Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1 and 4-22 were previously pending and subject to a non-final Office Action having a notification date of November 26, 2025 (“non-final Office Action”). Following the non-final Office Action, Applicant filed an amendment on May 14, 2026 (the “Amendment”), amending claims 1, 7, 10, and 21 and canceling claims 6 and 22.
The present Final Office Action addresses pending claims 1, 4, 5, and 7-21 in the Amendment.
Response to Arguments
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §112
While these rejections have been withdrawn, new rejections under 35 USC 112 are presented herein in view of the Amendment.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §101
On page 11-12 of the Amendment, Applicant takes the position that the present claims integrate the abstract idea into a "specific and tangible method that improves functioning of a computer and/or other technology by preserving computational resources" based on the newly added limitation "wherein processing the message comprises preserving computational resources by pre-processing, with the rule-based model, the set of inputs to check for a predetermined set of words selected through an optimization process and specific to a particular mental health condition, and not performing one or more steps of the method upon checking the set of inputs for the predetermined set of words."
The Examiner disagrees because a user could mentally determine that a set of words is present in the inputs and then just not perform the step of mentally determining the temporal parameter or weighting the scores. Regarding how computational resources are somehow preserved by the rule-based model checking for the keywords and then not performing steps of the model, the Examiner asserts that these additional limitations merely amount to reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Specifically, the claims do not include details regarding exactly how for instance the alleged reduction in computational resources owing to not performing one or more steps of the method would not be outweighed by the additional computational resources owing to the rule-based model checking for the predetermined set of words in the inputs.
On pages 12-13 of the Amendment, Applicant then asserts "wherein rule-based model is structured to provide traceability of data contributing to an output of the rule-based model and to prevent unexpected outputs by omitting end-to-end deep learning approaches" as recited in independent claim 10 also integrates the abstract idea into a specific and tangible method that improves technology functioning in the context of traceability. The Examiner disagrees and asserts that these additional limitations merely amount to reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)) because there are no details regarding exactly how the rule-based model is structured to provide such traceability and how omitting of the end-to-end DL approaches somehow prevents unexpected outputs.
In relation to Applicant's general assertion that the additional limitations "amount to other meaningful limitations beyond generally linking use of the judicial exception to a particular technological environment" (MPEP 2106.05(e)), the Examiner disagrees for at least all the reasons presented herein in relation to why the additional limitations do not provide a "practical application" of or amount to "significantly more" than the abstract idea.
The 35 USC 101 rejection is maintained.
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 10-20 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claim 10 has been amended to recite, inter alia, "wherein the rule-based model is structured to … prevent unexpected outputs by omitting end-to-end deep learning approaches" (Emphasis added). For reference, [0043] of the present specification discloses:
The set of models preferably includes a set of rule-based models and/or algorithms, which can function to provide reliable, traceable, and explainable analyses of the participants. For instance, in some implementations of the system and method, data collected from participants (e.g., participant messages) are processed with a set of rule-based models and/or algorithms such that the particular pieces of data which contribute to a model/algorithm output (e.g., indication that the patient should be escalated to clinical care) can be easily identified (e.g., for review and/or verification, for further analysis by a care team member, etc.). This can also prevent limitations resulting from "black box" (e.g., end-to-end deep learning) data analysis approaches, such as: unexpected results (e.g., which could cause harm to the participant in an event that escalation is not detected when it should have been, which could cause unnecessary hassle to the participant in an event that escalation is recommended when not needed, etc.), a lack of explainability, and/or any other limitations. Additionally or alternatively, the set of rule-based models and/or algorithms can perform any other functions. (Emphasis added).
Thus, [0043] of the present specification appears to disclose that the disclosed traceability of the rule-based models can prevent unexpected results that result from end-to-end deep learning approaches. However, this paragraph does not disclose actually omitting such deep learning approaches. Rather, this paragraph discloses that deep learning approaches can generate unexpected results and that the traceability of the rule-based models can somehow prevent such unexpected results.
Claims 11-20 are rejected based on their dependence from claim 10.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 4, 5, and 7-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Each of independent claims 1 and 10 has been amended to recite "wherein processing the message comprises preserving computational resources by pre-processing, with the rule-based model, the set of inputs to check for a predetermined set of words selected through an optimization process and specific to a particular mental health condition, and not performing one or more steps of the method upon checking the set of inputs for the predetermined set of words." However, the scope of these claims is now indefinite because it is unclear which steps of the method are or are not included as part of the claims. Stated differently, it is now difficult if not impossible for a third party to know how to avoid infringement of these claims because the third party would not precisely know which steps of the method are or are not included as part of the claims.
Furthermore, independent claim 10 has been amended to recite, inter alia, "wherein the rule-based model is structured to … prevent unexpected outputs by omitting end-to-end deep learning approaches." However, claim 10 also results how the escalation detection subsystem comprises "a set of trained neural networks" which are known be a deep learning approach thus leading to uncertainty as to whether or not the recited "omission" of the end-to-end deep learning approaches has any effect on the set of trained neural networks of the escalation detection subsystem.
The Examiner will attempt to examine the claims as best understood.
The remaining claims are rejected based on their dependence from claims 1 or 10.
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.
Claim 1, 4, 5, and 7-21 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Criteria - Step 1:
As claims 1, 4, 5, and 7-21 are directed to a method (i.e., a process), the claims are all within one of the four statutory categories. 35 USC §101.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 update issued by the USPTO as now incorporated into the MPEP, as supported by relevant case law), the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A method for computer-aided escalation of a member in a digital platform, the method comprising:
training an escalation detection subsystem comprising a machine learning model and a rule-based model comprising a heuristics-based natural language processing (NLP) model, to predict a score associated with a clinical care escalation outcome, wherein training comprises training with labeled data comprising portions of chat transcripts between a participant and a coach in which the participant provides information informative of escalation to clinical care;
processing a message associated with the member and a set of inputs with the trained escalation detection subsystem and using a set of prioritization parameters for the trained escalation detection subsystem to produce a predicted score for the member, the predicted score corresponding to a clinical care escalation outcome recommendation for the member, wherein processing the message comprises assigning, using the trained escalation detection subsystem, a set of scores to the message based upon a set of contexts determined from a set of keywords derived from the message,
wherein assigning the set of scores based upon the set of contexts comprises determining a temporal parameter from the set of keywords and weighting the set of scores,
wherein weighting the set of scores comprises downweighting a first score of the set of scores if a time period of the temporal parameter deviates from present time by more than a threshold, and downweighting a second score of the set of scores by a scaling factor corresponding to a duration of time of the temporal parameter,
wherein the predicted score is generated from the set of scores and corresponds to a care escalation outcome recommendation for the member, and
wherein processing the message comprises preserving computational resources by pre-processing, with the rule-based model, the set of inputs to check for a predetermined set of words selected through an optimization process and specific to a particular mental health condition, and not performing one or more steps of the method upon checking the set of inputs for the predetermined set of words;
retrieving a historical score associated with a historical message from the member;
aggregating the predicted score with the historical score to produce an aggregated score;
evaluating a set of satisfaction criteria based on the aggregated score, wherein:
in an event that the set of satisfaction criteria are satisfied, automatically triggering an adjustment in a care plan at the digital platform; and
preventing repetition of the adjustment in the care plan upon detecting prior triggering of the adjustment in the care plan for the member, wherein the adjustment comprises a change in features of the digital platform that are available to the member.
The Examiner submits that the foregoing underlined limitations constitute “mental processes” because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). As an example, a medical professional/clinician could readily analyze the sentiment of keywords/text in messages from a member/patient to assign a set of “scores” to the message based on contexts of the wording/text. For instance, the medical professional could readily assign higher scores to keywords/phrases such as “major pain,” “emergency,” “stroke,” and lower scores to keywords/phrases such as “resolved,” “feeling healthy,” etc. Furthermore, the medical professional could readily "downweight" scores based on a temporal parameter (e.g., age) of the keywords, such as downweighting a first score if a time period of the temporal parameter deviates from present time by more than a threshold and downweighting a second score by a scaling factor corresponding to a duration of time of the temporal parameter.
Thereafter, the medical professional could determine a predicted score for the message from the set of scores indicative of a relative level of danger of the member in relation to a medical condition that corresponds to a “care escalation outcome recommendation” for the member/patient (e.g., via adding up the set of scores), aggregate the scores over time, trigger an adjustment in a care plan for the member (e.g., a change in features available to the patient/member, such as further monitoring, reduced communications, etc.) when the aggregated score exceeds a threshold (when “satisfaction criteria” are satisfied), and prevent repetition of the adjustment in the care plan upon detecting prior triggering of the adjustment in the care plan for the member (e.g., not repeatedly adjusting the care plan until a previous adjustment has completed).
In relation to "pre-processing" the inputs by checking for a predetermined set of words through some "optimization" process specific to a particular mental health condition and then not performing one of the steps based on the checking, a user could readily perform such process in their mind. For instance, in the case the user mentally determined that a set of words is present in the inputs, the user could just not perform the step of mentally determining the temporal parameter or weighting the scores.
Furthermore, in relation to predicting from the temporal parameter of the message that the participant/member is experiencing suicidal ideation (from independent claim 10), this step constitutes “mental processes” because it is an evaluation/judgment that can, at the currently claimed high level of generality, be practically performed in the human mind.
These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
In relation to automatically initiating a call to a suicide hotline for the member based on the suicidal ideation prediction (from independent claim 10), the Examiner submits that this limitation constitutes “certain methods of organizing human activity” because it relates to managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions). That is, automatically initiating such a call relates to facilitating interactions between the member experiencing suicidal ideation and a healthcare worker trained to manage such situations.
Accordingly, the claim recites at least one abstract idea.
Furthermore, dependent claims 4, 5, 7, 12-15, 18, and 21 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below:
-Claims 4 and 18 call for preventing care plan adjustment when the satisfaction criteria is not met which can be practically performed in the human mind with pen and paper (“mental processes”). Furthermore, claim 18 calls for evaluating a set of satisfaction criteria based on the aggregated score which is practically performable in the human mind with pen and paper (“mental processes”)
-Claims 5 and 15 call for performing a (feature weighting) adjustment of the message using the prioritization parameters which can be practically performed in the human mind with pen and paper (“mental processes”). For instance, a clinician could assign higher weights to certain words and lower weights to other less important words.
-Claim 7 recites how the predicted score is determined based at least in part on the set of contexts (e.g., relative importance of certain words to certain medical conditions) associated with the message which can be practically performed in the human mind with pen and paper (“mental processes”).
-Claim 12 calls for performing a generic “adjustment” of the message (e.g., determining different treatment recommendations to be presented to a user) and determining a set of contexts associated with the adjusted message which again can be practically performed in the human mind with pen and paper (“mental processes”).
-Claim 13 calls for determining contexts associated with the message and predicting the score based on the contexts which can be practically performed in the human mind with pen and paper (“mental processes”).
-Claim 14 recites how the contexts are associated with an adjustment of the message which can be practically performed in the human mind with pen and paper (“mental processes”).
-Claim 21 calls for predicting from the temporal parameter of the message that the member is experiencing suicidal ideation which constitutes “mental processes” because it is an evaluation/judgment that can, at the currently claimed high level of generality, be practically performed in the human mind. Claim 21 also calls for automatically initiating a call to a suicide hotline for the member based on the suicidal ideation prediction which constitutes “certain methods of organizing human activity” because it relates to managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions). That is, automatically initiating such a call relates to facilitating interactions between the member experiencing suicidal ideation and a healthcare worker trained to manage such situations.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A method for computer-aided (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) escalation of a member in a digital platform (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), the method comprising:
training an escalation detection subsystem comprising a machine learning model and a rule-based model comprising a heuristics-based natural language processing (NLP) model (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)), to predict a score associated with a clinical care escalation outcome, wherein training comprises training with labeled data comprising portions of chat transcripts between a participant and a coach in which the participant provides information informative of escalation to clinical care (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f); mere field of use limitation as noted below, see MPEP § 2106.05(h));
processing a message associated with the member and a set of inputs with the trained escalation detection subsystem and using a set of prioritization parameters for the trained escalation detection subsystem (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)) to produce a predicted score for the member, the predicted score corresponding to a clinical care escalation outcome recommendation for the member, wherein processing the message comprises assigning, using the trained escalation detection subsystem (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)), a set of scores to the message based upon a set of contexts determined from a set of keywords derived from the message,
wherein assigning the set of scores based upon the set of contexts comprises determining a temporal parameter from the set of keywords and weighting the set of scores,
wherein weighting the set of scores comprises downweighting a first score of the set of scores if a time period of the temporal parameter deviates from present time by more than a threshold, and downweighting a second score of the set of scores by a scaling factor corresponding to a duration of time of the temporal parameter,
wherein the predicted score is generated from the set of scores and corresponds to a care escalation outcome recommendation for the member; and
wherein processing the message comprises preserving computational resources (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to "apply it," see MPEP § 2106.05(f)) by pre-processing, with the rule-based model (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to "apply it," see MPEP § 2106.05(f)), the set of inputs to check for a predetermined set of words selected through an optimization process and specific to a particular mental health condition, and not performing one or more steps of the method upon checking the set of inputs for the predetermined set of words;
retrieving a historical score associated with a historical message from the member (extra-solution activity as noted below (data gathering), see MPEP § 2106.05(g));
aggregating the predicted score with the historical score to produce an aggregated score;
evaluating a set of satisfaction criteria based on the aggregated score, wherein:
in an event that the set of satisfaction criteria are satisfied, automatically triggering an adjustment in a care plan at the digital platform (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)); and
preventing repetition of the adjustment in the care plan upon detecting prior triggering of the adjustment in the care plan for the member, wherein the adjustment comprises a change in features of the digital platform that are available to the member.
For the following reasons, the Examiner submits that the above identified additional limitations, when considered as a whole with the limitations reciting the at least one abstract idea, do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the method being computer-aided, the digital platform, and the escalation detection subsystem, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)). For instance, to the extent that the mentally-performable care plan adjustment trigger is performed at or by the generically-recited “digital platform,” the Examiner asserts that doing so just amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (“apply it”) (see MPEP § 2106.05(f)).
Regarding the additional limitations of receiving a historical score, the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)).
Regarding the additional limitations of training the escalation detection subsystem including an ML model (set of NNs in the case of independent claim 10) and a rule/heuristics-based NLP model with labeled data comprising portions of chat transcripts between a participant and a coach in which the participant provides information informative of escalation to clinical care, and then using the trained escalation detection subsystem and set of prioritization parameters to process the message and inputs to produce the predicted score, the Examiner asserts that these additional limitations merely amount to reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)).
Still further, these steps do not require any particular type of training methodology—they only serve to limit the “training” to the use of labeled chat transcripts between a participant and a coach in which the participant provides information informative of escalation to clinical care and thus merely “link[] the use of a judicial exception to a particular technological environment or field of use.” MPEP § 2106.05(h). Still further, the claims do not recite any details regarding how the escalation detection subsystem actually assigns the scores to generate the predicted score.
With reference to Applicant’s specification, [0042]-[0046] provide a generic discussion of rule-based models (e.g., heuristics-based models or other suitable algorithms) and ML models (e.g., deep learning models, NNs, etc. trained via supervised learning, unsupervised learning, etc.) for use in determining that a participant’s level of care should be adjusted. However, [0020]-[0028] and [0051] of the specification generally discuss how the escalation detection subsystem serves to automate the process of monitoring participants for escalation (which would normally be performed by a medical professional) rather than improving any underlying technologies for doing so.
Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Id., p. 12.
Regarding somehow "preserving computational resources" by checking for keywords in the inputs with the rule-based model and then not performing steps of the model, the Examiner asserts that these additional limitations merely amount to reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Specifically, the claims do not include details regarding exactly how for instance the alleged reduction in computational resources owing to not performing one or more steps of the method would not be outweighed by the additional computational resources owing to the rule-based model checking for the predetermined set of words in the inputs.
Regarding the rule-based model somehow being structured to provide traceability of data contributing to an output of the rule-based model and to prevent unexpected outputs by omitting end-to-end deep learning approaches (from independent claim 10), the Examiner asserts that these additional limitations merely amount to reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). More specifically, there are no details regarding exactly how the rule-based model is structured to provide such traceability and how omitting of the end-to-end DL approaches somehow prevents unexpected outputs.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Furthermore, looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 1 and analogous independent claim 10 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 1 and analogous independent claim 10 are directed to at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
-Claims 2 and 17 recite how the escalation detection subsystem uses labeled data including a training message labeled with the clinical care escalation outcome which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). For instance, as the escalation detection subsystem is configured to receive messages and output clinical care escalation outcome recommendations, then merely reciting how the subsystem uses labeled data including a training message labeled with the clinical care escalation outcome merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished.
-Claim 7 recites how it is the “rule-based model” that determines the set of contexts which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). For instance, what steps does the rule-based model perform to determine the set of contexts?
-Claims 8 and 19 recite updating the escalation detection subsystem in response to automatically triggering the adjustment which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). For instance, how is the escalation detection subsystem updated?
-Claims 9 and 20 recite how the updating includes retraining the escalation detection subsystem based on the message, the aggregated score, and the adjustment in the care plan which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). For instance, how are the message, the aggregated score, and the adjustment in the care plan used to retrain the system?
-Claim 11 recites how the NN set includes multiple NNs which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)).
-Claim 12 recites how the first NN performs the message adjustment, the rule-based model determines the set of contexts, and the second NN determines the aggregated score which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). For instance, what steps does the first NN perform that amount to the adjustment of the message, what steps does the rule-based model perform to determine the set of contexts, and what steps does the second NN perform to determine the aggregated score?
-Claim 13 recites how the set of contexts are determined by the rule-based model which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)).
-Claim 14 recites how the message adjustment is performed by the NN set which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)).
-Claim 15 recites how the learned set of prioritization parameters is learned during training of the escalation detection subsystem which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)).
-Claim 16 recites how the triggering of the care plan adjustment includes adjusting an member user device interface which amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
When the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations of the method being computer-aided, the digital platform, and the escalation detection subsystem, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)). For instance, to the extent that the mentally-performable care plan adjustment trigger is performed at or by the generically-recited “digital platform,” the Examiner asserts that doing so just amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (“apply it”) (see MPEP § 2106.05(f)).
Regarding the additional limitations of training the escalation detection subsystem including an ML model (set of NNs in the case of independent claim 10) and a rule/heuristics-based NLP model with labeled data comprising portions of chat transcripts between a participant and a coach in which the participant provides information informative of escalation to clinical care, and then using the trained escalation detection subsystem and set of prioritization parameters to process the message and inputs to produce the predicted score, the Examiner asserts that these additional limitations merely amount to reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)).
Still further, these steps do not require any particular type of training methodology—they only serve to limit the “training” to the use of labeled chat transcripts between a participant and a coach in which the participant provides information informative of escalation to clinical care and thus merely “link[] the use of a judicial exception to a particular technological environment or field of use.” MPEP § 2106.05(h). Still further, the claims do not recite any details regarding how the escalation detection subsystem actually assigns the scores to generate the predicted score.
With reference to Applicant’s specification, [0042]-[0046] provide a generic discussion of rule-based models (e.g., heuristics-based models or other suitable algorithms) and ML models (e.g., deep learning models, NNs, etc. trained via supervised learning, unsupervised learning, etc.) for use in determining that a participant’s level of care should be adjusted. However, [0020]-[0028] and [0051] of the specification generally discuss how the escalation detection subsystem serves to automate the process of monitoring participants for escalation (which would normally be performed by a medical professional) rather than improving any underlying technologies for doing so.
Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Id., p. 12.
Regarding somehow "preserving computational resources" by checking for keywords in the inputs with the rule-based model and then not performing steps of the model, the Examiner asserts that these additional limitations merely amount to reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Specifically, the claims do not include details regarding exactly how for instance the alleged reduction in computational resources owing to not performing one or more steps of the method would not be outweighed by the additional computational resources owing to the rule-based model checking for the predetermined set of words in the inputs.
Regarding the rule-based model somehow being structured to provide traceability of data contributing to an output of the rule-based model and to prevent unexpected outputs by omitting end-to-end deep learning approaches (from independent claim 10), the Examiner asserts that these additional limitations merely amount to reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). More specifically, there are no details regarding exactly how the rule-based model is structured to provide such traceability and how omitting of the end-to-end DL approaches somehow prevents unexpected outputs.
Regarding the additional limitation directed to receiving a historical score which the Examiner submits merely add insignificant extra-solution activity to the abstract idea, the Examiner has reevaluated such limitation and determined it to not be unconventional as it merely consists of receiving/transmitting data over a network. See Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362 (Fed. Cir. 2016); See MPEP 2106.05(d)(II).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
-Claims 2 and 17 recite how the escalation detection subsystem uses labeled data including a training message labeled with the clinical care escalation outcome which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). For instance, as the escalation detection subsystem is configured to receive messages and output clinical care escalation outcome recommendations, then merely reciting how the subsystem uses labeled data including a training message labeled with the clinical care escalation outcome merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished.
-Claim 6 generically recites how the trained escalation detection subsystem includes a combination of a rule-based model and a set of trained NNs which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). For instance, what steps does the set of trained NNs perform that amount to the generic “feature weighting adjustment” of the message?
-Claim 7 recites how it is the “rule-based model” that determines the set of contexts which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). For instance, what steps does the rule-based model perform to determine the set of contexts?
-Claims 8 and 19 recite updating the escalation detection subsystem in response to automatically triggering the adjustment which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). For instance, how is the escalation detection subsystem updated?
-Claims 9 and 20 recite how the updating includes retraining the escalation detection subsystem based on the message, the aggregated score, and the adjustment in the care plan which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). For instance, how are the message, the aggregated score, and the adjustment in the care plan used to retrain the system?
-Claim 11 recites how the NN set includes multiple NNs which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)).
-Claim 12 recites how the first NN performs the message adjustment, the rule-based model determines the set of contexts, and the second NN determines the aggregated score which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)). For instance, what steps does the first NN perform that amount to the adjustment of the message, what steps does the rule-based model perform to determine the set of contexts, and what steps does the second NN perform to determine the aggregated score?
-Claim 13 recites how the set of contexts are determined by the rule-based model which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)).
-Claim 14 recites how the message adjustment is performed by the NN set which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)).
-Claim 15 recites how the learned set of prioritization parameters is learned during training of the escalation detection subsystem which merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished (see MPEP § 2106.05(f)).
-Claim 16 recites how the triggering of the care plan adjustment includes adjusting an member user device interface which amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Therefore, claims 1, 4, 5, and 7-21 are ineligible under 35 USC §101.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JONATHON A. SZUMNY/Patent Examiner, Art Unit 3686
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