DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The status of the claims as of the response filed 11/19/2025 is as follows: Claims 2-3 and 12-13 are cancelled, and all previously given rejections for these claims are considered moot. Claims 1, 4, 11, 14, and 20 are currently amended. Claims 5-10 and 15-19 are original. Claims 21-24 are new. Claims 1, 4-11, and 14-24 are currently pending in the application and have been considered below.
Response to Arguments
Rejection Under 35 USC 101
On pages 11-12 of the response filed 11/19/2025 Applicant argues that the Examiner has impermissibly relied on unrecited claim limitations to support the finding that the claims recite an abstract idea, specifically asserting that “the mere generation and display of a report is not the actual recitation of a certain method of organizing human activity, and unrecited steps may not be used to argue that it is.” Applicant’s arguments are fully considered, but are not persuasive. Examiner maintains that the recited steps of generating and displaying a report do describe a certain method of organizing human activity such as managing personal behavior and/or interactions between people, because a human actor such as a clinician could manage their personal behavior and interactions with other people (e.g. a patient and/or colleague) to access microbiome data associated with a patient, think about the accessed patient data, and generate/write up a visually depicted report about the patient’s expected medical conditions, symptoms, and/or recommended actions, as well as interact with the patient or colleague to solicit adjustments to the analysis inputs and provide projected updates to the analysis outputs (e.g. by conversing with a patient who inquires “what if I start eating more fermented foods?” or “what if I stop taking a certain drug?” and providing predicted changes to the patient’s expected medical conditions, symptoms, and/or recommendations in the what-if scenarios based on their medical expertise). Examiner has provided the example scenario not to introduce unrecited limitations into the claim, but to explain how the italicized steps of the claim fit within the grouping of “certain methods of organizing human activity.”
On pages 12-13 of the response Applicant argues that the claims do not fit into any of the enumerated sub-groupings of certain methods of organizing human activity, and that the enumerated list “is not to be expanded beyond these enumerated sub-groupings except in rare circumstances.” Applicant’s arguments are fully considered, but are not persuasive. Examiner maintains that the claims do recite steps that fall into the sub-grouping of “managing personal behavior, and relationships or interactions between people,” because they recite steps that describe a diagnostic report generation operation that could be achieved by a human actor such as a clinician or other medical professional managing their personal behavior and/or interactions with others such as a patient and/or colleague. Accordingly, the rejections do not rely on expanding the enumerated sub-groupings beyond those listed in the 2019 PEG or MPEP.
For the reasons outlined above, the 35 USC 101 rejections are upheld.
Rejection Under 35 USC 102/103
On pages 13-14 of the response Applicant argues that adjusting the training options available to a user as in Jain is distinct from adjusting one or more elements of the first set of subject predispositions as in the amended independent claims. Applicant’s arguments are fully considered, but are not persuasive. Para. [00116] of Applicant’s specification defines “subject predispositions” in the following manner: “Information displayed about one or more subjects to one or more users based on subject data is termed herein as ‘subject predispositions.’” Because the training options available to a user as in Jain are information displayed about one or more subjects to one or more users based on subject data (see Jain [0351]-[0353]), they are considered to be encompassed as part of the first set of subject predispositions in accordance with Applicant’s definition of this type of data. Thus, Examiner maintains that the claim language of the independent claims as presently amended is met when a user selects or adjusts different training options to determine different predicted effects as in [0353] of Jain, as explained in more detail below.
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, 4-11, and 14-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
In the instant case, claims 1, 4-10, and 21-22 are directed to a method (i.e. a process), claims 11, 14-19, and 23-24 are directed to a device (i.e. a machine), and claim 20 is directed to a non-transitory computer-readable medium (i.e. a manufacture). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A – Prong 1
Independent claims 1, 11, and 20 recite steps that, under their broadest reasonable interpretations, cover certain methods of organizing human activity, e.g. managing personal behavior, relationships, or interactions between people. Specifically, claim 1 (as representative) recites:
A computer-implemented method of leveraging subject microbiome data, the computer-implemented method comprising operations including:
detecting, on a graphical user interface of an application platform of a user computing device, a selection of a subject by a user;
accessing, based on the detecting, data associated with the subject, wherein the data comprises the subject microbiome data;
generating, by a processor, an overview report comprising a first set of subject predispositions, wherein generating the overview report comprises: generating each of (1) a list of expected conditions associated with the subject microbiome data, (2) an expected symptom set associated with the subject microbiome data, and (3) one or more recommended actions to improve a condition of a subject microbiome based on the subject microbiome data;
displaying, on the application platform, the generated overview report;
receiving, at the application platform, a user modification input that adjusts one or more elements of the first set of subject predispositions;
identifying, using the processor, a predictive change to the subject microbiome data based on the user modification input; and
displaying, on the application platform, the predictive change to the subject microbiome.
But for the recitation of generic computer components like a GUI of an application platform of a computing device and a processor, the italicized functions, when considered as a whole, describe a diagnostic report generation operation that could be achieved by a human actor such as a clinician or other medical professional managing their personal behavior and/or interactions with others such as a patient and/or colleague. For example, a clinician could speak with a colleague who informs them of a selected patient for whom a diagnostic report should be created, then access microbiome data associated with that patient (e.g. from their knowledge of past interactions with the patient, medical records, patient files, communicating with a patient during an appointment, etc.). The clinician could use such accessed data to generate an overview report about the patient’s predispositions to different medical conditions, symptoms, and treatments, and then visually display the report for a colleague or the patient (e.g. by writing it down and sharing it). The clinician could then speak with the colleague or patient who revises or adds to the patient’s predispositions (e.g. clarifying a particular type of diabetes, including additional information about a patient’s diet, etc.) and then using their medical expertise to predict how such updated data impacts the patient’s microbiome data and sharing the updates back to the colleague or patient. Accordingly, claim 1 recites an abstract idea in the form of a certain method of organizing human activity. Claims 11 and 20 recite substantially similar subject matter as claim 1 and are found to recite an abstract idea under the same analysis.
Dependent claims 4-10, 14-19, and 21-24 inherit the limitations that recite an abstract idea from their dependence on claims 1 or 11, and thus these claims also recite an abstract idea under the Step 2A – Prong 1 analysis. In addition, claims 4-10, 14-19, and 21-24 recite additional limitations that further describe the abstract idea identified in the independent claims.
Specifically, claims 4 and 14 recite generating and displaying a second set of predispositions based on the predictive change, which a clinician could accomplish by thinking about an updated set of predispositions based on the changes they predicted and including such updated predispositions in the visually represented report.
Claims 5 and 15 specify that displaying the second set of subject predispositions comprises visually distinguishing differences between the first and second sets of predispositions that are greater than a threshold, which a clinician could achieve by thinking about which differences in the predisposition sets are most significant and visually highlighting, circling, bolding, pointing to, etc. such differences in the report.
Claims 6 and 16 recite receiving an indication of a target goal for the subject, retrieving microbiome data of a reference subject having achieved the target goal, comparing the subject microbiome data to the retrieved microbiome data, identifying a change to one or more elements of the first set of subject predispositions needed to adjust characteristics of the subject microbiome data to the microbiome data of the reference subject, generating a second set of subject predispositions that incorporate the change, and displaying a plan that includes the second set of subject predispositions. A clinician managing their personal behavior and/or interactions with other people could achieve such steps by speaking with a colleague or patient to understand a target goal for the patient, then retrieve microbiome data from past patients who have already achieved the target goal (e.g. pulling records of patients who have completed an exercise goal, adhered to a certain diet, etc.). The clinician could then compare the current patient’s microbiome data to microbiome data of the reference patient and identify differences between them that reflect changes the current patient could make to be more like the reference patient. The clinician could finally generate a second set of predispositions that incorporate the changes using their medical expertise, and display a plan with the second set of predispositions for sharing with the patient or colleague.
Claims 7 and 17 specify that the plan comprises a list of recommended actions the subject should take for each of the second set of predispositions to achieve the target goal, which a clinician would be capable of determining and including in a treatment/intervention plan that helps the patient achieve a health goal.
Claims 8 and 18 recite determining a coaching individual based on the target goal and/or data associated with the subject, and automatically matching the coaching individual with the user. A clinician could accomplish these functions by thinking about the patient’s current state and goals (e.g. current weight and target weight or body composition) and selecting an appropriate health coach (e.g. a personal trainer, dietician, etc.) for the patient.
Claims 9 and 19 recite identifying interaction data between the coaching individual and the subject, analyzing the interaction data with respect to a progress rate of the user toward the target goal, determining an aspect of the interaction data that accelerates or impedes the progress rate based on the analyzed interaction data, and transmitting instructions to the coaching individual to display the aspect. A clinician could accomplish these functions by communicating with the patient and their selected coach about their interactions and how much progress has been made towards the patient’s goal, determining what aspects of the coaching are most helpful and most unhelpful with respect to reaching the goal (e.g. noting that the patient does not seem to respond well to daily interactions and prefers weekly summarized action plans), and sharing the insights with the coach in the form of visual instructions regarding what works best for the patient.
Claim 10 specifies that the subject microbiome data is derived from a microbiome sample from the subject and/or a second microbiome sample from a second subject that is linked to the subject, which are types of data that a clinician would be capable of accessing and analyzing.
Claims 21 and 23 recite receiving a second user modification input that adjusts one or more linkages or user engagements associated with the subject, identifying a second predictive change to the subject microbiome data based on the second user modification input, and displaying the second predictive change to the subject microbiome. These steps could be accomplished as a certain method of organizing human activity in the same way as explained for similar subject data modification and predictive change identification steps of the independent claims above.
Claims 22 and 24 specify the type of updated linkages or updated user engagements each of which are types of data inputs that a human actor would be capable of changing to trigger updates to a clinical report.
However, recitation of an abstract idea is not the end of the analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A – Prong 2
The judicial exception is not integrated into a practical application. In particular, independent claims 1, 11, and 20 do not include additional elements that integrate the abstract idea into a practical application. The additional elements of claim 1 includes the method being computer-implemented, using a graphical user interface of an application platform of a user computing device to detect the selection of a subject by a user, using a processor to generate the overview report and identify the predictive change, and using the application platform to display the generated overview report and predictive change as well as receive a user modification. Claim 11 includes substantially similar additional elements as claim 1, further specifying that a user computing device comprises one or more computer processors and a non-transitory computer-readable storage medium storing instructions executable by the one or more processors that when executed by the processors cause the one or more processors to perform the operations of the invention. Claim 20 includes substantially similar additional elements as claims 1 and 11.
These additional elements, when considered in the context of each claim as a whole, merely serve to automate interactions that could be achieved by and among human actors (as described above), and thus amount to instructions to “apply” the abstract idea using generic computer components. For example, various entities of a patient care team can interact to share and access information about a patient for the purpose of generating and updating a report about patient microbiome and corresponding predispositions. The use of a GUI of an application platform of a user computing device to receive the inputs and display the outputs of this process merely digitizes the sharing of data that could otherwise be achieved by human actors, while the use of a computer processor executing stored instructions to access patient data, generate the report, and identify the predictive change merely automates the role of a clinician in gathering and analyzing patient data for diagnostic or health recommendation purposes. Accordingly, these generic computer elements are merely invoked to digitize or automate the behavior and interactions of human actors and amount to mere instructions to apply the abstract idea on a computer. Accordingly, claims 1, 11, and 20 as a whole are each directed to an abstract idea without integration into a practical application.
The judicial exception recited in dependent claims 4-10, 14-19, and 21-24 is also not integrated into a practical application under a similar analysis as above. The functions of claims 4-8, 10, 14-18, and 21-24 are performed with the same additional elements introduced in the independent claims, without introducing any new additional elements of their own, and accordingly also amount to mere instructions to apply the abstract idea using these same additional elements. Claims 9 and 19 recite the additional element of another computing device associated with the coaching individual that is used to display the aspect transmitted to it by the user computing device. This element again functions to digitize the role of a human actor (e.g. a health coach) such that otherwise-abstract data sharing operations occur in a digital environment, amounting to instructions to “apply” the exception with computing components.
Accordingly, the additional elements of claims 1, 4-11, and 14-24 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 1, 4-11, and 14-24 are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of computer processors executing stored instructions and a GUI of an application platform of a user device used for performing the detecting, accessing, generating, displaying, receiving, identifying, retrieving, comparing, determining, matching, analyzing, transmitting, etc. steps of the invention amount to mere instructions to apply the exception using generic computer components. As evidence of the generic nature of the above recited additional elements, Examiner notes paras. [0025]-[0028], [00110], & [00256]-[00259] of Applicant’s specification, where the computing hardware of the invention is disclosed in highly generic and non-limiting exemplary terms, which one of ordinary skill in the art would recognize as indicating that any type of known processing, memory, and user device components may be utilized to implement the invention. Note that despite Applicant’s assertions in para. [0029] that the data processing of the invention “requires specialized computer systems, algorithms, and computational resources beyond what can be performed solely within the human mind” and “although displaying analyzed data to a user is known in the art, the graphical rendering techniques leveraged here to construct an interactive and dynamic user interface are not known in the art and represent a unique computer construct,” there is no indication that the functions of the claims as presently drafted (e.g. receiving a user selection, accessing microbiome data, and generating and displaying a report based on the microbiome data) utilize particular machines as outlined in MPEP 2106.05(b) or differ from known methods of analyzing and displaying information on computing devices (note that “gathering and analyzing information using conventional techniques and displaying the result” as in TLI Communications was not found sufficient to show an improvement to technology as outlined in MPEP 2106.05(a)).
Analyzing these additional elements as an ordered combination adds nothing that is not already present when considering the elements individually; the overall effect of the computer implementation and GUI and user devices in combination is to digitize and/or automate a diagnostic report generation operation that could otherwise be achieved as a certain method of organizing human activity. Thus, when considered as a whole and in combination, claims 1, 4-11, and 14-24 are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4-5, 10-11, 14-15, and 20-24 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jain et al. (US 20210241139 A1).
Claims 1, 11, and 20
Jain teaches a computer-implemented method of leveraging subject microbiome data (Jain abstract, noting a computerized method of accessing and evaluating subject data for health and wellness determinations), the computer-implemented method comprising operations including:
detecting, on a graphical user interface of an application platform of a user computing device, a selection of a subject by a user (Jain [0278], noting an interface on a computing device is presented to a user for selecting a particular subject from a list of subjects);
accessing, based on the detecting, data associated with the subject, wherein the data comprises the subject microbiome data (Jain [0275], noting assessment analytical data for a particular subject may be presented on an interface (e.g. responsive to selection of the particular subject as in [0278]) and is populated with subject data acquired from the computer system; such subject data can include lab and diagnostic data, omics data including microbiomics, various physiological measurements, and other subject data as noted in [0432]-[0447]);
generating, by a processor, an overview report comprising a first set of subject predispositions, wherein generating the overview report comprises: generating each of (1) a list of expected conditions associated with the subject microbiome data, (2) an expected symptom set associated with the subject microbiome data, and (3) one or more recommended actions to improve a condition of a subject microbiome based on the subject microbiome data (Jain [0125]-[0126], [0163]-[0164], [0215]-[0219], noting various predictions, training programs, risk scores, trends, or other outputs about the subject (i.e. an overview report comprising a first set of subject predispositions in accordance with Applicant’s definition and examples of subject predispositions in para. [00116] of Applicant’s specification) may be generated based on the subject data; see also [0013], noting the outputs or predictions of the system (i.e. aspects of a report) can include modeling the progression of a disease (i.e. expected condition) and patient status or abilities (i.e. set of symptoms) based on the subject data (i.e. associated with the subject microbiome data as in [0434]); see also [0215]-[0220], [0322], noting the outputs displayed to the user can include recommended training actions to improve a user’s condition or capabilities based on the subject’s genomics, metabolomics, etc. (i.e. microbiome));
displaying, on the application platform, the generated overview report (Jain [0220], [0270], [0275], [0351]-[0352], noting the generated predictions and other outputs (i.e. the generated overview report) are provided to a user via display at a user interface);
receiving, at the application platform, a user modification input that adjusts one or more elements of the first set of subject predispositions; identifying, using the processor, a predictive change to the subject microbiome data based on the user modification input; and displaying, on the application platform, the predictive change to the subject microbiome (Jain [0313], [0353], noting a user may interact with the user interface to select adjusted inputs for training or intervention options (i.e. elements of the first set of subject predispositions) and the system utilizes the adjusted inputs to generate and display updated feature scores or other indicators; such features scores can include attributes of the subject derived from sensor data or other subject data as noted in [0326]-[0327] such that feature scores newly derived based on adjusted user inputs as in [0353] are considered equivalent to a predictive change to the subject microbiome data in accordance with the definition and examples of microbiome data in para. [0065]-[0066] of Applicant’s specification).
Regarding claim 11, Jain teaches a user computing device, comprising: one or more computer processors; and a non-transitory computer-readable storage medium storing instructions executable by the one or more computer processors, the instructions when executed by the one or more computer processors causing the one or more computer processors (Jain [0450]-[0454], noting the invention may be implemented with computing hardware such as a processor and computer-readable memory storing program instructions for execution at a computer device such as a tablet, mobile telephone, or other user device) to perform operations including those substantially similar as claim 1, as explained above.
Regarding claim 20, Jain teaches a non-transitory computer-readable medium storing instructions executable by one or more computer processors of a computer system, the instructions when executed by the one or more computer processors cause the one or more computer processors (Jain [0450]-[0454], claim 20, noting the invention may be implemented with computing hardware such as a processor and non-transitory computer-readable memory storing program instructions for execution at a computer device) to perform operations comprising those substantially similar as claim 1, as explained above.
Claims 4 and 14
Jain teaches the computer-implemented method of claim 1, and further teaches: generating, using the processor, a second set of subject predispositions based on the predictive change; and displaying, on the application platform and subsequent to the generating, the second set of subject predispositions (Jain [0353], noting the feature scores newly derived based on adjusted user inputs (i.e. the predictive change to microbiome data, as explained above) can be used as a basis for determining predictions of the ultimate effect of different training plans or options on the user (i.e. a second set of subject predispositions) and displayed for user comparison).
Claim 14 recites substantially similar subject matter as claim 4, and is also rejected as above.
Claims 5 and 15
Jain teaches the computer-implemented method of claim 4, and further teaches wherein the displaying the second set of subject predispositions comprises visually distinguishing, on the graphical user interface, differences between the first set of subject predispositions and the second set of subject predispositions that are greater than a predetermined threshold (Jain [0353], noting the user interface can display comparisons about the predicted effects of different treatment options to indicate, e.g., how much more efficiently the subject would achieve readiness with one option or another, a percentage of time that would be reduced for achieving readiness with different options, etc. This is considered equivalent to visually distinguishing differences between the first and second set of predispositions greater than a predetermined threshold percentage (e.g. greater than a threshold of 0%) because the differences are indicated at all).
Claim 15 recites substantially similar subject matter as claim 5, and is also rejected as above.
Claim 10
Jain teaches the computer-implemented method of claim 1, and further teaches wherein the subject microbiome data is derived from a microbiome sample from the subject and/or a second microbiome sample from a second subject that is linked to the subject (Jain [0259], [0433], noting subject data may be derived from bioassays of subject biological samples).
Claim 21
Jain teaches the computer-implemented method of claim 1, and further teaches: receiving, at the application platform, a second user modification input that adjusts one or more linkages associated with the subject; identifying, using the processor, a second predictive change to the subject microbiome data based on the second user modification input; and displaying, on the application platform, the second predictive change to the subject microbiome (Jain [0313], [0353], noting a user may interact with the user interface to select adjusted inputs for training or intervention options and the system utilizes the adjusted inputs to generate and display updated feature scores or other indicators; such intervention options can include assignment of a new technician, coach, doctor, manager, etc. to a subject as in [0371] (considered equivalent to adjusting one or more linkages associated with the subject), while such feature scores can include attributes of the subject derived from sensor data or other subject data as noted in [0326]-[0327] such that feature scores newly derived based on adjusted user inputs as in [0353] are considered equivalent to a predictive change to the subject microbiome data in accordance with the definition and examples of microbiome data in para. [0065]-[0066] of Applicant’s specification).
Claim 22
Jain teaches the computer-implemented method of claim 21, and further teaches wherein the one or more linkages associated with the subject include one or more of a subject-to-subject, a subject-to-coach, a subject-to-user, or a combination thereof (Jain [0371], noting adjusted intervention options as in [0353] may be related to assignment of a new technician, coach, doctor, manager, etc. to a subject (i.e. one or more of a subject-to-coach and a subject-to-user linkage)).
Claim 23
Jain teaches the user computing device of claim 11, and further teaches: receiving, at the application platform, a second user modification input that adjusts one or more user engagements associated with the subject; identifying, using the processor, a second predictive change to the subject microbiome data based on the second user modification input; and displaying, on the application platform, the second predictive change to the subject microbiome (Jain [0313], [0353], noting a user may interact with the user interface to select adjusted inputs for training or intervention options and the system utilizes the adjusted inputs to generate and display updated feature scores or other indicators; such intervention options can include interactions with mobile devices such as reminders, surveys, media, etc. as in [0370] (considered equivalent to adjusting one or more user engagements associated with the subject), while such feature scores can include attributes of the subject derived from sensor data or other subject data as noted in [0326]-[0327] such that feature scores newly derived based on adjusted user inputs as in [0353] are considered equivalent to a predictive change to the subject microbiome data in accordance with the definition and examples of microbiome data in para. [0065]-[0066] of Applicant’s specification).
Claim 24
Jain teaches the user computing device of claim of claim 23, and further teaches wherein the one or more user engagements associated with the subject includes one or more of a survey responses, a user log data, a user purchase history, or one or more user communications (Jain [0370], noting adjusted intervention options as in [0353] may be related to candidate actions for interactions with mobile devices like reminders, surveys, media, etc. (i.e. one or more user communications)).
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 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.
Claims 6-9 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Jain as applied to claims 1 or 11 above, and further in view of Frenkel-Morgenstern et al. (US 20230227908 A1).
Claims 6 and 16
Jain teaches the computer-implemented method of claim 1, and further teaches:
receiving, at the application platform, an indication of a target goal for the subject (Jain [0124], [0239], noting users may set targets for a subject as input at a computing device);
retrieving, using the processor, microbiome data of a reference subject having achieved the target goal; comparing, using the processor, the subject microbiome data to the microbiome data of the reference subject; (Jain [0112], noting the system identifies resources or interventions used by other subjects (i.e. reference subjects) with similar attributes or past activities of a current subject and successfully achieved the target goal; because the system is able to identify subjects with similar attributes to a current patient, the system is considered to retrieve subject attribute data (e.g. including microbiome data as in [0434]) of the reference subject for comparison to subject attribute data (e.g. including microbiome data) of the current subject to determine similarities);
identifying a change to one or more elements of the first set of subject predispositions needed (Jain [0111]-[0112], [0383], noting the system selects actions for a subject (i.e. a second set of subject predispositions incorporating a change) based on resources or interventions that were utilized by the reference subject to achieve the readiness criteria (i.e. target goal) and may change or adjust subject plans based on the selected actions); and
displaying, on the application platform, a plan that includes the second set of subject predispositions (Jain [0220], [0384], noting updated or current treatment plans (e.g. including the actions and interventions selected as in [0111]-[0112] & [0383]) are displayed to a user).
In summary, Jain teaches a system that allows a user to input a target goal and selects actions or interventions (i.e. a set of second predispositions) for a user to achieve that goal based on actions or interventions that have previously been successful for other patients with similar attributes achieving that goal. However, Jain does not appear to explicitly disclose the outcome of the actions or interventions being adjusting characteristics of the subject microbiome data to the microbiome data of the reference subject. However, Frenkel-Morgenstern teaches selecting therapies for a subject as needed to adjust characteristics of the subject’s microbiome data to the microbiome data of a reference subject (Frenkel-Morgenstern [0089], noting data from a population of subjects is used to characterize subjects according to their microbiome composition, and promote one or more therapies that can modulate the composition of a given subject’s microbiome towards desired equilibrium states in comparison with a healthy subject). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the selection of therapeutic interventions for achieving a desired target based on similar previous patients as in Jain to include selection of therapies that would promote adjusting a subject’s microbiome data to more closely resemble the microbiome data of a reference subject as in Frenkel-Morgenstern in order to provide tailored and personalized treatment solutions that address microbiome considerations and promote a healthy microbiome equilibrium, because the microbiome is a powerful indicator of subject wellness and wellbeing (as suggested by Frenkel-Morgenstern [0005]-[0008]).
Claim 16 recites substantially similar subject matter as claim 6, and is also rejected as above.
Claims 7 and 17
Jain in view of Frenkel-Morgenstern teaches the computer-implemented method of claim 6, and the combination further teaches wherein the plan comprises a list of recommended actions the subject should take for each of the second set of subject predispositions to achieve the target goal (Jain [0177]-[0178], [0332], noting a list of candidate actions or interventions and their respective impact scores or effectiveness towards meeting the readiness criteria (i.e. the target goal) are provided).
Claim 17 recites substantially similar subject matter as claim 7, and is also rejected as above.
Claims 8 and 18
Jain in view of Frenkel-Morgenstern teaches the computer-implemented method of claim 6, and the combination further teaches: determining, based on the target goal and/or the data associated with the subject, a coaching individual; and automatically matching, based on the determined coaching individual, the coaching individual with the user (Jain [0371], noting the candidate actions toward reaching the readiness criteria (i.e. the target goal) can include changing an assignment (i.e. matching) of an individual to assist the subject in achieving readiness, e.g. a coach, doctor, manager, etc.; the candidate interventions are based on attributes and other data associated with the subject and the readiness criteria (i.e. target goal) as noted in [0177]-[0178], [0332], & [0369]).
Claim 18 recites substantially similar subject matter as claim 8, and is also rejected as above.
Claims 9 and 19
Jain in view of Frenkel-Morgenstern teaches the computer-implemented method of claim 8, and the combination further teaches:
identifying interaction data between the coaching individual and the subject (Jain [0140], noting an assigned coach can provide third-party input to the system, including feedback relating to recent counselling or coaching sessions (i.e. interaction data between a coach and subject));
analyzing, using the processor, the interaction data with respect to a progress rate of the user toward the target goal (Jain [0332], noting the system evaluates data stored by the system (i.e. including the feedback/interaction data as explained above) to generate predictions about the effectiveness of different training activities or other factors of interventions towards successful completion of a goal for subjects with different attributes);
determining, using the processor and based on the analyzed interaction data, an aspect of the interaction data that accelerates or impedes the progress rate of the subject toward the target goal (Jain [0191], [0332], noting the system determines which actions are most likely to improve or be effective in the progression of a subject towards successful completion of the goal based on data stored by the system (i.e. analyzed interaction data as explained above)); and
transmitting, by the user computing device, instructions to another computing device associated with the coaching individual to display the aspect (Jain [0182], noting selected interventions for a subject (i.e. instructions) may be transmitted to the device of a coach, counselor, or other third party user).
Claim 19 recites substantially similar subject matter as claim 9, and is also rejected as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Warren et al. (US 20210050080 A1) and Dogra (US 20240006051 A1) describe systems for determining user microbiome profiles and associated health plan recommendations.
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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/KAREN A HRANEK/ Primary Examiner, Art Unit 3684