Openai Opco, LLC

Openal Opco, LLC

Noah Deutsch

Benjamin Zweig

Nicholas Turley

Madeline Simens

Andrew Schmidt

Thomas Dimson

Jotham Taylor, Iii

Raphael Gontijo Lopes

Key Shin

Kevin Yu

Michael Heaton

Javier Soto Bustos

Grant Jenks

Karina Nguyen

Kshitij Wadhwa

Vivek Verma

Valerie Zerfas

Constantin Koumouzelis

The present technology pertains to a system for generating automations based on a request/prompt from a user to a language model. The language model receives a prompt in a conversation thread with a user. When the prompt includes a request for a delayed action (i.e., an automation), the request is forwarded to an automations engine, which determines the delayed action from an action component of the request and a time at which to perform the delayed action from a temporal component of the request. A scheduling instruction based on the determined time is sent to a scheduler, which signals when the time occurs, triggering the automations engine to perform the delayed action, for example, by acting as the user's delegate and sending a delayed prompt to the language model to elicit a response and amending part or all of the resulting conversation to the conversation thread with the user.

Language model automations

Disclosed herein are methods and systems for generating metadata from content using one or more machine learning models. In an embodiment, a method may include receiving the content through a graphical user interface associated with the large language model, generating a first file by tokenizing the content into an input format for the large language model and merging the tokenized content with a content instruction, inputting the first file into the large language model, generating, using the large language model, metadata from at least the first file, the metadata reflecting a context associated with the content, generating a second file, the second file comprising the metadata, and displaying the generated metadata on the graphical user interface.

Systems and methods for generation of metadata by an artificial intelligence model based on context

Disclosed herein are methods, systems, and computer-readable media for interacting with a language model using custom instructions. In one embodiment a method includes receiving, through an interface, custom instructions, the custom instructions comprising at least one of personal information or a response type preference, storing the custom instructions temporarily within a session specific cache, in response to a trigger event, adding the custom instructions to a system message associated with the language model, the system message being a prompt modifier to the language model, in response to receiving a prompt, retrieving the custom instructions from the session specific cache, determining whether the custom instructions are relevant to the prompt, and in response to determining the custom instructions are relevant to the prompt, generating a response to the prompt based on the custom instructions.

Custom model instructions with language models

Disclosed embodiments may include a method of interacting with a multimodal machine learning model; the method may include providing a graphical user interface associated with a multimodal machine learning model. The method may further include displaying an image to a user in the graphical user interface. The method may also include receiving a textual prompt from the user and then generating input data using the image and the textual prompt. The method may further include generating an output at least in part by applying the input data to the multimodal machine learning model, the multimodal machine learning model configured using prompt engineering to identify a location in the image conditioned on the image and the textual prompt, wherein the output includes a first location indication. The method may also include displaying, in the graphical user interface, an emphasis indicator at the indicated first location in the image.

Systems and methods for interacting with a large language model

The present technology provides an interaction paradigm whereby a prompt source can continue to interact with the generative response engine through a conversational interface while the generative response engine is processing a task, especially a long-running task. A prompt source can provide additional prompts to modify or clarify the task. The prompt source can also provide additional tasks or subtasks. The generative response engine can also provide intermediate responses in the conversational interface. For example, the generative response engine can respond to prompts provided by the prompt source during the performance of the long-running task. The generative response engine can also determine that it should ask for additional details or clarification, and in response to such a determination, the generative response engine can provide intermediate responses in the conversation interface to encourage further input from the prompt source.

Interactions with a generative response engine during a long running task

The present technology pertains to a generative response engine that can adapt a user interface provided by its front end to receive inputs in a visual format and to provide visual formats in response to prompts. In some embodiments, the generative response engine can provide a greater variety of outputs that can be rendered by the front end. Collectively, the present technology can render dynamic user interface elements in response to prompts received by the generative response engine. Generative response engines that can provide dynamic and multimodal responses that are appropriate to a task are useful for an increased range of tasks.

Adaptive UI for rich output rendering of assistant messages

The disclosed embodiments may include a method of interacting with a multimodal machine learning model; the method may include providing a graphical user interface associated with a multimodal machine learning model. The method may further include displaying an image to a user in the graphical user interface. The method may also include receiving a textual prompt from the user and then generating input data using the image and the textual prompt. The method may further include generating an output at least in part by applying the input data to the multimodal machine learning model, the multimodal machine learning model configured using prompt engineering to identify a location in the image conditioned on the image and the textual prompt, wherein the output comprises a first location indication. The method may also include displaying, in the graphical user interface, an emphasis indicator at the indicated first location in the image.

Growing community of inventors

San Francisco, CA, United States of America

Noah Deutsch