Tenant-Isolated Custom Annotations for Semantic Search
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Solution Overview
Problem
Existing search engines face challenges in customizing annotations for tenant-specific searches within public corpora, especially when introducing new attributes post-runtime, which can be costly and inefficient, particularly in large datasets like healthcare documents.
Innovation Solution
A method for generating tenant-isolated custom annotations by incorporating new attribute definitions into existing indices using approximation techniques like collaborative filtering and topic modeling, allowing for improved search results through tenant-specific annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If new attribute definitions are introduced post-runtime in existing search engines, then search customization capability is improved, but system complexity and computational cost increase significantly
Solution Approach 1:
The patent segments the search system into public corpus indexing (shared across all tenants) and tenant-specific annotation layers (isolated per tenant). This allows new attribute definitions to be introduced at the tenant level without reprocessing the entire public corpus, reducing system complexity while maintaining customization capability.
Solution Approach 2:
The patent adds a new dimension of tenant-specific annotations layered over the existing public corpus index. Instead of modifying the base index structure, it creates a separate annotation layer that can be independently customized, avoiding complexity in the core system while enabling versatility.
2Adaptability or versatility
If new attribute definitions are introduced post-runtime in existing search engines, then search customization capability is improved, but computational cost increases significantly
Solution Approach 1:
The patent divides the annotation process into public corpus annotations (computed once) and tenant-specific annotations (computed only when needed). This segmentation avoids redundant computational work and reduces overall computational cost while maintaining the ability to customize searches.
Solution Approach 2:
The patent performs preliminary annotation of the public corpus in advance, storing results that can be reused across multiple tenants. This preliminary action eliminates the need to reprocess the entire corpus for each tenant, significantly reducing computational cost.
3Measurement precision
If tenant-specific annotations are created from public corpus, then search accuracy is improved, but data processing time increases
Solution Approach 1:
The patent performs preliminary annotation of the public corpus in advance, creating a foundation that can be reused. This eliminates redundant processing and reduces data processing time while maintaining annotation quality for tenant-specific searches.
Solution Approach 2:
The patent creates tenant-specific annotations by copying and adapting from the public corpus annotations rather than creating entirely new annotations. This copying approach maintains search accuracy while significantly reducing processing time.
Data Source
AI summary
Annotations are customized for a tenant-specific search within a public corpus. In a non-limiting embodiment of the invention, a cartridge file is received by a semantic search application. The cartridge file includes a new attribute definition that is not available in an index of the semantic search application. The new attribute definition is incorporated within the index based on an approximation of one or more existing attributes in the index. One or more documents are retrieved from the public corpus based on a concept search using the incorporated new attribute definition and the one or more documents are annotated based on the incorporated new attribute definition. The annotated one or more documents are stored in a tenant-specific dataset separate from the public corpus.