AI Workflow Platform for Multi-Format Data Processing
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Solution Overview
Problem
Current AI platforms face challenges in handling the complexity of multiple data formats, requiring significant time, manpower, and expertise for data preparation, visualization, labeling, and machine learning tasks, especially with the scarcity of tools for end-to-end AI model development and deployment in enterprises.
Innovation Solution
A comprehensive AI workflow platform that receives and indexes structured, semi-structured, and unstructured data, automatically schedules and uploads it to a database, visualizes and cleanses the data, labels and annotates it, and builds AI models based on user input and predefined rules, enabling seamless integration and management of AI models across various formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple data formats (structured, semi-structured, unstructured) are handled manually through separate tools and processes, then data processing can be performed, but the complexity of the system increases and requires significant time, manpower, and expertise
Solution Approach 1:
The patent implements a universal data processing platform that can handle multiple data formats (structured, semi-structured, and unstructured data) through a single integrated system. The platform uses common processing pipelines, visualization tools, and machine learning algorithms that work across all data types, eliminating the need for separate specialized tools for each format and thereby reducing overall system complexity.
2Reliability
If data preparation, cleaning, and integration are performed manually by data scientists, then data quality can be ensured, but 80-90% of time is spent on these tasks
Solution Approach 1:
The patent implements automated self-service mechanisms for data preparation, cleaning, and integration. The system automatically performs data validation, cleaning, and transformation operations without requiring manual intervention from data scientists. This includes automated detection of data quality issues, automatic correction of common errors, and self-organizing of data into appropriate formats, thereby ensuring data quality while dramatically reducing the time spent on preparation tasks.
Solution Approach 2:
The patent applies preliminary action by pre-processing and pre-validating data as it enters the system. Data is automatically cleaned, transformed, and validated before reaching the analysis stage, preparing it in advance for machine learning operations. This preliminary data preparation reduces the need for extensive manual cleaning and integration work later in the process.
3Measurement precision
If deep learning models are used for image and text processing, then accuracy improves, but large quantities of labeled training data are required
Solution Approach 1:
The patent uses semi-structured data as an intermediary representation that bridges unstructured data (images, text) and structured data formats. By converting unstructured data into semi-structured representations with embedded metadata and relationships, the system enables machine learning models to process complex data types without requiring vast amounts of manually labeled training data, thus maintaining accuracy while reducing data requirements.
4Productivity
If specialized knowledge in specific fields (Computer Vision, NLP, time-series) is required for data science tasks, then task performance improves, but the barrier to entry increases and requires multiple software products and experts
Solution Approach 1:
The patent segments specialized data science tasks into modular, reusable components that can be independently developed and combined. Each data type (images, text, time-series) has dedicated processing modules with specialized algorithms, but these modules expose standardized interfaces that can be orchestrated through a unified platform. This allows specialized functionality to be maintained while providing an easier-to-use integrated system that reduces the need for multiple separate software products and deep specialized knowledge across all areas.
Data Source
AI summary
A method of data mining and AI workflow platform for structured and unstructured data is described. The method comprises receiving data from a data source, wherein the data comprises at least one data format of at least one of structured data, semi-structured data and unstructured data; indexing and analyzing the received data; scheduling and uploading automatically the data to a database as per the indexing; visualizing the data and determining at least one of the structured data, the semi-structured data, and the unstructured data from the data uploaded; cleansing and filtering the data based on at least one of an input from a user, and a predefined rule; labeling and annotating seamlessly the data available in the database; and building an artificial intelligence (AI) model based on at least one of the data available in the database, the input from the user, and the predefined rule.