IP Library › Granted Patent US 12,748,916
Granted Patent B2
US 12,748,916 · App. 18/621,892 · Granted Sep 29, 2026

Generation of personalized and structured content using a collaborative online generator

Inventors: Behnoosh Hariri (New York, NY); Gregory George Galante (Little Silver, NJ); Rebecca Wenshu Hsieh (Sunnyvale, CA); Princeton Tirin Poe (New York, NY); Gonzalo Fiorina (Brooklyn, NY); Barak Ben Noon (Brooklyn, NY); Miles Henrichs (New York, NY); Ahsan Wahab (Tampa, FL); Amer Mograbi (New York, NY); Christopher Gregory Tong (Long Island, NY); Nicholas Joseph Pesce (New York, NY); Andrew James Motika (Birmingham, MI); John Gabriel D'Angelo (New York, NY); Tomer Aberbach (Hoboken, NJ); Grace Sytin Shih (Cupertino, CA); Albert Orriols Puig (Los Altos, CA); Jayakumar Hoskere (Bangalore, IN)
Assignee: GOOGLE LLC
G06F40/186G06F40/103G06F40/20
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Quick Facts
Patent No.
US 12,748,916
App. No.
18/621,892
Granted
Sep 29, 2026
Kind
B2
Abstract

Systems and methods for generating personalized and structured content using a collaborative generator provide a user interface to a user computing system and receive a prompt from the user computing system via the user interface. The systems and methods provide the prompt to a generative model, with the generative model being a machine-learned model trained to process language input prompts to generate a language output. The systems and methods receive a generative output generated by the generative model in response to the prompt. Additionally, the systems and methods generate a modified output by modifying the generative output based at least in part on historical user data for a user associated with the prompt, and then provide the modified output via the user interface.

Claims (46)

1 . A computing system for automatically generating personalized and structured content, the computing system comprising:

one or more processors; and

one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising:

providing a user interface to a user computing system, the user interface including an integrated development environment in which content is insertable in-line by at least a user of the user computing system;

receiving a prompt from the user of the user computing system via the user interface;

providing the prompt to a generative model, the generative model being a machine-learned model trained to process language input prompts to generate a language output;

receiving a generative output generated by the generative model in response to the prompt;

providing the generative output via the user interface in a generative area of the integrated development environment, the generative area separating the generative output from being in-line within the integrated development environment;

receiving an insertion request from the user of the user computing system via the user interface subsequent to providing the generative output, the insertion request being associated with accepting the generative output for insertion in-line within the integrated development environment;

generating, in response to receiving the insertion request, a modified output by modifying the generative output based at least in part on historical user data for the user; and

providing the modified output via the user interface and in-line within the integrated development environment.

2 . The computing system of claim 1 , wherein receiving the prompt from the user computing system via the user interface comprises receiving the prompt from within the generative area of the user interface.

3 . The computing system of claim 1 , wherein the integrated development environment comprises at least one formatting selection interface for selecting formatting rules for text in-line within the integrated development environment,

wherein providing the modified output via the user interface comprises inserting the modified output in-line within the integrated development environment and formatted according to the formatting rules.

4 . The computing system of claim 1 , wherein receiving the prompt from the user computing system via the user interface comprises receiving selection of text within the user interface, the text being formatted according to embedded formatting rules,

wherein generating the modified output comprises generating the modified output by modifying the generative output based at least in part on the historical user data and the embedded formatting rules received with the selection of text.

5 . The computing system of claim 1 , wherein the generative output comprises a block template generated by the generative model, the block template defining one or more fields associated with the prompt,

wherein generating the modified output comprises populating eligible fields of the one or more fields within the block template based on the historical user data, the eligible fields being associated with the historical user data.

6 . The computing system of claim 1 , wherein the historical user data is not provided to the generative model.

7 . The computing system of claim 1 , wherein the historical user data includes one or more of a name, contact information, contacts, calendar events, or location history associated with the user.

8 . The computing system of claim 1 , wherein the historical user data is defined by a user during set-up of a user profile for use with the integrated development environment.

9 . A computer-implemented method for automatically generating personalized and structured content, the method comprising:

providing, by a computing system comprising one or more processors, a user interface to a user computing system, the user interface including an integrated development environment in which content is insertable in-line by at least a user of the user computing system;

receiving, by the computing system, a prompt from the user of the user computing system via the user interface;

providing, by the computing system, the prompt to a generative model, the generative model being a machine-learned model trained to process language input prompts to generate a language output;

receiving, by the computing system, a generative output generated by the generative model in response to the prompt;

generating, by the computing system, a modified output by modifying the generative output based at least in part on historical user data for the user, wherein the historical user data is not provided to the generative model; and

providing, by the computing system, the modified output via the user interface and in-line within the integrated development environment.

10 . The computer-implemented method of claim 9 , wherein receiving, by the computing system, the generative output generated by the generative model comprises:

receiving, by the computing system, the generative output generated by the generative model; and

providing, by the computing system, the generative output via the user interface.

11 . The computer-implemented method of claim 10 , further comprising receiving, by the computing system, an insertion request from the user of the user computing system via the user interface subsequent to providing the generative output,

wherein generating, by the computing system, the modified output comprises generating, by the computing system, the modified output in response to receiving the insertion request.

12 . The computer-implemented method of claim 9 , wherein the generative output comprises a block template generated by the generative model, the block template defining one or more fields associated with the prompt,

wherein generating, by the computing system, the modified output comprises populating, by the computing system, eligible fields of the one or more fields within the block template based on the historical user data, the eligible fields being associated with the historical user data.

13 . The computer-implemented method of claim 9 , wherein the historical user data includes one or more of a name, contact information, contacts, calendar events, or location history associated with the user.

14 . One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations, the operations comprising:

providing a user interface to a user computing system, the user interface including an integrated development environment in which content is insertable in-line by at least a user of the user computing system;

receiving a prompt from the user of the user computing system via the user interface;

providing the prompt to a generative model, the generative model being a machine-learned model trained to process language input prompts to generate a language output;

receiving a generative output generated by the generative model in response to the prompt;

generating a modified output by modifying the generative output based at least in part on historical user data for the user, wherein the historical user data is not provided to the generative model; and

providing the modified output via the user interface.

15 . The one or more non-transitory computer-readable media of claim 14 , wherein the generative output comprises a block template generated by the generative model, the block template defining one or more fields associated with the prompt,

wherein generating the modified output comprises populating eligible fields of the one or more fields within the block template based on the historical user data, the eligible fields being associated with the historical user data.

16 . The one or more non-transitory computer-readable media of claim 14 , wherein the historical user data includes one or more of a name, contact information, contacts, calendar events, or location history associated with the user.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 2, 2024
From: HARIRI, BEHNOOSH; GALANTE, GREGORY GEORGE; HSIEH, REBECCA WENSHU; POE, PRINCETON TIRIN; FIORINA, GONZALO; NOON, BARAK BEN; HENRICHS, MILES; WAHAB, AHSAN; MOGRABI, AMER; TONG, CHRISTOPHER GREGORY; PESCE, NICHOLAS JOSEPH; MOTIKA, ANDREW JAMES; D'ANGELO, JOHN GABRIEL; ABERBACH, TOMER; SHIH, GRACE SYTIN; PUIG, ALBERT ORRIOLS; HOSKERE, JAYAKUMAR
To: GOOGLE LLC
Reel/Frame 066972/0709 →
Continuity (2)
Provisional Application 63492926 · Mar 29, 2023
Related Publication 20240330580A1 · Oct 3, 2024
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