DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to an application filed on 02/14/2025. Claims 1-17 are currently pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept — i.e. element that amount to significantly more than the abstract idea. The Claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea.
STEP 1
The claims are directed to a method which is included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims recite the abstract idea (based on claim 1) of:
A method for creating a Medical Data Ocean (MDO), comprising: receiving medical data from disparate sources; performing datum distillation on the received medical data and integration of the distilled medical data to form integrated and distilled medical data, wherein the datum distillation further includes standardizing and homogenizing the received medical data into a common format for integration and the standardization process involves converting the received medical data into a unified data structure and terminology, and the homogenization process involves removing inconsistencies and redundancies in the received medical data; storing the integrated and distilled medical data, wherein the stored integrated and distilled medical data comprises a source for individual medical data within the integrated and distilled medical data; receiving a request for information from the large-scale analytics of the integrated and distilled medical data; analyzing the integrated and distilled medical data to identify biological predictors and guide precision medicine; using the analysis of the integrated and distilled medical data to develop personalized treatment plans, predictive models, and/or providing a response to the request for information; and tracking the source of individual medical data from the integrated and distilled medical data used to determine the response to the requested information and/or to develop personalized treatment plans.
The claims, as illustrated by the limitations of Claim 1 above, recite an abstract idea within the “certain methods of organizing human activity” grouping — managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
The claims recite responding to a request for information and tracking response to the requested information for analyzed medical data that has been received, standardized, and homogenized. Responding to a request for information and tracking response to the requested information for analyzed medical data that has been received, standardized, and homogenized is a process that merely organizes human activity, as it involves receiving data, distilling data, storing data, receiving a request for the data, analyzing the data, providing a response to the request, and tracking the response to the request. As such, the claims recite an abstract idea within the category of mathematical concepts.
The dependent claims 2-6 and 9-15 recite further abstract ideas within the category of certain methods of organizing human activity, such as 2 receiving medical data from disparate sources further comprises: collecting medical data from various sources, including individual electronic health records (EHRs) containing patient medical histories, biomarkers and mutations in oncogenes for cancer progression, and data from micro-physiological systems (MPS) that simulate disease physiology, wherein the MPS data includes direct drug response information from patient- derived micro-tissue models; 3 the distillation of the received medical data comprises converting the received medical data into numbers and keywords; 4 the distillation of the received medical data comprises analyzing individual EHRs to extract information, including biomarkers, symptoms, and metadata, wherein the analysis is performed using natural language processing and learning techniques to identify relevant data from unstructured text and images to generate the numbers and keywords; 5 de-identifying the extracted information by removing personally identifiable information (PII) and retaining biomarkers and metadata, wherein the de-identification process involves any combination of deleting data, data masking, pseudonymization, and tokenization, wherein the de-identifying comprising retaining a source identifier for tracking the source of individual medical data; 6 not sharing the source identifier as part of the response to the request for information; 9 presenting a first user interface configured to a medical practitioner user and presenting a second user interface configured to a patient user; 10 the second user interface is configured to present medical implications based on similarities in a medical history of the patient to other patients in the MDO; 11 receiving a consent of a patient to receive information and providing new information to the patient based on a specific criteria related to the patient; 12 datum distillation comprises retaining biomarker-style data extracted from an EHR by using language analysis (NLP) where keywords and their relationships are taken from a written description within the EHR, and where image data of MRI (magnetic resonance imaging), IHC (immunohistochemistry), and/or FISH (Fluorescent in situ hybridization) are reduced to a set of numbers related to observable data sets; 13 the data distillation of the received medical data includes processing the received medical data to extract metrics comprising biomarkers, keywords, numerical weights, and measurements; 14 the data distillation further comprises supplements the received data to provide estimates to fill data gaps and adjust the data to account for variations between sources of the received data using sparce matrices; 15 the data distillation uses analytical processes to interpolate gaps in the biomarkers of the received medical data.
STEP 2A PRONG TWO
The claims recite additional elements beyond those that encompass the abstract idea above including:
Independent claim 1:
into a medical database
Dependent claim 4:
machine
Dependent claim 9:
on a first electronic device
on a second electronic device
Dependent claim 12:
automated
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with considerations laid out by the Supreme Court or the Federal Circuit. (see MPEP 2106.05 a-c and e) The additional elements integrate the abstract idea into a practical application when they: improve the functioning of a computer or improving any other technology, apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, apply the judicial exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. The additional limitations do not integrate the abstract idea into a practical application when they merely serve to link the use of the abstract idea to a particular technological environment or field of use — i.e. merely uses the computer as a tool to perform the abstract idea; or recite insignificant extra-solution activity (see MPEP 2106.05 f - h).
The database, machine, and devices are recited at a high level of generality such that it amounts to no more than instructions to apply the abstract idea using generic computer components. These elements merely add instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment. Nothing in the claim recites specific limitations directed to an improved database, machine, and devices. Similarly, the specification is silent with respect to these kinds of improvements. A general purpose computer that applies a judicial exception to computer functions, as is the case here, does not qualify as a particular machine, nor does the recitation of a basic computer impose meaningful limits in the claimed process. (see Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17 (Fed. Cir. 2014)). As such, the additional elements recited in the claims do not integrate the abstract medical data analysis process into a practical application of that process.
STEP 2B
The additional elements identified above do not amount to significantly more than the abstract medical data analysis process. The additional structural elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generic computer structure. Because the specification describes these additional elements in general terms, without describing particulars, Examiner concludes that the claim limitations may be broadly, but reasonably construed, as reciting basic computer components and techniques. The specification describes the elements in a manner that indicates that they are sufficiently straightforward such that the specification does not need to describe the particulars in order to satisfy U.S.C. 112. Considered as an ordered combination, the limitations recited in the claims add nothing that is not already present when the steps are considered individually.
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. For example, dependent claim limitations 2 receiving medical data from disparate sources further comprises: collecting medical data from various sources, including individual electronic health records (EHRs) containing patient medical histories, biomarkers and mutations in oncogenes for cancer progression, and data from micro-physiological systems (MPS) that simulate disease physiology, wherein the MPS data includes direct drug response information from patient- derived micro-tissue models; 3 the distillation of the received medical data comprises converting the received medical data into numbers and keywords; 4 the distillation of the received medical data comprises analyzing individual EHRs to extract information, including biomarkers, symptoms, and metadata, wherein the analysis is performed using natural language processing and learning techniques to identify relevant data from unstructured text and images to generate the numbers and keywords; 5 de-identifying the extracted information by removing personally identifiable information (PII) and retaining biomarkers and metadata, wherein the de-identification process involves any combination of deleting data, data masking, pseudonymization, and tokenization, wherein the de-identifying comprising retaining a source identifier for tracking the source of individual medical data; 6 not sharing the source identifier as part of the response to the request for information; 9 presenting a first user interface configured to a medical practitioner user and presenting a second user interface configured to a patient user; 10 the second user interface is configured to present medical implications based on similarities in a medical history of the patient to other patients in the MDO; 11 receiving a consent of a patient to receive information and providing new information to the patient based on a specific criteria related to the patient; 12 datum distillation comprises retaining biomarker-style data extracted from an EHR by using language analysis (NLP) where keywords and their relationships are taken from a written description within the EHR, and where image data of MRI (magnetic resonance imaging), IHC (immunohistochemistry), and/or FISH (Fluorescent in situ hybridization) are reduced to a set of numbers related to observable data sets; 13 the data distillation of the received medical data includes processing the received medical data to extract metrics comprising biomarkers, keywords, numerical weights, and measurements; 14 the data distillation further comprises supplements the received data to provide estimates to fill data gaps and adjust the data to account for variations between sources of the received data using sparce matrices; 15 the data distillation uses analytical processes to interpolate gaps in the biomarkers of the received medical data are directed to the abstract idea of certain methods of organizing human activity without integrating into a practical application or amounting to significantly more. Dependent claim limitations 7 the analytics involve techniques including any combination of machine learning, deep learning, and statistical modeling, and the identified predictors include biomarkers, genetic variants, and environmental factors associated with disease risk and treatment response; 8 a developed personalized treatment plan for an individual includes actionable medical insights for treating the individual based on presentation of biomarkers; 16 analyzing the integrated and distilled medical data further comprises using a digital twin and/or predictive algorithms that mimic medical systems; 17 analyzing the integrated and distilled medical data further comprises using a digital twin and/or predictive algorithms that mimic medical systems merely serve to further narrow the abstract idea above. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 8-11, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over McNutt, et al. (US 2016/0378919 A1) in view of Scripka, et al. (US 2023/0017211A1).
With regards to claim 1, McNutt teaches a method for creating a Medical Data Ocean (MDO), comprising: receiving medical data from disparate sources (see at least ¶ 0005, 0023, 0142, receiving a plurality of patient data collected from multiple institutions [disparate sources]); performing datum distillation on the received medical data and integration of the distilled medical data to form integrated and distilled medical data, wherein the datum distillation further includes standardizing and homogenizing the received medical data …for integration and the standardization process involves converting the received medical data into a unified data structure and terminology, and the homogenization process involves removing inconsistencies and redundancies in the received medical data (see at least figure 2, ¶ 0032, 0064-0065, 0067-0072, clinical data is collected and separated [datum distillation] into categories [terminology] in a schema/tables [unified data structure] where different data is directly transferred or derived; ¶ 0153, data stored in the database may be checked for data integrity and completeness. A component may search the database for data values that are missing and/or inconsistent with other recorded data values. For example, doses may be inadvertently entered that may be detected as incorrect based on known dosing. Once a data value has been identified, the value may be flagged to allow an operator to examine the data value and determine if it is incorrect. Bad data may be detected and removed to ensure quality and consistency to the data [homogenization process involves removing inconsistencies and redundancies in the received medical data]); storing the integrated and distilled medical data into a medical database, wherein the stored integrated and distilled medical data comprises a source for individual medical data within the integrated and distilled medical data (see at least figure 10 (1020), ¶ 0143, the collected medical data may be stored in a storage repository such as a relational database. The data stored in the relational database, for example, may be stored to facilitate fast retrieval for queried medical information for patients with a similar disease or prognosis. The medical data may be stored in tables); receiving a request for information from the large-scale analytics of the integrated and distilled medical data (see at least figure 10 (1050), a query may be received requesting medical information); analyzing the integrated and distilled medical data to identify biological predictors and guide precision medicine (see at least figure 10 (1060), ¶ 0147, aggregated medical data may be analyzed based on the received query); using the analysis of the integrated and distilled medical data to develop personalized treatment plans, predictive models, and/or providing a response to the request for information (see at least figure 10 (1070, 1080), ¶ 0148-0149, results of received query are produced and transmitted to requestor [providing a response to the request for information]); and tracking the source of individual medical data from the integrated and distilled medical data used to determine the response to the requested information and/or to develop personalized treatment plans (see at least ¶ 0110, comparing treatment variations between institutions [tracking the source]).
McNutt does not explicitly teach … into a common format. Scripka teaches …into a common format (see at least ¶ 0008). It would have been obvious to one of ordinary skill in the art to combine the standardized formatting of medical data of Scripka with the medical data analysis system of McNutt with the motivation of optimization of data analysis (Scripka, ¶ 0005-0008).
With regards to claim 8, McNutt teaches the method of claim 1, wherein a developed personalized treatment plan for an individual includes actionable medical insights for treating the individual based on presentation of biomarkers (see at least figures 3, 5-7, 9).
With regards to claim 9, McNutt teaches the method of claim 1, further comprising presenting a first user interface on a first electronic device configured to a medical practitioner user (see at least ¶ 0026) and presenting a second user interface on a second electronic device configured to a patient user (see at least ¶ 0027).
With regards to claim 10, McNutt teaches the method of claim 9, wherein the second user interface is configured to present medical implications based on similarities in a medical history of the patient to other patients in the MDO (see at least ¶ 0027).
With regards to claim 11, McNutt teaches the method of claim 1, further comprising receiving a consent of a patient to receive information and providing new information to the patient based on a specific criteria related to the patient (see at least ¶ 0027).
With regards to claim 16, McNutt teaches the method of claim 1, wherein analyzing the integrated and distilled medical data further comprises using a digital twin and/or predictive algorithms that mimic medical systems (see at least ¶ 0085-0087).
With regards to claim 17, McNutt teaches the method of claim 1, wherein tracking the source of individual medical data comprises tokenization and an open ledger system (see at least ¶ 0066, 0073).
Claims 2-7 and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over McNutt, et al. (US 2016/0378919 A1) in view of Scripka, et al. (US 2023/0017211 A1) in further view of Petak, et al. (US 2020/0098477 A1) in further view of Cirit, et al. (US 2021/0101146 A1).
With regards to claim 2, McNutt teaches the method of claim 1, wherein receiving medical data from disparate sources further comprises: collecting medical data from various sources, including individual electronic health records (EHRs) containing patient medical histories (see at least ¶ 0142), biomarkers (see at least ¶ 0050).
McNutt does not explicitly teach …and mutations in oncogenes for cancer progression, and data from micro-physiological systems (MPS) that simulate disease physiology, wherein the MPS data includes direct drug response information from patient- derived micro-tissue models.
Petak teaches …and mutations in oncogenes for cancer progression (see at least ¶ 0206, 0208, 0210-0211). It would have been obvious to one of ordinary skill in the art to combine the cancer mutation oncogenes of Petak with the medical data analysis system of McNutt with the motivation of sharing therapy used in genetic alterations to all users of a decision support system (Petak, ¶ 0004-0006, 0010).
Cirit teaches … and data from micro-physiological systems (MPS) that simulate disease physiology, wherein the MPS data includes direct drug response information from patient- derived micro-tissue models (see at least ¶ 0007). It would have been obvious to one of ordinary skill in the art to combine the MPS methods of Cirit with the medical data analysis system of McNutt with the motivation of optimizing patient therapy (Cirik, ¶ 0004).
With regards to claim 3, McNutt teaches the method of claim 2, wherein the distillation of the received medical data comprises converting the received medical data into numbers and keywords (see at least figures 2-9, data is converted to categories [keywords] and numbers).
With regards to claim 4, McNutt teaches the method of claim 3, wherein the distillation of the received medical data comprises analyzing individual EHRs to extract information, including biomarkers (see at least ¶ 0050), symptoms (see at least ¶ 0034), and metadata (see at least ¶ 0050), wherein the analysis is performed using …and machine learning techniques to identify relevant data from unstructured text and images to generate the numbers and keywords (see at least ¶ 0081-0084).
Furthermore, Scripka teaches …natural language processing (see at least ¶ 0018). It would have been obvious to one of ordinary skill in the art to combine the standardized formatting of medical data of Scripka with the medical data analysis system of McNutt with the motivation of optimization of data analysis (Scripka, ¶ 0005-0008).
With regards to claim 5, McNutt teaches the method of claim 4, further comprising de-identifying the extracted information by removing personally identifiable information (PII) and retaining biomarkers and metadata, wherein the de-identification process involves any combination of deleting data, data masking, pseudonymization, and tokenization (see at least ¶ 0066), wherein the de-identifying comprising retaining a source identifier for tracking the source of individual medical data (see at least ¶ 0110, comparing treatment variations between institutions [tracking the source]).
With regards to claim 6, McNutt teaches the method of claim 5, further comprising not sharing the source identifier as part of the response to the request for information (see at least figure 3, 5).
With regards to claim 7, McNutt teaches the method of claim 2, wherein the analytics involve techniques including any combination of machine learning, deep learning, and statistical modeling, and the identified predictors include biomarkers, genetic variants, and environmental factors associated with disease risk and treatment response (see at least ¶ 0081-0084).
With regards to claim 12, McNutt teaches the method of claim 2, wherein datum distillation comprises retaining biomarker-style data extracted from an EHR (see at least ¶ 0050) …where keywords and their relationships are taken from a written description within the EHR (see at least figure 2, ¶ 0032, 0064-0065, 0067-0072, clinical data is collected and separated [datum distillation] into categories [keywords] in a schema/tables where different data is directly transferred or derived, and where image data of MRI (magnetic resonance imaging), IHC (immunohistochemistry), and/or FISH (Fluorescent in situ hybridization) are reduced to a set of numbers related to observable data sets (see at least ¶ 0050, MRI data; figures 3, 5-9, data reduced to numbers in observable data sets).
Furthermore, Scripka teaches …by using automated language analysis (NLP) (see at least ¶ 0018). It would have been obvious to one of ordinary skill in the art to combine the standardized formatting of medical data of Scripka with the medical data analysis system of McNutt with the motivation of optimization of data analysis (Scripka, ¶ 0005-0008).
With regards to claim 13, McNutt teaches the method of claim 12, wherein the data distillation of the received medical data includes processing the received medical data to extract metrics comprising biomarkers (see at least ¶ 0050), keywords (see at least figure 2, ¶ 0032, 0064-0065, 0067-0072, clinical data is collected and separated [datum distillation] into categories [keywords] in a schema/tables, numerical weights, and measurements (see at least figures 3, 5-9, data in numerical weights and measurements).
With regards to claim 14, McNutt teaches the method of claim 13, wherein the data distillation further comprises supplements the received data to provide estimates to fill data gaps (see at least ¶ 0071, received clinical data (e.g., data from MOSAIQ) may be transferred through a direct extract, transform, and load (ETL) process between databases. In the ETL process, some data may be directly transferred, where other information may be derived from the clinical data in the ETL process. For example, PSA scores may be directly transferred from the lab result data. Alternatively, diagnosis PSA or pre-treatment PSA score may be calculated from the PSA score closest to and before the first date of treatment. Other data may include the raw data, acute toxicity, or late toxicity (e.g., 3 months after finished treatment). Dates may be converted to be relative days from a chosen reference date (e.g., first day of treatment)) and adjust the data to account for variations between sources of the received data using sparce matrices (at least ¶ 0072, Database schema 201 may support multiple patient representations and the transformations between the multiple patient representations (e.g., deformable or rigid) to support dose accumulation from multiple RT courses, or daily variations in the patient that can be accounted for dosimetrically. Ultimately, an accurate determination of actual dose delivered to the patient may be stored; ¶ 0115, plan quality may directly influence patient outcomes both in terms of local tumor control and normal tissue toxicities, the OVH can be used to “normalize” variations in plan quality to improve the consistency of multi-institutional studies).
With regards to claim 15, McNutt teaches the method of claim 13, wherein the data distillation uses analytical processes to interpolate gaps in the biomarkers of the received medical data gaps (see at least ¶ 0071, received clinical data (e.g., data from MOSAIQ) may be transferred through a direct extract, transform, and load (ETL) process between databases. In the ETL process, some data may be directly transferred, where other information may be derived from the clinical data in the ETL process. For example, PSA scores may be directly transferred from the lab result data. Alternatively, diagnosis PSA or pre-treatment PSA score may be calculated from the PSA score closest to and before the first date of treatment).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Colley, et al. (US 2024/0312581 A1) which discloses a method for identifying actionable care events includes receiving data sources relating to a subject; storing data from them in a first database; generating a database comprising structured data fields and metadata fields from the sources; generating output data related to fields within the data or metadata fields; populating the database with the output data; generating criteria sets corresponding to respective actionable care events; evaluating the generated database using the criteria sets; identifying whether any of the criteria sets are not sufficiently satisfied by the database, wherein an underlying error or an indication of missing or incomplete information within the database with respect to a criteria set indicates a corresponding actionable care event; determining that other data sources within the collection do not sufficiently satisfy any of the identified criteria sets; and generating, based on the identifying and determining, a notification that at least one actionable care event applies.
Swisher, et al. (US 2023/0260665 A1) which discloses systems and methods for modeling complex outcomes using similarity and machine learning algorithms. Machine learning algorithms and models can be implemented on platforms comprising one or more user interfaces and an insight engine. In these embodiments, insight engine comprises a machine learning software algorithm (or module) configured to ingest data and generate insights.
Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford). 2020 Jan 1;2020:baaa010. doi: 10.1093/database/baaa010. PMID: 32185396; PMCID: PMC7078068 which discloses precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joey Burgess whose telephone number is (571)270-5547. The examiner can normally be reached Monday through Friday 9-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached on 571-272-6702 The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSEPH D BURGESS/ Primary Examiner, Art Unit 3685
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