Logistical Relationship Inference from Mobile Device Location Data

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Solution Overview

Problem

Existing technologies are insufficient in automating the determination of logistical relationships between suppliers and customers in a supply chain, relying on manual workflows and supplier input, which is costly, inaccurate, and time-consuming, and fail to provide granular enough information using available data.

Innovation Solution

A system and method using clustering algorithms to analyze aggregate mobile device location data to identify user-defined Areas of Interest (AOIs) and quantify logistical relationships between them without requiring input from individual suppliers or customers, by determining paths and stationary geolocation data records to cluster and classify additional AOIs based on geographic proximity and timestamps.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual workflows and supplier input are used to determine logistical relationships, then data can be obtained, but the process is costly, time-consuming, and prone to inaccuracies

Engineering Contradiction:

Improveaccuracy of logistical relationship dataVSAvoidtime to determine relationships

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical workflows with an automated computer-based system that processes mobile device location data. The system automatically identifies AOIs, clusters location records, determines paths between AOIs, and infers logistical relationships without human intervention, thereby eliminating the time loss and accuracy issues associated with manual data collection and analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by automatically extracting logistical relationship information from existing mobile device location data without requiring suppliers or customers to manually input data. The automated clustering and path analysis algorithms independently identify relationships, making the process self-sufficient and eliminating reliance on external data provision.

Inventive Principle:

Principle #25Self-service

2Productivity

If manual supplier input is used to gather supply chain data, then relationships can be identified, but costs increase and data accuracy deteriorates due to reliance on manual entry

Engineering Contradiction:

Improvespeed of relationship determinationVSAvoidcost of data collection process

Core Design Contradiction:

ProductivityVSEase of manufacture

Solution Approach 1:

The patent replaces costly manual data collection processes with an automated computational system that processes mobile device location data. The system uses computer algorithms to automatically cluster location records, identify AOIs, and determine paths, eliminating the need for manual workflows and reducing both time loss and operational costs while improving productivity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system creates a digital copy of physical movement patterns by processing mobile device location data. Instead of manually recording supplier-customer interactions, the system copies and analyzes existing digital location records to infer logistical relationships, making the process more efficient and cost-effective while maintaining high productivity.

Inventive Principle:

Principle #26Copying

3Quantity of substance

If aggregate mobile device location data is used, then large volumes of data are available, but the data lacks granularity to identify specific logistical relationships between AOIs

Engineering Contradiction:

Improvevolume of location dataVSAvoidgranularity of relationship information

Core Design Contradiction:

Quantity of substanceVSMeasurement precision

Solution Approach 1:

The patent applies segmentation by dividing aggregate location data into discrete clusters representing specific AOIs. The system segments the continuous stream of location records into meaningful groups based on spatial and temporal patterns, then identifies paths between these segmented AOIs, thereby transforming voluminous aggregate data into granular relationship information with high measurement precision.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system applies local quality by focusing analysis on specific local patterns within the aggregate data. Instead of treating all location data uniformly, the system identifies and analyzes local clusters of AOIs and their interconnections, extracting granular relationship information from specific regions of the data while maintaining the benefit of having large volumes of source data.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS11523248B2Inference of logistical relationships from device location data

Publication Date: 2022.12.06 ORBITAL INSIGHT INC

AI summary

A system and method are disclosed for receiving a request to track data of an area of interest (AOI). The system receives a plurality of geolocation data records corresponding to a plurality of mobile devices. The system determines which mobile devices of the plurality of mobile devices have visited the AOI. For each mobile device determined to have visited the AOI, the system determines a path of the mobile device. The path including a prior location of the mobile device and a next location of the mobile device. The system transmits for display by a client device, a report including the path for each mobile device determined to have visited the AOI. The path indicating an identification of additional AOIs based on the prior location and the next location.