DETAILED ACTION
Status of Claims
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 .
This action is in reply to the Request for Continued Examination filed on 11/06/2025.
Claims 1, 9, 12, 20, 23, 32-33, 42 and 114-117 have been amended.
Claims 43-85 have been cancelled.
Claims 1-42 and 86-117 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/06/2025 has been entered.
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 1-42 and 86-117 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-11, 23-32, 86-92, 100-106, 114 and 116 are directed to a method (i.e., a process) and claims 12-22, 33-42, 93-99, 107-113, 115 and 117 are directed to a system (i.e., a machine). Accordingly, claims 1-42 and 86-117 are all within at least one of the four statutory categories.
Step 2A - Prong One:
An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claims 12 and 33 include limitations that recite an abstract idea.
Specifically, the abstract idea in independent claim 12 recites:
An apparatus for generating and monitoring a therapy regimen, the apparatus comprising:
a processor configured to:
generate, by machine learning software within the processor, a therapy regimen associated with a condition of a user, wherein the therapy regimen comprises one or more machine-readable computer files containing machine-executed instructions for an application and indicates a set of one or more pre-stored parameters associated with the condition;
provide the therapy regimen to the application on a user device of the user;
receive a plurality of inputs associated with the therapy regimen of the user from one or more devices via a network, wherein each an input contains a value of a parameter;
compare the value of the parameter against a corresponding pre-stored parameter stored in a first database;
monitor interactions between the user and the therapy regimen provided through the application;
determine a regimen status indicating a progress of the user in addressing the condition;
obtain, based on the regimen status, one or more data identifiers;
obtain content, based on the one or more data identifiers, by executing machine- executable instructions; and
update the one or more machine-readable computer files of the therapy regimen responsive to the regimen status not satisfying a threshold, wherein updating the one or more machine-readable computer files of the therapy regimen comprises updating the one or more GUIs with the content.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because generating a therapy regimen indicated by parameters associated with a user who has a condition, determining a regimen status for the therapy regimen of the user based on interactions between the user and the therapy regimen provided through the application, updating files of the therapy regimen responsive to the regimen status not satisfying a threshold and transmitting a milestone achievement interface all relate to treating, motivating a medical patient and providing healthcare services, which relate to managing human behavior/interactions between people. For example, an individual with a medical condition may use a medical app on the individual’s mobile phone for tracking amounts of physiological activities and providing feedback, as status to determine that the individual is achieving goals in following a therapy regimen. Furthermore, these limitations constitute (b) “a mental process” because comparing the value of the parameter against a corresponding pre-stored parameter, monitor interactions between the user and the therapy regimen and determining that a regimen status threshold is satisfied and not satisfied, monitoring a therapy regimen and comparing a value of a parameter against a corresponding pre-stored parameter are observations/evaluations/analysis that can be performed in the human mind or with a pen and paper. Accordingly, the claim describes at least one abstract idea.
Dependent claims 2-11, 13-22, 24-32, 34-42 and 86-117 inherit the limitations that recite an abstract idea from their dependence on claims 1, 12, 23 or 33, and thus these claims also recite an abstract idea under the Step 2A - Prong 1 analysis. In addition, claims 2-11, 13-22, 24-32, 34-42 and 86-117 recite further limitations that: 1) merely recite specific kinds of data and 2) under their broadest reasonable interpretations, amount to additional steps/functions in the method of organizing human activity.
Specifically, claims 2, 13, 24 and 34 recite storing, by the computer, each respective input into a user record of a second database. Claims 3, 14, 25 and 35 recite inputting a user-generated input received from the user device, and wherein the method further comprises calculating, by the computer, a score indicating a likelihood of success associated with the therapy regimen based upon a frequency of receiving each user-generated input. Claims 4 and 15 recite updating, by the computer, the user record of the second database to include the score indicating the likelihood of success. Claims 5, 16, 27 and 37 recite indicating the likelihood of success at a predetermined interval. Claims 6 and 17 recite the inputs is received from a device selected from the group comprising: a coach device, a third-party server, and an artificial intelligence server. Claims 7 and 18 recite the inputs is received from a device selected from the group comprising: a coach device, a third-party server, and an artificial intelligence server. Claim 8, 19, 31 and 41 recites generating, by the computer, a coaching interface configured to display the one or more coaching inputs received from the coach device; and transmitting, by the computer, the coaching interface to the user device. Claims 9 and 20 recites transmitting, by the computer, to the user device at least a portion of the machine-readable computer files of the therapy regimen to be executed by the mobile application of the user device. Claim 10 recites updating, by the computer, the therapy regimen associated with the condition of the user based upon the regimen status for the therapy regimen; and updating, by the computer, the therapy regimen and parameters in the user record of the second database. Claims 11, 22, 29 and 39 recite wherein at least one input includes a body metric measurement received from a device configured to generate the body metric measurement. Claim 13 recites wherein receiving the processor is further configured to store each respective input into a user record of a second database. Claim 15 recites the processor is further configured to update the user record of the second database to include the score indicating the likelihood of success. Claims 30 and 40 recites wherein the device configured to generate the body metric measurement is selected from the group comprising: a smart home device, a wearable device, and a fitness tracker. Claims 31 and 41 recites receiving, by the computer, via the network one or more coaching inputs from a coach device; and generating, by the computer, a coaching interface configured to display the one or more coaching inputs received from the coach device. Claims 32 and 42 recites configured to generate a prompt interface at a given time according to the instructions of the therapy regimen, the prompt interface configured to display a field that receives the input from the user via the graphical user interface of the computer. Claims 86, 93, 100 and 107 recite wherein the condition of the user is a diabetes health condition. Claims 87, 94, 101 and 108 recite wherein a health score model is trained with and uses data fields relevant to monitoring diabetes. Claims 88, 95, 102 and 109 recite wherein the data fields are selected from the group consisting of blood glucose, cholesterol, blood pressure, weight, and number of interactions with one or more of the one or more devices and a coach. Claims 89, 96, 103 and 110 recite wherein the condition of the user is type II diabetes. Claims 90, 97, 104 and 111 recite wherein the machine learning software executes machine learning algorithms and processes selected from the group consisting of generalized linear models, random forests, support vector machines, unsupervised and/or supervised clustering, and deep learning, wherein deep learning includes neural networks. Claims 91, 98, 105 and 112 recite comparing, by the computer, one or more data fields relevant to the user’s condition against pre-stored milestone parameters or data values at predetermined milestone intervals, wherein the prestored milestone values may operate as threshold values. Claims 92, 99, 106 and 113 recite wherein there are multiple pre-stored milestone values for multiple data fields, to compare multiple different customer values of different fields against multiple different pre-stored values. Claims 114-117 recite wherein indicators of interactions between the user and certain aspects of the mobile device are selected from the group consisting of whether a meal plan was followed; whether a therapy regimen task was completed; whether a coaching call was completed; whether one or more, or all, ingredients on a shopping list were procured; whether the subject consumed a specified amount of water or whether the subject consumed water; a number of meal plan meals consumed, a number of tasks completed, a total number of calories burned in one or more activities, a number of coaching calls completed, length of one or more coaching calls, a number or fraction of ingredients procured from a shopping list, a number of hydration events, or a total volume of hydration.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, 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 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.”
The limitations of claims 1, 12, 23 and 33, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting a computer, one or more processors, one or more devices via a network, one or more machine-readable computer files containing machine-executed instructions for a mobile application, graphical user interface (GUI), a first database, a second database, a coach device, a processor, and a user device to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment in the mind but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” and “Mental Process” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the computer, , one or more processors, devices via a network, machine-readable computer files containing machine-executed instructions for the mobile application, graphical user interface (GUI), first database, second database, coach device, processor, and user device are recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components.
Regarding the additional limitations “machine learning software”, the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitation “receiving, by the computer, a plurality of inputs associated with the therapy regimen of the user from one or more devices ……” and “the computer receives indicators of the interactions….”, the Examiner submits that this additional limitation merely adds insignificant pre-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea (see MPEP § 2106.05(g)).
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see 2019 PEG and MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
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 the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claims 12 and 33, regarding the additional limitations of the computer, , one or more processors, devices via a network, machine-readable computer files containing machine-executed instructions for the mobile application, graphical user interface, first database, second database, coach device, processor, and user device, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claims 12 and 33 and analogous independent claims 1 and 23 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 claim does not integrate the abstract idea into a practical application.
Claims 1, 12, 23 and 33 introduce elements of the machine learning software for performing the generating, determining, etc. steps of the invention amount to no more than mere instructions to apply the exception using generic computer components. As evidence of the generic nature of the above recited additional elements the machine learning software that carries out generating a therapy regimen associated with a condition of a user and determining a regimen status for the therapy regimen of the user merely digitize interactions that could occur between human actors such that they amount to the words “apply it” with a computer. Such processing thus utilizes computer components recited at a high level of generality that could otherwise occur between healthcare provider and a patient, and thus this element amounts to the words “apply it” with a computer.
Claims 6-8, 17-19, 31 and 41 specifies a coach device and coaching interface configured to display, which merely receives indicators of interactions between the customer and aspects of the digital therapy service and amounts to the words “apply it” with a computer.
The dependent claims no 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 reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 11-42 and 86-117 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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 1-42, 86-87, 89-94, 96-101, 103-108 and 110-117 are rejected under 35 U.S.C. 103 as being unpatentable over Nevo (US 2015/0313529 A1) in view of Brust (US 2017/0329933 A1).
Claim 1:
Nevo discloses a computer-implemented method for generating and monitoring a therapy regimen (See Fig. 1-2, data processing system 30, exemplary computing devices (P0048) and P0184-P0185, where exemplary smartphone app displays reminders for taking medications, personalized recommendations, general information about the subject's mental disorder, daily questions regarding the subject's mental state and daily routine serve as generating and monitoring a therapy regimen), the method comprising:
generating, by one or more processors executing a machine learning model, a therapy regimen associated with a condition of a user, wherein the therapy regimen comprises one or more machine-readable computer files containing machine-executed instructions for an application comprising one or more configurations for collecting a plurality of inputs and indicates a set of one or more pre stored parameters associated with the condition (See software instructions, embodiment of the invention performed by a data processor in Fig. 2, P00032, P0109. See P0131-P0132, P0171, where affective preventative medicine is achieved based on using classification and machine-learning algorithms to identify patterns of a user's behavior and detect deviations from these patterns and sensors record parameters about the user's daily routine and physiology. See [P0047] instruction on a computer readable medium.);
providing, by the one or more processors, the therapy regimen to the application on a user device of the user (See exemplary smart Phone App in [P0184] displays reminders for taking medications, personalized recommendations, general information about the subject's mental disorder.);
receiving, by the one or more processors, a plurality of inputs associated with the therapy regimen of the user from one or more devices via a network, wherein an input contains a value of a parameter (Besides the sensors embedded in the smartphone for recording parameters in P0131, see adjusted medical treatment from analyzing the parameters in P0029. Also, see network connection in P0017, P0032 and cellular network in P0142.);
comparing, by the one or more processors, the value of the parameter against a corresponding pre- stored parameter stored in a first database (Behavior patterns from sensors record parameters P0131 are compared to reference behavior patterns obtained from a database mentioned in P0170-P0172.);
monitoring, by the one or more processors, interactions between the user and the therapy regimen provided through the application (See electronic communication log data interactive with the phone app (P0050, P0056, P0176-P0184) when reminding a user to take medications, estimated from motion sensors for detecting sleep patterns (P0063).); and
determining, by the one or more processors, a regimen status indicating a progress of the user in addressing the condition (See activity log and reminders for taking medications in [P0181-P0185] the subject information regarding his behavioral patterns and alerts or indicates when there are deviations, displays reminders for taking medications, personalized recommendations, general information about the subject's mental disorder. Also, see P0215-P0217.).
Although Nevo discloses a computer-implemented method for generating and monitoring a therapy regimen by processors when determining based on providing the interactions and comparison to the machine learning model, a regimen status indicating a progress of the user in addressing the condition as mentioned above, Nevo does not explicitly teach GUIs obtaining data identifiers and content by executing machine- executable instructions and updating the therapy regimen responsive to the regimen status not satisfying a threshold. Brust teaches:
an application configured to generate one or more graphical user interfaces (GUIs) (See exemplary graphical user interface 420 mentioned in P0051-P0052.);
obtaining, based on the regimen status, one or more data identifiers (With data identifiers as a corresponding chatbot, see coaching avatar over time in P0049, Fig. 15 chat communication 1530 in P0151-P0152 is initiated after determining the user’s assessment, current state. Also, see P0146, [P0184] If the supporter would like to contact the human user 106 directly then a conversation box or other conversations widget can be opened for the supporter to chat with the human user 106 (operation 2126). If the supporter would like to send inspiration to the human user 106, a client inspiration widget may be accessed so that the supporter can determine what inspires the human user 106 (operation 2128).); and
obtaining content, based on the one or more data identifiers, by executing machine- executable instructions (Besides a virtual agent with adaptive learning, automation and customizable new tool in the form of a design-your-own-engagement-coach in P0049, see support coaching tools serve as a form of executing machine- executable instructions in [P0146-P0147] the health information system 102 to provide communications over a variety of mediums, enabling the professional user to chat, send images, send videos, monitor the member, and provide suggestions.).
updating, by the one or more processors, the one or more machine-readable computer files of the therapy regimen responsive to the regimen status not satisfying a threshold, wherein updating the one or more machine-readable computer files of the therapy regimen comprises updating the one or more GUIs with the content (See measuring and verifying compliance with a particular therapy activity in P0069-P0071 as a status tracker tracking exercise therapy and exercise target 687s shown in Fig. 6B-B construe not satisfying a threshold. Also, see Fig. 8 Action/Success Matrix 826 that tracks the success or failure of individual activities and obtaining user profile characteristics mentioned in P0086, P0094, P0148.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include teach GUIs obtaining data identifiers and content by executing machine- executable instructions and updating the therapy regimen responsive to the regimen status not satisfying a threshold as taught by Brust in to utilize related objective metrics from the performance of specific activities such as quality of motion and volume of activities mentioned in Brust’s P0089.
Claim 12:
Nevo discloses an apparatus for generating and monitoring a therapy regimen (See Fig. 1-2, data processing system 30, exemplary computing devices (P0048) and P0184-P0185, where exemplary smartphone app displays reminders for taking medications, personalized recommendations, general information about the subject's mental disorder, daily questions regarding the subject's mental state and daily routine.), the apparatus comprising:
a processor configured (See P0032) to:
generate, by machine learning model within the processor, a therapy regimen associated with a condition of a user, wherein the therapy regimen comprises one or more machine-readable computer files containing machine-executed instructions for an application comprising one or more configurations for collecting a plurality of inputs and indicates a set of one or more prestored parameters associated with the condition (See software instructions, embodiment of the invention performed by a data processor in Fig. 2, P00032, P0109. See P0131-P0132, P0171, where affective preventative medicine is achieved based on using classification and machine-learning algorithms to identify patterns of a user's behavior and detect deviations from these patterns and sensors record parameters about the user's daily routine and physiology. See [P0047] instruction on a computer readable medium.);
provide the therapy regimen to the application on a user device of the user (See exemplary smart Phone App in [P0184] displays reminders for taking medications, personalized recommendations, general information about the subject's mental disorder.);
receive a plurality of inputs associated with the therapy regimen of the user from one or more devices via a network, wherein an input contains a value of a parameter (Besides the sensors embedded in the smartphone for recording parameters in P0131, see adjusted medical treatment from analyzing the parameters in P0029. Also, see network connection in P0017, P0032 and cellular network in P0142.);
compare the value of the parameter against a corresponding pre-stored parameter stored in a first database (Behavior patterns from sensors record parameters P0131 are compared to reference behavior patterns obtained from a database mentioned in P0170-P0172.);
monitor interactions between the user and the therapy regimen provided through the application (See electronic communication log data interactive with the phone app (P0050, P0056, P0176-P0184) when reminding a user to take medications, estimated from motion sensors for detecting sleep patterns (P0063).); and
determine a regimen status indicating a progress of the user in addressing the condition (See activity log and reminders for taking medications in [P0181-P0185] the subject information regarding his behavioral patterns and alerts or indicates when there are deviations, displays reminders for taking medications, personalized recommendations, general information about the subject's mental disorder. Also, see P0215-P0217.).
Although Nevo discloses a computer-implemented method for generating and monitoring a therapy regimen by processors when determining based on providing the interactions and comparison to the machine learning model, a regimen status indicating a progress of the user in addressing the condition as mentioned above, Nevo does not explicitly teach GUIs obtaining data identifiers and content by executing machine- executable instructions and updating the therapy regimen responsive to the regimen status not satisfying a threshold. Brust teaches:
an application configured to generate one or more graphical user interfaces (GUIs) (See exemplary graphical user interface 420 mentioned in P0051-P0052.);
obtain, based on the regimen status, one or more data identifiers (With data identifiers as a corresponding chatbot, see coaching avatar over time in P0049, Fig. 15 chat communication 1530 in P0151-P0152 is initiated after determining the user’s assessment, current state. Also, see P0146, [P0184] If the supporter would like to contact the human user 106 directly then a conversation box or other conversations widget can be opened for the supporter to chat with the human user 106 (operation 2126). If the supporter would like to send inspiration to the human user 106, a client inspiration widget may be accessed so that the supporter can determine what inspires the human user 106 (operation 2128).); and
obtain content, based on the one or more data identifiers, by executing machine- executable instructions (Besides a virtual agent with adaptive learning, automation and customizable new tool in the form of a design-your-own-engagement-coach in P0049, see support coaching tools serve as a form of executing machine- executable instructions in [P0146-P0147] the health information system 102 to provide communications over a variety of mediums, enabling the professional user to chat, send images, send videos, monitor the member, and provide suggestions.).
update the one or more machine-readable computer files of the therapy regimen responsive to the regimen status not satisfying a threshold, wherein updating the one or more machine-readable computer files of the therapy regimen comprises updating the one or more GUIs with the content (See measuring and verifying compliance with a particular therapy activity in P0069-P0071 as a status tracker tracking exercise therapy and exercise target 687s shown in Fig. 6B-B construe not satisfying a threshold. Also, see Fig. 8 Action/Success Matrix 826 that tracks the success or failure of individual activities and obtaining user profile characteristics mentioned in P0086, P0094, P0148.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include teach GUIs obtaining data identifiers and content by executing machine- executable instructions and updating the therapy regimen responsive to the regimen status not satisfying a threshold as taught by Brust in to utilize related objective metrics from the performance of specific activities such as quality of motion and volume of activities mentioned in Brust’s P0089.
Claim 23:
Nevo discloses a computer-implemented method for generating and monitoring a therapy regimen (See Fig. 1-2, data processing system 30, exemplary computing devices (P0048) and P0184-P0185, where exemplary smartphone app displays reminders for taking medications, personalized recommendations, general information about the subject's mental disorder, daily questions regarding the subject's mental state and daily routine.), the method comprising:
receiving, by one or more processors, a therapy regimen associated with a condition of a user, wherein the therapy regimen comprises one or more machine-readable computer files containing machine-executed instructions for an application and indicates a set of one or more parameters associated with the condition (See P0131-P0132, P0171, where affective preventative medicine is achieved based on using classification and machine-learning algorithms to identify patterns of a user's behavior and detect deviations from these patterns and sensors record parameters about the user's daily routine and physiology. See [P0047] instruction on a computer readable medium.);
receiving, by the one or more processors, via a graphical user interface one or more inputs associated with the therapy regimen of the user, wherein an input contains a value of a parameter (Besides the sensors embedded in the smartphone for recording parameters in P0131, see adjusted medical treatment from analyzing the parameters in P0029.);
comparing, by the one or more processors, the value of the parameter against a corresponding pre- stored parameter stored in a first database (Behavior patterns from sensors record parameters P0131 are compared to reference behavior patterns obtained from a database mentioned in P0170-P0172.);
monitoring, by the one or more processors, interactions between the user and the therapy regimen provided through the application (See electronic communication log data interactive with the phone app (P0050, P0056, P0176-P0184) when reminding a user to take medications, estimated from motion sensors for detecting sleep patterns (P0063).);
determining, by the one or more processors, a regimen status indicating a progress of the user in addressing the condition (See activity log and reminders for taking medications in [P0181-P0185] the subject information regarding his behavioral patterns and alerts or indicates when there are deviations, displays reminders for taking medications, personalized recommendations, general information about the subject's mental disorder. Also, see P0215-P0217.).
Although Nevo discloses a computer-implemented method for generating and monitoring a therapy regimen by processors when determining based on providing the interactions and comparison to the machine learning model, a regimen status indicating a progress of the user in addressing the condition as mentioned above, Nevo does not explicitly teach GUIs obtaining data identifiers and content by executing machine- executable instructions and updating the therapy regimen responsive to the regimen status not satisfying a threshold. Brust teaches:
an application configured to generate one or more graphical user interfaces (GUIs) (See exemplary graphical user interface 420 mentioned in P0051-P0052.);
obtaining, based on the regimen status, one or more data identifiers (With data identifiers as a corresponding chatbot, see coaching avatar over time in P0049, Fig. 15 chat communication 1530 in P0151-P0152 is initiated after determining the user’s assessment, current state. Also, see P0146, [P0184] If the supporter would like to contact the human user 106 directly then a conversation box or other conversations widget can be opened for the supporter to chat with the human user 106 (operation 2126). If the supporter would like to send inspiration to the human user 106, a client inspiration widget may be accessed so that the supporter can determine what inspires the human user 106 (operation 2128).); and
obtaining content, based on the one or more data identifiers, by executing machine- executable instructions (Besides a virtual agent with adaptive learning, automation and customizable new tool in the form of a design-your-own-engagement-coach in P0049, see support coaching tools serve as a form of executing machine- executable instructions in [P0146-P0147] the health information system 102 to provide communications over a variety of mediums, enabling the professional user to chat, send images, send videos, monitor the member, and provide suggestions.).
updating, by the one or more processors, the one or more machine-readable computer files of the therapy regimen responsive to the regimen status not satisfying a threshold, wherein updating the one or more machine-readable computer files of the therapy regimen comprises updating the one or more GUIs with the content (See measuring and verifying compliance with a particular therapy activity in P0069-P0071 as a status tracker tracking exercise therapy and exercise target 687s shown in Fig. 6B-B construe not satisfying a threshold. Also, see Fig. 8 Action/Success Matrix 826 that tracks the success or failure of individual activities and obtaining user profile characteristics mentioned in P0086, P0094, P0148.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include teach GUIs obtaining data identifiers and content by executing machine- executable instructions and updating the therapy regimen responsive to the regimen status not satisfying a threshold as taught by Brust in to utilize related objective metrics from the performance of specific activities such as quality of motion and volume of activities mentioned in Brust’s P0089.
Claim 33:
Nevo discloses an apparatus for generating and monitoring a therapy regimen, the apparatus comprising: a processor (See Fig. 1-2, data processing system 30, exemplary computing devices (P0048) and P0184-P0185, where exemplary smartphone app displays reminders for taking medications, personalized recommendations, general information about the subject's mental disorder, daily questions regarding the subject's mental state and daily routine.), configured to:
receive a therapy regimen associated with a condition of a user, wherein the therapy regimen comprises one or more machine-readable computer files containing machine executed instructions for a mobile an application comprising one or more configurations for collecting a plurality of inputs and indicates a set of one or more parameters associated with the condition (See P0131-P0132, P0171, where affective preventative medicine is achieved based on using classification and machine-learning algorithms to identify patterns of a user's behavior and detect deviations from these patterns and sensors record parameters about the user's daily routine and physiology. See [P0047] instruction on a computer readable medium.);
receive, via a graphical user interface of the one or more GUIs, one or more inputs associated with the therapy regimen of the user, wherein an input contains a value of a parameter (Besides the sensors embedded in the smartphone for recording parameters in P0131, see adjusted medical treatment from analyzing the parameters in P0029. Also, see network connection in P0017, P0032 and cellular network in P0142.);
compare the value of the parameter against a corresponding pre-stored parameter stored in a first database (Behavior patterns from sensors record parameters P0131 are compared to reference behavior patterns obtained from a database mentioned in P0170-P0172.);
monitor interactions between the user and the therapy regimen provided through the application (See electronic communication log data interactive with the phone app (P0050, P0056, P0176-P0184) when reminding a user to take medications, estimated from motion sensors for detecting sleep patterns (P0063).); and
determine a regimen status indicating a progress of the user in addressing the condition
(See activity log and reminders for taking medications in [P0181-P0185] the subject information regarding his behavioral patterns and alerts or indicates when there are deviations, displays reminders for taking medications, personalized recommendations, general information about the subject's mental disorder. Also, see P0215-P0217.).
Although Nevo discloses a computer-implemented method for generating and monitoring a therapy regimen by processors when determining based on providing the interactions and comparison to the machine learning model, a regimen status indicating a progress of the user in addressing the condition as mentioned above, Nevo does not explicitly teach GUIs obtaining data identifiers and content by executing machine- executable instructions and updating the therapy regimen responsive to the regimen status not satisfying a threshold. Brust teaches:
an application configured to generate one or more graphical user interfaces (GUIs) (See exemplary graphical user interface 420 mentioned in P0051-P0052.);
obtain, based on the regimen status, one or more data identifiers (With data identifiers as a corresponding chatbot, see coaching avatar over time in P0049, Fig. 15 chat communication 1530 in P0151-P0152 is initiated after determining the user’s assessment, current state. Also, see P0146, [P0184] If the supporter would like to contact the human user 106 directly then a conversation box or other conversations widget can be opened for the supporter to chat with the human user 106 (operation 2126). If the supporter would like to send inspiration to the human user 106, a client inspiration widget may be accessed so that the supporter can determine what inspires the human user 106 (operation 2128).); and
obtain content, based on the one or more data identifiers, by executing machine- executable instructions (Besides a virtual agent with adaptive learning, automation and customizable new tool in the form of a design-your-own-engagement-coach in P0049, see support coaching tools serve as a form of executing machine- executable instructions in [P0146-P0147] the health information system 102 to provide communications over a variety of mediums, enabling the professional user to chat, send images, send videos, monitor the member, and provide suggestions.).
update the one or more machine-readable computer files of the therapy regimen responsive to the regimen status not satisfying a threshold, wherein updating the one or more machine-readable computer files of the therapy regimen comprises updating the one or more GUIs with the content (See measuring and verifying compliance with a particular therapy activity in P0069-P0071 as a status tracker tracking exercise therapy and exercise target 687s shown in Fig. 6B-B construe not satisfying a threshold. Also, see Fig. 8 Action/Success Matrix 826 that tracks the success or failure of individual activities and obtaining user profile characteristics mentioned in P0086, P0094, P0148.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include teach GUIs obtaining data identifiers and content by executing machine- executable instructions and updating the therapy regimen responsive to the regimen status not satisfying a threshold as taught by Brust in to utilize related objective metrics from the performance of specific activities such as quality of motion and volume of activities mentioned in Brust’s P0089.
Regarding claim 2, Nevo disclose wherein receiving the plurality of inputs further comprises storing, by the one or more processors, each respective input into a user record of a second database (Taught as co-occur frequently within databases in P0070.).
Regarding claim 3, Nevo and Brust teach the method of claim 2 mentioned above. However Nevo does not explicitly teach, while Brust teaches: wherein at least one of the inputs is a user-generated input received from the user device, and wherein the method further comprises calculating, by the one or more processors, a score indicating a likelihood of success associated with the therapy regimen based upon a frequency of receiving each user-generated input (See collected quality of therapy or quality of exercise as a frequency of receiving each user-generated input in [P0090-P0091] Based on the data collected, and the “Quality” score, a mobile app can coach a patient to perform a physical therapy exercise in a certain way, or provide a suggestion in real-time how to do the exercise more effectively or safely. Also, see trust score, confidence score and predictive score based on past data, patient behavior, monitored events, compliance, and therapy conditions, such predictive scores may be further adaptive to patient input and caregiver therapy considerations mentioned in [P0092].).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include the inputs is a user-generated input received from the user device, and wherein the method further comprises calculating, by the computer, a score indicating a likelihood of success associated with the therapy regimen based upon a frequency of receiving each user-generated input as taught by Brust in to utilize related objective metrics from the performance of specific activities such as quality of motion and volume of activities mentioned in Brust’s P0089.
Regarding claim 4, Brust teaches further comprising updating, by the computer, the user record of the second database to include the score indicating the likelihood of success (See P0092, where trust score, confidence score and predictive score based on past data, patient behavior, monitored events, compliance, and therapy conditions from wearable device input and therapy conditions data are all stored among databases (Fig, 22, P0185-P0188).).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include updating, by the computer, the user record of the second database to include the score indicating the likelihood of success as taught by Brust to remember what therapies inspire and support the user as mentioned in Brust’s P0184.
Regarding claims 5 and 16, Brust teaches wherein the computer updates the score indicating the likelihood of success at a predetermined interval (See Fig. 5B, [P0061] a selection interface 552 to define a time interval (such as 7 days, 14 days, 28 days, etc.) and a time-based graphical display such as a chart 554 that indicates the progress status over the time interval. Also, see P0116.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include updating the score indicating the likelihood of success at a predetermined interval as taught by Brust to achieve goals by providing reward points (e.g., kudos), other incentives, and encouraging content outputs as mentioned in Brust’s P0115.
Regarding claims 6 and 17, Brust teaches wherein at least one of the inputs is received from a device selected from the group comprising: a coach device, a third-party server, and an artificial intelligence server (See Fig. 17 and supporter coaching tools mentioned in P0049, P0087 and techniques such as artificial intelligence in P0112.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include a coaching device and artificial intelligence components as taught by Brust when applicable to human behaviors and goal-based activities in medical and non-medical settings as mentioned in Brust’s P0166.
Regarding claim 7, Brust teaches receiving, by the computer, via the network one or more coach inputs from a coach device (See Fig. 1, Fig. 3, therapist/physician 104, provide coaching over networks (P0033, P0035). Also, see Fig. 5C with inputting Bot activity settings (P0062).); and updating, by the computer, the user record of the second database to at least one coaching input received from the coach device (See update selection of suggestions in P0048, P0169.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include receiving via the network one or more coach inputs from a coach device and updating, by the computer, the user record of the second database to coaching input received from the coach device as taught by Brust prevent any suggestions from being suggested again for a period of time or indefinitely as mentioned in Brust’s P0170.
Regarding claim 8, Brust teaches generating, by the computer, a coaching interface configured to display the one or more coaching inputs received from the coach device (See Fig. 1, Fig. 3, therapist/physician 104, provide coaching. Also, see Fig. 5C Bot activity settings (P0062).); and transmitting, by the computer, the coaching interface to the user device (See Fig. 17 and interactive supporter coaching tools mentioned in P0049, P0087.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include generating a coaching interface configured to display coaching inputs received from the coach device and transmitting the coaching interface to the user device as taught by Brust prevent any suggestions from being suggested again for a period of time or indefinitely as mentioned in Brust’s P0170.
Regarding claim 9, Nevo discloses further comprising transmitting, by the computer, to the user device at least a portion of the one or more machine-readable computer files of the therapy regimen to be executed by the mobile application of the user device (See P0045-P0046 invention operations performed on computer programs distributed to users on a distribution medium such as, but not limited to, a floppy disk, CD-ROM, flash drives or the like.).
Regarding claim 10, Brust teaches further comprising: updating, by the computer, the therapy regimen associated with the condition of the user based upon the regimen status for the therapy regimen (See monitored therapy status as updating in P0036, and reviewed recommendations in P0038.); and updating, by the computer, the therapy regimen and parameters in the user record of the second database (See Fig. 1, [P0040] These initial inputs may include psychological profile questions, medical record information, and health status information. Besides therapy suggested action database (P0117), see expert-based content in P0162 the workflow may include interactions with the previously described information system and associated databases and graphical user interfaces, with individual or groups of users.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include updating the therapy regimen associated with the condition of the user based upon the regimen status for the therapy regimen and parameters in the user record of the second database as taught by Brust in to utilize multiple channels and ways of communication, through multiple people, to assist the delivery of the dynamic therapy content mentioned in Brust’s P0043.
Regarding claims 11 and 22, Nevo discloses wherein at least one input includes a body metric measurement received from a device configured to generate the body metric measurement (See calculated score pertaining to activity level of subject in P0017, P0207.).
Regarding claim 13, Nevo disclose wherein the processor is further configured to store each respective input into a user record of a second database (Taught as co-occur frequently within databases in P0070.).
Regarding claim 14, Nevo discloses the method of claim 2 mentioned above. However Nevo does not explicitly teach, while Brust teaches: wherein at least one of the inputs is a user- generated input received from the user device, and wherein the processor is further configured to calculate a score indicating a likelihood of success associated with the therapy regimen based upon a frequency of receiving each user-generated input (See collected quality of therapy or quality of exercise as a frequency of receiving each user-generated input in [P0090-P0091] Based on the data collected, and the “Quality” score, a mobile app can coach a patient to perform a physical therapy exercise in a certain way, or provide a suggestion in real-time how to do the exercise more effectively or safely. Also, see trust score, confidence score and predictive score based on past data, patient behavior, monitored events, compliance, and therapy conditions, such predictive scores may be further adaptive to patient input and caregiver therapy considerations mentioned in [P0092].).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include the inputs is a user-generated input received from the user device, and wherein the method further comprises calculating, by the computer, a score indicating a likelihood of success associated with the therapy regimen based upon a frequency of receiving each user-generated input as taught by Brust in to utilize related objective metrics from the performance of specific activities such as quality of motion and volume of activities mentioned in Brust’s P0089.
Regarding claim 15, Brust teaches wherein the processor is further configured to update the user record of the second database to include the score indicating the likelihood of success (See P0092, where trust score, confidence score and predictive score based on past data, patient behavior, monitored events, compliance, and therapy conditions from wearable device input and therapy conditions data are all stored among databases (Fig, 22, P0185-P0188).).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include updating, by the computer, the user record of the second database to include the score indicating the likelihood of success as taught by Brust to remember what therapies inspire and support the user as mentioned in Brust’s P0184.
Regarding claim 18, Brust teaches receive via the network one or more coaching inputs from a coach device (See Fig. 1, Fig. 3, therapist/physician 104, provide coaching over networks (P0033, P0035). Also, see Fig. 5C with inputting Bot activity settings (P0062).); and update the user record of the second database to at least one coaching input received from the coach device (See update selection of suggestions in P0048, P0169.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include updating the user record of the second database to at least one coaching input received from the coach device as taught by Brust prevent any suggestions from being suggested again for a period of time or indefinitely as mentioned in Brust’s P0170.
Regarding claim 19, Brust teaches generate a coaching interface configured to display the one or more coaching inputs received from the coach device (See Fig. 1, Fig. 3, therapist/physician 104, provide coaching. Also, see Fig. 5C Bot activity settings (P0062).); and transmit the coaching interface to the user device (See Fig. 17 and interactive supporter coaching tools mentioned in P0049, P0087.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include generating a coaching interface configured to display coaching inputs received from the coach device and transmitting the coaching interface to the user device as taught by Brust prevent any suggestions from being suggested again for a period of time or indefinitely as mentioned in Brust’s P0170.
Regarding claim 20, Nevo discloses the processor is further configured to transmit to the user device at least a portion of the one or more machine-readable computer files of the therapy regimen to be executed by the application of the user device (See P0045-P0046 invention operations performed on computer programs distributed to users on a distribution medium such as, but not limited to, a floppy disk, CD-ROM, flash drives or the like.).
Regarding claim 21, Brust teaches wherein the processor is further configured to: update the therapy regimen associated with the condition of the user based upon the regimen status for the therapy regimen (See monitored therapy status as updating in P0036, and reviewed recommendations in P0038.); and update the therapy regimen and parameters in the user record of the second database (See Fig. 1, [P0040] These initial inputs may include psychological profile questions, medical record information, and health status information. Besides therapy suggested action database (P0117), see expert-based content in P0162 the workflow may include interactions with the previously described information system and associated databases and graphical user interfaces, with individual or groups of users.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include updating the therapy regimen associated with the condition of the user based upon the regimen status for the therapy regimen and parameters in the user record of the second database as taught by Brust in to utilize multiple channels and ways of communication, through multiple people, to assist the delivery of the dynamic therapy content mentioned in Brust’s P0043.
Regarding claim 24, Nevo disclose wherein receiving the one or more inputs further comprises transmitting, by the one or more processors, each respective input to a second database configured to store data associated with the user in a database record for the user (Taught as co-occur frequently within databases in P0070.).
Regarding claim 25, Nevo discloses the method of claim 2 mentioned above. However Nevo does not explicitly teach, while Brust teaches: further comprising determining, by the one or more processors, a score indicating a likelihood of success associated with the therapy regimen based upon a frequency of receiving each respective input via the graphical user interface (See collected quality of therapy or quality of exercise as a frequency of receiving each user-generated input in [P0090-P0091] Based on the data collected, and the “Quality” score, a mobile app can coach a patient to perform a physical therapy exercise in a certain way, or provide a suggestion in real-time how to do the exercise more effectively or safely. Also, see trust score, confidence score and predictive score based on past data, patient behavior, monitored events, compliance, and therapy conditions, such predictive scores may be further adaptive to patient input and caregiver therapy considerations mentioned in [P0092].).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include the inputs is a user-generated input received from the user device, and wherein the method further comprises calculating, by the computer, a score indicating a likelihood of success associated with the therapy regimen based upon a frequency of receiving each user-generated input as taught by Brust in to utilize related objective metrics from the performance of specific activities such as quality of motion and volume of activities mentioned in Brust’s P0089.
Regarding claim 26, Brust teaches further comprising transmitting, by the one or more processors, the score indicating the likelihood of success to the second database to update the database record for the user (See P0092, where trust score, confidence score and predictive score based on past data, patient behavior, monitored events, compliance, and therapy conditions from wearable device input and therapy conditions data are all stored among databases (Fig, 22, P0185-P0188).).
Regarding claim 27, Brust teaches wherein the one or more processors updates the score indicating the likelihood of success at a predetermined interval (See Fig. 5B, [P0061] a selection interface 552 to define a time interval (such as 7 days, 14 days, 28 days, etc.) and a time-based graphical display such as a chart 554 that indicates the progress status over the time interval. Also, see P0116.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include updating the score indicating the likelihood of success at a predetermined interval as taught by Brust to achieve goals by providing reward points (e.g., kudos), other incentives, and encouraging content outputs as mentioned in Brust’s P0115.
Regarding claim 28, Nevo discloses receiving, by the one or more processors, one or more body metric inputs containing one or more values, each representing a body metric measurement (See calculated score pertaining to activity level of subject in P0017, P0207.).
Regarding claims 29 and 39, Nevo discloses wherein the computer receives at least one body metric input from a device configured to generate the body metric measurement (See calculated score pertaining to activity level of subject in P0017, P0207.).
Regarding claims 30 and 40, Nevo discloses wherein the device configured to generate the body metric measurement is selected from the group comprising: a smart home device, a wearable device, and a fitness tracker (See P0144, mobile device wearable or mountable.)
Regarding claim 31, Brust teaches receiving, by the one or more processors, via the network one or more coaching inputs from a coach device (See Fig. 1, Fig. 3, therapist/physician 104, provide coaching over networks (P0033, P0035). Also, see Fig. 5C with inputting Bot activity settings (P0062).); and generating, by the one or more processors, a coaching interface configured to display the one or more coaching inputs received from the coach device (See accessing selection of suggestions in P0048, P0169.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include receiving via the network one or more coach inputs from a coach device and generating, by the computer, the user record of the second database to coaching input received from the coach device as taught by Brust prevent any suggestions from being suggested again for a period of time or indefinitely as mentioned in Brust’s P0170.
Regarding claims 32 and 42, Brust teaches further comprising generating, by the one or more processors, a prompt interface at a given time according to the instructions of the therapy regimen, the prompt interface configured to display a field that receives the input from the user via the graphical user interface (See P0062, Bot activity includes prompts and additional prompting in P0104 and P0137.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include receiving via the network one or more coach inputs from a coach device and generating, by the computer, the user record of the second database to coaching input received from the coach device as taught by Brust prevent any suggestions from being suggested again for a period of time or indefinitely as mentioned in Brust’s P0170.
Regarding claim 34, Nevo disclose further configured to transmit each respective input to a second database configured to store data associated with the user in a database record for the user (Taught as co-occur frequently within databases in P0070.).
Regarding claim 35, Nevo discloses the method of claim 2 mentioned above. However Nevo does not explicitly teach, while Brust teaches: to determine a score indicating a likelihood of success associated with the therapy regimen based upon a frequency of receiving each respective input via the graphical user interface (See collected quality of therapy or quality of exercise as a frequency of receiving each user-generated input in [P0090-P0091] Based on the data collected, and the “Quality” score, a mobile app can coach a patient to perform a physical therapy exercise in a certain way, or provide a suggestion in real-time how to do the exercise more effectively or safely. Also, see trust score, confidence score and predictive score based on past data, patient behavior, monitored events, compliance, and therapy conditions, such predictive scores may be further adaptive to patient input and caregiver therapy considerations mentioned in [P0092].).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include the inputs is a user-generated input received from the user device, and wherein the method further comprises calculating, by the computer, a score indicating a likelihood of success associated with the therapy regimen based upon a frequency of receiving each user-generated input as taught by Brust in to utilize related objective metrics from the performance of specific activities such as quality of motion and volume of activities mentioned in Brust’s P0089.
Regarding claim 36, Brust teaches wherein the processor is further configured to transmit the score indicating the likelihood of success to the second database to update the database record for the user (See P0092, where trust score, confidence score and predictive score based on past data, patient behavior, monitored events, compliance, and therapy conditions from wearable device input and therapy conditions data are all stored among databases (Fig, 22, P0185-P0188).).
Regarding claim 37, Brust teaches wherein the processor is configured to update the score indicating the likelihood of success at a predetermined interval (See Fig. 5B, [P0061] a selection interface 552 to define a time interval (such as 7 days, 14 days, 28 days, etc.) and a time-based graphical display such as a chart 554 that indicates the progress status over the time interval. Also, see P0116.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include updating the score indicating the likelihood of success at a predetermined interval as taught by Brust to achieve goals by providing reward points (e.g., kudos), other incentives, and encouraging content outputs as mentioned in Brust’s P0115.
Regarding claim 38, Nevo discloses to receive one or more body metric measurement inputs containing one or more values, each representing a body metric measurement (See calculated score pertaining to activity level of subject in P0017, P0207.).
Regarding claim 41, Brust teaches receive via the network one or more coaching inputs from a coach device (See Fig. 1, Fig. 3, therapist/physician 104, provide coaching over networks (P0033, P0035). Also, see Fig. 5C with inputting Bot activity settings (P0062).); and generate a coaching interface configured to display the one or more coaching inputs received from the coach device (See accessing selection of suggestions in P0048, P0169.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include receiving via the network one or more coach inputs from a coach device and generating, by the computer, the user record of the second database to coaching input received from the coach device as taught by Brust to prevent any suggestions from being suggested again for a period of time or indefinitely as mentioned in Brust’s P0170.
Regarding claims 86 and 93, Brust teaches wherein the condition of the user is a diabetes health condition (See determining diabetes and glucose level in P0078, P0084.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include a user with a diabetes condition as taught by Brust when a user is following a low carbohydrate, low fat, or low calorie diet, a commercial weight loss or diet as mentioned in Brust’s P0128.
Regarding claims 87 and 94, Brust teaches wherein a health score model is trained with and uses data fields relevant to monitoring diabetes (See determining diabetes and glucose level serve as a health score in P0078, P0084.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include a health score model is trained with and uses data fields relevant to monitoring diabetes as taught by Brust when a user is following a special diet as mentioned in Brust’s P0128.
Regarding claims 89 and 96, Brust teaches wherein the condition of the user is type II diabetes (See P0032, P0040 where utilizing medical records allows a user to find a type II diabetes diagnosis in the medical records. See determining diabetes and glucose level serve as a health score in P0078, P0084.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo when the user is type II diabetes as taught by Brust when a user needs to follow a special diet as mentioned in Brust’s P0128.
Regarding claims 90 and 97, Nevo discloses wherein the machine learning model is selected from the group consisting of generalized linear models, random forests, support vector machines, unsupervised and/or supervised clustering, and deep learning, wherein deep learning includes neural networks (See clustering, neural networks and support vector machine in P0027, P0067.).
Regarding claims 91 and 98, Nevo discloses further comprising comparing, by the one or more processors, one or more data fields relevant to the user’s condition against pre-stored milestone parameters or data values at predetermined milestone intervals, wherein the pre-stored milestone values may operate as threshold values (See clustering, neural networks and support vector machine in P0027-P0028, P0067.).
Regarding claims 92 and 99, Nevo discloses wherein there are multiple pre-stored milestone values for multiple data fields, to compare multiple different customer values of different fields against multiple different pre-stored values (Taught as threshold data detected from multiple sensors in P0165-P016, P0226, P0228.).
Regarding claims 114-115, Nevo discloses wherein indicators of interactions are selected from the group consisting of whether a meal plan was followed; whether a therapy regimen task was completed; whether a coaching call was completed (See analysis of food selection in P0073, P0075 and P0081.); whether one or more, or all, ingredients on a shopping list were procured; whether the user consumed a specified amount of water or whether the user consumed water; a number of meal plan meals consumed, a number of tasks completed, a total number of calories burned in one or more activities, a number of coaching calls completed, length of one or more coaching calls, a number or fraction of ingredients procured from a shopping list, a number of hydration events, or a total volume of hydration (See Fig. 5A, P0270, P0199-P0200 Averages Calls Duration and P0131, P0150 Calls logs.).
Regarding claims 100 and 107, Brust teaches wherein the condition of the user is a diabetes health condition (See determining diabetes and glucose level in P0078, P0084.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include a user with a diabetes condition as taught by Brust when a user is following a low carbohydrate, low fat, or low calorie diet, a commercial weight loss or diet as mentioned in Brust’s P0128.
Regarding claims 101 and 108, Brust teaches wherein a health score model is trained with and uses data fields relevant to monitoring diabetes (See determining diabetes and glucose level serve as a health score in P0078, P0084.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo to include a health score model is trained with and uses data fields relevant to monitoring diabetes as taught by Brust when a user is following a special diet as mentioned in Brust’s P0128.
Regarding claims 103 and 110, Brust teaches wherein the condition of the user is type II diabetes (See P0032, P0040 where utilizing medical records allows a user to find a type II diabetes diagnosis in the medical records. See determining diabetes and glucose level serve as a health score in P0078, P0084.).
Therefore, it would have been obvious to one of ordinary skill in the art of adaptive therapy in health monitoring before the effective filing date of the claimed invention to modify the method and system of Nevo when the user is type II diabetes as taught by Brust when a user needs to follow a special diet as mentioned in Brust’s P0128.
Regarding claims 104 and 111, Nevo discloses wherein the machine learning software executes machine learning algorithms and processes selected from the group consisting of generalized linear models, random forests, support vector machines, unsupervised and/or supervised clustering, and deep learning, wherein deep learning includes neural networks (See clustering, neural networks and support vector machine in P0027, P0067.).
Regarding claims 105 and 112, Nevo discloses further comprising comparing, by the computer, one or more data fields relevant to the user’s condition against pre-stored milestone parameters or data values at predetermined milestone intervals, wherein the prestored milestone values may operate as threshold values (See clustering, neural networks and support vector machine in P0027-P0028, P0067.).
Regarding claims 106 and 113, Nevo discloses wherein there are multiple pre-stored milestone values for multiple data fields, to compare multiple different customer values of different fields against multiple different pre-stored values (Taught as threshold data detected from multiple sensors in P0165-P016, P0226, P0228.).
Regarding claims 116-117, Nevo discloses wherein indicators of interactions between the user and certain aspects of the mobile device are selected from the group consisting of whether a meal plan was followed; whether a therapy regimen task was completed; whether a coaching call was completed (See analysis of food selection in P0073, P0075 and P0081.); whether one or more, or all, ingredients on a shopping list were procured; whether the subject consumed a specified amount of water or whether the user consumed water; a number of meal plan meals consumed, a number of tasks completed, a total number of calories burned in one or more activities, a number of coaching calls completed, length of one or more coaching calls, a number or fraction of ingredients procured from a shopping list, a number of hydration events, or a total volume of hydration (See Fig. 5A, P0270, P0199-P0200 Averages Calls Duration and P0131, P0150 Calls logs.).
Claims 88, 95, 102 and 109 are rejected under 35 U.S.C. 103 as being unpatentable over Nevo (US 2015/0313529 A1) in view of Brust (US 2017/0329933 A1) further in view of Madan (US 2014/0052475 A1).
Regarding claims 88 and 95, Madan teaches wherein the data fields are selected from the group consisting of blood glucose, cholesterol, blood pressure, weight, and number of interactions with one or more of the one or more devices and a coach (See P0047 patient’s weight and blood pressure.).
Therefore, it would have been obvious to one of ordinary skill in the art of treatment regimens before the effective filing date of the claimed invention to modify the method and system of Nevo and Brust to include weight and blood pressure as data fields as taught by Madan to customize treatment regimens with patients with similar conditions as mentioned in Madan’s P0032.
Regarding claims 102 and 109, Madan teaches wherein the data fields are selected from the group consisting of blood glucose, cholesterol, blood pressure, weight, and number of interactions with one or more of a user device and a coach (See P0047 patient’s weight and blood pressure.).
Therefore, it would have been obvious to one of ordinary skill in the art of treatment regimens before the effective filing date of the claimed invention to modify the method and system of Nevo and Brust to include weight and blood pressure as data fields as taught by Madan to customize treatment regimens with patients with similar conditions as mentioned in Madan’s P0032.
Response to Arguments
Applicant alleges that amended claim 1 is not directed to any of the enumerated groupings of abstract ideas, not reciting a mental process and that cannot practically be performed in the human mind. see pgs. 17-18 of Remarks – Examiner disagrees.
Generating and monitoring a therapy regimen based on comparing relevant data to the user’s condition against pre-stored milestone parameters or data values at predetermined milestone intervals, when the milestone values may operate as threshold values does not even need a computer to perform. Rather, a human making observations could mentally and interactively perform.
Applicant alleges that amended claim 1 provides many technical improvements and solves several challenges in the fie. see pgs. 19-20 of Remarks – Examiner disagrees.
Using the digital therapies in the form of a chatbot, coach, calculating a health score, training and re-training are technologies for providing therapy regimens are problems that have already been solved, which leave the question, “what technologies are being used to provide therapy regimens that would integrate any purported judicial exception into a practical application? Using machine-readable computer files with machine-executed instructions to generate GUIs and providing the therapy regimen to an application on a user device are tasks that a generic computer would be expected to perform.
Regarding the prior art rejection, Applicant’s arguments have been fully considered, but are now moot in view of the new grounds of rejection. The Examiner has entered a new rejection under 35 USC § 103(a) and applied art already of record.
Conclusion
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/T.S.W./Examiner, Art Unit 3687 03/18/2026
/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687