IP Library Granted Patent US 12,339,129
Granted Patent B2
US 12,339,129 · App. 18/241,834 · Granted Jun 24, 2025

Methods and servers for generating a prediction score by a machine learning algorithm

Inventors: Ekaterina Gladkikh (Moscow, RU); Dmitry Shulchevskiy (Ryazan, RU)
Assignee: Y.E. Hub Armenia LLC
G01C21/3446G01C21/3461
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Quick Facts
Patent No.
US 12,339,129
App. No.
18/241,834
Granted
Jun 24, 2025
Kind
B2
Abstract

A method and server for generating a prediction score by a Machine Learned Algorithm (MLA). The method includes: acquiring route data, generating a plurality of route scores for the plurality of routes based on the route data, acquiring driver data, generating a plurality of driver scores for the plurality of drivers based on the driver data, and for a given route-driver pair: generating by the MLA a given prediction score indicative of a likelihood of the given driver having an accident on the given route within a predetermined amount of time after initiating the given route. The method also includes assigning, by employing a dispatching algorithm based on the prediction score, the given route to the given driver for providing a transportation service to the user.

Claims (37)

1. A method of generating a prediction score by a Machine Learned Algorithm (MLA), the MLA being executed by a server, the server executing an digital transportation service, the server being configured to be communicatively coupled to a plurality of electronic devices associated with respective users and respective drivers of the digital transportation service, the method executable by the server, the method comprising:

acquiring, by the server, route data associated with a plurality of routes requested by users of the digital transportation service;

generating, by the server, a plurality of route scores for the plurality of routes based on the route data, a given route score being indicative of how safe a respective route from the plurality of routes is;

acquiring, by the server, driver data associated with a plurality of drivers of the digital transportation service;

generating, by the server, a plurality of driver scores for the plurality of drivers based on the driver data, a given driver score being indicative of a driving behavior of the respective driver from the plurality of drivers;

for a given pair including the given route and the given driver:

generating, by the server employing the MLA, a given prediction score indicative of a likelihood of the given driver having an accident on the given route within a predetermined amount of time after initiating the given route;

assigning, by the server employing a dispatching algorithm, the given route to the given driver for providing a transportation service to the user, the assigning being based on the prediction score.

2. The method of claim 1 , wherein the generating the route score comprises generating, by the server, a ratio of accidents on the route over a route length of the route.

3. The method of claim 1 , wherein the generating the driver score comprises generating, by the server employing a driver model, the driver score based on driver behavioral data and driver profile data.

4. The method of claim 1 , wherein the method further comprises generating, by the server, a dispatch time value for the given pair, and wherein the assigning is further based on the dispatch time value.

5. The method of claim 1 , wherein the MLA is a logistic regression model.

6. The method of claim 1 , wherein the MLA is a pairwise ranking model.

7. The method of claim 1 , wherein the dispatching algorithm is a Hungarian algorithm.

8. A method of dispatching a driver to a ride request, the method executable by a server, the server executing an digital transportation service, the server being configured to be communicatively coupled to a plurality of electronic devices associated with respective users and respective drivers of the digital transportation service, the method comprising:

acquiring, by the server, route data associated with a plurality of routes requested by users of the digital transportation service, a given route from the plurality of routes being associated with a respective ride request;

generating, by the server, a plurality of route scores for the plurality of routes based on the route data, the given route score being indicative of how safe a respective route from the plurality of routes is;

acquiring, by the server, driver data associated with a plurality of drivers of the digital transportation service;

generating, by the server, a plurality of driver scores for the plurality of drivers based on the driver data, a given driver score being indicative of a driving behavior of the respective driver from the plurality of drivers;

for a given pair including the given route and the given driver:

generating, by the server employing the MLA, a given prediction score indicative of a likelihood of the given driver having an accident on the given route within a predetermined amount of time after initiating the given route;

assigning, by the server employing a dispatching algorithm, the given route to the given driver for providing a transportation service to the user, the assigning being based on the prediction score; and

transmitting, by the server, a request for confirmation to the given driver for accepting the given route.

9. A server for generating a prediction score by a Machine Learned Algorithm (MLA), the MLA being executed by the server, the server executing an digital transportation service, the server being configured to be communicatively coupled to a plurality of electronic devices associated with respective users and respective drivers of the digital transportation service, the server being configured to:

acquire route data associated with a plurality of routes requested by users of the digital transportation service;

generate a plurality of route scores for the plurality of routes based on the route data, a given route score being indicative of how safe a respective route from the plurality of routes is;

acquire driver data associated with a plurality of drivers of the digital transportation service;

generate a plurality of driver scores for the plurality of drivers based on the driver data, a given driver score being indicative of a driving behavior of the respective driver from the plurality of drivers;

for a given pair including the given route and the given driver:

generate, by employing the MLA, a given prediction score indicative of a likelihood of the given driver having an accident on the given route within a predetermined amount of time after initiating the given route;

assign, by employing a dispatching algorithm, the given route to the given driver for providing a transportation service to the user, the assigning being based on the prediction score.

10. The server of claim 9 , wherein to generate the route score comprises the server configured to generate a ratio of accidents on the route over a route length of the route.

11. The server of claim 9 , wherein to generate the driver score comprises the server configured to generate, by employing a driver model, the driver score based on driver behavioral data and driver profile data.

12. The server of claim 9 , wherein the server is further configured to generate a dispatch time value for the given pair, and wherein the assigning is further based on the dispatch time value.

13. The server of claim 9 , wherein the MLA is a logistic regression model.

14. The server of claim 9 , wherein the MLA is a pairwise ranking model.

15. The server of claim 9 , wherein the dispatching algorithm is a Hungarian algorithm.

Assignments (4)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 17, 2025
From: GLADKIKH, EKATERINA, MS.; SHULCHEVSKIY, DMITRY, MR.
To: YANDEX.TECHNOLOGIES LLC
Reel/Frame 070867/0519 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 17, 2025
From: YANDEX.TECHNOLOGIES LLC
To: YANDEX.TAXI, LLC
Reel/Frame 070867/0933 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 17, 2025
From: YANDEX.TAXI LLC
To: DIRECT CURSUS TECHNOLOGY L.L.C
Reel/Frame 070868/0113 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 9, 2024
From: DIRECT CURSUS TECHNOLOGY L.L.C
To: Y.E. HUB ARMENIA LLC
Reel/Frame 068534/0818 →
Priority Claims (1)
RU RU2022123664 · Sep 5, 2022 · national
Continuity (1)
Related Publication 20240077318A1 · Mar 7, 2024
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