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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/2/2026 has been entered.
Claims 1, 14, and 19 have been amended. Claim 21 has been added. Claims 1-6, 8-14, and 16-21 are currently pending and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63392572, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
With respect to claims 1 and 21, Application No. 63392572, fails to provide support for the limitations reciting (a) “assigning a clinical parameter to the user via a recurrent neural network according to a subset of the set of aggregate parameters that includes each of the set of aggregate parameters,” (b) “determining a threshold value associated with the clinical parameter from the set of baseline user data,” as recited in lines 27-31 and lines 27-29 and 32-33 respectively.
With respect to element (a), paragraph 42 states that “[t]he predictive model 124 can utilize one or more pattern recognition algorithms, each of which analyze the extracted features or a subset of the extracted features to assign a continuous or categorical clinical parameter to the user,” where examples of continuous parameters include:
“a likelihood that that user has or is about to contract a specific disease or disorder, a likelihood that the user is experiencing the effects of aging, a likelihood that the user is experiencing an onset of dementia, a likelihood that the user has or will develop a neurodegenerative disorder, a likelihood that the user will experience an intensifying of symptoms, or "flare-up," of a chronic condition, a likelihood that the user will use an addictive substance during rehabilitation or treatment, a current or predicted level of pain for the user, an expected performance level of the user associated with a current or future time for a particular activity or occupation, a change in symptoms associated with a disease or disorder, a current or predicted response to treatment, a likelihood that the user has experienced an increase in stress, or an overall wellness level of the user.”
Paragraph 21 also defines a clinical parameter as “any continuous or categorical parameter representing the mental, neurological, physical, cognitive, behavioral, social, and emotional health of a user and can represent any or all of the health, function, a zone or flow state, balance, resilience, homeostasis, disease, and condition of the user.”
Paragraph 43 further provides that “[t]he training process of a given classifier will vary with its implementation, but training generally involves a statistical aggregation of training data into one or more parameters associated with the output class.” No further description of specific training is provided beyond general statements that a model can be trained on data from a user or on data from other users.
Paragraph 47 provides a broad description of recurrent neural networks as a class of algorithm, but does not provide information on how such a model would specifically be trained and used to provide a clinical parameter. Paragraph 55 states that a predictive model can be a recurrent neural network, but then further only provides that “the aggregate parameters can be provided to the predictive model as time series along with other relevant data” and that “[a]n output of the model is an index representing the current level of overall wellness being experienced by the user.”
Examiner notes that Tables I-VII list 53 different potential wellness-relevant parameters, but that no description or examples are provided of any particular wellness-relevant parameters being used as part of a training process or as used with a specific model to determine a specific “clinical parameter.”
The above disclosure is not sufficient to provide written description support for assigning a clinical parameter to the user via a recurrent neural network according to a subset of the set of aggregate parameters and generating a clinical parameter via the predictive model, representing a current state of the user, from a subset of the novel set of aggregate parameters. Merely listing an extensive and diverse list of potential data types and a general statement that a class of algorithm such as a recurrent neural network could be applied is not sufficient to provide support for assigning and generating a particular clinical parameter. This is especially true given that the clinical parameter is disclosed as potentially being “any continuous or categorical parameter representing the mental, neurological, physical, cognitive, behavioral, social, and emotional health of a user and can represent any or all of the health, function, a zone or flow state, balance, resilience, homeostasis, disease, and condition of the user” and encompassing anything from likelihood of a disease to likelihood of using addictive substances to an expected performance level of the user for a particular activity or occupation to “an overall wellness level of the user.” The disclosure provides no effective boundaries on the clinical data used or clinical parameters generated, and only provides a general description of various types of machine learning models, along with statements that such models can be trained with user data. This is not sufficient to satisfy the written description requirement of 35 USC 112(a).
With respect to element (b) above Application No. 63392572 does not provide a disclosure of how a threshold is actually determined using the baseline data.
With regard to claim 14, Application No. 63392572, fails to provide support for the limitations reciting (a) “a recurrent neural network that assigns the clinical parameter to the user according to a subset of the set of aggregate parameters that includes the first aggregate parameter” and (b) “the threshold value being determined from previous clinical parameters assigned to the patient via the recurrent neural network” as recited in lines 25-27 and 30-31.
With respect to element (a), Examiner references the analysis provided above for element (a) in claim 1. Element (a) of claim 14 lacks support in Application No. 63392572 on the same basis provided above for these elements.
With respect to element (b), Examiner notes that Application No. 63392572 does not describe determining the threshold value from previous clinical parameters assigned to the patient via the predictive model. Paragraph 56 states that “the predictive model 124 can include a feedback component 128 can tune various parameters of the predictive model 124 based upon the accuracy of predictions made by the model” and that “[i]n one example, a continuous output of the system can be compared to a threshold value to determine if the user is increase or decrease in wellness related parameters…This threshold can be varied by the feedback model 128 to increase the accuracy of the determination.” However, the threshold being described is not disclosed as a threshold used to provide an intervention when an assigned clinical parameter meets the threshold value, but rather is referencing thresholds used to determine categorical outputs from continuous variables within the model. Regardless, even if this threshold were construed as the threshold recited in claim 14, this disclosure does not provide support for how the threshold is actually determined from the previous clinical parameters.
Application No. 63392572, additionally fails to provide support for one or more limitations in claims 12, 17, and 20 as identified below in the rejection of each claim under 35 USC 112(a) and under the same analysis.
The claims of the present application are therefore not entitled to the priority date of prior-filed Application No. 63392572.
Response to Arguments
A. Applicant's arguments with respect to the claim of priority to prior-filed Application 63/392572 have been fully considered. Applicant’s arguments with respect to claims 3, 7-9, and 11 are persuasive. Applicant’s arguments directed to claims 1, 12, 14, 17, and 20 are not persuasive for the reasons below.
Applicant argues starting on page 9 of the response that Application 63/392,572 provides support for the portions of claims 1 and 14 reciting assigning a clinical parameter to the user via a predictive model according to a subset of the set of aggregate parameters that includes the first aggregate parameter, asserting that “one of skill in the art is quite capable of training a predictive model to generate a clinical parameter given a set of parameters.” Examiner respectfully disagrees.
Applicant acknowledges that the written description requirement is distinct from that of enablement, but asserts that “the knowledge and ability of one skilled in the art is clearly relevant to the written description analysis, as one of skill in the art, seeing the elements necessary to provide the invention, would be expected to understand the basic tools needed to implement the invention given those elements, and expect the inventors to have access to those tools.” Applicant asserts that:
“For example, one of skill in the art, given the framework of FIG. 2 and the list of wellness-relevant parameters would be expected to have the capability to perform an appropriate feature selection algorithm to select a set of features appropriate to a given application, for example, for detecting symptoms associated with a given disorder. This can include so-called filter techniques, such as information gain, correlation coefficients, or Fisher's scores, wrapper methods, such as recursive feature elimination or forward selection, or embedded methods such as L1 regularization or gradient boosting. More relevantly, one of skill in the art would expect the inventors to have the capability to perform feature selection, and thus would not assume, from the large number of potential wellness-relevant parameters listed in the specification, that the inventors were not in possession of the application. No one of skill in the field of machine learning would fail to understand that the inventors were in possession of the invention at the time of filing merely because they did not provide an exact list of specific features for a given implementation.”
Examiner reiterates that whether it would be within the capability of one skilled in the art to devise an algorithm capable of assigning a particular clinical parameter by experimenting with different wellness-relevant parameters or combinations of wellness-relevant parameters is a question of enablement, which is distinct from the requirement that the disclosure provide adequate written description to support possession. Applicant’s argument that one of skill in the art, given the framework of FIG. 2 and the list of wellness-relevant parameters would be expected to have the capability to perform an appropriate feature selection algorithm to select a set of features appropriate to a given application, for example, for detecting symptoms associated with a given disorder speaks directly to enablement. Provisional Application 63/392,572 does not contain any of the listed feature selection algorithms, disclose the use of any such feature selection algorithm, or disclose how such an algorithm might be applied to the specifics of the claimed invention. Applicant’s argument relies on subject matter not present in the disclosure as originally filed, and attempts to import the enablement standard into the requirement for written description. Whether one of skill in the art could devise a way of implementing the claimed invention through experimentation does not establish that one of skill in the art would then assume that Applicant was in possession based on information not present in the original disclosure.
Applicant’s assertion that “one of skill in the art would appreciate that the inventors had a number of options for determining a threshold value associated with the clinical parameter from the set of baseline user data. A number of algorithms exist for unsupervised anomaly detection, which is well-suited for applications that seek determine what values of a given parameter or set of parameters represent a deviation from an established baseline, including one-class support vector machines, elliptic envelope approaches, and histogram-based outlier detection” falls under the same analysis. None of the suggested algorithms are disclosed in Application 63/392,572. Applicant is asserting that one skilled in the art could envision a number of un-disclosed algorithms by which Applicant might have performed the claimed function, and that therefore one skilled in the art would conclude that Applicant possessed some undisclosed manner for performing that function. The disclosure still fails to communicate to one skilled in the art what Applicant was in possession of at the time of filing.
Examiner draws attention again to paragraph 54 listing fourteen diverse potential parameters ranging from heart rate to “metrics of sleep quality” to “body orientation” to “physical activity of the user,” paragraph 55 listing sixteen broad and diverse potential data types ranging from “attention, alertness and fatigue” to “perceptual processing” to “neuro capacity” to “social systems,” and the listing of eleven diverse potential parameters in Table II. Examiner notes that the listed “Exemplary Tests and Methods to Measure Cognitive Parameters” do not necessarily directly produce outputs which can even be weighted, combined, or used in the context of a predictive model such as a recurrent neural network. For example, each of the Iowa Gambling Task, Psychomotor Vigilance Task, and Set Shifting Task produce multiple potential numerical and non-numerical data elements which must be interpreted in the context of the test.
Applicant argues with respect to claim 12 that Application 63/392,572 provides support for assigning the user a predicted value representing a future value of one of the subset of the set of aggregate parameters according to the set of wellness-relevant parameters and at least one previously determined value for the one of the set of aggregate parameters; and assigning the clinical parameter to the user according to at least the predicted value for the one of the subset of the set of aggregate parameters, referencing the arguments presented with respect to claim 1 and stating that “one of skill in the art would understand implementation of the models to be well within the capabilities of the inventors given the framework, wellness-parameters, models, and interventions disclosed at the time of filing.” Examiner respectfully disagrees, and notes that Applicant does not provide further support for this assertion.
Examiner draws special attention to Applicant’s statement that “[r]egarding the need to envision a method for implementing the models, it is respectfully submitted that that the written description is sufficient for Applicant's representative to understand how it might be implemented, one of skill in the art would clearly understand the inventors' possession of the invention from the same disclosure” (emphasis added). Whether Applicant is able to “understand how it might be implemented” is not sufficient to establish written description support absent disclosure of how Applicant actually implemented the claimed elements, and the fact that a hypothetical example must be “envisioned” directly speaks to a lack of written description support.
Applicant argues starting on page 13 with respect to claim 17, that the recited function of adjusting the threshold value associated with the patient according to the collected values for the set of wellness-relevant parameters retraining process “can be performed at least by the reinforcement learning process of claim 9.” Claim 9 recites “wherein retraining the recurrent neural network on the baseline set of user data comprises retraining the recurrent neural network via a reinforcement learning process.” However, a general disclosure of retraining a model “via a reinforcement learning process” is not sufficient to provide written description support given that there is already insufficient support for how the model is trained to produce a threshold value in the first place. The existence of reinforcement learning as a machine learning technique and the recitation of performing retraining via a reinforcement learning process does not provide written description support for how that technique is being applied specifically in the context of Applicant’s claimed invention. Without adequate written description support for how a recurrent neural network is trained to determine a threshold value associated with a specific clinical parameter from baseline data, simply stating that reinforcement learning is then used to adjust the threshold value according to collected values for the set of wellness-relevant parameters from the user over a period of time does itself provide written description support for how that function is performed.
Applicant lastly argues with respect to claim 20 that “one skilled in the art, once instructed to do so, is capable of collecting values for input parameters and collecting outcomes,” and that “[o]nce a training set is generated in this manner, as discussed previously, one of skill in the art is capable of training a machine learning system.” However, Examiner maintains that a general assertion that one skilled in the art would be capable of collecting values for input parameters and collecting outcomes speaks to the enablement requirement rather than the written description requirement. Applicant’s assertion that “adjusting the threshold value associated with the patient according to collected values for the set of wellness-relevant parameters from the user over a period of time” is, again, attempting to import the enablement standard into the requirement for written description. Whether one of skill in the art could devise a way of implementing the claimed invention through experimentation does not establish that one of skill in the art would then assume that Applicant was in possession based on information not present in the original disclosure.
Examiner maintains that the present claims are not entitled to the priority date of prior-filed Application 63/392572.
B. Applicant's arguments with respect to the rejection of claims 1-6, 8-14, and 16-21 under 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues starting on page 12 that the claims “represent a practical application of any abstract idea that may be represented in the claims.” Applicant reasserts that claim 1 is “tied to a particular machine, specifically the photoplethysmography sensor worn by the user,” and cites to MPEP 2106.05 asserting that the photoplethysmography sensor satisfies the three criteria for a “particular machine.” Examiner respectfully disagrees.
Applicant first asserts that the photoplethysmography sensor “is recited with a high degree of specificity, limiting the claim to not just a plethysmographic sensor, but one configured to be wearable, measure heart rate or heart rate variability, and to do so via optical means.”
The specific language of claim 1 involving the photoplethysmographic sensor recites “monitoring the one of heart rate and a heart rate variability of the user at the photoplethysmographic sensor worn by the user to produce a first set of values.”
Paragraph 29 of the specification describes a photoplethysmography (PPG) sensor, but only names it as an example device for measuring heart rate variability with no further description of structure. Examiner notes that a generic photoplethysmographic sensor is worn by a user, can obtain heart rate and heart rate variability values, and collects data “via optical means.” Indeed, Applicant specifies on page 13 that “one of skill in the art would understand that a photoplethysmographic sensor is an optical sensor, specifically a sensor that uses variations in light absorption or reflection to measure changes in blood volume.” Examiner reiterates that the characteristics being argued by Applicant are simply the characteristics of a generic photoplethysmographic sensor, and Applicant is relying on what one of ordinary skill would understand the sensor to encompass simply from its name. Given that the sensor is only recited in terms of its function of collecting the heart rate and heart rate variability data and the description in the specification being limited to statements that a photoplethysmographic sensor may be used to measure heart rate and heart rate variability, Examiner maintains that the photoplethysmographic sensor is only recited at a high level of generality.
Regardless, the photoplethysmographic sensor additionally fails the third element in 2106.05(b)(III), whether the machine’s involvement is extra-solution activity or a field of use. 2106.05(b)(III) explicitly states that “[u]se of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more.” While Applicant points to the consideration in 2106.05(g) of “Whether the limitation amounts to necessary data gathering and outputting, (i.e. all uses of the recited judicial exception require such data gathering or data output)” and argues that other methods of acquiring heart rate and heart rate variability exist, the actual insignificant extra solution activity in this context is the data gathering step of collecting the heart rate and heart rate variability data. Claim 1 requires collecting the heart rate and heart rate variability data, and this step therefore satisfies the third consideration in 2106.05(g).
To reiterate, the use of a photoplethysmographic sensor is mere instructions to apply the exception, not the actual insignificant extra-solution activity within the analysis explained in 2106.05(b)(III) and 2106.05(g). While Applicant states that “[a]ccordingly, the recited photoplethysmographic sensor does not represent necessary data gathering for any steps in the claim identified in the Office Action as judicial exceptions,” the photoplethysmographic sensor itself is not data gathering. The collection of the heart rate and heart rate variability data is, and 2106.05(b)(III) is unambiguous in its language setting out that a machine used merely in such a data-gathering step is not sufficient to integrate a judicial exception into a practical application or provide significantly more.
Lastly, and with respect to Applicant’s request “that Examiner identify the case law or MPEP section that generalizes the factor concerning "mere instructions to implement the abstract idea on a computer" to any particular machine,” Examiner draws Applicant’s attention to 2106.05(f)(2), which repeatedly specifies that the “mere instructions to apply” criterion applies to “computers or other machinery.” For example, the heading of consideration (2) is “Whether the claim invokes computers or other machinery merely as a tool to perform an existing process,” the first sentence of which providing that “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data)… does not integrate a judicial exception into a practical application or provide significantly more.” This section goes on to cite TLI Communications as providing “an example of a claim invoking computers and other machinery merely as a tool to perform an existing process,” where the machinery in question included a telephone unit being used in its ordinary capacity. While generic computing components are a common example provided of limitations which are merely instructions to apply a judicial exception, 2106.05(f) never states that this consideration is limited to generic computers and provides examples and case law to the contrary.
The rejection under 35 USC 101 is maintained.
C. Applicant's arguments with respect to the rejection of claims 1-6, 8-14, and 16-21 under 35 USC 112(a) have been fully considered but are not persuasive.
Applicant argues starting on page 15 of the response that the present disclosure provides support for the portions of claims 1 and 14 reciting assigning a clinical parameter to the user via a predictive model according to a subset of the set of aggregate parameters that includes the first aggregate parameter, asserting that “one of skill in the art is quite capable of training a predictive model to generate a clinical parameter given a set of parameters.” Examiner respectfully disagrees.
Applicant acknowledges that the written description requirement is distinct from that of enablement, but asserts that “the knowledge and ability of one skilled in the art is clearly relevant to the written description analysis, as one of skill in the art, seeing the elements necessary to provide the invention, would be expected to understand the basic tools needed to implement the invention given those elements, and expect the inventors to have access to those tools.” Applicant asserts that:
“For example, one of skill in the art, given the framework of FIG. 2 and the list of wellness-relevant parameters would be expected to have the capability to perform an appropriate feature selection algorithm to select a set of features appropriate to a given application, for example, for detecting symptoms associated with a given disorder. This can include so-called filter techniques, such as information gain, correlation coefficients, or Fisher's scores, wrapper methods, such as recursive feature elimination or forward selection, or embedded methods such as L1 regularization or gradient boosting. More relevantly, one of skill in the art would expect the inventors to have the capability to perform feature selection, and thus would not assume, from the large number of potential wellness-relevant parameters listed in the specification, that the inventors were not in possession of the application. No one of skill in the field of machine learning would fail to understand that the inventors were in possession of the invention at the time of filing merely because they did not provide an exact list of specific features for a given implementation.”
Examiner reiterates that whether it would be within the capability of one skilled in the art to devise an algorithm capable of assigning a particular clinical parameter by experimenting with different wellness-relevant parameters or combinations of wellness-relevant parameters is a question of enablement, which is distinct from the requirement that the disclosure provide adequate written description to support possession. Applicant’s argument that one of skill in the art, given the framework of FIG. 2 and the list of wellness-relevant parameters would be expected to have the capability to perform an appropriate feature selection algorithm to select a set of features appropriate to a given application, for example, for detecting symptoms associated with a given disorder speaks directly to enablement. The present disclosure does not contain any of the listed feature selection algorithms, disclose the use of any such feature selection algorithm, or disclose how such an algorithm might be applied to the specifics of the claimed invention. Applicant’s argument relies on subject matter not present in the disclosure as originally filed, and attempts to import the enablement standard into the requirement for written description. Whether one of skill in the art could devise a way of implementing the claimed invention through experimentation does not establish that one of skill in the art would then assume that Applicant was in possession based on information not present in the original disclosure.
Applicant’s assertion that “one of skill in the art would appreciate that the inventors had a number of options for determining a threshold value associated with the clinical parameter from the set of baseline user data. A number of algorithms exist for unsupervised anomaly detection, which is well-suited for applications that seek determine what values of a given parameter or set of parameters represent a deviation from an established baseline, including one-class support vector machines, elliptic envelope approaches, and histogram-based outlier detection” falls under the same analysis. None of the suggested algorithms are provided in the disclosure. Applicant is asserting that one skilled in the art could envision a number of un-disclosed algorithms by which Applicant might have performed the claimed function, and that therefore one skilled in the art would conclude that Applicant possessed some undisclosed manner for performing that function. The disclosure still fails to communicate to one skilled in the art what Applicant was in possession of at the time of filing.
Examiner draws attention again to paragraph 60 listing fourteen diverse potential parameters ranging from heart rate to “metrics of sleep quality” to “body orientation” to “physical activity of the user,” paragraph 61 listing sixteen broad and diverse potential data types ranging from “attention, alertness and fatigue” to “perceptual processing” to “neuro capacity” to “social systems,” and the listing in Table II of eleven diverse potential parameters. Examiner notes that the listed “Exemplary Tests and Methods to Measure Cognitive Parameters” do not necessarily directly produce outputs which can be weighted, combined, or used in the context of a predictive model such as a recurrent neural network. For example, each of the Iowa Gambling Task, Psychomotor Vigilance Task, and Set Shifting Task produce multiple potential numerical and non-numerical data elements which must be interpreted in the context of the test.
Applicant argues with respect to claim 12 that the disclosure provides support for assigning the user a predicted value representing a future value of one of the subset of the set of aggregate parameters according to the set of wellness-relevant parameters and at least one previously determined value for the one of the set of aggregate parameters; and assigning the clinical parameter to the user according to at least the predicted value for the one of the subset of the set of aggregate parameters, referencing the arguments presented with respect to claim 1 and stating that “one of skill in the art would understand implementation of the models to be well within the capabilities of the inventors given the framework, wellness-parameters, models, and interventions disclosed at the time of filing.” Examiner respectfully disagrees, and notes that Applicant does not provide further support for this assertion.
Examiner draws special attention to Applicant’s statement that “[r]egarding the need to envision a method for implementing the models, it is respectfully submitted that that the written description is sufficient for Applicant's representative to understand how it might be implemented, one of skill in the art would clearly understand the inventors' possession of the invention from the same disclosure” (emphasis added). Whether Applicant is able to “understand how it might be implemented” is not sufficient to establish written description support absent disclosure of how Applicant actually implemented the claimed elements, and the fact that a hypothetical example must be “envisioned” directly speaks to a lack of written description support.
Applicant argues starting on page 17 with respect to claim 17, that the recited function of adjusting the threshold value associated with the patient according to the collected values for the set of wellness-relevant parameters retraining process “can be performed at least by the reinforcement learning process of claim 9.” Claim 9 recites “wherein retraining the recurrent neural network on the baseline set of user data comprises retraining the recurrent neural network via a reinforcement learning process.” However, a general disclosure of retraining a model “via a reinforcement learning process” is not sufficient to provide written description support given that there is already insufficient support for how the model is trained to produce a threshold value in the first place. The existence of reinforcement learning as a machine learning technique and the recitation of performing retraining via a reinforcement learning process does not provide written description support for how that technique is being applied specifically in the context of Applicant’s claimed invention. Without adequate written description support for how a recurrent neural network is trained to determine a threshold value associated with a specific clinical parameter from baseline data, simply stating that reinforcement learning is then used to adjust the threshold value according to collected values for the set of wellness-relevant parameters from the user over a period of time does itself provide written description support for how that function is performed.
Applicant lastly argues with respect to claim 20 that “one skilled in the art, once instructed to do so, is capable of collecting values for input parameters and collecting outcomes,” and that “[o]nce a training set is generated in this manner, as discussed previously, one of skill in the art is capable of training a machine learning system.” However, Examiner maintains that a general assertion that one skilled in the art would be capable of collecting values for input parameters and collecting outcomes speaks to the enablement requirement rather than the written description requirement. Applicant’s assertion that “adjusting the threshold value associated with the patient according to collected values for the set of wellness-relevant parameters from the user over a period of time” is, again, attempting to import the enablement standard into the requirement for written description. Whether one of skill in the art could devise a way of implementing the claimed invention through experimentation does not establish that one of skill in the art would then assume that Applicant was in possession based on information not present in the original disclosure.
The rejection under 35 USC 112(a) is maintained.
D. Applicant’s statements regarding the rejection of claims 1-6, 8-14, and 16-21 under 35 USC 103 and the submitted declaration have been fully considered but are not persuasive.
Applicant notes that the D’Haese reference was published on September 30, 2021, and asserts that the exception under 35 USC 102(b) applies based on the submitted declaration. However, the current application is not entitled to the priority date of Provisional Application 63/392572 as set out above. The D’Haese reference therefore lies outside of the 1-year period provided in 35 USC 102(b)(1) from the present application’s effective filing date of 7/27/2023.
The rejection under 35 USC 103 is maintained.
Claim Objections
The previous objection to claims 1 and 14 is withdrawn based on the amendment filed 1/2/2026.
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 1-6, 8-14, and 16-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-6 and 8-13 are drawn to a method, claims 14 and 16-20 are drawn to a system, and claim 21 is drawn to a method, each of which is within the four statutory categories.
Step 2A(1)
Claim 1 recites, in part, performing the steps of
establishing a set of baseline user data that includes a time series of values representing one of a heart rate and a heart rate variability over a time period, a second plurality of wellness-relevant parameters representing the user, and a third plurality of wellness-relevant parameters representing the user retrieved from health records of the user;
monitoring the one of heart rate and a heart rate variability of the user to produce a first set of values over a defined period;
obtaining the second plurality of wellness-relevant parameters representing the user over the defined period to provide a second set of values;
retrieving the third plurality of wellness-relevant parameters representing the user from health records of the user over the defined period to provide a third set of values, the first set of values, the second set of values, and the third set of values collectively forming a set of wellness-relevant parameters;
generating a set of aggregate parameters from the set of wellness-relevant parameters, each of the set of aggregate parameters comprising a unique proper subset of the set of wellness-relevant parameters, wherein the set of aggregate parameters includes a first aggregate parameter representing autonomic function, the proper subset of the set of wellness-relevant parameters associated with the first aggregate parameter including the one of heart rate and a heart rate variability; and
assigning a clinical parameter to the user via a model according to a subset of the set of aggregate parameters that includes the first aggregate parameter;
determining a threshold value associated with the clinical parameter from the set of baseline user data; and
providing an intervention to the user if the clinical parameter meets the determined threshold.
The above steps constitute a form of managing personal behavior or relationships or interactions between people, and therefore fall within the scope of an abstract idea in the form of a method of organizing human activity. Fundamentally, the process is that of aggregating wellness parameters for a user, such as from monitoring data and health records, and using the aggregated parameters to generate a clinical parameter and provide an intervention to the user based on the clinical parameter and an associated threshold. Consolidating and analyzing a variety of clinical data from a patient in this manner to determine whether to provide an intervention is performed by clinicians as part of patient monitoring and intervention activities, and is a form of managing the interactions and relationship between the patient and clinician. These functions are also a form of managing the behavior of the patient themselves based on the provided intervention.
Claim 14 recites, in part, performing the steps of:
monitoring a first plurality of wellness-relevant parameters representing the user over a defined period, the first plurality of wellness-related parameters include one of a heart rate and a heart rate variability;
obtaining a second plurality of wellness-relevant parameters representing the user;
retrieving a third plurality of wellness-relevant parameters representing the user from health records, the first plurality of wellness-relevant parameters, the second plurality of wellness-relevant parameters, and the third plurality of wellness-relevant parameters collectively forming a set of wellness-relevant parameters;
generating a set of aggregate parameters from the set of wellness-relevant parameters, each of the set of aggregate parameters comprising a unique proper subset of the set of wellness-relevant parameters, the set of aggregate parameters including a first aggregate parameter representing autonomic function, the proper subset of the set of wellness-relevant parameters associated with the first aggregate parameter including the one of heart rate and a heart rate variability;
using a model to assign the clinical parameter to the user according to a subset of the set of aggregate parameters that includes each of the set of aggregate parameters; and
providing an intervention for the user when assigned clinical parameter meets a threshold value associated with the patient, the threshold value being determined from previous clinical parameters assigned to the patient via the model.
The above steps constitute a form of managing personal behavior or relationships or interactions between people, and therefore fall within the scope of an abstract idea in the form of a method of organizing human activity. Fundamentally, the process is that of aggregating wellness parameters for a user, such as from monitoring data and health records, and using the aggregated parameters to generate a clinical parameter and provide an intervention to the user based on the clinical parameter and an associated threshold. Consolidating and analyzing a variety of clinical data from a patient in this manner to determine whether to provide an intervention is performed by clinicians as part of patient monitoring and intervention activities, and is a form of managing the interactions and relationship between the patient and clinician. These functions are also a form of managing the behavior of the patient themselves based on the provided intervention.
Claim 21 recites, in part, performing the steps of:
establishing a set of baseline user data that includes a time series of values representing one of a heart rate and a heart rate variability over a time period, a second plurality of wellness-relevant parameters representing the user, and a third plurality of wellness-relevant parameters representing the user retrieved from health records of the user;
monitoring the one of heart rate and a heart rate variability of the user to produce a first set of values over a defined period;
obtaining the second plurality of wellness-relevant parameters representing the user over the defined period to provide a second set of values;
retrieving the third plurality of wellness-relevant parameters representing the user from health records of the user over the defined period to provide a third set of values, the first set of values, the second set of values, and the third set of values collectively forming a set of wellness-relevant parameters;
generating a set of aggregate parameters from the set of wellness-relevant parameters, each of the set of aggregate parameters comprising a unique proper subset of the set of wellness-relevant parameters, wherein the set of aggregate parameters includes a first aggregate parameter representing autonomic function, the proper subset of the set of wellness-relevant parameters associated with the first aggregate parameter including the one of heart rate and a heart rate variability; and
assigning a clinical parameter to the user via a model according to a subset of the set of aggregate parameters that includes each of the set of aggregate parameters;
collecting data from a plurality of other patients before establishing the baseline set of user data;
determining a threshold value associated with the clinical parameter from the set of baseline user data; and
providing an intervention to the user if the clinical parameter meets the determined threshold.
The above steps constitute a form of managing personal behavior or relationships or interactions between people, and therefore fall within the scope of an abstract idea in the form of a method of organizing human activity. Fundamentally, the process is that of aggregating wellness parameters for a user, such as from monitoring data and health records, and using the aggregated parameters to generate a clinical parameter and provide an intervention to the user based on the clinical parameter and an associated threshold. Consolidating and analyzing a variety of clinical data from a patient in this manner to determine whether to provide an intervention is performed by clinicians as part of patient monitoring and intervention activities, and is a form of managing the interactions and relationship between the patient and clinician. These functions are also a form of managing the behavior of the patient themselves based on the provided intervention.
Step 2A(2)
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
Claim 1 recites additional elements of a) a photoplethysmography sensor worn by the user used to obtain the time series of values representing the heart rate or heart rate variability, b) a portable computing device used to obtain the second plurality of wellness-relevant parameters, c) an electronic health records system used to provide the third plurality of wellness-relevant parameters, and d) a recurrent neural network recited as the model used to assign the clinical parameter.
Claim 14 recites additional elements of a) a photoplethysmography sensor worn by the user used to monitor the time series of values representing the heart rate or heart rate variability, b) a portable computing device used to obtain the second plurality of wellness-relevant parameters, c) a server comprising a processor and a non-transitory computer readable medium recited as storing and executing instructions to perform the subsequent steps, d) a network interface used to retrieve the third plurality of wellness-relevant parameters, e) an electronic health records system used to provide the third plurality of wellness-relevant parameters, f) a feature aggregator used to generate the set of aggregate parameters from the set of wellness-relevant parameters, g) a recurrent neural network recited as the model used to assign the clinical parameter and as trained on the data collected from a plurality of other patients before establishing the baseline set of user data and then retrained on the baseline set of user data, and h) an intervention selector used to provide the intervention.
Claim 21 recites additional elements of a) a photoplethysmography sensor worn by the user used to obtain the time series of values representing the heart rate or heart rate variability, b) a portable computing device used to obtain the second plurality of wellness-relevant parameters, c) an electronic health records system used to provide the third plurality of wellness-relevant parameters, and d) a recurrent neural network recited as the model used to assign the clinical parameter and as trained on the data collected from a plurality of other patients before establishing the baseline set of user data and then retrained on the baseline set of user data.
Paragraph 29 of the specification states that a photoplethysmography (PPG) sensor can be used to measure heart rate variability while Table I lists a photoplethysmogram as a method to measure heart rate. However, no further disclosure is provided of the photoplethysmography sensor itself. The photoplethysmographic sensor is therefore given its broadest reasonable construction as a generic PPG sensor.
Paragraph 23 states that “[a] "portable computing device," as used herein, is a computing device that can carried by the user, such as a smartphone, smart watch, tablet, notebook, and laptop, that can measure a wellness-relevant parameter either through sensors on the device or via interaction with the user.” The portable computing device is therefore construed as encompassing a generic computing device.
Paragraphs 26 and 56 describe an electronic health records system as an EMR, and only in terms of its function as a source of the third plurality of wellness-relevant parameters. The electronic health records system is therefore construed as encompassing a generic electronic health records system.
Paragraphs 26, 38, and 44 describe a network interface only in terms of its function of retrieving wellness-relevant parameters from an EHR system. The network interface is therefore construed as encompassing a generic computer network interface.
Paragraphs 26 and 39 describe a server as a remote server used to analyze the collected data. Paragraphs 72-75 and 80-82 describe the system including the server, processing devices such as ASICs and microprocessors, and computer readable media such as compact disks, RAM, ROM, flash memory, and others. The server, non-transitory computer readable medium, and processor are therefore each construed as encompassing generic types of computer hardware.
Paragraphs 38 and 44 describe a feature aggregator only in terms of its function of aggregating extracted features to provide aggregate parameters, while Figure 1 shows a labeled feature aggregator element within the server. The feature aggregator is therefore construed as encompassing software.
Paragraph 53 provides the only description of an intervention selector, and only describes this element in terms of its function of providing an intervention to the user such as by communicating with the user via the network interface, while Figure 1 shows a labeled intervention selector element within the server. The intervention selector is therefore construed as encompassing software.
Paragraph 47 states that “[t]he training process of a given classifier will vary with its implementation, but training generally involves a statistical aggregation of training data into one or more parameters associated with the output class. Paragraph 67 provides that “[i]n one example, training the predictive model is initially trained on data collected from other users while values for the subset of the set of wellness-relevant parameters are collected from the user over a period of time” and that “[o]nce a sufficient amount of data is available for the user, the predictive model is retrained on the collected values for the subset of the set of wellness-relevant parameters.” Paragraph 64 only further states that a predictive model can be “for example, implemented as a recurrent neural network,” along with a general description that the aggregate parameters can be provided as input to the model and that an index representing the current level of overall wellness being experienced by the user can be collected as an output. The recurrent neural network is therefore given its broadest reasonable interpretation as a generic form of recurrent neural network, while the training and retraining processes encompass any manner of training and retraining the algorithm.
Each of the above elements only amounts to mere instructions to implement the abstract idea using computing elements as tools. Each of the photoplethysmography sensor, portable computing device, and electronic health records system is only recited at a high level of generality as used to supply wellness-relevant parameters, while the feature aggregator and intervention selector are likewise only recited at a high level of generality as implementing their respective data processing functions. The recurrent neural network is likewise only recited at a high level of generality as used to assign the clinical parameter and as being trained using the data from the plurality of patients and then retrained using the baseline data. These elements are not sufficient to integrate the abstract idea into a practical application.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claims 1, 14, and 21 only recite the photoplethysmography sensor, portable computing device, network interface, electronic health records system, server, processor, non-transitory computer readable medium, feature aggregator, intervention selector, and recurrent neural network as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f)
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Depending Claims
Claim 2 recites wherein providing an intervention to the user comprises providing one of the set of wellness-relevant parameters, the set of aggregate parameters, and the clinical parameter to one of a medical professional, a caregiver, a therapist, a peer advisor, and a coach. These limitations fall within the scope of the abstract idea as set out above.
Claim 3 recites wherein the subset of the set of aggregate parameters comprises each of the set of aggregate parameters. These limitations fall within the scope of the abstract idea as set out above.
Claim 4 recites wherein the providing the intervention to the user comprises reporting the clinical parameter to one of a health care provider, an insurance company, a care team, a research team, a coach of the user, and a workplace of the user. These limitations fall within the scope of the abstract idea as set out above.
Claim 4 recites the additional element of a network interface as used to report the clinical parameter.
As cited above, paragraphs 26, 38, and 44 describe a network interface only in terms of its function of retrieving wellness-relevant parameters from an EHR system. The network interface is therefore construed as encompassing a generic computer network interface.
The recited network interface only amounts to mere instructions to implement the abstract idea using computing elements as tools. Specifically, the network interface is only recited at a high level of generality as used to implement the function of reporting the clinical parameter to one of the subsequently recited entities. This element is not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 5 recites wherein the providing the intervention to the user comprises transmitting a message to the user that guides the user through a stress reduction technique. These limitations fall within the scope of the abstract idea as set out above.
Claim 6 recites providing the intervention to the user. This limitation falls within the scope of the abstract idea as set out above.
Claim 6 recites the additional element of the portable computing device as used to provide the intervention to the user.
As cited above, paragraph 23 states that “[a] "portable computing device," as used herein, is a computing device that can carried by the user, such as a smartphone, smart watch, tablet, notebook, and laptop, that can measure a wellness-relevant parameter either through sensors on the device or via interaction with the user.” Paragraph 62 further states that a message can be transmitted to the user’s portable computing device suggesting a course of action for the user. The portable computing device is therefore construed as encompassing a generic computing device.
The recited portable computing device only amounts to mere instructions to implement the abstract idea using computing elements as tools. Specifically, the portable computing device is only recited at a high level of generality as used to implement the function of providing the intervention to the user. This element is not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 8 recites wherein the user is a first user of a plurality of users and collecting data from a plurality of other patients before establishing the baseline set of user data. These limitations fall within the scope of the abstract idea as set out above.
Claim 8 further recites the additional elements of training the recurrent neural network on the data collected from the plurality of users before establishing the baseline set of user data, and retraining the model on the baseline set of user data.
Paragraph 47 states that “[t]he training process of a given classifier will vary with its implementation, but training generally involves a statistical aggregation of training data into one or more parameters associated with the output class.” Paragraph 67 provides that “[i]n one example, training the predictive model is initially trained on data collected from other users while values for the subset of the set of wellness-relevant parameters are collected from the user over a period of time” and that “[o]nce a sufficient amount of data is available for the user, the predictive model is retrained on the collected values for the subset of the set of wellness-relevant parameters.” Paragraph 64 states that a predictive model can be “for example, implemented as a recurrent neural network,” along with a general description that the aggregate parameters can be provided as input to the model and that an index representing the current level of overall wellness being experienced by the user can be collected as an output. However, no further disclosure is provided of the training process beyond this broad description of training and retraining the model.
Each of the above elements only amounts to mere instructions to implement the abstract idea using computing elements as tools. The training of the recurrent neural network itself is only recited at a high level of generality as performed using data from the plurality of users previous to the current user, with the retraining likewise only being recited at a high level of generality as using the baseline set of user data. The training and retraining are also only disclosed broadly as being performed using these types of data and without specific reference to the model being a recurrent neural network. These elements are not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 9 recites the additional element of wherein retraining the recurrent neural network on the subset of wellness-relevant parameters comprises retraining the recurrent neural network via a reinforcement learning process.
Paragraph 64 states that a predictive model can be “for example, implemented as a recurrent neural network,” along with a general description that the aggregate parameters can be provided as input to the model and that an index representing the current level of overall wellness being experienced by the user can be collected as an output.
Paragraph 58 on page 20 of the specification states that “a reinforcement learning approach can be used to adjust the model parameters based on the accuracy of either predicted future values of wellness-relevant parameters at intermediate stages of the predictive model 124 or the output of the predictive model. Paragraph 60 on page 24 similarly provides that “the self-reported or measured value can be compared to the value assigned to the user via a predictive model, and a parameter associated with the predictive model can be changed according to the comparison” and that “this can be accomplished by generating a reward for a reinforcement learning process based on a similarity of the measured outcome to the value assigned to the user and changing the parameter via the reinforcement learning process.”
The use of a reinforcement learning process to retrain the recurrent neural network only amounts to mere instructions to implement functions within the abstract idea using computing elements as tools. Specifically, the claim only recites the reinforcement learning process at a high level of generality, i.e. that the recurrent neural network is retrained “via a reinforcement learning process,” and the disclosure likewise only describes the process at a high level of generality. This element is not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 10 recites wherein the set of aggregate parameters further comprises a second aggregate parameter representing a cognitive function of the user, and a third aggregate parameter representing a motor and musculoskeletal health of the user. These limitations fall within the scope of the abstract idea as set out above.
Claim 11 recites wherein generating the set of aggregate parameters from the set of wellness-relevant parameters comprises generating a time series for one of the set of aggregate parameters and assigning the clinical parameter to the user via the model comprises performing a wavelet decomposition on the time series of the one of the set of aggregate parameters to provide a set of wavelet coefficients, and assigning the clinical parameter according to at least a set of wavelet coefficients and the subset of the set of aggregate parameters. These limitations fall within the scope of the abstract idea as set out above.
Claim 11 further recites the additional element of a recurrent neural network used as the model to assign the clinical parameter.
Paragraph 51 provides a broad description of recurrent neural networks as a class of algorithm. Paragraph 64 only further states that a predictive model can be “for example, implemented as a recurrent neural network,” along with a general description that the aggregate parameters can be provided as input to the model and that an index representing the current level of overall wellness being experienced by the user can be collected as an output. The recurrent neural network is therefore given its broadest reasonable interpretation as a generic form of recurrent neural network.
The recited recurrent neural network only amounts to mere instructions to implement the abstract idea using computing elements as tools given that the recurrent neural network is only recited at a high level of generality as used to assign the clinical parameter. This element is not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 12 recites wherein assigning the clinical parameter to the user via the model comprises: assigning the user a predicted value representing a future value of one of the subset of the set of aggregate parameters according to the set of wellness-relevant parameters and at least one previously determined value for the one of the set of aggregate parameters; and assigning the clinical parameter to the user according to at least the predicted value for the one of the subset of the set of aggregate parameters. These limitations fall within the scope of the abstract idea as set out above.
Claim 12 further recites the additional element of a recurrent neural network used as the model to assign the clinical parameter.
Paragraph 51 provides a broad description of recurrent neural networks as a class of algorithm. Paragraph 64 only further states that a predictive model can be “for example, implemented as a recurrent neural network,” along with a general description that the aggregate parameters can be provided as input to the model and that an index representing the current level of overall wellness being experienced by the user can be collected as an output. The recurrent neural network is therefore given its broadest reasonable interpretation as a generic form of recurrent neural network.
The recited recurrent neural network only amounts to mere instructions to implement the abstract idea using computing elements as tools given that the recurrent neural network is only recited at a high level of generality as used to assign the clinical parameter. This element is not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 13 recites wherein the set of aggregate parameters includes at least a first aggregate parameter representing sleep and circadian rhythms of the user, a second aggregate parameter representing a socio-behavioral function of the user, and a third aggregate parameter representing a biomarkers and genomics of the user. These limitations fall within the scope of the abstract idea as set out above.
Claim 16 recites wherein at least one of the second plurality of wellness-relevant parameters is derived from psychosocial assessment data provided by the user. These limitations fall within the scope of the abstract idea as set out above.
Claim 16 recites the additional element of a portable computing device comprising a user interface as used to allow the user to interact with a psychosocial assessment application.
As cited above, paragraph 23 states that “[a] "portable computing device," as used herein, is a computing device that can carried by the user, such as a smartphone, smart watch, tablet, notebook, and laptop, that can measure a wellness-relevant parameter either through sensors on the device or via interaction with the user.” Paragraphs 31 and 60 further state that a smartphone, tablet, or smart watch can be used to measure cognitive parameters via an application, such as via “a graphical user interface 164 that allows a user to interact with one or more data gathering applications.” The portable computing device, user interface, and application are each therefore construed as encompassing generic computing elements such as generic user interfaces and software for interacting with the user.
The portable computing device, user interface, and application only amount to mere instructions to implement the abstract idea using computing elements as tools. Each of these elements is only recited at a high level of generality as used to implement the function of allowing the user to interact with a psychosocial assessment application. These elements are not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 17 recites collecting values for the set of wellness-relevant parameters from the user over a period of time and adjust the threshold value associated with the patient according to the collected values for the set of wellness-relevant parameters. These limitations fall within the scope of the abstract idea as set out above.
Claim 17 recites the additional elements of a) machine-readable instructions executable by the processor recited as used to perform the subsequent data processing functions, and b) a feedback component used to collect the values for the set of wellness-relevant parameters from the user over a period of time and adjust the threshold value.
Paragraphs 72-75 and 80-82 describe the system including processing devices such as ASICs and microprocessors as well as computer readable media such as compact disks, RAM, ROM, flash memory, and others storing executable instructions. The instructions and processor are therefore each construed as encompassing generic types of software and computer hardware.
Paragraph 56 states that “the predictive model 124 can include a feedback component 128 can tune various parameters of the predictive model 124 based upon the accuracy of predictions made by the model.” No further detail or structure of the feedback component is provided.
The above elements only amount to mere instructions to implement the abstract idea using computing elements as tools. Specifically, the machine-readable instructions and processor are only recited at a high level of generality as providing the subsequent functions, and the feedback component is only recited at a high level of generality as used to collect the values and adjust the threshold. These elements are not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 18 recites wherein the set of aggregate parameters comprises the first aggregate parameter representing autonomic function of the user, a second aggregate parameter representing a cognitive function of the user, a third aggregate parameter representing a motor and musculoskeletal health of the user, a fourth aggregate parameter representing sleep disruptions and general disruptions of circadian rhythm, a fifth aggregate parameter representing relevant biomarkers and generic information identified for the user, a sixth aggregate parameter representing sensory function and changes in function for the user, and a seventh parameter representing a socio-behavioral health of the user. These limitations fall within the scope of the abstract idea as set out above.
Claim 19 recites performing a wavelet decomposition on a time series of values for the one of the set of wellness-relevant parameters to provide a set of wavelet coefficients, and generating the set of aggregate parameters from according to at least the set of wavelet coefficients and the set of wellness-relevant parameters. These limitations fall within the scope of the abstract idea as set out above.
Claim 19 recites the additional elements of a) machine-readable instructions executable by the processor recited as used to perform the subsequent data processing functions, b) a feature extractor used to perform the wavelet decomposition, and c) a feature aggregator used to generate the set of aggregate parameters.
Paragraphs 72-75 and 80-82 describe the system including processing devices such as ASICs and microprocessors as well as computer readable media such as compact disks, RAM, ROM, flash memory, and others storing executable instructions. The instructions and processor are therefore each construed as encompassing generic types of software and computer hardware.
Paragraphs 38, 39, and 41 describe a feature extractor in terms of its functions of extracting parameters and performing a wavelet transform. No further structure is explicitly provided for the feature extractor. For purposes of the present analysis and to expedite prosecution, the feature extractor is therefore construed as encompassing a generic computing element.
Paragraphs 38 and 44 describe a feature aggregator only in terms of its function of aggregating extracted features to provide aggregate parameters. No further structure is explicitly provided for the feature aggregator. For purposes of the present analysis and to expedite prosecution, the feature aggregator is therefore construed as encompassing a generic computing element.
The above elements only amount to mere instructions to implement the abstract idea using computing elements as tools. Specifically, the machine-readable instructions and processor are only recited at a high level of generality as providing the subsequent functions while the feature extractor and feature aggregator are only recited at a high level of generality as performing corresponding data analysis functions. These elements are not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 20 recites collecting values for the set of wellness-relevant parameters from the user over a period of time and collecting values representing an outcome for the user. These limitations fall within the scope of the abstract idea as set out above.
Claim 20 recites the additional elements of a) machine-readable instructions executable by the processor recited as used to perform the subsequent data processing functions, b) a feedback component used to perform the subsequently recited data analysis functions of collecting the values for the set of wellness-relevant parameters and values representing an outcome for the patient, and c) a recurrent neural network used as the model and being retrained on the collected values for the set of wellness-relevant parameters and the values representing the outcome for the user.
Paragraphs 72-75 and 80-82 describe the system including processing devices such as ASICs and microprocessors as well as computer readable media such as compact disks, RAM, ROM, flash memory, and others storing executable instructions. The instructions and processor are therefore each construed as encompassing generic types of software and computer hardware.
Paragraph 56 states that “the predictive model 124 can include a feedback component 128 can tune various parameters of the predictive model 124 based upon the accuracy of predictions made by the model.”
Paragraph 47 states that “[t]he training process of a given classifier will vary with its implementation, but training generally involves a statistical aggregation of training data into one or more parameters associated with the output class. Paragraph 67 provides that “[i]n one example, training the predictive model is initially trained on data collected from other users while values for the subset of the set of wellness-relevant parameters are collected from the user over a period of time” and that “[o]nce a sufficient amount of data is available for the user, the predictive model is retrained on the collected values for the subset of the set of wellness-relevant parameters.” Paragraph 64 states that a predictive model can be “for example, implemented as a recurrent neural network,” along with a general description that the aggregate parameters can be provided as input to the model and that an index representing the current level of overall wellness being experienced by the user can be collected as an output. However, no further disclosure is provided of the training process beyond this broad description of training and retraining the model.
The above elements only amount to mere instructions to implement the abstract idea using computing elements as tools. The machine-readable instructions and processor are only recited at a high level of generality as providing the subsequent functions while the feedback component is only recited at a high level of generality as performing the subsequently recited data analysis functions. The recurrent neural network itself is only recited at a high level of generality and disclosed broadly, and the retraining process itself is likewise both recited at a high level of generality as using the collected types of data and only disclosed broadly. These elements are not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claims 1-6, 8-14, and 16-21 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-6, 8-14, and 16-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
In order to satisfy the written description requirement, the specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See MPEP 2161.01(I). However, generic claim language in the original disclosure does not satisfy the written description requirement if it fails to support the scope of the genus claimed, and even original claims may fail to satisfy the written description requirement when the invention is claimed and described in functional language but the specification does not sufficiently identify how the invention achieves the claimed function. See MPEP 2161.01(I) citing in part Ariad, 598 F.3d at 1349 ("[A]n adequate written description of a claimed genus requires more than a generic statement of an invention's boundaries."). “In Ariad, the court recognized the problem of using functional claim language without providing in the specification examples of species that achieve the claimed function:
The problem is especially acute with genus claims that use functional language to define the boundaries of a claimed genus. In such a case, the functional claim may simply claim a desired result, and may do so without describing species that achieve that result. But the specification must demonstrate that the applicant has made a generic invention that achieves the claimed result and do so by showing that the applicant has invented species sufficient to support a claim to the functionally-defined genus.” – MPEP 2161.01
Specifically with regard to computer-implemented functional claims, the specification must provide a disclosure of the computer and the algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention, including how to program the disclosed computer to perform the claimed function. MPEP 2161.01(I).
With regard to claims 1 and 21, the disclosure does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a method or system performing the functions of (a) “assigning a clinical parameter to the user via a recurrent neural network according to a subset of the set of aggregate parameters,” and (b) “determining a threshold value associated with the clinical parameter from the set of baseline user data,”.
With respect to element (a), paragraph 45 states that “[t]he predictive model 124 can utilize one or more pattern recognition algorithms, each of which analyze the extracted features or a subset of the extracted features to assign a continuous or categorical clinical parameter to the user,” where examples of continuous parameters include:
“a likelihood that that user has or is about to contract a specific disease or disorder, a likelihood that the user is experiencing the effects of aging, a likelihood that the user is experiencing an onset of dementia, a likelihood that the user has or will develop a neurodegenerative disorder, a likelihood that the user will experience an intensifying of symptoms, or "flare-up," of a chronic condition, a likelihood that the user will use an addictive substance during rehabilitation or treatment, a current or predicted level of pain for the user, an expected performance level of the user associated with a current or future time for a particular activity or occupation, a change in symptoms associated with a disease or disorder, a current or predicted response to treatment, a likelihood that the user has experienced an increase in stress, or an overall wellness level of the user.”
Paragraph 20 also defines a clinical parameter as “any continuous or categorical parameter representing the mental, neurological, physical, cognitive, behavioral, social, and emotional health of a user and can represent any or all of the health, function, a zone or flow state, balance, resilience, homeostasis, disease, and condition of the user.”
Paragraph 46 further provides that “[t]he training process of a given classifier will vary with its implementation, but training generally involves a statistical aggregation of training data into one or more parameters associated with the output class.” No further description of specific training is provided beyond general statements that a model can be trained on data from a user or on data from other users.
Paragraph 51 provides a broad description of recurrent neural networks as a class of algorithm, but does not provide information on how such a model would specifically be trained and used to provide a clinical parameter. Paragraph 64 states that a predictive model can be a recurrent neural network, but then further only provides that “the aggregate parameters can be provided to the predictive model as time series along with other relevant data” and that “[a]n output of the model is an index representing the current level of overall wellness being experienced by the user.”
Examiner notes that Tables I-VII list 53 different potential wellness-relevant parameters, but that no description or examples are provided of any particular wellness-relevant parameters being used as part of a training process or as used with a specific model to determine a specific “clinical parameter.”
The above disclosure is not sufficient to provide written description support for assigning a clinical parameter to the user via a recurrent neural network according to a subset of the set of aggregate parameters and generating a clinical parameter via the recurrent neural network, representing a current state of the user, from a subset of the set of aggregate parameters. Merely listing an extensive list of potential data types and stating that a general category of algorithm could be trained using user data is not sufficient to provide support for assigning and generating a particular clinical parameter. This is especially true given that the clinical parameter is disclosed as potentially being “any continuous or categorical parameter representing the mental, neurological, physical, cognitive, behavioral, social, and emotional health of a user and can represent any or all of the health, function, a zone or flow state, balance, resilience, homeostasis, disease, and condition of the user” and encompassing anything from likelihood of a disease to likelihood of using addictive substances to an expected performance level of the user for a particular activity or occupation to “an overall wellness level of the user.” The disclosure provides no effective boundaries on the clinical data used or clinical parameters generated, and only provides a general description of various types of machine learning models, along with statements that such models can be trained with user data. This is not sufficient to satisfy the written description requirement of 35 USC 112(a).
With respect to element (b) above, paragraph 57 states that “the thresholds used for a given model can be determined initially from data collected from other patients… As additional patient data is gathered over time to establish a set of baseline data, the thresholds can be refined to be specific to the user, representing a meaningful change in the value of the clinical parameter for that patient.” Paragraph 62 further provides that “[o]nce the baseline data is established, at least one threshold value associated with the clinical parameter can be determined from the set of baseline user data that is specific to the user, representing values of the clinical parameter that would be abnormal for that specific user.” However, no disclosure is provided of how a threshold is actually determined using the baseline data. Examiner notes the expansive and diverse list of potential wellness-relevant parameters, and that no examples are provided of how any particular combination might be used to generate a threshold as part of “baseline user data.” Merely stating that a threshold may be determined from a set of baseline user data, without any further disclosure of how that function is accomplished, is not sufficient to satisfy the written description requirement of 35 USC 112(a).
Claims 2-6 and 8-13 inherit the deficiencies of claim 1 through dependency and are likewise rejected.
With regard to claim 12, the disclosure does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a method or system performing the functions of assigning the user a predicted value representing a future value of one of the subset of the set of aggregate parameters according to the set of wellness-relevant parameters and at least one previously determined value for the one of the set of aggregate parameters; and assigning the clinical parameter to the user according to at least the predicted value for the one of the subset of the set of aggregate parameters.
Paragraph 52 states that “the predictive model 124 can include a constituent model that predicts future values for the aggregate parameters, such as a convolutional neural network that is provided with one or more two-dimensional arrays of wavelet transform coefficients as an input,” and that “the predictive model 124 can use constituent models that predict current or future values for the aggregate parameters, with these measures then used as features for generating the output of the predictive model.”
Paragraph 59 on page 24 similarly states that “the user can be assigned a predicted value representing a future value of a given aggregate parameter according to the values for the subset of aggregate parameters, and the value assigned to the user can be assigned based on the predicted value.”
However, no further disclosure is provided of how the system actually determines the predicted value representing a future value of one of the subset of the set of aggregate parameters, or how it then further assigns the clinical parameter to the user according to at least the predicted value. Examiner references the analysis provided above for element (a) in claim 1 addressing the lack of support for assigning a clinical parameter to the user via a predictive model according to a subset of the set of aggregate parameters.
With regard to claim 14, the disclosure does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a method or system incorporating or performing the functions of (a) “a recurrent neural network that assigns the clinical parameter to the user according to a subset of the set of aggregate parameters that includes the first aggregate parameter” and (b) “the threshold value being determined from previous clinical parameters assigned to the patient via the recurrent neural network”.
With respect to element (a), Examiner references the analysis provided above for element (a) in claim 1. Element (a) of claim 14 lacks written description support on the same basis provided above for these elements.
With respect to element (b), Examiner notes that the specification and drawings do not appear to explicitly describe determining the threshold value from previous clinical parameters assigned to the patient via the predictive model. Paragraph 56 states that “the predictive model 124 can include a feedback component 128 can tune various parameters of the predictive model 124 based upon the accuracy of predictions made by the model” and that “[i]n one example, a continuous output of the system can be compared to a threshold value to determine if the user is increase or decrease in wellness related parameters…This threshold can be varied by the feedback model 128 to increase the accuracy of the determination.” However, the threshold being described is not disclosed as a threshold used to provide an intervention when an assigned clinical parameter meets the threshold value, but rather is referencing thresholds used to determine categorical outputs from continuous variables within the model. Regardless, even if this threshold were construed as the threshold recited in claim 14, this disclosure does not provide support for how the threshold is actually determined from the previous clinical parameters.
Paragraph 57 states that “[f]or interventions, outcome data can also be collected and used to refine the thresholds for a patient, with the thresholds for interventions determined to be ineffective for a given patient adjusted to suggest them less frequently, and the thresholds for interventions determined to be effective changed to suggest these interventions more frequently.” However, no disclosure is provided of refining the thresholds based on previous clinical parameters assigned to the patient via the predictive model.
Claims 16-20 inherit the deficiencies of claim 14 through dependency and are likewise rejected.
With regard to claim 17, the disclosure does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a method or system performing the functions of adjusting the threshold value associated with the patient according to collected values for the set of wellness-relevant parameters from the user over a period of time.
Paragraph 57 states that “the thresholds used for a given model can be determined initially from data collected from other patients… As additional patient data is gathered over time to establish a set of baseline data, the thresholds can be refined to be specific to the user, representing a meaningful change in the value of the clinical parameter for that patient.” Paragraph 62 further provides that “[o]nce the baseline data is established, at least one threshold value associated with the clinical parameter can be determined from the set of baseline user data that is specific to the user, representing values of the clinical parameter that would be abnormal for that specific user.” However, similar to the analysis provided above with respect to element (b) of claim 1, no disclosure is provided of how a threshold is actually determined or adjusted using the collected data. Examiner notes the expansive and diverse list of potential wellness-relevant parameters, and that no examples are provided of how any particular combination might be used to generate or adjust a threshold. Merely stating that a threshold may be adjusted from a set of collected values for wellness-relevant parameters over a period of time, without any further disclosure of how that function is accomplished, is not sufficient to satisfy the written description requirement of 35 USC 112(a).
With regard to claim 20, the disclosure does not provide sufficient written description of the claimed subject matter to show that applicant had possession of a method or system performing the functions of retraining the recurrent neural network on collected values for the set of wellness-relevant parameters from the user over a period of time and values representing the outcome for the user.
Paragraph 56 on page 19 states that a feedback component “can tune various parameters of the predictive model 124 based upon the accuracy of predictions made by the model,” where “in one example, a continuous output of the system can be compared to a threshold value to determine if the user is increase or decrease in wellness related parameters” and that “[t]his threshold can be varied by the feedback model 128 to increase the accuracy of the determination.
However, no disclosure is provided of how the feedback component actually “tune[s] various parameters of the predictive model 124 based upon the accuracy of predictions made by the model.” This is especially true given that the predictive model is described in the specification as employing “any of a variety of techniques… including support vector machines, regression models, self-organized maps, fuzzy logic systems, data fusion processes, boosting and bagging methods, rule-based systems, or artificial neural networks,” and that training of any such model is only described as “generally involving a statistical aggregation of training data into one or more parameters associated with the output class” (see paragraph 46). Merely stating that a model can be retrained on collected values over a period of time and values representing the outcome of a user is not sufficient to satisfy the written description requirement of 35 USC 112(a).
Claim Rejections - 35 USC § 112
The previous rejection of claims 1-6, 8-14, and 16-20 under 35 USC 112(b) is withdrawn based on the amendment filed 1/2/2026.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4, and 8-13 are rejected under 35 U.S.C. 103 as being unpatentable over D’Haese et al (WO 2021/195616) in view of Brunner (US Patent Application Publication 2023/0082019).
With respect to claim 1, D’Haese discloses the claimed method for generating a clinical parameter for a user, the method comprising:
establishing a set of baseline user data (Figure 16 and [31] describe tracking biological rhythms to establish a normal pattern; [56] and [58] describe the system having established baseline values associated with a patient) that includes a time series of values representing one of a heart rate and a heart rate variability obtained from a photoplethysmography sensor worn by the user over a time period (Table 1 and [57] describe the information as including a time series of heart rate and heart rate variability values obtained via a photoplethysmogram as well as the use of a pulse oximeter), a second plurality of wellness-relevant parameters representing the user obtained via a portable computing device (Figure 15, [26], [30], [32], [42], [58], and [71] describe using a portable monitoring device to obtain wellness-relevant parameters, including cognitive assessments and mood), and a third plurality of wellness-relevant parameters representing the user ([36] and [41] describe the parameters including clinical data and medical history; Table 1 further provides parameters such as genetic and immune markers, symptom logs, and other parameters. Examiner notes that any set of the parameters described in [30]-[36] and [41] may be construed as a “third” plurality of parameters);
monitoring the one of heart rate and a heart rate variability of the user at the photoplethysmographic sensor worn by the user to produce a first set of values over a defined period ([26], [30], and [71] describe using a portable device having sensors, such as a smartwatch, to monitor wellness-relevant parameters of a user; Table 1 and [57] describe the information as including a time series of heart rate and heart rate variability values obtained via a photoplethysmogram as well as the use of a pulse oximeter; [68] and [71] describe collecting multiple sets of measurements; [55] and [56] describe the system receiving feedback regarding accuracy of predicted future parameters, i.e. the system is collecting new values for the parameters);
obtaining the second plurality of wellness-relevant parameters representing the user via the portable computing device over the defined period to provide a second set of values (Figure 15, [26], [30], [32], [42], [58], and [71] describe using a portable monitoring device to obtain wellness-relevant parameters, including cognitive assessments and mood; [68] and [71] describe collecting multiple sets of measurements; [55] and [56] describe the system receiving feedback regarding accuracy of predicted future parameters, i.e. the system is collecting new values for the parameters);
retrieving the third plurality of wellness-relevant parameters representing the user over the defined period to provide a third set of values ([36] and [41] describe the parameters including clinical data and medical history; Table 1 further provides parameters such as genetic and immune markers, symptom logs, and other parameters. Examiner notes that any set of the parameters described in [30]-[36] and [41] may be construed as a “third” plurality of parameters; [68] and [71] describe collecting multiple sets of measurements; [55] and [56] describe the system receiving feedback regarding accuracy of predicted future parameters, i.e. the system is collecting new values for the parameters), the first set of values, the second set of values, and the third set of values collectively forming a set of wellness-relevant parameters ([30]-[32], [34]-[36], [41], and [57] describe the various types of wellness-relevant parameters, which together are construed as a collective set of wellness-relevant parameters);
generating a set of aggregate parameters from the set of wellness-relevant parameters, each of the set of aggregate parameters comprising a unique proper subset of the set of wellness-relevant parameters (Tables 1-3 describe parameters categorized according to physiological parameters, cognitive parameters, and psychosocial or behavioral parameters; [30] describes the parameters as including physiological, cognitive, psychosocial, sensory, and behavioral parameters, where [36] describes “one or more combinations of physiological, cognitive, psychosocial, and behavioral parameters” in addition to “clinical” parameters; [40] and [73] describe aggregating wavelet coefficients to make weighted composite features), wherein the set of aggregate parameters includes a first aggregate parameter representing autonomic function, the proper subset of the set of wellness-relevant parameters associated with the first aggregate parameter including the one of heart rate and a heart rate variability (Table I, [30], [31], [36], [42], and [57] describe the combinations of parameters as including parameters related to autonomic function such as heart rate and heart rate variability); and
assigning a clinical parameter to the user via a recurrent neural network according to a subset of the set of aggregate parameters that includes the first aggregate parameter ([41]-[43], [59], [72], and [75] describe various types of indexes which could be assigned by a predictive model based on corresponding sets of aggregate parameters; Table I, [30], [31], [36], [42], and [57] describe the combinations of parameters as including parameters related to autonomic function such as heart rate and heart rate variability; Claim 17, [51], [59], and [68] describe the use of a recurrent neural network);
determining a threshold value associated with the clinical parameter from the set of baseline user data (Claims 5 and 17, [42], [55], [56], and [65] describe determining a threshold corresponding to a clinical parameter as well as determining the threshold from baseline user data);
providing an intervention to the user if the clinical parameter meets the determined threshold ([42] and [65]-[67] describe the decision threshold being used to provide specific outputs to the user);
but does not expressly disclose:
retrieving the third plurality of wellness-relevant parameters from an electronic health records (EHR) system.
However, Brunner teaches that it was old and well known in the art of health monitoring before the effective filing date of the claimed invention to retrieve a plurality of wellness-related parameters from an electronic health records (EHR) system (Figure 1, [33], [35], [37], [46], [96], and [157] describe acquiring a patient data of a plurality of different types from an external database or data repository containing health care records, i.e. an EHR system).
Therefore it would have been obvious to one of ordinary skill in the art of health monitoring before the effective filing date of the claimed invention to modify the system of D’Haese to retrieve a plurality of wellness-related parameters from an electronic health records (EHR) system as taught by Brunner since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case D’Haese already discloses retrieving a third plurality of wellness-relevant parameters and retrieving data from an external database (see paragraph 80), and retrieving the plurality of wellness-related parameters from an electronic health records (EHR) system as taught by Brunner would perform that same function in D’Haese, making the results predictable to one of ordinary skill in the art (MPEP 2143).
With respect to claim 4, D’Haese/Brunner teach the method of claim 1. D’Haese further discloses:
wherein the providing the intervention to the user comprises reporting the clinical parameter to one of a health care provider, an insurance company, a care team, a research team, a coach of the user, and a workplace of the user via a network interface ([66] describes a supervisor using the generated index to remove employees that are particularly susceptible or likely to become contagious from direct contact with customers, i.e. reporting the clinical parameter to a workplace of the user; [77]).
With respect to claim 8, D’Haese/Brunner teach the method of claim 1. D’Haese further discloses:
wherein the user is a first user of a plurality of users, the method further comprising:
training the recurrent neural network on data collected from the plurality of users before establishing the baseline set of user data ([41], [55], and [56] describe training the model on information from multiple individuals; [55], [56], and [74] describe acquiring new feedback parameter values; Claim 17, [51], [59], and [68] describe the use of a recurrent neural network); and
retraining the recurrent neural network on the baseline set of user data ([55], [56], and [74] describe retraining the model based on feedback using reinforcement learning).
With respect to claim 9, D’Haese/Brunner teach the method of claim 8. D’Haese further discloses:
wherein retraining the recurrent neural network on the baseline set of user data comprises retraining the recurrent neural network via a reinforcement learning process ([55], [56], [74], and Claim 6 describe retraining the model by means of a reinforcement learning process based on similarly of measured outcomes to the value assigned to the user).
With respect to claim 10, D’Haese/Brunner teach the method of claim 1. D’Haese further discloses:
wherein the set of aggregate parameters further comprises a second aggregate parameter representing a cognitive function of the user (Table II, [32], [33], [36], [42], and [58] describe the combinations of parameters as including parameters related to cognitive function), and a third aggregate parameter representing a motor and musculoskeletal health of the user (Table III, Figures 2 and 9, [23], and [58] describe the parameters as including parameters for motor and musculoskeletal health such as activity level, muscular strength, and movement).
With respect to claim 11, D’Haese/Brunner teach the method of claim 1. D’Haese further discloses:
wherein generating the set of aggregate parameters from the set of wellness-relevant parameters comprises generating a time series for one of the set of aggregate parameters (Figures 15-16, [37]-[41] describe generating a time series of aggregated composite features) and assigning the clinical parameter to the user via the recurrent neural network comprises performing a wavelet decomposition on the time series of the one of the set of aggregate parameters to provide a set of wavelet coefficients, and assigning the clinical parameter according to at least the set of wavelet coefficients and the subset of the set of aggregate parameters (Figure 15, [37]-[41], [53], and [73] describe performing wavelet decomposition and assigning the index based on wavelets and aggregate parameters; Claim 17, [51], [59], and [68] describe the use of a recurrent neural network).
With respect to claim 12, D’Haese/Brunner teach the method of claim 1. D’Haese further discloses:
wherein assigning the clinical parameter to the user via the recurrent neural network comprises:
assigning the user a predicted value representing a future value of one of the subset of the set of aggregate parameters according to the set of wellness-relevant parameters and at least one previously determined value for the one of the set of aggregate parameters; and assigning the clinical parameter to the user according to at least the predicted value for the one of the subset of the set of aggregate parameters ([53]-[56] and Claim 13 describe predicting a future value for parameters based on previous values, and using the prediction to assign the index; Claim 17, [51], [59], and [68] describe the use of a recurrent neural network).
With respect to claim 13, D’Haese/Brunner teach the method of claim 1. D’Haese further discloses:
wherein the set of aggregate parameters includes at least a first aggregate parameter representing sleep and circadian rhythms of the user (Figures 2 and 9, [25], [31], [42], [53], and [57] describe the grouped parameters as including those related to sleep and circadian rhythm), a second aggregate parameter representing a socio-behavioral function of the user (Table I and [42] describe the parameters including genetic and immune biomarkers), and a third aggregate parameter representing a biomarkers and genomics of the user (Table III, [24], [28], [34], and [35] describe the parameters including social and behavioral information).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over D’Haese et al (WO 2021/195616) in view of Brunner (US Patent Application Publication 2023/0082019) as applied to claim 1, and further in view of Rezai et al (US Patent Application Publication 2021/0162216).
With respect to claim 2, D’Haese/Brunner teach the method of claim 1. D’Haese does not expressly disclose wherein providing an intervention to the user comprises providing one of the set of wellness-relevant parameters, the set of aggregate parameters, and the clinical parameter to one of a medical professional, a caregiver, a therapist, a peer advisor, and a coach.
However, Rezai teaches that it was old and well known in the art of health monitoring before the effective filing date of the claimed invention to provide an intervention to a user by providing one of a wellness-relevant parameter, aggregate parameters, and clinical parameter to one of a medical professional, a caregiver, a therapist, a peer advisor, and a coach ([41]-[46] and [51] describe notifying individuals including a physician, therapist, nurse, coach, and 12-step sponsor of a determination that the patient’s addiction has not improved, i.e. a clinical parameter).
Therefore it would have been obvious to one of ordinary skill in the art of health monitoring before the effective filing date of the claimed invention to modify the combination of D’Haese and Brunner to provide an intervention to a user by providing one of a wellness-relevant parameter, aggregate parameters, and clinical parameter to one of a medical professional, a caregiver, a therapist, a peer advisor, and a coach as taught by Rezai since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case D’Haese already discloses providing an intervention to a user by providing a clinical parameter to an individual, and providing the intervention by providing one of a wellness-relevant parameter, aggregate parameters, and clinical parameter to one of a medical professional, a caregiver, a therapist, a peer advisor, and a coach as taught by Rezai would perform that same function in D’Haese and Brunner, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over D’Haese et al (WO 2021/195616) in view of Brunner (US Patent Application Publication 2023/0082019) as applied to claim 1, and further in view of Ash et al (US Patent Application Publication 2013/0174073).
With respect to claim 3, D’Haese/Brunner teach the method of claim 1. D’Haese does not expressly disclose wherein the subset of the set of aggregate parameters comprises each of the set of aggregate parameters.
However, Ash teaches that it was old and well known in the art of health monitoring before the effective filing date of the claimed invention to determine a value representing an overall wellness of a user based on each of a set of aggregated parameters (Figure 5, [46], [50], [51], and [69] describe determining an overall health score for a user based on factors for a plurality of health areas).
Therefore it would have been obvious to one of ordinary skill in the art of health monitoring before the effective filing date of the claimed invention to modify the combination of D’Haese and Brunner to determine a value representing an overall wellness of a user based on an entire set of aggregated parameters as taught by Ash since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case D’Haese already discloses determining a clinical parameter based on aggregated parameters, and determining the clinical parameter as a value representing an overall wellness of a user based on an entire set of aggregated parameters as taught by Ash would perform that same function in D’Haese and Brunner, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over D’Haese et al (WO 2021/195616) in view of Brunner (US Patent Application Publication 2023/0082019) as applied to claim 1, and further in view of Catani et al (US Patent Application Publication 2015/0364057).
With respect to claim 5, D’Haese/Brunner teach the method of claim 1. D’Haese does not expressly disclose wherein the providing the intervention to the user comprises transmitting a message to the user that guides the user through a stress reduction technique.
However, Catani teaches that it was old and well known in the art of health monitoring before the effective filing date of the claimed invention to provide an intervention to a user by transmitting a message to the user guiding the user through a stress reduction technique (Figures 49, 89-92, 97-99 and [386] describe a message guiding the user through deep breathing exercises and other stress-reduction techniques).
Therefore it would have been obvious to one of ordinary skill in the art of health monitoring before the effective filing date of the claimed invention to modify the combination of D’Haese and Brunner to provide an intervention to a user by transmitting a message to the user guiding the user through a stress reduction technique as taught by Catani since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case D’Haese already discloses providing an intervention to a user, and providing a message to the user guiding the user through a stress reduction technique as taught by Catani would perform that same function in D’Haese and Brunner, making the results predictable to one of ordinary skill in the art (MPEP 2143).
With respect to claim 6, D’Haese/Brunner teach the method of claim 1. D’Haese does not expressly disclose wherein the providing the intervention to the user comprises providing the intervention to the user via the portable computing device.
However, Catani teaches that it was old and well known in the art of health monitoring before the effective filing date of the claimed invention to provide an intervention to a user via a portable computing device (Figures 49, 89-92, 97-99 and [207], [213], [213], and [386] describe interventions provided to a user on a mobile device).
Therefore it would have been obvious to one of ordinary skill in the art of health monitoring before the effective filing date of the claimed invention to modify the combination of D’Haese and Brunner to provide an intervention to a user via a portable computing device as taught by Catani since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case D’Haese already discloses providing an intervention to a user as well as mobile devices associated with the user, and provide an intervention to the user via a portable computing device as taught by Catani would perform that same function in D’Haese and Brunner, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Claims 14 and 16-21 are rejected under 35 U.S.C. 103 as being unpatentable over D’Haese et al (WO 2021/195616) in view of Brunner (US Patent Application Publication 2023/0082019) and Ash et al (US Patent Application Publication 2013/0174073).
With respect to claim 14, D’Haese discloses the claimed system for generating a clinical parameter for a user, the system comprising:
a photoplethysmographic sensor worn by the user that monitors a first plurality of wellness-relevant parameters representing the user over a defined period, the first plurality of wellness-relevant parameters include one of a heart rate and a heart rate variability ([26], [30], and [71] describe using a portable device having sensors, such as a smartwatch, to monitor wellness-relevant parameters of a user; Table 1 and [57] describe the information as including a time series of heart rate and heart rate variability values obtained via a photoplethysmogram as well as the use of a pulse oximeter);
a portable computing device that obtains a second plurality of wellness-relevant parameters representing the user via a portable computing device (Figure 15, [26], [30], [32], [42], [58], and [71] describe using a portable monitoring device to obtain wellness-relevant parameters, including cognitive assessments and mood);
a server (Figure 1 element 120 and [30] describe the system comprising a server) comprising:
a processor ([30] and [76]-[78] describe the server including a processing unit);
a network interface ([77] and [80] describe a network interface and external data source);
retrieving a third plurality of wellness-relevant parameters representing the user ([36] and [41] describe the parameters including clinical data and medical history; Table 1 further provides parameters such as genetic and immune markers, symptom logs, and other parameters. Examiner notes that any set of the parameters described in [30]-[36] and [41] may be construed as a “third” plurality of parameters), the first plurality of wellness-relevant parameters, the second plurality of wellness-relevant parameters, and the third plurality of wellness-relevant parameters collectively forming a set of wellness-relevant parameters ([30]-[32], and [34]-[36] describe the various types of wellness-relevant parameters, which together are construed as a collective set of wellness-relevant parameters); and
a non-transitory computer readable medium storing machine-readable instructions executable by the processor (Figure 17, [77], and [79]-[81] describe the system including memory devices storing executable logic) to provide:
a feature aggregator that generates a set of aggregate parameters from the set of wellness-relevant parameters, each of the set of aggregate parameters comprising a unique proper subset of the set of wellness-relevant parameters (Tables 1-3 describe parameters categorized according to physiological parameters, cognitive parameters, and psychosocial or behavioral parameters; [30] describes the parameters as including physiological, cognitive, psychosocial, sensory, and behavioral parameters, where [36] describes “one or more combinations of physiological, cognitive, psychosocial, and behavioral parameters” in addition to “clinical” parameters; [40] and [73] describe aggregating wavelet coefficients to make weighted composite features; Figures 1 and 17 and [30]), the set of aggregate parameters including a first aggregate parameter representing autonomic function, the proper subset of the set of wellness-relevant parameters associated with the first aggregate parameter including the one of heart rate and a heart rate variability (Table I, [30], [31], [36], [42], and [57] describe the combinations of parameters as including parameters related to autonomic function such as heart rate and heart rate variability);
a recurrent neural network that assigns the clinical parameter to the user according to a subset of the set of aggregate parameters ([41]-[43], [59], [72], and [75] describe various types of indexes which could be assigned by a predictive model based on corresponding sets of aggregate parameters; Table I, [30], [31], [36], [42], and [57] describe the combinations of parameters as including parameters related to autonomic function such as heart rate and heart rate variability; Claim 17, [51], [59], and [68] describe the use of a recurrent neural network), wherein the recurrent neural network is trained on data collected from a plurality of other patients before establishing the baseline set of user data ([41], [55], and [56] describe training the model on information from multiple individuals; [55], [56], and [74] describe acquiring new feedback parameter values; Claim 17, [51], [59], and [68] describe the use of a recurrent neural network) and then retrained on the baseline set of user data ([55], [56], and [74] describe retraining the model based on feedback using reinforcement learning); and
an intervention selector that provides an intervention for the user when assigned clinical parameter meets a threshold value associated with the patient, the threshold value being determined from previous clinical parameters assigned to the patient via the recurrent neural network (Claims 5 and 17, [42], [55], [56], and [65] describe determining a threshold corresponding to a clinical parameter as well as determining the threshold based on previous determinations such as the user’s infection risk category; [42] and [65]-[67] describe the decision threshold being used to provide specific outputs to the user; Claim 17, [51], [59], and [68] describe the use of a recurrent neural network),
but does not expressly disclose:
the network interface retrieving the third plurality of wellness-relevant parameters from an electronic health records (EHR) system;
the subset of the set of aggregate parameters comprising each of the set of aggregate parameters.
However, Brunner teaches that it was old and well known in the art of health monitoring before the effective filing date of the claimed invention to retrieve a plurality of wellness-related parameters from an electronic health records (EHR) system (Figure 1, [33], [35], [37], [46], [96], and [157] describe acquiring a patient data of a plurality of different types from an external database or data repository containing health care records, i.e. an EHR system).
Therefore it would have been obvious to one of ordinary skill in the art of health monitoring before the effective filing date of the claimed invention to modify the system of D’Haese to retrieve a plurality of wellness-related parameters from an electronic health records (EHR) system as taught by Brunner since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case D’Haese already discloses retrieving a third plurality of wellness-relevant parameters and retrieving data from an external database (see paragraph 80), and retrieving the plurality of wellness-related parameters from an electronic health records (EHR) system as taught by Brunner would perform that same function in D’Haese, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Ash further teaches that it was old and well known in the art of health monitoring before the effective filing date of the claimed invention to determine a value representing an overall wellness of a user based on each of a set of aggregated parameters (Figure 5, [46], [50], [51], and [69] describe determining an overall health score for a user based on factors for a plurality of health areas).
Therefore it would have been obvious to one of ordinary skill in the art of health monitoring before the effective filing date of the claimed invention to modify the combination of D’Haese and Brunner to determine a value representing an overall wellness of a user based on an entire set of aggregated parameters as taught by Ash since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case D’Haese already discloses determining a clinical parameter based on aggregated parameters, and determining the clinical parameter as a value representing an overall wellness of a user based on an entire set of aggregated parameters as taught by Ash would perform that same function in D’Haese and Brunner, making the results predictable to one of ordinary skill in the art (MPEP 2143).
With respect to claim 16, D’Haese/Brunner/Ash teach the system of claim 14. D’Haese further discloses:
wherein at least one of the second plurality of wellness-relevant parameters is derived from psychosocial assessment data provided by the user, the portable computing device comprising a user interface that allows the user to interact with a psychosocial assessment application ([71] and claim 16 describe collecting psychosocial data from a user by means of a psychosocial assessment application on the mobile device).
With respect to claim 17, D’Haese/Brunner/Ash teach the system of claim 14. D’Haese further discloses:
machine-readable instructions executable by the processor to (Figure 17, [77], and [79]-[81] describe the system including memory devices storing executable logic) provide a feedback component that collects values for the set of wellness-relevant parameters from the user over a period of time and adjusts the threshold value associated with the patient according to the collected values for the set of wellness-relevant parameters ([41], [55], [56], and Claim 17 describe using feedback for the parameters to adjust the threshold).
With respect to claim 18, D’Haese/Brunner/Ash teach the system of claim 14. D’Haese further discloses:
wherein the set of aggregate parameters (Tables 1-3 describe parameters categorized according to physiological parameters, cognitive parameters, and psychosocial or behavioral parameters; [30] describes the parameters as including physiological, cognitive, psychosocial, sensory, and behavioral parameters, where [36] describes “one or more combinations of physiological, cognitive, psychosocial, and behavioral parameters” in addition to “clinical” parameters; [40] and [73] describe aggregating wavelet coefficients to make weighted composite features) comprises
the first aggregate parameter representing autonomic function of the user (Table I, [30], [31], [36], [42], and [57] describe the combinations of parameters as including parameters related to autonomic function such as heart rate and heart rate variability),
a second aggregate parameter representing a cognitive function of the user (Table II, [32], [33], [36], [42], and [58] describe the combinations of parameters as including parameters related to cognitive function),
a third aggregate parameter representing a motor and musculoskeletal health of the user (Table III, Figures 2 and 9, [23], and [58] describe the parameters as including parameters for motor and musculoskeletal health such as activity level, muscular strength, and movement),
a fourth aggregate parameter representing sleep disruptions and general disruptions of circadian rhythm (Figures 2 and 9, [25], [31], [42], [53], and [57] describe the grouped parameters as including those related to sleep and circadian rhythm),
a fifth aggregate parameter representing relevant biomarkers and generic information identified for the user (Table I and [42] describe the parameters including genetic and immune biomarkers),
a sixth aggregate parameter representing sensory function and changes in function for the user (Table I, Figure 2, [24], [30], and [58] describe the parameters including sensory and functional information), and
a seventh parameter representing a socio-behavioral health of the user (Table III, [24], [28], [34], and [35] describe the parameters including social and behavioral information).
With respect to claim 19, D’Haese/Brunner/Ash teach the system of claim 14. D’Haese further discloses:
machine-readable instructions executable by the processor to (Figure 17, [77], and [79]-[81] describe the system including memory devices storing executable logic) provide a feature extractor that performs a wavelet decomposition on a time series of values for the one of the first plurality of wellness-relevant parameters to provide a set of wavelet coefficients, the feature aggregator generating the set of aggregate parameters from according to at least the set of wavelet coefficients and the first plurality of wellness-relevant parameters ([37]-[41] and claims 18-19 describe a feature extractor performing wavelet decomposition on a time series of parameter values to provide wavelet coefficients and aggregating said information).
With respect to claim 20, D’Haese/Brunner/Ash teach the system of claim 14. D’Haese further discloses:
machine-readable instructions executable by the processor to (Figure 17, [77], and [79]-[81] describe the system including memory devices storing executable logic) provide a feedback component that collects values for the set of wellness-relevant parameters from the user over a period of time, collecting values representing an outcome for the user, and retraining the recurrent neural network on the collected values for the set of wellness-relevant parameters and the values representing the outcome for the user ([55], [56], [74], and Claim 6 describe collecting parameter values over time and retraining the model based on a similarly of measured outcomes to the value assigned to the user; Claim 17, [51], [59], and [68] describe the use of a recurrent neural network).
With respect to claim 21, D’Haese discloses the claimed method for generating a clinical parameter for a user, the method comprising:
establishing a set of baseline user data (Figure 16 and [31] describe tracking biological rhythms to establish a normal pattern; [56] and [58] describe the system having established baseline values associated with a patient) that includes a time series of values representing one of a heart rate and a heart rate variability obtained from a photoplethysmography sensor worn by the user over a time period (Table 1 and [57] describe the information as including a time series of heart rate and heart rate variability values obtained via a photoplethysmogram as well as the use of a pulse oximeter), a second plurality of wellness-relevant parameters representing the user obtained via a portable computing device (Figure 15, [26], [30], [32], [42], [58], and [71] describe using a portable monitoring device to obtain wellness-relevant parameters, including cognitive assessments and mood), and a third plurality of wellness-relevant parameters representing the user ([36] and [41] describe the parameters including clinical data and medical history; Table 1 further provides parameters such as genetic and immune markers, symptom logs, and other parameters. Examiner notes that any set of the parameters described in [30]-[36] and [41] may be construed as a “third” plurality of parameters);
monitoring the one of heart rate and a heart rate variability of the user at the photoplethysmographic sensor worn by the user to produce a first set of values over a defined period ([26], [30], and [71] describe using a portable device having sensors, such as a smartwatch, to monitor wellness-relevant parameters of a user; Table 1 and [57] describe the information as including a time series of heart rate and heart rate variability values obtained via a photoplethysmogram as well as the use of a pulse oximeter; [68] and [71] describe collecting multiple sets of measurements; [55] and [56] describe the system receiving feedback regarding accuracy of predicted future parameters, i.e. the system is collecting new values for the parameters);
obtaining the second plurality of wellness-relevant parameters representing the user via the portable computing device over the defined period to provide a second set of values (Figure 15, [26], [30], [32], [42], [58], and [71] describe using a portable monitoring device to obtain wellness-relevant parameters, including cognitive assessments and mood; [68] and [71] describe collecting multiple sets of measurements; [55] and [56] describe the system receiving feedback regarding accuracy of predicted future parameters, i.e. the system is collecting new values for the parameters);
retrieving the third plurality of wellness-relevant parameters representing the user over the defined period to provide a third set of values ([36] and [41] describe the parameters including clinical data and medical history; Table 1 further provides parameters such as genetic and immune markers, symptom logs, and other parameters. Examiner notes that any set of the parameters described in [30]-[36] and [41] may be construed as a “third” plurality of parameters; [68] and [71] describe collecting multiple sets of measurements; [55] and [56] describe the system receiving feedback regarding accuracy of predicted future parameters, i.e. the system is collecting new values for the parameters), the first set of values, the second set of values, and the third set of values collectively forming a set of wellness-relevant parameters ([30]-[32], [34]-[36], [41], and [57] describe the various types of wellness-relevant parameters, which together are construed as a collective set of wellness-relevant parameters);
generating a set of aggregate parameters from the set of wellness-relevant parameters, each of the set of aggregate parameters comprising a unique proper subset of the set of wellness-relevant parameters (Tables 1-3 describe parameters categorized according to physiological parameters, cognitive parameters, and psychosocial or behavioral parameters; [30] describes the parameters as including physiological, cognitive, psychosocial, sensory, and behavioral parameters, where [36] describes “one or more combinations of physiological, cognitive, psychosocial, and behavioral parameters” in addition to “clinical” parameters; [40] and [73] describe aggregating wavelet coefficients to make weighted composite features), wherein the set of aggregate parameters includes a first aggregate parameter representing autonomic function, the proper subset of the set of wellness-relevant parameters associated with the first aggregate parameter including the one of heart rate and a heart rate variability (Table I, [30], [31], [36], [42], and [57] describe the combinations of parameters as including parameters related to autonomic function such as heart rate and heart rate variability); and
assigning a clinical parameter to the user via a recurrent neural network according to a subset of the set of aggregate parameters ([41]-[43], [59], [72], and [75] describe various types of indexes which could be assigned by a predictive model based on corresponding sets of aggregate parameters; Table I, [30], [31], [36], [42], and [57] describe the combinations of parameters as including parameters related to autonomic function such as heart rate and heart rate variability; Claim 17, [51], [59], and [68] describe the use of a recurrent neural network), wherein the recurrent neural network is trained on data collected from a plurality of other patients before establishing the baseline set of user data ([41], [55], and [56] describe training the model on information from multiple individuals; [55], [56], and [74] describe acquiring new feedback parameter values; Claim 17, [51], [59], and [68] describe the use of a recurrent neural network) and then retrained on the baseline set of user data ([55], [56], and [74] describe retraining the model based on feedback using reinforcement learning);
determining a threshold value associated with the clinical parameter from the set of baseline user data (Claims 5 and 17, [42], [55], [56], and [65] describe determining a threshold corresponding to a clinical parameter as well as determining the threshold from baseline user data);
providing an intervention to the user if the clinical parameter meets the determined threshold ([42] and [65]-[67] describe the decision threshold being used to provide specific outputs to the user);
but does not expressly disclose:
retrieving the third plurality of wellness-relevant parameters from an electronic health records (EHR) system;
the subset of the set of aggregate parameters comprising each of the set of aggregate parameters.
However, Brunner teaches that it was old and well known in the art of health monitoring before the effective filing date of the claimed invention to retrieve a plurality of wellness-related parameters from an electronic health records (EHR) system (Figure 1, [33], [35], [37], [46], [96], and [157] describe acquiring a patient data of a plurality of different types from an external database or data repository containing health care records, i.e. an EHR system).
Therefore it would have been obvious to one of ordinary skill in the art of health monitoring before the effective filing date of the claimed invention to modify the system of D’Haese to retrieve a plurality of wellness-related parameters from an electronic health records (EHR) system as taught by Brunner since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case D’Haese already discloses retrieving a third plurality of wellness-relevant parameters and retrieving data from an external database (see paragraph 80), and retrieving the plurality of wellness-related parameters from an electronic health records (EHR) system as taught by Brunner would perform that same function in D’Haese, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Ash further teaches that it was old and well known in the art of health monitoring before the effective filing date of the claimed invention to determine a value representing an overall wellness of a user based on each of a set of aggregated parameters (Figure 5, [46], [50], [51], and [69] describe determining an overall health score for a user based on factors for a plurality of health areas).
Therefore it would have been obvious to one of ordinary skill in the art of health monitoring before the effective filing date of the claimed invention to modify the combination of D’Haese and Brunner to determine a value representing an overall wellness of a user based on an entire set of aggregated parameters as taught by Ash since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case D’Haese already discloses determining a clinical parameter based on aggregated parameters, and determining the clinical parameter as a value representing an overall wellness of a user based on an entire set of aggregated parameters as taught by Ash would perform that same function in D’Haese and Brunner, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Griffin (US Patent Application Publication 2023/0042882);
Amerasingham et al (US Patent Application Publication 2015/0213225);
Heldman (US 11,367,519).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM G LULTSCHIK whose telephone number is (571)272-3780. The examiner can normally be reached 9am - 5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached at (571) 270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Gregory Lultschik/ Examiner, Art Unit 3682