Automated Shopping Assistant Natural Language Search Refinement
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Solution Overview
Problem
Users face inefficiencies in finding desired products through existing search interfaces, which are often complex and not user-friendly, leading to excessive time spent browsing unwanted products before selecting the desired item, especially when unclear about specific products they seek.
Innovation Solution
A system and method for an automated shopping assistant that processes successive user natural language communications to refine search results by extracting intents and entities, generating action signals, and performing queries on a database to provide refined product information, enabling efficient retrieval of relevant information from a knowledge base.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If existing search interfaces are used to help users find products, then users can access product information, but users spend excessive time browsing unwanted products before selecting the desired item
Solution Approach 1:
The system performs preliminary actions by proactively analyzing user communications to extract search criteria, entities, and intents before the user completes their search process. The automated assistant pre-processes user statements, identifies product attributes, and formulates search queries in advance, eliminating the need for users to manually navigate through complex search interfaces and browse numerous products.
Solution Approach 2:
The automated assistant serves as an intermediary between the user and the product database. It translates natural language user communications into structured search queries, acts as a mediator that understands user intent, and returns refined search results. This intermediary function simplifies the interaction by handling the complexity of search processing behind the scenes while presenting simplified results to the user.
2Adaptability or versatility
If users are not clear about specific products they seek, then users can explore product options, but users spend considerable time browsing before finding a product that meets requirements
Solution Approach 1:
The search system dynamically adapts to user needs by processing successive communications and refining search results in real-time. As users provide additional information or express preferences through natural language, the system dynamically adjusts search criteria, re-ranks results, and narrows down options. This dynamic refinement process maintains versatility in exploring options while significantly reducing browsing time through iterative improvement of search results.
Solution Approach 2:
The system implements feedback mechanisms by analyzing user responses to search results and using this information to refine subsequent searches. When users interact with presented products or provide additional comments, the system learns from this feedback and adjusts the search parameters accordingly, creating a closed-loop process that continuously improves result relevance and reduces the time needed to find suitable products.
3Measurement precision
If complex search interfaces are used to refine search results, then search accuracy can be improved, but the interface becomes less user-friendly and increases browsing time
Solution Approach 1:
The system replaces complex mechanical search interface operations with automated natural language processing. Instead of requiring users to manually navigate complex filters, categories, and search parameters, the system uses speech recognition and natural language understanding to automatically extract search criteria from user communications. This substitution of automated processing for manual interface manipulation maintains high search accuracy while eliminating interface complexity.
Solution Approach 2:
The search system performs self-service by automatically processing user communications, extracting search parameters, and refining results without requiring user intervention in complex interface operations. The system autonomously handles the complexity of search refinement by processing user statements, identifying entities and attributes, and generating optimized search queries automatically, freeing users from interacting with complex search mechanics.
Data Source
AI summary
The disclosed subject matter relates to a system and method for providing an automated assistant that retrieves information from a knowledge base in response to a user's natural language communications. A refinement signal based on a subsequent user communication relevant to a previous search, establishes the type of search to be performed by the automated assistant in generating a response to the user communications. A reply communication to the user includes the selected results from search type selected. The types of searches are SPECIFIC, RELATIVE and NEGATION searches.