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 .
Response to Amendment
Applicant’s amendments to the claims, filed 05/20/2026, are accepted and appreciated by the
Examiner.
Response to Arguments
Applicant’s arguments, see Remarks, filed 05/20/2026, with respect to the 35 U.S.C. 101 rejections of claims 1, 11, and 21 have been fully considered and are persuasive. Example 39 of the 2019 Revised Patent Subject Matter Eligibility Guidance also includes limitations that involve training a machine learning model using generated data sets that comprise different data. These limitations in that example were deemed as not including mathematical concepts or mental processes. As a result, the similar limitations in the instant application that relate to creating training data and training the machine learning model to predict the trajectory of a patient’s menopause represent additional elements. These additional elements are viewed as integrating the claim into a practical application, because they apply the judicial exception to predicting a menopause trajectory of a patient. This represents using the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception as seen in MPEP 2106.05. Therefore, the 35 U.S.C. 101 rejection of claims 1, 11, and 21 have been withdrawn.
Applicant’s arguments, see Remarks, filed 05/20/2026, with respect to the rejection(s) of claims 1, 11, and 21 under 35 U.S.C. 103 have been fully considered and are persuasive. The combination of Zambotti and Steinberg-Koch (US 20220223293 A1) does not explicitly teach wherein each historical menopause outcome trajectory includes an age of onset of at least one of perimenopause, menopause, or post-menopause for the respective historical patient. Therefore, the rejection has been withdrawn. However, Zambotti does teach generating a training set that includes a feature vector as seen in Para. [0102]. In addition, Steinberg-Koch also teaches using training data that includes feature vectors as seen in Para. [0043] and the Abstract. Even though, the method in Steinberg-Koch uses historical medical data to train the machine learning model to diagnosis autoimmune conditions in a patient. The machine learning model could be trained on different data in order to find a different result such as menopause trajectory. It would be obvious to use feature vectors to train the machine learning model because feature vectors are used widely in machine learning, as a consequence of their effectiveness and practicality in representing objects in a numerical way as seen in The Uses of Feature Vector Section of Feature Vector. Brilliant.org. As a result, upon further consideration, a new ground(s) of rejection is made in view of Zambotti (US 20200013511 A1), Steinberg-Koch (US 20220223293 A1), and Zambotti (2024) (US 20240016456 A1).
Claim Rejections - 35 USC § 103
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.
Claims 1-19 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Zambotti (US 20200013511 A1), Steinberg-Koch (US 20220223293 A1), and Zambotti (2024) (US 20240016456 A1).
With respect to claims 1, 11, and 21,
Zambotti teaches,
one or more processors; and a non-transitory program memory communicatively coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the processors to: (Para. [0010] teaches “A number of embodiments can include a non-transitory computer-readable storage medium comprising instructions that when executed cause a processor circuit of a computing device to receive a plurality of input parameters indicative of hot flash factors for a user and other users,”)
analyzing, by the one or more processors, the historical EMR data associated with the plurality of historical patients to determine one or more patient parameters associated with each historical patient and a historical menopause outcome trajectory associated with each historical patient; (Para. [0042] teaches “Generating the predictive model 109 includes receiving input parameters and identification of past hot flashes for the user 108 or other users, identifying different patterns or correlation of occurred hot flashes and the input parameters.”)
generating, by the one or more processors, a training dataset that includes a set of feature vectors, each feature vector of the set of feature vectors associating one or more patient parameters with a corresponding historical menopause outcome trajectory for a respective historical patient (Para. [0102] teaches “As shown, data is input at 661 for preprocessing and feature extraction, at 662. A feature vector is output at 663 and input for performing machine learning to generate the predictive model at 666.” Para. [0042] teaches “Generating the predictive model 109 includes receiving input parameters and identification of past hot flashes for the user 108 or other users, identifying different patterns or correlation of occurred hot flashes and the input parameters, and based on the patterns, identifying predictive probabilities of the user 108 having a hot flash at dates and times using additionally received input parameters.” Para. [0104] teaches “In the batch mode, all available training data are provided to the network to calculate the optimum parameters, while in the incremental style, the parameters are updated each time a training sample is presented to the network.”)
and training, by the one or more processors, a menopause outcome machine learning model, using the training dataset, to generate a predicted menopause outcome trajectory for a patient based on patient parameters associated with the patient, wherein the predicted menopause outcome trajectory includes or consists of predictions of one or more of: an age of onset of perimenopause for the patient, an age of onset of menopause for the patient, an age of onset of post-menopause for the patient, particular menopause symptoms experienced by the patient, an age of onset of each menopause symptom experienced by the patient, or a severity level associated with each menopause symptom experienced by the patient. (Para. [0103] teaches “The predictive model gives the probability of the hot flash occurrence based on the observed and/or collected data. A simple example is the logistic regression model which defines a linear decision boundary between the training samples associated with a hot flash occurrence and those that are not. A more complex model can be built when there is a more complex or non-linear relationship between the inputs and output.” (i.e. a hot flash is a particular menopause symptom.)
Zambotti does not explicitly teach,
obtaining, by one or more processors, historical electronic medical record (EMR) data associated with a plurality of historical patients wherein each historical menopause outcome trajectory includes an age of onset of at least one of perimenopause, menopause, or post-menopause for the respective historical patient;
Steinberg-Koch teaches,
obtaining, by one or more processors, historical electronic medical record (EMR) data associated with a plurality of historical patients. (Para. [0037] teaches “The presently disclosed system uses AI-based methods employing machine learning, deep learning, NLP techniques, and other advances learning methods trained on data acquired from multiple data sources such as EMRs, EHRs and claims data. Training the method entails building a mathematical model based on sample data, known as “training data”, in order to enable the algorithmic method to make predictions or decisions without being explicitly programmed to do so.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zambotti obtaining, by one or more processors, historical electronic medical record (EMR) data associated with a plurality of historical patients such as that of Steinberg-Koch.
One of ordinary skill would have been motivated to modify Zambotti, because as seen in Para. [0028] of Steinberg-Koch “The service enables providers to seamlessly integrate this solution into their current workflow by either integrating the algorithms and software into the existing EMR system or by providing a separate software interface.” Therefore, using EHR or EMR data will allow the system to be implemented with already existing systems. Furthermore, Para. [0072] of Zambotti teaches that the data may come from other applications or websites not just from local systems.
The combination of Zambotti and Steinberg-Koch does not explicitly teach,
wherein each historical menopause outcome trajectory includes an age of onset of at least one of perimenopause, menopause, or post-menopause for the respective historical patient;
Zambotti (2024) teaches,
wherein each historical menopause outcome trajectory includes an age of onset of at least one of perimenopause, menopause, or post-menopause for the respective historical patient; (Para. [0033] teaches “Predicting the onset of menopause can involve considering several features including age.” Para. [0058] teaches “In some embodiments, the processor circuitry 100 can generate the at least one ML model. For example, the processor circuitry 100 can generate the at least one ML model based on general population trends and demographic information associated with the user. Generating a ML model, as used herein, can include constructing the ML model and training the constructed ML model using known inputs and known outputs. The ML model can be trained using known inputs that include a set of features for other users, such as demographically similar users (and/or the user), and with the known outputs including indicators of known menopause states (e.g., clinically identified)” Para. [0076] teaches “In some embodiments, the current state of menopause that is predicted can be based on past predicted states. The states generally follow the order of pre-menopause, menopause transition (e.g., perimenopause and/or early and late menopause), and post-menopause.”))
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 combination of Zambotti and Koch wherein each historical menopause outcome trajectory includes an age of onset of at least one of perimenopause, menopause, or post-menopause for the respective historical patient such as that of Steinberg-Koch.
One of ordinary skill would have been motivated to modify Zambotti, because age at the onset of a menopause transition is an important predictor of female health and is an important piece of data for determining future menopause trajectory as seen in Para. [0033] of Zambotti (2024).
With respect to claims 2 and 12,
Zambotti further teaches,
The computer-implemented method of claim 1, further comprising: applying, by the one or more processors, the trained menopause outcome machine learning model to one or more patient parameters associated with a new patient; (Para. [0076] teaches “The calculation of the probability of occurrence, e.g., the predictive model, is further described below. With regard to the analysis of triggers, the analysis module 433 can analyze all data that is available to it along with the data of hot flash occurrence and determine the conditions that are most likely to cause hot flashes for the particular subject, which are sometimes herein also referred to as “hot flash factors”” Para. [0080] teaches “The above analysis may be done with an analysis module 433 used for performing computations and/or with a machine learning (ML) processes.”)
and generating, by the one or more processors, based on applying the trained menopause outcome machine learning model to the one or more patient parameters associated with the new patient, a predicted menopause outcome trajectory associated with the new patient. (Para. [0096] teaches “Therefore, the output of the machine learning process consists of m hot flash occurrence probabilities corresponding to m time intervals. Machine learning methods such as multiple regression, genetic programming, support vector regression, and difference structures of neural networks can be used for this purpose.”)
With respect to claims 3 and 13,
Zambotti further teaches,
The computer-implemented method of claim 1, wherein analyzing the historical EMR data associated with the plurality of historical patients to determine one or more patient parameters associated with each historical patient and a historical menopause outcome trajectory associated with each historical patient includes analyzing the historical EMR data using natural language processing (NLP) techniques. (Para. [0097] teaches “The different events in the calendar may be extracted using natural language processing (NLP) techniques and are classified or clustered into several group according to their similarity.”)
With respect to claims 4 and 14,
Zambotti further teaches,
The computer-implemented method of claim 1, wherein the patient parameters include one or more of: patient medical condition parameters, patient demographic parameters, or patient lifestyle parameters. (Para. [0005] teaches “For each specific user and/or users in general, there are specific biopsychosocial factors (e.g., stress, drinking hot beverages, eating spicy food, hot environments), physiological state, demography and behaviors that are associated with a greater probability of having a hot flash.” Para. [0077] teaches “The system 430 can generate a table such as Table 1, with the conditions that occurred when each hot flash occurred, or did not occur, being noted. The conditions can be the inputs that the system 430 gathers. While Table 1 shows three conditions, the number of columns are not so limited and can encompass any number of inputs. Other non-limiting examples of user conditions may be skin conductance, humidity, time of day, location, meeting with a particular person, stress level, and so on”)
With respect to claims 5 and 15,
Zambotti further teaches,
The computer-implemented method of claim 4, wherein the patient medical condition parameters include previous or current medical conditions or symptoms associated with the patient, including one or more of: number or frequency of hot flashes, sleep disruption, fatigue, sexual dysfunction, vaginal dryness, presence of menstruation, characteristics of menstruation, instances of amenorrhea, night sweats, urinary symptoms, mood symptoms, brain fog, changes in ability to focus, hair loss, weight gain, perimenopause or menopause status, arthritis, depression, anxiety, breast cancer, osteoporosis, dizziness, vertigo, inflammation, cardiovascular conditions, diabetes, or central nervous system conditions experienced by the patient. (Para. [0008] teaches “The input parameters are indicative of or otherwise include hot flash factors for a user and/or other users. For example, the logic circuitry receives the plurality of input parameters that comprise reported hot flashes and timing information, schedule or calendar data, stress level, general mood, dietary information, exercise data, sleep data, health information, among other information and a combination thereof.”
With respect to claims 6 and 16,
Zambotti further teaches,
The computer-implemented method of claim 4, wherein the patient demographic parameters include one or more of: an age, a location, or a race or ethnicity associated with the patient. (Para. [0064] teaches “such as but not limited to demographics, body mass index (BMI), ethnicity, age, menopausal status, medications, mood during a particular time of the day, anxiety level, activity level, allergies, types of food ingested, etc.”)
With respect to claims 7 and 17,
Zambotti further teaches,
The computer-implemented method of claim 4, wherein the patient lifestyle parameters include one or more of: a body mass index, a volume or frequency of alcohol use, a duration or frequency of smoking use, a diet, a number of children, a relationship status, or a stress level associated with the patient. (Para. [0008] teaches “schedule or calendar data, stress level, general mood, dietary information, exercise data, sleep data, health information, among other information and a combination thereof.”)
With respect to claims 8 and 18,
Zambotti further teaches,
The computer-implemented method of claim 1, wherein the menopause symptoms of the historical menopause outcome trajectory and the predicted menopause trajectory each include one or more of: number or frequency of hot flashes, sleep disruption, fatigue, sexual dysfunction, vaginal dryness, presence of menstruation, characteristics of menstruation, instances of amenorrhea, night sweats, urinary symptoms, mood symptoms, brain fog, changes in ability to focus, hair loss, weight gain, depression, anxiety, dizziness, vertigo, inflammation, or central nervous system conditions experienced by the patient. (Para. [0094] teaches “These form the inputs to the sub-model 2 543-2 that outputs the probability Pi of the hot flash occurrence over any specific time frame i.”)
With respect to claims 9 and 19,
Zambotti further teaches,
The computer-implemented method of claim 1, further comprising analyzing the historical EMR data associated with the plurality of historical patients to determine one or more menopause symptom treatments associated with each historical patient; (Para. [0120] teaches “The different categories of data have different weights based on success (e.g., past success for the particular user or other users) in predicting a hot flash occurrence and which can be updated over time. The actions module is triggered based on source of data and the probability exceeding the threshold. In specific embodiments, as shown by the predictive model, the actions module can be triggered a threshold period of time before the probability exceeds the threshold, such that the hot flash is anticipated, mitigated, and/or prevented. Although embodiments are not limited to hot flash mitigation and can be used to mitigate or otherwise manage other symptoms of menopause.”
wherein the training dataset includes menopause symptom treatments associated with each historical patient, and wherein training the menopause outcome machine learning model to generate the predicted menopause outcome trajectory for a patient based on patient parameters associated with the patient includes training the menopause outcome machine learning model to generate predicted menopause symptom treatments for alleviating or preventing one or more symptoms of the predicted menopause outcome trajectory. (Para. [0034] teaches “Para. [0034] teaches "Such a hot flash or menopausal symptom management tool can be linked with non-pharmacological therapies, such as cooling devices and/or stress relieving devices, which can provide sufficient relief from hot flashes in cases where pharmacological treatment is contra-indicated or not preferred. In related and specific embodiments, the system suggests mitigation techniques or triggers a mitigating action such as activating (e.g., turning) on a cooling device.”)
With respect to claim 10,
Zambotti further teaches,
The computer-implemented method of claim 9, wherein the menopause symptom treatments include one or more of: biologics, steroids, nonsteroidal anti-inflammatory drugs (NSAIDs), gabapentin or pregablin, leuperelin, hormone therapy, chemotherapy, metformin, sodium-glucose co-transporter-2 (SGLT2), peroxisome proliferator-activated receptors (PPAR), sulfonylureas, dipeptidyl-peptidase 4 (DPP4), insulin, orlistat, synthetic thyroid, statins, glucagon-like peptide-1 (GLP-1), angiotensin II receptor blockers (ARBs), calcium channel blockers (CCBs), angiotensin converting enzyme (ACE), diuretics, ambien, selective serotonin reuptake inhibitors (SSRIs), norepinephrine and dopamine reuptake inhibitors (NDRI), monoamine oxidase inhibitors (MAOIs), oral contraceptives, hormone replacement therapy, topical or vaginal hormone creams, natural or herbal remedies, cognitive therapy, exercise, meditation, vaginal lubricants, diet alterations, psychotherapy, vitamin D, or clonidine. (Para. [0010] teaches “In specific embodiments, the communicated data is an instruction that activates cooling circuitry worn by the user, and in response to the activation, the cooling circuitry provides cooling to the user to mitigate or prevent an imminent or occurring hot flash. The computing device further generates another instruction to deactivate the cooling circuitry worn by the user in response to a further revised probability being within the threshold, the further revised probability being based on an additionally received physical measurement.” (i.e. cooling the user is viewed as a natural remedy))
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to JOSHUA L FORRISTALL whose telephone number is 703-756-4554. The examiner
can normally be reached Monday-Friday 8:30 AM- 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use
the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached on 571-272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like
assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or
571-272-1000.
/JOSHUA L FORRISTALL/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights