Entity-Independent Time-Series Data Disambiguation via Attribute Querying

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Solution Overview

Problem

Conventional systems fail to store and retrieve entity-independent time-series data generated by monitoring devices, such as those used in hospitals, effectively, leading to loss of historical measurement data and trends, as they are not associated with the entity being monitored.

Innovation Solution

A method and system that disambiguates entity-independent time-series data by obtaining attributes like date, time, and location from the data, generating queries to identify entities, and associating the data with entity identifiers in a database, allowing for the storage and retrieval of time-series data linked to specific entities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If entity-independent time-series data is stored without association to entities, then storage simplicity is improved, but data retrieval and analysis capability deteriorates

Engineering Contradiction:

Improvestorage simplicityVSAvoiddata retrieval capability

Core Design Contradiction:

Ease of manufactureVSLoss of information

Solution Approach 1:

The patent introduces an intermediary processing system that receives entity-independent time-series data, extracts attributes (location, time, sensor type), queries an entity database to identify the associated entity, and stores the data with entity association. This intermediary layer maintains storage efficiency while enabling data retrieval through entity identification.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Speed

If real-time data processing is implemented without historical data storage, then processing speed is improved, but historical analysis capability deteriorates

Engineering Contradiction:

Improveprocessing speedVSAvoidhistorical data availability

Core Design Contradiction:

SpeedVSDuration of action of moving object

Solution Approach 1:

The system performs preliminary actions by continuously storing time-series data in an entity-independent format as it is generated, without waiting for entity identification. This allows real-time processing continuity while preserving all historical data for future analysis once entities are identified through attribute matching.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If entity identification queries are executed for every data point, then data association accuracy is improved, but system complexity increases

Engineering Contradiction:

Improvedata association accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system applies partial action by executing entity identification queries selectively rather than for every single data point. It uses attribute extraction and matching to identify entities for relevant data points, achieving sufficient association accuracy without the excessive complexity of querying every data point.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20240403308A1Systems and methods for disambiguating time-series data generated by monitoring devices

Publication Date: 2024.12.05 JOHNS HOPKINS UNIVERSITY

AI summary

Systems, methods, and computer programs for disambiguating time-series data generating by a monitoring device. In one aspect, the method can include obtaining entity independent time-series data broadcast by a monitoring device, determining, based on the obtained entity independent time-series data, one or more attributes of the entity independent time-series data, generating a query that includes the one or more attributes of the entity independent time-series data, executing the generated query against an entity database to obtain query results that identify an entity, and generating data that associates the obtained entity independent time-series data with the identified entity.