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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/7/2026 was received and placed in the record on file. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Currently, no claim limitations are being interpreted as invoking a 35 USC 112(f) interpretation.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claims invention is directed to an abstract idea without significantly more.
Regarding claims 1-20; using the two-part test for subject matter eligibility, independent claims 1, 11 and 20 are the exemplary independent claims and are directed to a process (claim 1), a manufacture (claim 11) and a machine (claim 20) (Step 1: Yes). The claims are also directed to a judicial exception regarding an abstract idea (Step 2A, Prong 1: Yes). The claims are recreated below and the abstract idea is bolded and italicized:
Claim 1:
A method of operating a binary classification artificial intelligence (AI) model to perform pregnancy classification, comprising:
inputting, into the binary classification AI model, labeled training data comprising:
a first set of training data comprising temperature data, heart rate data, breath rate data, and heart-rate-variability data corresponding to a first set of users labeled as pregnant, and
a second set of training data comprising temperature data, heart rate data, breath rate data, and heart-rate-variability data corresponding to a second set of users labeled as not pregnant;
determining, using a loss function, an error margin for the binary classification AI model based at least in part on inputting the labeled training data, wherein the loss function:
imposes, for false positive pregnancy classifications, a first penalty factor that is weighted relative to a menstruation start date;
imposes a second penalty factor for classification confidences that change by a threshold amount between two consecutive days;
imposes a third penalty factor for false positive pregnancy classifications that is greater than a reward factor for true positive pregnancy classifications; and
adjusting one or more parameters of the binary classification AI model.
Claim 11:
A non-transitory computer-readable medium storing code for operating a binary classification artificial intelligence (AI) model to perform pregnancy classification, the code comprising instructions executable by one or more processors to cause the one or more processors to:
input, into the binary classification AI model, labeled training data comprising:
a first set of training data comprising temperature data, heart rate data, breath rate data, and heart-rate-variability data corresponding to a first set of users labeled as pregnant, and
a second set of training data comprising temperature data, heart rate data, breath rate data, and heart-rate-variability data corresponding to a second set of users labeled as not pregnant;
determine, using a loss function, an error margin for the binary classification AI model based at least in part on inputting the labeled training data, wherein the loss function:
imposes, for false positive pregnancy classifications, a first penalty factor that is weighted relative to a menstruation start date and that is greater than a reward factor for true positive pregnancy classifications;
imposes a second penalty factor for classification confidences that change by a threshold amount between two consecutive days;
imposes a third penalty factor for false positive pregnancy classifications that is greater than a reward factor for true positive pregnancy classifications; and
adjust one or more parameters of the binary classification AI model.
Claim 20:
An apparatus for operating a binary classification artificial intelligence (AI) model to perform pregnancy classification, the apparatus comprising:
one or more memories storing processor-executable code; and
one or more processors coupled with the one or more memories, the one or more processors individually or collectively operable to execute the code to cause the apparatus to:
input, into the binary classification AI model, labeled training data comprising:
a first set of training data comprising temperature data, heart rate data, breath rate data, and heart-rate-variability data corresponding to a first set of users labeled as pregnant, and
a second set of training data comprising temperature data, heart rate data, breath rate data, and heart-rate-variability data corresponding to a second set of users labeled as not pregnant;
determine, using a loss function, an error margin for the binary classification AI model based at least in part on inputting the labeled training data, wherein the loss function:
imposes, for false positive pregnancy classifications, a first penalty factor that is weighted relative to a menstruation start date and that is greater than a reward factor for true positive pregnancy classifications;
imposes a second penalty factor for classification confidences that change by a threshold amount between two consecutive days; and
imposes a third penalty factor for false positive pregnancy classifications that is greater than a reward factor for true positive pregnancy classifications; and
adjust one or more parameters of the binary classification AI model.
The independent claims encompass an abstract idea drawn to mental processes and mathematical concepts that can be performed in the human mind and/or by hand using a pen and paper. In this case, “determining, using a loss function, an error margin for the binary classification AI model based at least in part by on inputting the labeled training data” (mathematical concept of using the loss function to determine an error margin based on the output data and/or the mental process of determining the penalty factor imposed by the loss function) of claims 1, 11 and 20 are drawn to mental processes and mathematical relationships. In other words, the mathematical concept/mental process is the determining of the error margin using an abstract loss function determined by the mental process of the user/operator, wherein the mental process includes observation, evaluation, judgment and opinion (in this case observing the inputting of the labeled data sets, evaluating and judging how the inputs affect the output from the model to mentally determine the parameters of the loss function to thus determine the error margin).
Further, the claims do not recite additional elements that integrate the judicial exception into a practical application (Step 2A, Prong 2: No). The claims fail to recite additional element or combinations of elements to apply, rely on, or use the judicial exception in a manner that imposes meaningful limitation of the judicial exception. In the instant claims, the identified additional elements of: a binary classification AI model (claims 1, 11 and 20); inputting first and second labeled training data into a binary classification model (claim 1, 11 and 20); adjusting one or more parameters of the binary classification AI model (claim 1, 11 and 20); a non-transitory computer readable medium storing code for operating a binary classification AI model, the code comprising instructions executable by one or more processors to cause the processors the execute steps (claim 11); and an apparatus for operating a binary classification AI model, one or more memories storing processor-executable code and one or more processors coupled with the one or more memories, the one or more processors operable to execute the code to cause the apparatus to (claim 20) do not integrate the judicial exception into a practical application. Instead, the additional limitations amount to merely applying the judicial exception by including the instruction to implement on a computer, or merely using a computer as a tool to perform the abstract idea (i.e. implementing the steps on a general purpose computer/processor or storing the steps on a generic readable storage medium for execution on by a general purpose computer/processor; wherein the computer elements are all recited at a high level of generality); and generally linking the use of the judicial exception to a particular technological environment (i.e. to a computer or computer readable medium). Further the additional elements of inputting labeled training data sets into a classification model and adjusting one or more parameters of the binary classification AI model are mere extra-solution activity that do not meaningfully implement the abstract idea to the practical application. Specifically, inputting labeled training data sets into a classification model does not meaningfully integrate the abstract idea into a practical application because the inputting labeled training because this the inputting is performed to get data for the mental analysis/mathematical concept step, and is a necessary precursor for all uses of the recited exception. Further the step of adjusting one or more parameters of the binary classification AI model does not meaningfully integrate the abstract idea into practical application because it is not tied to the abstract idea/mathematical concept as currently recited (i.e. the adjusting of the parameters as currently recited does not implement the abstract idea as the adjustment is not based on the mental process/mathematical concept, instead it just recites the general statement of adjusting one or more parameters of the binary classification AI model without implementing the abstract idea).
Finally, the claims as a whole do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B: No). The additional elements of the computer a binary classification AI model (claims 1, 11 and 20); inputting first and second labeled training data into a binary classification model (claim 1, 11 and 20); adjusting one or more parameters of the binary classification AI model (claim 1, 11 and 20); a non-transitory computer readable medium storing code for operating a binary classification AI model, the code comprising instructions executable by one or more processors to cause the processors the execute steps (claim 11); and an apparatus for operating a binary classification AI model, one or more memories storing processor-executable code and one or more processors coupled with the one or more memories, the one or more processors operable to execute the code to cause the apparatus to (claim 20), do not amount to or contribute to the inventive concept recigted in the abstract idea. The additional elements, considered individually and as a whole, amount to merely implementing the abstract idea on a computer by reciting the implementation of the process on one or more computer processor or on a computer readable medium for causing computers/processors to carry out the abstract idea; wherein the processors, computer readable medium, and apparatus are recited at a high level of generality and do not attempt to meaningfully limit the abstract idea. Further regarding the binary classification AI model, inputting of the labeled data sets and the adjusting of one or more parameters of a binary classification AI model, are all conventional, routine and well understood concepts in artificial intelligence. Binary classification AI models and their training are well known, routine and conventional as evidenced by Roepke (examiner included NPL “Everything You Need to Know to Build an Amazing Binary Classifier”) which describe how to build and train a binary machine learning classifier which can be applied to any situation with a binary result. As such, the additional elements, when considered individually and as a whole do not amount to significantly more than the abstract idea.
Accordingly, claims 1-20 are rejected as non-statutory as being directed to a judicial exception (abstract idea: mental process and mathematical concepts) without significantly more.
Furthermore, dependent claims 2-10 and 12-19 do not recite any additional elements that implement the abstract idea into a practical application or amount to significantly more than the abstract idea. Regarding claim 2 and 12; the claims recite an extra solution activity of mere data gathering (receiving an inference data set) and outputting a result (determining a pregnancy classification with the model) which do not implement into a practical application or amount to more than the abstract idea. Regarding claims 3, 4, 8-10, 13 and 17-19, , they claim merely further specify the details of the abstract idea or recite further steps to the abstract idea wherein eligibility cannot be furnished by the unpatentable abstract idea itself (MPEP 2106.04, II, A, 2). Further regarding claims 5, 6, 14 and 15; the claims merely recite mere extra-solution activity of outputting a result which is well known, routine and conventional. Finally, regarding claims 7 and 16; the additional element of a wearable device is well known, routine and conventional as paragraphs [0015]-[0017] of the user’s own spec contemplates that the wearable are known wearable computing devices such as rings, watches, phones, etc….
Accordingly, the dependent claims do not include any additional limitations that reduce the abstract idea to a practical application or amount to significantly more than the abstract idea, and are thus are rejected as directed to non-statutory subject matter.
Examiner’s Comment on Claims Over Prior Art
Claims 1-20 are rejected over 35 USC 101, but would be allowable amended to overcome the previously held 35 USC 101 rejections as described above. The examiner reserves the right to re-evaluate allowability over the prior art of record if the scope of the claim is changed via the amendment to overcome the 35 USC 101 rejections that would necessitate a rejection over 35 USC 102 or 103.
The following is a statement of reasons for the indication of allowable subject matter over the prior art of record: The instant method differs from the closest prior art of record in that it determines an error margin using a loss function which imposes penalty factors based on false positive pregnancy classifications that are weighted relative to a menstruation start date, changes in the classification confidence between consecutive days that are greater than a threshold value; and where the penalty factor for a false positive pregnancy classification is greater than a reward factor for true positive pregnancy classifications. The closest prior art of record to Thigpen et al (US 2022/0313146 A1), Shinar et al (US 2016/0058429 A1) and Stein (US 12,446,865) all disclose various systems and methods for monitoring a user’s biometric data and use algorithms (including machine learning/artificial intelligence) to recognize patterns in the data, including determining pregnancy state of the user. However, they do not explicitly disclose determining an error margin using the parameters as claimed. Anderson (US 2023/0044102 A1) and Singh et al (US 2025/0225169) teach specifics about the application of machine learning including for binary classification models that are trained using labeled data and the use of loss functions with penalties factors to determine an classification confidence/error margin and further adjusting weighting parameters of the model based on the determined error margin to improve the accuracy of the model. So while it may have been obvious to implement the specifics of developing and training a binary machine learning model into the determining a pregnancy status of an individual based on a plurality of monitored biometric parameters, and to implement a loss function to determine a error margin and adjust the binary model accordingly; it would not have been obvious to one of ordinary skill in the art at the time of filing to use the specific parameters as claimed for imposing penalty factors with the loss function and adjusting the parameters of the binary classification AI model based on the error margin to improve the model accuracy.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2019/0110692 A1 to Pardey et al; discloses a system for processing a physical signal which monitors and analyzes the signal and can detect pregnancy using pattern recognition algorithm.
US 2025/0225169 A1 to Singh et al; discloses details of training a machine learning model and implementing a loss function to improve the model.
US 2008/0275349 A1 to Halperin et al; discloses a system for monitoring, predicting and treating clinical episodes which monitors user’s physiological parameters and can identify conditions of the user.
US 2023/0044102 A1 to Anderson et al; discloses details of training a machine learning model and implementing a loss function to improve the model.
US 2013/0245389 A1 to Schultz et al; discloses learning patient monitoring intervention system which is a system which uses machine learning models to identify conditions of the users.
US 2016/0058429 A1 to Shinar et al; discloses a pregnancy state monitoring system.
US 12,446,865 B2 to Stein; discloses a system and method for detecting pregnancy related events.
US 12,426,858 B2 to Clements et al; discloses an in-bed temperature array for menstrual cycle monitoring.
CN 118201545 A to Park et al; discloses disease prediction using analyte measurement features and machine learning.
“A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery” to Yaseen et al.; discloses the use of machine learning and artificial intelligence in monitoring females in maternal care.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM J EISEMAN whose telephone number is (571)270-3818. The examiner can normally be reached Monday - Friday (7:00 AM - 4:00 PM).
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/ADAM J EISEMAN/ Primary Examiner, Art Unit 3791
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