Environment-Scan Content Selection for Goods And Services Matching
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Solution Overview
Problem
Existing systems for enhancing environments, such as home improvement applications, rely heavily on user input and do not effectively analyze spatial information to recommend products or services that enhance the environment without user awareness or intent.
Innovation Solution
A system that analyzes spatial information of an environment to select and recommend products or services by integrating multi-dimensional scans, object recognition, and semantic segmentation, generating personalized content items that fit the environment's needs and aesthetics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a system uses manual user input to identify areas for modification, then the system can obtain user preferences and intentions, but the process becomes time-consuming and requires significant user effort
Solution Approach 1:
The system performs self-service by automatically scanning the environment using sensors and cameras, extracting spatial information and object data without requiring user input. The system independently identifies areas for modification and selects appropriate products based on environmental analysis rather than waiting for user directives.
Solution Approach 2:
The system performs preliminary actions by continuously scanning and analyzing the environment in advance, maintaining an updated understanding of spatial布局和 existing objects. This pre-analysis enables the system to immediately recommend products when opportunities arise without requiring users to initiate the discovery process.
2Ease of operation
If a system uses AR to project product images into the environment, then users can visualize changes, but users must still manually discover and select products to place
Solution Approach 1:
The system uses feedback from environmental scans to automatically identify suitable locations for products and select appropriate items from a database. The system continuously monitors the environment, compares current state against ideal configurations, and automatically generates product recommendations with AR visualizations based on detected opportunities.
Solution Approach 2:
The system introduces an intermediary layer of environmental analysis and product recommendation algorithms between the user and the product selection process. This intermediary automatically processes spatial information, identifies placement opportunities, and presents curated product options with AR visualizations, eliminating the need for users to manually discover products.
3Measurement precision
If a system creates detailed mappings of environments, then spatial information becomes available for analysis, but the system lacks the capability to assess and make recommendations
Solution Approach 1:
The system segments the environmental mapping data into meaningful components such as spatial zones, object categories, and functional areas. By dividing the comprehensive spatial information into structured segments, the system can analyze specific regions independently and make targeted product recommendations for each segment based on its characteristics and needs.
Solution Approach 2:
The system transforms static spatial parameters from environmental mappings into dynamic assessment criteria by analyzing multiple attributes simultaneously (spatial dimensions, object types, lighting conditions, existing decor). This parameter transformation enables the system to evaluate environmental suitability and generate context-aware product recommendations rather than merely storing geometric data.
Data Source
AI summary
The present disclosure is directed to systems and methods for selecting objects and content items related to a given environment. In some embodiments, the systems and methods receive a mapping of the environment and extract layout, available space, and existing object data of the environment from the mapping. The systems and methods receive a storage of supplement content items, and select a supplemental content item from the storage of content items based on the received mapping. The systems and methods may further select the supplemental content item based on a user profile received from a data source. The systems and methods then generate for display the supplemental content item on a device to a user.