Cross-Product Data Encoding for Distributed Query Response
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Solution Overview
Problem
Current approaches to managing value chain networks are limited by centralized data collection due to bandwidth, storage, processing, and other limitations, leading to overwhelmed data transmission and ineffective automated decisions.
Innovation Solution
A method for processing queries in a distributed database, where an edge device receives queries, stores them on a dynamic ledger, detects summary data, generates an approximate response based on this data, and transmits it back, optimizing data processing and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If centralized data collection is used to manage value chain networks, then comprehensive data availability is improved, but system complexity and processing burden increase due to bandwidth, storage, and processing limitations
Solution Approach 1:
The patent segments the centralized data collection system into distributed edge devices that autonomously process and encode data locally. Each edge device maintains a digital twin and processes queries independently, eliminating the need for a single centralized data collection point and reducing system complexity while maintaining comprehensive data availability across the network.
Solution Approach 2:
The patent introduces a new dimensional approach by encoding cross-product data structures that represent relationships between different data entities in a compact format. This encoding dimension reduces the storage and transmission burden by representing complex multi-dimensional relationships in a more efficient structure.
2Loss of information
If all data is transmitted to centralized systems for processing, then complete information analysis is improved, but transmission time and network bandwidth consumption increase
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing encoded cross-product data structures at edge devices before queries are received. Digital twins are maintained locally with pre-processed data relationships, enabling rapid query response without transmitting all underlying data to centralized systems, thus reducing transmission time while maintaining information completeness.
Solution Approach 2:
The patent extracts only the essential encoded data structures and digital twins to edge devices, removing the need to transmit entire raw datasets for processing. This extraction approach maintains the ability to perform complete information analysis locally while minimizing network transmission requirements.
3Measurement precision
If detailed data is stored and transmitted across the network, then query accuracy is improved, but network bandwidth and storage requirements increase
Solution Approach 1:
The patent applies parameter changes by transforming detailed raw data into encoded cross-product data structures that change the representation parameters from verbose detailed formats to compact encoded formats. This transformation maintains the semantic meaning and query accuracy while dramatically reducing the volume of data that needs to be stored and transmitted across the network.
Data Source
AI summary
A digital product network system includes a set of digital products each having a product processor, a product memory, and a product network interface. The digital product network system includes a product network control tower having a control tower processor, a control tower memory, and a control tower network interface. The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to generate product level data at the product processor, transmit the product level data from the product network interface, receive the product level data at the control tower network interface, encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products, and write the product level data structure to at least one of the product memory and the control tower memory.