Synthetic Data Aggregation for Disparate Dataset Time Series

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Solution Overview

Problem

Conventional data aggregation platforms struggle to manage and present disparate datasets that do not naturally fit into traditional time series formats, leading to technical difficulties in real-time data distribution, incomplete data presentation, and increased computational burden, especially with rapidly changing and large volumes of data.

Innovation Solution

The system aggregates disparate datasets into a singular datapoint using a synthetic aggregation wizard, enabling user-customizable filtering and display through an interactive GUI, supporting dynamic real-time updates and historical time series analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional data aggregation platforms use traditional time series formats and workflows, then data management is straightforward for structured data, but disparate datasets that do not naturally fit into traditional time series formats cannot be easily presented or managed

Engineering Contradiction:

Improveability to handle disparate datasetsVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces a synthetic aggregation engine as an intermediary layer between disparate data sources and the user interface. This engine automatically creates synthetic time series data from non-time-series datasets by applying filtering and aggregation logic, enabling traditional time series workflows to handle disparate data types without requiring complex custom processing for each data type.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes the parameter representation of disparate datasets by transforming them into synthetic time series format with standardized parameters including time, value, and metadata fields. This parameter transformation allows diverse data types to be processed through统一的 time series workflows while maintaining their unique characteristics through metadata attributes.

Inventive Principle:

Principle #35Parameter changes

2Speed

If data aggregation platforms distribute large volumes of digital data content in real-time, then users receive up-to-date information, but transmission delays and data handling delays introduce significant errors

Engineering Contradiction:

Improvereal-time data distribution speedVSAvoiddata accuracy

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The synthetic aggregation engine performs preliminary data processing, filtering, and aggregation before data is distributed to users. By pre-processing data into standardized time series format with all necessary transformations applied in advance, the system eliminates real-time processing delays during data distribution while ensuring data accuracy through server-side validation and normalization.

Inventive Principle:

Principle #10Preliminary action

3Ease of operation

If conventional platforms use one-size-fits-all workflows with fixed filters, then system operation is simplified, but datasets with attributes that do not align with platform filters produce incomplete data presentation

Engineering Contradiction:

Improveworkflow simplicityVSAvoiddata completeness

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The system implements dynamic filter generation where the synthetic aggregation engine automatically creates customized filters based on the specific attributes and characteristics of each disparate dataset. This dynamic adaptation allows the standardized workflow to automatically adjust to different data types, ensuring complete data presentation while maintaining operational simplicity through automated filter selection and configuration.

Inventive Principle:

Principle #15Dynamics

4Quantity of substance

If data aggregation platforms manage large amounts of disparate datasets with unique identifiers and attributes, then comprehensive data coverage is achieved, but the amount of key-value-pairs becomes effectively infinite and difficult to search, maintain, and support

Engineering Contradiction:

Improvedata volume coverageVSAvoiddata maintenance complexity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The patent merges disparate datasets with unique identifiers into unified synthetic time series representations. By combining multiple data sources and their attributes into a standardized time series format with consolidated metadata, the system reduces the complexity of managing infinite key-value-pairs while maintaining comprehensive data coverage through the unified time series structure.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20260016932A1Systems and methods for synthetic data aggregations

Publication Date: 2026.01.15 INTERCONTINENTAL EXCHANGE HOLDINGS INC

AI summary

Systems and methods for aggregating data. The system is configured to receive metadata from an interactive graphical user interface (GUI) of a user device, aggregate field values from the data stored on one or more databases based on the received metadata and generate filter instructions based on the received metadata. The system is further configured to transmit the aggregated field values and the filter instructions to the user device, receive a user-customized filter set and subscription request for a synthetic symbol associated with the user-customized filter set from the user device, and create the synthetic symbol responsive to the subscription request. Moreover, the system aggregates one or more data values from the data stored on the databases associated with the created synthetic symbol and generates instructions to display the data values on the interactive GUI in accordance with the user-customized filter set associated with the created synthetic symbol.