DATA-DRIVEN CONTENT INTERACTION

Number: 20260211914
Published: 2026-07-23
Filed: 2025-01-23
Assignee: DISNEY ENTERPRISES, INC.
Inventors: YU; Yang, ZHANG; Yan, WANG; Zhe, YE; Zhaohui, GAO; Pengfei
CPC: G06F16/33295; G06F16/9024; G06F16/3329; G06F16/90332; G06F40/30; G06F40/35
Verdict: High Notable software
First reported: 2026-W36 (2026-09-05)
Source: Google Patents · FreePatentsOnline

The keeper's note

Interprets user input across LED, haptic and wearable channels to drive content — new inventor cluster, worth a look.

Abstract

One embodiment of the present invention sets forth a technique for processing user input. The technique includes determining, via execution of a first machine learning model, a first intent associated with a first message that is received from a user during an interaction associated with a first content item. The technique also includes matching the first intent to a first portion of a graph associated with the interaction. The technique further includes determining, based on the first portion of the graph, a first response to the first message, and causing a second content item corresponding to the first response to be outputted to the user.

Background

BACKGROUND Field of the Various Embodiments

Embodiments of the present disclosure relate generally to machine learning and content generation and, more specifically, to data-driven content interaction. Description of the Related Art

Advances in computer and network technology have led to an increase in the availability and use of digital content in various contexts and environments. For example, users may access content in the form of images, video, audio, text, graphics, animations, multimedia, and/or other types of digital from personal computers, laptop computers, workstations, mobile phones, tablet computers, game consoles, eBook readers, televisions, projectors, speakers, virtual reality (VR) devices, augmented reality (AR) devices, mixed reality (MR) devices, extended reality (XR) devices, wearable devices, music players, and/or other types of electronic devices. The digital content may be delivered via one or more files, streams of network packets, and/or digital broadcasts.

However, user experiences with digital content tend to be one-directional and static. More specifically, digital content is typically stored in a pre-recorded and/or pre-generated form and subsequently delivered to a user on demand, based on a schedule, and/or based on other factors. While the user can consume the delivered content in various forms via various output devices (e.g., display, speaker, haptic device, etc.), the user is limited in the ability to interact with the d

Claims

1. A computer-implemented method for processing user input, the method comprising: determining, via execution of a first machine learning model, a first intent associated with a first message that is received from a user during an interaction associated with a first content item; matching the first intent to a first portion of a graph associated with the interaction; determining, based on the first portion of the graph, a first response to the first message; and causing a second content item corresponding to the first response to be outputted to the user. || 11. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: determining, via execution of a first machine learning model, a first intent associated with a first message that is received from a user during an interaction associated with a first content item; matching the first intent to a first portion of a graph associated with the interaction; determining, based on the first portion of the graph, a first response to the first message; and causing a second content item corresponding to the first response to be outputted to the user. || 20. A system, comprising: one or more memories that store instructions, and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps of: determining, via execution of a first machine learning model, a plurality of questions associated with a content item; generating, via execution of a second machine learning model, a plurality of responses to the plurality of questions; generating a graph that includes a plurality of edges representing the plurality of questions and a plurality of nodes representing the plurality of responses; and processing an interaction between a user and the content item based on the graph.