Cloud Softbots for Inter-Entity Prediction Analysis
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Solution Overview
Problem
Existing digital data processing solutions lack a comprehensive framework for considering inter-entity influences when making predictions, fail to present predictions in an optimized manner, struggle with distributed processing of large volumes of data, and do not provide user flexibility in time-based predictions.
Innovation Solution
A cloud-based system using software agents (Softbots) that process and classify time-based documents to generate predictions through distributed machine learning, allowing for inter-entity influence analysis and visualization of predictions, and enabling user-defined time periods for data consideration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing solutions process predictions in isolation without considering inter-entity influences, then the prediction generation process remains simple, but the prediction accuracy and relevance deteriorate due to lack of contextual understanding
Solution Approach 1:
The system segments the prediction task by creating separate software agents for different entities (e.g., one agent for Microsoft, another for Yahoo), each responsible for processing documents and generating predictions specific to that entity. This segmentation allows independent processing while maintaining the ability to integrate results through inter-entity influence analysis, resolving the contradiction between prediction accuracy and framework complexity.
Solution Approach 2:
The system implements a nested architecture where software agents are contained within a cloud-based platform, and each agent further contains document processing modules, classification components, and prediction generation mechanisms. This nesting allows complex prediction frameworks to be organized in manageable layers, improving prediction accuracy through comprehensive entity consideration while keeping the overall system architecture organized and controllable.
2Ease of operation
If existing solutions present predictions without visualizations and explanatory statements, then the system remains simple to operate, but user understanding and trust in the predictions deteriorate
Solution Approach 1:
The system introduces visualizations and explanatory statements as intermediary elements between the prediction generation process and the user. These intermediaries translate complex computational results into intuitive graphical representations and contextual explanations, maintaining ease of operation while preventing loss of information through comprehensive presentation of prediction context, influencing factors, and relationships.
Solution Approach 2:
The system employs visualizations that use color coding and graphical representations to convey different aspects of prediction data. Different colors represent different entities, relationships, or prediction confidence levels, allowing users to quickly grasp complex information patterns. This approach maintains simple user interaction while enriching the presentation with comprehensive contextual information through visual encoding.
3Productivity
If existing solutions process large volumes of data without distributed processing, then the system architecture remains simple, but the processing speed and scalability deteriorate
Solution Approach 1:
The system segments the data processing workload across multiple software agents running in parallel on the cloud platform. Each agent processes documents and generates predictions for its assigned entity independently, then results are aggregated. This segmentation enables concurrent processing of large volumes of data, significantly improving prediction processing speed while managing architecture complexity through modular agent design.
Solution Approach 2:
The system implements multiple instances of software agents that can be replicated across the cloud infrastructure. Each agent is a copy of the same functional template but operates independently on different data subsets. This copying approach enables scalable processing of large volumes of data through parallel execution, improving productivity while keeping individual agent complexity manageable through template-based design.
4Adaptability or versatility
If existing solutions provide fixed time-based predictions without user choice, then the system remains simple to implement, but user flexibility and adaptability deteriorate
Solution Approach 1:
The system implements dynamic time parameter configuration where users can specify custom time periods (e.g., last 7 days, last month) for document processing. The system adapts its behavior based on user-defined time ranges, allowing flexible prediction generation tailored to specific needs. This dynamic approach improves adaptability while managing complexity through standardized time parameter interfaces and automated document filtering based on user selections.
Data Source
AI summary
A system and method for generating a prediction are disclosed. In one embodiment, the method includes receiving a plurality of time-based documents; receiving a user query including a time period of interest defining a subset of the time-based documents from which to generate a prediction; and a plurality of cloud-based software agents classifying the subset of the plurality of time-based documents into a plurality of classes for the plurality entities, wherein the plurality of cloud-based software agents intercommunicate using distributed processing, wherein each of the plurality of cloud-based software agents is dedicated to one of the entities in the plurality of entities; and generating, using at least one machine learning method, the prediction based on the subset of the plurality of time-based documents for at least one of a plurality of categories. However, other embodiments are disclosed.