# US 12,411,893 B2 · Proactively generated content and personalized feeds available for audiences > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,411,893 B2 - Document ID: US12411893B2 - Status: Granted - Kind: B2 - Application number: 18/783,424 - Filed: 2024-07-25 - Published: 2025-06-19 - Granted: 2025-09-09 - Earliest claimed: 2023-02-08 - Assignees: Typeface Inc. - Applicants: Typeface Inc. - Inventors: Abhay Parasnis; Saachi Shah; Jonathan Moreira; Perraju Bendapudi; Hema Vishnu Pola; Vishal Sood; Nipun Dave; Anish Pimpley - Web page: https://patentgrep.com/patent/US12411893B2 ## CPC - G06F 16/9035: Filtering based on additional data, e.g. user or group profiles - G06F 3/0482: Interaction with lists of selectable items, e.g. menus - G06F 3/0483: Interaction with page-structured environments, e.g. book metaphor - G06F 3/04842: Selection of displayed objects or displayed text elements (G06F3/0482 takes precedence) - G06F 3/04845: for image manipulation, e.g. dragging, rotation, expansion or change of colour - G06F 9/451: Execution arrangements for user interfaces - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/047: Probabilistic or stochastic networks - G06N 3/0475: Generative networks - G06N 3/08: Learning methods - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G06N 20/20: Ensemble learning - G06Q 30/0244: {Optimization} - G06Q 30/0269: {based on user profile or attribute} - G06Q 30/0271: {Personalized advertisement} - G06Q 30/0276: {Advertisement creation} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - 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 ## Abstract Methods, systems, and computer programs are presented for generating personalized content. One method includes an operation for identifying audience parameter values that indicate which users are members of an audience. The method further includes operations for determining attribute values for attributes used to generate items based on the audience parameter values, and generating items for the audience based on the attribute values. Generating each item comprises creating a prompt based on a type of the item, the attribute values, and the audience parameter values; selecting a generative artificial intelligence (GAI) tool to generate the item based on the type of item; and providing the created prompt to the selected GAI tool. The method further includes causing presentation of the generated items in a user interface (UI), receiving on the UI a selection of one of the items; and transmitting the selected item to one or more members of the audience. ## Claims ### Claim 1 A computer-implemented method comprising: identifying audience parameter values, the audience parameter values indicating which users are members of an audience; determining attribute values for a plurality of attributes used to generate items based on the audience parameter values, the attribute values used to identify the members of the audience; generating a plurality of items for the audience based on the attribute values, wherein generating each item from the plurality of items comprises: creating a prompt based on a type of the item, the attribute values, and the audience parameter values, the prompt being a text description for generating new content; selecting a generative artificial intelligence (GAI) tool to generate the item based on the type of item; and providing the created prompt to the selected GAI tool to obtain the item; causing presentation of the generated plurality of items in a user interface (UI); receiving on the UI a selection of one of the items from the generated plurality of items; and transmitting the selected item to one or more members of the audience. ### Claim 2 (depends on 1) The method as recited in claim 1, further comprising: providing a first user interface (UI) displaying information regarding different audiences, audience preferences, and content designed for each audience, the first UI providing an option to select and filter information by audience. ### Claim 3 (depends on 2) The method as recited in claim 2, wherein the first UI provides cards showing documents generated for each communication channel. ### Claim 4 (depends on 1) The method as recited in claim 1, further comprising: providing a second UI showing a performance of content presented to users and a feed of items available for future communications to the audience. ### Claim 5 (depends on 4) The method as recited in claim 4, wherein the performance is a measurement of a response of users to communications, and the performance is based on one or more of a number of views, a percentage of users that selected a link for additional information, a percentage of users that bought a product or service associated with a transmitted item. ### Claim 6 (depends on 4) The method as recited in claim 4, wherein generating each item further comprises utilizing past performance data to generate the item. ### Claim 7 (depends on 1) The method as recited in claim 1, wherein determining attribute values further comprises: utilizing an image-recognition model to determine features of images sent to users; determining a relevance score for the features based on tracked performance of the images sent to users; and selecting the attribute values based on the relevance score of the features. ### Claim 8 (depends on 7) The method as recited in claim 7, wherein the image-recognition model is trained using a dataset of images with known performance metrics, the image-recognition model learning correlations between visual elements and item performance, wherein the image-recognition model predicts a performance of new images based on visual features of the new images. ### Claim 9 (depends on 1) The method as recited in claim 1, further comprising: providing a second UI showing audience information on a feed, the second UI showing content organized by audience for a plurality of audiences, the second UI presenting the audience information in a table organized by calendar date, with each column for a different day and each row showing communications planned or delivered for each day for each audience. ### Claim 10 (depends on 1) The method as recited in claim 1, further comprising: generating a feed for the audience, wherein the items generated for the feed are deliverable via a plurality of channels that comprise publishing to social media, connecting to email automation systems, providing audience-level personalization on a webpage, and distributing through customer journey orchestrators. ### Claim 11 A system comprising: a memory comprising instructions; and one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the system to perform operations comprising: identifying audience parameter values, the audience parameter values indicating which users are members of an audience; determining attribute values for a plurality of attributes used to generate items based on the audience parameter values, the attribute values used to identify the members of the audience; generating a plurality of items for the audience based on the attribute values, wherein generating each item from the plurality of items comprises: creating a prompt based on a type of the item, the attribute values, and the audience parameter values, the prompt being a text description for generating new content; selecting a generative artificial intelligence (GAI) tool to generate the item based on the type of item; and providing the created prompt to the selected GAI tool to obtain the item; causing presentation of the generated plurality of items in a user interface (UI); receiving on the UI a selection of one of the items from the generated plurality of items; and transmitting the selected item to one or more members of the audience. ### Claim 12 (depends on 11) The system as recited in claim 11, wherein the instructions further cause the one or more computer processors to perform operations comprising: providing a first user interface (UI) displaying information regarding different audiences, audience preferences, and content designed for each audience, the first UI providing an option to select and filter information by audience. ### Claim 13 (depends on 12) The system as recited in claim 12, wherein the first UI provides cards showing documents generated for each communication channel. ### Claim 14 (depends on 11) The system as recited in claim 11, wherein the instructions further cause the one or more computer processors to perform operations comprising: providing a second UI showing a performance of content presented to users and a feed of items available for future communications to the audience. ### Claim 15 (depends on 14) The system as recited in claim 14, wherein the performance is a measurement of a response of users to communications, and the performance is based on one or more of a number of views, a percentage of users that selected a link for additional information, a percentage of users that bought a product or service associated with a transmitted item. ### Claim 16 A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising: identifying audience parameter values, the audience parameter values indicating which users are members of an audience; determining attribute values for a plurality of attributes used to generate items based on the audience parameter values, the attribute values used to identify the members of the audience; generating a plurality of items for the audience based on the attribute values, wherein generating each item from the plurality of items comprises: creating a prompt based on a type of the item, the attribute values, and the audience parameter values, the prompt being a text description for generating new content; selecting a generative artificial intelligence (GAI) tool to generate the item based on the type of item; and providing the created prompt to the selected GAI tool to obtain the item; causing presentation of the generated plurality of items in a user interface (UI); receiving on the UI a selection of one of the items from the generated plurality of items; and transmitting the selected item to one or more members of the audience. ### Claim 17 (depends on 16) The non-transitory machine-readable storage medium as recited in claim 16, wherein the machine further performs operations comprising: providing a first user interface (UI) displaying information regarding different audiences, audience preferences, and content designed for each audience, the first UI providing an option to select and filter information by audience. ### Claim 18 (depends on 17) The non-transitory machine-readable storage medium as recited in claim 17, wherein the first UI provides cards showing documents generated for each communication channel. ### Claim 19 (depends on 16) The non-transitory machine-readable storage medium as recited in claim 16, wherein the machine further performs operations comprising: providing a second UI showing a performance of content presented to users and a feed of items available for future communications to the audience. ### Claim 20 (depends on 19) The non-transitory machine-readable storage medium as recited in claim 19, wherein the performance is a measurement of a response of users to communications, and the performance is based on one or more of a number of views, a percentage of users that selected a link for additional information, a percentage of users that bought a product or service associated with a transmitted item.