Published Technical Disclosure Archive

Tennis Stroke Analytics and Performance Platform

Publication ID: 24-11857862_0010_PTD
Published: October 28, 2025
Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Tennis Stroke Analytics and Performance Platform,” Published Technical Disclosure No. 24-11857862_0010_PTD, Published October 28, 2025, available at https://archive.pr1or.art/24-11857862_0010_PTD
This technical disclosure describes improvements that would be readily apparent to a Person Having Ordinary Skill In The Art (PHOSITA) when considered in combination with the foundational architecture disclosed in U.S. Patent No. 11,857,862.

Summary of the Inventive Concept

A comprehensive platform for tennis players, coaches, and analysts to assess and improve tennis stroke performance using advanced sensor data and AI-driven analytics, enabling data-driven decision-making and strategic play.

Background and Problem Solved

The original patent for assessing tennis stroke heaviness, while innovative, had limitations in its ability to provide real-time feedback, integrate with other data sources, and offer predictive insights. The new inventive concept addresses these limitations by envisioning a next-generation platform that leverages AI, sensor data, and cloud-based analytics to provide a holistic view of tennis stroke performance.

Detailed Description of the Inventive Concept

The Tennis Stroke Analytics and Performance Platform consists of multiple components, including a neural network-based predictive model, a real-time sensor data processing module, a cloud-based database for storing and analyzing large datasets of tennis match statistics and player profiles, and a wearable device for tracking player performance. The platform uses advanced AI algorithms to analyze sensor data and generate a heaviness index, which is then used to predict player performance, provide personalized coaching, and offer real-time commentary. The platform also integrates with video broadcasts, enabling a seamless and immersive experience for players, coaches, and spectators.

Novelty and Inventive Step

The new inventive concept introduces several novel and non-obvious features, including the use of AI-driven predictive models, real-time sensor data processing, and cloud-based analytics. The integration of these components enables a holistic view of tennis stroke performance, which is not achievable with the original patent's limited scope.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept could include variations in sensor types, AI algorithms, and data sources. For example, the platform could be adapted for use with different types of sports equipment, or integrated with virtual reality training environments. Additionally, the platform could be modified to provide real-time feedback on other aspects of tennis performance, such as footwork or strategy.

Potential Commercial Applications and Market

The Tennis Stroke Analytics and Performance Platform has significant commercial potential in the tennis industry, with potential applications in professional tennis, tennis academies, and individual player training. The platform could also be adapted for use in other sports, such as baseball, cricket, or golf, offering a broad market opportunity.

CPC Classifications

SectionClassGroup
A A63 A63B71/0622
A A63 A63B43/004
A A63 A63B69/38
A A63 A63B2071/0625
A A63 A63B2214/00
A A63 A63B2220/05
A A63 A63B2220/20
A A63 A63B2220/35
A A63 A63B2220/62
A A63 A63B2220/806
A A63 A63B2220/808
A A63 A63B2220/89

Field of Art

Sports technology, sensor-based performance analytics, machine learning applications in athletic performance measurement, with expertise in motion tracking, data processing, and biomechanical analysis of athletic movements

Person of Ordinary Skill (PHOSITA) Profile

A professional with advanced engineering or computer science degree, experience in sports biomechanics, sensor design, machine learning, and data analytics, capable of integrating sensor technologies with predictive computational models

Obviousness Rationale

A PHOSITA would recognize that extending the source patent's tennis stroke measurement system with AI-driven predictive analytics and cloud-based processing represents a natural technological progression. The fundamental concept of quantifying tennis stroke characteristics is already established, making the integration of advanced machine learning and real-time data processing a predictable enhancement. The PTD's variations leverage known machine learning techniques to expand the original patent's measurement methodology into a comprehensive performance analysis platform.

Obvious Combinations & Variations

Source Patent Element
Sensor device detecting tennis ball movement parameters and generating a heaviness value
PTD Variation
Neural network-based predictive model generating heaviness index from sensor data
Obviousness Reasoning
Applying machine learning to sensor-derived measurements is a known technique for extracting more sophisticated insights, representing an obvious extension of existing measurement methodologies
Source Patent Element
System for measuring linear and rotational speeds of tennis ball
PTD Variation
Cloud-based database storing and analyzing large datasets of tennis match statistics
Obviousness Reasoning
Aggregating and analyzing sensor-derived performance data is a predictable application of big data techniques in sports analytics
Source Patent Element
Processor and memory system for calculating tennis stroke characteristics
PTD Variation
Real-time sensor data processing module with AI algorithms for performance prediction
Obviousness Reasoning
Enhancing computational systems with predictive AI is a standard approach for extracting more advanced insights from existing measurement technologies
Source Patent Element
Camera-based ball movement tracking
PTD Variation
Wearable device with haptic feedback for providing real-time stroke technique guidance
Obviousness Reasoning
Extending sensor tracking to provide immediate athlete feedback is a logical and foreseeable technological progression
Source Patent Element
Translational and rotational kinetic energy calculations for stroke assessment
PTD Variation
Natural language processing module generating descriptive match commentary based on sensor data
Obviousness Reasoning
Transforming quantitative sensor measurements into narrative descriptions is an obvious application of natural language processing techniques
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857862 and the disclosed technical variations, a person having ordinary skill in the art would find the claimed innovations obvious, as they represent predictable combinations of known sensor measurement techniques, machine learning approaches, and sports performance analytics methodologies. The published technical disclosure demonstrates that the claimed innovations are straightforward extensions of existing technological capabilities in sports performance measurement and analysis.

Original Patent Information

Patent NumberUS 11,857,862
TitleMethod and system for assessing tennis stroke heaviness