Amdocs has been granted a patent for a no-code, model-agnostic, cloud-agnostic onboarding platform. This platform features a universal interface for onboarding and deploying machine learning models, utilizing structured query language (SQL) for input and output management, and enabling seamless integration across various environments. GlobalData’s report on Amdocs gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on Amdocs, Hybrid cloud mgmt was a key innovation area identified from patents. Amdocs's grant share as of June 2024 was 81%. Grant share is based on the ratio of number of grants to total number of patents.

No-code machine learning model onboarding and deployment platform

Source: United States Patent and Trademark Office (USPTO). Credit: Amdocs Ltd

The granted patent US12045698B1 introduces a non-transitory computer-readable medium that stores executable code for a Model-Agnostic Onboarding Workflow (MAOW) platform. This platform is designed as a no-code, model-agnostic, and cloud-agnostic solution, providing a universal unified interface that includes dictionaries for machine learning model input SQL statements and output destinations. The method outlined in the claims involves onboarding a chain of machine learning models, creating an onboarding configuration file, and establishing a machine learning model pipeline for sequential execution. The MAOW platform caches input and output data, facilitating the flow of data between models in the chain, and allows for deployment to various target environments, including cloud and on-premises settings.

Additionally, the patent details features such as the ability to interface with development environments, automatic no-code configuration, and customization of machine learning models. It specifies that the universal unified interface can be defined as a Unified Universal Interface Contract (UUIC), which manages both input and output between the models and the MAOW platform. The claims also highlight the platform's capability to validate model configurations through local prototyping and testing, as well as its use of a directed acyclic graph (DAG) for task management. Furthermore, the MAOW platform provides metadata for output database tables, which aids in the validation of output datasets, thereby expediting the productization phase of machine learning models.

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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.