Multi-modal Mobile Device Sensor Integration for Automated Contextual Actions
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Solution Overview
Problem
Current handheld devices with multiple sensors lack applications that fully utilize these sensors to provide multi-modal, multi-lingual assistance, limiting their ability to automate actions based on user criteria, preferences, and environmental contexts.
Innovation Solution
A multi-modal, multi-lingual mobile device equipped with sensors and AI reasoning/learning techniques that automatically detect and respond to user intentions and preferences by synchronizing schedules, identifying individuals, translating languages, and suggesting actions based on environmental, conversation, and temporal contexts, using a combination of detection and analysis components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are equipped in handheld devices, then sensing capability is improved, but the ability to automate actions based on integrated sensor data is insufficient
Solution Approach 1:
The patent combines data from multiple sensors (camera, microphone, GPS, accelerometer) with user context information (schedule, contacts, preferences) into a unified analysis framework. The system merges sensor readings with contextual data to enable automated actions, such as combining location data with calendar events to automatically schedule appointments or send notifications based on contextual understanding rather than isolated sensor triggers.
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes sensor data before triggering automated actions. This intermediary component evaluates sensor inputs against user preferences, contextual information, and decision rules to determine whether and what automated action to take. This mediator prevents premature or inappropriate automation while enabling intelligent decision-making based on integrated sensor and contextual data.
2Extent of automation
If AI reasoning and rules-based logic are implemented, then automation intelligence is improved, but device complexity increases
Solution Approach 1:
The patent segments the automation system into distinct modular components: sensor data acquisition module, contextual information retrieval module, analysis engine (with AI reasoning and rules-based logic), and action execution module. Each component has a specific function, allowing the complex automation intelligence to be built from manageable, independent modules that can be developed, tested, and maintained separately.
Solution Approach 2:
The patent implements a universal analysis engine that handles multiple types of sensor data (visual, audio, location, motion) and contextual information (schedule, contacts, preferences) through a single integrated framework. This multi-functional engine applies both AI reasoning and rules-based logic uniformly across different data types and automation scenarios, reducing overall system complexity compared to having separate specialized systems for each function.
Data Source
AI summary
A multi-modal multi-lingual mobile device that facilitates intelligently automating an action. The device can automatically synchronize a user schedule based upon a user state, intention, preference and/or limitation. The device can employ sensors to automatically detect criteria by which to automatically implement an action. Moreover, the system can interrogate a user thus converging upon a user intention and/or preference. An analyzer component can intelligently evaluate the compiled criterion in order to automatically perform an action. The multi-modal multi-lingual mobile device can automatically facilitate identification of an individual. Other actions that are automatically performed can include modifying personal information manager data, translating languages into a language comprehendible to a user, etc. Implementation of these actions can be based at least in part upon an environmental factor, a conversation, a location factor and a temporal factor.