DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to a response filed 14 October 2025, on an application filed 4 April 2022, which is a continuation in part of application 16/589066 (now U.S. Patent # 11,384,671) filed 30 September 2019.
Claims 1 and 11 have been amended.
Claims 1-3, 5-13 and 15-20 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 1-3, 5-13 and 15-20 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) without significantly more.
Step 1
Claims 1-3, 5-13 and 15-20 are within the four statutory categories. Claims 1-3 and 5-10 are drawn to a system for selecting a prescriptive element based on user implementation inputs, which is within the four statutory categories (i.e. machine). Claims 11-13 and 15-20 are drawn to a method for selecting a prescriptive element based on user implementation inputs, which is within the four statutory categories (i.e. process).
Prong 1 of Step 2A
Claim 1 recites: A system for selecting a prescriptive element based on user implementation inputs, wherein the system comprises a computing device configured to:
receive at least a diagnosis descriptor including a disease classifier and a disease stage descriptor from a user client device;
generate a prescriptive model using a machine-learning process, wherein generating the prescriptive model comprises receiving at least one domain restriction and filtering training data for the machine-learning process as a function of the at least one domain restriction and the prescriptive model is configured to:
receive the at least a diagnostic descriptor;
output a plurality of prescriptive elements, wherein each prescriptive element includes a prescriptive allocation resource calculation; and
wherein the prescriptive model is iteratively trained with output from training data, wherein the prescriptive model is configured to receive the at least one domain restriction and output the plurality of prescriptive elements, wherein iteratively training the prescriptive model comprises:
training the prescriptive model using training data as input, wherein the training data comprises the at least one domain restriction;
adjusting one or more connections and one or more weights between nodes in adjacent layers of the prescriptive model; and
retraining the prescriptive model as a function of the connections to produce the output layer of nodes; and
transmit the plurality of prescriptive elements to the user client device;
receive, as a function of the transmission, a user implementation response including at least a prescriptive element indicator and a prescriptive allocation standard response from the user client device, wherein the user implementation response further comprises a user willingness indicator, and wherein the prescriptive allocation standard response includes a prescriptive allocation estimation comprising a treatment accessibility factor, wherein the treatment accessibility factor comprises geographic data, and wherein the geographic data indicates a quantity of medical professionals associated with a specific geographic region; and
select a prescriptive element from the plurality of prescriptive elements as a function of the user implementation response, the treatment accessibility factor and a loss function.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions), for example a mental process that a neurologist should follow when testing a patient for nervous system malfunctions (i.e. in this case the system creates a model by associating diagnostic information with treatment information, receives diagnostic information and determines a corresponding treatment), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for claim 11 is identical as the abstract idea for claim 1, because the only difference between claim 11 and claim 1 is that claim 1 recites a system, whereas claim 11 recites a method.
Dependent claims 2, 3 and 5-10 and 12, 13 and 15-20 include other limitations, for example claims 3, 4, 6, 9, 13, 14, 16 and 19 further describes claimed elements initially described in the independent claims, claims 7, 8, 17 and 18 describe the loss minimization functions, and claims 10 and 20 describe the display of data, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Hence dependent claims 2, 3 and 5-10 and 12, 13 and 15-20 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 11.
Prong 2 of Step 2A
Claims 1 and 11 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the structural components of the computing device, which amounts to merely invoking a computer as a tool to perform the abstract idea, as well as the elements of domain restriction and filtering training data of the machine learning model and providing output from the machine learning model, see MPEP 2106.05(f); and/or
adding insignificant extrasolution activity to the abstract idea, for example mere data gathering, selecting a particular data source or type of data to be manipulated, and/or insignificant application, such as the transmission of data (e.g. see MPEP 2106.05(g)).
Additionally, dependent claims 2, 3 and 5-10 and 12, 13 and 15-20 include other limitations, but these limitations also amount to no more than amount to mere instructions to apply the exception (e.g. the adjustments to the treatments disclosed in dependent claims 7 and 19-20), generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data disclosed in dependent claims 3, 5, 6, 13, 14, 16, 9 and 19), and/or do not include any additional elements beyond those already recited in independent claims 1 and 11, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claims 1 and 11 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
Paragraphs 9 and 10 of the Specification discloses that the additional elements (i.e. the computing components) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receive and process data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare); Paragraph 25 of the Specification discloses that the additional elements directed to the training of the model are composed of old and well known model training functions that are well-understood, routine, and conventional activities previously known to the pertinent industry (machine learning modelling);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.").
Dependent claims 2, 3 and 5-10 and 12, 13 and 15-20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1 and 11, and/or (e.g. the adjustments to the treatments disclosed in dependent claims 7 and 19-20), generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data disclosed in dependent claims 3, 5, 6, 13, 14, 16, 9 and 19), and hence do not amount to “significantly more” than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-3, 5-13 and 15-20 are nonetheless 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-3, 5-13 and 15-20 are rejected under 35 U.S.C. 103 as being obvious over McRaith et al. (U.S. PG-Pub 2017/0329917), hereinafter McRaith, further in view of Caffarel et al. (U.S. PG-Pub 2016/0171177 A1), hereinafter Caffarel, further in view of Hirsch et al. (U.S. PG-Pub 2019/0096526 A1), hereinafter Hirsch.
As per claims 1 and 11, McRaith discloses a method and system for selecting a prescriptive element based on user implementation inputs, wherein the system comprises a computing device (McRaith, Figs. 1 and 4) configured to:
receive at least a diagnosis descriptor including a disease classifier and a disease stage descriptor from a user client device (McRaith receives initial user data, including diagnosis data for a patient and stage/severity of the disease, see paragraph 44, from various devices of Fig. 1. It is the Office’s position that the indication of a patient having diabetes inherently describes the body system impacted by the disease, the endocrine system, as it is old and well known in the medical arts that diabetes affects the endocrine system.);
generate a prescriptive model using a machine-learning process … the prescriptive model is configured to: receive at least a diagnostic descriptor; and output a plurality of prescriptive elements … (Using the input patient data, the system generates a patient cohort [prescriptive model] related to the patient, and using the cohort and patient diagnosis information, the system outputs a plurality of prescriptive elements, such as determined goals and treatment plans related to the identified cohort, see paragraphs 47 and 48: “The goal may include improving one or more health parameters of the user, such as, e.g., blood glucose level, hemoglobin A1C level, blood pressure, low-density lipid level, high-density lipid level, triglyceride cholesterol level, total cholesterol level, body mass index (BMI), weight, user activity level, sleep duration, sleep quality, adherence to prescribed medication, nutrition (e.g., carbohydrate intake), psycho-social determinants, and blood glucose level testing frequency correlated to its effect on said blood glucose level, among others. The goal may be determined by mHealth application based on the previously entered information, including information based on the user's disease state, history of user's disease, and/or other initial user data received at step 401. The goal also may be determined based on the cohort associated with the user 8 at step 404. One or more machine learning algorithms may be used by the server 29 to help determine the goal.” A treatment plan for the patient is generated using goals (prescriptive elements), user data (diagnosis etc.) and cohort (generated prescriptive model), see paragraphs 51-55; this treatment plan would also be a prescriptive element.) McRaith discloses a machine learning process at paragraphs 69-71.); and
wherein the prescriptive model is iteratively trained with output from training data, wherein the prescriptive model is configured to receive the at least one … restriction and output the plurality of prescriptive elements, wherein iteratively training the prescriptive model (McRaith Fig. 4 discloses a system for providing a treatment plan recommendation composed of prescriptive elements. Fig. 4 #412 and paragraphs 51-53 discloses wherein the system generates an initial plan. System takes the initial plan, herein also the restriction, and accesses additional data related to the plan, see paragraphs 67-68 and #418, in order to analyze/train the input data using the machine learning model, see paragraphs 69-71 and #422, and output new recommendations/prescriptive elements via an updated goal plan #428. This process is looped/iteratively implemented, see Fig. 4 #414-428.) comprises:
training the prescriptive model using training data as input, wherein the training data comprises the at least one … restriction (McRaith, Fig. 4 #412 and paragraphs 51-53 discloses wherein the system generates an initial plan. System takes the initial plan, herein also the restriction, and accesses additional data related to the plan, see paragraphs 67-68 and #418, in order to analyze/train the input data using the machine learning model, see paragraphs 69-71 and #422, and output new recommendations/prescriptive elements via an updated goal plan #428.;
adjusting one or more connections and one or more weights between nodes in adjacent layers of the prescriptive model (McRaith discloses iteratively training a neural network as the prescriptive model, which inherently comprises adjusting weight between nodes of the layers, see Fig. 4 and paragraph 70.); and
retraining the prescriptive model as a function of the connections to produce the output layer of nodes (McRaith discloses iteratively training a neural network as the prescriptive model, which inherently comprises retraining the model as a function of the connections, see Fig. 4 and paragraph 70.); and
transmit the plurality of prescriptive elements to the user client device (Treatment plan is transmitted back to the patient’s electronic device #19, see paragraph 66 and Fig. 4 #414.);
receive, as a function of the transmission, a user implementation response including at least a prescriptive element indicator … including a … data from the user client device, wherein the user implementation response further comprises a user willingness indicator … (System receives data relating to plan, which can include updates to original data received, see Fig. 4 #418 and paragraph 68. Initial data received can include “the healthcare provider's subjective opinions regarding the user's motivation, compliance, overall health, and the like. … For example, if the provider's subjective opinion of the user 8 is that the user 8 has a high compliance to medication and dietary regimens, but a low compliance to exercise regimens, then a subsequent treatment plan generated by mHealth application 1 may include a larger emphasis on medication and diet, as opposed to exercise” [paragraph 46], which would comprise a user implementation response that includes a prescriptive element indicator, such as medication/dietary/exercise. Provider’s subjective opinion regarding user’s motivation and compliance would comprise a user willingness indicator. Further, user inputs feedback on implemented plan, see Fig. 4 #428 and paragraphs 88-90. System receives GPS and geo-tagged location data of various restaurants, and compares them to output restaurant-treatment suggestion recommendations based thereon [restaurants are also considered in view of their alignment with patients health issues], see paragraphs 45, 103 and claim 7.); and
select a prescriptive element from the plurality of prescriptive elements as a function of the user implementation response … (System generates/updates plans based on user implementation response, see paragraphs 46, 68 and 88-90, which would comprise selecting a prescriptive element to generate/update.).
McRaith fails to explicitly disclose to provide:
wherein each prescriptive element includes a prescriptive allocation resource calculation … wherein the prescriptive allocation standard response includes a prescriptive allocation estimation;
a prescriptive allocation standard response; and
selecting a treatment based on a loss function.
However, Caffarel discloses that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to provide wherein each prescriptive element includes a prescriptive allocation resource calculation; and a prescriptive allocation standard response … wherein the prescriptive allocation standard response includes a prescriptive allocation estimation (Caffarel discloses display and consideration of treatment costs and consideration of a patient’s treatment budget, see paragraphs 34 and 68.), in order to provided “methods for matching specific services or tailoring the elements of the service according to a patient's current status and assessment of acuity level/risk in order to maximize outcomes” (Caffarel, paragraph 12.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare before the effective filing date of the claimed invention to modify the treatment plan generation system of McRaith to include wherein each prescriptive element includes a prescriptive allocation resource calculation; and a prescriptive allocation standard response, as taught by Caffarel, in order to arrive at a treatment plan generation system that provides “methods for matching specific services or tailoring the elements of the service according to a patient's current status and assessment of acuity level/risk in order to maximize outcomes” (Caffarel, paragraph 12.).
Neither McRaith nor Caffarel disclose:
receiving at least one domain restriction and filtering training data for the machine-learning process as a function of the at least one domain restriction;
accessing data regarding a treatment accessibility factor … wherein the treatment accessibility factor comprises geographic data, and wherein the geographic data indicates a quantity of medical professionals associated with a specific geographic region; and
selecting a treatment based on utilization of a loss function and a treatment accessibility factor.
However, Hirsch discloses that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to provide:
receiving at least one domain restriction and filtering training data for the machine-learning process as a function of the at least one domain restriction (Hirsch discloses the use of supervised machine learning to create a model, wherein training data is filtered to those datum that have known results, which would comprise the domain restriction, see paragraphs 172, 174 and 84.);
accessing data regarding a treatment accessibility factor … wherein the treatment accessibility factor comprises geographic data, and wherein the geographic data indicates a quantity of medical professionals associated with a specific geographic region (Hirsch provides data regarding the number of medical professionals associated with a specific geographic region, see page 4, Table 5 which includes information on all hospital addresses and servicing medical professionals identified by specialties.); and
selecting a treatment based on utilization of a loss function and a treatment accessibility factor (Hirsch discloses a system that utilizes loss functions to provide treatment recommendations, see paragraphs 16, 63, 101, 119, 135, 142-149, 155-159 and 170-171, and uses all available information, including and a treatment accessibility factor, see Table 5, page 4. The Office notes that a cost function is also a loss function.)
in order to provide a recommendation system wherein “Health sciences professionals can use these health science recommendations as a basis for making health science diagnosis and/or treatments for a patient” (Hirsch, paragraph 12.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare before the effective filing date of the claimed invention to modify the treatment plan generation system of McRaith/Caffarel to include receiving at least one domain restriction and filtering training data for the machine-learning process as a function of the at least one domain restriction, accessing data regarding and a treatment accessibility factor and selecting a treatment based on utilization of a loss function and a treatment accessibility factor, as taught by Hirsch, in order to arrive at a treatment plan generation system wherein “Health sciences professionals can use these health science recommendations as a basis for making health science diagnosis and/or treatments for a patient” (Hirsch, paragraph 12.).
McRaith, Caffarel and Hirsch are all directed to the electronic processing of patient healthcare data and specifically to the determination of patient treatment plans. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
As per claims 3, 5, 6, 9, 10, 13, 15, 16, 19 and 20, McRaith/Caffarel/Hirsch disclose claims 1 and 11, above. McRaith/Caffarel also discloses:
3,13. wherein each prescriptive element includes data describing a prescriptive process (A treatment plan for the patient is generated using goals (prescriptive elements), user data (diagnosis etc.) and cohort (generated prescriptive model), see McRaith paragraphs 51-55; this treatment plan would also be a prescriptive element.);
5,15. wherein the user implementation response includes a numerical response reflecting a user willingness indicator and an effort descriptor (Feedback includes treatment plan rating, which would comprise a numerical response reflecting a user willingness indicator and an effort descriptor, see McRaith paragraph 88.);
6,16. wherein the user implementation response further includes a user willingness score containing a user effort factor (Feedback includes treatment plan rating, which would comprise a user willingness score containing a user effort factor, see McRaith paragraph 88.);
9,19. wherein a selected prescriptive element comprises a price associated with the selected prescriptive element (McRaith discloses information corresponding to selected elements, see McRaith paragraphs 46, 68 and 88-90. As shown above, Caffarel discloses display of pricing information corresponding to treatments, see paragraphs 34 and 68.); and
10,20. wherein computing device is configured to display the price associated with the selected prescriptive element at the user client device (McRaith discloses display information corresponding to selected elements, see McRaith paragraphs 46, 68 and 88-90. As shown above, Caffarel discloses display of pricing information corresponding to treatments, see paragraphs 34 and 68.).
As per claims 2, 7, 8, 12, 17 and 18, McRaith/Caffarel/Hirsch disclose claims 1 and 11, above. McRaith further discloses:
8,18. wherein minimizing the loss function further includes:
evaluating the user implementation response (System receives data relating to plan, which can include updates to original data received, see Fig. 4 #418 and paragraph 68. Initial data received can include “the healthcare provider's subjective opinions regarding the user's motivation, compliance, overall health, and the like. … For example, if the provider's subjective opinion of the user 8 is that the user 8 has a high compliance to medication and dietary regimens, but a low compliance to exercise regimens, then a subsequent treatment plan generated by mHealth application 1 may include a larger emphasis on medication and diet, as opposed to exercise.” [paragraph 46], which would comprise a user implementation response that includes a prescriptive element indicator, such as medication/dietary/exercise.” Data is evaluated to make or update treatment plans. Further, user inputs feedback on implemented plan, see Fig. 4 #428 and paragraphs 88-90.);
obtaining a user implementation factor comprising a numerical scored response as a function of the evaluation (Feedback includes treatment plan rating, which would comprise a numerical scored response as a function of the evaluation, see paragraph 88.).
McRaith fails to explicitly disclose:
2,12. wherein the computing device is further configured to receive at least the diagnosis descriptor as a function of an expert knowledge database;
7,17. generating a plurality of user implementation neutralizers and generating a plurality of user implementation neutralizers and minimizing the loss function as a function of the plurality of user implementation neutralizers as a function of the plurality of user implementation neutralizers;
8,18. assigning a weighted variable to the user implementation response as a function of the user implementation factor.
Hirsch discloses that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to provide:
2,12. wherein the computing device is further configured to receive at least the diagnosis descriptor as a function of an expert knowledge database (Hirsch, Fig. 4 discloses output of a diagnosis for a patient based on expert knowledge contained within databases shown in Fig. 3.);
7,17. generating a plurality of user implementation neutralizers and generating a plurality of user implementation neutralizers and minimizing the loss function as a function of the plurality of user implementation neutralizers as a function of the plurality of user implementation neutralizers (Hirsch discloses a plurality of weighting of various variables, some of more weight then others, which meets Applicant’s description of a neutralizer, see Hirsch, paragraphs 55, 59, 62, 66, 76, 82, 90-92, etc.); and
8,18. assigning a weighted variable to the user implementation response as a function of the user implementation factor (Hirsch discloses weighting of various variables, see Hirsch, paragraphs 55, 59, 62, 66, 76, 82, 90-92, etc.);
in order to provide a recommendation system wherein “Health sciences professionals can use these health science recommendations as a basis for making health science diagnosis and/or treatments for a patient” (Hirsch, paragraph 12.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare before the effective filing date of the claimed invention to modify the treatment plan generation system of McRaith/Caffarel/Hirsch to include wherein the computing device is further configured to receive at least the diagnosis descriptor as a function of an expert knowledge database; generating a plurality of user implementation neutralizers and generating a plurality of user implementation neutralizers and minimizing the loss function as a function of the plurality of user implementation neutralizers as a function of the plurality of user implementation neutralizers; and assigning a weighted variable to the user implementation response as a function of the user implementation factor, as taught by Hirsch, in order to arrive at a treatment plan generation system wherein “Health sciences professionals can use these health science recommendations as a basis for making health science diagnosis and/or treatments for a patient” (Hirsch, paragraph 12.).
Response to Arguments
Applicant’s arguments filed 14 October 2025 concerning the rejection of all claims under 35 U.S.C. 101 and 103(a) have been fully considered but they are not persuasive.
With regard to the rejection of the claims under 35 USC 101, Applicant argues on pages 7-13 that the claims comprise statutory material because:
A. Step 2A, Prong One: The claims are not directed to “a method of organizing human activity” because they recite elements that are performed by a machine learning model, such as weight adjustment, and are not directed to the abstract ideas of mathematical concepts.
B. Step 2A, Prong Two: Similar to Example 47, Claim 3 of the 2024 Subject Matter Eligibility Examples, the present claims comprises various limitations that integrate any alleged abstract idea into a practical application by improving the functionality and adaptability of the machine-learning system itself.
C. Step 2B:Applicant cites various limitations from the independent claims and indicates that they present a specific and non-conventional combination of steps results in a machine-learning system that dynamically adapts prescriptive outputs based on real-world diagnostic and geographic data and therefore provides a technical improvement in both computational performance and prescriptive decision accuracy.
The Office respectfully disagrees. Please see the statutory rejection above where the claims are shown to be directed to an abstract idea without significantly more.
Regarding A., The cited material directed to the machine learning model is not part of the abstract idea, as it is an additional element that amount to mere instructions to apply the exception, as indicated above. The cited material directed to the training of the model is shown to be directed to an abstract idea, as it merely encompasses what a human would do in adjusting their personal or professional analysis of how to determine what treatment to provide to a patient or group of patients. Further, the claims are not currently indicated as being directed to the abstract ideas of mathematical concepts.
MPEP 2106. 04(a)(2)(11) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Office submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to create a model by associating diagnostic information with treatment information, receive diagnostic information and then determine a corresponding treatment. Furthermore, the Office submits that healthcare itself is inherently represents the organization of human activity. Applicant has not pointed to anything in the claims that fall outside of this characterization. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to create a model by associating diagnostic information with treatment information, receive diagnostic information and then determines a corresponding treatment, the claimed invention is directed to an abstract idea.
Regarding B., MPEP 2106.04(d)(1) states that a practical application may be present where the claimed invention improves the functioning of a computer. See also MPEP 2106.05(a)(I). The technological environment of Applicant’s claim is a general-purpose computer (see specification paragraph [0009]). Applicant has not identified nor can the Office locate any physical improvement to the functioning of the computer that results from the implementation of Applicant’s claim. There is no indication that the computer is made to run faster, more efficiently, or utilize less power. In fact, the computer may be caused to operate slower and less efficiently through the implementation of Applicant’s claimed invention; we do not know. There is no clear indication that the various generic training elements cited by the Applicant improve anything. Because there is no improvement to the function of the computer, a practical application is not present.
Regarding C., MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Examiner cannot find, nor has the Applicant identified, any technological problem that was caused by the technological environment to which the claims are confined.
Accordingly, the rejection is upheld.
With regard to the rejection of the claims under 35 USC 103, Applicant argues on pages 14-16 that the cited references fail to disclose the amended language directed to use of a treatment accessibility factor.
The Office respectfully disagrees. As shown above, Hirsch discloses Hirsch provides data regarding the number of medical professionals associated with a specific geographic region, see page 4, Table 5 which includes information on all hospital addresses and servicing medical professionals identified by specialties and uses that data in determining treatment for a patient.
Accordingly, the rejection is upheld.
In conclusion, all of the limitations which Applicant disputes as missing in the applied references, including the features newly added by amendment, have been fully addressed by the Office as either being fully disclosed or obvious in view of the collective teachings of McRaith, Caffarel and Hirsch, based on the logic and sound scientific reasoning of one ordinarily skilled in the art at the time of the invention, as detailed in the remarks and explanations given in the preceding sections of the present Office Action and in the prior Office Actions (13 May 2025, 1 November 2024 and 25 June 2024), and incorporated herein.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Mark Holcomb, whose telephone number is 571.270.1382. The Examiner can normally be reached on Monday-Friday (8-5). If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Kambiz Abdi, can be reached at 571.272.6702.
/MARK HOLCOMB/
Primary Examiner, Art Unit 3685
10 November 2025