Context Dependent Recognition Using Local Data Storage
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Solution Overview
Problem
Existing methods for identifying content using electronic devices require active internet connections, which can be unreliable and slow, leading to delayed or unavailable results, especially in situations where processing large amounts of data is necessary.
Innovation Solution
Implementing a system where a computing device determines its context using various sensors and algorithms, allowing it to locally store relevant data for quick recognition and reduce reliance on cloud-based services by capturing and analyzing context data such as images, audio, and location, enabling faster and more accurate identification of objects without constant internet connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud-based services are used for content identification, then recognition accuracy can be improved through access to large datasets, but response time increases and reliability decreases due to network dependency
Solution Approach 1:
The system divides the recognition task into two parts: local processing for immediate responses and cloud processing for enhanced accuracy. The device performs initial analysis locally using stored context data, while selectively uploading only necessary information to cloud services for sophisticated analysis, thus balancing speed and accuracy.
Solution Approach 2:
The system pre-loads and stores context data locally on the device before recognition tasks are needed. This preliminary preparation of relevant information enables faster local processing and reduces the need for time-consuming cloud queries during actual recognition operations.
2Adaptability or versatility
If cloud-based services are used for content identification, then recognition capabilities are enhanced, but device autonomy decreases due to constant internet connection requirements
Solution Approach 1:
The device is designed to perform content identification autonomously using locally stored context data and processing capabilities. It can independently handle recognition tasks without requiring continuous cloud connectivity, thereby maintaining device autonomy while still benefiting from cloud services when needed.
Solution Approach 2:
The system uses local context data as an intermediary between the device and cloud services. This intermediate layer enables the device to operate autonomously by providing sufficient context for recognition tasks without needing direct cloud connectivity, while still allowing cloud enhancement when beneficial.
3Measurement precision
If large datasets are processed for content identification, then recognition accuracy improves, but processing power requirements and time consumption increase
Solution Approach 1:
The system extracts and processes only the most relevant context data locally rather than processing entire large datasets. By identifying and working with the essential information needed for recognition, the system achieves adequate accuracy without the computational burden of processing all available data.
Solution Approach 2:
The system implements different processing qualities at different locations: lightweight local processing for immediate responses using stored context data, and heavier cloud-based processing for enhanced accuracy when needed. This localized approach to processing quality optimizes the balance between computational resources and recognition accuracy.
Data Source
AI summary
Context data can be used to determine the current context and/or to predict the future context of a user. When the disclosed technology knows of the user's likely context, it can prepare for object recognition (e.g., image recognition, speech recognition, etc.) by (downloading and) locally storing (i.e., holding) object data related to the context. This allows for the object recognition to be performed locally and for any additional information about the object to be provided without communication over a network, thereby reducing resources such as time, cost, and processing power. If, however, the object data related to the context is not available locally, such object data can still be downloaded from a server/cloud. In some embodiments, if a likely future context is predicted and the object data related to that future context is not available locally, the object data can be downloaded from a server/cloud prior to the future context.