# US 2026/0100845 A1 · Method and computer programs for determining human condition of a target

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- Number: US 2026/0100845 A1
- Document ID: US20260100845A1
- Status: Published application
- Kind: A1
- Application number: 19/343,206
- Filed: 2025-09-29
- Published: 2026-04-09
- Earliest claimed: 2024-10-04
- Assignees: Telefónica Innovacion Digital S.L.
- Applicants: Telefónica Innovación Digital, S.L.
- Inventors: Pablo Ignacio LOPEZ COYA
- Web page: https://patentgrep.com/patent/US20260100845A1

## CPC

- G06Q 30/018: Certifying business or products
- H04L 9/3236: {using cryptographic hash functions}
- G06F 16/29: Geographical information databases
- H04W 12/104: Location integrity, e.g. secure geotagging
- H04W 12/61: Time-dependent
- H04W 12/64: using geofenced areas
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

A method and computer program for determining human condition of a target are provided. The method obtains validated geolocation data points of said target from a database, and determines whether a given trajectory depicted by a set of the validated geolocation data points belongs to a human by computing a plurality of models using a behavioural algorithm, the plurality of models including two or more of the following models: a first model evaluating a likelihood of geographical location in terms of human habitability, a second model evaluating a likelihood of social interaction, identifying places that are typically frequented by humans, and a third model evaluating a likelihood of the given trajectory being physically possible for a human within a specified time frame. Finally, the method provides a weight to each one of the computed plurality of models, and computes a human condition score based on a combination of the provided weights.

## Claims

### Claim 1

A method for determining human condition of a target, the method comprising performing by one or more processors the following steps:
obtaining, from a database, validated geolocation data points of said target, each validated geolocation data point comprising three parameters including longitude, latitude and timestamp of a given geolocation, and being associated with an International Mobile Subscriber Identity, IMSI;
determining whether a given trajectory depicted by a set of the validated geolocation data points belongs to a human by computing a plurality of models using a behavioural algorithm, the plurality of models including two or more of the following models:
a first model evaluating a likelihood of geographical location in terms of human habitability,
a second model evaluating a likelihood of social interaction, identifying places that are typically frequented by humans, and
a third model evaluating a likelihood of the given trajectory being physically possible for a human within a specified time frame;
providing a weight to each one of the computed plurality of models; and
computing a human condition score based on a combination of the provided weights.

### Claim 2 (depends on 1)

The method of claim 1, wherein the geolocation data points are acquired using geolocation information from different communication network segments or input computing devices, the geolocation information being adjusted based on at least one of: a window-size parameter that adjusts a volume of the geolocation information acquisition and/or a granularity parameter that adjusts a sampling rate of the geolocation information acquisition.

### Claim 3 (depends on 2)

The method of claim 2, further comprising processing the geolocation information using noise removal, normalization, and/or feature extraction techniques.

### Claim 4 (depends on 1)

The method of claim 1, wherein the first model is computed by:
identifying habitable points using the set of validated geolocation data points and one or more of the following datasets: a dataset that assess the suitability of locations for human habitation, a dataset that provides human population distribution, a dataset that provides spatial information on human settlements, a dataset that provides spatial information on the risk level of natural disasters; and
computing a probability of the target being human as the ratio of the identified habitable points to the total number of geolocation data points in the set.

### Claim 5 (depends on 1)

The method of claim 1, wherein the places in the second model are identified by obtaining places sorted by proximity with their associated establishment types by querying a social context database.

### Claim 6 (depends on 5)

The method of claim 5, wherein the querying to the social context database comprises:
representing each irregular polygon of a set of irregular polygons defining a given geographic space as a set of N geographical coordinates;
computing a centroid of each irregular polygon using Green's Theorem, and averaging the coordinates of all vertices of the irregular polygon;
computing a distance to the computed centroid for each side of each irregular polygon;
computing a radius of an inscribed circle using a minimum distance of the computed distance;
using the computed centroid and radius of each irregular polygon as parameters in the query to the social context database; and
generating a histogram based on the types of establishments obtained from the social context database.

### Claim 7 (depends on 5)

The method of claim 5, further comprising:
generating a list that only includes establishment types by filtering the generated histogram; and
ranking the generated list based on a ratio of significant human places to a total number of places included in the histogram.

### Claim 8 (depends on 1)

The method of claim 1, wherein the third model is computed based on a speed required to cover a distance of the given trajectory in the specified time frame, and the method further comprising comparing the computed speed with a speed threshold.

### Claim 9 (depends on 8)

The method of claim 8, wherein the speed is calculated by:
obtaining positional data of the target at two different points in time and associated timestamps;
converting the obtained positional data to radians, and calculating a difference of the positional data between the two different points;
calculating a distance between the two different points on an Earth's surface;
calculating a time different using the timestamps; and
calculating the speed based on the distance of the given trajectory and the calculated time.

### Claim 10 (depends on 9)

The method of claim 9, wherein the positional data comprises a latitude and longitude of the target.

### Claim 11 (depends on 8)

The method of claim 8, wherein the speed threshold comprises a maximum speed threshold, and the computed speed is below the maximum speed threshold, the method further comprising computing a ratio of one-hour segments where the computed speed is below the maximum speed threshold to a total number of segments into which the set of validated geolocation data points is divided.

### Claim 12 (depends on 8)

The method of claim 8, wherein the speed threshold comprises a maximum speed threshold, and the computed speed is below the maximum speed threshold, the method further comprising computing a ratio of one-hour segments in which the computed speed is below the maximum speed threshold and no extreme mobility pattern is detected to a total number of segments into which the set of validated geolocation data points is divided, an extreme mobility pattern meaning being stationary for more time than a first predefined threshold or being in continuous movement for more time than a second predefined threshold.

### Claim 13 (depends on 11)

The method of claim 11, further comprising determining a means of transport of the target using the computed ratio of one-hour segments and at least one social context database.

### Claim 14 (depends on 1)

The method of claim 1, further comprising applying a hashing algorithm to each validated geolocation data point.

### Claim 15 (depends on 1)

A non-transitory computer readable medium including code instructions that when executed in a computer system implement the steps of the method of claim 1.