Detection System Polling With Raw and Calculated Data

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Solution Overview

Problem

Conventional mouse devices only output calculated displacement data, limiting their application in machine learning tasks and reducing user experience.

Innovation Solution

A detection system that transmits both calculated and raw data to a post processor in response to polling requests, utilizing optical mice and processors to facilitate machine learning operations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If the mouse device only outputs calculated displacement data, then the device complexity is reduced and energy consumption is lower, but the adaptability for machine learning applications is limited

Engineering Contradiction:

Improvemachine learning application capabilityVSAvoiddata transmission complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The optical mouse performs preliminary processing by calculating displacement data from raw image data locally before transmission. This preliminary action enables the device to provide both processed results (for immediate use) and raw data (for machine learning), resolving the contradiction between simplicity and adaptability

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The detection system is designed to serve multiple functions: it can transmit only calculated data for basic cursor control, only raw data for machine learning tasks, or both simultaneously. This multi-functionality approach allows the same device to adapt to different application scenarios without increasing structural complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If the mouse device transmits both calculated data and raw data, then the machine learning capability is improved, but the quantity of data transmitted increases

Engineering Contradiction:

Improvemachine learning application capabilityVSAvoiddata transmission volume

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The system dynamically adjusts the data transmission mode based on application needs. The post processor can request different data types (calculated data, raw data, or both) at different polling rates, allowing the data transmission volume to adapt to actual requirements rather than always transmitting maximum data

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The data transmission is segmented into different types (calculated displacement data and raw image data) that can be transmitted independently or together. This segmentation allows the system to transmit only the necessary data portion for each specific application, reducing unnecessary data volume while maintaining machine learning capability

Inventive Principle:

Principle #1Segmentation

3Productivity

If the post processor requests raw data frequently, then the machine learning performance is improved, but the loss of time for data transmission increases

Engineering Contradiction:

Improvemachine learning processing efficiencyVSAvoiddata transmission time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The optical mouse continuously captures and stores raw image data in its buffer before any request is made. This preliminary action ensures that when the post processor requests raw data, it is already available in the device's memory, reducing the time loss associated with data acquisition and transmission

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250298765A1Detection system sending calculated data and raw data

Publication Date: 2025.09.25 PIXART IMAGING INC

AI summary

There is provided a detection system including a detection device and a post processor. The detection device and the post processor exchange data therebetween using a predetermined communication protocol. The detection device outputs at least one of calculated data and raw data to the post processor in response to each polling according to a request from the post processor. The raw data is provided to the post processor for the machine learning.