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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 December 11, 2025 has been entered.
Status of Claims
This office action for the 18/099809 application is in response to the communications filed December 11, 2025.
Claims 1, 8 and 15 were amended December 11, 2025.
Claims 1, 2, 4-9, 12-16 and 18-20 are currently pending and considered below.
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, 2, 4-9, 12-16 and 18-20 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.
As per claim 1,
Step 1: The claim recites subject matter within a statutory category as a process.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of a method comprising: generating a training dataset including historical holistic treatment processes and data associated with a first user, determining that a quantity of data in the training dataset is below a threshold, augmenting the training data by including data associated with one or more second users, wherein the first user and the one or more second users are associated with at least one common feature, receiving an identification of one or more symptoms, the one or more symptoms being associated with a user profile; using the identification of the one or more symptoms and the user profile, generate a holistic treatment process; receiving on-going performance data corresponding to execution of the holistic treatment process over a first time interval; using the on-going performance data and the user profile to generate a predicted degree of interdependence between holistic classes, wherein the predicted degree of interdependence is represented by a weight value usable to generate holistic treatment processes, and using the weight value and the performance data generate a revised holistic treatment process that is more likely to alleviate the one or more symptoms. These steps, as drafted, under the broadest reasonable interpretation are directed to:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. The identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recites a list of rules or instructions that a human person can follow in the course of their personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“computer-implemented”, “training a machine-learning model using the training dataset and over a set of continuous iterations according to an accuracy threshold”, “executing a machine-learning model”, “the machine-learning model being configured to”, “executing the machine learning model”, “dynamically executing a reinforcement training iteration for the machine-learning model”, and “to generate an updated machine-learning model, wherein the updated machine-learning model is configured to” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0064] of the as-filed specification describes that the technology that implements the steps of the abstract idea is at a level of a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 2,
Claim 2 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 2 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the first time interval is dynamically defined based on the performance data and an accuracy metric associated with the machine-learning model.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 4,
Claim 4 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 4 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein presenting the holistic treatment process includes presenting a tutorial corresponding to the treatment protocols.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 5,
Claim 5 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 5 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein a portion of the performance data associated with a particular interdependent holistic class is received from a remote device” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“wherein the remote device hosts an application that corresponds to the particular interdependent holistic class.” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 6,
Claim 6 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 6 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“further comprising: receiving, after an expiration of the first time interval, feedback corresponding to the holistic treatment process from the user and at least one user device, wherein the feedback includes an indication as to whether the one or more symptoms have been alleviated;” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“training the machine-learning model using reinforcement learning based on the feedback, wherein training the machine-learning model improves a subsequent holistic treatment process generated for the user.” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 7,
Claim 7 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“further comprising: generating … a value for each interdependent holistic class, wherein the value represents a degree of user wellness relative to the interdependent holistic class;” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“by the machine-learning model using the user profile” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
“generating a first user interface including a representation of each interdependent holistic class of the set of interdependent holistic classes, wherein the representation of each interdependent holistic class is based on the value associated with that interdependent holistic class; and presenting the first user interface.” introduces additional elements that is insufficient to provide a practical application or significantly more:
Step 2A Prong 2: In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as:
“generating a first user interface including a representation of each interdependent holistic class of the set of interdependent holistic classes, wherein the representation of each interdependent holistic class is based on the value associated with that interdependent holistic class; and presenting the first user interface.” which corresponds to mere data gathering and/or output.
Step 2B: As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, identified as insignificant extra-solution activity to the abstract idea, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as:
computer functions that have been identified by the courts 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, see MPEP 2106.05(d)(II), such as:
“generating a first user interface including a representation of each interdependent holistic class of the set of interdependent holistic classes, wherein the representation of each interdependent holistic class is based on the value associated with that interdependent holistic class; and presenting the first user interface.” which corresponds to receiving or transmitting data over a network.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 8,
Claim 8 is substantially similar to claim 1. Accordingly, claim 8 is rejected for the same reasons as claim 1.
As per claim 9,
Claim 9 is substantially similar to claim 2. Accordingly, claim 9 is rejected for the same reasons as claim 2.
As per claim 11,
Claim 11 is substantially similar to claim 4. Accordingly, claim 11 is rejected for the same reasons as claim 4.
As per claim 12,
Claim 12 is substantially similar to claim 5. Accordingly, claim 12 is rejected for the same reasons as claim 5.
As per claim 13,
Claim 13 is substantially similar to claim 6. Accordingly, claim 13 is rejected for the same reasons as claim 6.
As per claim 14,
Claim 14 is substantially similar to claim 7. Accordingly, claim 14 is rejected for the same reasons as claim 7.
As per claim 15,
Claim 15 is substantially similar to claim 1. Accordingly, claim 15 is rejected for the same reasons as claim 1.
As per claim 16,
Claim 16 is substantially similar to claim 2. Accordingly, claim 16 is rejected for the same reasons as claim 2.
As per claim 18,
Claim 18 is substantially similar to claim 4. Accordingly, claim 18 is rejected for the same reasons as claim 4.
As per claim 19,
Claim 19 is substantially similar to claim 5. Accordingly, claim 19 is rejected for the same reasons as claim 5.
As per claim 20,
Claim 20 is substantially similar to claim 6. Accordingly, claim 20 is rejected for the same reasons as claim 6.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 4-9, 12-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bharmi et al. (US 2021/0020294; herein referred to as Bharmi) in view of Brunner (US 2017/0249434).
As per claim 1,
Bharmi teaches a computer-implemented method comprising: generating a training dataset including historical holitstic processes and data associated with a first user, augmenting the training data by including data associated with one or more second users, wherein the first user and the one or more second users are associated with at least one common feature, receiving an identification of one or more symptoms, the one or more symptoms being associated with a user profile and executing a machine-learning model using the identification of the one or more symptoms and the user profile, the machine-learning model being configured to generate a holistic treatment process:
(Paragraphs [0006], [0016], [0238], and [0313] of Bharmi. The teaching describes methods, systems and devices are provided that integrate external diagnostic information (e.g., laboratory or point of care diagnostic information) with streaming data from implantable medical devices in order to provide a holistic patient management environment. The method and system may apply one or more application specific models (ASM) to calculate the health risk index. The ASM may be implemented as at least one of a threshold-based algorithm, template correlation algorithm, lookup table, decision tree, or machine learning algorithm. The ASM may utilize one or more sources of information as confirmatory evidence. For example, behavior related medical data may be used as confirmatory evidence, such by having the patient enter self-reported quality of life information from the patient, signs and symptoms indicating fatigue, lack of mobility/exercise and the like. The prioritization algorithm may utilize predetermined weights for each of the risk score, time since last appointment, and medication titration data in determining the priority score. In an example the prioritization algorithm is a machine learning algorithm and modifies the weights over time based on patient data related to previous patients that experience the event, such as a heart failure event of a patient compared to their risk score at the time of the event.)
Bharmi further teaches receiving on-going performance data corresponding to execution of the holistic treatment process over a first time interval:
(Paragraph [0189] of Bharmi. The teaching describes that the processor updates a patient medical record with the BGA Index and BGA data, as well as any medication information (e.g., a current prescription, a prescription change, compliance with prescribed medication, or the like) and any other test results of interest from the BGA data. Optionally, the processor may update the patient medical record to include historic information as appropriate based on patient history.)
Bharmi further teaches dynamically executing a reinforcement training iteration for the machine-learning model using the performance data to generate an updated machine-learning model, wherein the updated machine-learning model is configured to generate a revised holistic treatment process that is more likely to alleviate the one or more symptoms:
(Paragraph [0211] of Bharmi. The teaching describes that the ASM may determine that additional information is warranted. For example, the ASM may determine that updates or re-measurements of one or more IMD and/or BGA parameters is warranted. In the event that additional IMD data is needed, the ASM may convey a device command to the IMD to obtain and return the additional desired IMD data. Additionally or alternatively, the ASM may desire additional BGA data. For example, the ASM may provide an instruction to the patient to remeasure the PAP, if the patient has not already performed a remeasurement. Once the additional information is collected, the ASM may complete the analysis of the original and additional IMD, BGA and/or BRM data. In the event that the PAP level remains elevated, the ASM may render a diagnosis and treatment recommendation to treat the elevated PAP level)
Bharmi does not explicitly teach executing the machine-learning model using the on-going performance data and the user profile to generate a predicted degree of interdependence between holistic classes, wherein the predicted degree of interdependence is represented by a weight value usable to generate holistic treatment processes.
However, Brunner teaches a machine-learning model that uses medical information to generate a predicted degree of interdependence between variables wherein the predicted degree of interdependence is represented by a weight value usable to generate treatment recommendations:
(Paragraphs [0020]-[0031], [0200] and [0221] of Brunner. The teaching describes monitoring a present or prospective condition of a first subject which may comprise: processing the dataset against two or more analytical algorithms in a plurality of analytical algorithms to obtain a plurality of analytical algorithm results; selecting weights for each respective functional domain in the dataset; and applying a metalearner ensemble algorithm to integrate and weight the individual analytical algorithm results to create an integrated answer for the query thereby monitoring a present or prospective condition of the first subject. In some embodiments, the processing a), selecting b), and applying c) may be repeated until the integrated answer satisfies an optimization threshold. In some embodiments, prior to executing the query, the method further comprises structuring any unstructured data in said dataset using a data formatting algorithm. Prior to executing the query, the method may further comprise the step of analyzing the dataset to determine if it is incomplete and, when the dataset is deemed incomplete, the method further comprises imputing additional data points in the dataset, wherein the additional data points are derived from data relating to the subject, a group of subjects similar to the first subject, or a normative dataset. In some embodiments, the method may further comprise comprising treating or modifying a current treatment of the first subject for the present or prospective condition based upon the integrated answer. In some embodiments, the method may comprise treating or modifying a current treatment of the first subject for the present or prospective condition based upon the integrated answer that satisfies the optimization threshold. The manner in which algorithms are combined can be dynamically improved by analysis of the correlation between their answers. Combining answers from multiple non-independent algorithms may produce a suboptimal solution to a query. In some embodiments, it is preferable to have fewer independent algorithms that many correlated algorithms. The ability to explore correlations between algorithms in a large dataset allows the examination of their interdependence. For example, simple and polynomial algorithms could be reasonably expected to be non-independent. FIG. 8 exemplifies a network depicting different insomnia types (see Data Analysis Example 1), visualized with TDA after PCA dimensionality reduction, in which clusters are composed of subjects presenting with similar sleep patterns.)
Brunner further teaches determining that a quantity of data in the training dataset is below a threshold:
(Paragraph [0208] of Brunner. The teaching describes that different types of training sets (where training sets are subsets of the data used to train classifiers, as opposed to testing sets which are subsets of data kept aside to assess the accuracy of trained classifiers) may be used for each classifier in need of training, to reduce the amount of correlation between trained algorithms and reduce classification error due to inter algorithm-dependencies. In another embodiment, this can be accomplished through the training sets using only a subset of the available features from each domain to train the different algorithms, thus providing again some variability in the ability of the trained classifiers to model that data, and make predictions and classifications. Features can be withheld uniformly across domains (feature reduction) or from a particular domain (domain reduction). Diversity between training sets can also be achieved by resampling the original dataset with replacement (bagging), thus artificially and differentially enlarging the different training sets.)
Brunner further teaches training a machine-learning model using the training dataset and over a set of continuous iterations according to an accuracy threshold:
(Paragraph [0196] of Brunner. The teaching describes that once the query is processed the resulting answer is improved through an iterative process triggered by Ensemble Metalearner Module 17. Such algorithm may request alternative domain gains and or algorithms to improve the answer accuracy. The final optimal answer is available to the user through an Answer Output Module 18. Optimizing the answers in a dynamic way is one embodiment of this analytical platform.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the principle component analysis teachings of Bharmi, the weighted principle component analysis teachings of Brunner. Paragraph [0045] of Brunner teaches that the integrated platform may provide for the development of better and/or more effective therapies. In some embodiments, the integrated platform may allow the correct therapy to be identified for a patient. The ability of the present invention to capture subtle yet reliable health profiles and acute signatures allows for accurate tracking of people's response to treatments and improvement in treatment options. One of ordinary skill in the art in possession of Bharmi would have looked to Brunner to achieve these advantages. One of ordinary skill in the art would have added to the teaching of Bharmi, the teaching of Brunner based on these incentives without yielding unexpected results.
As per claim 2,
The combined teaching of Bharmi and Brunner teaches the limitations of claim 1.
Bharmi further teaches wherein the first time interval is dynamically defined based on the performance data and an accuracy metric associated with the machine-learning model:
(Paragraphs [0295] and [0296] of Bharmi. The teaching describes methods and systems for analyzing any and all available sources of patient data (e.g., IMD data, BGA data, etc.) and performing a risk assessment that the patient will experience a predetermined event, such as a heart failure (HF) episode or other episode warranting hospitalization. The risk assessment includes an assessment of whether the patient will experience the predetermined event within a predetermined period of time (e.g., the next 30 days, 60 days). Additionally or alternatively, embodiments herein may prioritize patients for clinician management. By integrating an analysis of multiple types of data from dis-similar data sources, embodiments afford a more accurate determination of risk assessment. The ASM analyzes IMD and BGA data to generate a diagnosis and treatment recommendation.)
As per claim 4,
The combined teaching of Bharmi and Brunner teaches the limitations of claim 1.
Bharmi further teaches wherein presenting the holistic treatment process includes presenting a tutorial corresponding to the treatment protocols:
(Paragraphs [0013] and [0173] of Bharmi. The teaching describes that methods, systems and devices are provided that incorporate digital healthcare management and patient application features, as implemented on handheld devices, to leverage information known about various food products and a patient's diet. For example, smart phone-based applications make it simple to track calorie, salt and fat intake, and to combine such information with activity collected by an IMD, wearable device and the like. The information is analyzed to identify a treatment diagnosis and recommendation. Among other things, the recommendation may include a communication to a patient and/or caregiver that includes educational material and/or feedback regarding negative/positive health consequences of the patient's lifestyle choices. The recommendation may inform a patient of trends in certain physiologic characteristics of interest and provide feedback and/or encouragement when patient data improves or indicates a positive trend. FIG. 5B illustrates a distributed healthcare system 550 that collects and analyzes patient data in accordance with embodiments herein. The system 550 includes one or more patient data entry (PDE) devices 560 that communicate over a network 562 with various other devices, such as IMDs, BGA test devices, MP devices, local external devices, servers and the like. The network 562 may represent the World Wide Web, a local area network, a wide area network and the like. When the PDE device 560 includes a GUI, the patient or other user may input patient data in addition to IMD data and BGA data. Here, the words of the recommendation is construed as a tutorial corresponding to the treatment protocols.)
As per claim 5,
The combined teaching of Bharmi and Brunner teaches the limitations of claim 1.
Bharmi further teaches wherein a portion of the performance data associated with a particular interdependent holistic class is received from a remote device, wherein the remote device hosts an application that corresponds to the particular interdependent holistic class:
(Paragraph [0013] of Bharmi. The teaching describes methods, systems and devices are provided that incorporate digital healthcare management and patient application features, as implemented on handheld devices, to leverage information known about various food products and a patient's diet. For example, smart phone-based applications make it simple to track calorie, salt and fat intake, and to combine such information with activity collected by an IMD, wearable device and the like.)
As per claim 6,
The combined teaching of Bharmi and Brunner teaches the limitations of claim 1.
Bharmi further teaches further comprising: receiving, after an expiration of the first time interval, feedback corresponding to the holistic treatment process from the user and at least one user device, wherein the feedback includes an indication as to whether the one or more symptoms have been alleviated; and training the machine-learning model using reinforcement learning based on the feedback, wherein training the machine-learning model improves a subsequent holistic treatment process generated for the user:
(Paragraph [0189] of Bharmi. The teaching describes that the processor updates a patient medical record with the BGA Index and BGA data, as well as any medication information (e.g., a current prescription, a prescription change, compliance with prescribed medication, or the like) and any other test results of interest from the BGA data. Optionally, the processor may update the patient medical record to include historic information as appropriate based on patient history.)
(Paragraph [0211] of Bharmi. The teaching describes that the ASM may determine that additional information is warranted. For example, the ASM may determine that updates or re-measurements of one or more IMD and/or BGA parameters is warranted. In the event that additional IMD data is needed, the ASM may convey a device command to the IMD to obtain and return the additional desired IMD data. Additionally or alternatively, the ASM may desire additional BGA data. For example, the ASM may provide an instruction to the patient to remeasure the PAP, if the patient has not already performed a remeasurement. Once the additional information is collected, the ASM may complete the analysis of the original and additional IMD, BGA and/or BRM data. In the event that the PAP level remains elevated, the ASM may render a diagnosis and treatment recommendation to treat the elevated PAP level)
As per claim 7,
The combined teaching of Bharmi and Brunner teaches the limitations of claim 1.
Bharmi further teaches further comprising: generating, by the machine-learning model using the user profile, a value for each interdependent holistic class, wherein the value represents a degree of user wellness relative to the interdependent holistic class, generating a first user interface including a representation of each interdependent holistic class of the set of interdependent holistic classes, wherein the representation of each interdependent holistic class is based on the value associated with that interdependent holistic class presenting the first user interface:
(Paragraphs [0013] and [0173] of Bharmi. The teaching describes that methods, systems and devices are provided that incorporate digital healthcare management and patient application features, as implemented on handheld devices, to leverage information known about various food products and a patient's diet. For example, smart phone-based applications make it simple to track calorie, salt and fat intake, and to combine such information with activity collected by an IMD, wearable device and the like. The information is analyzed to identify a treatment diagnosis and recommendation. Among other things, the recommendation may include a communication to a patient and/or caregiver that includes educational material and/or feedback regarding negative/positive health consequences of the patient's lifestyle choices. The recommendation may inform a patient of trends in certain physiologic characteristics of interest and provide feedback and/or encouragement when patient data improves or indicates a positive trend. FIG. 5B illustrates a distributed healthcare system 550 that collects and analyzes patient data in accordance with embodiments herein. The system 550 includes one or more patient data entry (PDE) devices 560 that communicate over a network 562 with various other devices, such as IMDs, BGA test devices, MP devices, local external devices, servers and the like. The network 562 may represent the World Wide Web, a local area network, a wide area network and the like. When the PDE device 560 includes a GUI, the patient or other user may input patient data in addition to IMD data and BGA data.)
As per claim 8,
Claim 8 is substantially similar to claim 1. Accordingly, claim 8 is rejected for the same reasons as claim 1.
As per claim 9,
Claim 9 is substantially similar to claim 2. Accordingly, claim 9 is rejected for the same reasons as claim 2.
As per claim 11,
Claim 11 is substantially similar to claim 4. Accordingly, claim 11 is rejected for the same reasons as claim 4.
As per claim 12,
Claim 12 is substantially similar to claim 5. Accordingly, claim 12 is rejected for the same reasons as claim 5.
As per claim 13,
Claim 13 is substantially similar to claim 6. Accordingly, claim 13 is rejected for the same reasons as claim 6.
As per claim 14,
Claim 14 is substantially similar to claim 7. Accordingly, claim 14 is rejected for the same reasons as claim 7.
As per claim 15,
Claim 15 is substantially similar to claim 1. Accordingly, claim 15 is rejected for the same reasons as claim 1.
As per claim 16,
Claim 16 is substantially similar to claim 2. Accordingly, claim 16 is rejected for the same reasons as claim 2.
As per claim 18,
Claim 18 is substantially similar to claim 4. Accordingly, claim 18 is rejected for the same reasons as claim 4.
As per claim 19,
Claim 19 is substantially similar to claim 5. Accordingly, claim 19 is rejected for the same reasons as claim 5.
As per claim 20,
Claim 20 is substantially similar to claim 6. Accordingly, claim 20 is rejected for the same reasons as claim 6.
Response to Arguments
Applicant's arguments filed December 11, 2025 have been fully considered.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 101 are not persuasive.
The Applicant argues that the pending claims integrate the alleged abstract idea into a practical application of improving machine-learning operations and recite significantly more than the abstract idea.
The Examiner respectfully disagrees. There is no evidence presented by the Applicant to support their argument. There is nothing in the pending claims that the Examiner sees that would lend them to establishing an improvement to technology. Accordingly, this is a bare assertion of improvement and such an argument is not persuasive to the Examiner.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 103 are not persuasive.
The Applicant argues that the pending claims are not taught by the prior art of record.
The Examiner respectfully disagrees. The Applicant has not presented any reasoning to support their position. In contrast, the Examiner has provided detailed reasoning as to why the combine detaching of Bharmi and Brunner teaches the limitations of the pending claims.
Conclusion
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/CHAD A NEWTON/Primary Examiner, Art Unit 3686