Information Processing Device for Dynamic Store Crowding Recommendations
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Solution Overview
Problem
Users arriving at crowded stores face inconvenience as they must manually check crowding levels of other stores, leading to wasted time and effort in finding less crowded alternatives within the same genre but from a different affiliated group.
Innovation Solution
An information processing device that acquires the crowding level of a destination store and recommends a less crowded store within the same genre but from a different affiliated group, generating a proposal advertisement to suggest changing the destination, which is presented to the user via a mobile terminal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually check crowding levels of other stores to find alternatives, then they can find less crowded stores, but they waste time and effort
Solution Approach 1:
The system enables self-service by automatically providing crowding level information and store recommendations without requiring users to manually check each store. The server device collects crowding data from multiple stores and delivers it to users' mobile terminals, allowing users to make informed decisions instantly.
Solution Approach 2:
The system performs preliminary action by pre-collecting and organizing crowding level data from multiple stores before users need it. The server device continuously monitors and stores crowding information, so when a user queries for alternatives, the data is already prepared and can be immediately presented.
2Ease of operation
If users manually check crowding levels of other stores to find alternatives, then they can find less crowded stores, but they spend excessive effort
Solution Approach 1:
The system performs the complex task of comparing multiple stores and their crowding levels automatically. The server device handles the entire process of data collection, comparison, and recommendation generation, freeing users from the complexity of manual store evaluation.
Solution Approach 2:
The server device acts as an intermediary between users and multiple stores. Instead of users directly interacting with each store's information system, the server consolidates crowding data from all stores and presents a simplified recommendation to the user.
3Reliability
If the system recommends stores from the same affiliated group, then it maintains brand consistency, but it reduces competition and alternative quality
Solution Approach 1:
The system applies local quality by differentiating between affiliated and non-affiliated stores in the recommendation logic. Non-affiliated stores are prioritized when they offer lower crowding levels, while affiliated stores serve as fallback options, creating a nuanced recommendation strategy that balances brand loyalty with user benefit.
Solution Approach 2:
The system changes the selection parameter from brand affiliation to crowding level. Instead of always recommending affiliated stores, the system dynamically adjusts recommendations based on the crowding parameter, selecting non-affiliated stores when they provide better service quality in terms of lower crowding.
4Ease of operation
If the system provides detailed crowding information and recommendations, then it improves user convenience, but it increases system complexity
Solution Approach 1:
The system extracts the complex data processing and recommendation logic from the user's mobile terminal and places it on the server device. The mobile terminal only needs to send simple queries and display received recommendations, while the server handles all the complex operations of data collection, comparison, and analysis.
Data Source
AI summary
An information processing device includes a control unit configured to execute acquiring a crowding level of a destination store that is a destination of a vehicle in which a user rides, extracting a store with a crowding level lower than the crowding level of the destination store as a predetermined recommended store from among other stores that belong to the same genre as the destination store and that do not belong to the same affiliated group as the destination store when the crowding level of the destination store is higher than a predetermined threshold value, generating a proposal advertisement for proposing to change the destination of the vehicle from the destination store to the predetermined recommended store, and presenting the proposal advertisement to the user who rides in the vehicle via a mobile terminal that moves with the vehicle.