Recommendation Engine for Golf Wedge Bounce Optimization
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Solution Overview
Problem
Current golf club fitting techniques do not adequately account for varying environmental conditions, leading to suboptimal equipment recommendations for golfers playing on different terrains.
Innovation Solution
A system that integrates terrestrial parameters, such as turf conditions, with user-operated mechanical device data, like golf swing characteristics, to determine a personalized bounce recommendation for wedge-type golf clubs using a look-up table or database, correlating effective attack angles with geographical factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional golf club fitting techniques are used, then the fitting process is simple and quick, but the recommendations do not account for varying environmental conditions leading to suboptimal equipment selection
Solution Approach 1:
The fitting system is segmented into multiple independent modules: a terrestrial parameter module that collects environmental data (turf conditions, weather, location), a user operation module that captures swing characteristics, and a recommendation engine that processes both data types. This segmentation allows each module to be developed and maintained independently while improving overall recommendation accuracy.
Solution Approach 2:
The system merges previously separate fitting approaches by integrating terrestrial environmental parameters with user-specific operational data. The recommendation engine combines these diverse data sources using a unified algorithm that considers both the golfer's swing characteristics and the environmental conditions where the clubs will be used, creating comprehensive recommendations that neither approach could achieve alone.
2Adaptability or versatility
If a one-size-fits-all approach is used, then the equipment selection process is straightforward, but it fails to provide personalized recommendations for different terrains and conditions
Solution Approach 1:
The system performs preliminary data collection by automatically gathering terrestrial parameters (location, weather, turf conditions) before the fitting session begins. This pre-collected environmental data is stored and ready to be combined with user swing data during the actual fitting process, eliminating the need for time-consuming on-site environmental assessments.
Solution Approach 2:
The system enables self-service functionality where the recommendation engine automatically processes both terrestrial and user operation data to generate personalized club recommendations. The system serves itself by autonomously analyzing the combined dataset and providing adaptive recommendations for different terrain types without requiring manual intervention for each environmental condition.
Data Source
AI summary
Generally, described are methods, apparatuses and systems for a recommendation engine which determines a recommendation based upon a terrestrial parameter and an overall factor unrelated to the terrestrial parameter. The recommendation engine may receive as inputs: (1) some data relating to the terrestrial parameter and (2) some data relating to user operation of a mechanical device. An overall factor may be calculated or obtained, at least in part, from the data relating to user operation of the mechanical device. And based upon the data relating to the terrestrial parameter and the overall factor, a recommendation may be determined and transmitted. In one embodiment, as part of the transmission process, the recommendation may be displayed to a user requesting the recommendation.