Probabilistic Multi-Application Data Network for Incident Output Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Integrating and leveraging data from multiple devices, applications, and networks is challenging due to disparate interfaces, leading to inefficient data sharing, processing, and output generation during incidents, which can be arduous for participants and interested parties.
Innovation Solution
A multi-application network utilizing a probabilistic network and knowledge base to transform data by comparing new data with historic data, recognizing patterns, and generating improved output data through machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data from multiple devices, applications, and networks is integrated using traditional methods, then data sharing and processing can be achieved, but the process becomes arduous and inefficient due to disparate interfaces
Solution Approach 1:
The patent implements a universal data exchange platform that enables multiple disparate applications and devices to communicate through a common interface. The system provides standardized data collection, processing, and distribution mechanisms that work across different applications (e.g., insurance, automotive, healthcare) without requiring application-specific integration logic, thereby improving both productivity and ease of operation.
Solution Approach 2:
The patent introduces an intermediary data processing layer that sits between multiple applications and devices. This intermediary system receives data from various sources, processes it through standardized protocols, and distributes it to interested parties. The intermediary eliminates the need for direct point-to-point integration between disparate systems, making data sharing easier and more efficient.
2Productivity
If traditional data collection and processing methods are used across multiple applications, then data can be gathered and processed, but time constraints are excessive and the process is arduous
Solution Approach 1:
The patent implements preliminary data processing and validation steps that occur automatically as data is collected. The system pre-processes incoming data by validating formats, enriching with contextual information, and preparing for distribution before actual processing requests are made. This preliminary action reduces the time required for subsequent processing and output generation.
Solution Approach 2:
The patent establishes continuous data processing pipelines that operate without interruption. The system maintains continuous connections with data sources and interested parties, enabling real-time or near-real-time data exchange. This continuous operation eliminates batch processing delays and keeps the data flow moving efficiently throughout the system.
3Reliability
If multiple interested parties with unique interfaces are coordinated during an incident, then comprehensive data collection is possible, but the coordination process becomes arduous for participants
Solution Approach 1:
The patent creates a universal coordination platform that handles interactions with multiple interested parties through a single standardized interface. The system manages data collection, validation, and distribution to various parties (e.g., insurance companies, repair shops, medical providers) without requiring participants to navigate multiple unique interfaces, thereby maintaining reliability while improving ease of operation.
4Productivity
If data from disparate sources is integrated without transformation, then data collection is straightforward, but data analysis and pattern recognition are hindered
Solution Approach 1:
The patent systematically transforms data from disparate sources by changing its parameters to a standardized format. The system applies consistent data validation rules, normalizes data types, and transforms varied input formats into a unified structure suitable for analysis. This parameter transformation enables efficient pattern recognition and analysis while managing complexity through automated processes.
Data Source
AI summary
Disclosed are systems and methods for intelligently transforming data to generate improved output data, including, for example, for use in a multi-application network with disparate parties. The systems and methods include transforming data using a probabilistic network and a knowledge base generated using historic data to generate improved output data and include the steps of receiving first data associated with a first user and associated with a first incident object. In some embodiments, the systems and methods include generating a first computing object, transmitting the first computing object, receiving a first selection, transmitting a data collection computing input tool, receiving a second selection, receiving second data comprising a first image of a first incident object, transmitting the second data, transforming the second data using a probabilistic network, a machine learning model, a knowledge base, and a data group associated with patterns of processed historic data, and generating improved output data.