Social Media Atmosphere Classification via Image Audio Recognition
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Solution Overview
Problem
Event and location review websites rely heavily on subjective user opinions, lacking objective information about atmosphere and other relevant details, which can be crucial for users seeking specific experiences, such as a casual or upscale environment.
Innovation Solution
Systems and methods that retrieve and analyze social media content from social networking servers to categorize items related to locations and events, allowing users to search and filter results based on specific criteria like atmosphere, using image and audio recognition to extract relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional review websites are used, then users can access location and event information, but the information quality is limited by subjective reviewer opinions and lacks objective atmospheric details
Solution Approach 1:
The patent introduces social media content as an intermediary source between the location/event and the user. Instead of relying directly on subjective reviewer opinions, the system retrieves and analyzes social media posts, images, and check-ins that objectively capture the actual atmosphere and conditions at locations and events, thereby mediating the information gap.
Solution Approach 2:
The patent replaces the manual review mechanism (human reviewers providing subjective opinions) with an automated image and audio recognition system. This system automatically analyzes social media content to extract objective atmospheric information, substituting the mechanical process of human review with automated computational analysis.
2Measurement precision
If social media content analysis is implemented, then objective atmosphere information is extracted, but system complexity increases due to image and audio recognition requirements
Solution Approach 1:
The patent makes the system universally capable by integrating multiple functions into a single platform: retrieving social media content, performing image recognition, performing audio recognition, categorizing items, and providing location/event recommendations. This multi-functional approach consolidates complexity into a unified system rather than requiring separate systems for each function.
Solution Approach 2:
The system performs self-service by automatically retrieving, analyzing, and categorizing social media content without requiring manual intervention. The image and audio recognition systems automatically extract atmospheric information, and the system self-manages the entire process from data retrieval to recommendation generation, reducing operational complexity.
3Loss of information
If image and audio recognition are used to categorize items, then relevant atmospheric information is extracted, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-retrieving and pre-processing social media content before user queries are submitted. The system proactively gathers and analyzes social media data, extracts atmospheric information through image and audio recognition, and organizes it into categories in advance, so that when users search, the processing time is significantly reduced.
Solution Approach 2:
The system dynamically adjusts its processing based on user needs and query types. Rather than uniformly processing all social media content at maximum depth, the system adapts the level of analysis to the specific search requirements, optimizing the balance between information extraction and processing time.
Data Source
AI summary
Systems and methods of returning location and/or event results using information mined from non-textual information are provided. Non-textual information is captured using a hardware component of a user device. Text-based social media content input on the user device is then retrieved. A location of the user device is determined using a global positioning system module in the user device. The non-textual information is converted to a machine-analyzable format, and the converted non-textual information is compared to a database of converted non-textual information samples to analyze and classify the converted non-textual information. The classification is sent to a server for storage in a database in a manner that ties the classification to the geographical location of the user device.