Wireless Golf Putter Laser Alignment and Training System with Feedback Tracking for Improved Aim and Putter Fitting

Description

BACKGROUND OF THE INVENTION

Field of Invention

This invention relates to golf training technology, specifically a putting alignment and stroke analysis system designed to improve golfers' putting accuracy. The system combines a laser-guided alignment tool for real-time aim feedback and an AI-powered motion capture device to quantify putting stroke metrics such as face angle, path, and tempo. The motion capture device ALSO calculates the outcome of a golfer's putt when placed some distance away from the player. Outcome measures include strokes gained, speed trends, and L/R miss trends. By integrating these three features, this invention offers golfers, coaches, and club fitters a comprehensive tool to analyze and enhance putting performance through precise visual and motion-based data.

BRIEF SUMMARY OF THE INVENTION

The present invention is a golf putting training system that combines laser-guided alignment feedback and AI-driven motion capture for stroke analysis. The system consists of a laser alignment device that attaches to the putter, projecting a beam for real-time visual feedback, and an AI motion capture module that records essential stroke metrics such as face angle, path, and tempo. Together, these components provide golfers with comprehensive, data-rich feedback, enabling precise alignment adjustments and stroke refinement. This integrated approach enhances putting accuracy, delivering a powerful training solution for golfers, coaches, and club fitters.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1: This figure shows the laser device attached to various parts of the putter, such as the shaft, grip, or head, with a ball joint mechanism that enables easy aim adjustments.

FIG. 2: Depicts the attachment setup of the button, secured to the putter grip with a rubber tube or clamp, alongside the aiming board positioned over the target, featuring degree markings for measuring aim accuracy.

FIG. 3: Illustrates the integrated phone app using AI and motion detection to analyze the putter stroke, capturing data such as tempo, club path, and spin, and providing feedback for putting improvement drills.

FIG. 4: This flowchart illustrates the golf putter laser alignment process, where golfers use a wireless laser and feedback system to enhance putting accuracy through sequential steps of aim preparation, alignment verification, adjustment, and performance tracking.

DETAILED DESCRIPTION

This invention introduces a comprehensive golf putting alignment and training system that merges laser-guided aim feedback, AI-driven motion capture for stroke analysis, and an adjustable slope board with a vestibular balance training system. This system is designed to improve a golfer's ability to read and adapt to green slopes, aim with precision, and execute a consistent putting stroke. Each component contributes to the golfer's overall putting skillset by combining spatial awareness of slope with advanced aim and stroke metrics.

The invention consists of three integrated components:

Laser Alignment Device for real-time visual aim feedback,

AI Motion Capture Module of Putting Stroke to quantify and analyze stroke metrics,

AI Motion Cature Module of Putt Outcomes to analyze outcome of putts (strokes gained, etc)

Slope Estimation Board with adjustable incline and vestibular balance features to simulate and train slope perception and its impact on aim.

These components collectively enable golfers to improve putting accuracy and develop a refined sense of slope estimation, ultimately translating to better putting performance on the green.

Laser Alignment Device

The laser alignment device is a precision-guided aiming tool that projects a visible laser beam along the intended putting line, allowing golfers to check and adjust their aim in real time. This device is attachable to various parts of the putter, such as the head, shaft, or grip, providing flexibility based on the golfer's preference. Made from lightweight, durable materials like anodized aluminum or composite polymers, the laser device ensures minimal impact on putter balance.

Adding dots or lines onto the ball in a specific orientation to pick up the spin/rotation of the ball may also be helpful, as would be the scaling of the distance. The system would also be able to calculate the slope and the speed of the green after recording balls hit from different angles.

Key Technical Specifications:

Ball Joint Adjustment Mechanism: The laser device is mounted on a ball joint mechanism, allowing multi-directional aim adjustments while maintaining beam stability. The ball joint has a frictional resistance coating, preventing unintentional shifts once set, which ensures the laser remains aligned with the golfer's intended target.

Beam Specifications: The laser emits a high-intensity, collimated beam with a narrow divergence angle (typically ≤1 milliradian) to maintain precision even over longer distances. Wavelength selection (typically 532 nm green or 650 nm red) provides optimal visibility in indoor and low-light outdoor settings.

Power Source: The laser is powered by a lithium-ion rechargeable battery, compatible with USB-C charging, providing a battery life of approximately 10-12 hours in continuous mode.

The laser alignment device includes a wireless activation button on the putter grip, as shown in FIG. 2. This button, encased in a moisture-resistant silicone or thermoplastic elastomer housing, operates via Bluetooth or radio frequency (RF) and enables activation without disturbing the golfer's stance or alignment. With this wireless feature, the device provides an uninterrupted alignment experience that enhances putting focus.

Operational Modes:

Continuous Mode: The laser projects continuously, allowing golfers to verify alignment visually throughout the setup process.

Pulse Mode: In pulse mode, the laser emits intermittent beams, which the AI motion capture module can detect and analyze. This mode assists in recording alignment deviation and bias errors, feeding the data into the mobile app for post-session analysis.

AI Motion Capture Module for Putting Stroke

The AI motion capture module is an advanced sensor array that attaches to the putter head or shaft and provides detailed metrics on the golfer's putting stroke. By employing AI algorithms, this module captures and analyzes critical elements of the stroke, including face angle, path consistency, stroke tempo, and speed. Alternatively, motion capture could be accomplished using the camera on a smart phone, and AI algorithms can be used to optimize the camera of a smart phone for this purpose.

Technical Specifications

Sensors: The module incorporates a three-axis accelerometer, a three-axis gyroscope, and a magnetometer. These sensors operate at high sampling rates (e.g., ≥100 Hz) to capture nuanced movements in the putting stroke, offering a detailed representation of the face angle, path, and motion dynamics.

Sensor Fusion Algorithm: Data from the accelerometer, gyroscope, and magnetometer are processed using a Kalman filter to reduce noise and improve accuracy in angle and speed measurements. This sensor fusion algorithm enables the device to achieve precise motion tracking, essential for stroke consistency analysis.

Communication Protocol: Data is transmitted via Bluetooth Low Energy (BLE) to the mobile app, reducing power consumption while maintaining a stable connection.

Captured Metrics:

Face Angle at Impact: Measures the angle of the putter face relative to the target line at impact, with accuracy to +0.1 degrees. This is essential for understanding directional control.

Path Consistency: Tracks the putter's movement along the stroke path, indicating deviations from a straight line and assessing directional consistency.

Stroke Tempo and Speed: Measures the timing and velocity of the stroke, with data on stroke cadence and acceleration patterns.

Impact Metrics: Measures variables like ball speed and spin, providing insights into stroke efficiency and power transfer.

These metrics are transmitted to the mobile application, as illustrated in FIG. 3, where the AI algorithms process and compile the data into an overall “putting stroke score.” This score reflects the golfer's performance across consistency, accuracy, and tempo, offering quantifiable feedback for improvement.

AI Motion Capture Module to analyze outcome of putts (strokes gained, etc)

When placed some distance away (15-40 feet), a smartphone camera can track a golfer's putts on a green. It will count the number of attempts, provide miss patterns long, short, right/left, and strokes gained. It will store all the information graphically and in a table and use AI algorithms to recommend putting drills. The AI algorithms are implemented via an application-specific integrated circuit (ASIC) for an artificial neural network connected to the computer memory device, the ASIC comprising: a plurality of neurons organized in an array, wherein each neuron comprises a register, a processing element and at least one input, and a plurality of synaptic circuits, each synaptic circuit including a memory for storing a synaptic weight, wherein each neuron is connected to at least one other neuron via one of the plurality of synaptic circuits configured to ingest putt data and provide insights therefrom.

Adjustable Slope Board with Vestibular Balance System

The slope board component is a training tool designed to help golfers develop an accurate sense of slope estimation using their vestibular sense. The board can be inclined from 0 to 5 degrees along both the X (sagittal) and Y (frontal) planes, allowing golfers to simulate various slopes and practice compensatory aim adjustments.

Technical Specifications

Incline Adjustment Mechanism: Each leg of the board is independently adjustable, allowing precise modification of the board's angle in both planes. Two methods of adjustment are available:

Manual Screw Adjustment: A threaded screw system enables each leg to be raised or lowered manually, allowing for fine-tuning of the slope angle.

Hydraulic System: An optional hydraulic adjustment system enables smooth, quick incline changes, especially useful in instructional or high-frequency training settings.

Slope Range and Tolerances: The board can adjust from 0 to 5 degrees in 0.1-degree increments, offering granular control over slope simulation. The incline mechanism's tolerance ensures stable, repeatable settings, critical for consistent training.

Balance Feedback: Equipped with two bubble levels—one for each directional plane—the board provides visual feedback on the angle of incline. An optional digital display can offer precise numerical feedback, further enhancing training accuracy.

The slope board is designed to activate the golfer's vestibular system, improving their ability to estimate slope through proprioceptive feedback. By practicing on a slope board, golfers learn to perceive slope angles and translate that awareness into aiming adjustments.

System Integration and Training Process

The combination of laser alignment, AI motion capture, and slope estimation training offers a complete putting improvement system.

Session Initialization: The golfer initiates a training session in the mobile app, which connects to the laser alignment device, AI motion capture module, and slope board (if digital integration is enabled).

Slope Estimation Practice: The golfer stands on the slope board and adjusts it to a desired incline angle, confirmed via bubble levels or the digital display. This practice helps develop a refined sense of slope perception, enabling the golfer to intuitively sense the degree of slope underfoot without visual confirmation.

Aiming and Alignment Adjustment: Once the golfer has estimated the slope, they can use the laser alignment device to align the putter, adjusting aim to compensate for the slope's effect on ball trajectory. For example, if the golfer perceives a 2-degree right-to-left slope, they would aim slightly right to counteract the slope, using the laser beam as a reference point.

Stroke Execution and Data Capture: As the golfer executes the putting stroke, the AI motion capture module records all relevant metrics, including face angle at impact, path, tempo, and speed. The laser, if in pulse mode, provides periodic feedback points that are recorded in the app, allowing for detailed alignment analysis.

Session Analysis and Drill Recommendations: At the end of the session, the mobile app compiles alignment, stroke, and slope estimation data into a comprehensive report. Metrics such as aim deviation, stroke consistency, and slope estimation accuracy are displayed alongside a “putting stroke score.” Based on performance, the app suggests specific drills—such as balance training on the slope board or alignment drills with the laser—to target areas of improvement.

This invention offers significant improvements over existing putting aids that typically focus on isolated aspects of putting. Traditional tools lack slope estimation training, an essential skill for real-world conditions, and rarely integrate alignment and motion capture into a unified system. This invention's unique combination of vestibular slope training, laser-guided aim correction, and AI-driven motion analysis provides a holistic approach to putting improvement.

Some embodiments include motion capture of putting session. This feature could be app on a mobile phone. The phone could be positioned 15-25 feet (or more) from the area of putting. The golfer would hit putts from various areas around one hole. The motion capture system of the app, implemented using inputs from the smartphone camera, would track among other factors distance of each putt, make or miss, trends of misses (left, right, or short), entry speed of the ball at the time it reaches the hole. This would be the ultimate test of “strokes gained putting” while a player putts and provide players a report of their putting session in real time.

The mobile application offers a seamless interface for session management, data analysis, and performance tracking. By combining alignment, stroke, and slope metrics into a single system, this invention gives golfers a precise, data-rich training experience, enabling both immediate feedback and long-term improvement. Below is the foundational code framework

• • :
  • Backend (Flask-Python)
  • #app.py-Flask Backend
  • from flask import Flask, request, jsonify
  • from flask_cors import CORS
  • import numpy as np
  • from sklearn.linear_model import LinearRegression #Example model for motion analysis
  • app=Flask (_name_)
  • CORS(app) #Allow cross-origin requests from the React Native app
  • #Mock database to store user session data
  • user_data={ }
  • @app.route(‘/start-session’, methods=[‘POST’])
  • def start_session( ):
    • “““Initialize a new putting session for the user.”””
    • user_id=request.json[‘user_id’]
    • user_data[user_id]={‘alignment_data’: [ ], ‘motion_data’: [ ]}
    • return jsonify({“status”:“Session started”, “user_id”: user_id})
  • @app.route(‘/record-alignment’, methods=[‘POST’])
  • def record_alignment( ):
    • “““Record alignment data from the laser alignment device.”””
    • user_id=request.json[‘user_id’]
    • alignment_value=request.json[‘alignment_value’]
    • user_data[user_id][‘alignment_data’].append(alignment_value)
    • return jsonify({“status”: “Alignment data recorded”})
  • @app.route(‘/record-motion’, methods=[‘POST’])
  • def record_motion( ):
    • “““Record motion capture data.”””
    • user_id=request.json[‘user_id’]
    • motion_values=request.json[‘motion_values’] #Expects an array of sensor data points
    • user_data[user_id][‘motion_data’].extend(motion_values)
    • return jsonify({“status”: “Motion data recorded”})
  • @app.route(‘/analyze-session’, methods=[‘POST’])
  • def analyze_session( ):
    • “““Perform data analysis on the recorded session data.”””
    • user_id=request.json[‘user_id’]
    • alignment_data=np.array(user_data[user_id][‘alignment_data’]).reshape(−1, 1)
    • motion_data=np.array(user_data[user_id][‘motion_data’])
    • #Example analysis: Simple linear regression to model alignment vs. motion correlation
    • model=LinearRegression( )
    • model.fit(alignment_data, motion_data)
    • alignment_score=model.score(alignment_data, motion_data)
    • return jsonify({
      • “alignment_score”: alignment_score,
      • “average_alignment”: np.mean (alignment_data),
      • “average_motion”: np.mean (motion_data)
    • })
  • if_name_==‘_main_’:
    • app.run(debug=True)

Frontend(React Native-JavaScript)

For this example, installed dependencies with:

• • npm install axios react-navigation
  • //App.js-React Native Frontend
  • import React, {useState} from ‘react’;
  • import {StyleSheet, Text, View, Button, TextInput} from ‘react-native’;
  • import axios from ‘axios’;
  • export default function App( ) {
    • const [userId, setUserId]=useState (″);
    • const [alignmentValue, setAlignmentValue]=useState (″);
    • const [motion Values, setMotionValues]=useState([ ]);
    • const [sessionResult, setSessionResult]=useState(null);
    • const startSession=async( )=> {
      • const response=await axios.post(‘http://127.0.0.1:5000/start-session’, {user_id: userId});
      • alert (response.data.status);
    • };
    • const recordAlignment=async( )=> {
      • await axios.post(‘http://127.0.0.1:5000/record-alignment’, {
        • user_id: userId,
        • alignment_value: alignmentValue
      • });
      • alert (“Alignment data recorded”);
    • };
    • const recordMotion=async( )=> {
      • await axios.post(‘http://127.0.0.1:5000/record-motion’, {
        • user_id: userId,
        • motion_values: motion Values
      • });
      • alert (“Motion data recorded”);
    • };
    • const analyzeSession=async( )=> {
      • const response=await axios.post(‘http://127.0.0.1:5000/analyze-session’, {user_id: userId});
      • setSessionResult (response.data);
    • };
    • return (
      • <View style={styles.container}>
        • <Text style={styles.title}>Golf Putting Training</Text>
        • <TextInput
        •  placeholder=“User ID”
        •  onChangeText={setUserId}
        •  value={userid}
        •  style={styles.input}
        • />
        • <TextInput
        •  placeholder=“Alignment Value”
        •  onChangeText={setAlignmentValue} value={alignmentValue}
        •  style={styles.input}
        • />
        • <Button title=“Start Session” onPress={startSession}/>
        • <Button title=“Record Alignment” onPress={recordAlignment}/>
        • <Button title=“Record Motion” onPress={( )=>setMotionValues ([0.1, 0.5, 0.3])}/> {/*Mock data*/}
        • <Button title=“Analyze Session” onPress={analyzeSession}/>
        • {sessionResult && (
        •  <View style={styles.resultContainer}>
        •  <Text>Alignment Score: {sessionResult.alignment_score}</Text>
        •  <Text>Average Alignment: {sessionResult.average_alignment}</Text>
        •  <Text>Average Motion: {sessionResult.average_motion}</Text>
        •  </View>)
        • }
      • </View>)
    • );
  • }
  • const styles-StyleSheet.create({
    • container: {
      • flex: 1,
      • justifyContent: ‘center’,
      • padding: 20,
      • backgroundColor: ‘#fff’,
    • },
    • title: {
      • fontSize: 24,
      • marginBottom: 20,
      • textAlign: ‘center’,
    • },
    • input: {
      • height: 40,
      • borderColor: ‘gray’,
      • borderWidth: 1,
      • marginBottom: 12,
      • padding: 8,
    • };
    • resultContainer: {
      • marginTop: 20,
      • padding: 10,
      • backgroundColor: ‘#eee’,
    • },
  • });

Explanation of the Code

Flask Backend: Provides endpoints for starting a session, recording alignment and motion data, and analyzing the data. For simplicity, LinearRegression is used to mock the AI analysis, though a more sophisticated model can be applied in production.

React Native Frontend: Allows users to initiate a training session, input alignment and motion data, and retrieve analyzed results. The analyzeSession function calls the Flask backend to get a summary of the alignment and motion analysis.

Data Flow:

Start a session (startSession).

Record alignment data (user's alignment setup, tracked through recordAlignment).

Record motion data, which could be expanded with real sensor input.

Analyze data and get feedback on alignment and stroke consistency.

In the fully functional product, actual sensor data will come from Bluetooth-connected devices, such as a laser alignment sensor or motion capture module on the putter, with data processed by using a more sophisticated AI or machine learning model on the backend.

DETAILED DESCRIPTION OF FIGURES

FIG. 1:

This figure illustrates the laser alignment device attached to a putter head, shaft, or grip using a flexible attachment system designed for easy removal and repositioning. The device is mounted on a ball joint mechanism, allowing the laser to pivot freely and adjust aim angles while maintaining stability once aligned. This adjustability enhances accuracy by accommodating each golfer's unique stance and alignment preferences without compromising the putter's natural balance. This laser device, powered by a USB-C rechargeable battery, provides sufficient intensity for indoor and low-light outdoor use, enabling golfers to achieve consistent alignment feedback across varied practice environments.

FIG. 2:

This figure shows the wireless activation button secured to the putter grip via a rubber tube or clamp, allowing convenient access during alignment without disrupting the golfer's stance. The button, operable via Bluetooth or radio frequency (RF), remotely activates the laser, creating a seamless interface that maintains the golfer's focus on alignment. Additionally, FIG. 2 depicts the optional aiming board placed over the target hole. The board, marked with degree-based tic marks, allows for precise measurement of alignment deviations by providing a visual reference for slight alignment errors. The hollow underside enables balls to pass under the board, facilitating continuous putting practice. In pulse mode, the laser projects onto the board to record alignment accuracy through a mobile app, which calculates aim deviation and bias errors for training insights.

FIG. 3:

This figure illustrates the use of the system's mobile app to capture detailed motion analysis data. When positioned above, behind, or slightly to the side of the putting area, the app uses the phone's camera and LiDAR (if available) to monitor stroke mechanics and alignment. The app tracks key performance metrics, including tempo, club speed, club path, face angle, and ball spin, quantifying these factors to create a “putting stroke score” for quality and consistency assessment. The app also includes training features such as a clock putting drill, which monitors putts from different angles and distances, providing insights on miss patterns and recommending targeted drills. This advanced functionality offers a data-rich feedback loop for golfers, club fitters, and coaches aiming to enhance putting technique through detailed analysis and personalized drill suggestions.

FIG. 4.101: Start—This initial step marks the beginning of the alignment session, where the golfer positions themselves with the intent to practice and improve their putter aim.

FIG. 4.103: Prepare to Aim the Putter—The golfer assumes a stable stance and positions the putter head in line with the intended target, establishing a consistent baseline for alignment.

FIG. 4.105: Align Putter with Target—The golfer visually aligns the putter with the target hole or intended line of aim. This step sets the reference point that the laser will validate, providing the first stage of alignment feedback.

FIG. 4.107: Activate Laser with Button—Once aligned, the golfer activates the laser by pressing the wireless button on the putter grip. Using Bluetooth or radio frequency technology, the button instantly projects a laser beam from the putter head, delivering a real-time visual alignment guide.

FIG. 4.109: Check Laser Alignment with Target—The golfer observes the laser's position relative to the target, assessing if the putter is correctly aligned or deviates from the intended aim. This immediate feedback facilitates accurate alignment adjustments.

FIG. 4.111: Adjust Aim Based on Feedback—Using the laser feedback, the golfer adjusts the putter's position to correct any detected misalignment, refining aim to enhance accuracy before recording or practicing.

FIG. 4.113: Record Aim Accuracy—After achieving optimal alignment, the golfer records the aim results in the app, which calculates metrics such as aim deviation and bias error. This data is tracked for ongoing progress, providing insights into alignment tendencies and improvement areas.