Persona Data Object Aggregation Without Losing Individual Characteristics
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Solution Overview
Problem
As the amount of data generated from medication prescriptions, insurance policies, and patient information increases, existing systems struggle to effectively aggregate and preserve individual characteristics while analyzing big data for trends and insights, leading to a loss of personal data granularity.
Innovation Solution
A system and method for generating persona data objects using big data analytics, which includes receiving input data objects, determining their format, storing or updating data based on expected formats, and classifying fields to create aggregated data objects that maintain individual characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data aggregation is performed to identify trends and averages from vast amounts of prescription and insurance data, then productivity and analytical capability are improved, but individual characteristics and personal data nuances are lost
Solution Approach 1:
The patent segments the data processing into distinct stages: individual data object creation, validation against schemas, classification, and aggregation. This segmentation allows individual characteristics to be preserved in the data object structure while enabling aggregated analysis at higher levels, resolving the contradiction between aggregation capability and individual detail retention
Solution Approach 2:
The patent introduces a hierarchical dimension to data organization, creating multiple levels of data abstraction (individual data objects, classified data objects, aggregated data objects). This dimensional approach allows simultaneous access to both individual characteristics and aggregated trends by navigating different hierarchical levels, thus resolving the contradiction between loss of individuality and gain in analytical productivity
2Manufacturing precision
If data validation and formatting checks are performed on each field of input data objects, then manufacturing precision and data quality are improved, but processing time and complexity increase
Solution Approach 1:
The patent applies preliminary action by establishing data schemas and validation rules before data processing begins. Input data objects are pre-defined with expected field structures, and validation checks are prepared in advance, allowing systematic verification without adding significant processing complexity during actual data operations
Solution Approach 2:
The patent changes the parameter of data validation from ad-hoc checking to structured schema-based validation. By transforming validation into a parameter-driven process where schemas define expected formats and classifications, the system achieves high data precision while managing complexity through standardized parameter sets rather than complex custom validation logic
Data Source
AI summary
A method includes receiving at least one input data object corresponding to an individual and determining whether a field of the input data object includes data formatted according to an expected format. The method also includes, storing the data associated with the field of the input data object in the corresponding field of the plurality of fields of the intermediate data object and determining, for the corresponding field of the plurality of fields of the intermediate data object, a classification. The method also includes identifying an aggregated data object having a field having a classification corresponding to the classification of the corresponding of the plurality of fields of the intermediate data object. The method also includes updating a value associated with at least one field of the aggregated data object using the corresponding field of the plurality of fields of the intermediate data object.