Implicit Input Processing for Software Functionality Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current software applications are unable to accurately and efficiently process implicit input, such as environmental noises, facial expressions, and user utterances, leading to increased computational resource demands and potential misinterpretation of user intent.
Innovation Solution
Employing machine learning models, particularly natural language processing (NLP) and multimodal large language models (LLMs), to determine the semantic meaning of implicit input and modify software application functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex sequences of explicit input are used to modify software functionality, then the software can achieve its goals, but computational resources (processor, memory, network capacity) are increased
Solution Approach 1:
The patent introduces an implicit input processing system that acts as an intermediary between the user and the software application. This system captures implicit input (environmental noises, facial expressions, utterances) and automatically translates it into meaningful commands, eliminating the need for complex explicit input sequences and reducing computational resource consumption.
Solution Approach 2:
The patent replaces traditional mechanical input methods (keyboard typing, mouse clicking) with automated sensing and processing systems. Sensors capture implicit input modalities such as audio, visual, and environmental data, which are then processed by machine learning models to generate software commands, substituting manual mechanical operations with automated systems.
2Adaptability or versatility
If current software applications process implicit input using traditional methods, then they can handle basic inputs, but they are unable to interpret implicit input accurately and efficiently
Solution Approach 1:
The patent transforms implicit input from raw sensor data into meaningful parameters by applying machine learning models. The system changes the parameter representation from unstructured environmental data to structured intent classifications, enabling accurate interpretation of user meaning while maintaining versatility in handling diverse input modalities.
Solution Approach 2:
The patent performs preliminary processing of implicit input through trained machine learning models before the software application needs to act on it. The system pre-interprets environmental noises, facial expressions, and utterances to determine user intent in advance, making the subsequent software response more accurate and efficient.
3Measurement precision
If machine learning models are deployed on client devices to process implicit input, then user intent interpretation accuracy is enhanced, but device processing power and memory requirements increase
Solution Approach 1:
The patent segments the implicit input processing system into multiple components distributed across different devices. Machine learning models can be deployed on remote servers to handle complex interpretation tasks, while client devices perform lighter preprocessing and receive processed results, dividing the computational burden and reducing individual device complexity requirements.
Data Source
AI summary
An implementation may involve: receiving audio input that contains utterances; determining, by a speech-to-text engine that receives the audio input, a textual representation of the utterances; providing, to a natural language model, a request to determine an intent of the textual representation of the utterances, wherein the request indicates that the intent is to be selected from a plurality of predefined intents; receiving, from the natural language model, the intent; determining, based on the intent, an action; and based on the action, modifying operation of a software application.