Electronic List Interface Parsing Mixed Inputs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Inputs to electronic list systems, such as voice and text inputs, are often sporadic, generalized, and poorly focused, mixing product descriptions, prices, and brand information, which creates challenges in reconciling differences and providing specific product descriptions required by shopping services in electronic commerce.
Innovation Solution
The system parses user inputs to identify product genus, subgenus, name, brand, price, and retailer information, queries user activity history, and presents organized product descriptions hierarchically, allowing users to select and order items from associated shopping services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system accepts generalized user inputs (voice/text), then ease of operation is improved, but measurement precision of product information deteriorates
Solution Approach 1:
The patent introduces an intermediary processing layer that includes parsing modules to extract structured data from unstructured user inputs, history querying modules to retrieve relevant contextual information, and reconciliation modules to resolve ambiguities. This intermediary layer transforms generalized user inputs into precise product descriptions without requiring users to provide detailed information directly.
Solution Approach 2:
The system performs preliminary actions by pre-querying user activity history and pre-processing potential product matches before the user completes their input. This allows the system to anticipate user intent and prepare structured product information in advance, improving both ease of operation and measurement precision.
2Manufacturing precision
If the system parses and processes mixed inputs thoroughly, then manufacturing precision of product information is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex processing task into distinct modular components: input reception modules, parsing modules that extract specific data types, history querying modules, reconciliation modules, and output generation modules. Each module handles a specific aspect of the processing pipeline, making the overall complex system manageable and maintainable while achieving high manufacturing precision.
3Reliability
If the system queries user activity history, then reliability of product recommendations is improved, but loss of time in processing increases
Solution Approach 1:
The system applies partial action by querying only the most relevant portions of user activity history based on the current input context, rather than retrieving and processing the entire history. The reconciliation module prioritizes matching against recently purchased items and frequently bought together items, achieving high recommendation accuracy with reduced processing time.
Data Source
AI summary
Processing inputs to electronic list systems. Receiving, from a user device, a user input. Parsing the received user input for at least one of {product genus, product subgenus, product name, brand name, price information, retailer name, manufacturer name}. Querying a history of user activity using the parsed input. Receiving at least one product description responsive to the query. Presenting, via the user device, each received at least one product description to the user.