Digital Workplace Audio Assistance Using LLM Context Synthesis
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Solution Overview
Problem
Visually impaired individuals face limited assistance in work environments, and there is no comprehensive solution for a digital workplace.
Innovation Solution
A vision augmentation system using audio cues, synthesized speech, natural language processing, and generative AI to provide verbal and auditory information based on environmental data, integrating with enterprise products for end-to-end job functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If comprehensive assistance systems are implemented for visually impaired users, then accessibility and productivity are improved, but device complexity and integration requirements increase
Solution Approach 1:
The system is divided into distinct functional modules: an extraction pipeline that collects data from multiple sources (calendars, emails, task management tools, messaging platforms), a large language model that processes the extracted data, and interface products that deliver information to users. This segmentation allows each component to specialize in specific tasks while reducing overall system complexity through modular architecture.
Solution Approach 2:
The large language model serves multiple functions within the system: it synthesizes information from diverse data sources, generates natural language descriptions of visual content, provides contextual awareness of the user's environment, and adapts to different operating modes (work mode, travel mode, meeting mode). This multi-functionality reduces the need for separate specialized systems while improving accessibility.
2Loss of information
If multiple data sources are integrated through an extraction pipeline, then information completeness is improved, but processing complexity and time requirements increase
Solution Approach 1:
The extraction pipeline continuously pre-processes and extracts data from multiple sources in the background before it is needed for user interaction. By performing extraction operations in advance and maintaining updated information about the user's environment, calendar events, emails, and tasks, the system reduces real-time processing requirements and provides faster responses when users query information.
Solution Approach 2:
The large language model autonomously synthesizes information from the extracted data without requiring manual intervention or complex coordination between data sources. The model independently determines what information is relevant, how to combine it coherently, and how to present it to the user, thereby reducing processing time while maintaining information completeness.
3Productivity
If personalized and adaptive audio assistance is provided, then user productivity is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system dynamically adapts its behavior based on the user's context and needs. Different operating modes (work mode, travel mode, meeting mode) activate specific information synthesis strategies and data source priorities. The large language model adjusts its information retrieval and synthesis approach in real-time based on the selected mode, providing personalized assistance without requiring separate systems for each scenario.
Solution Approach 2:
The large language model acts as an intermediary between the complex multi-source data extraction pipeline and the user's audio interface. It translates diverse structured and unstructured data from multiple sources into coherent natural language audio responses, simplifying the interaction while maintaining access to comprehensive information from all integrated data sources.
Data Source
AI summary
A computer-implemented method of providing information to a user is provided. The method comprises receiving input of a selected operating mode and extracting, via an extraction pipeline, data from a number of data sources according to the selected operating mode. The extracted data is fed into a large language model (LLM), and the LLM generates verbal and auditory information for the user based on the data. The LLM conveys the verbal and auditory information to the user via a number of interface products according to the selected operating mode.