Accenture’s patented AI-based data processing system analyzes current data quality and determines if impacted or incremental data should be provided to consumers. Machine learning models are used to make predictions and can be automatically retrained. The system also determines the resources needed for data upload based on different configurations. GlobalData’s report on Accenture gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Accenture, was a key innovation area identified from patents. Accenture's grant share as of February 2024 was 59%. Grant share is based on the ratio of number of grants to total number of patents.
Ai-based system for processing and uploading data to consumers
A recently granted patent (Publication Number: US11928124B2) discloses an Artificial Intelligence (AI) based data processing system designed to efficiently handle and analyze current data for uploading to a data consumer. The system utilizes machine learning (ML) models to aggregate data into clusters, generate real-time patterns, and determine the quality of the aggregated data for uploading. It includes features such as identifying differences in current data compared to historical data, uploading impacted or incremental data based on these variances, and dynamically configuring processing units for efficient data loading. Additionally, the system can provide feedback on ML model accuracy, trigger retraining of models, and optimize memory utilization for cloud processors based on data volume.
Furthermore, the patent describes a method for processing data that involves partitioning current data, identifying anomalies, aggregating data at multiple levels, obtaining predictions from ML models, comparing predictions with historical data, and retraining models as needed. The method also includes determining processing units required for uploading incremental data, based on various factors such as data volume and historical comparisons. It emphasizes the importance of continuous monitoring of ML model accuracy, automatic retraining based on error trends, and selecting the most suitable ML model for retraining. The method aims to ensure data quality criteria are met before uploading, considering differences in current and historical data, and efficiently managing resources for data loading operations.
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