warning the driver or prompting a driver intervention - B60K 2360/11: Instrument graphical user interfaces or menu aspects - B60K 2360/1868: according to driving situations - B60W 2040/0818: {Inactivity or incapacity of driver} - B60W 2050/146: {Display means} - B60W 2540/229: Attention level, e.g. attentive to driving, reading or sleeping - B60K: ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES - B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT ## Abstract Systems and techniques are described herein for a context-aware intelligent cockpit. For example, a computing device can capture, using a plurality of sensors, context data associated with user operation of a vehicle. The computing device can determine a distraction score based on the context data. The computing device can adjust a user interface of the vehicle based on the distraction score. ## Claims ### Claim 1 An apparatus for context-aware driving assistance, the apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory and configured to: capture, using a plurality of sensors, context data associated with user operation of a vehicle; determine a distraction score based on the context data; and adjust a user interface of the vehicle based on the distraction score. ### Claim 2 (depends on 1) The apparatus of claim 1, wherein the context data includes a count of user interactions with the user interface over a predetermined period of time. ### Claim 3 (depends on 1) The apparatus of claim 1, wherein the context data includes data associated with at least one of an amount of traffic on a road where the vehicle is located, pedestrian distance from the vehicle, curvature of the road, or a number of lanes of the road. ### Claim 4 (depends on 1) The apparatus of claim 1, wherein the context data includes data associated with a number of passengers in the vehicle. ### Claim 5 (depends on 1) The apparatus of claim 1, wherein the context data includes data associated with a sound level within the vehicle. ### Claim 6 (depends on 1) The apparatus of claim 1, wherein the context data includes data associated with a date and time of operation of the vehicle. ### Claim 7 (depends on 1) The apparatus of claim 1, wherein the at least one processor is configured to: disable features of the vehicle based on the distraction score. ### Claim 8 (depends on 1) The apparatus of claim 1, wherein the at least one processor is configured to: delay notifications based on the distraction score. ### Claim 9 (depends on 1) The apparatus of claim 1, wherein the at least one processor is configured to: determine the distraction score is greater than a first predetermined threshold; and generate, using a machine learning model, a summary of the user operation of the vehicle based on the distraction score being greater than the first predetermined threshold. ### Claim 10 (depends on 9) The apparatus of claim 9, wherein the first predetermined threshold is adjustable based on the context data. ### Claim 11 (depends on 9) The apparatus of claim 9, wherein the at least one processor is configured to: output the summary as audio. ### Claim 12 (depends on 9) The apparatus of claim 9, wherein the machine learning model is a large language model (LLM) and text to speech model (TTS). ### Claim 13 (depends on 9) The apparatus of claim 9, wherein the at least one processor is configured to: determine the distraction score is greater than a second predetermined threshold, wherein the second predetermined threshold is greater than the first predetermined threshold; and generate, using the machine learning model, the summary of the user operation of the vehicle based on the distraction score being greater than the second predetermined threshold, wherein the summary of the user operation includes an adjusted tone based on the distraction score being greater than the second predetermined threshold. ### Claim 14 A method for context-aware driving assistance, the method comprising: capturing, using a plurality of sensors, context data associated with user operation of a vehicle; determining a distraction score based on the context data; and adjusting a user interface of the vehicle based on the distraction score. ### Claim 15 (depends on 14) The method of claim 14, wherein the context data includes a count of user interactions with the user interface over a predetermined period of time. ### Claim 16 (depends on 14) The method of claim 14, wherein the context data includes data associated with at least one of an amount of traffic on a road where the vehicle is located, pedestrian distance from the vehicle, curvature of the road, or a number of lanes of the road. ### Claim 17 (depends on 14) The method of claim 14, wherein the context data includes data associated with a number of passengers in the vehicle. ### Claim 18 (depends on 14) The method of claim 14, wherein the context data includes data associated with a sound level within the vehicle. ### Claim 19 (depends on 14) The method of claim 14, wherein the context data includes data associated with a date and time of operation of the vehicle. ### Claim 20 (depends on 14) The method of claim 14, further comprising: determining the distraction score is greater than a first predetermined threshold; and generating, using a machine learning model, a summary of the user operation of the vehicle based on the distraction score being greater than the first predetermined threshold.