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 .
Status of the Claims
The office action is in response to the claim amendments and remarks filed on February 5, 2025 for the application filed August 17, 2023 which claims priority to a provisional application filed on August 17, 2022. Claim 1 has been amended and claim 9 has been cancelled. Claims 1-8 and 10-18 are currently pending and have been examined.
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 1-8 and 10-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Eligibility Step 1:
Under step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, claims 1-8 and 10-18 are directed towards a system (i.e. a machine), which is a statutory category. Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea). In the instant application, the claims are directed towards an abstract idea.
Eligibility Step 2A, Prong One:
Under step 2A, prong one of the 2019 Revised Patent Subject Matter Eligibility Guidance, independent claim 1 is determined to be directed to an judicial exception because an abstract idea is recited in the claims which fall within the subject matter groupings of abstract ideas. The abstract idea (identified in bold) recited in claim 1 is identified as:
1. A data presentation system configured to present healthcare information to a patient, the system comprising:
storage, including non-transient memory, configured to store patient medical histories and outcomes for a plurality of past patients;
outcome prediction logic configured to predict a healthcare outcome for a current patient based on one or more healthcare actions;
profile comparison logic configured to compare the past patient medical histories to a medical history of the patient;
a cohort identification logic configured to identify a cohort of members of the past patients having medical histories similar to the medical history of the patient;
user interface logic configured to generate a user interface for presentation to the patient, the user interface including a representation of the predictions of healthcare outcomes generated by the outcome prediction logic, and also including interactive comparisons between the medical histories of the patient and medical histories of the cohort identified by the cohort identification logic, wherein the predictions of healthcare outcomes included in the user interface include quantitative comparisons of expected outcomes based on the one or more healthcare actions as determined using the outcome prediction logic; and
a microprocessor configured to execute at least the user interface logic or the outcome prediction logic.
The identified limitations of the abstract idea of claim 1 falls within the subject matter grouping of certain methods of organizing human activity related and the sub grouping of managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). The claims recite steps a healthcare provider follows when predicting outcomes for a patient based on similar patients and presenting information to a patient. Therefore, the claims are directed to certain methods of organizing the human activities between a healthcare provider and a patient.
The identified limitations of predicting, comparing and identifying of claim 1 falls within the subject matter grouping of mental processes. If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Here, a human can mentally predict outcome using data, compare patient data and identify similar patients using observations, judgments and opinions.
Accordingly, claim 1 recites an abstract idea under step 2A, prong one.
Eligibility Step 2A, Prong Two:
Under step 2A, prong two of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the identified abstract ideas are integrated into a practical application. After evaluation, there is no indication that any additional elements or combination of elements integrate the abstract idea into a practical application, such as through: an additional element that reflects an improvement to the functioning of a computer, or an improvements to any other technology or technical field; an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; an additional element that implements the judicial exception with, or uses the judicial exception in connection with, a particular machine or manufacture that is integral to the claim; an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses 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. As shown below, the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than a recitation of: generally linking the abstract idea to a particular technological environment or field of use; insignificant extra-solution activity to the judicial exception; and/or adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea as evidenced below.
The additional elements recited in claim 1 are identified in italics as:
1. A data presentation system configured to present healthcare information to a patient, the system comprising:
storage, including non-transient memory, configured to store patient medical histories and outcomes for a plurality of past patients;
outcome prediction logic configured to predict a healthcare outcome for a current patient based on one or more healthcare actions;
profile comparison logic configured to compare the past patient medical histories to a medical history of the patient;
a cohort identification logic configured to identify a cohort of members of the past patients having medical histories similar to the medical history of the patient;
user interface logic configured to generate a user interface for presentation to the patient, the user interface including a representation of the predictions of healthcare outcomes generated by the outcome prediction logic, and also including interactive comparisons between the medical histories of the patient and medical histories of the cohort identified by the cohort identification logic, wherein the predictions of healthcare outcomes included in the user interface include quantitative comparisons of expected outcomes based on the one or more healthcare actions as determined using the outcome prediction logic; and
a microprocessor configured to execute at least the user interface logic or the outcome prediction logic.
The additional limitations of “system”, “storage, including non-transient memory, configured to…”, “…logic configured to”, “user interface” and “a microprocessor configured to execute at least the user interface logic or the outcome prediction logic” are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f). These limitations are recited at a high level of generality are no more than mere instructions to implement an abstract idea or other exception on a computer.
The additional limitations of “store patient medical histories and outcomes for a plurality of past patients” are determined to be no more than insignificant extra-solution activity to the judicial exception under MPEP §2106.05(g). Storing medical histories and outcomes is mere necessary data gathering.
Accordingly, claim 1 does not recite additional elements which integrate the abstract idea into a practical application.
Eligibility Step 2B:
Under step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether provide an inventive concept by determining if the claims include additional elements or a combination of elements that are sufficient to amount to significantly more than the judicial exception. After evaluation, there is no indication that an additional element or combination of elements 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 limitations are mere instructions to apply an abstract idea under MPEP §2106.05(f) and insignificant extra-solution activity to the judicial exception under MPEP §2106.05(g). Evidence that storing data is well-understood- routine and conventional is provided by MPEP §2106.05(d), subsection II. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements amounts to an inventive concept.
Dependent Claims:
The dependent claims merely present additional abstract information in tandem with further details regarding the elements from the independent claims and are, therefore, directed to an abstract idea for similar reasons as given above. None of the limitations are deemed to integrate the claims into a practical application or to amount to significantly more than the abstract idea as detailed below.
Claims 2-6, 11, 13-16 and 18 are directed towards the abstract ideas in claim 1 as they are steps performed by a healthcare provider and can be performed mentally.
Claims 7-10 merely define what and how information is conveyed to a patient and the abstract human activity of exploring/navigating information.
Claim 12 is directed to the abstract idea grouping of mathematical concepts and mental process.
Claim 17 recites using a graph to generate the prediction, which is a mental process and storing the graph is mere instructions to apply an abstract idea under MPEP §2106.05(f).
Therefore, whether taken individually or as an ordered combination, 1-8 and 10-18 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8-9 and 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Apte et al. (U.S. Pub. No. 2019/0078142) in view of Loewke et al. (U.S. Pub. No. 2022/0399091).
Regarding claim 1, Apte discloses a data presentation system configured to present healthcare information to a patient (Paragraph [0228]), the system comprising:
storage, including non-transient memory, configured to store patient medical histories and outcomes for a plurality of past patients (Paragraph [0111], Embodiments of the method 100 can additionally or alternatively include Block S120, which can include processing (e.g., receiving, collecting, transforming, determining supplementary features. Paragraph [0112], Supplementary data can include: female reproductive system-related condition data (e.g., data informative of different female reproductive system-related conditions, such as in relation to microbiome characteristics, therapies, users, etc.); user data (e.g., user medical data current and historical medical data such as historical therapies, historical medical examination data.);
outcome prediction logic configured to predict a healthcare outcome for a current patient based on one or more healthcare actions (Paragraph [0161], Performing a characterization process S130 (e.g., performing a female reproductive system-related therapy) can include Block S140, which can include determining one or more therapies (e.g., therapies configured to modulate microbiome composition, function, diversity, and/or other suitable aspects, such as for improving one or more aspects associated with female reproductive system-related conditions, such as in users characterized based on one or more characterization processes; etc.). Block S140 can function to identify, select, rank, prioritize, predict, discourage, and/or otherwise determine therapies (e.g., facilitate therapy determination, etc.).Paragraph [0168], identifying therapeutic measures that provide desired outcomes for subjects based upon different female reproductive system-related characterizations. Paragraph [0172], Processing one or more therapy models is preferably based on microbiome features. For example, generating a therapy model can based on microbiome features associated with one or more female reproductive system-related conditions, therapy-related aspects such as therapy efficacy in relation to microbiome characteristics, and/or other suitable data. Paragraph [0189], Block S180 can function to gather additional data regarding positive effects, negative effects, and/or lack of effectiveness of one or more therapies (e.g., suggested by the therapy model for users of a given characterization, etc.). Paragraph [0190], used to generate a therapy-effectiveness model for each characterization provided by the characterization process of Block S130, and each recommended therapy measure provided in Blocks S140 and S170. Also see paragraph [0193]. Using a therapy model to determine/predict a therapy that provides a desired outcome for subject based on microbiome features associated with one or more female reproductive system-related conditions and therapy-related aspects such as therapy efficacy in relation to microbiome characteristics necessarily involves predicting the outcome of different therapies in order to determine the therapy which is predicted to achieve the desired outcome.);
profile comparison logic configured to compare the past patient medical histories to a medical history of the patient (Paragraph [0130], Block S130 and/or other portions of embodiments of the method 100 can include applying computer-implemented rules (e.g., models, feature selection rules, etc.) to process population-level data, but can additionally or alternatively include applying computer-implemented rules to process microbiome-related data on a demographic characteristic-specific basis (e.g., subgroups sharing one or more demographic characteristics such as therapy regimens, dietary regimens, physical activity regimens, ethnicity, age, gender, weight, behaviors, etc.), condition-specific basis (e.g., subgroups exhibiting a specific female reproductive system-related condition, a combination of female reproductive system-related conditions, triggers for the female reproductive system-related conditions, associated symptoms, etc.), a sample type-specific basis (e.g., applying different computer-implemented rules to process microbiome data derived from different collection sites; etc.), a user basis (e.g., different computer-implemented rules for different users; etc.) and/or any other suitable basis. As such, Block S130 can include assigning users from the population of users to one or more subgroups. Also see paragraphs [0049] and [0192].);
a cohort identification logic configured to identify a cohort of members of the past patients having medical histories similar to the medical history of the patient (Paragraph [0130], Block S130 and/or other portions of embodiments of the method 100 can include applying computer-implemented rules (e.g., models, feature selection rules, etc.) to process population-level data, but can additionally or alternatively include applying computer-implemented rules to process microbiome-related data on a demographic characteristic-specific basis (e.g., subgroups sharing one or more demographic characteristics such as therapy regimens, dietary regimens, physical activity regimens, ethnicity, age, gender, weight, behaviors, etc.), condition-specific basis (e.g., subgroups exhibiting a specific female reproductive system-related condition, a combination of female reproductive system-related conditions, triggers for the female reproductive system-related conditions, associated symptoms, etc.), a sample type-specific basis (e.g., applying different computer-implemented rules to process microbiome data derived from different collection sites; etc.), a user basis (e.g., different computer-implemented rules for different users; etc.) and/or any other suitable basis. As such, Block S130 can include assigning users from the population of users to one or more subgroups. Paragraph [0192], determining a comparison between microbiome characteristics of the user and reference microbiome characteristics corresponding to a user subgroup sharing at least one of a behavior and an environmental factor (and/or other suitable characteristic) associated with the female reproductive system-related condition, based on the post-therapy microbiome features. Also see paragraphs [0049] and Fig. 22.);
user interface logic configured to generate a user interface for presentation to the patient, the user interface including a representation of the predictions of healthcare outcomes generated by the outcome prediction logic, and also including interactive comparisons between the medical histories of the patient and medical histories of the cohort identified by the cohort identification logic (Paragraph [0178], Female reproductive system-related characterizations can include one or more of: diagnoses (e.g., presence or absence of a female reproductive system-related condition; etc.); risk (e.g., risk scores for developing and/or the presence of a female reproductive system-related condition; information regarding female reproductive system-related characterizations (e.g., symptoms, signs, triggers, associated conditions, etc.); comparisons (e.g., comparisons with other subgroups, populations, users, historic health statuses of the user such as historic microbiome compositions and/or functional diversities; comparisons associated with female reproductive system-related conditions; etc.); therapy determinations; other suitable outputs associated with characterization processes; and/or any other suitable data. Paragraph [0183], as shown in FIG. 22, Block S160 can include presenting female reproductive system-related characterizations (e.g., information extracted from the characterizations; as part of facilitating therapeutic intervention; etc.), such as at a web interface, a mobile application, and/or any other suitable interface, but presentation of information can be performed in any suitable manner. Paragraph [0188], a web interface of a personal computer or laptop associated with a user can provide access, by the user, to a user account of the user, where the user account includes information regarding the user's female reproductive system-related characterization, detailed characterization of aspects of the user's microbiome (e.g., in relation to correlations with female reproductive system-related conditions; etc.), and/or notifications regarding suggested therapeutic measures (e.g., generated in Blocks S140 and/or S170, etc.). The determined therapies are construed as the representation of the predictions of healthcare outcomes. Also see paragraph [0188] and [0228] and Figs. 22 and 25. Fig. 22 shows that the interface is interactive, therefore, under broadest reasonable interpretation, the comparisons are interactive.),
a microprocessor configured to execute at least the user interface logic or the outcome prediction logic (Paragraph [0233], embodiments of the method 100 and/or system 200 can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions can be executed by computer-executable components that can be integrated with the system. The computer-readable medium can be stored on any suitable computer-readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device. The computer-executable component can be a general or application specific processor, but any suitable dedicated hardware or hardware/firmware combination device can alternatively or additionally execute the instructions.).
Apte does not appear to explicitly disclose wherein the predictions of healthcare outcomes included in the user interface include quantitative comparisons of expected outcomes based on the one or more healthcare actions as determined using the outcome prediction logic.
Loewke teaches that it was old and well known in the art of reproductive healthcare at the time of the filing wherein the predictions of healthcare outcomes included in the user interface include quantitative comparisons of expected outcomes based on the one or more healthcare actions as determined using the outcome prediction logic (Loewke, paragraph [0092], he RE may be able to view in real-time how the egg outcome 556 may be altered for various measurements of suppressor 554 a, stimulant 554 b, and hormone 554 c. Additionally or alternatively, the RE may be able to change the day on which the final trigger injection is to be administered (e.g., by clicking on the toggle button above hormone 554 c). Changing the day may change the egg outcome 556. In some variations, RE Application 408 may also display a graph 558 illustrating the egg outcome for various days of a menstrual cycle. This may provide the RE with the necessary information to determine the day on which the final trigger injection is to be administered. Paragraph [0081], The machine-learning model(s) may predict egg outcome for a patient. For example, the patient-specific data may be used to train a machine-learning model for selecting a stimulation protocol for a patient. The machine-learning model may predict the egg outcome of various stimulation protocols (e.g., Antagonist Protocol, Long Protocol, Flare Protocol, etc.) for the patient) to assist in successful pregnancy and a potential live birth (Loewke, paragraph [0003]).
Therefore, it would have been obvious to one of ordinary skill in the art of reproductive healthcare at the time of the filing to modify the system of Apte such that the predictions of healthcare outcomes included in the user interface include quantitative comparisons of expected outcomes based on the one or more healthcare actions as determined using the outcome prediction logic, as taught by Loewke, in order to assist in successful pregnancy and a potential live birth.
Furthermore, Loewke teaches user interface logic configured to generate a user interface for presentation to the patient, the user interface including interactive comparisons between the medical histories of the patient and medical histories of the cohort identified by cohort identification logic (Fig. 24 and paragraph [0167], In FIG. 24 , the statistics of the patients that are similar to “Lisa Jones” may be displayed (e.g., 2452). As shown in FIG. 24 , the FSH dose model may use historical patient cycle data of 100 similar patients. The statistics of these similar 100 patients is shown in 2452. The dose response curve may be fitted/generated for “Lisa Jones” based on these statistics 2452. More specifically, the FSH dose response model may use the baseline characteristics 2451 for “Lisa Jones” and the statistics 2452 of the 100 similar patients to generate the dose response curve 2453. Paragraph [0089], Additionally, the REs may access, review, and/or edit the patient-specific data associated with the patient in real-time through the EMR 204 connected to the RE Application 208.).
Regarding claim 2, Apte does not appear to explicitly disclose, but Loewke teaches that it was old and well known in the art of reproductive healthcare at the time of the filing wherein the outcome prediction logic is configured to predict invitro fertilization (IVF) outcomes (Loewke, paragraph [0071], The computer-implemented method may include receiving a first patient-specific data associated with a first patient undergoing an IVF treatment (e.g., an IVF cycle). The computer-implemented method may include predicting an egg outcome for the first patient based on an implementation of the predictive model for the first patient-specific data associated with the first patient. Also see abstract.) to assist in successful pregnancy and a potential live birth (Loewke, paragraph [0003]).
Therefore, it would have been obvious to one of ordinary skill in the art of reproductive healthcare at the time of the filing to modify the system of Apte such that the outcome prediction logic is configured to predict invitro fertilization (IVF) outcomes, as taught by Loewke, in order to assist in successful pregnancy and a potential live birth.
Regarding claim 3, Apte does not appear to explicitly disclose, but Loewke teaches that it was old and well known in the art of reproductive healthcare at the time of the filing wherein the outcome prediction logic is configured to predict invitro fertilization outcomes based on selection among alternative embryos, based on selection of a hormonal treatment, based on timing of embryo cell division, or based on morphology of an embryo (Loewke, abstract, predicting an egg outcome for the patient for each of a plurality of treatment options for an ovarian stimulation process based on at least one predictive model and the patient-specific data. Paragraph [0062], During an ovarian stimulation stage of an IVF cycle, a patient may be prescribed hormones in order to promote multi-follicular development so that numerous mature eggs can be retrieved. The combination of drugs, dosages, and/or injections prescribed to promote the multi-follicular development may constitute a stimulation protocol. Also see paragraphs [0077]-[0078].). to assist in successful pregnancy and a potential live birth (Loewke, paragraph [0003]).
Therefore, it would have been obvious to one of ordinary skill in the art of reproductive healthcare at the time of the filing to modify the system of Apte such that the outcome prediction logic is configured to predict invitro fertilization outcomes based on selection among alternative embryos, based on selection of a hormonal treatment, based on timing of embryo cell division, or based on morphology of an embryo, as taught by Loewke, in order to assist in successful pregnancy and a potential live birth.
Regarding claim 4, Apte further discloses wherein the outcome prediction logic is configured to predict healthcare outcomes based on alternative medical treatments or to provide a comparison between expected outcomes of the alternative medical treatments (Paragraph [0188], providing a therapy to a user can include providing therapy recommendations (e.g., substantially concurrently with providing information derived from a female reproductive system-related characterization for a user; etc.) and/or other suitable therapy-related information (e.g., therapy efficacy; comparisons to other individual users, subgroups of users, and/or populations of users; therapy comparisons; historic therapies and/or associated therapy-related information; psychological therapy guides such as for cognitive behavioral therapy; etc.), such as through presenting notifications at a web interface (e.g., through a user account associated with and identifying a user; etc.).
Regarding claim 5, Apte further discloses wherein the user interface is configured for comparison of a profile of a current patient to multiple profiles of historical patients or cohorts thereof (Paragraph [0188], providing a therapy to a user can include providing therapy recommendations (e.g., substantially concurrently with providing information derived from a female reproductive system-related characterization for a user; etc.) and/or other suitable therapy-related information (e.g., therapy efficacy; comparisons to other individual users, subgroups of users, and/or populations of users; therapy comparisons; historic therapies and/or associated therapy-related information; psychological therapy guides such as for cognitive behavioral therapy; etc.), such as through presenting notifications at a web interface. Also see fig. 22.).
Regarding claim 6, Apte further discloses wherein the user interface is configured to wherein the profile of the current patient is compared to profiles of similar historical patients, as determine by the profile comparison logic (Paragraph [0188], providing a therapy to a user can include providing therapy recommendations (e.g., substantially concurrently with providing information derived from a female reproductive system-related characterization for a user; etc.) and/or other suitable therapy-related information (e.g., therapy efficacy; comparisons to other individual users, subgroups of users, and/or populations of users; therapy comparisons; historic therapies and/or associated therapy-related information; psychological therapy guides such as for cognitive behavioral therapy; etc.), such as through presenting notifications at a web interface. Also see fig. 22.).
Regarding claim 8, Apte further discloses wherein the user interface is configured to show a similarity between a current patient and an historical patient or a cohort thereof (Paragraph [0188], providing a therapy to a user can include providing therapy recommendations (e.g., substantially concurrently with providing information derived from a female reproductive system-related characterization for a user; etc.) and/or other suitable therapy-related information (e.g., therapy efficacy; comparisons to other individual users, subgroups of users, and/or populations of users; therapy comparisons; historic therapies and/or associated therapy-related information; psychological therapy guides such as for cognitive behavioral therapy; etc.), such as through presenting notifications at a web interface. Also see fig. 22.).
Regarding claim 9, Apte further discloses wherein the user interface is configured to (Not further limiting).
Regarding claim 13, Apte further discloses wherein the profile comparison logic is configured to distinguish between static characteristics and dynamic characteristics, the dynamic characteristics including hormone levels, patient weight, or embryo incubation temperature (Paragraph [0113], supplementary data can include… physiological data, demographic data. Physiological data can include information related to physiological features (e.g., height, weight, body mass index, body fat percent, body hair level, medical history, etc.). Demographic data can include information related to demographic characteristics (e.g., gender, age, ethnicity, marital status, number of siblings, socioeconomic status, sexual orientation, etc.).).
Regarding claim 14, Apte further discloses wherein the cohort identification logic is configured to identify the cohort of members of the past patents having medical histories similar to the medical history of the patient (Paragraph [0130], Block S130 and/or other portions of embodiments of the method 100 can include applying computer-implemented rules (e.g., models, feature selection rules, etc.) to process population-level data, but can additionally or alternatively include applying computer-implemented rules to process microbiome-related data on a demographic characteristic-specific basis (e.g., subgroups sharing one or more demographic characteristics such as therapy regimens, dietary regimens, physical activity regimens, ethnicity, age, gender, weight, behaviors, etc.), condition-specific basis (e.g., subgroups exhibiting a specific female reproductive system-related condition, a combination of female reproductive system-related conditions, triggers for the female reproductive system-related conditions, associated symptoms, etc.), a sample type-specific basis (e.g., applying different computer-implemented rules to process microbiome data derived from different collection sites; etc.), a user basis (e.g., different computer-implemented rules for different users; etc.) and/or any other suitable basis. As such, Block S130 can include assigning users from the population of users to one or more subgroups. Also see paragraphs [0049] and [0192].), t
Apte does not appear to explcity disclose but Loewke teaches that it was old and well known in the art of healthcare machine learning at the time of the filing the similarity being weighted with regard to characteristics relevant to IFV outcomes. (Loewke, paragraph [0115], the parameters in the feature vector may be weighted (e.g., with a respective coefficient) to reflect the importance of each parameter (e.g., a first weight for a first parameter and a second weight for a second parameter, where the first weight is greater than a second weight when the first parameter is more important than the second parameter for establishing patient similarity). Paragraph [0117], the similarity matching (e.g., matching a set of similar patients to a patient-of interest) may be distance based. Paragraph [0120], For example, similar patients may be identified by comparing feature vectors that represent metrics such as age, BMI, race/ethnicity, diagnosis, AFC, AMH, prior history, and/or others. Also see paragraph [0003]) to reflect the importance of each parameter (Loewke, paragraph [0115]).
Therefore, it would have been obvious to one of ordinary skill in the art of medical treatment modelling at the time of the filing to modify the system of Apte such that the similarity is weighted with regard to characteristics relevant to IFV outcomes, as taught by Loewke, in order to reflect the importance of each parameter.
Regarding claim 15, Apte further discloses wherein the patient medical histories include: medical histories of a prospective birth mother, an egg donor, and/or an embryo generated by fertilization of an egg from the egg donor (Loewke, paragraph [0008], suitable patient-specific data may include age, body mass index, ethnicity, diagnosis of infertility, prior pregnancy history, prior birth history, information relating to one or more prior IVF treatments (e.g., data retrieved during ovarian stimulation, number of eggs retrieved, number of mature eggs, number of successfully fertilized eggs, number of blastocysts, number of usable blastocysts, pregnancy outcome, and live birth outcome, etc.) and/ortone or more treatment variables (e.g., type of medication, a type of hormonal trigger injection to cause follicle maturation, and number of cycle(s) associated with the patient, etc.). Also see paragraph [0077]-[0078].).
Regarding claim 16, Apte further discloses wherein the patient medical histories include: reproductive histories, hormone levels, birth mother age, egg donor age, embryo growth rates, and/or embryo division times (Loewke, paragraph [0008], suitable patient-specific data may include age, body mass index, ethnicity, diagnosis of infertility, prior pregnancy history, prior birth history, information relating to one or more prior IVF treatments (e.g., data retrieved during ovarian stimulation, number of eggs retrieved, number of mature eggs, number of successfully fertilized eggs, number of blastocysts, number of usable blastocysts, pregnancy outcome, and live birth outcome, etc.) and/or tone or more treatment variables (e.g., type of medication, a type of hormonal trigger injection to cause follicle maturation, and number of cycle(s) associated with the patient, etc. Also see paragraphs [0077]-[0078].).
Regarding claim 17, Apte further discloses wherein the storage is further configured to store a graph, and the outcome prediction logic is configured to use the graph to generate a prediction (Paragraph [0129]).
Claims 7, 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Apte et al. (U.S. Pub. No. 2019/0078142) in view of Loewke et al. (U.S. Pub. No. 2022/0399091) and Heywood et al. (U.S. Pub. No. 2020/0279622).
Regarding claim 7, Apte does not appear to explicitly disclose wherein the user interface is configured to display percentages of historical patients that chose different medical actions at a particular point in IVF treatment.
Loewke teaches that it was old and well known in the art of reproductive healthcare at the time of the filing to choose different medical actions at a particular point in IVF treatment (Loewke, paragraph [0065], Once an IVF treatment and an IVF cycle are recommended for the patient, the treatment may proceed to the ovarian stimulation phase. During the ovarian stimulation phase, the RE may be faced with multiple decisions that may affect the outcome of the IVF cycle and the health of the patient. One such decision may include determining the stimulation protocol (e.g., 112 a) to be prescribed for the patient. For example, the RE may determine the drugs to be used and the starting dosage of the drugs. After the stimulation protocol (e.g., 112 a) has been selected, the RE may monitor the patient regularly to assess the response of the patient to the stimulation protocol. Based on the patient's response, the RE may modify the stimulation protocol (e.g., 112 b) and/or may cancel the IVF cycle.) to assist in successful pregnancy and a potential live birth (Loewke, paragraph [0003]); and
Heywood teaches that it was old and well known in the art of medical information reporting at the time of the filing wherein the user interface is configured to display percentages of historical patients that chose different medical actions (Heywood, Fig. 19C. Also see paragraphs [0104]-[0108].) to help users and their healthcare providers make the best preventive care, lifestyle, and treatment decisions (Heywood, paragraph [0111]).
Therefore, it would have been obvious to one of ordinary skill in the art of reproductive healthcare and medical information reporting at the time of the filing to modify the system of Apte such that the user interface is configured to display percentages of historical patients that chose different medical actions at a particular point in IVF treatment, as taught by Loewke and Heywood, in order to assist in successful pregnancy and a potential live birth and help users and their healthcare providers make the best preventive care, lifestyle, and treatment decisions.
Regarding claim 10, Apte does not appear to explicitly disclose, but Heywood teaches that it was old and well known in the art of medical information reporting at the time of the filing wherein the user interface includes a map of decisions or similar patient profiles, and is configured for a current patient to navigate this map to explore predicted outcomes following different medical actions (Heywood, Figs. 20A-20H) to help users and their healthcare providers make the best preventive care, lifestyle, and treatment decisions (Heywood, paragraph [0111]).
Therefore, it would have been obvious to one of ordinary skill in the art of medical information reporting at the time of the filing to modify the system of Apte such the user interface includes a map of decisions or similar patient profiles, and is configured for a current patient to navigate this map to explore predicted outcomes following different medical actions, as taught by Heywood, in order to help users and their healthcare providers make the best preventive care, lifestyle, and treatment decisions.
Regarding claim 11, Apte does not appear to explicitly disclose, but Heywood teaches that it was old and well known in the art of medical information reporting at the time of the filing wherein the profile comparison logic is configured for a user to select between comparisons of their profile to a cohort of historical users or comparisons of their profile to individual historical users (Heywood, figs. 20A and 20B show that a user can select to view insights for a cohort of similar patients (fig. 20A) and individual similar patients (fig. 20B), which is based on comparing user data to historical users data as discussed in paragraph [0104].) to help users and their healthcare providers make the best preventive care, lifestyle, and treatment decisions (Heywood, paragraph [0111]).
Therefore, it would have been obvious to one of ordinary skill in the art of medical information reporting at the time of the filing to modify the system of Apte such that the profile comparison logic is configured for a user to select between comparisons of their profile to a cohort of historical users or comparisons of their profile to individual historical users, as taught by Heywood, in order to help users and their healthcare providers make the best preventive care, lifestyle, and treatment decisions.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Apte et al. (U.S. Pub. No. 2019/0078142) in view of Loewke et al. (U.S. Pub. No. 2022/0399091) and Ghazaleh et al. (U.S. Pub. No. 2020/0152320).
Regarding claim 12, Apte does not appear to explicitly disclose, but Ghazaleh teaches that it was old and well known in the art of medical treatment modeling at the time of the filing wherein the profile comparison logic is configured to 1) compare medical profiles in multiple dimensions, 2) generate a Cosine distance between medical profiles, 3) generate a value representing profile distance based on a weighting of profile characteristics (Ghazaleh, paragraph [0006], Patient features vectors of the plurality of patients can be clustered into a first set of first clusters based on, for example, cosine distance clustering, and a cluster features vector can be computed for each cluster of the first set of first clusters. A cluster features vector may be computed for each first cluster to represent a distribution of each for each of the plurality of data categories in the first cluster. The cluster features vectors, which represent the first set of first clusters of patient characteristics data and treatment administration data, can be further clustered into a second set of second clusters based on, for example, Euclidean distance clustering (e.g., K-means clustering). Each second cluster of the second set of second clusters can include a subset of the first set of first clusters represented by the cluster features vectors, and each cluster feature vector itself can represent a cluster of patients in the first set of first clusters. Through the two-stage clustering, the patients data can be divided into clusters according to the clustering of the cluster features vectors. Also see paragraphs [0042]-[0058].) to accurately identify actions that influence the quality and effectiveness of care to improve clinical decisions and medical resource managements (Ghazaleh, paragraph [0002]).
Therefore, it would have been obvious to one of ordinary skill in the art of medical treatment modelling at the time of the filing to modify the system of Apte such that he profile comparison logic is configured to 1) compare medical profiles in multiple dimensions, 2) generate a Cosine distance between medical profiles, 3) generate a value representing profile distance based on a weighting of profile characteristics, as taught by Ghazaleh, in order to accurately identify actions that influence the quality and effectiveness of care to improve clinical decisions and medical resource managements.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Apte et al. (U.S. Pub. No. 2019/0078142) in view of Loewke et al. (U.S. Pub. No. 2022/0399091) and Dvorak et al. (U.S. Pub. No. 2010/0070306).
Regarding claim 18, Apte does not appear to explicitly disclose, but Dvorak teaches that it was old and well known in the art of patient healthcare platforms at the time of the filing to provide a system further comprising anonymization logic configured to anonymize the past patient medical histories (Dvorak, Abstract) to comply with the Health Insurance Portability and Accountability Act (HIPAA) (Dvorak, paragraph [0020]).
Therefore, it would have been obvious to one of ordinary skill in the art of patient healthcare platforms at the time of the filing to modify the system of Apte such that the system further comprises anonymization logic configured to anonymize the past patient medical histories, as taught by Dvorak, in order to comply with the Health Insurance Portability and Accountability Act (HIPAA).
Response to Arguments
Applicant's arguments filed February 5, 2026 regarding claims 1-8 and 10-18 being rejected under 35 U.S.C. §101 have been fully considered but they are not persuasive.
Applicant argues that in light of Desjardins, any abstract idea in the claims is integrated into a practical application because making healthcare outcome predictions for different actions has practical benefits, identification of cohorts of similar patients has practical applications in better comparisons and more accurate predictions, and communication predicted outcomes in an interactive manner has the practical application of providing a way for patients to compare outcomes of various medical options.
In response, integrating the abstract idea into a practical application under step 2A, prong 2 can be shown by additional elements that improve the functioning of a computer or improves another technology or technical field. The additional elements in the claims provide no improvement to the functioning of a computer, another technology or technical field. As discussed in the previous and instant rejection, the addition limitations of “logic configured to” achieve the functionality identified as the abstract idea are recited at a high level and devoid of any details as to how the logic is actually configured to achieve the claimed functions such that these limitations cannot amount to an improvement to the functioning of a computer, another technology or technical field. The above argued improvements are a result of the abstract idea itself and are not improvements to technology.
Applicant's arguments filed February 5, 2026 regarding claims 1-8 and 10-18 being rejected under 35 U.S.C. §102 have been fully considered but they are either moot in view of the new grounds of rejection or are not persuasive.
Applicant argues that Apte does not disclose outcome prediction logic to predict a healthcare outcome.
In response, Using a therapy model to determine/predict a therapy that provides a desired outcome for subject based on microbiome features associated with one or more female reproductive system-related conditions and therapy-related aspects such as therapy efficacy in relation to microbiome characteristics necessarily involves predicting the outcome of different therapies in order to determine the therapy which is predicted to achieve the desired outcome. Loewke also teaches this limitations.
Applicant argues that Apte does not disclose cohort identification logic to identify a cohort of members of past patients having medical histories similar to the medical history of the patient.
In response, paragraph [0192], discusses determining a user subgroup sharing at least one of a behavior and an environmental factor (and/or other suitable characteristic) associated with the female reproductive system-related condition and paragraph [0130] discusses that the subgroups sharing one or more demographic characteristics such as therapy regimens, dietary regimens, physical activity regimens. Also see fig. 22 which clearly shows identifying a subgroup of users sharing demographic, behavior or environmental characteristics.
Applicant argues that Apte does not disclose the user interface logic that present interactive comparisons.
In response, Fig. 22 shows that the interface is interactive and includes the comparisons. Loewke also teaches this limitations.
The remaining arguments are moot in view of the new grounds of rejection.
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.
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/DEVIN C HEIN/Examiner, Art Unit 3686