Semantic Index for Contextual Data Retrieval
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Solution Overview
Problem
Users face difficulties in recalling details from past events due to the unstructured nature of contextual information collected by computing devices, such as weather, location, and audio/video data, which are not effectively helpful for memory recall and can be time-consuming and computationally expensive to search through.
Innovation Solution
Generating a semantic index by labeling and grouping content data based on semantic context using machine-learning models, allowing for efficient retrieval of relevant information and actions, such as annotating displays or generating emails, by clustering feature vectors and storing them in a database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If computing devices collect and store unstructured contextual information (weather, location, audio/video data), then the quantity of information available for recall is improved, but the time and computational resources required to search through this data increases
Solution Approach 1:
The patent applies preliminary action by pre-labeling content data with semantic context tags before storage. The system automatically annotates audio, video, and other content data with contextual labels (e.g., location, weather, entities) at the time of capture, so that when a user searches, the pre-organized data can be quickly retrieved without requiring extensive real-time processing. This resolves the contradiction by preparing the data structure in advance, reducing search time while maintaining comprehensive information storage.
2Quantity of substance
If computing devices collect and store unstructured contextual information, then the quantity of information available for recall is improved, but the computational resources required to process and search this data increases
Solution Approach 1:
The patent applies segmentation by dividing content data into distinct labeled components based on semantic context. Instead of storing monolithic unstructured data, the system segments content into labeled segments (e.g., separate tags for location, weather, entities, time) that can be independently processed and searched. This segmentation reduces computational resources by allowing the system to query only relevant segments rather than processing entire datasets, thus resolving the contradiction between information quantity and computational resource usage.
3Ease of manufacture
If unstructured contextual information is stored without labeling, then storage simplicity is maintained, but the ability to retrieve and use specific information is worsened
Solution Approach 1:
The patent applies self-service by implementing automated semantic labeling that operates without manual intervention. The system automatically analyzes content data and assigns contextual labels using machine learning models and natural language processing, making the labeling process self-serve rather than requiring manual annotation. This resolves the contradiction by maintaining storage simplicity while dramatically improving information retrieval capability through automated organization.
Data Source
AI summary
Methods and systems for generating and using a semantic index are provided. In some examples, content data is received. The content data includes a plurality of subsets of content data. Each of the plurality of subsets of content data are labelled, based on a semantic context corresponding to the content data. The plurality of subsets of content data and their corresponding labels are stored. The plurality of subsets of content data are grouped, based on their labels, thereby generating one or more groups of subsets of content data. Further, a computing device is adapted to perform an action, based on the one or more groups of subsets of content data.