# US 2021/0342913 A1 · Methods and systems for providing a vehicle recommendation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0342913 A1 - Document ID: US20210342913A1 - Status: Published application - Kind: A1 - Application number: 16/862,777 - Filed: 2020-04-30 - Published: 2021-11-04 - Earliest claimed: 2020-04-30 - Assignees: Capital One Services, LLC - Applicants: Capital One Services, LLC - Inventors: Chih-Hsiang CHOW; Steven DANG; Elizabeth FURLAN - Web page: https://patentgrep.com/patent/US20210342913A1 ## CPC - G06Q 30/0631: {Recommending goods or services} - B60R 16/037: for occupant comfort {, e.g. for automatic adjustment of appliances according to personal settings, e.g. seats, mirrors, steering wheel} - G06F 16/9535: Search customisation based on user profiles and personalisation - G06F 30/12: characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD - G06F 30/15: Vehicle, aircraft or watercraft design - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 20/00: Machine learning - G06Q 30/0282: Rating or review of business operators or products - G06Q 30/0621: {by configuring or customising goods or services} - G06Q 30/0627: {by specifying product or service characteristics, e.g. product dimensions} - G06Q 30/0629: {by pre-processing results, e.g. ranking or ordering results} - G06Q 30/0643: {graphically representing goods, e.g. 3D product representation} - G06T 19/20: Editing of three-dimensional [3D] images, e.g. changing shapes or colours, aligning objects or positioning parts - G06T 2200/24: involving graphical user interfaces [GUIs] - G06T 2219/2012: Colour editing, changing, or manipulating; Use of colour codes - G06T 2219/2021: Shape modification - G06T 2219/2024: Style variation - 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 - B60R: VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A computer-implemented method for providing a vehicle recommendation may include obtaining characteristic data of a user-defined vehicle based on one or more interactive activities of a user with a first three-dimensional model of a model vehicle; generating a second three-dimensional model of the user-defined vehicle based on the first three-dimensional model and the characteristic data; obtaining one or more images of the second three-dimensional model; generating a selection of one or more pre-stored vehicles based on a comparison between the one or more images of the second three-dimensional model and pre-stored image data of the one or more pre-stored vehicles other than the user-defined vehicle; obtaining user feedback data based on the selection of the one or more pre-stored vehicles; generating the vehicle recommendation for the user based on the user feedback data; and transmitting, to a device associated with the user, a notification indicative of the vehicle recommendation. ## Claims ### Claim 1 A computer-implemented method for providing a vehicle recommendation to a user, the method comprising: obtaining, via one or more processors, characteristic data of a user-defined vehicle based on one or more interactive activities of the user with a first three-dimensional model of a model vehicle, wherein the one or more interactive activities of the user comprises at least altering one or more features of the model vehicle; generating, via the one or more processors, a second three-dimensional model of the user-defined vehicle based on the first three-dimensional model of the model vehicle and the characteristic data of the user-defined vehicle; obtaining, via the one or more processors, one or more images of the second three-dimensional model of the user-defined vehicle, wherein the one or more images comprise at least one of a front side image, a back side image, a left side image, or a right side image of the second three-dimensional model of the user-defined vehicle; generating, via the one or more processors, a selection of one or more pre-stored vehicles based on a comparison between the one or more images of the second three-dimensional model of the user-defined vehicle and pre-stored image data of the one or more pre-stored vehicles other than the user-defined vehicle via a trained machine learning algorithm; obtaining, via the one or more processors, user feedback data based on the selection of the one or more pre-stored vehicles, wherein the user feedback data indicates a user's preference of a given pre-stored vehicle of the selection of the one or more pre-stored vehicles; generating, via the one or more processors, the vehicle recommendation for the user based on the user feedback data via the trained machine learning algorithm; and transmitting, to a device associated with the user, a notification indicative of the vehicle recommendation. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the one or more images include a vectorized image of a portion of the second three-dimensional model of the user-defined vehicle. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the one or more features of the model vehicle include one or more exterior features or one or more interior features of the model vehicle. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein the one or more features of the model vehicle include the one or more exterior features of the model vehicle, and wherein the one or more exterior features of the model vehicle include at least one of a wheel feature, a color feature, or a shape feature of the model vehicle. ### Claim 5 (depends on 3) The computer-implemented method of claim 3, wherein the one or more features of the model vehicle include the one or more interior features of the model vehicle, and wherein the one or more interior features of the model vehicle include at least one of a material feature, an internal shape feature, an electronics feature, an engine feature, or an add-on feature of the model vehicle. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the trained machine learning algorithm includes a neural network. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the selection of the one or more pre-stored vehicles includes a rank of the pre-stored vehicles based on a level of similarity between the pre-stored vehicles and the user-defined vehicle. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein the pre-stored image data of the one or more pre-stored vehicles includes one or more pre-stored images of the one or more pre-stored vehicles. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the vehicle recommendation includes a recommended vehicle available for purchase. ### Claim 10 A computer-implemented method for providing a vehicle recommendation to a user, the method comprising: obtaining, via one or more processors, a first three-dimensional model of a model vehicle based on automotive data provided by the user, wherein the automotive data comprises at least one of a make or model of the model vehicle; obtaining, via the one or more processors, characteristic data of a user-defined vehicle based on one or more interactive activities of the user with the model vehicle, wherein the one or more interactive activities of the user comprise at least altering one or more features of the model vehicle; generating, via the one or more processors, a second three-dimensional model of the user-defined vehicle based on the first three-dimensional model of the model vehicle and the characteristic data of the user-defined vehicle; obtaining, via the one or more processors, one or more images of the second three-dimensional model of the user-defined vehicle, wherein the one or more images comprise at least one of a front side image, a back side image, a left side image, or a right side image of the second three-dimensional model of the user-defined vehicle; obtaining, via the one or more processors, pre-stored image data of one or more pre-stored vehicles other than the user-defined vehicle based on the one or more images of the second three-dimensional model of the user-defined vehicle, wherein the pre-stored image data of the one or more pre-stored vehicles comprises one or more pre-stored images of the one or more pre-stored vehicles; generating, via the one or more processors, a selection of the one or more pre-stored vehicles based on the one or more images of the second three-dimensional model of the user-defined vehicle and the pre-stored image data of the one or more pre-stored vehicles other than the user-defined vehicle via a trained machine learning algorithm; obtaining, via the one or more processors, user feedback data based on the selection of the one or more pre-stored vehicles, wherein the user feedback data indicates a user's preference of a given pre-stored vehicle of the selection of the one or more pre-stored vehicles; generating, via the one or more processors, the vehicle recommendation for the user based on the user feedback data via the trained machine learning algorithm; and transmitting, to a device associated with the user, a notification indicative of the vehicle recommendation. ### Claim 11 (depends on 10) The computer-implemented method of claim 10, wherein the one or more images include a vectorized image of a portion of the second three-dimensional model of the user-defined vehicle. ### Claim 12 (depends on 10) The computer-implemented method of claim 10, wherein the one or more features of the model vehicle include one or more exterior features or one or more interior features of the model vehicle. ### Claim 13 (depends on 12) The computer-implemented method of claim 12, wherein the one or more features of the model vehicle include the one or more exterior features of the model vehicle, and wherein the one or more exterior features of the model vehicle include at least one of a wheel feature, a color feature, or a shape feature of the model vehicle. ### Claim 14 (depends on 12) The computer-implemented method of claim 12, wherein the one or more features of the model vehicle include the one or more interior features of the model vehicle, and wherein the one or more interior features of the model vehicle include at least one of a material feature, an internal shape feature, an electronics feature, an engine feature, or an add-on feature of the model vehicle. ### Claim 15 (depends on 10) The computer-implemented method of claim 10, wherein the trained machine learning algorithm includes a neural network. ### Claim 16 (depends on 10) The computer-implemented method of claim 10, wherein the selection of the one or more pre-stored vehicles includes a rank of the pre-stored vehicles based on a level of similarity between the pre-stored vehicles and the user-defined vehicle. ### Claim 17 (depends on 10) The computer-implemented method of claim 10, wherein the vehicle recommendation includes a recommended vehicle available for purchase. ### Claim 18 (depends on 17) The computer-implemented method of claim 17, wherein the recommended vehicle is one of the one or more pre-stored vehicles. ### Claim 19 (depends on 10) The computer-implemented method of claim 10, wherein the automotive data is provided by the user via the device associated with the user. ### Claim 20 A computer system for providing a vehicle recommendation to a user, comprising: a memory storing instructions; and one or more processors configured to execute the instructions to perform operations including: obtaining characteristic data of a user-defined vehicle based on one or more interactive activities of the user with a first three-dimensional model of a model vehicle, wherein the one or more interactive activities of the user comprise at least altering one or more features of the model vehicle; generating a second three-dimensional model of the user-defined vehicle based on the first three-dimensional model of the model vehicle and the characteristic data of the user-defined vehicle; obtaining one or more images of the second three-dimensional model of the user-defined vehicle, wherein the one or more images comprise at least one of a front side image, a back side image, a left side image, or a right side image of the second three-dimensional model of the user-defined vehicle; generating a selection of one or more pre-stored vehicles based on a comparison between the one or more images of the second three-dimensional model of the user-defined vehicle and pre-stored image data of the one or more pre-stored vehicles other than the user-defined vehicle via a trained machine learning algorithm; obtaining user feedback data based on the selection of the one or more pre-stored vehicles, wherein the user feedback data indicates a user's preference of a given pre-stored vehicle of the selection of the one or more pre-stored vehicles; generating the vehicle recommendation for the user based on the user feedback data via the trained machine learning algorithm; and transmitting, to a device associated with the user, a notification indicative of the vehicle recommendation.