SMS Dictionary Compression for Network Bandwidth Bottlenecks
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Solution Overview
Problem
The increasing volume of information communicated through mobile and other communication networks leads to bandwidth insufficiency, causing delays or preventing communication due to excessive resource consumption.
Innovation Solution
A method and system for compressing short text data, such as SMS messages, by using dictionaries sorted by symbol probability, where each symbol is encoded based on its proximity in the text, reducing bandwidth usage between devices and network facilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If compression techniques are applied to reduce bandwidth usage, then network resource consumption decreases, but device complexity increases due to dictionary encoding/decoding operations
Solution Approach 1:
The patent pre-generates dictionaries of frequently occurring symbol sequences (n-grams) before compression operations. These dictionaries are stored in advance and reused during encoding, eliminating the need to compute probabilities on-the-fly. This preliminary preparation reduces the computational complexity during actual compression while maintaining high compression ratios.
Solution Approach 2:
The patent applies different dictionary encoding strategies to different parts of the text based on local characteristics. Specifically, it uses context-aware dictionaries that are selected or adapted based on the local context of the text being encoded, allowing the system to optimize compression for specific regions without uniformly increasing complexity across the entire system.
2Productivity
If dictionaries sorted by symbol probability are used for compression, then compression efficiency improves, but processing time increases due to multiple dictionary lookups
Solution Approach 1:
The patent pre-sorts and organizes dictionaries by symbol probability in advance, creating optimized lookup structures before compression operations begin. This preliminary organization allows for rapid retrieval during encoding without requiring complex probability calculations during the actual compression process, thus maintaining high compression efficiency while reducing processing time.
Solution Approach 2:
The patent divides the text into segments or n-grams and applies dictionary encoding to each segment independently. By segmenting the processing task, the system can efficiently lookup and encode common patterns in each segment without requiring exhaustive searches across the entire text, thereby reducing overall processing time while maintaining compression efficiency.
Data Source
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AI summary
The present disclosure is directed towards systems and methods for compressing messages, such as Short Message Service (SMS) or text messages between fixed or mobile devices through communications networks. The data of, for example, SMS messages is compressed and forwarded through a communication network to an appliance having a processing unit. The appliance decompresses the message and controls its delivery through network communication devices, where the decompressed SMS message is forwarded to its destination.