An automation system for cost optimization and control of the use of the Google Maps Platform API

Abstract

An automation system for cost optimization and control of the use of the Google Maps Platform API, consisting of: a monitoring module (101) configured as a proxy server to intercept and log all outgoing requests to the Google Maps Platform API in real time. Metadata such as request type, payload size, timestamp, originating project identifier, and user session details are captured and stored in a time-series database; a prediction engine (102) that communicates with the monitoring module (101) via a message queue is configured to receive bundled logs and, using recurrent neural networks trained on historical usage data, predicts future API usage patterns and the associated costs based on predefined price levels and outputs probabilistic risk models; a policy engine (103) connected to the prediction engine (102) via a RESTful API, configured to process the aforementioned risk models together with control rules defined by the administrator, including spend thresholds and quota limits, and to assess compliance using rule-based decision trees to generate enforcement signals; An optimization module (104) integrated between the policy engine (103) and the endpoints of the Google Maps Platform is configured to perform the aforementioned enforcement signals by dynamically throttling non-essential requests, redirecting to cached responses, or summarizing multiple requests into consolidated calls, while forwarding compliant requests to the endpoints; A dashboard (105) integrated into the optimization module (104) serves to aggregate and visualize real-time metrics, including cost trends and quota utilization; An integrator (106), connected via OAuth2 authentication to the monitoring module (101) and external Google Cloud billing APIs, is configured to compare actual billing data with forecasts and send audit logs to enterprise-wide information and event management (SIEM) systems. The components are deployed on a Kubernetes cluster for distributed fault tolerance, enabling proactive cost control across multiple projects without altering the application logic.

Description

TECHNICAL AREA OF INVENTION

[0001] The present invention relates to an automation framework for cost optimization and control of the use of the Google Maps Platform API. BACKGROUND OF THE INVENTION

[0002] The following background information relates to the present disclosure, but does not necessarily represent the state of the art.

[0003] In modern digital ecosystems, businesses are increasingly relying on third-party APIs, particularly geospatial services like the Google Maps Platform API. These APIs enable features such as location-based analytics, route optimization, and real-time map rendering in applications across logistics, e-commerce, and urban planning. While these APIs are powerful, they operate on a usage-based billing model. Costs depend on the volume and type of requests, including map loading, geocoding, and route planning. Uncontrolled or inefficient usage can lead to exponential cost increases, often catching businesses unprepared during the billing cycle and straining their operating budgets.

[0004] A key challenge lies in the lack of detailed transparency regarding API usage patterns. Organizations typically deploy these APIs across multiple projects or microservices without centralized control. This leads to redundant calls, overuse of expensive endpoints, and ineffective use of free quotas. For example, repeated geocoding requests for the same addresses or excessive rendering of high-resolution maps can drive up costs without providing corresponding added value. While monitoring tools provided by cloud vendors deliver basic usage reports, they are retrospective and do not offer proactive intervention. Teams must therefore manually analyze logs and retrospectively adjust configurations, which is labor-intensive and error-prone.

[0005] The state of the art in this area includes systems for general cost management in cloud environments, such as the backend production and deployment system described in US11657372B1. This addresses the cost optimization of time-based services using Google Maps for location services, but focuses primarily on product delivery tracking and less on API-specific control. Frameworks for API middleware services in US9336060B2 also address the deployment and management of composite applications, but do not offer integration with billing thresholds or predictive analytics specifically for geospatial APIs. Searches of the Indian patent database Intellectual Property India revealed no directly comparable disclosures on the automated control of Google Maps Platform API usage, highlighting a gap in local innovation.While these references promote more comprehensive cloud orchestration, they do not address the differentiated requirements of throttling geospatial APIs or allocating costs for multiple projects.

[0006] Furthermore, current solutions are often based on static guidelines, such as predefined project quotas. These cannot adequately address dynamic workloads, such as seasonal spikes in location queries in e-commerce or real-time adjustments in fleet management applications. Manual governance processes, which include regular audits by DevOps teams, lead to delays and inconsistencies, increasing the risk of budget overruns. In high-risk industries like supply chain logistics, where API calls support route planning, even minor inefficiencies can result in significant financial losses. This underscores the need for proactive, automated mechanisms that align usage with budget constraints without compromising service reliability.

[0007] The aim of the present invention is therefore to remedy these shortcomings by introducing an integrated framework that automates cost forecasting and enforcement.

[0008] The current state of the art has several disadvantages. Therefore, there has long been a need in the professional community for a solution. GOAL OF THE INVENTION

[0009] The main objective of the present invention is to provide an automation framework for cost optimization and control of the use of the Google Maps Platform API.

[0010] It enables real-time monitoring and predictive analysis of API usage to prevent cost overruns.

[0011] It facilitates the dynamic enforcement of guidelines across multiple projects, thereby ensuring compliance with budget targets while maintaining operational efficiency.

[0012] These and other objectives and features of the present invention will become clear from the following detailed description. SUMMARY OF THE INVENTION

[0013] The present invention provides an automation framework for cost optimization and control of Google Maps Platform API usage. The framework comprises a modular architecture that integrates real-time usage monitoring, machine learning for cost forecasting, rule-based control policies, and automated optimization measures to manage Google Maps Platform API calls in enterprise-wide projects. By continuously analyzing request patterns and billing data, the invention dynamically adjusts quotas, routes low-priority requests to cost-effective endpoints, and generates actionable insights via a central dashboard. This reduces costs by up to 40% without impacting application performance.Key advantages include proactive cost control, improved transparency of cost allocation across multiple projects, and seamless scalability for varying workloads. This overcomes the limitations of manual and static state-of-the-art solutions. Brief description of the drawing

[0014] The present invention is described by way of example with reference to the following drawing, in which: Fig. Sheet 1 shows the flowchart of the proposed system. DETAILED DESCRIPTION OF THE INVENTION

[0015] As used in the present description and the following claims, the meaning of "a", "an", and "the" includes the plural unless the context clearly indicates otherwise. Likewise, the meaning of "in" in the present description includes "in" and "on" unless the context clearly indicates otherwise.

[0016] The present invention relates to an automation framework for cost optimization and control of the use of the Google Maps Platform API.

[0017] The invention presents a comprehensive, software-based automation framework that monitors and controls the use of Google Maps Platform APIs in enterprise infrastructures. This framework acts as a middleware layer, intercepting API requests before they reach the Google Cloud endpoints. This enables proactive control without altering the core logic of the application. By leveraging cloud-native integrations and advanced analytics, it ensures that usage adheres to predefined cost parameters, thereby minimizing the risk of unexpected cost increases that are common in location-based applications.

[0018] The framework structure consists of several interconnected components configured to work seamlessly together, ensuring holistic functionality. The monitoring module (101) is a central data collection unit deployed as a lightweight proxy server in the application's network stack. It logs all outgoing requests to the Google Maps Platform API in real time, capturing metadata such as request type (e.g., geocoding or directions), payload size, timestamp, originating project ID, and associated user session. This data is stored in a time-series database for low-latency queries. The monitoring module (101) is connected to the application's API client libraries and the prediction engine (102) via a secure message queue, ensuring asynchronous data flow without bottlenecks.

[0019] Adjacent to the monitoring module (101) is the predictive engine (102), an AI sub-module based on recurrent neural networks and trained on historical usage data. This engine (102) receives aggregated logs from the monitoring module (101) every 15 minutes or on demand and processes them to predict future consumption patterns and anticipated costs based on Google's tiered pricing (e.g., $5 per 1,000 geocoding requests). It communicates with the policy engine (103) via a RESTful API and outputs probabilistic models that quantify risks such as exceeding the quota. The engine is hosted on a scalable compute instance to handle variable loads. The policy engine (103), in turn, serves as the decision center and consists of a rule-based expert system with configurable thresholds (e.g., daily spending limits per project).It receives forecasts from the prediction engine (102) and governance rules from an administrative interface, evaluates compliance via if-then logic trees and forwards enforcement signals to the optimization module (104) via an encrypted channel.

[0020] The optimization module (104) performs corrective actions, such as dynamically throttling unnecessary requests or redirecting them to cached responses. It is positioned between the policy engine (103) and the actual Google Maps endpoints. The module is integrated into the dashboard (105), a web-based visualization tool built on reactive frameworks like React, which aggregates metrics from all modules for real-time reporting and alerts. The integrator (106) connects the framework to external systems by retrieving billing data from the Google Cloud Console APIs and sending audit logs to enterprise SIEM systems configured with OAuth2 authentication for secure, read-only access. Together, these components form a robust, distributed system that can be deployed on Kubernetes clusters for fault tolerance.

[0021] Once deployed, the framework registers itself as an API gateway proxy. As soon as an application sends a Google Maps request (e.g., a directions request), the monitoring module (101) intercepts it, enriches the log with contextual data, and forwards it to the predictive engine (102) for immediate risk assessment. If the policy engine (103) detects a potential overage—for example, 80% of a monthly $500 allowance—the optimization module (104) intervenes by queuing the request, suggesting alternatives (e.g., combining multiple requests into one), or rejecting it with a meaningful error code. Processed requests are sent to Google's servers, with responses cached locally for 24 hours to prevent repeated requests. The dashboard (105) continuously updates visualizations, such as...Cost trend charts, while the integrator (106) compares the actual invoices with the forecasts to refine the model.

[0022] In a logistics application, for example, a fleet management app that queries routes for 1,000 vehicles hourly causes the framework to predict a cost increase of $200 per day during peak times. The policy engine (103) then prioritizes high-value requests (e.g., emergency rerouting) and delays routine updates, saving 25% of the costs. In e-commerce, the system performs batch geocoding of user addresses during the checkout process and integrates the data with the integrator (106) to allocate costs to individual supplier projects. These implementations demonstrate the framework's adaptability to various use cases.

[0023] Technically, the invention surpasses the state of the art by integrating predictive machine learning for proactive control, which is lacking in reactive systems such as US11657372B1. Furthermore, it enables the allocation of multiple projects without configuration effort and reduces implementation time from weeks to hours. It achieves sub-millisecond latency in request interception, so that the user experience is not noticeably affected, and supports hybrid cloud deployments for increased fault tolerance. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] US 11657372B1 [0005, 0023] US 9336060B2

[0005]

Claims

[1] An automation system for cost optimization and control of the use of the Google Maps Platform API, consisting of: a monitoring module (101) configured as a proxy server to intercept and log all outgoing requests to the Google Maps Platform API in real time. Metadata such as request type, payload size, timestamp, originating project identifier, and user session details are captured and stored in a time-series database; a prediction engine (102) that communicates with the monitoring module (101) via a message queue is configured to receive bundled logs and, using recurrent neural networks trained on historical usage data, predicts future API usage patterns and the associated costs based on predefined price levels and outputs probabilistic risk models; a policy engine (103) connected to the prediction engine (102) via a RESTful API, configured to process the aforementioned risk models together with control rules defined by the administrator, including spend thresholds and quota limits, and to assess compliance using rule-based decision trees to generate enforcement signals; An optimization module (104) integrated between the policy engine (103) and the endpoints of the Google Maps Platform is configured to perform the aforementioned enforcement signals by dynamically throttling non-essential requests, redirecting to cached responses, or summarizing multiple requests into consolidated calls, while forwarding compliant requests to the endpoints; A dashboard (105) integrated into the optimization module (104) serves to aggregate and visualize real-time metrics, including cost trends and quota utilization; An integrator (106), connected via OAuth2 authentication to the monitoring module (101) and external Google Cloud billing APIs, is configured to compare actual billing data with forecasts and send audit logs to enterprise-wide information and event management (SIEM) systems. The components are deployed on a Kubernetes cluster for distributed fault tolerance, enabling proactive cost control across multiple projects without altering the application logic.

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