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 .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/16/2026 has been entered.
Status of the Application
Claims 1-4, 6-11, and 13-16 are currently pending in this case and have been examined and addressed below. This communication is a Non-Final Rejection in response to the Amendment to the Claims and Remarks filed on 02/16/2026.
Claims 1, 4, 6-8, 11, and 13-14 have been currently amended.
Claims 5 and 12 remain cancelled and are not considered at this time.
Claims 15-16 are newly added.
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 1-4, 6-11, and 13-16 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-4, 6-7, and 15 fall within the statutory category of an apparatus or system. Claims 8-11, 13-14, and 16 fall within the statutory category of a process.
Step 2A, Prong One
As per Claim 1, the limitations of analyzing at least a first part of said retrieved medical data, and creating medical guidelines based on said at least first part of said retrieved medical data, said at least one simulated medical case, and said expert’s labeling, under its broadest reasonable interpretation, covers performance of the limitation in the mind. The steps of analyzing medical data and creating medical guidelines are concepts performed including observation, evaluation, judgement and opinion in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers the performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
As per Claim 8, the limitations of generating at least one simulated medical case, analyzing at least a first part of said retrieved medical data, labeling at least a second part of said retrieved medical data by an expert, and creating medical guidelines based on said at least first part of said retrieved medical data, said at least one simulated medical case, and said expert labeling, under its broadest reasonable interpretation, covers performance of the limitation in the mind. The steps of generating a simulated medical case, analyzing medical data, labeling medical data, and creating medical guidelines are concepts performed including observation, evaluation, judgement and opinion in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers the performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The claims also recite training the machine learning module based on said expert’s labeling and said simulated medical case. The type of training utilized by the claimed invention is not describe by the Applicant. As such, the Examiner is required to analyze the training step given the broadest reasonable interpretation. The training of the machine learning module is considered to be part of the abstract idea because they fall under data manipulations that humans perform and thus are part of the mental process. Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional element – a system server and a database. The system server and database in these steps is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims recite that the system server communicates with a medical source and stores medical data from said medical source in a database, wherein the medical data comprises at least one synthetic medical case generated by the system (Claim 1) and retrieving medical data from a medical source and storing retrieved data (Claim 8). These elements invoke computers in their ordinary capacity for tasks such as receiving, storing, or transmitting data, as per MPEP 2106.05(f)(2), which amount to mere instructions to apply the exception. The claims also recite using a machine learning module for generating a simulated medical case and creating medical guidelines. As generating a simulated medical case and creating medical guidelines is directed to the abstract idea, the use of a machine learning module to execute the abstract idea amounts to mere instructions to apply the exception. As per MPEP 2106.05(f)(2), the use of a mathematical algorithm applied on a general purpose computer has been found by the courts to do no more than invoke computers as a tool and amount to mere instructions to apply the exception. Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional element of a system server and database to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component. The system server and database are recited at a high level of generality and are recited as generic computer components by reciting the system server as preferably a web server and comprising a processor and computerized modules (Specification page 11) and the database which is not further specified, which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception. Claim 1 also recites communicating with a medical source and storing medical data from said medical source in a database, and claim 8 also recites retrieving medical data from a medical source and storing retrieved data which are also mere instructions to apply the exception as detailed above. The claims also recite the use of a machine learning module to execute the abstract idea. The machine learning module is described as a general purpose mathematical algorithm in the specification as known machine learning techniques such as random forests or deep learning (page 9 and 20) and the use of a known mathematical algorithm to apply the abstract idea amounts to mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims
Dependent Claims 2-4, 6-7, 9-11, and 13-16 add further limitations which are also directed to an abstract idea. For example, Claims 2, 4, 6-7, 9-11, and 13-16 further specify or limit the elements of the independent claims, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1 and 11. Claims 3 and 10 include the use of natural language processing and artificial intelligence to carry out the steps of the abstract idea. Similar to the independent claims, the use of known mathematical algorithms to execute the abstract idea amounts to mere instructions to apply the exception. Because the additional elements do not impose meaningful limitations on the judicial exception and the additional elements are well-understood, routine and conventional functionalities in the art, the claims are directed to an abstract idea and are not patent eligible.
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 (i.e., changing from AIA to pre-AIA ) 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-11, and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Bettencourt-Silva (US 2021/0118578 A1), in view of Gheorghita (US 2022/0093270 A1), hereinafter Gheorghita.
As per Claim 1, Bettencourt discloses an automated Al-based system for creating and providing medical guidelines (Abstract, [0022] generate clinical practice guidelines), comprising:
a system server (see fig. 1; [0014]) configured to:
communicate with at least one medical source ([0069] communicate with external devices, [0038] data sources include sensor-based devices, etc.);
store medical data from said at least one medical source in a database (see Fig. 5 aggregated patient data database/memory);
analyze at least a first part of said medical data (see [0004]/Fig. 8, 804 analyze data from data sources); and
use machine learning module for creating medical guidelines (see Fig. 8, 806 generate clinical practice guidelines for selected cohorts using evidence and patient data; [0087-0088] producing guidelines (CPG) based on patient data and evidence where data includes criteria, i.e. labeling and patient health data, [0123] machine learning mechanism to create guidelines (CPG)).
However, Bettencourt may not explicitly disclose the following which is taught by Gheorghita:
wherein said medical data comprise at least one simulated medical case generated by a machine learning module ([0009] sample data includes synthetic examples, [0042-0043] data is augmented with synthetically created data in which a processor generated synthetic samples derived using simulation, [0024-0025] machine-learned generator generates synthetic masks, i.e. patient samples);
creating medical guidelines using said machine learning module is based on said at least first part of said medical data, said at least one simulated medical case, and said expert’s labeling (Abstract a machine-trained classifier, i.e. machine learning module, applied to patient medical data for clinical decision support, [0012] classifier, i.e. machine learning module, is trained with patient data samples having known values, where the known values are ground truth, i.e. labels, [0011] where the classification is used for clinical decision support generated from machine-learned model, [0009] the patient data samples include first medical data as the actual patient data and second data set including the synthetic data samples; Examiner interprets clinical decision support to be analogous to medical guidelines under BRI of medical guidelines, [0066] ground truths are annotations from experts, [0031] training data includes samples and ground truths for samples);
train said machine learning module based on said expert’s labeling and said simulated medical case ([0043-0044] training data includes both actual patient data and synthetically created samples of patient data generated by simulation, classifier trained with datasets, [0012] machine training for disease classification using data samples as training data. [0056] train the classifier, see also [0058] data samples are training data and are from actual patients with known ground truths and synthesized data sets, [0066] ground truths are annotations from experts).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing date of the current application to combine the known concept of generating a clinical recommendation for treatment using medical data, generated medical case, and expert’s labeling from Gheorghita with the known medical guideline generation system using machine learning and generated medical case as a synthetic case with labeling from Bettencourt in order to learn patterns in medical data for making clinical decisions and recommendations in a less time-consuming and less costly manner (Gheorghita [0004]).
As per Claim 8, Bettencourt discloses an automated Al-based method of creating and providing medical guidelines (Abstract, [0022] generate clinical practice guidelines), comprising:
retrieving medical data from at least one medical source and storing said retrieved data ([0098] query a data source to extract data, [0094] data aggregator module may aggregate data from various input data sources, see Fig. 5 aggregated patient data database/memory);
storing simulated medical case in said retrieved data (see Fig. 5 aggregated patient data database/memory)
analyzing, in a server, at least a first part of said retrieved medical data (see [0004]/Fig. 8, 804 analyze data from data sources); and
using a said machine learning module for creating medical guidelines (see Fig. 8, 806 generate clinical practice guidelines for selected cohorts using evidence and patient data; [0087-0088] producing guidelines (CPG) based on patient data and evidence where data includes criteria, i.e. labeling and patient health data, [0123] machine learning mechanism to create guidelines (CPG)).
However, Bettencourt may not explicitly disclose the following which is taught by Gheorghita:
generating, by a machine learning module, at least one simulated medical case ([0009] sample data includes synthetic examples, [0042-0043] data is augmented with synthetically created data in which a processor generated synthetic samples derived using simulation, [0024-0025] machine-learned generator generates synthetic masks, i.e. patient samples);
labeling, by an expert, at least a second part of said retrieved medical data ([0012] classifier, i.e. machine learning module, is trained with patient data samples having known values, where the known values are ground truth, i.e. labels, [0066] ground truths are annotations from experts);
using said machine learning module for creating medical guidelines using said machine learning module is based on said at least first part of said medical data, said at least one simulated medical case, and said expert’s labeling (Abstract a machine-trained classifier, i.e. machine learning module, applied to patient medical data for clinical decision support, [0012] classifier, i.e. machine learning module, is trained with patient data samples having known values, where the known values are ground truth, i.e. labels, [0011] where the classification is used for clinical decision support generated from machine-learned model, [0009] the patient data samples include first medical data as the actual patient data and second data set including the synthetic data samples; Examiner interprets clinical decision support to be analogous to medical guidelines under BRI of medical guidelines, [0066] ground truths are annotations from experts);
training said machine learning module based on said expert’s labeling and said simulated medical case ([0043-0044] training data includes both actual patient data and synthetically created samples of patient data generated by simulation, classifier trained with datasets, [0012] machine training for disease classification using data samples as training data. [0056] train the classifier, see also [0058] data samples are training data and are from actual patients with known ground truths and synthesized data sets, [0066] ground truths are annotations from experts).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing date of the current application to combine the known concept of generating a clinical recommendation for treatment using medical data, generated medical case, and expert’s labeling from Gheorghita with the known medical guideline generation system using machine learning and generated medical case as a synthetic case with labeling from Bettencourt in order to learn patterns in medical data for making clinical decisions and recommendations in a less time-consuming and less costly manner (Gheorghita [0004]).
As per Claims 2 and 9, Bettencourt and Gheorghita discloses the limitations of Claims 1 and 8. Bettencourt also teaches said at least one medical source comprises at least one of internal data source and external data source ([0038] data sources include sensor-based devices, other computing systems which are external sources).
As per Claims 3 and 10, Bettencourt and Gheorghita discloses the limitations of Claims 1 and 8. Bettencourt also teaches said analysis of said at least first part of said medical data is performed using at least one of Natural Language Processing (NLP) and artificial intelligence tools ([0032] analyzing the user data to determine activities of daily living using machine learning, [0038] machine learning/artificial reasoning used to interpret data from the data sources, [0040] where the artificial intelligence is specifically natural language processing).
As per Claims 4 and 11, Bettencourt and Gheorghita discloses the limitations of Claims 1 and 8. Bettencourt also teaches said medical data comprises at least one report generated by a reports and statistics module ([0121] analyze evidential data, [0122] transform patient data into statistical patterns and metadata, i.e. a report of the data which is an organized account of the information, also see [0079]); and
wherein said machine learning module is further configured to create medical guidelines based on said at least first part of said medical data, said at least one report and said labeling (see Fig. 8 generate guidelines based on the analyzed data, [0122] report/statistical patterns and metadata are used to generate guidelines, [0081] guidelines generated from the statistical patterns generated).
However, Bettencourt may not explicitly disclose the following which is taught by Gheorghita: the labeling is based on expert labeling of data ([0012] classifier, i.e. machine learning module, is trained with patient data samples having known values, where the known values are ground truth, i.e. labels, [0066] ground truths are annotations from experts).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing date of the current application to combine the known concept of generating a clinical recommendation for treatment using expert’s labeling from Gheorghita with the known medical guideline generation system using machine learning and generated medical case as a synthetic case with labeling from Bettencourt in order to learn patterns in medical data for making clinical decisions and recommendations in a less time-consuming and less costly manner (Gheorghita [0004]).
As per Claims 6 and 13, Bettencourt and Gheorghita discloses the limitations of Claims 1 and 8. Bettencourt also teaches said medical data comprises prospective medical data ([0021-0024] evidence includes literature, research evidence, and clinical trial data, [0080] evidence includes clinical trial data); and
wherein said machine learning module is further configured to create medical guidelines based on said at least first part of said medical data, said prospective medical data and said labeling ([0021] guidelines (CPG) developed based on the evidence including the prospective data as described above).
However, Bettencourt may not explicitly disclose the following which is taught by Gheorghita: the labeling is based on expert labeling of data ([0012] classifier, i.e. machine learning module, is trained with patient data samples having known values, where the known values are ground truth, i.e. labels, [0066] ground truths are annotations from experts).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing date of the current application to combine the known concept of generating a clinical recommendation for treatment using expert’s labeling from Gheorghita with the known medical guideline generation system using machine learning and generated medical case as a synthetic case with labeling from Bettencourt in order to learn patterns in medical data for making clinical decisions and recommendations in a less time-consuming and less costly manner (Gheorghita [0004]).
As per Claims 7 and 14, Bettencourt and Gheorghita discloses the limitations of Claims 1 and 8. Bettencourt also teaches said medical data further comprises at least one patient's outcome ([0089] various data sources include literature which includes outcomes related to the selection criteria); and
wherein said machine learning module is further configured to create medical guidelines based on said at least first part of said medical data, said at least one patient's outcome and said labeling ([0090] guideline generation uses the selection criteria and datasets and the terminology which includes outcomes as indicated above from [0089]).
However, Bettencourt may not explicitly disclose the following which is taught by Gheorghita: the labeling is based on expert labeling of data ([0012] classifier, i.e. machine learning module, is trained with patient data samples having known values, where the known values are ground truth, i.e. labels, [0066] ground truths are annotations from experts).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing date of the current application to combine the known concept of generating a clinical recommendation for treatment using expert’s labeling from Gheorghita with the known medical guideline generation system using machine learning and generated medical case as a synthetic case with labeling from Bettencourt in order to learn patterns in medical data for making clinical decisions and recommendations in a less time-consuming and less costly manner (Gheorghita [0004]).
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Bettencourt-Silva (US 2021/0118578 A1), in view of Gheorghita (US 2022/0093270 A1), in view of Neumann (2021/0004714 A1), hereinafter Neumann.
As per Claims 15 and 16, Bettencourt and Gheorghita discloses the limitations of Claims 1 and 8. Bettencourt and Gheorghita may not explicitly disclose the following which is taught by Neumann: expert labeling is indicative of a justification of a medical procedure ([0059] data in the training dataset includes expert submission which includes reason for collection of samples, i.e. a procedure).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing date of the current application to combine the known concept of using expert’s labeling which includes a reason for a procedure from Neumann with the known medical guideline generation system using machine learning and generated medical case as a synthetic case with labeling from Bettencourt and Gheorghita in order to use machine learning to improve the physical condition of a patient by the use of expert labeling (Neumann [0032]).
Response to Arguments
Applicant’s arguments, see Pages 5-6, “Claim Rejections - 35 U.S.C. 101”, filed 02/16/2026 with respect to claims 1-4, 6-11, and 13-16 have been fully considered but they are not persuasive.
Applicant argues that the claims of the present application are not directed to a mental process, because they recite features that cannot be practically performed in the human mind. Specifically, the use of a machine learning module to generate a simulated medical case and use of a machine learning module to create medical guidelines are features which cannot practically be performed in the human mind. Examiner respectfully disagrees. The use of a machine learning model to carry out the abstract idea (generating a simulated medical case and creating medical guidelines) is an additional element that is claimed at a high level of generality. The machine learning module is described in the specification as machine learning techniques such as random forests or deep learning (Page 1120), which are known mathematical algorithms. The use of known mathematical algorithms to execute the abstract idea amounts to mere instructions to apply the exception, as per MPEP 2106.05(f)(2). This additional element is not considered when determining if the claims recite an abstract idea.
Applicant additionally argues that the claims recite training the machine learning module based on the expert labeling and said simulated medical case which is not directed to an abstract idea and integrates the abstract idea into a practical application because it improves the technical field of AI training by using expert labeling. Examiner respectfully disagrees. The training is recited at a very high-level of generality in the claim and the specification does not describe any particular method of training the machine learning module. The training is “based on” the expert’s labeling and simulated medical case, but this does not specify how the training is accomplished merely data which is involved in training the module. The type of training utilized by the claimed invention is not describe by the Applicant. As such, the Examiner is required to analyze the training step given the broadest reasonable interpretation. The training of the machine learning module is considered to be part of the abstract idea because they fall under data manipulations that humans perform and thus are part of the mental process.
Applicant’s arguments, see Pages 6-14, “Claim Rejections - 35 U.S.C. 103”, filed 02/16/2026 with respect to claims 1-4, 6-11, and 13-14 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Gheorghita.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Evangeline Barr whose telephone number is (571)272-0369. The examiner can normally be reached Monday to Friday 8:00 am to 4:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached at 571-270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EVANGELINE BARR/Primary Examiner, Art Unit 3682
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