US 2026/0037668 A1 · APPLYING TRANSFORMATIONS ON STREAMING OUTPUT

Abstract

Techniques for streaming generative machine learned model (or LLM) output to a virtual space are described herein. A system may receive a request to perform an action. The system may leverage LLMs to assist in performing aspects of the requested action. The system can generate input data to input into the LLM. When generating the input data, the system can identify sensitive data associated with the request. The system can modify the input data to mask and/or anonymize the sensitive data. The system can input the input data into an LLM trained to output a subset (e.g., less than all) of the response at a time. The system can add the output subset to a buffer and upon identifying the masked data in the buffer, the system can demask the sensitive data and output the sensitive data to the user profile in a streaming manner.

Claims20

Dependent claims

A system comprising:

one or more processors;
and
one or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause the one or more processors to perform operations comprising:
receiving, from a user profile of a user, a request for a generative machine learned model to perform an action;
identifying, prior to inputting data associated with the request into the generative machine learned model, sensitive data in the data;
generating, in response to identifying the sensitive data, modified data that includes a mask to anonymize the sensitive data;
inputting the modified data into the generative machine learned model that is configured to output a response;
receiving, from the generative machine learned model, a subset of the response;
causing the subset of the response to be added to a buffer;
generating, in response to determining that the buffer includes the mask, a modified subset of the response that includes the sensitive data;
and
causing, in response to generating the modified subset of the response, the modified subset to be output to the user profile.

9 more elements

One or more non-transitory computer-readable media storing instructions executable by one or more processors, wherein the instructions, when executed, cause the one or more processors to perform operations comprising:

receiving, from a user profile of a user, a request for a generative machine learned model to perform an action;
identifying, prior to inputting data associated with the request into the generative machine learned model, sensitive data in the data;
generating, in response to identifying the sensitive data, modified data that includes a mask to anonymize the sensitive data;
inputting the modified data into the generative machine learned model that is configured to output a response;
receiving, from the generative machine learned model, a subset of the response;
generating a modified subset of the response that includes the sensitive data;
and
causing, in response to generating the modified subset of the response, the modified subset to be output to the user profile.

5 more elements

Dependencies are inferred from the claim text.

Applying transformations on streaming output

Life of this patent

Applying transformations on streaming output

Life of this patent

Abstract

Claims20