# US 12,002,079 B2 · Method, system, and non-transitory computer readable medium for providing product image recommendations > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,002,079 B2 - Document ID: US12002079B2 - Status: Granted - Kind: B2 - Application number: 17/836,233 - Filed: 2022-06-09 - Published: 2022-09-22 - Granted: 2024-06-04 - Earliest claimed: 2019-10-24 - Assignees: Shopify Inc. - Applicants: Shopify Inc. - Inventors: Franklyn D'Souza; Jonathan Wade; Juho Mikko Haapoja - Web page: https://patentgrep.com/patent/US12002079B2 ## CPC - G06Q 30/0244: {Optimization} - G06Q 30/0627: {by specifying product or service characteristics, e.g. product dimensions} - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/045: Combinations of networks - G06N 3/08: Learning methods - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 20/00: Machine learning - G06Q 30/0276: {Advertisement creation} - G06Q 30/0609: {Qualifying participants for shopping transactions (payment transaction verification G06Q20/401)} - G06Q 30/0631: {Recommending goods or services} - G06Q 30/0643: {graphically representing goods, e.g. 3D product representation} - H04N 23/64: {Computer-aided capture of images, e.g. transfer from script file into camera, check of taken image quality, advice or proposal for image composition or decision on when to take image} - 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 - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## Abstract For products that are sold online, the manner in which a product is displayed in an image can affect sales of the product. Embodiments of the present disclosure relate to computer-implemented systems and methods to provide a user with recommendations when generating an image of a product. A method includes obtaining a product image and determining parameters of the product image. A recommendation for modifying the product image is then generated using a model to relate these parameters to market success of the product image. The recommendation is displayed on the user device, and a user can potentially improve subsequent product images by following the recommendation. ## Claims ### Claim 1 A computer-implemented method comprising: obtaining a first product image captured by a user device, the first product image having a resolution less than a particular resolution achievable by the user device; obtaining data indicating a capability of the user device, the data including an indication of the particular resolution achievable by the user device; determining particular parameters of the first product image, wherein determining the particular parameters of the first product image includes determining a parameter of the first product image based on the particular resolution achievable by the user device that is higher than the resolution of the first product image; generating, based on the particular parameters, a recommendation for capturing a further product image with the user device; and instructing, based on the recommendation, the user device to display at least a portion of the recommendation in a viewfinder on the user device and automatically adjust a setting used for capturing the further product image by the user device. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the setting automatically adjusted includes at least one of: a sensitivity of a camera of the user device, an aperture size of the camera of the user device, an exposure time of the camera of the user device, a zoom setting of the camera of the user device, a filtering performed by the user device, and/or a flash setting of the camera of the user device. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the recommendation displayed is in the form of a user notification. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, further comprising instructing the user device to automatically take a picture to capture the further product image. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the particular resolution is the resolution at which a picture is taken by a camera of the user device. ### Claim 6 (depends on 5) The computer-implemented method of claim 5, wherein the particular resolution is the true resolution of the camera. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the recommendation is generated based on both the particular parameters and a model that relates parameters of a product image to market success of the product image. ### Claim 8 (depends on 7) The computer-implemented method of claim 7, wherein the model is implemented using a machine learning algorithm, and generating the recommendation comprises: inputting the particular parameters into the machine learning algorithm; and calculating, using the machine learning algorithm, a prediction of market success of the first product image, wherein the recommendation is associated with an improvement to the prediction of the market success of the first product image. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein determining the particular parameters of the first product image comprises performing image analysis on the first product image. ### Claim 10 A system comprising: a processor; and a memory storing processor-executable instructions that, when executed, cause the processor to: obtain a first product image captured by a user device, the first product image having a resolution less than a particular resolution achievable by the user device; obtain data indicating a capability of the user device, the data including an indication of the particular resolution achievable by the user device; determine particular parameters of the first product image, wherein determining the particular parameters of the first product image includes determining a parameter of the first product image based on the particular resolution achievable by the user device that is higher than the resolution of the first product image; generate, based on the particular parameters, a recommendation for capturing a further product image with the user device; and instruct, based on the recommendation, the user device to display at least a portion of the recommendation in a viewfinder on the user device and automatically adjust a setting used for capturing the further product image by the user device. ### Claim 11 (depends on 10) The system of claim 10, wherein the setting automatically adjusted includes at least one of: a sensitivity of a camera of the user device, an aperture size of the camera of the user device, an exposure time of the camera of the user device, a zoom setting of the camera of the user device, a filtering performed by the user device, and/or a flash setting of the camera of the user device. ### Claim 12 (depends on 10) The system of claim 10, wherein the recommendation displayed is in the form of a user notification. ### Claim 13 (depends on 10) The system of claim 10, wherein the processor is to instruct the user device to automatically take a picture to capture the further product image. ### Claim 14 (depends on 10) The system of claim 10, wherein the particular resolution is the resolution at which a picture is taken by a camera of the user device. ### Claim 15 (depends on 10) The system of claim 10, wherein the recommendation is generated based on both the particular parameters and a model that relates parameters of a product image to market success of the product image. ### Claim 16 (depends on 15) The system of claim 15, wherein the model is implemented using a machine learning algorithm, and generating the recommendation comprises: inputting the particular parameters into the machine learning algorithm; and calculating, using the machine learning algorithm, a prediction of market success of the first product image, wherein the recommendation is associated with an improvement to the prediction of the market success of the first product image. ### Claim 17 (depends on 10) The system of claim 10, wherein determining the particular parameters of the first product image comprises performing image analysis on the first product image. ### Claim 18 A non-transitory computer readable medium having stored thereon computer-executable instructions that, when executed by a computer, cause the computer to perform operations comprising: obtaining a first product image captured by a user device, the first product image having a resolution less than a particular resolution achievable by the user device; obtaining data indicating a capability of the user device, the data including an indication of the particular resolution achievable by the user device; determining particular parameters of the first product image, wherein determining the particular parameters of the first product image includes determining a parameter of the first product image based on the particular resolution achievable by the user device that is higher than the resolution of the first product image; generating, based on the particular parameters, a recommendation for capturing a further product image with the user device; and instructing, based on the recommendation, the user device to display at least a portion of the recommendation in a viewfinder on the user device and automatically adjust a setting used for capturing the further product image by the user device. ### Claim 19 (depends on 18) The non-transitory computer readable medium of claim 18, wherein the setting automatically adjusted includes at least one of: a sensitivity of a camera of the user device, an aperture size of the camera of the user device, an exposure time of the camera of the user device, a zoom setting of the camera of the user device, a filtering performed by the user device, and/or a flash setting of the camera of the user device. ### Claim 20 (depends on 19) The non-transitory computer readable medium of claim 19, wherein the recommendation displayed is in the form of a user notification.