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 Claims
Claims 1-20 were previously pending and subject to a non-final Office Action having a notification date of September 11, 2025 (“non-final Office Action”). Following the non-final Office Action, Applicant filed an amendment on December 1, 2025 (the “Amendment”), amending claims 1-3, 5, 6, 9-11, 13, 14, 16, 17, and 19.
The present Final Office Action addresses pending claims 1-20 in the Amendment.
Response to Arguments
Response to Applicant’s Arguments Regarding Double Patenting Claim Rejections
These rejections are withdrawn in view of the Amendment.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §101
At pages 11-12 of the Amendment, Applicant reiterates substantially all the limitations of the independent claims and then asserts that the limitations i) cannot be reasonably practically performed in the human mind and ii) provide additional, technical limitations that recite not only storing specific types of data in a specialized data structure, but retrieving that data from the specialized data structure in a structured way and using the retrieved data by a ML model to generate a specialized recommendation based on a specific goal received and addressed by the ML model.
The Examiner disagrees that the present claims have no limitations that can be practically performed in the human mind. As set forth in the rejection below, a medical professional could practically in their mind with pen and paper present a questionnaire via writing it down on paper, receive (e.g., via listening or observing) one or more responses to the questionnaire including a goal associated with a health outcome including a stage of menopause, generate "clusters" of existing user profiles (e.g., where the existing profiles in each cluster includes data representative of common characteristics of a group of people such as different menopause stages, etc. that are similar above a "similarity threshold"), generate a "profile" for a new user (e.g., collection of characteristics for the new user), associate the generated profile into one of the clusters (e.g., the cluster having characteristics most similar to those of the generated profile, e.g., above the "similarity threshold"), determine a "persona" including artificial profile representing one or more data values of the generated profile in the one of the clusters (e.g., women ages 45-49 in the perimenopause stage with "mild" symptoms), identify a highest ranked intervention for the determined health outcome for the determined persona for the one of the clusters, and generate a recommendation for the user based on the cluster and its persona including the highest ranked intervention for the associated cluster that has a likelihood of accomplishing the goal of addressing the user's determined health outcome. These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
Furthermore, the foregoing underlined limitations constitute "certain methods of organizing human activity” because they relate to managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions). The limitations are similar to a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). MPEP 2106.04(a)(2)(II)(C).
Regarding the additional limitations of the generic computer-implementation including the UI, memory/non-transitory computer readable media including instructions, processor, and "specialized" data structure storage, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of recommendations being generated via an ML model, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). This additional limitation provides only a result-oriented solution and lacks details as to how the ML model is trained and/or executed in a manner that provides a technological solution/improvement. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13. Allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system. Id.
Applicant next contends that the present claims compare favorably to those in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335, 118 USPQ2d 1684, 1688 (Fed. Cir. 2016) (hereinafter, "Enfish") because the present claims address an alleged similar issue of a solution to a particular problem of "the consumer identifying incorrect or incomplete information, leading to an incorrect self-diagnosis, and/or the selected products failing to address the symptoms felt by the consumer" as noted at [0002] of the present specification. Furthermore, Applicant makes reference to how [0020] of the present specification discloses how "systems and methods described herein operate in an unconventional manner by collecting data from various sources and in various formats for a new profile, converting the collected data into a standardized format, generating clusters of similar profiles and associating the new profile with a particular cluster, determining an expected health outcome for the profiles in the cluster and identifying an associated intervention, and generating a recommendation that, when applied, implements the intervention to address the expected health outcome" and how "the present disclosure therefore provides numerous technical effects, including an improved data structure that stores the converted data originally collected from multiple sources that facilitates improved retrieval of the information, an improved recommendation engine that implements a trained machine learning model to optimizes the content delivered, i.e., the generated recommendation, to a consumer based on specific historical user characteristics, taking into account changes in the consumer's profile over time."
To be clear, the claims of the patents at issue in Enfish describe the steps of configuring a computer memory in accordance with a self-referential table. The court asked whether the focus of the claims is on the specific asserted improvement in computer capabilities (i.e., the self-referential table for a computer database), or instead on a process that qualifies as an "abstract idea" for which computers are invoked merely as a tool. To make the determination of whether these claims are directed to an improvement in existing computer technology, the court looked to the teachings of the specification. Specifically, the court identified the specification's teachings that the claimed invention achieves other benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. It was noted that the improvement does not need to be defined by reference to "physical" components. Instead, the improvement here is defined by logical structures and processes, rather than particular physical features. The Federal Circuit stated that the Enfish claims were not ones in which general-purpose computer components are added after the fact to a fundamental economic practice or mathematical equation, but were directed to a specific implementation of a solution to a problem in the software arts, and concluded that the Enfish claims were thus not directed to an abstract idea (under Step 2A).
In the present case, and contrary to the Enfish claims, there are no limitations specifying how a computer memory is specifically configured or how an improved data structure is specifically provided. For instance, while the Enfish claim recites a specific manner of configuring memory according to a logical table including logical rows with an ID number corresponding to an information record, logical columns including an identifier and intersecting the rows to define logical cells along with a means for indexing data stored in the table, the present claims merely generically recite storing the mentally-determinable clusters/personas in a "specialized data structure." Accordingly, the generic recitation of storing the clusters/personas in a "specialized data structure" amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea and/or merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)).
The Examiner disagrees with Applicant's assertion that the present claims address an alleged similar issue (i.e., to that being addressed in Enfish) of "the consumer identifying incorrect or incomplete information, leading to an incorrect self-diagnosis, and/or the selected products failing to address the symptoms felt by the consumer." In Enfish, the technical issues being addressed were slower data searching with existing relational database structures, less effective storage of images and unstructured text, and extension modeling and configuration of various tables and relationships in advance of launching the database. In the present case and in contrast, the above problems being solved by Applicant's claims (identifying incorrect information leading to incorrect self-diagnoses, etc.) have nothing to do with technical issues related to database/memory storage but instead relate to mental processes and certain methods of organizing human activity. Specifically, the presently recited steps directed to receiving questionnaire responses, generating clusters, associating a new user cluster into one of the generated clusters, determining personas, etc. all describe mental processes and certain methods of organizing human activity at the claimed high level of generality.
Regarding the additional limitations of recommendations being generated via an ML model as noted by Applicant at the top of page 13 of the Amendment, the Examiner submits that this limitation amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). This additional limitation provides only a result-oriented solution and lacks details as to how the ML model is trained and/or executed in a manner that provides a technological solution/improvement. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13. Allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system. Id.
Regarding Applicant's position on page 13 that the present claims allegedly improve the functioning of a computer by providing "an improved data structure that stores the converted data originally collected from multiple sources that facilitates improved retrieval of the information, an improved recommendation engine that implements a trained machine learning model to [optimize] the content delivered, i.e., the generated recommendation, to a consumer based on specific historical user characteristics, taking into account changes in the consumer's profile over time," the Examiner initially notes that the present claims do not even recite collecting data from multiple sources, converting the data, optimizing delivered content based on specific historical user characteristics, or taking into account changes in profiles over time in the first place. Furthermore, and as noted previously, there are no limitations in the present claims specifying how a computer memory is specifically configured or how an improved data structure is specifically provided, how the ML model is trained/executed to optimize delivered recommendations, etc.
Finally, the Examiner disagrees with Applicant's assertions on pages 14-15 of the Amendments for reasons similar to those presented above.
The 35 USC 101 rejection is maintained.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §102/103
On page 16 of the Amendment, Applicant asserts Mason fails to teach, expressly or inherently, "presenting, on a user interface (UI), a questionnaire," "receiving, on the UI, one or more responses to the questionnaire, the received one or more responses including a goal of a new user, the goal associated with a health outcome, wherein the health outcome is a stage of menopause," and "generating, by a machine learning (ML) model, a recommendation for the new user based on the associated cluster and the determined persona, wherein the generated recommendation includes the identified highest ranked intervention that, when applied, has a
likelihood of accomplishing the goal associated with addressing the determined health outcome"
as recited in claim 1.
The Examiner disagrees with Applicant's assertion because Mason discloses the aforementioned limitations as follows:
presenting, on a user interface (UI), a questionnaire (the end of [0069] discloses presenting a question/questionnaire on a UI);
receiving, on the UI, one or more responses to the questionnaire, the received one or more responses … of a new user (the end of [0069] discusses receiving a patient (new user) response to the question (which would be via the patient interface 50 in Figures 1 and 4), …;
generating, by a machine learning (ML) model, a recommendation for the user based on the associated cluster and the determined persona, wherein the generated recommendation includes the identified highest ranked intervention that, when applied, has a likelihood of accomplishing [a] goal associated with addressing the determined health outcome ([0031], [0045]-[0046], Figure 7 disclose/illustrate using an ML model to select one of a number of treatment plans/interventions based on the cohort/cluster to which the patient/user is assigned (which has an associated "persona" as discussed above) to achieve a desired result ("goal") such as most effective recovery, speed of recovery, recovering to threshold range of motion such as 75%, etc. associated with addressing the determined health outcome such as obesity, diabetes, comorbid diseases/conditions, etc. per [0030]-[0032], [0093] ("highest ranked" treatment plan/intervention that when applied has a likelihood of accomplishing a goal associated with addressing determined health outcome)).
While Mason might be silent specifically regarding the received one or more responses to the questionnaire including the goal associated with the health outcome as recited, Mason does disclose how the new user may have a "goal" associated with a "health outcome" as noted above ([0028], [0033], [0046], [0091]-[0094], [0101]) which can be obtained from the user such as during a telemedicine session ([0119]) or the like. Furthermore, the end of [0069] of Mason already discloses how a questionnaire can be presented to a user to obtain pain levels from the patient which would provide an effective manner of soliciting important information from the patient to facilitate recommendation/adjustment of treatment plans/interventions thereby improving patient outcomes.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the received one or more responses to the questionnaire to specifically include the goal associated with the health outcome as taught by Mason to facilitate recommendation/adjustment of treatment plans/interventions thereby improving patient outcomes, because a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention, and because there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). The courts have made clear that the teaching, suggestion, or motivation test is flexible and an explicit suggestion to combine the prior art is not necessary. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007).
While Applicant takes the position (page 18 of the Amendment) that Mason and Behal do not disclose or suggest the following limitations of claim 16, the Examiner disagrees as set forth below.
generate, for a new user, a profile ([0027]-[0029] of Mason discuss collecting characteristics for users (including a new/current patient/user per [0031], [0042]), where a particular collection of characteristics for a particular patient/user is a "profile"; also see patient profile 130 in Figures 5 and 7) based on one or more responses to a questionnaire ([0074], [0106] of Mason disclose how the characteristics of the patient ("profile") can include pain levels which are reported in response to a questionnaire per the end of [0069]), the generated profile including at least an identified [medical condition] ([0027] of Mason discloses how the collected characteristics can include personal information while [0024] discloses how personal information can include a medical condition) and user symptoms experienced related to [the medical condition] ([0027] of Mason discloses how the collected characteristics can include performance information while [0024] discloses how performance information can include pain levels (e.g., related to diabetes/medical condition per [0093]), …;
associate the generated profile into a cluster of the plurality of clusters ([0031] and [0042] of Mason discuss assigning the new/current patient to a particular one of the cohorts based on their characteristics (based on their "profile"); also see Figure 7); and
generate, by a machine learning (ML) model, a recommendation for the user based on the associated cluster and the determined persona, wherein the generated recommendation includes [an] intervention that, when applied, has a likelihood of accomplishing [a] goal associated with addressing the identified [medical condition] ([0031], [0045]-[0046], Figure 7 of Mason disclose/illustrate using an ML model to select one of a number of treatment plans/interventions based on the cohort/cluster to which the patient/user is assigned (which has an associated "persona" as discussed above) to achieve a desired result ("goal") such as most effective recovery, speed of recovery, recovering to threshold range of motion such as 75%, etc. associated with addressing the determined health outcome such as obesity, diabetes, comorbid diseases/conditions, etc. per [0030]-[0032], [0093] (treatment plan/intervention that when applied has a likelihood of accomplishing a goal associated with addressing determined health outcome)).
While Mason discloses how the new user may have a "goal" associated with a health outcome/medical condition as noted above ([0028], [0033], [0046], [0091]-[0094], [0101]) which can be obtained from the user such as during a telemedicine session ([0119]) or the like, Mason might be silent specifically regarding the one or more responses to the questionnaire including the goal of the new user.
However, the end of [0069] of Mason already discloses how a questionnaire can be presented to a user to obtain pain levels from the patient which would provide an effective manner of soliciting important information from the patient to facilitate recommendation/adjustment of treatment plans/interventions thereby improving patient outcomes.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the one or more responses to the questionnaire to specifically include the goal associated with new user as taught by Mason to facilitate recommendation/adjustment of treatment plans/interventions thereby improving patient outcomes, because a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention, and because there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). The courts have made clear that the teaching, suggestion, or motivation test is flexible and an explicit suggestion to combine the prior art is not necessary. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007).
Furthermore, while Mason does not appear to disclose the medical condition/health outcome to specifically be a user stage of menopause, Mason does disclose how the medical condition/health outcome to be treated can be medical conditions/diseases such as obesity ([0030], [0032]), diabetes ([0093]), and/or comorbid conditions/diseases ([0093]) as already discussed above.
Furthermore, Behal teaches that it was known in the healthcare informatics art to execute a clustering algorithm to cluster individuals into a plurality of different clusters or subclusters based on various cardiometabolic conditions/diseases ([0046]-[0052]) such as diabetes, obesity, etc. ([0025]) and women during menopause transition years, post-menopause, or early menopause (stage of menopause) ([0069], where menopause is a cardiometabolic condition as evidenced by "Menopause: a cardiometabolic transition" to Nappi et al. ("Nappi")), assign a new individual to an already generated cluster with an associated medical classification ([0066], which can be a stage of menopause per [0069]), and generate a care strategy for individuals in the cluster to treat the corresponding medical condition(s) including stage of menopause ([0067], [0069]). This arrangement advantageously facilitates treatment of a plurality of related cardiometabolic conditions including stage of menopause, obesity, etc., thereby resulting in improved patient outcomes.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the medical condition/health outcome of Mason to include a user stage of menopause as taught by Behal as Mason already discloses that the system can address obesity, diabetes, and other comorbid conditions ([0093]) and Behal teaches (as evidenced by Nappi above) that menopause is a medical condition that can be comorbid with obesity, diabetes, etc. as already disclosed by Mason. Therefore, including user stage of menopause as one of the medical conditions/health outcomes of Mason as taught by Behal would advantageously allow corresponding treatments for the user stage of menopause to be determined for a user thereby improving patient outcomes. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Claim Objections
Claim 16 is objected to because of the following informalities:
In claim 16, the second to last line, "intervention" should be changed to --an intervention--.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the determined health outcome" in line 17. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation "the determined health outcome" in line 21. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-8 and 10-15 are rejected based on their dependencies from claims 1 or 9.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more:
Subject Matter Eligibility Criteria - Step 1:
Claims 1-8 are directed to a method (i.e., a process), claims 9-15 are directed to an apparatus (i.e., a machine), and claims 16-20 are directed to one or more non-transitory computer readable media (i.e., a manufacture). Accordingly, claims 1-20 are all within at least one of the four statutory categories. 35 USC §101.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 and July 2024 updates issued by the USPTO as incorporated into the MPEP, as supported by relevant case law), the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A computer-implemented method, comprising:
presenting, on a user interface (UI), a questionnaire;
receiving, on the UI, one or more responses to the questionnaire, the received one or more responses including a goal of a new user, the goal associated with a health outcome, wherein the health outcome is a stage of menopause;
generating a plurality of clusters of a plurality of existing profiles having a similarity above a similarity threshold;
generating, for the new user, a profile;
associating the generated profile into a cluster of the plurality of clusters based on the similarity for the generated profile being above the similarity threshold;
determining a persona for the cluster of the plurality of clusters, wherein the determined persona is an artificial profile representing one or more data values of the generated profile in the cluster;
storing, in a specialized data structure, the cluster together with the determined persona for the cluster;
for the associated cluster, identifying, in the specialized data structure, a highest ranked intervention for the determined health outcome for the determined persona; and
generating, by a machine learning (ML) model, a recommendation for the new user based on the associated cluster and the determined persona, wherein the generated recommendation includes the identified highest ranked intervention that, when applied, has a likelihood of accomplishing the goal associated with addressing the determined health outcome.
The Examiner submits that the foregoing underlined limitations constitute "mental processes" because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). For example, a medical professional could practically in their mind with pen and paper present a questionnaire via writing it down on paper, receive (e.g., via listening or observing) one or more responses to the questionnaire including a goal associated with a health outcome including a stage of menopause, generate "clusters" of existing user profiles (e.g., where the existing profiles in each cluster includes data representative of common characteristics of a group of people such as different menopause stages, etc. that are similar above a "similarity threshold"), generate a "profile" for a new user (e.g., collection of characteristics for the new user), associate the generated profile into one of the clusters (e.g., the cluster having characteristics most similar to those of the generated profile, e.g., above the "similarity threshold"), determine a "persona" including artificial profile representing one or more data values of the generated profile in the one of the clusters (e.g., women ages 45-49 in the perimenopause stage with "mild" symptoms), identify a highest ranked intervention for the determined health outcome for the determined persona for the one of the clusters, and generate a recommendation for the user based on the cluster and its persona including the highest ranked intervention for the associated cluster that has a likelihood of accomplishing the goal of addressing the user's determined health outcome. These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
Furthermore, the foregoing underlined limitations constitute "certain methods of organizing human activity” because they relate to managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions). The limitations are similar to a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). MPEP 2106.04(a)(2)(II)(C).
Independent claim 9 includes limitations that recite at least one abstract idea. Specifically, independent claim 9 recites:
An apparatus comprising:
a user interface (UI) configured to present a questionnaire;
a memory configured to store a specialized data structure; and
a processor coupled to the memory configured to:
receive, via the UI, one or more responses to the questionnaire, the received one or more responses including a goal of a new user, the goal associated with a health outcome, wherein the health outcome is a stage of menopause;
generate a plurality of clusters of a plurality of existing profiles having a similarity above a similarity threshold;
generate, for the new user, a profile associated with the user based on the received responses to the questionnaire;
associate the generated profile into a cluster of a plurality of clusters based on the similarity for the generated profile being above the similarity threshold;
determine a persona for the cluster of the plurality of clusters, wherein the determined persona is an artificial profile representing one or more data values of the generated profile in the cluster;
store, in a specialized data structure, the cluster together with the determined persona for the cluster;
for the associated cluster, identifying, in the specialized data structure, a highest ranked intervention for the determined health outcome for the determined persona; and
execute a machine learning (ML) model to generate a recommendation for the new user based on the associated cluster and the determined persona, wherein the generated recommendation includes the identified highest ranked intervention that, when applied, has a likelihood of accomplishing the goal associated with addressing the determined health outcome.
The Examiner submits that the foregoing underlined limitations constitute "mental processes" because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). For example, a medical professional could practically in their mind with pen and paper present a questionnaire via writing it down on paper, receive (e.g., via listening or observing) one or more responses to the questionnaire including a goal associated with a health outcome including a stage of menopause, generate "clusters" of existing user profiles (e.g., where the existing profiles in each cluster includes data representative of common characteristics of a group of people such as different menopause stages, etc. that are similar above a "similarity threshold"), generate a "profile" for a new user (e.g., collection of characteristics for the new user), associate the generated profile into one of the clusters (e.g., the cluster having characteristics most similar to those of the generated profile, e.g., above the "similarity threshold"), determine a "persona" including artificial profile representing one or more data values of the generated profile in the one of the clusters (e.g., women ages 45-49 in the perimenopause stage with "mild" symptoms), identify a highest ranked intervention for the determined health outcome for the determined persona for the one of the clusters, and generate a recommendation for the user based on the cluster and its persona including the highest ranked intervention for the associated cluster that has a likelihood of accomplishing the goal of addressing the user's determined health outcome. These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
Furthermore, the foregoing underlined limitations constitute "certain methods of organizing human activity” because they relate to managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions). The limitations are similar to a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). MPEP 2106.04(a)(2)(II)(C).
Independent claim 16 includes limitations that recite at least one abstract idea. Specifically, independent claim 16 recites:
One or more non-transitory computer readable media storing instructions that, when executed by a processor, cause the processor to:
generate a plurality of clusters, each cluster of the generated plurality of clusters including at least a stage of menopause and symptoms experienced related to menopause;
determine a persona for the cluster of the plurality of clusters, wherein the determined persona is an artificial profile representing one or more data values of the generated profile in the cluster;
store, in a specialized data structure, the cluster together with the determined persona for the cluster;
generate, for a new user, a profile based on the received responses to the questionnaire, the generated profile including at least an identified user stage of menopause and user symptoms experienced related to menopause, wherein the one or more responses include a goal of the new user;
associate the generated profile into a cluster of the plurality of clusters; and
generate, by a machine learning (ML) model, a recommendation for the user based on the associated cluster and the determined persona, wherein the generated recommendation includes [an] intervention that, when applied, has a likelihood of accomplishing the goal associated with addressing the identified user stage of menopause.
The Examiner submits that the foregoing underlined limitations constitute "mental processes" because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). For example, a medical professional could practically in their mind with pen and paper generate "clusters" including data representative of common characteristics of a group of people including menopause stages and associated experienced symptoms, determine a "persona" including artificial profile representing one or more data values of each of the generated profiles (e.g., Persona 1: women ages 45-49 in the perimenopause stage with "mild" symptoms; Persona 2: women ages 50-54 in the menopause stage with "moderate" symptoms; etc.), generate a "profile" for a new user based on responses to a questionnaire (e.g., including identified user menopause stage and associated user symptoms), associate the generated profile into one of the clusters (e.g., the cluster having characteristics most similar to those of the generated profile), and generate a recommendation for the user based on the cluster and its associated persona including an intervention that has a likelihood of accomplishing the goal of addressing the user's menopause stage. These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
Furthermore, the foregoing underlined limitations constitute "certain methods of organizing human activity” because they relate to managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions). The limitations are similar to a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). MPEP 2106.04(a)(2)(II)(C).
Accordingly, the claims recite at least one abstract idea.
Furthermore, dependent claims 2-8, 10-15, and 17-20 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below:
-Claims 2, 10, and 18 recite how generating the clusters further includes identifying the plurality of existing profiles including a set of variables; identifying for each of the existing profiles a value for each variable of the set of variables; determining, by a clustering algorithm, that first and second existing profiles have a similarity above the similarity threshold; and generating, by the clustering algorithm, the cluster including the first and second existing profiles. These limitations just further define the at least one abstract idea discussed above.
-Claims 3, 11, and 19 recite how associating the generated profile into the cluster includes identifying a new set of variables for the generated profile; identifying a value for each of the new variables; determining, by the clustering algorithm, the cluster of the plurality of clusters most similar to the generated profile based on the identified value for each of the new variables; and associating the generated profile into the determined cluster. These limitations just further define the at least one abstract idea discussed above.
-Claims 4 and 12 recite how the variables include are related to at least one of age, number of symptoms, types of symptoms, severity of symptoms, ethnicity, income, geographical location, or awareness of menopause. These limitations just further define the at least one abstract idea discussed above.
-Claims 5, 6, 13, 14, and 17 recite how generating the recommendation for the user further includes determining the health outcome (e.g., stage of menopause) associated with the cluster of the plurality of clusters; identifying the intervention that, when applied, has a likelihood of addressing the determined health outcome (e.g., treatment for symptom of stage of menopause or educational content for stage of menopause); and generating, by the ML model, the recommendation for the user, the recommendation including the intervention. These limitations just further define the at least one abstract idea discussed above.
-Claim 7 calls for receiving feedback indicating a result of the recommendation which just further defines the at least one abstract idea discussed above.
-Claims 8, 15, and 20 call for receiving updated information from the new user; associating the generated profile into a different second of the clusters based on the received updated information; and generating a second recommendation for the user based on the associated second cluster. These limitations just further define the at least one abstract idea discussed above.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
Independent claim 1:
A computer-implemented (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) method, comprising:
presenting, on a user interface (UI) (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), a questionnaire;
receiving, on the UI (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), one or more responses to the questionnaire, the received one or more responses including a goal of a new user, the goal associated with a health outcome, wherein the health outcome is a stage of menopause;
generating a plurality of clusters of a plurality of existing profiles having a similarity above a similarity threshold;
generating, for the new user, a profile;
associating the generated profile into a cluster of the plurality of clusters based on the similarity for the generated profile being above the similarity threshold;
determining a persona for the cluster of the plurality of clusters, wherein the determined persona is an artificial profile representing one or more data values of the generated profile in the cluster;
storing, in a specialized data structure, the cluster together with the determined persona for the cluster (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f));
for the associated cluster, identifying, in the specialized data structure (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), a highest ranked intervention for the determined health outcome for the determined persona; and
generating, by a machine learning (ML) model (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)), a recommendation for the new user based on the associated cluster and the determined persona, wherein the generated recommendation includes the identified highest ranked intervention that, when applied, has a likelihood of accomplishing the goal associated with addressing the determined health outcome.
Independent claim 9:
An apparatus comprising:
a user interface (UI) configured to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f))present a questionnaire;
a memory configured to store a specialized data structure (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)); and
a processor coupled to the memory configured to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)):
receive, via the UI (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), one or more responses to the questionnaire, the received one or more responses including a goal of a new user, the goal associated with a health outcome, wherein the health outcome is a stage of menopause;
generate a plurality of clusters of a plurality of existing profiles having a similarity above a similarity threshold;
generate, for the new user, a profile associated with the user based on the received responses to the questionnaire;
associate the generated profile into a cluster of a plurality of clusters based on the similarity for the generated profile being above the similarity threshold;
determine a persona for the cluster of the plurality of clusters, wherein the determined persona is an artificial profile representing one or more data values of the generated profile in the cluster;
store, in a specialized data structure, the cluster together with the determined persona for the cluster (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f));
for the associated cluster, identifying, in the specialized data structure (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), a highest ranked intervention for the determined health outcome for the determined persona; and
execute a machine learning (ML) model to (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)) generate a recommendation for the new user based on the associated cluster and the determined persona, wherein the generated recommendation includes the identified highest ranked intervention that, when applied, has a likelihood of accomplishing the goal associated with addressing the determined health outcome.
Independent claim 16:
One or more non-transitory computer readable media storing instructions that, when executed by a processor, cause the processor to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)):
generate a plurality of clusters, each cluster of the generated plurality of clusters including at least a stage of menopause and symptoms experienced related to menopause;
determine a persona for the cluster of the plurality of clusters, wherein the determined persona is an artificial profile representing one or more data values of the generated profile in the cluster;
store, in a specialized data structure, the cluster together with the determined persona for the cluster (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f));
generate, for a new user, a profile based on the received responses to the questionnaire, the generated profile including at least an identified user stage of menopause and user symptoms experienced related to menopause, wherein the one or more responses include a goal of the new user;
associate the generated profile into a cluster of the plurality of clusters; and
generate, by a machine learning (ML) model (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)), a recommendation for the user based on the associated cluster and the determined persona, wherein the generated recommendation includes the identified highest ranked intervention that, when applied, has a likelihood of accomplishing the goal associated with addressing the determined health outcome.
For the following reasons, the Examiner submits that the above-identified additional limitations, when considered as a whole with the limitations reciting the at least one abstract idea, do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the generic computer-implementation including the UI, memory/non-transitory computer readable media including instructions, and processor, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitation of the specialized data structure storage, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea and/or merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)).
Regarding the additional limitations of recommendations being generated via an ML model, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). This additional limitation provides only a result-oriented solution and lacks details as to how the ML model is trained and/or executed in a manner that provides a technological solution/improvement. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13. Allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system. Id.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Furthermore, looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, independent claim 1, 9, and 16 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, independent claims 1, 9, and 16 are directed to at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
-Claims 5, 7, 8, 13, 15, 17, and 20 recite generic use of the ML model to generate the first/second recommendation or update the ML model based on feedback which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how the ML model is trained and/or executed in a manner that provides a technological solution/improvement. As noted above, claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Id., p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
When the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, independent claims 1, 9 and 16 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations of the generic computer-implementation including the UI, memory/non-transitory computer readable media including instructions, processor, and specialized data structure storage, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of recommendations being generated via an ML model, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). This additional limitation provides only a result-oriented solution and lacks details as to how the ML model is trained and/or executed in a manner that provides a technological solution/improvement. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13. Allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system. Id.
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
-Claims 5, 7, 8, 13, 15, 17, and 20 recite generic use of the ML model to generate the first/second recommendation or update the ML model based on feedback which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how the ML model is trained and/or executed in a manner that provides a technological solution/improvement. As noted above, claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Id., p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 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 and 8-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2020/0275886 to Mason ("Mason") in view of U.S. Patent App. Pub. No. 2024/0071627 to Behal ("Behal"):
Regarding claim 1, Mason discloses a computer-implemented method (server 30 in Figure 1 includes instructions 40 that necessarily perform a computer-implemented method), comprising:
presenting, on a user interface (UI), a questionnaire (the end of [0069] discloses presenting a question/questionnaire on a UI);
receiving, on the UI, one or more responses to the questionnaire, the received one or more responses … of a new user (the end of [0069] discusses receiving a patient (new user) response to the question (which would be via the patient interface 50 in Figures 1 and 4), …;
generating a plurality of clusters of a plurality of existing profiles having a similarity above a similarity threshold ([0030], [0039]-[0040] discloses processing data to group people into different cohorts (generating clusters) such that the data in each cohort/cluster includes people having "similar" characteristics/variables, where the plurality of characteristics/variables of each person collectively represents a "profile" for the person (plurality of existing profiles), and where the various profiles/characteristics of each respective cohort/cluster necessarily have a similarity over some similarity threshold so as to distinguish the cohort/cluster from other cohorts/clusters);
generating, for the new user, a profile ([0027]-[0029] discuss collecting characteristics for users (including a new/current patient/user per [0031], [0042]), where a particular collection of characteristics for a particular patient/user is a "profile"; also see patient profile 130 in Figures 5 and 7);
associating the generated profile into a cluster of the plurality of clusters based on the similarity for the generated profile being above the similarity threshold ([0031] and [0042] discuss assigning the new/current patient to a particular one of the cohorts/clusters based on their characteristics (based on their "profile"); also see Figure 7; similar to how the existing profiles of each cohort/cluster have a similarity above the similarity threshold as discussed above, the generated profile of the new/current patient would have a similarity above the similarity threshold for the particular one of the cohorts/clusters);
determining a persona for the cluster of the plurality of clusters, wherein the determined persona is an artificial profile representing one or more data values of the generated profile in the cluster ([0030], [0086]-[0087], and Figure 6 discuss/illustrate how each cohort/cluster includes a different collection of characteristics, treatment plans, results, etc. (a "persona" representing an artificial profile representing data values of the generated profile in the cluster));
storing, in a specialized data structure, the cluster together with the determined persona for the cluster ([0040]-[0041] and Figure 1 discuss/illustrate how the cohorts/clusters along with their respective characteristics/treatment plans/results (collectively, personas) are stored in patient-cohort databases 44 (specialized data structure(s)) in server 30);
for the associated cluster, identifying, in the specialized data structure, a highest ranked intervention for [a] determined health outcome for the determined persona ([0031] and [0043] discuss assigning the new patient to the particular cohort/cluster and selecting the treatment plan; for instance, [0030]-[0033], [0046], [0091]-[0094], [0101], and Figure 7 discuss/illustrate how a plurality of treatment plans are determined and the system recommends the treatment plan (stored in the "specialized data structure as noted above") to achieve a desired result ("goal") such as most effective recovery, speed of recovery, recovering to threshold range of motion, etc. in relation to obesity, diabetes, etc. (identifying "highest ranked intervention for [a] determined health outcome"); furthermore, as each treatment plan is recommended based on the cohort/cluster into which the patient is assigned/matched (where each cohort/cluster includes a particular set of characteristics/treatment plans/results/etc. ("persona")), then the "highest ranked" treatment plan/intervention is "for the determined persona"); and
generating, by a machine learning (ML) model, a recommendation for the user based on the associated cluster and the determined persona, wherein the generated recommendation includes the identified highest ranked intervention that, when applied, has a likelihood of accomplishing [a] goal associated with addressing the determined health outcome ([0031], [0045]-[0046], Figure 7 disclose/illustrate using an ML model to select one of a number of treatment plans/interventions based on the cohort/cluster to which the patient/user is assigned (which has an associated "persona" as discussed above) to achieve a desired result ("goal") such as most effective recovery, speed of recovery, recovering to threshold range of motion such as 75%, etc. associated with addressing the determined health outcome such as obesity, diabetes, comorbid diseases/conditions, etc. per [0030]-[0032], [0093] ("highest ranked" treatment plan/intervention that when applied has a likelihood of accomplishing a goal associated with addressing determined health outcome)).
While Mason discloses how the new user may have a "goal" associated with a "health outcome" as noted above ([0028], [0033], [0046], [0091]-[0094], [0101]) which can be obtained from the user such as during a telemedicine session ([0119]) or the like, Mason might be silent specifically regarding the received one or more responses to the questionnaire including the goal associated with the health outcome.
However, the end of [0069] of Mason already discloses how a questionnaire can be presented to a user to obtain pain levels from the patient which would provide an effective manner of soliciting important information from the patient to facilitate recommendation/adjustment of treatment plans/interventions thereby improving patient outcomes.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the received one or more responses to the questionnaire to specifically include the goal associated with the health outcome as taught by Mason to facilitate recommendation/adjustment of treatment plans/interventions thereby improving patient outcomes, because a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention, and because there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). The courts have made clear that the teaching, suggestion, or motivation test is flexible and an explicit suggestion to combine the prior art is not necessary. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007).
Furthermore, while Mason does not appear to disclose the health outcome to specifically be a stage of menopause, Mason does disclose how the medical condition/health outcome to be treated can be medical conditions/diseases such as obesity ([0030], [0032]), diabetes ([0093]), and/or comorbid conditions/diseases ([0093]) as already discussed above.
Furthermore, Behal teaches that it was known in the healthcare informatics art to execute a clustering algorithm to cluster individuals into a plurality of different clusters or subclusters based on various cardiometabolic conditions/diseases ([0046]-[0052]) such as diabetes, obesity, etc. ([0025]) and women during menopause transition years, post-menopause, or early menopause (stage of menopause) ([0069], where menopause is a cardiometabolic condition as evidenced by "Menopause: a cardiometabolic transition" to Nappi et al. ("Nappi")), assign a new individual to an already generated cluster with an associated medical classification ([0066], which can be a stage of menopause per [0069]), and generate a care strategy for individuals in the cluster to treat the corresponding medical condition(s) including stage of menopause ([0067], [0069]). This arrangement advantageously facilitates treatment of a plurality of related cardiometabolic conditions including stage of menopause, obesity, etc., thereby resulting in improved patient outcomes.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the health outcome of Mason to include a stage of menopause as taught by Behal as Mason already discloses that the system can address obesity, diabetes, and other comorbid conditions ([0093]) and Behal teaches (as evidenced by Nappi above) that menopause is a medical condition that can be comorbid with obesity, diabetes, etc. as already disclosed by Mason. Therefore, including stage of menopause as one of the health outcomes of Mason as taught by Behal would advantageously allow corresponding treatments for the stage of menopause to be determined for a user thereby improving patient outcomes. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Regarding claim 2, the Mason/Behal combination discloses the computer-implemented method of claim 1 further including wherein generating the plurality of clusters further comprises:
identifying the plurality of existing profiles, each existing profile of the plurality of existing profiles including a set of variables ([0030] and [0040] of Mason discuss obtaining pluralities of characteristics/variables (each plurality collectively representing a "profile") for a plurality of people/patients);
for each of the existing profiles, identifying a value for each variable of the set of variables ([0030] of Mason discusses how for instance one characteristic/variable is body type where one value is "athletic"; another characteristic/variable is minutes of exercise and days per week of exercise where the values are 30 min and 10 min for minutes of exercise and 5 days and 3 days for days per week); and
determining, by a clustering algorithm, a first existing profile and a second existing profile, of the plurality of existing profiles, have a similarity above the similarity threshold ([0030] and [0040] of Mason discuss how the data of a first group of people having "similar" characteristics/variables (which would mean the "values" of the characteristics/variables as noted above, where a collection of characteristics/variables and their respective values for each person/patient is a "profile") can be processed and grouped into a first cohort/cluster and a second group of people having "similar" characteristics/variables (similar profiles) can be grouped into a second cohort/cluster; with respect to each cohort, the various profiles/characteristics of the cohort necessarily have a similarity over some similarity threshold so as to distinguish such profiles from those of other cohorts/clusters; also, as the data is processed so as to segment the first and second cohorts/clusters in respective databases per [0030] and [0040], there is necessarily some instructions ("clustering algorithm") to make the above similarity determination); and
generating, by the clustering algorithm, the cluster including the first existing profile and the second existing profile (as noted above per [0030] and [0040] of Mason, various cohorts/clusters are formed, each including at least first and second "profiles").
Regarding claim 3, the Mason/Behal combination discloses the computer-implemented method of claim 2 further including wherein associating the generated profile into the cluster of the plurality of clusters further comprises:
identifying a new set of variables for the generated profile ([0031] and [0041] of Mason discuss receiving a set of characteristics/variables for a new/current patient (where the set of characteristics/variables make up a "profile" for the new/current patient);
identifying a value for each variable of the new set of variables for the generated profile (each variable/characteristic in the "profile" (the collective collection of variables/characteristics) has a value as noted in [0030] of Mason (e.g., 30 min for minutes of exercise per day, obese for body type, etc.));
based on the identified value for each variable of the new set of variables for the generated profile, determining, by the clustering algorithm, the cluster of the plurality of clusters most similar to the generated profile ([0031] and [0042] of Mason discuss matching a pattern of characteristics (which would be their values as noted in [0030]) of the new patient and those of one or more patients of a particular one of the cohorts/clusters; in the case of first and second cohorts as discussed in [0030] and [0040], then the cohort/cluster to which the new patient is matched/assigned is "most similar" to the new patient's profile (e.g., as compared to the other cohort); and
associating the generated profile into the determined cluster (the new patient is assigned to the particular cohort/cluster per [0031] and [0042] of Mason; also see Figure 7).
Regarding claim 4, the Mason/Behal combination discloses the computer-implemented method of claim 3 further including wherein the set of variables include variables related to at least one of age, number of symptoms, types of symptoms, severity of symptoms, ethnicity, income, geographical location, or awareness of menopause ([0024] of Mason discloses age and geographic information; [0069], [0093] disclose pain level symptoms; and [0040] discloses injury which is interpreted to be related to a symptom).
Regarding claim 5, the Mason/Behal combination discloses the computer-implemented method of claim 1 further including wherein generating the recommendation for the user further comprises:
determining the health outcome associated with the cluster of the plurality of clusters ([0040] of Mason notes how the patients in each cohort have a particular similar injury/medical condition (health outcome));
identifying the intervention that, when applied, has a likelihood of addressing the determined health outcome ([0033] and [0046] of Mason disclose how the recommended treatment plan can be for a full or most effective recovery (where a "full" or "most effective" recovery would necessarily having a likelihood of addressing the health outcome); also note how [0093] discloses a treatment including a medication that manages pain associated with a medical disease/condition (likelihood of addressing health outcome)); and
generating, by the ML model, the recommendation for the user, the recommendation including the intervention ([0031], [0033], [0043], and [0046] of Mason discuss how the ML model generates the recommended treatment plan for the user (e.g., based on the cohort/cluster to which the patient/user is assigned); also see Figure 7).
Regarding claim 6, the Mason/Behal combination discloses the computer-implemented method of claim 5, further including wherein:
the identified intervention includes at least one of a treatment for a symptom of the stage of menopause or educational content associated with the stage of menopause ([0093] of Mason discloses a treatment plan including a medication to manage pain (symptom) of a medical disease/health outcome which includes a stage of menopause per the above combination with Behal; again, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the health outcome of Mason to include a stage of menopause as taught by Behal as Mason already discloses that the system can address obesity, diabetes, and other comorbid conditions ([0093]) and Behal teaches (as evidenced by Nappi above) that menopause is a medical condition that can be comorbid with obesity, diabetes, etc. as already disclosed by Mason. Therefore, including stage of menopause as one of the health outcomes of Mason as taught by Behal would advantageously allow corresponding treatments for the stage of menopause to be determined for a user thereby improving patient outcomes. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.).
Regarding claim 8, the Mason/Behal combination discloses the computer-implemented method of claim 1, further including
receiving updated information from the new user ([0032] and [0098] of Mason discuss receiving change/updated characteristics/information) of the new patient);
based on the received updated information, associating the generated profile into a second cluster of the plurality of clusters, the second cluster different than the cluster ([0032] and [0100] of Mason discuss reassigning the patient to a different cohort/cluster based on the updated information; also see Figure 8); and
generating, by the ML model, a second recommendation for the user based on the associated second cluster ([0032] and [0100] of Mason discuss how the ML model selects a new treatment plan/recommendation based on the reassigned cohort/cluster).
Regarding claim 9, Mason discloses an apparatus (system 10 in Figure 1) comprising:
a user interface (UI) (patient interface 50 in Figure 1) configured to present a questionnaire (the end of [0069] discloses presenting a question/questionnaire on a UI);
a memory (memory 62, 38 in Figure 1) configured to store a specialized data structure ([0040]-[0041] and Figure 1 discuss/illustrate how the cohorts/clusters along with their respective characteristics/treatment plans/results (collectively, personas) are stored in patient-cohort databases 44 (specialized data structure(s)) in memory 38 server 30); and
a processor coupled to the memory (processor 60, 36 in Figure 1).
The remaining limitations of claim 9 are rejected in view of the Mason/Behal combination as discussed above in relation to claim 1.
Claims 10-15 are rejected in view of the Mason/Behal combination as respectively discussed above in relation to claims 2-6 and 8.
Regarding claim 16, Mason discloses one or more non-transitory computer readable media storing instructions that, when executed by a processor, cause the processor to (the server 30 of Figure 1 includes memory 38 storing instructions 40 executable by a processor 36):
generate a plurality of clusters ([0030], [0039]-[0040] discloses processing data to group people into cohorts (generating clusters)), each cluster of the generated plurality of clusters including [a medical condition] and symptoms experienced related to [the medical condition] ([0040] notes how the patients in each cohort have a particular similar injury/medical condition (health outcome) while [0093] discloses pain (symptom) associated with diabetes (medical condition));
determine a persona for the cluster of the plurality of clusters, wherein the determined persona is an artificial profile representing one or more data values of the generated profile in the cluster ([0030], [0086]-[0087], and Figure 6 discuss/illustrate how each cohort/cluster includes a different collection of characteristics, treatment plans, results, etc. (a "persona" representing an artificial profile representing data values of the generated profile in the cluster));
store, in a specialized data structure, the cluster together with the determined persona for the cluster ([0040]-[0041] and Figure 1 discuss/illustrate how the cohorts/clusters along with their respective characteristics/treatment plans/results (collectively, personas) are stored in patient-cohort databases 44 (specialized data structure(s)) in server 30);
generate, for a new user, a profile ([0027]-[0029] discuss collecting characteristics for users (including a new/current patient/user per [0031], [0042]), where a particular collection of characteristics for a particular patient/user is a "profile"; also see patient profile 130 in Figures 5 and 7) based on one or more responses to a questionnaire ([0074], [0106] disclose how the characteristics of the patient ("profile") can include pain levels which are reported in response to a questionnaire per the end of [0069]), the generated profile including at least an identified [medical condition] ([0027] discloses how the collected characteristics can include personal information while [0024] discloses how personal information can include a medical condition) and user symptoms experienced related to [the medical condition] ([0027] discloses how the collected characteristics can include performance information while [0024] discloses how performance information can include pain levels (e.g., related to diabetes/medical condition per [0093]), …;
associate the generated profile into a cluster of the plurality of clusters ([0031] and [0042] discuss assigning the new/current patient to a particular one of the cohorts based on their characteristics (based on their "profile"); also see Figure 7); and
generate, by a machine learning (ML) model, a recommendation for the user based on the associated cluster and the determined persona, wherein the generated recommendation includes [an] intervention that, when applied, has a likelihood of accomplishing [a] goal associated with addressing the identified [medical condition] ([0031], [0045]-[0046], Figure 7 disclose/illustrate using an ML model to select one of a number of treatment plans/interventions based on the cohort/cluster to which the patient/user is assigned (which has an associated "persona" as discussed above) to achieve a desired result ("goal") such as most effective recovery, speed of recovery, recovering to threshold range of motion such as 75%, etc. associated with addressing the determined health outcome such as obesity, diabetes, comorbid diseases/conditions, etc. per [0030]-[0032], [0093] (treatment plan/intervention that when applied has a likelihood of accomplishing a goal associated with addressing determined health outcome)).
While Mason discloses how the new user may have a "goal" associated with a health outcome/medical condition as noted above ([0028], [0033], [0046], [0091]-[0094], [0101]) which can be obtained from the user such as during a telemedicine session ([0119]) or the like, Mason might be silent specifically regarding the one or more responses to the questionnaire including the goal of the new user.
However, the end of [0069] of Mason already discloses how a questionnaire can be presented to a user to obtain pain levels from the patient which would provide an effective manner of soliciting important information from the patient to facilitate recommendation/adjustment of treatment plans/interventions thereby improving patient outcomes.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the one or more responses to the questionnaire to specifically include the goal associated with new user as taught by Mason to facilitate recommendation/adjustment of treatment plans/interventions thereby improving patient outcomes, because a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention, and because there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). The courts have made clear that the teaching, suggestion, or motivation test is flexible and an explicit suggestion to combine the prior art is not necessary. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007).
Furthermore, while Mason does not appear to disclose the medical condition/health outcome to specifically be a user stage of menopause, Mason does disclose how the medical condition/health outcome to be treated can be medical conditions/diseases such as obesity ([0030], [0032]), diabetes ([0093]), and/or comorbid conditions/diseases ([0093]) as already discussed above.
Furthermore, Behal teaches that it was known in the healthcare informatics art to execute a clustering algorithm to cluster individuals into a plurality of different clusters or subclusters based on various cardiometabolic conditions/diseases ([0046]-[0052]) such as diabetes, obesity, etc. ([0025]) and women during menopause transition years, post-menopause, or early menopause (stage of menopause) ([0069], where menopause is a cardiometabolic condition as evidenced by "Menopause: a cardiometabolic transition" to Nappi et al. ("Nappi")), assign a new individual to an already generated cluster with an associated medical classification ([0066], which can be a stage of menopause per [0069]), and generate a care strategy for individuals in the cluster to treat the corresponding medical condition(s) including stage of menopause ([0067], [0069]). This arrangement advantageously facilitates treatment of a plurality of related cardiometabolic conditions including stage of menopause, obesity, etc., thereby resulting in improved patient outcomes.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the medical condition/health outcome of Mason to include a user stage of menopause as taught by Behal as Mason already discloses that the system can address obesity, diabetes, and other comorbid conditions ([0093]) and Behal teaches (as evidenced by Nappi above) that menopause is a medical condition that can be comorbid with obesity, diabetes, etc. as already disclosed by Mason. Therefore, including user stage of menopause as one of the medical conditions/health outcomes of Mason as taught by Behal would advantageously allow corresponding treatments for the user stage of menopause to be determined for a user thereby improving patient outcomes. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
Regarding claim 17, the Mason/Behal combination discloses the one or more non-transitory computer readable media of claim 16, further including storing instructions for generating the recommendation for the user that, when executed by the processor, cause the processor to:
identify an intervention associated with the associated cluster that, when applied, has a likelihood of addressing the identified user stage of menopause ([0033] and [0046] of Mason disclose how the recommended treatment plan can be for a full or most effective recovery (where a "full" or "most effective" recovery would necessarily having a likelihood of addressing the health outcome); also note how [0093] discloses a treatment including a medication that manages pain associated with a medical disease/condition (likelihood of addressing health outcome), where the medical/health condition is a user stage of menopause per the combination with Behal as discussed in relation to claim 16), the identified intervention including at least one of a treatment for a symptom of the stage of menopause or educational content associated with the stage of menopause ([0093] of Mason discloses a treatment plan including a medication to manage pain (symptom) of a medical disease (health outcome) while [0069] of Behal discusses a tailored intervention program for women with a particular stage of menopause (to necessarily treat symptoms thereof); again, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the medical condition/health outcome of Mason to include a user stage of menopause as taught by Behal as Mason already discloses that the system can address obesity, diabetes, and other comorbid conditions ([0093]) and Behal teaches (as evidenced by Nappi above) that menopause is a medical condition that can be comorbid with obesity, diabetes, etc. as already disclosed by Mason. Therefore, including user stage of menopause as one of the medical conditions/health outcomes of Mason as taught by Behal would advantageously allow corresponding treatments for the user stage of menopause to be determined for a user thereby improving patient outcomes. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.); and
generating, by the ML model, the recommendation for the user, the recommendation including the intervention ([0031], [0033], [0043], and [0046] of Mason discuss how the ML model generates the recommended treatment plan for the user (e.g., based on the cohort/cluster to which the patient/user is assigned); also see Figure 7).
Regarding claim 18, the Mason/Behal combination discloses the one or more non-transitory computer readable media of claim 16, further including further storing instructions for generating the plurality of clusters that, when executed by the processor, cause the processor to:
identify a plurality of existing profiles, each existing profile of the plurality of existing profiles including a set of variables ([0030] and [0040] of Mason discuss obtaining pluralities of characteristics/variables (each plurality collectively representing a "profile") for a plurality of people/patients);
for each of the existing profiles, identify a value for each variable of the set of variables ([0030] of Mason discusses how for instance one characteristic/variable is body type where one value is "athletic" and another value is "obese"; another characteristic/variable is minutes of exercise and days per week of exercise where the values are 30 min and 10 min for minutes of exercise and 5 days and 3 days for days per week); and
determine, by a clustering algorithm, a first existing profile and a second existing profile, of the plurality of existing profiles, have a similarity above a similarity threshold ([0030] and [0040] of Mason discuss how the data of a first group of people having "similar" characteristics/variables (which would mean the "values" of the characteristics/variables as noted above, where a collection of characteristics/variables and their respective values for each person/patient is a "profile") can be processed and grouped into a first cohort/cluster and a second group of people having "similar" characteristics/variables (similar profiles) can be grouped into a second cohort/cluster; with respect to each cohort, the various profiles/characteristics of the cohort necessarily have a similarity over some similarity threshold so as to distinguish such profiles from those of other cohorts/clusters; also, as the data is processed so as to segment the first and second cohorts/clusters in respective databases per [0030] and [0040] of Mason, there is necessarily some instructions ("clustering algorithm) to make the above similarity determination); and
generate, by the clustering algorithm, the cluster including the first existing profile and the second existing profile (as noted above per [0030] and [0040] of Mason, various cohorts/clusters are formed, each including at least first and second "profiles").
Regarding claim 19, the Mason/Behal combination discloses the one or more non-transitory computer readable media of claim 18, further including further storing instructions for associating the generated profile into the cluster of the plurality of clusters that, when executed by the processor, cause the processor to:
identify a new set of variables for the generated profile ([0031] and [0041] of Mason discuss receiving a set of characteristics/variables for a new/current patient (where the set of characteristics/variables make up a "profile" for the new/current patient);
identify a value for each variable of the new set of variables for the generated profile (each variable/characteristic in the "profile" (the collective collection of variables/characteristics) has a value as noted in [0030] of Mason (e.g., 30 min for minutes of exercise per day, obese for body type, etc.));
based on the identified value for each variable of the new set of variables for the generated profile, determine, by the clustering algorithm, the cluster of the plurality of clusters most similar to the generated profile ([0031] and [0042] of Mason discuss matching a pattern of characteristics (which would be their values as noted in [0030] of Mason) of the new patient and those of one or more patients of a particular one of the cohorts/clusters; in the case of first and second cohorts as discussed in [0030] and [0040] of Mason, then the cohort/cluster to which the new patient is matched/assigned is "most similar" to the new patient's profile (e.g., as compared to the other cohort); and
associate the generated profile into the determined cluster (the new patient is assigned to the particular cohort/cluster per [0031] and [0042] of Mason; also see Figure 7 of Mason).
Regarding claim 20, the Mason/Behal combination discloses the one or more non-transitory computer readable media of claim 16, further including further storing instructions that, when executed by the processor, cause the processor to:
receive updated information from the new user ([0032] and [0098] of Mason discuss receiving change/updated characteristics/information) of the new patient);
based on the received updated information, associate the generated profile into a second cluster of the plurality of clusters, the second cluster different than the cluster ([0032] and [0100] of Mason discuss reassigning the patient to a different cohort/cluster based on the updated information; also see Figure 8); and
generate, by the ML model, a second recommendation for the user based on the associated second cluster ([0032] and [0100] of Mason discuss how the ML model selects a new treatment plan/recommendation based on the reassigned cohort/cluster).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent App. Pub. No. 2020/0275886 to Mason ("Mason") in view of U.S. Patent App. Pub. No. 2024/0071627 to Behal ("Behal"), and further in view of U.S. Patent App. Pub. No. 2022/0172838 to Besanson et al. ("Besanson"):
Regarding claim 7, the Mason/Behal combination discloses the computer-implemented method of claim 1, further including
receiving feedback ([0049] of Mason discloses receiving feedback)…; and
…
However, Mason appears to be silent regarding the received feedback indicating a result of the recommendation; and based on the received feedback, updating the ML model.
Nevertheless, Besanson teaches ([0064]) that it was known in the healthcare informatics and machine learning art to receive outcome feedback from users regarding recommended treatments and update a trained treatment model (ML per [0074]) to advantageously optimize treatment policies associated with a particular cluster of users ([0055]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have received feedback indicating a result of the recommendation and updated the ML model based on the received feedback in the system of the Mason/Behal combination as taught by Besanson to advantageously optimize treatment policies associated with a particular cluster of users. A person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Furthermore, all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. Id.
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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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686