Contextual Document Attribute Generation for Search Efficiency
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Solution Overview
Problem
Users face difficulties in locating documents due to static default attribute values in systems like portals and content management systems, which provide little useful information for searches unless manually modified, a time-consuming process.
Innovation Solution
Generating metadata with contextual attribute values for a document based on associated documents, including key words, which can be modified by users or automatically saved, and linked to the document upon triggering events such as creation or upload, leveraging contextual information from other documents accessed by the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static default attribute values are used for documents, then system simplicity is maintained, but document location and search efficiency deteriorate
Solution Approach 1:
The system performs preliminary action by automatically generating and populating attribute values for documents before users need to search or locate them. When a document is created or modified, the system proactively generates relevant attribute values based on document content and context, so that search functionality is already optimized and ready for use, eliminating the need for users to manually fill in search-related fields.
Solution Approach 2:
The system implements self-service by enabling documents to automatically generate their own attribute values based on their content and context. Instead of requiring users to manually assign attributes, the system analyzes the document itself (through keyword extraction, content analysis, and contextual relationships with other documents) to autonomously determine and assign appropriate attribute values, making the system serve itself rather than relying on user intervention.
2Ease of operation
If manual modification of attribute values is required, then attribute customization is possible, but user time consumption increases
Solution Approach 1:
The system performs preliminary action by pre-populating attribute values with relevant information extracted from document content and context before the user needs to use the document. This preliminary generation of attribute values based on keywords, document type, and relationships with other documents eliminates the need for users to spend time manually filling in these fields, while still allowing users to review and modify the values if necessary.
Solution Approach 2:
The system implements self-service by automatically generating and suggesting attribute values based on document analysis, so that the system serves the user's need for customized attributes without requiring manual input. Users can accept the automatically generated values or make minor modifications, significantly reducing the time and effort compared to manually creating all attributes from scratch.
3Measurement precision
If contextual attribute values are generated automatically, then search relevance is improved, but information accuracy may deteriorate
Solution Approach 1:
The system implements feedback by allowing users to review, correct, and modify the automatically generated attribute values. The system presents suggested attribute values to users, and users can provide feedback by confirming the suggestions or correcting them if necessary. This feedback mechanism ensures that the final attribute values are both contextually relevant and accurate to the specific document and user needs.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting the generation and presentation of attribute values based on contextual factors such as document type, content analysis, relationships with other documents, and user profile. The system can modify which attributes are generated, how they are generated, and what defaults are used, allowing optimization of both relevance and accuracy for different scenarios while maintaining user control over final values.
Data Source
AI summary
Metadata is generated that contains attribute values for a first document that in turn contains one or more key words. The attribute values of the first document are based on contextual information such as attribute values associated with other documents that contain at least one of the key words. Once generated, the metadata may be associated with the first document. Related techniques, apparatuses, and articles are also described.