US 2021/0201186 A1Published application

Utilizing Machine Learning to Predict Information Corresponding to Merchant Offline Presence

AssigneePaypal, Inc.

InventorItay Margolin

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Abstract

A machine learning process includes first, second, third and fourth phases. The first phase includes accessing geographical locations and economic traits for merchants. First merchants have offline locations. Second merchants have no offline locations. The second phase includes labeling third merchants as having offline locations and labeling fourth merchants as having no offline locations. The third phase includes training a machine learning model via the economic trait data of the first, second, third, and fourth merchants. A first probability of having the offline location and a second probability of having no offline location are determined via the trained model and for each of the remaining merchants. Fifth merchants whose predicted first probability exceeds a first predefined threshold are labeled as having offline locations. Sixth merchants whose predicted second probability exceeds a second predefined threshold are labeled as having no offline locations. The fourth phase repeats the second and third phases.

Claims20

Dependent claims

A method, comprising:

accessing a geographical location and one or more attributes of each entity of a plurality of entities, wherein a first subset of the entities of the plurality of entities each have an offline presence, respectively, wherein a second subset of the entities of the plurality of entities each have no offline presence, and wherein a third subset of the entities of the plurality of entities each have an unknown offline presence;
identifying, from the third subset of the entities and based on the accessing, one or more first entities having geographical locations within a predefined distance from the geographical location of any of the entities in the first subset of the entities, and one or more second entities having geographical locations within the predefined distance from the geographical location of any of the entities in the second subset of the entities;
grouping the one or more identified first entities into the first subset of the entities and grouping the one or more identified second entities into the second subset of the entities;
removing the one or more identified first entities and second entities from the third subset of the entities;
training a machine learning model based on the one or more attributes;
determining, via the machine learning model, a first probability and a second probability for each remaining entity in the third subset of the entities, the first probability corresponding to a probability of having the offline presence, the second probability corresponding to a probability of having no offline presence;
identifying, from the third subset of the entities and based on the determined first probability and second probability, one or more third entities each having the determined first probability exceeding a first predefined threshold and one or more fourth entities each having the determined second probability exceeding a second predefined threshold;
grouping the one or more identified third entities into the first subset of the entities and grouping the one or more identified fourth entities into the second subset of the entities;
and
removing the one or more identified third entities and fourth entities from the third subset of the entities.

7 more elements

A system, comprising:

a non-transitory memory;
and
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
performing a first phase of a machine learning process, including accessing geographical location data and economic trait data for a plurality of merchants, wherein one or more first merchants of the plurality of merchants have an offline location for conducting transactions, and wherein one or more second merchants of the plurality of merchants have no offline location;
performing a second phase of the machine learning process by labeling one or more third merchants of the plurality of merchants as having the offline location and by labeling one or more fourth merchants of the plurality of merchants as having no offline location, wherein the one or more third merchants are located within a first predefined geographical distance from at least one of the one or more first merchants, and wherein the one or more fourth merchants are located within a second predefined geographical distance from at least one of the one or more second merchants;
performing a third phase of the machine learning process that comprises:
training a machine learning model via the economic trait data of the one or more first, second, third, and fourth merchants;
predicting, via the trained machine learning model and for each of the merchants other than the first, second, third, and fourth merchants, a first probability of having the offline location and a second probability of having no offline location;
labeling one or more fifth merchants whose predicted first probability exceeds a first predefined confidence threshold as having the offline location;
and
labeling one or more sixth merchants whose predicted second probability exceeds a second predefined confidence threshold as having no offline location.

8 more elements

A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:

accessing geographical location data and non-geographical-location data of each merchant of a plurality of merchants, wherein a first subset of the merchants of the plurality of merchants each have an offline presence, respectively, wherein a second subset of the merchants of the plurality of merchants each have no offline presence, and wherein the offline presence is unknown for a third subset of the merchants of the plurality of merchants;
identifying, from the third subset of the merchants and based on the accessing of the geographical location data, one or more first merchants that are each located within a first predefined proximity from any of the merchants in the first subset of the merchants, and one or more second merchants that are each located within a second predefined proximity from any of the merchants in the second subset of the merchants;
labeling the one or more identified first merchants as belonging to the first subset of the merchants;
labeling the one or more identified second merchants as belonging to the second subset of the merchants;
training a machine learning model based on the non-geographical-location data of the first subset of the merchants and the second subset of the merchants;
predicting, via the trained machine learning model, a first probability and a second probability for each remaining merchant in the third subset of the merchants, the first probability corresponding to a probability of having the offline presence, the second probability corresponding to a probability of having no offline presence;
identifying, from the third subset of the merchants and based on the predicted first probability and the predicted second probability, one or more third merchants whose predicted first probability exceeds a first predefined threshold and one or more fourth merchants whose predicted second probability exceeds a second predefined threshold;
labeling the one or more identified third merchants as belonging to the first subset of the merchants;
and
labeling the one or more identified fourth merchants as belonging to the second subset of the merchants.

7 more elements

Dependencies are inferred from the claim text.