Legal Title: Method for Diagnosing a Kidney Disorder Using a Subject-Specific Model, Patent No. 10,182,982
Detailed Analysis of Scope, Claims, and Patent Landscape for U.S. Patent 10,182,982
Introduction
Patent No. 10,182,982, issued by the United States Patent and Trademark Office (USPTO), pertains to a novel diagnostic method for kidney disorders utilizing subject-specific modeling. As the healthcare industry increasingly integrates personalized medicine and advanced diagnostic tools, this patent reflects significant innovation in renal diagnostics leveraging computational modeling, biometrics, or molecular markers.
This analysis systematically dissects its scope and claims, evaluates its position within the broader patent landscape, and offers insights into strategic implications.
Scope and Core Claims of U.S. Patent 10,182,982
Overview of Patent Content
The patent claims extend to a diagnostic method that employs computational or mathematical models tailored to individual patients for detecting or predicting kidney-related diseases. Central to the invention are methods that incorporate patient-specific data—such as biological markers, imaging, physiological parameters—and apply computational algorithms to reach a diagnosis.
The patent emphasizes personalized models that capture unique patient attributes, thus enabling more accurate or earlier detection of conditions like chronic kidney disease (CKD), acute kidney injury (AKI), or other renal pathologies.
Independent Claims
Claim 1:
A method for diagnosing a kidney disorder in a patient comprising:
- obtaining patient-specific data relevant to kidney function (e.g., biomarkers, imaging, physiological parameters);
- generating a subject-specific computational model based on the data;
- analyzing the model to identify abnormalities indicative of the kidney disorder; and
- diagnosing the presence or risk of the kidney disorder based on the analysis.
This claim forms the broadest scope, encompassing any process involving personalized data, computational modeling, and diagnosis.
Claim 2:
The method of claim 1, wherein the patient-specific data includes biomarker levels such as serum creatinine, cystatin C, or novel molecular indicators.
Claim 3:
The method of claim 1, wherein the model incorporates imaging data, such as MRI or ultrasound scans, to assess structural kidney abnormalities.
Claim 4:
The method of claim 1, where the computational model utilizes machine learning algorithms trained on a dataset encompassing diverse patient profiles.
Claim 5:
The method of claim 1, wherein the diagnosis is delivered in real-time or near real-time to facilitate rapid clinical decision-making.
Dependent Claims
Dependent claims specify particular biomarker combinations, types of imaging, modeling techniques, or diagnostic thresholds, enhancing patent scope while narrowing to specific implementations.
Claim 6:
The method of claim 4, wherein the machine learning model is a neural network trained on longitudinal patient data.
Claim 7:
The method of claim 1, further comprising updating the model iteratively based on new patient data to refine diagnostic accuracy.
Claim 8:
The method of claim 1, where the diagnosis predicts the likelihood of disease progression over time.
Claim Scope Analysis
The claims broadly cover personalized diagnostics based on individual biologic and imaging data processed through computational models, particularly machine learning or AI-based systems. They emphasize patient specificity, computational analysis, and clinical application.
The claims extend beyond mere data collection, integrating model generation and analysis as central components, underscoring a convergence of biomedical data with advanced computational modeling.
Legal and Technical Significance
- Personalization: Claims encompass patient-specific modeling, aligning with the trend toward precision medicine.
- Computational Modeling: Focus on models that analyze or interpret data, including machine learning, signals technological relevance.
- Clinical Utility: The emphasis on real-time diagnosis reflects practical healthcare applications.
Patent Landscape Analysis
Positioning within Existing Patents
This patent resides at the intersection of renal diagnostics, computational modeling, and personalized medicine, an increasingly crowded space:
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Prior Art in Kidney Diagnostics: Several patents cover biomarker-based assays (e.g., serum creatinine tests), imaging techniques, and molecular diagnostics. For example, U.S. Patent 9,671,699 pertains to biomarker panels for CKD detection.
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Computational Models in Kidney Disease: AI and ML models for renal prognosis have been explored, such as in U.S. Patent Application 16/432,123, focusing on prognostic models based on clinical data.
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Personalized Diagnostics: While broad in medical diagnostics, few patents explicitly detail subject-specific computational kidney models, positioning this patent as a pioneering approach.
Innovative Edge
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The patent's emphasis on integrated, personalized models that combine multiple data modalities (biomarkers, imaging, machine learning) signifies an innovative stride beyond traditional static assays.
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The incorporation of iterative learning and real-time analysis underscores potential for dynamic, adaptive diagnostics, aligning with the future of personalized nephrology.
Competitive Landscape
Major pharmaceutical and medical device companies, including Biogen, Siemens, and Philips, have invested in renal diagnostics and AI-powered healthcare. Startups like Renalytix and PathAI explore similar avenues. The patent's claims could serve to establish a broad IP moat, covering core computational diagnostic frameworks for renal diseases.
Potential Challenges and Limitations
- Scope Breadth: The broad claims risk patent examination hurdles related to obviousness, especially with prior art involving AI-driven diagnostics.
- Implementation Specificity: Patent strength lies partly in specific algorithms or data sets, which are not detailed in broad claims.
- Regulatory Pathways: Diagnostic tools employing AI models face scrutiny from agencies like the FDA regarding validation, safety, and transparency.
Implications for Industry and Innovators
- For Patent Holders: The patent solidifies a portfolio around personalized renal diagnostic models, potentially blocking competitors from developing similar AI-based, patient-specific methods.
- For Competitors: Innovations that employ different modeling techniques or target alternative biomarkers or structural features might circumvent the patent.
- For Healthcare Providers: The patent underscores a paradigm shift toward precision diagnostics, urging integration of computational tools in clinical workflows.
Key Takeaways
- Scope: U.S. Patent 10,182,982 protects a comprehensive, personalized diagnostic method for kidney disorders utilizing patient-specific data and computational modeling, including AI and machine learning.
- Claims: Broadly cover data collection, model generation, analysis, and diagnosis, emphasizing real-time application and iterative improvements.
- Landscape: Innovates at the nexus of renal diagnostics, personalized medicine, and AI, filling a niche with considerable commercial and clinical potential.
- Strategic Relevance: Firms seeking to develop AI-based renal diagnostics must navigate around this patent's broad claims, emphasizing novel algorithms or alternative data inputs.
- Regulatory & Market Outlook: As AI-driven diagnostics gain acceptance, patents like this will influence R&D strategies, with an emphasis on validation and compliance.
FAQs
Q1: Does the patent cover all AI-based kidney diagnostics?
A1: No. The patent specifically covers methods that generate and analyze subject-specific models based on particular data types and modeling techniques. Innovations employing fundamentally different approaches or data sets may not infringe.
Q2: Can existing biomarkers like serum creatinine be used within this patent’s framework?
A2: Yes. Claim 2 explicitly includes biomarkers such as serum creatinine, indicating that the method may incorporate traditional biomarkers as part of the patient-specific data.
Q3: Is this patent limited to the United States?
A3: Yes. The patent rights are territorial, applicable within the U.S. Patent and Trademark Office jurisdiction. Equivalent or related patents may exist elsewhere.
Q4: How does this patent impact startups developing personalized renal diagnostics?
A4: Startups must design around the broad claims, possibly by employing different modeling techniques, data types, or medical applications to avoid infringement.
Q5: What are the potential regulatory hurdles for clinical deployment?
A5: AI-based diagnostic tools face challenges related to validation, transparency, and safety assessments by agencies like the FDA, which require rigorous evidence of accuracy and reliability.
References
[1] USPTO Patent No. 10,182,982. "Method for Diagnosing a Kidney Disorder Using a Subject-Specific Model." Issued November 6, 2018.
[2] Prior Art: U.S. Patent No. 9,671,699. Biomarker Panels for Renal Disease.
[3] Prior Art: U.S. Patent Application 16/432,123. AI-Based Kidney Prognosis Models.
[4] FDA Guidance. Software as a Medical Device (SaMD): Clinical Evaluation and Regulatory Considerations.
This comprehensive analysis offers business professionals and technologists an authoritative perspective on U.S. Patent 10,182,982, illuminating its innovative scope, strategic implications, and positioning within the evolving landscape of personalized renal diagnostics.
