Hands-Free Content Casting via Sensor and Demographic Analysis

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Solution Overview

Problem

Users face challenges in intuitively triggering content casting from computing devices to larger display devices, such as TVs, due to varying device types and locations of interaction, leading to an unintuitive and sometimes unreliable user experience.

Innovation Solution

Implementing a computing device with sensors and machine learning algorithms to detect typical patterns of movement and gestures, associating these with actions, and using demographic data to determine when to cast content to a secondary display device, allowing seamless and accurate user interactions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If traditional manual interaction methods are used to trigger content casting, then the device can reliably identify user intent, but the user experience becomes unintuitive and requires excessive interaction steps

Engineering Contradiction:

Improveuser interaction intuitivenessVSAvoiduser intent identification accuracy

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system automatically detects user intent through sensor data and demographic information without requiring explicit user commands. The computing device serves itself by interpreting contextual signals (device movements, location changes, time of day) to determine when casting should occur, eliminating the need for users to navigate complex menus or press specific buttons.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces traditional mechanical interaction methods (buttons, switches, manual selections) with sensor-based detection systems. Accelerometers, gyroscopes, and location sensors capture physical device movements and environmental context, which are then processed by machine learning algorithms to infer user intent, substituting physical interaction with automated environmental sensing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If the system uses multiple sensors and demographic data to detect user intent, then the accuracy of action initiation improves, but the device complexity increases

Engineering Contradiction:

Improveuser action detection accuracyVSAvoidsensor and processing system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system employs a multi-functional architecture where a single machine learning processing unit handles diverse sensor inputs (accelerometer, gyroscope, location, time) and performs multiple functions: filtering noise, identifying device movements, determining user intent, and triggering appropriate actions. This universal processor reduces overall system complexity compared to having separate dedicated systems for each sensor type.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent combines multiple data sources (sensor readings, demographic information, contextual data) into a unified analysis framework. The machine learning system merges these diverse inputs to form a comprehensive understanding of user intent, allowing the system to achieve high detection accuracy while managing complexity through integrated processing rather than separate analysis pipelines.

Inventive Principle:

Principle #5Merging (Combining)

3Productivity

If the system automatically determines casting actions based on detected patterns, then the productivity of content management improves, but the reliability of action execution may decrease due to false detections

Engineering Contradiction:

Improvecontent casting efficiencyVSAvoidaction execution accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system incorporates feedback mechanisms where the outcomes of automatically triggered actions are monitored and used to refine future detections. When the system correctly identifies user intent and executes the appropriate casting action, this positive feedback strengthens the associated detection patterns. Conversely, false detections are corrected through feedback loops that adjust the machine learning models, improving reliability over time while maintaining high productivity.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The machine learning system is pre-trained with extensive demographic data and contextual information before deployment. This preliminary training establishes baseline detection accuracy, allowing the system to reliably distinguish between intentional user actions and incidental device movements. The pre-configured knowledge base enables rapid, accurate action execution without requiring extensive real-time processing, thus maintaining both productivity and reliability.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11936937B2Multi sensory input to improve hands-free actions of an electronic device

Publication Date: 2024.03.19 GOOGLE LLC

AI summary

In one general aspect, a method can include detecting at least one indicator of user-initiated interaction with a computing device, obtaining data related to a demographic of a user of the computing device, identifying a current state of the computing device, determining that content displayed on a first display device included in the computing device is to be casted to a second display device separate from the computing device based on the at least one indicator of the user-initiated interaction with the computing device, the data related to a demographic of a user of the computing device, and the current state of the computing device, and casting the content displayed on the first display device to the second display device.