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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 20, 2026 has been entered.
Response to Amendment
In the amendment filed on January 20, 2026, the following has occurred: claim(s) 1, 11 have been amended. Now, claim(s) 1-20 are pending.
Notice to Applicant
As of March 5, 2026 the Examiner has not received a Supplemental Information Disclosure Statement to correct the issue of the named inventor for U.S. Patent Pre-Grant Publication No. 2017/0249434.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word "means," but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: "the machine-learning module designed and configured to" in claim 1, "the machine-learning module is further configured to" in claim 4, "the machine-learning module is further configured to" in claim 5, "the machine-learning module is further configured to" in claim 6, "the machine- learning module is further configured to" in claim 7, and "the machine-learning module is further configured to" in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. l 12(f) or
pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C.
112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-10: Step 2A Prong One
Claim 1 recite(s)
record a physiological input related to lung function pertaining to a human subject;
categorize uncategorized elements of the first training data set using statistical analysis to assign descriptors and format the data for compatibility;
receive an update to the plurality of physiological data in the first training data set, wherein the update comprises an identification of an erroneous recordation of an element of physiological data of the plurality of physiological data in the first training data set;
modify the first training data set based on the identification of the erroneous recordation of the element of physiological data of the plurality of physiological data;
generate a diagnostic output; and
transmit the diagnostic output
These limitations as drafted given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. For example, but for the “classification device” language, the “record” function in the context of this claim encompasses a person following instructions to record a physiological input related to lung function pertaining to a human subject. Similarly, but for the “a machine-learning module operating on the classification device” and “processing algorithm” language, the “categorize” function in the context of this claim encompasses a person following instructions to organize and classify the information. Similarly, but for the “a machine-learning module operating on the classification device” language, the “receive” and “modify” functions in the context of this claim encompasses a person following instructions to make an update to the information based on an instruction. Similarly, but for the “a machine-learning module operating on the classification device” and “the trained machine-learning module” language, the “generate” function in the context of this claim encompasses a person following instructions to make a determination of a diagnostic. Finally, but for the “a machine-learning module operating on the classification device” and “client device” language, the “transmit” function in the context of this claim encompasses a person following instructions to relay the information to another person. These steps could be accomplished by a person following instructions to determine diagnostics, such steps encompass Certain Methods of Organizing Human Activity.
Claims 2-10 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, claim 2 includes the abstract idea identified above and further expands on the diagnostic output. Similarly, claim 3 includes the abstract idea identified above and describes a user indicating a follow-up test. Similarly, claim 4 includes the abstract idea identified above and describes limiting information according to the diagnostic constraint. Similarly, claim 5 includes the abstract idea identified above and further describes the generic computer components and utilizing the diagnostic output. Similarly, claim 6 includes the abstract idea identified above and further expands on the diagnostic outputs and limiting the diagnostic outputs using the diagnostic constraint. Similarly, claims 7-8 include the abstract idea identified above and describe determining a plurality of diagnostic outputs, ranking the plurality of diagnostic outputs by using the expert submission, and using a threshold to eliminate a diagnostic output as a function of the comparison. Similarly, claim 9 includes the abstract idea identified above and further expands on the generic computer components. Finally, claim 10 includes the abstract idea identified above and describes receiving a second training data set to be used in determining an ameliorative output of the diagnostic output. These claims also encompass Certain Methods of Organizing Human Activity for the reasons set forth above.
Claims 1-10: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining
elements amount to no more than general purpose computer components programmed to perform the abstract ideas, insignificant extra-solution activity, and generally linking the abstract idea to a
technical environment.
Claims 1-10, directly or indirectly, recite the following generic computer components configured to implement the abstract idea: "a classification device, the classification device designed and configured to:", "an expert database", "a client device", and "a machine-learning module operating on the classification device, the machine-learning module designed and configured to:". The written description discloses that the recited generic computer components encompass a generic computer "Classification device 104 may include any computing de vice as described below in reference to FIG. 10, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described below in reference to FIG. 10. Classification device 104may be housed with, may be incorporated in, or may incorporate one or more sensors of at least a sensor. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone." (See Specification in Paragraph [0008]) As set forth in the MPEP 2106.04(f) "merely including instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claims recite “retrieve from an expert database an expert submission, the expert submission including a diagnostic constraint”, “receive a first training data set, wherein the first training data set correlates a plurality of physiological data as inputs to a plurality of diagnostic data as outputs and comprises uncategorized data related to the plurality of physiological data and the plurality of diagnostic data;” at a high degree of generality, amount no more than receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). As set forth in MPEP 2106.05(d)(II), computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claims recite “a machine-learning module”, “…a processing algorithm configured to apply…”, and the “training” step at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words "apply it" to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words "apply it" or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claim 1-10: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at
223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.")
Receiving and transmitting data over a network (i.e., receiving and communicating data or signals) has been recognized as well-understood, routine, and conventional activity of a general-purpose computer (see MPEP 2106.05(d)) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)).
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2016.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)).
Claims 11-20 recite the same functions as claims 1-10 but in method form, and with the addition of "a computing device" in place of "a classification device" (in claim 1). The recitation of "a computing device" recites additional generic computer component as the written description discloses "Classification device 104 may include any computing device as described below in reference to FIG. 10, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described below in reference to FIG. 10. Classification device 104 may be housed with, may be incorporated in, or may incorporate one or more sensors of at least a sensor. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone." (See Specification in Paragraph [0008]) Therefore, these claims also recite an abstract idea that falls into the Mental Processes grouping of abstract ideas as explained above.
Accordingly, claims 1-20 are directed to an abstract idea without significantly more.
Therefore claims 1-20 are rejected under 35 U.S.C. § 101.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kalafut et al. (U.S. Patent Pre-Grant Publication No. 2020/0211692) in view of Takla et al. (U.S. Patent Pre-Grant Publication No. 2016/0042134).
As per independent claim 1, Kalafut discloses a system for classification of prognostic labels using expert inputs, the system comprising: a machine-learning module operating on the classification device, the machine-learning module designed and configured to:
receive a first training data set, wherein the first training data set correlates a plurality of physiological data as inputs to a plurality of diagnostic data as outputs (See Paragraphs [0033]-[0041], [0056]-[0057], [0090]-[0084], [0159]-[0160], [0167]: The collection component can provide or otherwise facilitate provision of the structured diagnostic training data to one or more machine learning systems of the AI model development system, wherein based on reception of the structured diagnostic data, the one or more machine learning systems employ the structured diagnostic training data as training data to generate or train one or more diagnostic models configured to provide AI based diagnostic evaluations of new medical image data, and the related diagnostic input data can include raw image data associated with the new medical image data, which the Examiner is interpreting the one or more machine learning systems to encompass a machine-learning module, the diagnostic training data to encompass a first training data set, and the structured training data database can collate medical image data, medical report data, and the like that associates information identifying and defining key data points in the medical image data and the medical report data to encompass a plurality of physiological data as inputs to a plurality of diagnostic data as outputs) and comprises uncategorized data related to the plurality of physiological data and the plurality of diagnostic data (See Paragraphs [0159]-[0160]: The indexing component can facilitate cleansing, and indexing or organizing the structured diagnostic training data generated by the diagnostic data development component in accordance with one or more defined data models and ontologies to facilitate efficient usage of the structured diagnostic training data 506 for model development and training, which the Examiner is interpreting the function of the indexing component to facilitate cleansing, and indexing or organizing the structured diagnostic training data to encompass the structured diagnostic training data is unorganized before the indexing component organizes the structured diagnostic training data);
categorize uncategorized elements of the first training data set using a processing algorithm configured to apply statistical analysis to assign descriptors and format the data for compatibility with the machine-learning module (See Paragraphs [0143], [0159]-[0160]: The server device can include application program interface (API) component, inference component, indexing component and collection component, the indexing component can be used for cleansing, indexing or organizing the structured diagnostic training data generated by the diagnostic data development component in accordance with one or more defined data models and ontologies to facilitate efficient usage of the structured diagnostic training data for model development and training, which the Examiner is interpreting the indexing component to encompass a processing algorithm, accordance with one or more defined data models and ontologies to encompass statistical analysis, indexing to encompass assign descriptors, and cleansing to encompass format the data for compatibility with the machine-learning module.);
receive an update to the plurality of physiological data in the first training data set, wherein the update comprises an identification of an erroneous recordation of an element of physiological data of the plurality of physiological data in the first training data set (See Fig. 11 and Paragraphs [0067], [0181]-[0183]: The new training data and/or aggregated training data included in the structured training data database can be used to periodically update the one or more diagnostic models, however if the feedback indicates the interpretation is incorrect, the interpretation results can be discarded (or partially discarded to remove only the portions of the results that are incorrect), which the Examiner is interpreting the structured training data database can be used to periodically update the one or more diagnostic models to encompass receive an update to the plurality of physiological data in the first training data set, and the interpretation is incorrect to encompass an identification of an erroneous recordation of an element of physiological data of the plurality of physiological data in the first training data set);
modify the first training data set based on the identification of the erroneous recordation of the element of physiological data of the plurality of physiological data (See Fig. 11 and Paragraphs [0067], [0181]-[0183]: The image data and associated interpretation results can be added to the training data database (e.g., the structured straining data database), the new training data and/or aggregated training data included in the structured training data database can be used to periodically update the one or more diagnostic models, however, if the feedback indicates the interpretation is incorrect the interpretation results can be discarded (or partially discarded to remove only the portions of the results that are incorrect), and the corrected interpretation results can be received that correct the errors in the AI based interpretation results, which the Examiner is interpreting discarding the incorrect interpretation results (or partially discarded to remove only the portions of the results that are incorrect) to encompass modify the first training data set based on the identification of the erroneous recordation of the element of physiological data);
train, iteratively, the machine-learning module using the modified first training data set, wherein training the machine-learning module includes retraining the machine-learning module with feedback from previous iterations of the machine-learning module (See Paragraphs [0090]-[0094]: The AI model development system can further regularly receive feedback regarding results generated by the AI healthcare models when applied to actual workflows and employ the feedback to regularly update and/or to optimize the one or more healthcare AI models provided in the AI model database, which the Examiner is interpreting employ the feedback to regularly update and/or to optimize the one or more healthcare AI models to encompass retraining the machine-learning module with feedback from previous iterations of the machine-learning module), and wherein additional supervised learning processes are performed to detect previously unknown or unsuspected relationships between the physiological data and prognostic labels (See Paragraphs [0173]-[0175]: The system provides an optimization cycle for regularly receiving new training data that provides information regarding new coefficients/model parameters or identifies errors in previously set coefficients/model parameters that can be used to further adapt, train, and improve model performance, which the Examiner is interpreting information regarding new coefficients/model parameters to encompass detect previously unknown or unsuspected relationships between the physiological data and prognostic labels);
generate a diagnostic output using the trained machine-learning module (See Paragraphs [0038]-[0040], [0087]-[0091], [0123]-[0125], [0129]: The ordering models can generate information regarding recommended medical tests/studies to be performed, procedures to be performed, medications to administer, physical activities to perform (or not perform) rehabilitation/therapy activities to perform, dietary restrictions, etc., which the Examiner is interpreting the generated information to encompass generate at least a diagnostic output using the trained machine learning module); and
transmit the diagnostic output to a client device (See Paragraphs [0134]-[0135], [0208]: Communication can be between a client and a server in the form of a data packet transmitted between two or more computer processes wherein the data packet may include healthcare related data, AI models, input data for the AI models, and the like.)
While Kalafut teaches the system as described above, Kalafut may not explicitly teach a classification device, the classification device designed and configured to:
record a physiological input related to lung function pertaining to a human subject; and
retrieve from an expert database an expert submission, the expert submission including a diagnostic constraint.
Takla teaches a system for a classification device (See Paragraphs [0174]-[0176]: The calculation unit may be embodied as a processor or a CPU), the classification device designed and configured to:
record a physiological input related to lung function pertaining to a human subject (See Paragraphs [0018], [0026]-[0028], [0122], [0188]-[0189], [0194]-[0195]: The method generates a medical suggestion as upon the receipt of input data, i.e., upon receipt of the received known facts, scores for selected ones of the medical suggestions are calculated, which the Examiner is interpreting the input data to encompass a physiological input pertaining to a human subject, and the medical facts F.sub.j are respectively embodied as an element chosen from the group comprising an age of the patient, a gender of the patient, a body weight of the patient, a body height of the patient, a physiological parameter, a biological parameter, a chemical parameter, a medical parameter, a symptom, an information associated with a medical complaint, a result of a medical finding, which the Examiner is interpreting a physiological parameter to encompass a physiological input related to lung function); and
retrieve from an expert database an expert submission, the expert submission including a diagnostic constraint (See Paragraphs [0014]-[0018], [0021], [0094], [0189]-[0190], [0268]: An association between a medical suggestion MS.sub.i and a medical fact F.sub.j, is comprised by the database, the database comprises or defines a relationship or link between said medical suggestion MS.sub.i and said medical fact F.sub.j, each test case comprises medical facts and a plurality of constraints which the Examiner is interpreting the medical suggestion to encompass an expert submission.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Kalafut to include a classification device, the classification device designed and configured to: record a physiological input related to lung function pertaining to a human subject; and retrieve from an expert database an expert submission, the expert submission including a diagnostic constraint as taught by Takla as data collection for the utilization of the machine learning systems of Kalafut. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Kalafut with Takla with the motivation of improving support for medical decision making (See Summary of the Invention of Takla in Paragraph [0007]).
Claim 11 mirrors claim 1 only within a different statutory category, and is rejected for the same reason as claim 1. The addition of and utilization of "a computing device" is encompassed by Takla in Paragraphs [0174]-[0176]: The calculation unit may be embodied as a processor or a CPU.
As per claim 2, Kalafut/Takla discloses the system of claim 1 as described above. Kalafut further teaches wherein: transmit the selected diagnostic output to the client device (See Paragraphs [0056], [0210]: Server can store the file, decode the file, or transmit the file to another client.)
While Kalafut discloses a system that can transmit the selected diagnostic output to the client device, Kalafut may not explicitly teach wherein: the diagnostic output further comprises a plurality of diagnostic outputs; and
the classification device is further configured to: display the plurality of diagnostic outputs to an expert; receive a selection of a diagnostic output from the expert.
Takla teaches a system wherein: the diagnostic output further comprises a plurality of diagnostic outputs (See Paragraphs [0187], [0191]: A plurality of medical suggestions can be generated, which the Examiner is interpreting a plurality of medical suggestions to encompass a plurality of diagnostic outputs); and
the classification device is further configured to: display the plurality of diagnostic outputs to an expert (See Paragraphs [0128], [0195]: The output may be a ranking/an order of scores of medical suggestions MS.sub.i, of the subset Sl and may be transmitted to a display to be displayed to a user, which the Examiner is interpreting user to be an expert); receive a selection of a diagnostic output from the expert (See Paragraphs [0168] [0187]-[0188]: If a plurality of medical suggestions MS.sub.i are presented to the user as a result of the method and the user selects one of said suggestions, this users election may be provided as a feedback information to the system or the database, such that an adaption of the corresponding associations of said medical fact and/or an adaption of the corresponding weight W.sub.i,j can be carried out, which the Examiner is interpreting the plurality of medical suggestions to encompass a diagnostic output.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Kalafut to include the diagnostic output further comprises a plurality of diagnostic outputs; and the classification device is further configured to: display the plurality of diagnostic outputs to an expert; receive a selection of a diagnostic output from the expert as taught by Takla. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Kalafut with Takla with the motivation of improving support for medical decision making (See Summary of the Invention of Takla in Paragraph [0007]).
Claim 12 mirrors claim 2 only within a different statutory category, and is rejected for the same reason as claim 2.
As per claim 3, Kalafut/Takla discloses the system of claims 1-2 as described above.
Kalafut further teaches wherein the classification device is further configured to display an indication of a follow-up test (See Paragraphs [0086]-[0087]: The models included AI model database can include medical ordering models configured to determine information regarding how to treat a patient based on a diagnosis of the patient, a current state of the patient, medical history of the patient, signs and symptoms, demographic information, insurance information and the like and the ordering models can generate information regarding recommended medical tests/studies to be performed, procedures to be performed, medications to administer, physical activities to perform (or not perform) rehabilitation/therapy activities to perform, dietary restrictions, etc.)
Claim 13 mirrors claim 3 only within a different statutory category, and is rejected for the same reason as claim 3.
As per claim 4, Kalafut/Takla discloses the system of claim 1 as described above. Kalafut further teaches wherein the machine-learning module is further configured to filter the first training data set according to the diagnostic constraint (See Paragraph [0105]: Clinical decision support refers to any tool that provides clinicians, administrative staff, patients, caregivers, or other members of the care team with information that is filtered or targeted to a specific person or situation, which the Examiner is interpreting the specific person or situation to encompass the diagnostic constraint.)
Claim 14 mirrors claim 4 only within a different statutory category and without the recitation of "the machine-learning module", and is rejected for the same reason as claim 4.
As per claim 5, Kalafut/Takla discloses the system of claim 1 as described above. Kalafut further teaches wherein:
the machine-learning module is configured to generate a plurality of machine-learning models (See Paragraphs [0102]-[0104]: The AI workflow assistance system can include a plurality of AI workflow assistance subsystems that can respectively be configured to apply the respective models to facilitate different types of workflows); and
the machine-learning module is configured to generate the diagnostic output by:
selecting a machine-learning model as a function of the diagnostic constraint (See Paragraphs [0105]- [0106]: The respective AI workflow assistance subsystem can be configured to provide clinical decision support by not only selecting and applying the appropriate AI models to a clinical context or workflow, but further delivering the right information (evidence-based guidance using the appropriate AI model or models and in response to a clinical need) to the right people (entire care team-including the patient) through the right channels (e.g., EHR, mobile device, patient portal) in the right intervention formats (e.g., order sets, flow-sheets, dashboards, patient lists) at the right points in workflow (for decision making or action)); and
generating the diagnostic output using the selected machine-learning model (See Paragraphs [0105]-[0106]: respective AI workflow assistance subsystem can be configured to provide clinical decision support by not only selecting and applying the appropriate AI models to a clinical context or workflow, but further delivering the right information (evidence-based guidance using the appropriate AI model or models and in response to a clinical need) to the right people (entire care team-including the patient) through the right channels (e.g., EHR, mobile device, patient portal) in the right intervention formats (e.g., order sets, flow-sheets, dashboards, patient lists) at the right points in workflow (for decision making or action).)
Claim 15 mirrors claim 5 only within a different statutory category, and is rejected for the same reason as claim 5.
As per claim 6, Kalafut/Takla discloses the system of claim 1 as described above. Kalafut further teaches wherein the machine-learning module is configured to generate the diagnostic output by: generating a plurality of diagnostic outputs (See Paragraphs [0139]- [0141]: It should be appreciated that the AI diagnostic model development subsystem 508 can further be divided into a plurality of different subsystems that are respectively configured to generate different types of diagnostic models respectively configured to interpret different data points and generate different outputs); and
filtering the plurality of diagnostic outputs using the diagnostic constraint (See Paragraph [0105]: Clinical decision support refers to any tool that provides clinicians, administrative staff, patients, caregivers, or other members of the care team with information that is filtered or targeted to a specific person or situation, which the Examiner is interpreting the specific person or situation to encompass a diagnostic constraint.)
Claim 16 mirrors claim 6 only within a different statutory category, and is rejected for the same reason as claim 6.
As per claim 7, Kalafut/Takla discloses the system of claim 1 as described above. Kalafut further teaches wherein the machine-learning module is further configured to generate a plurality of diagnostic outputs and rank the plurality of diagnostic outputs using the expert submission (See Paragraphs [0086]-[0087]: The diagnostic models can be configured to not only generate diagnosis information that diagnosis a patient condition or state, but also provide information regarding a level of confidence of the diagnosis (e.g., 76% probability of a diagnosis of condition A, 24% probability of diagnosis of condition B, etc.), which the Examiner is interpreting the level of confidence of the diagnosis to encompass rank the plurality of diagnostic outputs.)
Claim 17 mirrors claim 7 only within a different statutory category and without the recitation of "the machine-learning module", and is rejected for the same reason as claim 7.
As per claim 8, Kalafut/Takla discloses the system of claims 1 and 7 as described above.
Kalafut may not explicitly teach wherein the classification device is further configured to: compare a ranking of each of the plurality of diagnostic outputs to a threshold; and
eliminate a diagnostic output of the plurality of diagnostic outputs as a function of the comparison.
Takla teaches a system wherein the classification device is further configured to: compare a ranking of each of the plurality of diagnostic outputs to a threshold (See Paragraphs [0035], [0135]-[0137], [0189]: A threshold may be used to determine whether the score is high enough, in case the score of the medical suggestion exceeds said threshold, the method automatically provides this medical suggestion as a known fact F.sub.j for the next process which again carries out steps 2 to 4, which the Examiner is interpreting determine whether the score is high enough, in case the score of the medical suggestion exceeds said threshold to encompass compare a ranking of each of the plurality of diagnostic outputs to a threshold); and
eliminate a diagnostic output of the plurality of diagnostic outputs as a function of the comparison (See Fig. 2 and Paragraphs [0035], [0135]-[0137], [0189]: The principle of the process of restricting the basic set SO to the subset S1 and the criteria used by the presented method for that restriction/selection can also be gathered from FIG. 4 and in the example of FIG. 2, only the medical suggestions MS.sub.3, MS.sub.4, and MS.sub.6 do fulfil the subset criterion of S1, which the Examiner is interpreting the presented method for that restriction/selection to encompass eliminate a diagnostic output of the plurality of diagnostic outputs as a function of the comparison.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Kalafut to include the classification device is further configured to: compare a ranking of each of the plurality of diagnostic outputs to a threshold, and eliminate a diagnostic output of the plurality of diagnostic outputs as a function of the comparison as taught by Takla. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Kalafut with Takla with the motivation of improving support for medical decision making (See Summary of the Invention of Takla in Paragraph [0007]).
Claim 18 mirrors claim 8 only within a different statutory category, and is rejected for the same reason as claim 8.
As per claim 9, Kalafut/Takla discloses the system of claim 1 as described above. Kalafut further teaches wherein the machine-learning module is further configured to combine a machine-learning output with the expert submission (See Paragraphs [0036], [0102]-[0103]: The distributed learning architecture can further facilitate a continuous cycle of self-sustained model optimization and expansion based on model validation feedback received in association with usage of the models in the actual workflows and the collaboration and sharing of diverse annotated and raw training data between disparate healthcare systems and services using one or more centralized health data consortiums, and some AI models can be related in a manner such that an output of one model can be used as an input for another, which the Examiner is interpreting the feedback to encompass an expert submission and some AI models can be related in a manner such that an output of one model can be used as an input for another to encompass a machine-learning output.)
Claim 19 mirrors claim 9 only within a different statutory category and without the recitation of "the machine-learning module", and is rejected for the same reason as claim 9.
As per claim 10, Kalafut/Takla discloses the system of claim 1 as described above.
Kalafut further teaches wherein the machine-learning module further comprises an ameliorative label learner operating on the classification device, the ameliorative label learner designed and configured to: receive a second training data set, wherein the second training data set includes a plurality of second data entries, each second data entry of the second training set including a second prognostic label and a correlated ameliorative process label (See Paragraphs [0033], [0036]-[0038], [0058], [0101]: The distributed learning architecture can amass large datasets (labeled and unlabeled) from various disparate systems and data sources, the distributed learning architecture can provide infrastructure solutions that allow seamless access to large volumes of data for teaching and training purposes to produce trustworthy results, the research registry can associate various types of metadata with distinct data sets that facilitates evaluating correlations between various types of data points associated within the respective data sets, and a second model can be utilized, which the Examiner is interpreting the research registry can associate various types of metadata with distinct data sets that facilitates evaluating correlations between various types of data points associated within the respective data sets to encompass each second data entry of the second training set including at least a second prognostic label and at least a correlated ameliorative process label); and
generate an ameliorative output of the diagnostic output as a function of the second training set and a prognostic output (See Paragraphs [0033], [0036]-[0038], [0058], [0101]: The distributed learning architecture can amass large datasets (labeled and unlabeled) from various disparate systems and data sources, the distributed learning architecture can provide infrastructure solutions that allow seamless access to large volumes of data for teaching and training purposes to produce trustworthy results, the research registry can associate various types of metadata with distinct data sets that facilitates evaluating correlations between various types of data points associated within the respective data sets, and some AI models can be related in a manner such that an output of one model can be used as an input for another, and a second model can be utilized, which the Examiner is interpreting the some AI models can be related in a manner such that an output of one model can be used as an input for another to encompass an ameliorative output of the diagnostic output as a function of the second training set and the at least prognostic output when the output of the first model is used as an input for the second model.)
Claim 20 mirrors claim 10 only within a different statutory category and without the recitation of "the machine-learning module", and is rejected for the same reason as claim 10.
Response to Arguments
In the Remarks filed on January 20, 2026, the Applicant argues that the newly amended and/or added claims overcome the Claim Interpretation(s), Claim Objection(s), 35 U.S.C. 101 rejection(s), and 35 U.S.C. 103 rejection(s). The Examiner acknowledges that the newly added and/or amended claims overcome the Claim Objection(s). However, the Examiner does not acknowledge that the newly added and/or amended claims overcome the Claim Interpretation(s), 35 U.S.C. 101 rejection(s), and 35 U.S.C. 103 rejection(s).
The Applicant argues that:
(1) claim 1 as amended recites a process to "train, iteratively, the machine-learning module using the modified first training data set, wherein training the machine-learning module includes retraining the machine-learning module with feedback from previous iterations of the machine-learning module, and wherein additional supervised learning processes are performed to detect previously unknown or unsuspected relationships between the physiological data and prognostic labels... " The process, just like in Synopsys, except in its most simplistic form, could not conceivably be performed in the human mind or with a pen and paper because it would require leveraging supervised learning processes to identify previously unknown or unsuspected relationships between physiological data and prognostic labels based on the iterations of the machine-learning process and its previous outputs of the detection. This is not akin to a human judgment/observation as the system may be configured to go through large volume of input data and previous outputs of the detection of relationships between physiological data and prognostic labels. Even in its most simplistic form, no human judgment/observation could achieve and execute the aforementioned limitation. Clearly, in Synopsys, the Court has set a benchmark for separating the simplistic form of data manipulation from inventions involve several-step manipulation of data that could not conceivably be performed in the human mind or with a pen and paper, and "train, iteratively, the machine-learning module using the modified first training data set, wherein training the machine-learning module includes retraining the machine-learning module with feedback from previous iterations of the machine-learning module, and wherein additional supervised learning processes are performed to detect previously unknown or unsuspected relationships between the physiological data and prognostic labels... " is above that benchmark. In addition, "[t]he mental process is not without limits. Examiners are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind... Claim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within this grouping." See Memorandum to Technology Center 3600. USPTO, Memorandum to Tech. Centers 2100, 2600, and 3600 (August 4, 2025). (Hereinafter "The August 2025 Memo"). Further, in evaluating Step 2A, prong 1, "[e]xaminers should ... be careful to distinguish claims that recite an exception... and claims that merely involve an exception." MPEP § 2106.04(II)(A)(l). Claims that recite an exception require further eligibility analysis, whereas claims that merely involve an exception do not. See MPEP § 2106.04(II)(A)(l). The USPTO recently clarified the difference between claims that "recite" an abstract idea and claims that just "involve" an abstract idea in the August 2025 Memo by considering the published USPTO examples 39 and 47. Example 39 recites limitation "training the neural network in a first stage using the first training set" does not recite a judicial exception. The August 2025 Memo, p. 3. Similarly, claim 1 as amended recites the limitation "train, iteratively, the machine-learning module using the modified first training data set, wherein training the machine-learning module includes retraining the machine-learning module with feedback from previous iterations of the machine-learning module, and wherein additional supervised learning processes are performed to detect previously unknown or unsuspected relationships between the physiological data and prognostic labels... ", which does not recite a judicial exception. Finally, Applicant respectfully notes that Examiners must establish unpatentability under 35 U.S.C. § 101 by the "preponderance of the evidence." The August 2025 Memo, p. 5; MPEP § 706. This means that Examiners should only make a rejection when it is "more likely than not" that a claim is ineligible under 35 U.S.C. § 101, not when "an examiner is uncertain as to the claim's eligibility." The August 2025 Memo, p. 5. Therefore, the amended claim recites technical operations that cannot be performed mentally and are therefore not directed to a mental process under Step 2A, Prong One;
(2) "The claim itself does not need to explicitly recite the improvement described in the specification." The August 2025 Memo, p. 4. "The examiner is reminded to consult the specification to determine whether the disclosed invention improves technology or a technical field, and evaluate the claim to ensure it reflects the disclosed improvement. The specification does not need to explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art." Id. The analysis in Step 2A Prong Two considers the claim as a whole... [T]he analysis should take into consideration all the claim limitations and how these limitations interact and impact each other when evaluating whether the exception is integrated into a practical application. Id. In addition, the July 2024 Guidelines teach that integration of a judicial exception into a practical application may be achieved when "[(l)] the specification ... set[s] forth an improvement in the technology[;] and[, (2)] the claim ... reflect[s] the disclosed improvement" July 2024 Subject Matter Eligibility Guidelines, p. 12. The July 2024 Guidelines show that Example 47 claim 3 integrates the judicial exception into a practical application because (1) the specification teaches an improvement to network security "by acting in real time to proactively prevent network intrusions"; and (2) "[t]he claimed invention reflects this improvement in the field of network intrusion detection". July 2024 Subject Matter Eligibility Guidelines, p. 12. Specific examples of claims that are eligible under 35 U.S.C. § 101 are found in at least Example 47 (Artificial Neural Network for Anomaly Detection - claim 1 and 3) presented with the July 2024 Subject Matter Eligibility Update. Example 47 illustrates the application of the eligibility analysis to claims that recite limitations specific to artificial intelligence, particularly the use of an artificial neural network to identify or detect anomalies. With reference to Example 47 and claim 3, the July 2024 Subject Matter Eligibility Update states with respect to (1) above: "according to the background section, existing systems use various detection techniques for detecting potentially malicious network packets and can alert a network administrator to potential problems. The disclosed system detects network intrusions and takes real-time remedial actions, including dropping suspicious packets and blocking traffic from suspicious source addresses. The background section further explains that the disclosed system enhances security by acting in real time to proactively prevent network intrusions." July 2024 Subject Matter Eligibility Guidelines, p. 12. Analogous to Example 47 and claims 1 and 3, in the present application, claim 1 as amended also teaches technological improvement that is integrated into a practical application. Paragraphs [0082]-[0083] of the present application discloses improvement that would be apparent to one of ordinary skill in the art. For instance, "[a]s a non-limiting example, a particular set of blood test biomarkers and/or sensor data may be typically used by cardiologists to diagnose or predict various cardiovascular conditions, and a supervised machine-learning process may be performed to relate those blood test biomarkers and/or sensor data to the various cardiovascular conditions; in an embodiment, domain restrictions of supervised machine-learning procedures may improve accuracy of resulting models by ignoring artifacts in training data 120. Domain restrictions may be suggested by experts and/or deduced from known purposes for particular evaluations and/or known tests used to evaluate prognostic labels. Additional supervised learning processes may be performed without domain restrictions to detect, for instance, previously unknown and/or unsuspected relationships between physiological data and prognostic labels." Accordingly, and for at least the above-described reasons, Applicant respectfully submits that claim 1 as amended is not directed to an abstract idea. Therefore, pursuant to Step 2A of the § 101 analysis prescribed in Alice, claim 1 as amended recites patentable subject matter. Applicant respectfully requests that this rejection be withdrawn;
(3) claim 1 as amended recites details of a particular way to identify relevant relationship between physiological data and prognostic labels using supervised machine learning processes. Claim 1 as amended purports to improve existing computing technology by integrating one or more supervised machine-learning algorithms, whether restricted to a particular domain or not for instance to perform, with respect to a given set of parameters and/or categories of parameters that have been suspected to be related to a given set of prognostic labels, and/or are specified as linked to a medical specialty and/or field of medicine covering a particular set of prognostic labels. As such, Applicant submits that claim 1 as amended is allowable under 35 U.S.C. §101, at least for the reasons stated above. Claim 11 recites, substantially, the same limitations as claim 1. Therefore, Applicant submits that the rejection to claim 11 has been overcome for the same reasons as to claim 1. Applicant respectfully requests reconsideration and withdrawal of the rejection. Claims 2-10 and 12-20 depend, directly or indirectly, on claims 1 or 11 and thus recite all of the same elements as claim 1 and claim 11. Applicant, therefore, submits that claims 2-10 and 12-20 overcome these rejections for at least the same reasons as discussed above with reference to claims 1 and 11;
(4) Applicant respectfully asserts that Kalafut does not disclose "wherein additional supervised learning processes are performed to detect previously unknown or unsuspected relationships between the physiological data and prognostic labels" as recited in amended claim 1. As such, claim 1 as amended is patentably distinguishable over Kalafut, for at least the reasons discussed above. Takla does not cure the deficiency of Kalafut. The Office has not asserted that Takla teaches, suggests or motivates "wherein additional supervised learning processes are performed to detect previously unknown or unsuspected relationships between the physiological data and prognostic labels," as in claim 1. Accordingly, Applicant respectfully submits that claim 1 as amended is patentably distinguishable over Kalafut and Takla, alone or in combination, for at least the reasons discussed above. Therefore, Applicant respectfully requests the withdrawal of this rejection;
(5) claim 11 as amended recites similar limitations to claim 1. As noted above, claim 1 is patentably distinguishable over Kalafut and Takla, alone or in combination, for at least the reasons discussed above. Accordingly, Applicant respectfully submits that claim 11 as amended is patentably distinguishable over Kalafut and Takla, alone or in combination, for at least the reasons discussed above. Therefore, Applicant respectfully requests the withdrawal of this rejection. Each of claims 2-10 and 12-20 depends, directly or indirectly, from claims 1 or 11. As noted above, claims 2-10 and 12-20 are patentably distinguishable over Kalafut and Takla, alone or in combination, for at least the reasons discussed above. Accordingly, Applicant respectfully submits that claims 2-10 and 12-20 are patentably distinguishable over Kalafut and Takla, alone or in combination, for at least the reasons discussed above. Therefore, Applicant respectfully requests the withdrawal of these rejections.
In response to argument (1), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the limitations as drafted given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. Example 39 recites the limitation “training the neural network in a first stage using the first training set”, but this limitation is not the sole reason that the claim does not recite a judicial exception as the “Applicant’s invention addresses this issue by using a combination of features to more robustly detect human faces.” The Examiner maintains that the Applicant’s newly amended claims do not recite similar limitations to Example 39 that identified that Example 39 does not recite a judicial exception. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (2), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the Applicant’s newly amended claims are similar to “iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48” (See MPEP 2106.05(a)(II)) that the courts have indicated may not be sufficient to show an improvement to technology. The Examiner does not acknowledge that Example 47 claim 3 is similar to the Applicant’s newly amended claims, the Examiner maintains that the Applicant’s newly amended claims are more similar to Example 47 claim 2 which is not eligible. The Examiner maintains that the judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas, insignificant extra-solution activity, and generally linking the abstract idea to a technical environment. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (3), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the claims recitation of “a machine-learning module”, “…a processing algorithm configured to apply…”, and the “training” step at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words "apply it" to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words "apply it" or an equivalent, is an example of when an abstract idea has not been integrated into a practical application. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (4), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that Kalafut describes “wherein additional supervised learning processes are performed to detect previously unknown or unsuspected relationships between the physiological data and prognostic labels” as recited in amended claim 1 as Paragraphs [0173]-[0175] that the system provides an optimization cycle for regularly receiving new training data that provides information regarding new coefficients/model parameters or identifies errors in previously set coefficients/model parameters that can be used to further adapt, train, and improve model performance. The 35 U.S.C. 103 rejection(s) stand.
In response to argument (5), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that claim 11 is similarly rejected as claim 1 as described above in the 35 U.S.C. 103 rejection(s). Claims 2-10 and 12-20 are rejected individually and due to their dependence on independent claims 1 and 11. The 35 U.S.C. 103 rejection(s) stand.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Adamo et al. (U.S. Patent Publication No. 11,113,175), describes a system and accompanying methods may be utilized to utilize data and textual information obtained from outputs of a software application, internal documents, external documents, hierarchical and/or graphical models, other information sources, or a combination thereof, to determine or infer the proper constraints across complex parameters and/or across related parameter fields to allow for the successful navigation, exercise, and/or testing of the software application.
Brunner (U.S. Patent Pre-Grant Publication No. 2017/0249434), describes a method for the detection of early signals of disease and recovery thereof comprising a universal yet personalized health, monitoring solution using cell phones or other wearable smart device data that generate extensive real-time data.
Yang (“Machine Learning for Collocation Identification”), describes a new decision tree learning algorithm based on C4.5 to be used for learning tasks with unbalanced data.
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/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683