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 .
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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 9/17/2025 has been entered.
Priority
The present application claims priority to provisional application 63/218,695 filed on 7/6/2021.
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63/218,695, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Specifically, claims 4-5 and 12-13 are not disclosed in the provisional application and therefore receive no priority benefit.
Claims 1, 2, 6-11 and 14-16 have an effective filing date of 7/6/2021, which is the filing date of Provisional Application No. 63/218,695. Claims 4-5, 12 and 13 have an effective filing date of 7/4/2022, which is the filing date of this application.
Status of the Claims
Claims 1-2 and 4-16 are pending; claims 1-2 and 4-16 are amended, claim 3 is canceled; no claims are withdrawn. Claims 1-2 and 4-16 are examined below.
Specification
The disclosure is objected to because of the following informalities:
• Step 308 from Fig. 3 is missing in the specification
• In page 10, paragraph 5 and 6, “generate prediction model” should read “generate a prediction model”.
• In page 10, paragraph 6, "and target datasets baseline datasets" should read "target datasets and baseline datasets".
• In page 12, paragraph 5, need to spell out "POCT".
• Method 500 in Fig. 5 is missing in the specification.
• In page 18, paragraph 3, "hormonal Therapy" should read "hormonal therapy".
• In page 18, paragraph 6, "can be giving to achieve" should read "can be given to achieve".
• In page 18, paragraph 6, "TMB" should read "TDM".
• In page 19, paragraph 1, "kidney disease t thyroid disease" should read "kidney disease, thyroid disease".
• In page 19, paragraph 1, "physicians monitored maintain drug" should read "physicians monitor and maintain drug".
The above appear to be typographical errors. Appropriate correction is required.
The use of the term Bluetooth on paragraph 66, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore, the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
New Objection
Claim Objections
Claim 14 is objected to because of the following informalities:
In lines 12-14, it appears that "to aserver-based" is a typographical error, namely it suggested that "to aserver-based" read as "to a server-based" (it seems that there is a space missing between "a" and "server-based").
Furthermore, in page 6 line 1, it appears that “comprisng” is a typographical error, namely it suggested that “comprisng” read as “comprising”.
Appropriate correction is required.
New Rejections
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-2 and 4-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. This is a new matter rejection.
The claims recite the new limitation “(b) measuring a plurality of hormone and non-hormone parameters from said sample”. However, the only support for measuring “non-hormone parameters” found in the specification is in the last paragraph of page 8 and first paragraph of page 9, “[a] hormone parameter may be the level of a non-hormone that directly relates to the hormone level…or the ratio of two non-hormones…Non-hormones that may be used as parameters include enzymes, electrolytes and constituents of blood serum or whole blood”. Therefore, the specification discloses measuring a non-hormone or ratio of two non-hormones for it to be used as the hormone parameter because it “directly relates to the hormone level”. The specification does not have support for measuring any non-hormone parameter from the blood or serum sample from the patient. Although the specification discloses that “Apparatus 100 determines at least hormone related parameters from measurements taken from the blood of the subject as previously described” (page 7 para. 2), this is not sufficient support for measuring a plurality of non-hormone parameters from said sample. It seems that while the specification does have support for measuring non-hormones, these are not contemplated as a measured “parameter”, but instead as a way to determine the related hormone parameter (“one or more hormone related parameters are measured in step 304. As previously described, hormone related parameters include parameters that are measured by the apparatus which are indicative of a level of hormone, but which may not actually include an absolute determination of the hormone levels. Instead, preferably the hormone levels are calculated in step 306 from these measured parameters” page 8 para. 5).
Claim 1 and its dependent claims also recite the new limitation “mapping the plurality of measured parameters to the hormone level”. However, there is no disclosure in the specification about “mapping the plurality of measured parameters to the hormone level”. While the specification does disclose “a machine learning algorithm for determining hormone levels” (page 4 para. 4), there is no support in the specification for the mapping of the parameters to the hormone level.
Claim 1 and its dependent claims also recite the new limitation, “(f)…(ii) repeated measurements at 6-8 hour intervals over at least 48 hours”. However, “over at least 48 hours” is not disclosed in the specification. The specification discloses “[r]epeating the test every 6-8 hours will help to monitor drug/ hormone elevation and/or reduction in the blood” (page 18 last paragraph), “of course, if measurements were taken ever six hours ( or more frequently), more detectable levels of the surge will be seen” (page 19 para. 2), but the specification fails to disclose “over at least 48 hours”. Therefore, there appears to be no support for the limitation in step (f) “over at least 48 hours”.
Dependent claims 2 and 15 recite the new limitation “a clinician user interface coupled to the server”. However, the specification does not disclose “a clinician user interface”. While the specification does disclose “user interface 204, for example, to contact the user's doctor” (page 8 para. 3), this user interface is meant for the user and is not contemplated for the clinician’s side. It appears that the specification does not support a clinician user interface.
For these reasons, the claims are rejected under 112a as reciting new matter.
Claims 1-2 and 4-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. This is a written description rejection.
The claims require “a trained supervised machine-learning model mapping the plurality of measured parameters to the hormone level, the model being trained with baseline and target datasets and variance decomposition to identify significant variables”.
However, Applicant lacks adequate written description of the claimed trained supervised machine-learning model. The specification does not describe sufficient structure of the components encompassed by the trained supervised machine-learning model. The specification also fails to disclose examples of trained supervised machine-learning models to be used for mapping the plurality of measured parameters to the hormone level, the model being trained with baseline and target datasets and variance decomposition to identify significant variables. In the absence of a known or disclosed correlation between structure and function, claims which encompass variants defined by their function are generally not considered described. Applicant is directed to MPEP § 2163 for guidelines on compliance with the written description requirement.
The specification page 10 paragraph 3 discloses that “[i]n step 406, preferably the data set is divided into a training and test data set. In step 408, the training data set is trained on one or more models. These are models for machine learning or deep learning. Preferably, training is performed on a plurality of models in order to determine which model performs best”. Therefore, the specification suggests that both the data is what is trained (“the training data set is trained on one or more models”) and that the models are trained (“training is performed on a plurality of models”), which is unclear. The specification page 10 paragraph 5 also discloses that “[s]upervised learning will utilize known datasets of baseline datasets and target datasets to a) identify significant variables and generate prediction model of change from baseline to target datasets, as a consequence of medical intervention, and b) to define expected variance (y = ax2+bx+c), where x is an independent variable, representing the amount of the hormone required to cause the change from baseline to target value and y is the dependent value that changes corresponding to x, i.e. the predicted level of hormonal as a function of hormone dosage”. Therefore, the specification discloses that a supervised machine-learning model identifies significant variables, generates a prediction model of change from baseline to target datasets, as a consequence of medical intervention, and defines expected variance (y = ax2+bx+c). However, a person having ordinary skill in the art would not recognize sufficient description of what are “variables” and how y = ax2+bx+c is variance. Although the specification page 11 paragraph 3 suggests that “age” is an example of “variables” (“variables (for example, age)”), it is not clear what else is encompassed by “variables” and how these are identified by the supervised machine-learning model. There is also a lack of written description as to how the machine-learning model generates a prediction model of change from baseline to target datasets, as a consequence of medical intervention. Furthermore, y = ax2+bx+c is a quadratic equation, i.e. a parabola; thus, there is also not enough disclosed to show how y = ax2+bx+c is the variance or how the variance is defined by the supervised machine-learning model.
The machine-learning art ( Linardatos et al. Entropy 2021, 23(1), 18; https://doi.org/10.3390/e23010018 “Linardatos”) teaches that these systems have turned “into “black box” approaches…causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare” (Abstract).
Given that the specification fails to clearly describe a trained supervised machine-learning model, a person having ordinary skill in the art would not be able to recognize that Applicant was in possession of the trained supervised machine-learning model claimed.
Vas-Cath Inc. v. Mahurkar, 19 USPQ2d 1111 (Fed. Cir. 1991), clearly states that “applicant must convey with reasonable clarity to those skilled in the art that, as of the filing date sought, he or she was in possession of the invention. The invention is, for purposes of the ‘written description’ inquiry, whatever is now claimed.” (See page 1117). The specification does not “clearly allow persons of ordinary skill in the art to recognize that [he or she] invented what is claimed.” (See Vas-Cath at page 1116).
Therefore, the instant claims do not meet the written description provision of 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph.
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 1-2 and 4-16 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.
The claims recite “(b) measuring a plurality of hormone and non-hormone parameters from said sample”. However, it is not clear what are “hormone and non-hormone parameters”, more specifically what is a “parameter”. The specification fails to clearly define “parameter” and seems to suggest different meanings of “parameter” throughout the specification. On page 7 paragraph 5 of the specification, it seems that “parameter” is somewhat equivalent to “level”, i.e. a hormone/non-hormone parameter refers to the hormone/non-hormone level (“Display 206 optionally then shows one or more parameters related to the status of the user's, or in this case the subject's, blood hormone levels. User interface 204 enables the user to control user computational device 202 and, for example, to obtain then this information from apparatus 100. This information, such as the hormone parameter levels”). On page 8 paragraph 5 of the specification, “parameter” seems to be analogous to “analyte” (“hormone related parameters include parameters that are measured by the apparatus which are indicative of a level of hormone”). Then, page 8 paragraph 6 and page 9 paragraph 1 of the specification disclose that “[a] hormone parameter may be the level of a non-hormone that directly relates to the hormone level; the level of a different hormone from which the level of the target hormone can be determined; the ratio of two hormones; the ratio of a hormone to a non-hormone; or the ratio of two non-hormones. Also combinations of the above examples of parameters may be used if they can be extrapolated to a particular hormone level”, which suggests that “hormone parameter” is its own separate term. However, the specification also suggests that “parameters” are “calculations” (“includes these hormone related parameters and may also include other types of calculations” page 10 para. 1). Furthermore, the specification also discloses that parameters are variables (“on individual parameters (variables)” page 12 para. 5). Given the numerous possible interpretations to “hormone and non-hormone parameters”, a person having ordinary skill in the art would not recognize the metes and bounds of the claimed invention.
The claims further recite “(c) determining a level of a particular hormone from the plurality of parameters”. However, it is not clear what “the plurality of parameters” refers to because “parameters” is not clear (see above). Furthermore, “the plurality of parameters” in step (c) can be interpreted in multiple ways. For example, “the plurality of parameters” could be referring to the plurality of hormone parameters, the plurality of non-hormone parameters, or both. Note that, in case that the intended interpretation encompasses “non-hormone parameters”, it is not clear how one can determine a level of a particular hormone from a “non-hormone parameter”. While the specification discloses that non-hormones can be used as a “hormone related parameter” (page 8 para. 5), the specification does not disclose a “non-hormone parameter” (see new matter rejection above).
The claims further recite “female sub-phase (follicular, periovulatory, midcycle, luteal)”. However, it is not clear if female sub-phase must consist of follicular, periovulatory, midcycle and luteal or rather is these are merely exemplary. The specification page 5 discloses follicular, periovulatory, and luteal subphases for the hormone estradiol; and follicular, midcycle and luteal for the hormones LH and progesterone. Therefore, the specification discloses different subphases for different hormones. Furthermore, the list “follicular, periovulatory, midcycle, luteal” is unclear because it appears the list is incomplete. In other words, it appears there is missing an “and” before “luteal” to signify the list is complete. A person having ordinary skill in the art would therefore not be able to recognize what is encompassed by the “female sub-phase” recited.
The claims further recite “(e) automatically adjusting dosage of said particular hormone”. However, a person having ordinary skill in the art would not recognize what is being referred to by adjusting the dosage of said particular hormone because there is no step of dosing, or administering recited.
Claim 1 and its dependent claims further recite “(e)…mapping the plurality of measured parameters to the hormone level”. However, it is not clear what “the plurality of measured parameters” refers to because “parameters” is not clear (see above). Furthermore, “the plurality of measured parameters” in step (e) can be interpreted in multiple ways. For example, “the plurality of parameters” could be referring to the plurality of hormone parameters, the plurality of non-hormone parameters, or both. Furthermore, the term “mapping” is vague. It is not clear what is meant by “mapping the plurality of measured parameters to the hormone level” and the specification does disclose what is meant by “mapping” (see new matter rejection above).
Claim 1 and its dependent claims further recite “(e)… the model being trained with baseline and target datasets and variance decomposition to identify significant variables”. However, it is not clear what baseline and target datasets are being referred to because no actual baseline and target datasets are recited. Furthermore, it is not clear how the model is trained with variance decomposition to identify significant variables. Also, “variance” and “variables” is not clear (see above).
The claims further recite “(f) automatically generating a therapeutic drug monitoring (TDM) plan specifying (i) a measurement at 24-48 hours after administration and (ii) repeated measurements at 6-8 hour intervals over at least 48 hours, and updating the dosage using feedback from predicted versus actual outcomes”(claim 1) and “(f) automatically issuing a TDM plan comprising a measurement at 24-48 hours post-administration and repeated measurements at 6-8 hour intervals with feedback updating of dosage” (claim 14). However, it is not clear what is being referred to by “after/post administration” because there is no step of administering recited. Furthermore, it is not clear what is being referred to by “updating the dosage using feedback from predicted versus actual outcomes” (claim 1) because no outcomes are recited. Similarly, “with feedback updating of dosage” is vague. The claims fail to recite a “feedback” or “outcome” step that clarifies step (f). A person having ordinary skill in the art would be confused as to what is encompassed by the claimed step (f).
Claim 4 recites “wherein said at least one parameter is a ratio of two hormones comprising a ratio of estradiol to progesterone used as an input feature to the trained model”. Claim 12 recites “wherein said at least one hormone parameter is a ratio of estradiol to progesterone provided as an input feature to the trained model and used in the TDM plan”.
However, it is not clear how the ratio is used as an input feature to the trained model. As mentioned above, the trained supervised machine-learning model, the mapping done by the model and the training is not clear. Therefore, a person having ordinary skill in the art would not be able to recognize how the ratio is used as an input feature to the trained model. Furthermore, it is not clear how the ratio is used in the TDM plan.
Claim 5 recites “wherein said at least one parameter is a level of a non-hormone selected from sex hormone-binding globulin (SHBG), dehydroepiandrosterone (DHEA), 25-hydroxyvitamin D, parathyroid hormone (PTH), and insulin-like growth factor”.
However, “parameters” is not clear (see above). Furthermore, sex hormone-binding globulin (SHBG), dehydroepiandrosterone (DHEA), 25-hydroxyvitamin D, parathyroid hormone (PTH), and insulin-like growth factor are all hormones. Therefore, the claim is unclear because these are claimed as non-hormones, but are actually hormones.
Claim 6 recites “wherein said particular population is female and the female cohort is divided into menstrual sub-phases”.
However, it is not clear what is meant by “the female cohort” because “cohort” is not recited in claim 1. Furthermore, it is not clear what is encompassed by menstrual sub-phases because the specification fails to define menstrual subphases. As mentioned above, the specification discloses different subphases for different hormones. Therefore, a person having ordinary skill in the art would not be able to readily recognize what is meant by menstrual subphases.
Claim 7 recites “…and the model applies age-matched population norms for dosage prediction”.
However, it is not clear how the model applies age-matched population norms for dosage prediction. As mentioned above, the trained supervised machine-learning model, the mapping done by the model and the training is not clear. Therefore, a person having ordinary skill in the art would not be able to recognize how the model applies age-matched population norms for dosage prediction.
Claim 8 recites “…and the score is computed against a phase-specific normal range”.
However, it is not clear how the score is computed against a phase-specific normal range because a step of computing the score is not recited. Claim 1 recites “comparing the level…to determine a score”, but there is no computing involved. Therefore, a person having ordinary skill in the art would not be able to readily recognize what are the metes and bounds of the claim.
Claim 10 recites “wherein the particular hormone is selected from estradiol or progesterone and is predicted from the plurality of parameters via the trained model”. Claim 11 recites “wherein the particular hormone is chosen from the group consisting of luteinizing hormone (LH), beta-human Chorionic Gonadotropin (beta-hCG), follicle-stimulating hormone (FSH), and testosterone and is predicted from the plurality of parameters via the trained model”. Claim 16 recites “wherein the particular hormone is chosen from the group consisting of luteinizing hormone (LH), beta-hCG, follicle-stimulating hormone (FSH), and testosterone and is predicted from the plurality of parameters via the trained model”.
However, it is not clear what is meant by predicting the hormone. Does Applicant mean predicting the hormone level? Furthermore, it is not clear how the particular hormone is predicted from the plurality of parameters via the trained model. As mentioned above, the parameters, the trained supervised machine-learning model, the mapping done by the model and the training is not clear. Therefore, a person having ordinary skill in the art would not be able to recognize how the particular hormone is predicted from the plurality of parameters via the trained model.
Claim 13 recites “wherein the hormone parameter is chosen from the group consisting of: sex hormone-binding globulin (SHBG), Dehydroepiandrosterone (DHEA), 25-hydroxyvitamin D, parathyroid hormone (PTH), and insulin-like growth factor and at least one such non-hormone parameter is incorporated in the trained model for dosage adjustment”.
However, sex hormone-binding globulin (SHBG), Dehydroepiandrosterone (DHEA), 25-hydroxyvitamin D, parathyroid hormone (PTH), and insulin-like growth factor are all hormones. Therefore the claim reciting “and at least one such non-hormone parameter” is unclear. Furthermore, it is not clear how at least one such non-hormone parameter is incorporated in the trained model for dosage adjustment because, as mentioned above, the parameters, the trained supervised machine-learning model, the mapping done by the model and the training is not clear.
For these reasons the claims are indefinite.
Maintained Rejections
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 and 4-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The rejection has been modified to address applicant’s amendments.
The claims are drawn to a “method of human hormone monitoring and control comprising:…(c) determining a level of a particular hormone from the hormone parameter; (d) comparing the level of the particular hormone against a population database containing a sample space of a particular population stratified by at least age, body mass index (BMI), and female sub-phase (follicular, periovulatory, midcycle, luteal) to determine a score based on whether the hormone level falls in a normal region for said particular population; (e) automatically adjusting dosage of said particular hormone to the particular patient based on the score; and (f) automatically generating a therapeutic drug monitoring (TDM) plan specifying (i) a measurement at 24-48 hours after administration and (ii) repeated measurements at 6-8 hour intervals over at least 48 hours, and updating the dosage using feedback from predicted versus actual outcomes.” Limitation (c) requires evaluating a level of a measured hormone parameter to determine a level of a particular hormone. The determination of a level of a particular hormone can be performed in the human mind. Limitation (d) requires a comparison of a level to a level in a database, which is reasonably interpreted as a collection of data. The comparison can be performed in the mind. Furthermore, the automatic adjustment of the dosage reasonably reads on a mental step of changing the dosage. New limitation (f) is drawn to an automatic generation of a plan to make measurements and dosage updating, which can be performed in the human mind. Thus, the limitations of claim 1 and 14 fall into the “mental process” grouping of abstract ideas.
Further, claim 15 recites, “requiring a medical professional to allow or disallow said automatically adjusting in step (e) before said adjusting occurs”. The limitation of “allow or disallow” is another “mental process” carried out by the medical professional.
Dependent claim 4 recites, “wherein said at least one parameter is a ratio of two hormones.” Claim 12 is interpreted as requiring the at least one hormone parameter to be a ratio of estradiol to progesterone. The ratio is a mathematical relationship between two measured levels. The ratio is a mathematical calculation, i.e. another abstract idea that can be performed in the human mind.
Dependent claims 6-9 further limit the population used in limitation (d). The particular populations are used in a mental comparison. Thus, dependent claims 6-9 are a further statement of the judicial exception, i.e. are also directed to the abstract idea.
Dependent claims 10-11 and 16 further limit the particular hormone of limitation (c). The determination of the particular hormones of claims 10 and 11 can be carried out in the human mind. The limitations of claims 10-11 and 16 are a further statement of the judicial exception, i.e. are also directed to the abstract idea.
This judicial exception is not integrated into a practical application. Claims 1 and 14 recite the additional elements of “(a) taking a sample (i.e., claim 14) or human blood or serum sample (i.e., claim 1) from a particular patient; (b) measuring a plurality of hormone and non-hormone parameters from said sample, and (e) wherein the measured parameters from step (b) are telecommunicated from a patient-side device to a server-based central processing and storage unit that executes an analysis engine comprising a trained supervised machine learning model mapping the plurality of measured parameters to the hormone level, the model being trained with baseline and target datasets and variance decomposition to identify significant variables (claim 1) or (e) wherein the measured parameters are telecommunicated to a server-based analysis engine executing a trained supervised machine-learning model (claim 14). Limitations (a) and (b) are directed to mere data gathering in order to make the determination, comparison, and adjustment, i.e. data gathering for mental steps, i.e. the judicial exception. The limitation wherein the parameters from step (b) are telecommunicated to a central processing and storage unit, that executes an analysis engine comprising a trained supervised machine-learning model (wherein clause of (e)), amounts to mere instructions to implement the abstract idea on a computer. Thus, the claims recite insignificant extra-solution activity. Limitations (a) and (b) and the wherein clause of (e), do not integrate the judicial exception into a practical application. Dependent claim 2 requires wherein a clinician user interface coupled to the server displays the score, the stratified normal range, and the TDM plan before said adjusting occurs. Therefore, claim 2 also recites mere instructions to apply the abstract idea on a computer (display), i.e. insignificant extra-solution activity. Dependent claim 4 requires measurement of two hormones comprising a ratio of estradiol to progesterone used as an input feature to the trained model. Therefore, claim 4 also recite insignificant presolution activity, i.e. limitations drawn to data gathering in order to make the determination, comparison, and adjustment in (c)-(e). Dependent claim 5 requires measurement of a non-hormone selected from sex hormone-binding globulin (SHBG), dehydroepiandrosterone (DHEA), 25-hydroxyvitamin D, parathyroid hormone (PTH), and insulin-like growth factor. Dependent claim 12 requires the measurement of estradiol and progesterone provided as an input feature to the trained model and used in the TDM plan. Dependent claim 13 requires the measurement of sex hormone-binding globulin (SHBG), Dehydroepiandrosterone (DHEA), 25- hydroxyvitamin D, parathyroid hormone (PTH), or insulin-like growth factor and at least one such non-hormone parameter is incorporated in the trained model for dosage adjustment. Thus, similarly claims 5 and 12-13 recite insignificant presolution activity, i.e. steps drawn to data gathering. Dependent claim 7 recites the additional element “and the model applies age-matched population norms for dosage prediction”. Dependent claim 8 recites the additional element “and the score is computed against a phase-specific normal range”. Dependent claim 9 recites the additional element “with a distinct normal range in the database”. Dependent claims 10-11 and 16 recite the additional element “and is predicted from the plurality of parameters via the trained model”. Thus, the additional elements of claims 7-11 and 16 are mere steps drawn to data gathering that fail to integrate the judicial exception into a practical application. Dependent claim 15 recites the additional element “a clinician user interface coupled to the server”. However, a clinician user interface coupled to the server is considered as mere instructions to apply the judicial exception on a computer. The additional elements of the claims fail to use, rely on or apply the judicial exception such to amount to a practical application thereof.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because collecting a biological sample from a subject, analyzing the sample, and determining the level of the analyte, is well understood, routine, conventional in the art and does not add a meaningful limitation to the method. Dependent claims 4-5, and 12-13 are also well understood, routine, and conventional in the art. For example, Norman (WO 2020/081369) teaches the measurement of hormone (page 29, lines 15-17), or non-hormone (page 21, lines 14-18), levels in patient samples including blood and serum (page 3, lines 6-7, Fig. 2), as well as the ratio of two hormones, such as estradiol and progesterone (page 29, lines 15-19, page 32, lines 11-14). Norman further teaches the measurement of dehydroepiandrosterone (DHEA) (page 22, line 19). Furthermore, Norman teaches wherein the measured hormone parameter from step (b) is telecommunicated to a central processing and storage unit, wherein determining the level of a particular hormone in step (c) and the score in step (d) is performed using machine learning algorithms (“Figure 2 depicts an example treatment system 200... The system 200 includes an artificial intelligence (AI) computer system 202 that interacts with one or more patients 204, … one or more biological sample assays…to compute a recommended prescription for hormone therapy treatment with respect to patient 204 based on the patient's biochemical, symptomatic, and genetic status” page 7 lines 23-28, “mobile application and/or a web application can be made available to the patient 204 to provide the patient 204 with an effective mechanism for communicating such symptom data to the computer system 202” page 9 lines 1-3, “The AI computer system 202 can take the form of one or more servers that are configured for electronic communication via networks such as the Internet and/or wireless telecommunications networks with the patients 204” page 10 lines 13-15, “Memory 302 can store various data about the patient 204 as discussed above” page 10 line 23, “Computer system 202 can also provide powerful learning and feedback capabilities so that the prescriptions it recommends can improve over time. Such learning/feedback can be implemented at an individual patent level and/or an aggregated multi-patient level” page 38 lines19-21). Also, the courts have recognized the following laboratory technique as well-understood, routine, conventional activity in the life science arts when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: i. Determining the level of a biomarker in blood by any means, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017); furthermore, the courts have recognized as natural phenomena a correlation that is the consequence of how a certain compound is metabolized by the body, Mayo Collaborative Servs. v. Prometheus Labs., 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012).
Furthermore, using a trained supervised machine-learning model mapping the plurality of measured parameters to the hormone level, the model being trained with baseline and target datasets and variance decomposition to identify significant variables appears to be well understood routine and conventional. For example, Divaraniya et al. (WO 2019246361 A1) teaches that “[t]he methods, devices, and systems of the disclosure can be used to quantify analytes such as luteinizing hormone, progesterone, estradiol, or testosterone” (Abstract). Divaraniya further teaches that “[a] machine learning algorithm of the disclosure can utilize a supervised classification inference model, a schematic of which is shown in FIG. 9. …. The model takes features as input and classifies them into 1 of N classes… supervised classification learning model described herein can be used for future prediction or inference of new users or patients into the clusters defined by the unsupervised learning model. The model can be trained, and its performance measured, by splitting the data into test and training sets. The model can map a set of inputs to outputs by learning on the train set of input and output pairs” (paras. 205-206).
Also, Lefkofsky (US 20210118559 A1) teaches “[a] system and method, the method comprising receiving a laboratory diagnostic testing result associated with a specimen of a subject, the steps of receiving a clinomic profile of the subject, identifying a cohort of similar subjects based at least in part on the clinomic profile of the subject, providing the diagnostic testing results, clinomic profile, and the cohort of similar subjects to a smart output module to generate a personalized, precision medicine based laboratory diagnostic testing result as a smart output and displaying the smart output to a user” (Abstract). Lefkofsky further teaches “[a]n analysis module optimization dataset…machine learning models, or neural networks which improve the performance of the respective module for the subject results being processed at the time of operation…A MLA [machine learning algorithm] or a NN [neural network] may be trained from a training data set. In an exemplary analysis module, a training data set may include imaging, pathology, clinical, and/or molecular reports and details of a subject, such as those curated from an EHR [electronic health records] or genetic sequencing reports. The training data may be based upon features of the subject from the subject's clinomic profile. MLAs include supervised algorithms (such as algorithms where the features/classifications in the data set are annotated) using linear regression, logistic regression, decision trees, classification and regression trees, Naïve Bayes, nearest neighbor clustering” (para. 94). For all of these reasons, the claims fail to include additional elements that are sufficient to amount to significantly more than the judicial exception(s).
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Divaraniya (WO 2019246361 A1) suggests a method of human hormone monitoring and control (Abstract, “The present disclosure provides a system comprising: a) a lateral flow device; and b) a computer-implemented system” para. 109, “The sample pad of a lateral flow device acts as a sponge and holds a biological sample fluid, such as urine, blood” para. 106, “Therapeutic intervention using methods of the disclosure: Unable to conceive naturally for over 1 year, a woman is prescribed clomiphene citrate (Clomid.sup.®). The woman visits the clinic on day 3 of her menstrual cycle to get baseline hormone levels from a blood sample…On day 1 of her menstrual cycle, the woman starts taking daily urine tests to monitor her hormone levels using a device and methods of the disclosure. On days 7, 8, 9, 10, and 11 of her menstrual cycle, she takes Clomid®. On day 12 of her menstrual cycle, the woman self- administers an hCG shot (“trigger shot”) to induce ovulation” para. 325) comprising: (a) taking a human blood or serum sample from a particular patient (para. 106 and 325); (b) measuring a plurality of hormone and non-hormone parameters from said sample; (c) determining a level of a particular hormone from the plurality of parameters (“Sandwich assays can use various antibodies to detect the presence of an analyte. For example, antibodies that can be used in sandwich assays include antibodies against bilirubin, testosterone, follicle stimulating hormone (FSH), anti-mullerian hormone (AMH), estrogen, acylglycines, beta-carotene, cholesterol, creatinine” para. 110); (d) comparing the level of the particular hormone against a population database containing a sample space of a particular population stratified by at least age, body mass index (BMI), and female sub-phase (follicular, periovulatory, midcycle, luteal) to determine a score based on whether the hormone level falls in a normal region for said particular population (“During registration a mobile application captures critical information (including but not limited to): height, weight, age, and date of last menstrual cycle. This information is input into the machine learning pipeline and a woman is classified into one of six clusters…Cluster 1 are women in their mid-20s, high BMI, experience a long cycle, have periods longer than 5 days, generally experience late ovulation, and have extended number of peak ovulatory days…Cluster 2…The cluster a woman is classified into drives the type of information she will receive her reports…The report shows how a user’s hormone levels differ from the average woman and explain meaningful differences” paras. 330-337 and 340); (e) automatically adjusting dosage of said particular hormone to the particular patient based on the score (“Using all of the inputs, the algorithm is able to make the suggestions needed to help regulate her hormone level” para. 338); wherein the measured parameters from step (b) are telecommunicated from a patient-side device to a server-based central processing and storage unit (“The computer system 501 can be operatively coupled to a computer network (“network”) 530 with the aid of the communication interface 520. The network 530 can be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet. The network 530 in some cases is a telecommunication and/or data network. The network 530 can include one or more computer servers, which can enable distributed computing, such as cloud computing” para. 179, “[t]he computer system 501 in some cases can include one or more additional data storage units that are external to the computer system 501, such as located on a remote server” para. 181) that executes an analysis engine comprising a trained supervised machine-learning model mapping the plurality of measured parameters to the hormone level, the model being trained with baseline and target datasets and variance decomposition to identify significant variables (paras. 205-206). Divaraniya further teaches that “[t]he algorithm will make two recommendations: 1) Dietary supplements and 2) Diagnostic testing” (para. 339)
Divaraniya fails to teach step (f).
Lefkofsky (US 20210118559 A1) suggests a method of human hormone monitoring and control (Abstract, “These test results may be of the same tests taken repeatedly over a period of time or may be results from similar tests” para. 17, “Test results may include results… for the purposes of providing information for the diagnosis, prevention or treatment of any disease or impairment” para. 80) comprising: (a) taking a human blood or serum sample from a particular patient (“Exemplary medical tests include, but are not limited to, a blood test” para. 10); (b) measuring a plurality of hormone and non-hormone parameters from said sample; (c) determining a level of a particular hormone from the plurality of parameters (“Specific examples of laboratory tests include…a comprehensive molecular or metabolic panel, a lipid panel, a liver panel, a thyroid stimulating hormone test…specific examples of laboratory tests include those listed in the World Health Organization Model List of Essential in Vitro Diagnostics” para. 11, “The laboratory 140 may include equipment, materials, and supplies for detecting substances that have antigenic properties such as proteins, hormones… ELISA equipment may include… photometers, spectrophotometers, microplate readers” para. 64); (d) comparing the level of the particular hormone against a population database containing a sample space of a particular population stratified by at least age, body mass index (BMI), to determine a score based on whether the hormone level falls in a normal region for said particular population (“the test result may be compared against one or more sets of data reflective of health information of the subject, individuals medically similar to the subject…to contextualize the result in a manner that is specific for the subject” para. 16, “The identified cohort may then be processed to identify results which may be expected for the subject, outcomes for therapies which may be suggested to the subject, and a personalized subject diagnostic results threshold may be generated” para. 23, “Additional subject clinomic profile features … may include age,… body mass index” para. 276); (e) automatically adjusting dosage of said particular hormone to the particular patient based on the score (“In this way, any therapies which may improve or hinder the prognosis of the subject are visible to the treating physician and may be considered in generating a treatment plan for the subject” para. 100); wherein the measured parameters from step (b) are telecommunicated from a patient-side device to a server-based central processing and storage unit that executes an analysis engine comprising a trained supervised machine-learning model mapping the plurality of measured parameters to the hormone level, the model being trained with baseline and target datasets and variance decomposition to identify significant variables (para. 94, “The system 800 can be a content server (also referred to as a prediction engine), which is hardware or a combination of both hardware and software… The information acquired, processed, and generated by the content server 800 is stored on one or more of the network-based storage devices. The user can interact with the content server to access the information stored in the network-based storage devices, and the content server can receive user-supplied information, apply the one or more models stored in the network-based storage to the information, and to provide, in an electronic form, results of the model application to the user on a graphical user interface of the user device” para 183-184).
Lefkofsky fails to teach stratified by female sub-phase (follicular, periovulatory, midcycle, luteal) and step (f).
There were no references found that taught step (f). It seems that the art teaches away from step (f).
Kang et al. Korean J Intern Med. 2009 Mar 6;24(1):1–10. doi: 10.3904/kjim.2009.24.1.1 (“Kang”) teaches an “overview of therapeutic drug monitoring” (Title). Kang further teaches that “Therapeutic drug monitoring (TDM) is the clinical practice of measuring specific drugs at designated intervals to maintain a constant concentration in a patient's bloodstream, thereby optimizing individual dosage regimens” (Abstract). However, Kang teaches that the measurements are dependent on the specific drug treatment, namely on the pharmacokinetics of the drug (“To interpret a blood plasma concentration properly, the TDM team must be informed as to when a plasma sample was obtained in relation to the last dose administered and when the drug regimen was initiated. If a plasma sample is obtained before distribution of the drug into tissue is complete, for example with digoxin, the plasma concentration will be higher than predicted on the basis of dose and response. Peak plasma concentrations are helpful in evaluating the dose of antibiotics used to treat severe, life-threatening infections. Although serum concentrations for many drugs peak 1 to 2 h after an oral dose is administered, factors such as slow or delayed absorption can significantly delay the time at which peak serum concentrations are attained. Therefore, with few exceptions, plasma samples should be drawn at trough or just before the next dose (Css min; minimal steady state concentration) when determining routine drug plasma concentrations. These trough levels are less likely to be influenced by absorption and distribution problem… Blood samples should be collected once the drug concentrations have attained steady state, for example, after at least 5 half-lives at the current dosage regimen. Levels approximating steady state may be reached earlier if a loading dose has been administered. However, drugs with long half-lives should be monitored before steady state is achieved to ensure that individuals with impaired metabolism or renal excretion are not at risk of developing toxicity at the initial dosage regimen prescribed, as can occur with amiodarone and perhexiline” page 6). Furthermore, Kang teaches away from “routine measurement of the plasma drug concentration without a clear purpose” (page 5 col. 1 para. 1) and teaches “[c]riteria that a drug should satisfy for plasma concentration measurements to be useful” (Table 2, page 4 col. 2).
Ghiculescu Aust Prescr 2008;31:42-4 1 April 2008 DOI: 10.18773/austprescr.2008.025 teaches that “[d]rug assays are costly, so the reason for monitoring and the additional information to be gained (if any) should be carefully considered. For some drugs, therapeutic drug monitoring helps to increase efficacy (vancomycin), to decrease toxicity (paracetamol) and to assist diagnosis (salicylates). Routine monitoring is not advocated for most drugs. Only clinically meaningful tests should be performed” (page 42, col. 2 para. 5). Ghiculescu further teaches that “[c]orrect sample timing should also take into account absorption and distribution” (page 43 col. 2 para. 3).
Given that the art teaches that measurements are costly and that the frequency of measurements in therapeutic drug monitoring plans depend on the specific drug (with most drugs not requiring monitoring) and on the pharmacokinetics of the drug, a person having ordinary skill in the art would not be motivated in automatically generating a therapeutic drug monitoring plan specifying (i) a measurement at 24-48 hours after administration and (ii) repeated measurements at 6-8 hour intervals over at least 48 hours. Therefore, the claims appear to be free of the art.
Response to Arguments
Applicant's arguments filed 9/17/2025 have been fully considered but they are not persuasive.
Regarding the 101 rejections, Applicant argues that “[t]he amended claims now expressly require (i) remote telecommunication of measured parameters to a server-based analysis engine, (ii) execution of a trained supervised machine-learning model (trained with baseline and target datasets using variance decomposition to identify significant variables) to compute an actual hormone level from a plurality of parameters, including ratios and non-hormone biomarkers, (iii) generation of a therapeutic drug monitoring (TDM) plan specifying concrete measurement windows (24-48 hours post-administration and repeated every 6-8 hours), and (iv) feedback updating of dosage based on predicted vs. actual outcomes. These are not mental steps. These steps are computer-implemented control operations that structurally tie the claims to a specific medical telemetry/ML/TDM workflow and effect a real-world dosage change” (page 8 para. 1). However, the requirements (i)-(ii) remarked are reasonably interpreted as limitations drawn to simple implementation of the judicial exception on a computer, i.e. mere instructions to apply an exception (see MPEP 2106.05(f)). Therefore, (i)-(ii) fail to integrate the judicial exception into a practical application. Furthermore, (i)-(ii) are also well-understood, routine and conventional (see rejection above). Also, contrary to Applicant’s argument, requirements (iii)-(iv) are drawn to abstract ideas, i.e. mental steps. The generation of a plan ((iii)) and updating of the dosage (iv) can be performed in the human mind (see rejection above). Therefore, the claims are not patent eligible.
Applicant further remarks that “[s]upport for these amendments may found in at least FIGS. 1-2 (telemetry/system), FIGS. 4A-4B and ¶¶[0077]-[0084] (supervised training, variance decomposition), FIG. 5 and ¶¶ [013 l][0139] (control loop), and ¶¶ [0134], [0149] (concrete TDM timing), as well as database stratification and clinician UI (¶¶ [0065]-[007 l ])” (page 8 para. 2). However, there was no support found for “measuring a plurality of non-hormone parameters from said sample”, “mapping the plurality of measured parameters to the hormone level”, “(f)…(ii) repeated measurements at 6-8 hour intervals over at least 48 hours” (emphasis added), and “a clinician user interface” (see 112a new matter rejection above). Although Applicant argues that “¶¶ [0134], [0149]” teaches “(concrete TDM timing)” and that “(¶¶ [0065]-[007 l ])” teaches “clinician UI”, it is noted that the specification does not contain paragraph or section numbers, and that a search by the Examiner did not showed no support for such new limitations. Therefore, the claims are rejected under 112a new matter (see rejection above).
Conclusion
No claims allowed.
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/Fernando Ivich/Examiner, Art Unit 1678
/CHRISTOPHER L CHIN/Primary Examiner, Art Unit 1677