US-20260127157-A1 - STAND-IN MODEL FOR DOMAIN LEVEL SERVICES

US20260127157A1US 20260127157 A1US20260127157 A1US 20260127157A1US-20260127157-A1

Abstract

System and methods for providing stand-in services at a domain can include obtaining a service request at a domain, determining one or more services at the domain to fulfill the service request, in response to determining a service of the one or more services is unavailable, providing a model as a stand-in service for the service, determining, by the one or more services and the model, a decision based on the service request, and sending, in response to the service request, the decision as output by the domain level. The model can provide the stand-in service by obtaining data for the service based on the service request context, identifying one or more keys based on the obtained context data, retrieving, based on the one or more keys, data from a cache, and applying the data to the model, the decision being based on the data applied to the model.

Inventors

Prabin Patodia
Rajendra Bhat

Assignees

PAYPAL, INC.

Dates

Publication Date: 20260507
Application Date: 20241101

Claims (20)

1 . A computer-implemented method comprising: obtaining a service request at a domain level of a first computing device, the domain level including a plurality of services; determining one or more services of the plurality of services at the domain level to fulfill the service request; in response to determining at least one service of the one or more services is unavailable, providing a model as a stand-in service for the at least one service; determining, by the one or more services and the model, a decision based on the service request; and sending, in response to the service request, the decision as output by the domain level, wherein the model is configured to serve as a stand-in service for at least two services of the plurality of services responsive to becoming unavailable.
2 . The computer-implemented method of claim 1 , further comprising: obtaining a first dataset corresponding to an activity of the plurality of services from a data store; determining a dependency of the data in the first dataset to the plurality of services; and storing the first dataset including the determined dependency data in a cache as a second dataset, wherein the model utilizes data stored in the cache including the second dataset as training data to train the model to serve as the stand-in service for the at least one service.
3 . The computer-implemented method of claim 2 , wherein the first dataset obtained from the data store corresponds to the activity of the plurality of services since a last update.
4 . The computer-implemented method of claim 2 , wherein determining the dependency of the first dataset to the plurality of services comprises: extracting one or more features corresponding to the activity of the plurality of services from the data store; aggregating the extracted one or more features to generate the first dataset; and associating each data subset of a plurality of data subsets of the first dataset to a respective service of the plurality of services, wherein each data subset of the plurality of data subsets includes one or more data points of the first dataset.
5 . The computer-implemented method of claim 4 , further comprising: allocating an identifier to each subset of the plurality of data subsets based on the determined data dependency; and storing the plurality of data subsets including corresponding identifiers in the cache, wherein each identifier corresponds to a key-value pair for retrieving a corresponding data subset of the plurality of data subsets from the cache by the model based on the identifier matching a corresponding key of a given key set.
6 . The computer-implemented method of claim 5 , wherein providing the model as the stand-in service for the at least one service further comprises: obtaining data for the at least one service based on a context of the service request; generating one or more keys based on the context of the service request from the obtained data; retrieving, based on the one or more keys, a corresponding one or more data subsets from the cache; and applying the retrieved one or more data subsets as training data to train the model to serve as the stand-in service for the at least one service, wherein the decision is based on the one or more data subsets applied to the model.
7 . The computer-implemented method of claim 1 , further comprising: obtaining, by the first computing device, a user request from a second computing device; identifying one or more domain levels to fulfill the user request based on a context of the user request; and determining one or more service requests based on the user request and allocating the one or more service requests to a respective domain level of the one or more domain levels, wherein the one or more domain levels comprise the domain level.
8 . The computer-implemented method of claim 1 , further comprising: training the model using a training dataset corresponding to an activity of the plurality of services, the training dataset being based on data obtained from a data store, wherein the trained model comprises the model.
9 . The computer-implemented method of claim 8 , wherein the trained model is configured to serve as a stand-in for the plurality of services at the domain level.
10 . A system comprising: a processor; and a non-transitory computer readable media having stored thereon instructions executable by the processor to perform operations comprising: obtain a service request at a domain level, the domain level including a plurality of services; determine one or more services of the plurality of services at the domain level to fulfill the service request; obtain a first dataset corresponding to an activity of the plurality of services from a data store; determine a dependency of the data in the first dataset to the plurality of services; store the first dataset including the determined dependency data in a cache as a second dataset; in response to determining at least one service of the one or more services is unavailable, provide a model as a stand-in service for the at least one service; determine, by the one or more services and the model, a decision based on the service request and the second dataset; and send, in response to the service request, the decision as output by the domain level, wherein the model utilizes data stored in the cache including the second dataset to enable the model to serve as the stand-in service for the at least one service, wherein the model can be trained to serve as a stand-in service for at least two services of the plurality of services responsive to becoming unavailable.
11 . The system according to claim 10 , the operations further comprising: obtain a user request from a user computing device; identify one or more domain levels to fulfill the user request based on the user request; and determine one or more service requests based on the user request and allocate each service requests of the one or more service requests to a respective domain level of the one or more domain levels, wherein the one or more domain levels comprises the domain level.
12 . The system according to claim 10 , wherein determining the data dependency of the first dataset to the plurality of services comprises: extract one or more features corresponding to the activity of the plurality of services from the data store; aggregate the extracted one or more features to generate the first dataset; and associate each data subset of a plurality of data subsets of the first dataset to a respective service of the plurality of services, wherein the first dataset corresponds to the activity of the plurality of services since a last update, and wherein each data subset of the plurality of data subsets includes one or more data points of the first dataset.
13 . The system according to claim 12 , the operations further comprising: allocate an identifier to each data subset of the plurality of data subsets based on the determined data dependency; and store the plurality of data subsets including corresponding identifiers in the cache, wherein the identifier corresponds to a key-value pair for retrieving a corresponding data subset of the plurality of data subsets from the cache by the model.
14 . The system according to claim 13 , the operations further comprising: obtain data for the at least one service based on a context of the service request; identify one or more keys based on the obtained context data; retrieve, based on the one or more keys, a corresponding one or more data subsets from the cache; and apply the retrieved one or more data subsets to the model; wherein the decision is based on the one or more data subsets applied to the model.
15 . The system according to claim 10 , the operations further comprising: train the model using a training dataset corresponding to the activity of the plurality of services from the data store, the training dataset being based on data obtained from the data store; wherein the trained model comprises the model.
16 . The system according to claim 15 , wherein the trained model is configured to serve as a stand-in for the plurality of services at the domain level.
17 . A non-transitory computer readable media having stored thereon instructions executable by a processor of a computing device to cause the computing device to perform operations comprising: train a model using a training dataset corresponding to an activity of a plurality of services at a domain level; obtain a service request at the domain level, the domain level including the plurality of services; determine two or more services of the plurality of services at the domain level to fulfill the service request; in response to determining at least one service of the two or more services is unavailable, provide the trained model as a stand-in service for the at least one service; determine, by a remaining service of the two or more services and the trained model, a decision based on the service request; and send, in response to the service request, the decision as output by the domain level, wherein the model can serve as a stand-in service for the two or more services responsive to becoming unavailable.
18 . The non-transitory computer readable media of claim 17 , the operations further comprising: store data corresponding to the activity of the plurality of services in a data store at the domain level; and obtain the data corresponding to the activity of the plurality of services from the data store to generate the training dataset, the obtained data from the data store corresponding to the activity of the plurality of services since a last update of the training dataset, wherein the trained model is configured to serve as a stand-in for the plurality of services at the domain level.
19 . The non-transitory computer readable media of claim 18 , the operations further comprising: obtain a first dataset corresponding to the activity of the plurality of services from the data store; determine a data dependency of the first dataset to the plurality of services, wherein determining the data dependency of the first dataset to the plurality of services comprising: extract one or more features corresponding to the activity of the plurality of services from the data store; aggregate the extracted one or more features to generate the first dataset; and associate each data subset of a plurality of data subsets of the first dataset to a respective service of the plurality of services, wherein each data subset of the plurality of data subsets includes one or more data points of the first dataset; allocate an identifier of a plurality of identifiers to each respective subset of the plurality of data subsets based on the determined data dependency; and store the first dataset including the determined dependency data and the plurality of identifiers as a second dataset in a cache, the identifier corresponding to a key-value pair for retrieving the corresponding data subset of the plurality of data subsets from the cache by the model, wherein the first dataset corresponds to the activity of the plurality of services since the last update, and wherein the model utilizes data stored in the cache including the second dataset to enable the model to serve as the stand-in service for the at least one service.
20 . The non-transitory computer readable media of claim 19 , the operations further comprising: obtain, by the trained model, data corresponding to a context of the service request for the at least one service; identify, by the trained model, one or more keys based on the obtained context data; retrieve, by the trained model based on the one or more keys, a corresponding one or more data subsets from the cache; and apply the one or more data subsets to the trained model, wherein the decision is based on the one or more data subsets applied to the trained model.

Description

FIELD The present disclosure relates to the field of data architecture. More particularly, to a stand-in model for domain level services in data architecture. BACKGROUND An electronic service request received by a system or network of an entity from a user computing device can be fulfilled by leveraging one or more domains of the system data architecture to make respective determinations of whether to fulfill the user's service request. Each domain includes a number of micro-services, and the system can utilize a subset of these micro-services at a given domain to fulfill the service request, or a respective portion thereof, according to the models, policies, rules, and standards of the entity. In fulfilling the service request, the service request data can be provided as input to the subset of micro-services of the domain, and the subset of micro-services can interact with each other and output a determination based on the data in fulfillment of the service request to the domain. BRIEF DESCRIPTION OF THE DRAWINGS Some embodiments of the disclosure are herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the embodiments shown are by way of example and for purposes of illustrative discussion of embodiments of the disclosure. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the disclosure may be practiced. FIG. 1 is a block diagram of an example system for providing stand-in service functionality at a domain, according to some embodiments. FIG. 2 is a flow chart illustrating an example method of providing the stand-in service functionality. FIG. 3 is a block diagram view of an example system for performing the method of FIG. 2. FIG. 4 is a block diagram view of an example domain level in system for performing the method of FIG. 2. FIG. 5 is a flow diagram of an example method for processing data at a domain level, according to some embodiments. FIG. 6 is a flow diagram of an example method for processing data at a domain level, according to some embodiments. FIG. 7 is a flow diagram of an example method for processing the data at a domain level, according to some embodiments. FIG. 8 is a flow diagram of an example method for providing the stand-in service, according to some embodiments. FIG. 9 is a block diagram of an example domain level of system, according to some embodiments. FIG. 10 is a block diagram of an example computing system, according to some embodiments. DETAILED DESCRIPTION A system can implement a data architecture that includes a number of domains that perform different processing tasks such as, for example, fulfillment evaluation, risk compliance, IP address validation, electronic transaction fulfillment, etc. Each domain can include one or more micro-services, and the system can leverage the micro-services at each domain to perform the respective processing task. For example, the one or more micro-services at a particular domain can be utilized to validate a user authentication based on data input to the one or more micro-services at the domain. To utilize a domain, the system can generate a service request and send the service request to a corresponding domain. The service request can include, for example, request data including parameters of the request, user profile data, account data, personal data, device data, behavioral data, and the like. For example, the service request can include data in context of a transaction. In another example, the service request can include credit history data of a user. In some embodiments, the system can generate one or more service requests, and each service request can be allocated to a respective domain of the one or more domains of the system. For example, the system can generate multiple service requests for multiple domains of the system in response to a single transaction request from a user. The system can determine which of the one or more domains and corresponding micro-services to utilize to fulfill a request. In some embodiments, the domain can include a plurality of micro-services, and the domain can determine a subset of the micro-services to utilize to fulfill the request. In some embodiments, the domain can determine which micro-services to utilize to fulfill the service request based on a context of the service request. In this regard, the domain can obtain data corresponding to the service request and can apply the data, or one or more portions thereof, to one or more of the micro-services of the domain to fulfill the service request. For example, a domain for performing risk evaluations of electronic transactions can include 100 distinct micro-services, and the domain can utilize 80 of these micro-services to fulfill a service request for determining a risk level associated with fulfilling an electronic transaction. In some embodiments, the domain can determine