Master Language Model Synchronization Across Devices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional language recognition systems require each device to individually learn and maintain a local language model, leading to inconsistencies and misrecognitions, especially with proper names and infrequently used words, as they are not trained with all user inputs across multiple devices.
Innovation Solution
A master language model is created and synchronized across devices, allowing for real-time or near real-time updates and prioritization of changes, enabling consistent language recognition across multiple devices and improving input accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If each device maintains a local language model trained only on device-specific inputs, then device-specific input processing is simple and fast, but language recognition accuracy deteriorates due to inconsistencies and missing proper names from other devices
Solution Approach 1:
The patent merges language model data across multiple devices by implementing a centralized language model server that aggregates inputs from all user devices. This centralization allows the system to maintain a comprehensive language model that includes proper names and infrequently used words from all devices, resolving the accuracy issue while keeping individual devices relatively simple.
Solution Approach 2:
The patent introduces a language model server as an intermediary between user devices and the language model data. This mediator receives training inputs from multiple devices, manages the centralized language model, and distributes updated models back to devices, thereby improving accuracy without requiring complex peer-to-peer synchronization between devices.
2Stability of the object's composition
If language model updates are synchronized across all devices in real-time, then language recognition consistency is improved, but network bandwidth and processing resources are consumed
Solution Approach 1:
The patent implements partial synchronization by selectively updating only those devices that need updates based on their specific input patterns and model versions. Rather than forcing all devices to synchronize continuously, the system performs updates only when necessary, reducing network traffic and processing overhead while maintaining adequate consistency across the device fleet.
Solution Approach 2:
The patent employs periodic synchronization intervals where language model updates are distributed to devices at scheduled times rather than in real-time. This periodic approach allows the system to batch updates efficiently, reducing the frequency of network communications and processing events on individual devices while still maintaining model consistency over time.
Data Source
AI summary
Systems and methods for intelligent language models that can be used across multiple devices are provided. Some embodiments provide for a client-server system for integrating change events from each device running a local language processing system into a master language model. The change events can be integrated, not only into the master model, but also into each of the other local language models. As a result, some embodiments enable restoration to new devices as well as synchronization of usage across multiple devices. In addition, real-time messaging can be used on selected messages to ensure that high priority change events are updated quickly across all active devices. Using a subscription model driven by a server infrastructure, utilization logic on the client side can also drive selective language model updates.