US-20260127505-A1 - COORDINATING COMPLEX INTERACTIONS OVER COMPUTER NETWORKS USING MACHINE LEARNING

US20260127505A1US 20260127505 A1US20260127505 A1US 20260127505A1US-20260127505-A1

Abstract

Methods, systems, and apparatus, including computer-readable media, for coordinating complex interactions over computer networks using machine learning. In some implementations, a system receives a request from a remote system over a communication network. The request identifies a particular entity and indicates characteristics of a requested or proposed interaction. The system accesses a profile for the particular entity, where the profile comprises profile data that indicates patterns or characteristics determined from records of previous interactions. The system generates an output from each of multiple machine learning models that have each been trained to predict a likelihood of an outcome. The system generates a prediction for the outcome based on the outputs of the machine learning models. Based on the prediction the system selectively reserves a resource or service from one or more service provider systems over the communication network.

Inventors

Veysel Sinan Geylani
Mirela Dimofte
Umut Sevin
Elif Beyza Peker
Abdullah Avcu
Betül Salt Çelik
Rasha Salim
Vinayakk Garg
Kevin Medri

Assignees

Touch Risk GmbH

Dates

Publication Date: 20260507
Application Date: 20251105
Priority Date: 20241105

Claims (20)

1 . A method performed by one or more computers, the method comprising: receiving, by the one or more computers, a request from a remote system over a communication network, wherein the request identifies a particular entity and indicates characteristics of a requested or proposed interaction involving the particular entity; accessing, by the one or more computers, a profile for the particular entity, wherein the profile comprises profile data that indicates patterns or characteristics of activity of the particular entity that were determined from records of previous interactions of the entity or of other entities; generating, by the one or more computers, an output from each of multiple machine learning models that have each been trained to predict a likelihood of an outcome, wherein each output indicates a likelihood of the outcome for the requested or proposed interaction, and wherein each output is generated by the corresponding machine learning model based on input indicating (i) characteristics of the requested or proposed interaction and (ii) information from the profile for the particular entity; generating, by the one or more computers, a prediction for the outcome based on the outputs of the machine learning models; and based on the prediction generated based on the outputs of the machine learning models, selectively reserving, by the one or more computers, a resource or service from one or more service provider systems over the communication network and providing a response to the remote system.
2 . The method of claim 1 , wherein the machine learning models comprise at least one of a neural network, a reinforcement learning model, a support vector machine, a classifier, a regression model, a clustering model, a decision tree, an anomaly detection model, a random forest model, a genetic algorithm, a Bayesian model, or a Gaussian mixture model.
3 . The method of claim 1 , further comprising determining that the prediction satisfies one or more predetermined criteria for providing the resource or service for the requested or proposed interaction; wherein selectively reserving the resource or service from the one or more service provider systems comprises: in response to determining that the prediction satisfies the one or more predetermined criteria, communicating with the one or more service provider systems over the communication network to cause the resource or service to be provided for the requested or proposed interaction.
4 . The method of claim 3 , wherein communicating with the one or more service provider systems comprises providing, over the communication network, (i) information from the request indicating the characteristics of the requested or proposed interaction, and (ii) an indication of the prediction or an indication that the prediction satisfies the one or more predetermined criteria.
5 . The method of claim 1 , further comprising determining that the prediction does not satisfy one or more predetermined criteria for providing the resource or service for the requested or proposed interaction; wherein selectively reserving the resource or service from the one or more service provider systems comprises: in response to determining that the prediction does not satisfy the one or more predetermined criteria, (i) not reserving the resource or service for the requested or proposed interaction, and (ii) sending a message to the remote system indicating that the resource or service is not provided for the requested or proposed interaction.
6 . The method of claim 1 , wherein selectively reserving, by the one or more computers, the resource or service from one or more service provider systems comprises coordinating with each of multiple service providers such that the multiple service providers respectively provide different resources or services to complete the requested or proposed interaction.
7 . The method of claim 6 , wherein a first resource or service provided by at least one of the multiple service providers is provided conditionally based on availability of a second resource or service from another of the multiple service providers; and wherein coordinating with each of multiple service providers comprises obtaining confirmation of availability of the second resource or service to enable the first resource or service to be provided.
8 . The method of claim 1 , wherein selectively reserving, by the one or more computers, the resource or service from one or more service provider systems comprises: coordinating with a first service provider system to provide a first type of resource or service and a second service provider system to provide a second type of resource or service, wherein the second type of resource or service is different from the first type of resource or service, and wherein providing the second type resource or service of second is conditioned on providing the first type of resource or service, wherein the coordinating comprises: providing, to the first service provider system, prediction data indicating the prediction for the outcome for the proposed or requested interaction or an indication that the prediction satisfies one or more predetermined criteria; after providing the prediction data, receiving, from the first service provider system, a first confirmation message indicating that the first type of resource or service is allocated for the requested or proposed interaction; providing, to the second service provider system, an indication that the first service is allocated for the proposed or requested interaction; and after providing the indication to the second service provider system, receiving, from the second service provider system, a confirmation that the second type of resource service is reserved or allocated for the requested or proposed interaction.
9 . The method of claim 1 , wherein the output from each of the machine learning models comprises at least one of a probability score, a classification result, a detected anomaly, or a confidence score.
10 . The method of claim 1 , wherein generating the prediction for the outcome based on the outputs of the machine learning models comprises: generating the prediction based on an ensemble of the machine learning models, including determining an amount or proportion of the outputs that indicate that a likelihood of the outcome exceeds a predetermined threshold or represents an anomaly.
11 . The method of claim 1 , wherein generating the prediction for the outcome based on the outputs of the machine learning models comprises generating a combined score based on the outputs of the multiple machine learning models.
12 . The method of claim 11 , wherein generating the combined score comprises determining a weighted combination in which different outputs have different levels of contribution or influence to the combined score based on previous performance of the machine learning models.
13 . The method of claim 1 , wherein the output from each of the machine learning models comprises an importance score for each of multiple different factors, wherein the importance scores in the output of a machine learning model indicate relative levels of contribution of the corresponding factors to a prediction output of the machine learning model.
14 . The method of claim 1 , wherein each of the machine learning models is configured to receive input indicating a value for each of multiple different features; and wherein generating the output from each of the machine learning models comprises generating, for each of the multiple machine learning models, (i) a prediction output indicating a likelihood of the outcome or a likelihood of an anomaly in the outcome and (iii) an importance score for each of the multiple different features, wherein each importance score indicates a level of influence or impact of the corresponding feature on the prediction output of the machine learning model.
15 . The method of claim 1 , further comprising: receiving multiple requests for different types of interactions or for different entities; and using a dynamic ensemble of models to generate predictions for the multiple requests, including, for each of the multiple requests, using a different subset of machine learning models based on one or more of the entity involved, the type of interaction, characteristics of the interaction, or content of a profile of an entity involved in the interaction or one or more associated entities.
16 . The method of claim 1 , further comprising: storing a plurality of machine learning models that have each been trained to predict a likelihood of the outcome; and selecting a subset of the machine learning models in the plurality of machine learning models for the requested or proposed interaction; wherein the generating the output from each of the multiple machine learning models comprises generating an output from each of the machine learning models in the subset of the machine learning models.
17 . The method of claim 16 , further comprising determining performance measures indicating accuracy of the outputs of the different machine learning models in the plurality of machine learning models in different contexts; wherein selecting the subset of the machine learning models comprises selecting, from the plurality of machine learning models, a subset of the machine learning models that the performance measures indicate to have the highest performance in a context corresponding to the requested or proposed interaction.
18 . The method of claim 16 , further comprising: accessing a selection model that has been trained to predict or score the relevance of different machine learning models for different contexts; and generating, for each of the machine learning models in the plurality of machine learning models, an output of the selection model indicating a relevance of the machine learning model to a context corresponding to the requested or proposed interaction; wherein selecting the subset of the machine learning models in the plurality of machine learning models for the requested or proposed interaction comprises selecting the subset based on the outputs of the selection model.
19 . A system comprising: one or more computers; and one or more computer-readable media storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving, by the one or more computers, a request from a remote system over a communication network, wherein the request identifies a particular entity and indicates characteristics of a requested or proposed interaction involving the particular entity; accessing, by the one or more computers, a profile for the particular entity, wherein the profile comprises profile data that indicates patterns or characteristics of activity of the particular entity that were determined from records of previous interactions of the entity or of other entities; generating, by the one or more computers, an output from each of multiple machine learning models that have each been trained to predict a likelihood of an outcome, wherein each output indicates a likelihood of the outcome for the requested or proposed interaction, and wherein each output is generated by the corresponding machine learning model based on input indicating (i) characteristics of the requested or proposed interaction and (ii) information from the profile for the particular entity; generating, by the one or more computers, a prediction for the outcome based on the outputs of the machine learning models; and based on the prediction generated based on the outputs of the machine learning models, selectively reserving, by the one or more computers, a resource or service from one or more service provider systems over the communication network and providing a response to the remote system.
20 . One or more computer-readable media storing instructions that are operable, when executed by one or more computers, to cause the one or more computers perform the operations comprising: receiving, by the one or more computers, a request from a remote system over a communication network, wherein the request identifies a particular entity and indicates characteristics of a requested or proposed interaction involving the particular entity; accessing, by the one or more computers, a profile for the particular entity, wherein the profile comprises profile data that indicates patterns or characteristics of activity of the particular entity that were determined from records of previous interactions of the entity or of other entities; generating, by the one or more computers, an output from each of multiple machine learning models that have each been trained to predict a likelihood of an outcome, wherein each output indicates a likelihood of the outcome for the requested or proposed interaction, and wherein each output is generated by the corresponding machine learning model based on input indicating (i) characteristics of the requested or proposed interaction and (ii) information from the profile for the particular entity; generating, by the one or more computers, a prediction for the outcome based on the outputs of the machine learning models; and based on the prediction generated based on the outputs of the machine learning models, selectively reserving, by the one or more computers, a resource or service from one or more service provider systems over the communication network and providing a response to the remote system.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority under Section 119(a) to Indian provisional patent application Ser. No. 202411084617, filed in India on Nov. 5, 2024, which is incorporated by reference herein. BACKGROUND The present specification relates to coordinating interactions over computer networks, including using machine learning to facilitate interactions among multiple systems over the Internet. SUMMARY In some implementations, a computer system is configured to use machine learning to evaluate and manage the allocation of resources among client devices, servers, and other systems that interact over computer networks such as the Internet. For example, an artificial intelligence or machine learning (AI/ML) platform can be configured to gather monitoring data indicating the interactions of many systems over time. With the monitoring data, the AI/ML platform trains multiple machine learning models based on the examples of interactions, so that the models learn the patterns of activity of various types of systems and in various different contexts. The AI/ML platform can also create profiles for individual systems or entities and update the profiles over time to track the status and performance capability of these systems or entities. Using the machine learning models and the profiles, the AI/ML platform can then predict the likelihoods of various outcomes as specific interactions or transactions occur. The AI/ML platform can then selectively establish connections or selectively reserve resources based on the predicted likelihoods, on an interaction-by-interaction or transaction-by-transaction basis. As an example, if a prediction made using the machine learning models indicates that a requested interaction is likely to be performed successfully (e.g., with certain performance criteria satisfied), the AI/ML platform can interact with one or more service providers to allocate resources or provide services to complete the requested interaction. On the other hand, if a prediction indicates that the interaction is not likely be performed successfully, and thus that resources or services provided would be ineffective or wasted, the AI/ML platform can decline to have resources or services reserved or allocated for the interaction. As a result, the AI/ML platform can facilitate and accelerate interactions that are predicted to succeed, as well as block or prevent resources from being expended unnecessarily for an interaction that is unlikely to succeed. Even if the AI/ML platform declines to allocate resources or services for a requested interaction, the system initiating the interaction receives fast feedback that resources or services are unavailable, which allows the system to quickly source resources through an alternative channel. Client systems that request resources and service providers benefit from quick feedback that is often provided in real time or near real time. Service providers can use the AI/ML platform to more accurately allocate limited resources or finite capacity to interactions with predicted favorable outcomes, and by avoiding providing services that would result in inefficiency, waste, errors, or other poor outcomes. In many cases, when a user or client device initiates an interaction over the Internet, a single request may result in many different systems contributing or cooperating to fulfill the request. For example, for a given request, multiple different servers, databases, networks, and other elements in the network may each participate in fulfilling the request. The interactions that may be needed are varied and complex, and some interactions may be conditioned on or be dependent on other interactions being performed. As an additional complication, many entities and systems that interact over the Internet are not trusted, and the performance characteristics (e.g., timing, accuracy, reliability, etc.) can vary significantly. The AI/ML platform can use its predictions to improve interactions among systems over computer networks, especially where resources or services from multiple different service providers are needed. In many cases, there are dependencies where one system or entity does not or cannot provide a resource or service unless or until another system or entity provides a different resource or service. The AI/ML platform can use its machine learning predictions to manage the uncertainty inherent in multi-system interactions, giving increased confidence to service providers that particular interactions or transactions can be completed successfully. In other words, out of a set of requests or possible interactions, the AI/ML platform can use its predictions to identify the requests or interactions most likely to succeed, allowing service providers to allocate limited capacity for those requests. In many cases, this confidence and selectivity enables service providers to more readily provide the resources and services that further ser