Intent Encoder Vector Space Search Optimization
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Solution Overview
Problem
Existing machine-trained models for interpreting user input queries often produce unsatisfactory results by failing to accurately capture the user's intent, leading to inefficient use of computing resources and poor user experience, especially when dealing with queries that have lexical and semantic variations.
Innovation Solution
A computer-implemented technique that uses a machine-trained intent encoder component to map input expressions into intent vectors within a continuous intent vector space, trained using a system that iteratively decreases distances between positive examples and increases distances between negative examples, allowing for accurate intent modeling and retrieval of relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine-trained models are used to interpret user queries, then the system can process queries, but the models fail to accurately capture user intent leading to unsatisfactory results
Solution Approach 1:
The patent transforms the query interpretation approach by changing the parameter representation from traditional text matching to continuous vector space embeddings. Queries are mapped to dense vector representations that capture semantic meaning, allowing the system to measure intent similarity through vector distance metrics rather than lexical matching, thereby improving both intent capture accuracy and result quality
Solution Approach 2:
The patent introduces a new dimensional representation by projecting queries into a continuous vector space with multiple dimensions. Instead of processing queries in their original text form, the system creates high-dimensional vector representations where each dimension captures different aspects of query intent, enabling more nuanced and accurate intent differentiation
2Productivity
If traditional search models are used, then the system can return results, but it requires multiple searches and consumes excessive computing resources
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing query embeddings in the vector space before actual search execution. When a user submits a query, the system quickly retrieves relevant results through efficient vector similarity search rather than performing multiple traditional searches, significantly reducing computing resource consumption and improving search efficiency
Solution Approach 2:
The patent creates vector space copies or approximations of the query intent that can be efficiently searched. Instead of repeatedly processing the original query through complex models, the system uses pre-computed vector representations and efficient nearest-neighbor search algorithms to quickly identify relevant results, reducing energy consumption while maintaining productivity
3Adaptability or versatility
If the system uses fixed model structures, then the model is stable, but it cannot adapt to new intents without predefined taxonomies
Solution Approach 1:
The patent introduces dynamics by making the model structure flexible and adaptable. The vector space representation and training mechanism allow the model to dynamically learn new query patterns and intents from data without requiring predefined taxonomies. The system can continuously adapt to emerging query types through incremental training while maintaining the core vector space architecture
Solution Approach 2:
The patent creates a universal vector space representation that can handle diverse query types and intents through a single unified model. The continuous vector space framework is versatile enough to represent different domains, query formats, and intent types without requiring separate specialized models, achieving adaptability while managing complexity through a single multi-functional architecture
Data Source
AI summary
A computer-implemented technique receives an input expression that a user submits with an intent to accomplish some objective. The technique then uses a machine-trained intent encoder component to map the input expression into an input expression intent vector (IEIV). The IEIV corresponds to a distributed representation of the intent associated with the input expression, within a vector intent vector space. The technique then leverages the intent vector to facilitate some downstream application task, such as the retrieval of information. Some application tasks also use a neighbor search component to find expressions that express an intent similar to that of the input expression. A training system trains the intent encoder component based on the nexus between queries and user clicks, as recorded in a search engine's search log.