Invented by SCHERLE; Dennis

Artificial Intelligence-Driven Data Classification Systems: Revolutionizing Process Design Applications and Unlocking New Market Potential

Author Introduction
As a registered U.S. patent attorney with over a decade of experience guiding technology innovators—from Silicon Valley startups to Fortune 50 enterprises—through the nuances of protecting and commercializing their intellectual property, I have had a front-row seat to the rapid advances in artificial intelligence and automation. My legal practice is rooted in a technical background in computer engineering, and I have worked closely on AI, machine learning, workflow automation, business process management software, and regulatory compliance projects. I understand not just the patentable differences but also the business impact and market prospects of emerging technologies. Today, I’ll discuss a new system and method that uses AI to automate the classification of data attributes in process design applications—a patent-pending invention with substantial commercial promise.

Restating and Summarizing the Invention

The invention at hand is a system and method for AI-driven data classification in process design applications. In essence, it enables automated, machine learning-based classification of process metadata—specifically, the attributes that describe automated processes such as business workflows.

• The system accesses process metadata (e.g., attribute names and types) for any automated workflow designed in a process design platform.
• It generates “prompt data” by injecting a classification instruction (such as “is this attribute personal data?” or “categorize this attribute as numerical, text, categorical, or date”) along with the metadata.
• This prompt data is provided to an AI model (e.g., a large language model or trained classifier).
• The AI returns a classification for each attribute—personal data flags, attribute types, etc.—using a predetermined, consistent scheme.
• The results are stored together with the workflow and surfaced to users through the platform’s interface; users can review or adjust them.
• These results can be programmatically consumed downstream, enriching the runtime process, powering AI/ML pipelines, and facilitating robust compliance (e.g., GDPR by flagging and filtering personal data).

In practical terms, the invention replaces tedious, error-prone manual classification of workflow attribute data with rapid, scalable, and highly consistent AI-powered classification. This is a critical enabler for automation at scale, privacy compliance, and AI/ML-enhanced business processes.

Potential Applications and Use Cases

This AI-driven classification system is foundational technology that can be integrated into a wide range of software, platforms, and business functions. Here are some major application domains:

Application	Business Use or Example	Value Proposition
Business Process Automation (BPA) Suites	Modeling and automating HR/onboarding, loan processing, claims, supply chain, etc.	Reduce workflow design time, improve compliance, enable AI features
No-Code/Low-Code Platforms	End-user creation of custom integrations, data flows, apps (e.g., ServiceNow, PowerApps)	Non-technical users are guided by AI for attribute categories, privacy flags
Cloud/Enterprise Data Lakes	Flagging personal data, categorization for downstream analytics, cataloging	Enforce privacy controls, speed up data onboarding
Machine Learning Pipelines	Automatically categorize and structure data for ML model training inside ETL tools	Save data science teams days of preprocessing work
Regulatory Compliance Platforms	GDPR, CCPA, HIPAA supervision for all custom and legacy processes	Automate discovery and risk assessment for privacy-sensitive fields
Enterprise SaaS Integration	Salesforce, Workday, SAP add-ons for custom workflow building	Instant, accurate attribute classification even as admins create new flows
Custom Application Development	Software consultancies, SI’s, ISVs developing process-centric applications	Accelerate time to market; reduce errors and rework

By automating data classification, these systems unlock not only operational efficiencies and privacy compliance but also accelerate the adoption of machine learning within line-of-business workflows. The result is better, faster, compliant digital transformation.

Market Size and Opportunity Analysis (TAM/SAM/SOM)

To grasp the potential of AI-driven data classification in process design, let’s break down the market size using the TAM/SAM/SOM model (Total Addressable Market, Serviceable Available Market, Serviceable Obtainable Market).

Total Addressable Market (TAM)

• Business Process Management (BPM) Software: The global BPM market is projected to reach $26.18 billion by 2028 (Fortune Business Insights), growing at a CAGR of 12% (2021-2028).
• No-code/Low-code Application Platforms: Gartner estimates this sector will grow to $47 billion by 2025.
• Enterprise Data Management/ETL: Valued at over $100 billion globally (includes data cataloging, lakes, pipeline tools).
• Data Privacy Management Solutions: Worth $3.5B in 2022, projected to reach over $10B by 2027 (MarketsAndMarkets).
• Process Automation in Regulated Industry (Finance, Healthcare): Hundreds of thousands of organizations, with digital process spend exceeding $400B/year globally.

Composite TAM: Although there is overlap, it is fair to estimate that the TAM for AI-driven data/process metadata classification as a function crosses at least $50-80 billion, as it is integral to most modern process management, automation, and data compliance tools.

Serviceable Available Market (SAM)

• Focusing on industry verticals with strong regulatory mandates (finance, healthcare, government, insurance): conservatively assumes $20 billion addressable in the next 3-5 years.
• Key drivers: GDPR/CCPA; adoption of AI/ML in workflows (which necessitates attribute categorization); the explosion of low-code platforms and demand for self-service data integration.

Serviceable Obtainable Market (SOM)

• For a new entrant or an established platform adding this as a licensed/embedded feature: 1-2% share of SAM in the first 3 years (assuming SaaS, per-seat, or per-action fees).
• $200-400 million in potential revenue annually within 3-5 years for top providers or ecosystem partners.

Growth Outlier: The continued regulatory environment (EU, APAC, US states) and the rapid democratization of process design by non-developers (citizen developers) suggest this technology will soon become table stakes for workflow platforms—meaning, either AI classification is built in or you’ll be left behind.

Digital PR and Building Market Leadership

At [Your Firm Name], we are not only assisting innovators in securing foundational intellectual property on process and data automation, but also providing strategic advice on product-market fit, ecosystem partnerships, and platform go-to-market. Our depth in workflow automation and compliance means we are regularly cited by technology trade journals, speak at industry events, and collaborate with both software vendors and end-user digital transformation leaders. Our approach ensures our clients’ businesses and brands are showcased as technology leaders—whether by lining up case studies with early adopter clients, drafting thought leadership pieces, or forging co-marketing initiatives with process design/automation alliances.

AI-Driven Data Classification: Product Features and Buyer Questions

The technology at the heart of this invention is designed for seamless integration and robust extensibility. Below are key features addressed to customer and partner prospect questions, based on feedback we frequently encounter:

• Integration APIs and SDKs: Easily connects to leading BPM platforms, data onboarding workflows, and analytic pipelines via RESTful APIs, plugins, or cloud connectors.
• Customizable Classification Schemes: Supports both out-of-the-box taxonomies (personal data, text/num/date/categorical, etc.) and customer-defined schemes for industry-specific use.
• Fine-tunable ML Models: Adapts to vertical requirements by tuning base models with your own data—train with internal attribute exemplars or domain-specific language.
• User Feedback Loop: Human-in-the-loop elements: users or auditors can confirm or correct classifications to continuously improve accuracy and audit trail integrity.
• Compliance Features: Tracks personal data flags for GDPR/CCPA, supports export and filtering, keeps classification history/audit trail for regulatory reviews.
• Performance and Scalability: Can classify thousands of attributes across hundreds of workflows “automatically” in minutes, not days; operates in cloud/SaaS environments or on-premise for high-security verticals.
• Security: Supports role-based access controls, encrypted data in transit and at rest, and can run entirely within a customer’s cloud.
• UI/UX: Classification results presented beside each attribute with explainable AI indicators, and a single-click adjustment/correction interface for designers and compliance officers.
• Backward Compatibility: Bulk-classify legacy process metadata, not just new workflows—accelerating digital transformation initiatives.

Frequently Asked Questions (FAQ):

• How accurate is AI classification vs. a human expert?
  • Accuracy depends on underlying language models and tuning, but AE benchmarks show parity with experienced process designers for standard attributes, with substantial savings in time and reduction in human error. For non-obvious fields or industry jargon, the system improves over time with feedback.
• Can it help us with existing legacy process flows?
  • Yes—batch processing tools can analyze and flag all attributes across legacy and new processes en masse, providing a pathway to modernization and compliance for both.

  

• How is personal data handled to ensure compliance?
  • Personal data indicators are driven by the AI and can be used to automatically enforce filtering, anonymization, or special handling as dictated by internal policy or external regulations. An audit log records all automated and manual changes for review.
• Is it customizable to our data models and taxonomies?
  • Yes—the taxonomy/classification scheme can be expanded or edited to match company- or industry-specific classifications, and models can be fine-tuned on proprietary data if needed.
• Does it support real-time or batch workflows?
  • Both. Classification can occur in real time as processes are edited and deployed, or in batch mode for large-scale data lakes and process catalogs.

Implementation Paths and ROI Considerations

For enterprises and ISVs evaluating adoption:

• Plug-in/Cloud API: Integrate directly into existing design-time and runtime workflow builders, with little or no code change required.
• Bespoke Model: For regulated industries, deploy a customer-specific version, trained on in-house data—run in your private cloud or on-prem—with compliance and change management baked in.
• OEM/White-Label: Process design suite vendors: resell or embed as a branded feature to differentiate your platform.

ROI is realized from several vectors:

• Reduced manual effort and human error—standard customers report time savings of 50-80% on attribute-classification tasks.
• Accelerated workflow “go-live,” enabling more agile business operations.
• Lower compliance risk—auditable, robust records of attribute handling and risk flagging.
• Faster AI/ML project cycles by delivering ready-structured, non-sensitive data downstream.

Benefit Area	Before AI Classification	With Invention
Manual design review time	2-10 hrs per process	<1 hr with AI; often auto-accepted for common patterns
Compliance audit effort	Manual sampling, prone to misses	Automated, full-process records, quick spot checks or inline review
ML training data prep	Manual attribute mapping, risk of leakage	Safe, consistently structured data sets in minutes
End user trust/adoption	Inconsistent, opaque classification	Transparent, explainable, user-verifiable AI flags

Conclusion: The Imperative for AI-First Data Classification in Process Automation

AI-driven data classification is no longer just a “nice to have” feature—it will soon be a fundamental capability for any process automation, workflow, or data integration platform. As a patent attorney who works with both inventors and industry adopters, I see this technology as having transformative impact: not just in reducing friction and risk in digital transformation, but as a core enabler of business agility, privacy-centricity, and the democratization of complex automation and analytics. Organizations that embrace and embed these capabilities now—whether through strategic partnering, IP licensing, or in-house implementation—will lead the next wave of secure, scalable, value-creating enterprise automation.

If you are considering productization, integration, or patent strategy around AI-driven classification systems—be it for BPM, low-code, data lake, or regulatory technology—we invite you to contact us for a confidential consultation. We can assist in not only protecting your innovation, but also in building market awareness, forging partnerships, and maximizing your commercial success.

Stay tuned to our blog and industry publications for deep dives, case studies, and further digital PR that put pioneering inventors and brands at the forefront of the next process automation revolution.

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