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 .
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 1-19 and 21 rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
With respect to claims 1, 11, and 21, the following bold limitations are considered abstract:
“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:
obtain historical electronic medical record (EMR) data associated with a plurality of historical patients;
analyze 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;
generate a training dataset that includes patient parameters and menopause outcome trajectories associated with each historical patient;
and train a menopause outcome machine learning model, using the training dataset, to generate a predicted menopause outcome trajectory for patients 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.”
The above bolded limitations are directed to abstract ideas and would fall within the “Mathematical Concept” and “Mental Process” groupings of abstract ideas. Analyzing data to generate training data is a mental process as it can be done in the human mind using observation, judgement, and opinion. Training a machine learning model and running the model is viewed as using an algorithm to determine information which is a mathematical concept. A cited example of an algorithm being a mathematical concept is seen in MPEP 2106.04(a)(2) “using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979).” Furthermore, according to Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025), simply applying generic AI-based models to new environments is not enough to secure patent protection under 35 U.S.C. § 101. The court reaffirmed that without concrete technical improvements to the AI technology itself, claims will be dismissed as abstract and patent-ineligible.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements –
“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:
obtain historical electronic medical record (EMR) data associated with a plurality of historical patients;”
Examiner views these limitations amount to generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
As such Examiner does NOT view that the claims
-Improve the functioning of a computer, or to any other technology or technical field
-Apply the judicial exception with, or by use of, a particular machine - see MPEP
2106.05(b)
-Effect a transformation or reduction of a particular article to a different state or thing -
see MPEP 2106.05(c)
-Apply or use the judicial exception in some other 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 - see MPEP
2106.05(e) and Vanda Memo.
Moreover, Examiner views the claims to be merely generally linking the use of the judicial exception to a computer system and generic data.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “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: obtain historical electronic medical record (EMR) data associated with a plurality of historical patients;” amount to well-known computer components used as a tool and obtaining medical record data which is seen as mere data gathering.
Examiner further notes that such additional elements are viewed to be well known routine and conventional as evidenced by
Zambotti (US 20200013511 A1)
Steinberg-Koch (US 20220223293 A1)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way. As currently claimed, Examiner views that the additional elements do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, because the claim fails to recite clearly how the judicial exception is applied in a manner that does not monopolize the exception because the limitations “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: obtain historical electronic medical record (EMR) data associated with a plurality of historical patients;” just tie the claim to a well-known computer system and generic data.
Dependent claims 2-10 and 12-19 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claims are not directed to an abstract idea, as detailed below:
The dependent claims are directed to determining other parameters or a path of treatment from the data which is further seen as a mental process.
Therefore, dependent claims 2-10 and 12-19 further limit the abstract idea with an abstract idea and thus the claims are still directed to an abstract idea without significantly more
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) and Steinberg-Koch (US 20220223293 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 patient parameters and historical menopause outcome trajectories associated with each historical patient; (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.
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.
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
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