Vector-Based Call Data Indexing for Retrieval Speed
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Solution Overview
Problem
Current call data management systems in call centers are inefficient, as they lack the ability to easily access, retrieve, and process call data based on content, classification, and statistical features, leading to slow data access and high processing costs due to meaningless data structures and lack of knowledge representation.
Innovation Solution
A system that transcribes call content into text, projects it into vector space using an atomic-class dictionary for indexing and classification, enabling efficient retrieval and mining of call data through a relational database structure, which automatically updates frequency and classification hierarchies without modifying existing records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data structures are used for storing call data, then data storage is simple, but data retrieval and processing are extremely slow and inefficient
Solution Approach 1:
The patent segments call data into multiple dimensions including audio content, transcribed text, control data, and statistical features. Each segment is stored and processed independently through specialized indexing structures, allowing parallel retrieval operations that dramatically improve data access speed while maintaining manageable complexity through modular organization.
Solution Approach 2:
The patent transforms traditional linear data storage into multi-dimensional indexing structures that organize call data by multiple attributes simultaneously (time, content, classification, statistical features). This dimensional transformation enables efficient retrieval along any dimension without requiring full data scans, resolving the contradiction between retrieval speed and structure complexity.
2Adaptability or versatility
If basic SQL query structures are used, then database connectivity is straightforward, but complex call content searching becomes extremely difficult or impossible
Solution Approach 1:
The patent introduces intermediate indexing structures that bridge the gap between simple SQL queries and complex call content analysis. These intermediaries include content-based indexes, transcribed text indexes, and statistical feature indexes that translate complex search requirements into efficient query operations, enabling versatile search capabilities while keeping the query system manageable through layered abstraction.
3Quantity of substance
If large volumes of call data are stored, then data coverage is comprehensive, but access speed and processing efficiency deteriorate significantly
Solution Approach 1:
The patent performs preliminary organization and indexing of call data during the ingestion phase, creating optimized data structures before retrieval operations are needed. Call data is pre-segmented, pre-indexed by multiple attributes, and pre-processed into standardized formats, enabling fast access to large data volumes without compromising speed during actual retrieval operations.
4Loss of information
If conventional recording systems are used, then implementation is simple, but knowledge representation and data reusability are completely absent
Solution Approach 1:
The patent transforms call data from simple audio recordings into rich multi-parameter records that include transcribed text, control data, statistical features, and classification metadata. This parameter enrichment captures knowledge representation without requiring complete system redesign by adding informational layers to existing data structures, enabling data reusability while maintaining reasonable system complexity through incremental enhancement.
Data Source
AI summary
A system and method for representing call content in a searchable database includes transcribing call content to text. The call content is projected to vector space, by creating a vector by indexing the call based on the content and determining a similarity of the call to an atomic-class dictionary. The call is classified in a relational database in accordance with the vector.