Adaptive Information Retrieval System with Learning Component

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Solution Overview

Problem

Traditional information retrieval systems fail to effectively utilize data structure and metadata, leading to inefficient information retrieval and requiring users to manually analyze retrieved documents to determine relevance, while existing relational database systems struggle with unstructured text and scalability.

Innovation Solution

An adaptive information retrieval system combining machine learning techniques with a scalable relational database and natural language processor, which uses implicit and explicit feedback to infer user intent and provide direct answers, integrating statistical models like Bayesian classification for efficient query term modeling and ranked retrieval.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional information retrieval systems are used, then document retrieval capability is provided, but data structure and metadata are not effectively utilized, leading to inefficient information retrieval

Engineering Contradiction:

Improveinformation retrieval efficiencyVSAvoiddata structure and metadata utilization

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent combines information retrieval functionality with relational database management system capabilities, merging the strengths of both systems. The IR system leverages the database's ability to efficiently store and query structured metadata while maintaining full-text search capabilities, thereby resolving the contradiction between retrieval efficiency and data structure utilization.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system provides multi-functionality by enabling both traditional IR operations and structured data queries within a single framework. Users can perform full-text searches, structured metadata queries, and hybrid searches that combine both approaches, making the system universally applicable to diverse information retrieval needs while effectively utilizing data structures.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Stability of the object's composition

If relational database systems are used, then structured data management is improved, but support for unstructured text and scalability are limited

Engineering Contradiction:

Improvestructured data managementVSAvoidunstructured text support and scalability

Core Design Contradiction:

Stability of the object's compositionVSAdaptability or versatility

Solution Approach 1:

The system segments data into structured metadata portions and unstructured text portions, storing each in appropriate formats within the relational database. This segmentation allows the system to maintain structured data integrity while simultaneously handling unstructured text through specialized indexing mechanisms, thereby improving both structured data management and unstructured text support.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adapts its processing approach based on the nature of the query and data type. It can switch between structured query processing, full-text search, and hybrid approaches, providing scalability and versatility while maintaining stable structured data management through adaptive query routing and processing strategies.

Inventive Principle:

Principle #15Dynamics

3Measurement precision

If specialized IR tools are used, then text search capability is enhanced, but storage overhead increases due to duplicate text storage

Engineering Contradiction:

Improvetext search capabilityVSAvoidstorage overhead

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

Instead of maintaining separate duplicate text storage as in traditional IR systems, the patent utilizes the relational database's existing text storage capability and creates virtual copies through indexing mechanisms. The system creates inverted indexes that reference the stored text without requiring physical duplication, thereby reducing storage overhead while maintaining enhanced text search capability.

Inventive Principle:

Principle #26Copying

4Reliability

If users manually analyze retrieved documents, then relevance determination is possible, but time consumption increases

Engineering Contradiction:

Improverelevance determinationVSAvoidmanual analysis time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system implements feedback mechanisms that learn from user interactions with search results. By analyzing user behavior patterns, click-through rates, and result selections, the system refines its ranking algorithms to automatically prioritize more relevant results, thereby reducing the time users need to spend manually analyzing documents while maintaining or improving relevance determination accuracy.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS7627564B2High scale adaptive search systems and methods

Publication Date: 2009.12.01 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

The subject invention relates to systems and methods that employ automated learning techniques to database and information retrieval systems in order to facilitate knowledge capabilities for users and systems. In one aspect, an adaptive information retrieval system is provided. The system includes a database component to store structured and unstructured data values. A search component queries the data values from the database, wherein a learning component associated with the search component or the database component is provided to facilitate retrieval of desired information.