Location Evaluation Using Continuous Learning Engine
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Solution Overview
Problem
Existing methods for evaluating business locations are cumbersome, expensive, and lack accuracy, requiring significant investment and manual labor to assess factors like traffic, reputation, competition, and costs, often leading to inefficient and potentially faulty analyses.
Innovation Solution
A computer-implemented method using a continuous learning engine to refine a database of business success attributes and weights, analyzing historical performance data from various sources, including social networks and mobile devices, to rank candidate locations based on anticipated success for a proposed business.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to evaluate business locations, then detailed analysis can be performed, but the process becomes cumbersome and expensive
Solution Approach 1:
The patent replaces manual mechanical evaluation processes with an automated computer system that uses digital map data, social network information, and mobile device data to assess location attributes. The system automatically processes and analyzes multiple data sources to evaluate business locations, eliminating the need for manual field research and traditional evaluation methods.
Solution Approach 2:
The evaluation system is designed to handle multiple business segments and location types through a unified platform. The system can analyze different business categories (retail, dining, services) and apply appropriate evaluation criteria automatically, making the same system versatile across various business contexts without requiring separate evaluation protocols for each type.
2Reliability
If comprehensive location factors are analyzed manually, then evaluation thoroughness improves, but time investment increases significantly
Solution Approach 1:
The system performs preliminary data collection and processing by automatically gathering digital map data, social network information, and mobile device data before the actual evaluation occurs. Historical performance data of similar businesses is pre-analyzed and stored, allowing the system to quickly compare candidate locations against established success criteria without requiring time-consuming manual research during the evaluation process.
Solution Approach 2:
The evaluation system operates continuously by automatically updating and refining its database of business success attributes and weights based on ongoing analysis of historical performance data. The continuous learning engine constantly improves evaluation accuracy through feedback from actual business outcomes, ensuring that the evaluation process remains both thorough and increasingly efficient over time.
3Loss of information
If traditional evaluation methods are used, then local insights can be gathered, but cost and resource expenditure increase
Solution Approach 1:
The system creates digital copies of real-world location data through automated collection of digital map information, social network data, and mobile device information. Instead of requiring physical visits and manual observation, the system uses these digital representations to analyze local market conditions, customer demographics, and competition patterns, significantly reducing the resources needed while maintaining comprehensive local insights.
Solution Approach 2:
The evaluation system serves itself by automatically collecting, processing, and analyzing location data without requiring extensive human intervention. The continuous learning engine self-improves by analyzing historical performance data and automatically refining its evaluation models, reducing the need for ongoing manual adjustment and expert judgment while maintaining high-quality local market analysis.
Data Source
AI summary
Data-driven evaluation of locations for a proposed business is provided by a method that includes identifying and weighing business success attributes for a desired business segment and business segment requirements for a proposed business, identifying candidate locations, calling to devices to request attribute data for the business success attributes, obtaining success rates and attribute data for other businesses, ranking the candidate locations on anticipated success of the proposed business, and generating digital documents informing of the candidate locations, providing at least some of the attribute data, and identifying a preferred location, of the candidate locations, for the proposed business. Aspects also include refining a database indicating the business success attributes, weights thereof, and business segment requirements applicable to different business segments, based on historical performance of businesses in the different business segments to identify relevant business success attributes and relative importance thereof as an indicator of business success.