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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 9/19/2025 has been entered.
Status of the Claims
The current status of the claims is as follows: Claims 6-7 and 16-17 remain cancelled. Claims 1-5, 8-15, and 18-20 are currently amended. Claims 21-22 are new. Claims 1-5, 8-15, and 18-22 are currently pending in the application and have been considered below.
Response to Amendment
Rejection Under 35 USC 112(a)
The claims have been amended to remove the subject matter previously rejected under 35 USC 112(a) such that the corresponding rejections are withdrawn.
Rejection Under 35 USC 101
The claims have been amended but the 35 USC 101 rejections are upheld.
Rejection Under 35 USC 103
The amendments made to the claims introduce limitations that are not fully addressed in the previous office action, and thus the corresponding 35 USC 103 rejections are withdrawn. However, Examiner will consider the amended claims in light of an updated prior art search and address their patentability with respect to prior art below.
Response to Arguments
Rejection Under 35 USC 101
On page 11 of the response filed 9/19/2025 Applicant argues that the amended claims are “directed to a specific technological solution to a medical problem, that is, how to efficiently and quickly generate an anti-aging treatment customized for an individual patient.” Applicant further asserts that the amended claims “generate a tangible, practical outcome, the customized final anti-aging treatment” by using “an apparatus with a processor and memory that processes specific data (patient data, diagnostic test data) and applies specific computational steps (machine learning, titration)” and thus are patent eligible. Applicant’s arguments are fully considered, but are not persuasive. Examiner maintains that the functions of evaluating patient and diagnostic test data to generate BHRT and anti-aging treatments describe an abstract idea in the form of a certain method of organizing human activity because a human actor such as a clinician managing their personal behavior and interactions with a patient may achieve the same functions. The use of a processor and machine learning to automate/digitize these otherwise-abstract steps such that they occur in a more efficient manner in an electronic environment amount to instructions to “apply” the exception (see MPEP 2106.05(f)) and do not confer eligibility. Examiner notes that Examiner notes that per MPEP 2106.05(f)(2), “‘claiming the improved speed or efficiency inherent with applying the abstract idea on a computer’ does not integrate a judicial exception into a practical application or provide an inventive concept.” Applicant has not provided any evidence that the implementation of the abstract idea with computing components provides any technical improvements beyond the improved speed or efficiency inherent with applying treatment recommendation and adjustment operations in a computing environment.
On pages 12-13 of the response Applicant argues that the amended claims “integrat[e] specific, non-conventional analyses and processes to solve the specific technical problem of providing a customized anti-aging treatment” and are directed “to an apparatus configured to perform complex computational tasks that are beyond the mental capacity of a human.” Applicant further points to aspects of training/retraining the machine learning module as “an integral part of the solution” that amount to a practical application. Applicant’s arguments are fully considered, but are not persuasive. Though Examiner concedes that use of a machine learning module to perform the analysis and treatment generation steps is not within a human actor’s capacity, none of the underlying claimed functions are beyond a human actor’s capacity. Examiner maintains that the steps of analyzing user data and diagnostic test data to generate a first BHRT by comparing diagnostic test data to baseline hormone levels from a database, generating a customized treatment program based on known correlations between patient information and treatment outcomes, and titrating the first BHRT to generate a second BHRT based on the patient’s response to a first BHRT are all steps that could practically be achieved during ordinary patient diagnosis and treatment recommendation and monitoring interactions between a patient and clinician. The use of specifically a machine learning module to achieve some of these otherwise-abstract analysis functions then amounts to instructions to “apply” the exception with a high-level computing component because it is merely invoked as a tool with which to automate or digitize the ordinary clinician-patient interactions. Further, no improvements to the training or retraining of such a module are indicated; machine learning itself is the process by which a model is trained and retrained as new data becomes available, such that this element is claimed in its ordinary capacity as being applied to the business use case of medical treatment recommendation and adjustment. Despite Applicant’s assertions, no specific technical problem appears to be solved by the present invention, nor are any improvements to computer technology or the technical field of machine learning being made; rather, known computer components and machine learning techniques are being applied to the established business field of anti-aging medical evaluation and treatment recommendation.
On page 13 Applicant argues that “claims 1 and 11 recite a transformative process whereby raw patient and diagnostic data is transformed into a practical medical prescription, i.e., the customized anti-aging treatment.” Applicant’s arguments are fully considered, but are not persuasive. The transformation of patient and diagnostic data into an anti-aging treatment recommendation does not amount to a particular transformation as described in MPEP 2106.05(c): “An ‘article’ includes a physical object or substance. The physical object or substance must be particular, meaning it can be specifically identified. ‘Transformation’ of an article means that the ‘article’ has changed to a different state or thing. Changing to a different state or thing usually means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that the article has been transformed. Purely mental processes in which thoughts or human based actions are ‘changed’ are not considered an eligible transformation. For data, mere ‘manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’’ has not been deemed a transformation” (emphasis added). Thus, Applicant’s arguments are not persuasive at least because patient data and diagnostic test data are not physical or tangible ‘articles’ and because the manipulation or analysis of data does not amount to a particular transformation.
On pages 13-15 of the response Applicant argues that “the following elements at least would amount to significantly more than an abstract idea: (i) the machine learning module for customized treatment, (ii) epigenetic and telomere diagnostic data, and (iii) the automated titration of bioidentical hormones.” Applicant’s arguments are fully considered, but are not persuasive. Examiner has addressed the use of high-level, known machine learning techniques to the use case of anti-aging and BHRT treatment fields above, showing that this element amounts to instructions to “apply” the abstract idea in a digitized/automated manner. Examiner further notes that it is well-understood, routine, and conventional to utilize machine learning modules iteratively trained using correlations between user data and treatment types to generate a customized treatment plan for a patient, as evidenced by at least Goldsmith et al. (US 20200350073 A1) [0096]-[0101]; Haase (US 20220406430 A1) abstract, [0017], & [0043]-[0045]; and Shtraizent et al. (US 20230015833 A1) Fig. 6, [0077], [0094]-[0100], & [0139]-[0144]. Such use and training of machine learning modules thus does not provide improved technological results over conventional industry practice because no improved methods of machine learning use or training are achieved beyond that which is conventional in the field.
Regarding (ii), Examiner notes that epigenetic and telomere diagnostic data are not required in the claims as presently drafted, which could be met by the consideration of a hormone level test. Regardless, the content or type of the evaluated diagnostic test data does not amount to an additional element, because these are types of tests that a clinician would be able to obtain and evaluate the results of when making treatment recommendations or adjustments. Accordingly, (ii) is considered part of the abstract idea itself. Similarly, (iii) describes a process for titration (i.e. dosage adjustment) of bioidentical hormone therapies, which is a process that a clinician would be capable of achieving during ordinary treatment monitoring interactions with a patient, as explained above. Accordingly, (iii) is also considered part of the abstract idea itself. Because these functions are part of the abstract idea itself, they cannot provide “significantly more” than the abstract idea and thus do not confer eligibility (see MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” See also 2106.05(a)(II): “it is important to keep in mind that an improvement in the abstract idea itself… is not an improvement in technology.”)
For the reasons explained above, the 35 USC 101 rejections are upheld.
Rejection Under 35 USC 103
On pages 15-16 of the response Applicant argues that neither Haase nor Norman teach titrating the first BHRT to generate a second BHRT based on the patient’s response to the first BHRT. Applicant specifically submits that Norman does not mention the word ‘titrate’ and instead merely mentions treatment adjustments at a high level “without any teaching or suggestion of how such adjustment may be performed,” pointing to portions of [0118] & [0122] of that reference. Applicant further alleges that “paragraph [0122] actually teaches away from automatically adjusting the prescription and instead recommends generating a notification to a doctor who would presumably make a manually [sic] adjustment to the prescribed dosage. In contrast, amended claim 1 of the present application requires the apparatus to automatically perform the titration.” Applicant’s arguments are fully considered, but are not persuasive. Though para. [0019] of Applicant’s specification includes the definition of titration in the field of chemistry (stating that “’Titration as used in this disclosure is defined as a technique where a solution of a known concentration is used to determine the concentration of an unknown solution. Typically the titrant (known solution) is added from a burette to a known quantity of the analyte (unknown solution) until the reaction is complete”), the example instances of titration that it goes on to explain do not reflect this chemistry definition, and instead reflect the definition of titration in drug or pharmacology fields (see Reference V on the accompanying PTO-892, noting “drug titration is the process of adjusting the dose of a medication for the maximum benefit without adverse effects”). For example, para. [0019] states (with emphasis added):
For example, the processor 108 may receive the patient response such as patient's clinical and laboratory results according to the first bioidentical hormone replacement therapy 136. For example, patient response may include a changed hormone level. Based on the results, it may be advantageous for the processor 108 to titrate or modify the first BHRT. A processor 108 may receive information that such as patient's age, gender, lifestyle, stress level and severity of symptoms and information that patient's symptoms have not been alleviated. Processor 108 may then titrate or modify the first BHRT to a slightly higher dose in order to alleviate patient's symptoms. Processor 108 may be configured to titrate or modify the first BHRT to a higher dose by comparing the original first BHRT to a stored value and identifying the appropriate titration. For example, if patient presents with complaints of low energy levels processor 108 may receive patient's laboratory blood results which show a low level of testosterone and processor 108 may titrate the first BHRT to a slightly higher dose in order to increase patient's energy levels. For example, processor may obtain the standard/normal testosterone hormone level for patient based on patient input and compare it to patient's actual testosterone hormone level considering the first BHRT, based on this information, the processor 108 may titrate the first BHRT in order to optimize patient's testosterone level.
These examples show that “titration” in the context of the invention can include the common pharmacology definition of adjusting the dosage of a drug based on a patient’s response to the treatment. Further, there is no disclosure of any mechanisms by which the processor would be able to achieve the chemistry-based process of titration in the manner claimed; it is unclear how a processor would “generate a second BHRT based on the patient’s response to the first BHRT” by adding a solution of a known concentration to a known volume of a substance of unknown concentration, and there is no description of the apparatus including any laboratory hardware (e.g. beakers, burettes, robotic controllers, etc.) that would allow it to perform a chemistry-based titration. Because “titrating” a substance in the instant invention is clearly intended to include a processor making determinations about adjusting the dosage of a medication, Examiner maintains that Norman does adequately teach this aspect of claims 1 and 11. For example, paras. [0117]-[0122] describe a process by which a computerized system iteratively adjusts the dosage of one or more hormones in a hormone therapy regimen based on the patient’s reaction or response to the hormones, which amounts to “titrating” the treatments as currently recited by the claims. Further, Norman does not “teach away” from the apparatus automatically performing this operation – it shows that the processor automatically performs the titration determinations via analysis program 304.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-5, 8-10, and 21-22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “receive diagnostic test data from a plurality of medical diagnostic testing procedures, the plurality of testing procedures including one of an epigenetic test, a telomere test, and a hormone level test.” Since the claim recites “a plurality” (i.e. two or more) of medical diagnostic testing procedures, but then specify that the plurality of testing procedures includes “one of” three types of tests, it is unclear how many testing procedures are actually required by the claim, rendering its scope indefinite. For purposes of examination, Examiner will interpret this limitation as receiving diagnostic test data from at least one medical diagnostic testing procedure including at least one of an epigenetic test, a telomere test, and a hormone level test. Claims 2-5, 8-10, and 21-22 are also rejected on this basis because they inherit the indefinite limitation due to their dependence on claim 1.
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 1-5, 8-15, and 18-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
In the instant case, claims 1-5, 8-10, and 21-22 are directed to an apparatus (i.e. a machine) and claims 11-15 and 18-20 are directed to a method (i.e. a process). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A – Prong 1
Independent claims 1 and 11 recite steps that, under their broadest reasonable interpretations, cover certain methods of organizing human activity, e.g. managing personal behavior, relationships, or interactions between people. Specifically, claim 1 (as representative) recites:
An apparatus for generating a customized anti-aging treatment, the apparatus comprising:
a processor; and
a memory communicatively connected to the processor, the memory containing instructions that, when executed by the processor, configure the apparatus to:
receive patient data comprising patient stress level and severity of aging-related symptoms;
receive diagnostic test data from a plurality of medical diagnostic testing procedures, the plurality of testing procedures including one of an epigenetic test, a telomere test, and a hormone level test;
analyze the patient data and diagnostic test data to generate a first bioidentical hormone replacement therapy (BHRT), wherein the analysis includes comparing the diagnostic test data to baseline hormone levels from a database;
generate a customized treatment program utilizing a machine learning module, the machine learning module trained using historical patient and diagnostic test data to correlate patient information with treatment outcomes;
titrate the first BHRT to generate a second BHRT based on the patient’s response to the first BHRT; and
generate a final anti-aging treatment by combining, at least, the first BHRT, the second BHRT, and the customized treatment program.
But for the recitation of generic computing components like a processor, memory, and high-level machine learning, each of the italicized steps, when considered as a whole, describe interactions that could occur between a patient and a clinician during ongoing treatment planning appointments. For example, a patient could converse with a clinician to let them know about their recent stress levels and aging-related symptom severities. The clinician could also solicit and receive the results of diagnostic testing procedures (e.g. hormone level tests) and use all of the gathered patient information to come up with a first BHRT treatment for the patient by using their medical expertise and making comparisons of the patient-specific data to baseline or known normal hormone levels, and create a customized treatment program utilizing recommendation models trained/fitted based on historical patient data correlated with treatment outcomes. The clinician could then continue communicating with the patient to monitor the results of ongoing BHRT treatment and make adjustments to the regimen, and generate a final anti-aging treatment for the patient based on all of the available information. Thus, this claim describes steps for managing personal behavior of a clinician and interactions with a patient during a diagnostic and treatment planning and monitoring process, and accordingly claim 1 recites an abstract idea in the form of a certain method of organizing human activity. Claim 11 recites substantially similar limitations such that it also recites an abstract idea under the same analysis.
Dependent claims 2-5, 8-10, 12-15, and 18-22 inherit the limitations that recite an abstract idea from their dependence on claims 1 or 11, and thus these claims also recite an abstract idea under the Step 2A – Prong 1 analysis. In addition, claims 2-5, 8-10, 12-15, and 18-22 recite further limitations that, under their broadest reasonable interpretations, further describe the abstract idea recited in the independent claims. Specifically, claims 2 and 12 recite receiving updated diagnostic test data after the patient has undergone at least a round of customized treatment, using the updated data to retrain or adjust the treatment recommendation model, and processing the updated diagnostic test data with the updated model to output the customized treatment program, which a clinician could accomplish as indicated above my continuing to monitor and interact with the patient during their treatment, using such data to update/refit any diagnostic or treatment recommendation models they use, and adjusting the patient’s treatment accordingly. Claims 2 and 12 further specify that the model is a neural network with various node layers and that training the neural network includes adjusting the connections and weights between nodes of adjacent layers, which describe mathematical operations for training an ANN such as applying training data to node layers, adjusting connections and weights between the layers, detecting additional mathematical correlations, and retraining the ANN based on the additional correlations. Claims 3-4 and 13-14 specify particular types of diagnostic test data that is received and analyzed, each of which are types of data that a clinician would be capable of receiving and analyzing during a treatment planning interaction. Claims 5, 8-9, 15, 18-19, and 21-22 specify various types of bioidentical hormone replacement therapy and anti-aging treatments, each of which are treatment types that a clinician would be capable of identifying as indicated for a patient based on received patient data. Claims 10 and 20 recite generating the final anti-aging treatment using updated diagnostic test data after the first BHRT, which a clinician would be able to achieve by updating a treatment recommendation based on updated data provided by a patient after they have tried out a first treatment.
However, recitation of an abstract idea is not the end of the analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A – Prong 2
The judicial exception is not integrated into a practical application. In particular, independent claims 1 and 11 do not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes the additional elements of an apparatus comprising a processor and a memory communicatively connected to the processor, the memory containing instructions that, when executed by the processor, configure the apparatus to perform the various steps; while claim 11 includes the additional element of a computing device with a processor performing the various steps. Claims 1 and 11 also each include the additional elements of generating the customized treatment program utilizing a trained machine learning module.
The use of high-level computing components (e.g. a computing device processor configured to perform the various functions via instructions stored in a memory) as in these claims merely serves to automate the abstract idea such that they amount to the words “apply it” with a computer. For example, receiving data, identifying therapies for a user, comparing various data, and adjusting treatments are steps that could otherwise be achieved by a clinician managing their personal behavior and interactions with a patient in a certain method of organizing human activity as explained above, and the use of the processor and computing device elements merely digitize/automate these functions so that they are achieved in an electronic environment. The use of the trained machine learning module to generate the customized treatment program also amounts to the words “apply it” with a computer because the actual use/execution of the machine learning module is recited at a high level of generality. That is, the claims merely recite that the trained machine learning module is used to provide the outcome of a customized treatment program, without placing any limits on how the trained ML module functions to achieve these outputs. Accordingly, each independent claim as a whole is directed to an abstract idea without integration into a practical application.
The judicial exception recited in dependent claims 2-5, 8-10, 12-15, and 18-22 is also not integrated into a practical application under a similar analysis as above. Claims 3-5, 8-10, 13-15, and 18-22 do not recite any new additional elements and are performed with the same computing elements as the independent claims such that they also amount to the words “apply it” with a computer as explained above. Claims 2 and 12 specify that the machine learning module is a neural network with a layered node structure that is trained by adjusting connections and weights between nodes in adjacent layers with a training algorithm. Examiner notes that the level of detail in these claims does not expand beyond what machine learning via neural networks itself actually is, which is a node-based layered data representation that may iteratively learn in some fashion by adjusting weights and connections in the network based on training data and subsequent retraining using prior outputs. Accordingly, the use of trained or retrained machine learning modules amount to the words “apply it” with computer components because they serve to merely automate and/or digitize steps/functions that are otherwise abstract (e.g. determining therapy regimens based on patient data and adjusting therapy regimens based on newly learned patient data).
Accordingly, the additional elements of claims 1-5, 8-15, and 18-22 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 1-5, 8-15, and 18-22 are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computing device processor configured with instructions stored in a memory to perform the receiving, analyzing, comparing, generating, titrating, adjusting, processing, outputting, etc. steps of the invention amount to mere instructions to apply the exception using generic computer components. As evidence of the generic nature of the above recited additional elements, Examiner notes at least paras. [0044]-[0052] of Applicant’s specification, disclosing many types and examples of known processor-based computing devices that can implement the invention. From these disclosures, one of ordinary skill in the art would understand that many types of generic processor-based computing devices could be used to implement the invention.
Regarding the use, training, and retraining of a machine learning module comprising an artificial neural network to generate the treatment programs, Examiner notes at least paras. [0018], [0025], & [0028]-[0035] of Applicant’s specification, disclosing many types and examples of known machine learning methods that may be utilized to achieve the functions of the invention. Further, Examiner notes that it is well-understood, routine, and conventional to utilize machine learning modules including neural networks iteratively trained using correlations between user data and treatment types to generate a customized treatment plan for a patient, as evidenced by at least Goldsmith et al. (US 20200350073 A1) [0096]-[0101]; Haase (US 20220406430 A1) abstract, [0017], [0043]-[0045], & [0059]; Crotts et al. (WO 2023164206 A1) [040]-[041] & [068]; and Shtraizent et al. (US 20230015833 A1) Fig. 6, [0077], [0094]-[0100], & [0139]-[0144].
Analyzing these additional elements as an ordered combination adds nothing that is not already present when considering the elements individually; the overall effect of the computer device processor and machine learning module in combination is to digitize and/or automate a treatment planning and adjustment operation that could otherwise be achieved as an abstract idea. Thus, when considered as a whole and in combination, claims 1-5, 8-15, and 18-22 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 8-15, and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Haase (US 20220406430 A1) in view of Norman (US 20200118662 A1).
Claims 1 and 11
Haase teaches an apparatus for generating a customized anti-aging treatment, the apparatus comprising: a processor; and a memory communicatively connected to the processor, the memory containing instructions that, when executed by the processor, configure the apparatus to (Haase [0080]-[0086], noting a system can be implemented as a processor configured to perform various operations by executing instructions stored in memory; see also [0036], noting the system can be directed to evaluating and treating age-related conditions like Alzheimer’s, dementia, late life depression, age related decline in immune responses, hair greying, etc.):
receive patient data comprising patient stress level and (Haase [0018]-[0020], noting the system receives user input, including user identifiers, constitutional history, and any data indicative of a person’s physiological state (considered to include aging-related symptoms like loss of capillary density as in [0021]); see also [0023], noting user input can include measures of cortisol, ratio of DHEAS to cortisol, considered to correlate with user stress level);
receive diagnostic test data from a plurality of medical diagnostic testing procedures, the plurality of testing procedures including one of an epigenetic test, a telomere test, and a hormone level test (Haase [0018], [0021]-[0033], noting the system receives user input including various types of diagnostic test data from a plurality of diagnostic tests, e.g. various assays, laboratory tests results, imaging results, biomarkers, etc.; see specifically [0023], noting the measurements may be related to endocrine function such as quantities of hormones like testosterone, estrogen, growth hormone, progesterone, etc. );
analyze the patient data and diagnostic test data to generate a first bioidentical hormone replacement therapy (BHRT), wherein the analysis includes comparing the diagnostic test data to baseline hormone levels from a database (Haase abstract, [0041]-[0043], noting the system can identify one or more compounds for a replacement therapy based on the user data and biomarkers from the diagnostic test data; such compounds can include bioidentical compounds and hormones for hormone replacement therapy as noted in [0003] & [0071], indicating that at least a first bioidentical hormone replacement therapy may be generated. See also [0038]-[0039], noting the system analysis can include comparison of a biomarker level for a patient (considered to include hormone levels as in [0023]) to known values or ranges from a control (i.e. baseline) group/sample);
generate a customized treatment program utilizing a machine learning module, the machine learning module trained using historical patient and diagnostic test data to correlate patient information with treatment outcomes (Haase abstract, [0043], [0055], [0059], noting a treatment composition (i.e. a first BHRT comprising a customized treatment program) may be determined via a first machine learning model (including an artificial neural network as in [0059]) created with training data that correlate user data and diagnostic test data inputs with therapy composition outputs);
(Haase [0017], noting that each step of the method may be repeated iteratively using outputs of previous steps as inputs to subsequent repetitions; see also [0053], noting user historical data including user outcome data of previously administered treatments may be stored and utilized by the system, indicating that prior treatments may be used as a basis for adjusting the first BHRT to generate a second BHRT, similar to the process described in [0043]); and
generate a final anti-aging treatment by combining, at least, the first BHRT, the second BHRT, and the customized treatment program (Haase [0041]-[0043], noting the system can output a composition of a replacement therapy treatment (e.g. an anti-aging treatment for an age-related condition as in [0036]) that is intended to alter or correct a user’s condition using a trained model; the trained model can incorporate historical data including user outcome of previously administered treatments as explained above, such that the system is considered to use a combination of the first and second BHRT and customized treatment programs when generating the anti-aging treatment).
Though Haase teaches that any type of user data (including cortisol levels considered to correlate with stress level of a user, and symptoms associated with aging as explained above) may be input to the system (see [0018], [0021], & [0023]), it fails to explicitly disclose that user data comprises severity of aging-related symptoms. However, Norman teaches a machine-learning-based hormone therapy recommendation system that allows for the input of user data comprising severity of symptoms (Norman Fig. 8C, [0101], noting a user can quantify the severity of symptoms like anxiety, crankiness, pain, unexplained weight gain, insomnia, etc. (considered to encompass aging-related symptoms) for use in hormone therapy recommendation). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the user inputs of Haase to include symptom severity as in Norman in order for a user to be able to precisely quantify the degree or severity of their symptoms so that more accurate therapy recommendations can be provided (as suggested by Norman [0012] & [0101]).
In summary, Haase in view of Norman teaches a system for machine learning-based generation of treatment regimens for a patient (including hormone replacement therapy) that allows for the iterative training and use of the machine learning model (see Haase [0017], [0043], & [0059]) as well as consideration of user historical data representing data from prior treatments administered and associated outcomes (see Haase [0053]). Haase further teaches the use of monitoring biomarkers to assess the effects of a therapeutic agent on a patient (see [0038]). Though the combination of these disclosures might suggest the titration of a current treatment based on patient response to generate a second treatment, Haase fails to actually explicitly disclose such an operation. That is, Haase fails to explicitly disclose titrate the first BHRT to generate a second BHRT based on the patient’s response to the first BHRT.
However, Norman further teaches a hormone therapy recommendation system that includes machine-learning analysis of historical user data to adjust (i.e. titrate) the dose of one or more hormonal compounds based on the user’s response to one or more other hormonal compounds (Norman Fig. 9, [0117]-[0122], noting that data indicating a user’s hormonal response to hormones H1 and H2 (i.e. first and second BHRTs) can be used to help guide adjustments in doses to H1 and/or H2 going forward). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system of the combination to include the actual titration/modification of a first treatment to arrive at a second treatment based on receipt and analysis of actual hormonal outcome/ response data to each round of treatment as in Norman in order to provide powerful learning and feedback capabilities so that recommended treatments can improve over time (as suggested by Norman [0117]).
Claim 11 recites substantially similar subject matter as claim 1, and is also rejected as above.
Claims 2 and 12
Haase in view of Norman teaches the apparatus of claim 1, and the combination further teaches wherein the machine learning module is a neural network that includes an input layer of nodes, at least one intermediate layer, and an output layer of nodes (Haase [0059], noting the machine learning model can be an artificial neural network comprising an input layer of nodes, at least one intermediate layers, and an output layer of nodes), and wherein the instructions, when executed by the processor, further configure the apparatus to:
receive, at the input layer of nodes, updated diagnostic test data of the patient after the patient has undergone at least a round of customized treatment; train the neural network by adjusting the connections and weights between nodes in adjacent layers using a training algorithm, wherein the training is based on the updated diagnostic test data, thereby correlating patient information with treatment outcomes to generate a refined model of the patient’s biological response (Haase [0059], noting machine learning algorithms can use training data to train the ANN by adjusting the weights connecting each adjacent layer of nodes. See also [0017], noting each step may be repeated iteratively, as well as [0043], noting a training dataset may be developed by receiving data from past iterations of previous user training data vectors (e.g. patient treatment response data as in [0053] and specifically including updated diagnostic tests as in Norman [0117]-[0122]), indicating that the training dataset may be updated as new iterations of inputs result in new outputs during the iterations described in [0017], thereby amounting to detecting additional correlations between output and input layers and retraining the machine-learning module as a function of the additional correlations);
process the updated diagnostic test data with the refined model of the patient’s biological response; and output the customized treatment program from the output layer of nodes (Haase [0017], noting each step of the method may be repeated iteratively using outputs of previous steps as inputs to subsequent repetitions, indicating that an iteratively retrained neural network with output nodes as in [0059] may be used to output the customized treatment program as described in [0043] by processing updated input data (e.g. diagnostic test data from after a first round of treatment as in [0053] and Norman [0117]-[0122])).
Claim 12 recites substantially similar subject matter as claim 2, and is also rejected as above.
Claims 3 and 13
Haase in view of Norman teaches the apparatus of claim 1, and the combination further teaches wherein the diagnostic testing procedures comprises an autoimmune test (Haase [0023], [0028], noting the user data from diagnostic tests can include data such as antinuclear antibody levels and brain autoimmunity markers, i.e. data from autoimmune tests).
Claim 13 recites substantially similar subject matter as claim 3, and is also rejected as above.
Claims 4 and 14
Haase in view of Norman teaches the apparatus of claim 1, and the combination further teaches wherein: the plurality of testing procedures comprises a plurality of male patient tests if the patient is male; and the plurality of testing procedures comprises a plurality of female patient tests if the patient is female (Haase [0023], noting the diagnostic test results can include male user tests like testosterone measurements, prostate-specific Ag measurements, and DHEA measurements, which are in line with the examples of male user tests provided in para. [0015] of Applicant’s specification; the diagnostic test results can also include female user tests like estradiol measurements, progesterone measurements, testosterone measurements, and DHEA measurements, which are in line with the examples of female user tests provided in para. [0015] of Applicant’s specification).
Claim 14 recites substantially similar subject matter as claim 4, and is also rejected as above.
Claims 5 and 15
Haase in view of Norman teaches the apparatus of claim 1, and the combination further teaches wherein the first BHRT comprises topical optimized hormones (Haase [0041], noting the identified replacement therapies (e.g. including bioidentical and/or hormone compounds as in [0071]) can have a variety of delivery mechanisms, including dermal delivery such that an identified therapy is considered to include topical optimized hormones. See also Norman [0094], noting hormonal treatments are often administered as topical creams).
Claim 15 recites substantially similar subject matter as claim 5, and is also rejected as above.
Claims 8 and 18
Haase in view of Norman teaches the apparatus of claim 1, and the combination further teaches wherein the second BHRT comprises a target (Haase [0041], noting the replacement therapy treatments are used for remediation of a therapeutic health problem and/or imbalance, i.e. the therapies have a target or goal).
Claim 18 recites substantially similar subject matter as claim 8, and is also rejected as above.
Claims 9 and 19
Haase in view of Norman teaches the apparatus of claim 8, and the combination further teaches wherein the second BHRT comprises customized peptides for the target (Haase [0042], noting a compound of a replacement therapy regimen can include peptides to address the target health problem and/or imbalance).
Claim 19 recites substantially similar subject matter as claim 9, and is also rejected as above.
Claims 10 and 20
Haase in view of Norman teaches the apparatus of claim 1, and the combination further teaches wherein generating the final anti-aging treatment comprises using updated diagnostic test data after the first BHRT (Haase [0017], [0043], noting the machine learning model may iteratively provide therapy recommendations, including anti-aging treatments for age-related condition as noted in [0036]; such recommendations can be based on user historical data representing data from prior treatments administered and associated outcomes as in [0043] & [0053]. See also Norman [0117]-[0122], noting treatments may be iteratively adjusted based on user responses indicating updated diagnostic test data after at least a first BHRT is administered. Taken together in the context of the combination, these disclosures teach that the anti-aging treatment may be based on updated diagnostic test data reflecting responses to administration of the first BHRT).
Claim 20 recites substantially similar subject matter as claim 10, and is also rejected as above.
Claim 21
Haase in view of Norman teaches the apparatus of claim 1, and the combination further teaches wherein the final anti-aging treatment comprises a customized peptide (Haase [0042], noting a compound of a replacement therapy regimen can include peptides to address the target health problem and/or imbalance).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Haase in view of Norman as applied to claims 1 and 21 above, and further in view of Zheng et al. (Reference U on the accompanying PTO-892).
Claim 22
Haase in view of Norman teaches the apparatus of claim 21, but the combination fails to explicitly disclose where the customized peptide is MOTS-c. However, Zheng teaches that MOTS-c is a type of peptide that may be beneficially applied in anti-aging, cardiovascular disease, insulin resistance, and other treatment fields (Zheng abstract, Fig. 1, Pg 03-05). It therefore would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the unspecified peptide compositions of the combination (which may be utilized for anti-aging, cardiovascular, and diabetes applications as in Haase [0036]) to specifically include MOTS-c as in Zheng in order to allow the system to recommend specific treatments known to be potentially beneficial for anti-aging and other treatment goals (as suggested by Zheng Fig. 1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Stafira et al. (US 20250174329 A1) describes systems for using machine learning to recommend and titrate a BHRT treatment based on patient data and diagnostic test results.
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/KAREN A HRANEK/ Primary Examiner, Art Unit 3684