Intelligent Multi-Dimensional Product Recommendations
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Solution Overview
Problem
Conventional online shopping platforms overwhelm customers with large search results and inadequate product recommendations, leading to delayed purchasing decisions and potential loss of business as users seek external sources for product information.
Innovation Solution
A computer-implemented system that processes search queries by determining product types, retrieving associated metadata, filtering products based on conditions, ranking relevant items, and displaying selective label values and dimensions to streamline product selection, thereby providing intelligent multi-dimensional recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional online shopping platforms display all search results, then customers can see complete product options, but customers become overwhelmed by the large quantity of products
Solution Approach 1:
The patent segments the large set of search results into multiple subsets based on different dimensions (e.g., popularity, price range, brand). Each dimension creates a separate view that customers can explore, transforming one overwhelming list into multiple manageable categories.
Solution Approach 2:
The patent introduces multiple viewing dimensions beyond the traditional single list view. Customers can navigate products through different dimensional lenses such as popularity ranking, price segmentation, and brand categorization, adding complexity to the navigation space while simplifying individual views.
2Adaptability or versatility
If conventional systems provide broad search results, then customers have more product choices, but customers cannot effectively identify noteworthy products
Solution Approach 1:
The system pre-calculates and tags products with metadata indicating their notability across different dimensions before the customer even searches. This preliminary organization allows the system to quickly present differentiated product quality information without requiring customers to manually evaluate each product.
Solution Approach 2:
Different products are assigned different quality characteristics based on their attributes. Rather than treating all products uniformly, the system identifies and highlights specific noteworthy aspects of each product (e.g., best seller status, high rating, special offer) to provide localized quality information where needed.
3Measurement precision
If conventional platforms display all attribute filters, then customers can narrow search results precisely, but the amount of filters becomes overwhelming
Solution Approach 1:
The filter interface is made dynamic rather than static. The system adaptively adjusts which filters are displayed based on the current search context, customer preferences, and relevance to the query. Filters can be added or removed dynamically as the customer navigates through different product dimensions.
4Loss of information
If customers use external sources for product research, then they can find detailed product information, but the shopping process is delayed and business is lost
Solution Approach 1:
The online shopping platform integrates multiple functions that were previously required from external sources. The system provides product comparisons, detailed specifications, customer reviews, and recommendation engines all within the platform, eliminating the need for customers to switch to external websites while maintaining information completeness.
Data Source
AI summary
One aspect of the present disclosure is directed to a computer-implemented system for streamlined product searching configured to receive a search query comprising at least one keyword; determine a product type; retrieve a first record of products associated with the product type; determine whether the search query meets a condition; and if the search query meets the condition: determine, a plurality of label values, and for each label value: generate a second record of products associated with the label value; determine whether each product in the second record meets a condition; deleting each product that meets the condition from the second record; ranking each remaining product in each second record; display the plurality of label values for selection; receive a selected label value; and display at least a portion of the remaining ranked products associated with the selected label value.