Social Shopping App Query Assembly for Remote Advice
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Solution Overview
Problem
Current communication tools do not provide specific support for obtaining remote shopping advice, which is an emerging trend where shoppers seek feedback on potential purchases using mobile phones, often relying on inefficient methods like phone calls or limited social media interactions.
Innovation Solution
A method and system that utilizes a social shopping app to assemble media representations of items into a query, allowing shoppers to select parameters for reviewers, and sends the query to a server or directly to reviewers for feedback, which is then presented back to the shopper.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If current communication tools (phone calls, limited social media) are used for remote shopping advice, then basic communication is possible, but the shopping experience lacks efficiency and specific support
Solution Approach 1:
The system segments the shopping advice process into distinct functional modules: media capture module for capturing item images, parameter selection module for choosing reviewer criteria, query assembly module for creating structured requests, and feedback presentation module for displaying results. This segmentation enables each module to operate efficiently with specialized functions, resolving the contradiction between ease of operation and process efficiency.
Solution Approach 2:
The patent introduces a server as an intermediary between shoppers and reviewers. The server receives structured queries with media representations and parameters, processes them through crowd labor markets or social networks, and returns organized feedback. This intermediary layer automates coordination and communication tasks, dramatically improving efficiency while maintaining ease of use for end users.
2Reliability
If multiple media representations are assembled into structured queries with parameters, then feedback quality and diversity improve, but system complexity increases
Solution Approach 1:
The patent implements a universal query structure that handles multiple media types (images, videos) and various parameter categories (demographics, expertise, location) through a single integrated framework. The server processes all query types using the same infrastructure, enabling high feedback quality through diverse inputs while keeping the app interface simple and unified for users.
Solution Approach 2:
The system uses parameter-based querying where shoppers select from predefined parameter categories (e.g., reviewer age, expertise level, location) rather than configuring complex settings. This parameter approach maintains reliability by ensuring comprehensive reviewer matching while significantly reducing the perceived complexity for end users through standardized selection interfaces.
3Adaptability or versatility
If feedback is obtained from diverse sources including strangers via crowd labor markets, then advice diversity and value increase, but coordination and communication overhead increases
Solution Approach 1:
The system implements self-service mechanisms where the server automatically matches queries with appropriate reviewers based on selected parameters, retrieves feedback, and presents results without requiring shopper intervention in coordination tasks. This enables diverse feedback sources including crowd labor markets while keeping the shopper interface simple, as the system autonomously handles the complex coordination of multiple reviewers.
Data Source
AI summary
Various methods and systems for obtaining remote shopping advice are described herein. In one example, a method includes taking two or more media representations of items to be discussed. A parameter is selected within a social shopping app for obtaining feedback on the items to be discussed. The two or more media representations are assembled into a query within the social shopping app based, at least in part, on the parameter. The query is sent to a reviewer from within the social shopping app. Feedback on the items to be discussed is received from the reviewer within the social shopping app.