Transaction Protocol Recommendation via Machine Learning
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Solution Overview
Problem
The generation of viable transaction protocols using automated processes lacks precision due to complexity and uncertainty in the data being analyzed.
Innovation Solution
An apparatus and method that utilize a processor and memory to receive entity data, identify entity matches, determine protocol metrics, train a policy machine-learning model, select protocol objects, and generate policy agreements, enabling the recommendation of transaction protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated processes are used to generate transaction protocols, then productivity is improved, but manufacturing precision deteriorates due to data complexity and uncertainty
Solution Approach 1:
The patent introduces machine learning models as intermediary components between the automated process and the transaction protocol generation. These models process and analyze entity data, transforming complex uncertain data into structured insights that improve protocol accuracy while maintaining automated generation speed.
Solution Approach 2:
The system changes the parameters of data processing by applying machine learning techniques that dynamically adjust analysis parameters based on data characteristics. This allows the system to adapt to data complexity and uncertainty, improving precision without sacrificing the automated generation capability.
2Manufacturing precision
If machine-learning models are introduced to improve precision, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The machine learning models serve multiple functions: they analyze entity data, identify patterns, assess risks, and generate protocol recommendations. This multi-functionality reduces the need for separate specialized components, thereby limiting the increase in overall system complexity while achieving improved precision.
3Measurement precision
If entity data analysis is deepened to improve protocol accuracy, then measurement precision is improved, but loss of time increases due to data processing requirements
Solution Approach 1:
The system performs preliminary actions by pre-processing and pre-analyzing entity data using machine learning models before actual transaction protocol generation. This prepares the data in advance, creating structured insights that can be quickly retrieved and applied, thereby reducing the time required for deep analysis during protocol generation.
Data Source
AI summary
An apparatus for determining and recommending transaction protocols, wherein the apparatus includes at least a processor configured to receive entity data, identify one or more entity matches as a function of the entity data, determine at least a protocol metric for each protocol object of a plurality of protocol objects as a function of the entity data and the one or more entity matches, select at least one protocol object of the plurality of protocol objects as a function of the at least a protocol metric, generate at least one policy agreement as a function of the at least one protocol object and transmit the at least one policy agreement to at least a remote device.