Sentiment Heatmaps for Crowded Space Management
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Solution Overview
Problem
Managing the diverse needs of multiple users in a shared environment, such as a restaurant, is challenging due to the difficulty in intuitively discerning and addressing psycho-physical needs in real-time, as these needs are often personal and immediate, and constant inquiry from users is burdensome.
Innovation Solution
The method involves analyzing conversational audio streams using directional microphones to identify sentiments towards environmental entities like temperature, lighting, and service levels, creating heatmaps, and generating automated actions to optimize these entities through a cognitive system trained on sample heatmaps and operational policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If constant inquiry from users is used to manage their needs, then user needs can be addressed, but user burden increases and real-time management becomes difficult
Solution Approach 1:
The system enables self-service by automatically analyzing conversational audio streams to extract user sentiments and needs without requiring users to actively inquire or input data. The cognitive system processes unstructured conversation data to identify environmental entities and user preferences, allowing the system to self-manage user needs based on passive audio monitoring and sentiment analysis.
2Measurement precision
If unstructured conversational data is analyzed to identify user needs, then real-time sentiment detection is achieved, but system complexity increases
Solution Approach 1:
The patent introduces a cognitive system as an intermediary layer between raw conversational audio data and facility control actions. This cognitive system includes natural language processing components that act as mediators to transform unstructured audio streams into structured sentiment data, identifying environmental entities and user preferences without requiring direct complex processing in the facility control system.
Solution Approach 2:
The system replaces traditional mechanical or manual methods of need assessment with cognitive processing of audio data. Instead of using structured surveys or direct user input mechanisms, the patent substitutes these with automated speech-to-text conversion and natural language sentiment analysis to extract user needs from casual conversations.
3Ease of operation
If automated actions are generated based on sentiment analysis, then user comfort is improved, but energy consumption increases
Solution Approach 1:
The system applies local quality by generating facility control actions targeted at specific areas where user needs are detected through audio analysis. Rather than uniformly adjusting all facilities, the cognitive system identifies specific environmental entities and locations mentioned in conversations, then generates localized control actions only where needed, optimizing energy consumption by avoiding unnecessary adjustments in areas without detected user needs.
Data Source
AI summary
Facilities of a shared environment are automatically optimized by inferring sentiment from unstructured conversational data towards various environmental entities such as heat, light, service levels, etc. Conversational audio streams from different areas are analyzed to identify an entity and associated sentiment, and a heatmap is created representing the sentiment across the different areas. The conversational audio streams are captured by directional microphones and are assigned metadata such as a location tag indicating a position of a microphone within the shared environment. Heatmap creation can be supplemented by other sensory data. A cognitive system is used to generate actions for control of the facilities based on the heatmap. A suggested action may still be subject to operational policies for the facility. In some scenarios a first suggested facility action compensates for an effect of a second suggested facility action.