DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/12/2025 has been entered.
Response to Arguments
Rejection Under 101
Applicant's arguments filed 10/12/2025 have been fully considered.
Applicant argues that due to the amendments, the asserted characterization under 101 no longer applies. Concrete, machine-implemented operations are now explicitly recited in the amended claims.
In response to Applicant’s argument, the argument appears to be directed toward the amendments and is therefore moot. See the updated rejection for further clarification.
Applicant argues that the structural and operational features of the amended claims are not abstract.
In response to Applicant’s argument, the argument appears to be directed toward the amendments and is therefore moot. See the updated rejection for further clarification. Additionally, the limitations at issues are considered additional elements and not part of the abstract idea. See the updated rejection below.
Applicant argues that the use of the machine learning techniques are neither mental processes nor insignificant extrasolution activity but are performed by configured “computing hardware under machine control.”
In response to Applicant’s argument, the argument appears to be directed toward the amendments and is therefore moot. See the updated rejection for further clarification. Additionally, the limitations at issues are considered additional elements and not part of the abstract idea. The amended limitations directed toward machine learning are construed as additional elements that merely apply the abstract idea in a computer environment, as evidenced by Applicant’s own arguments: “technical data-processing steps… performed entirely by configured computing hardware under machine control”, thus the abstract idea is being carried out in a computing environment under machine control using machine learning data processing techniques. This amounts to merely invoking computers to carry out the abstract idea. See the updated rejection below.
Applicant argues that the claimed architecture improves the computer functionality itself.
In response to Applicant’s argument, the argument appears to be directed toward the amendments and is therefore moot. See the updated rejection for further clarification. Additionally, the amended limitations at issues are considered additional elements and not part of the abstract idea. The additional elements are merely applying the abstract idea in a computer environment as discussed above and thus do not recite an improvement.
Applicant argues that the display is a physical hardware component with the integrated with the processor and ML module to dynamically visualize computed rational responses and system-generated scores. The scoring mechanism is not mere presentation but an automated feedback signal controlling operations.
In response to Applicant’s argument, the use of a display to output information has been found by the courts to be analogous to outputting information and thus insignificant extrasolution activity. See the rejection below.
Applicant argues that the limitations of claims 5-10 modify the underlying computational behavior of the system. The steps directly influence how the processor manages allocations, databases, and model updates, thus integrating the abstract idea into a practical application.
In response to Applicant’s argument, as discussed in the rejection below, the dependent claims further narrow or define the abstract idea. Beyond the limitations consistent with the independent claims, the additional limitations amount to nothing more than merely amount to invoking computers as a tool to perform the abstract idea and are generally linking the abstract idea to a particular field of environment.
Applicant argues that the claims are not mere instructions to apply an exception but integrated into a practical application that improves the operation of a multi-component computer system performing specialized data transformations.
In response to Applicant’s argument, the additional elements do not amount to a practical application since the recited additional elements are recited at a high level of generality (e.g., memory, processor, etc.).
Applicant argues that the amended claims now recite significantly more than any alleged abstract idea by defining a specific, non-conventional machine configuration and data processing method.
In response to Applicant’s argument, the additional elements do not amount to a practical application since the recited additional elements are recited at a high level of generality (e.g., memory, processor, etc.). These generic computer components are also used for their intended purposes (e.g., storing data, processing data, etc.). Therefore, the additional elements do not amount to significantly more than the abstract idea. See below for further clarification.
Applicant argues that the claims recite concrete components and operations (e.g., processor, display, machine learning module, etc.). These elements improve how patients interactions are managed. Our claims recite specific implementations of a solution to a problem in the software arts (integrating ML, embeddings, thresholds, etc.) and thus are eligible. The training requires specialized algorithms and hardware and thus improves computer operations.
In response to Applicant’s argument, as previously discussed the computer components (e.g., processor, display, machine learning module, etc.) are recited at a high level of generality and used for their intended purposes. The claims are not directed at a way to improve machine learning or other computer functions, but rather the claim is directed toward a method and system for patient engagement. The claims recite these computer components merely as a way to carry out the abstract idea.
Applicant argues that the claims are rooted in computer technology and address a technical problem on computing devices.
In response to Applicant’s argument, as previously discussed the computer components merely carry out the abstract idea, there is no improvement to computer technology.
Applicant argues that the claims integrate the abstract idea into a practical application.
In response to Applicant’s argument, as previously discussed the additional elements amount to computer components merely carry out the abstract idea or insignificant extrasolution activity. See the updated rejection for clarification.
Applicant argues that the claims recite processor, memory, display, ML module, pre-learned embeddings, S-BERT embeddings and thus show integration into a practical application. These are not abstract placeholders but concrete machine components to execute specific computational tasks.
In response to Applicant’s argument, as previously discussed the additional elements amount to computer components merely carry out the abstract idea. See the updated rejection for clarification. Additionally, while the claim recite embeddings, S-BERT embeddings are not recited. Although claims are interpreted in light of the specification, limitations from the specification are not read into the claims. In re Van Geuns, 26 USPQ2d 1057 (CA FC 1993).
Applicant argues that the pretrained embeddings (such as S-BERT) and retrieval and pre-learned embeddings is concrete and transform raw text to vector space representation. The ML techniques are not merely applying ML but materially ties the abstract to the specific machine learning operations. Courts treat such improvement as technical.
In response to Applicant’s argument, the amended claims are merely invoking the use of pretrained embeddings to carry out the abstract idea. See the updated rejection below.
Applicant argues that determining whether the crowd stipend meets threshold and sending requests amounts to a practical application.
In response to Applicant’s argument, this limitation at issue is part of the abstract idea. See the updated rejection below for further clarification.
Applicant argues that upon acceptance record acceptance feedback for subsequent model updates amounts to a practical application.
In response to Applicant’s argument, this limitation at issue is part of the abstract idea. See the updated rejection below for further clarification.
Applicant argues that the amended claims recite significantly more than the abstract idea by reciting unconventional steps and components, such as, ML module (S-BERT embedding retrieval), dynamic crowd stipend threshold logic, feedback-based model improvement, database logging and concrete hardware components.
In response to Applicant’s argument, as discussed with the practical application analysis, the additional elements amount to nothing more than merely applying the abstract idea in a computer environment and insignificant extrasolution activity. See the updated rejection below for further clarification.
Applicant argues the legal standards/authorities. Enfish – claims directed to improved computer functionality can be patient eligible. Bascom – an inventive, non-conventional ordered combination of elements can be significantly more. McRO – claims that automate a manual process via detailed rules in a manner that improves computer automation are eligible. DDR – solving problems specific to internet/information technology and rooted in computer technology supports eligibility. Berkheimer – Examiner must provide evidence is asserting conventionality.
In response to Applicant’s arguments, regarding the Enfish argument, the claims are not improving computer functionality but improving the abstract idea of patient engagement. Bascom argument – as previously discussed the additional elements do not amount to significantly more, see the rejection and arguments. McRO argument – again the claims are not automating a manual process with detailed rules to improve computer automation, but rather improving the abstract idea of patient engagement. DDR argument – as discussed previously the use of the computer components is merely used to invoke the use of computer technology to carry out the abstract idea. Berkheimer argument – evidence of conventionality is provided in the analysis below in Step 2B of the analysis. See the updated rejection below for further clarification.
Applicant argues that the claims recite significantly more, such as, transformation of unstructured text into structured embeddings; concreted retrieval and decision pipeline; crowd stipend logic; data architecture supporting privacy, retrievability and analytics; closed feedback loop for adaptive ML; elements (S-Bert embeddings, pre-learned embeddings, explicit DB architecture, etc.) must have evidence of conventionality in Step 2B rejection.
In response to Applicant’s arguments, the additional elements amount to merely applying the abstract idea in a computer environment and insignificant extrasolution activity. Some of the limitations in Applicant’s argument, for example, the crowd stipend are part of abstract idea and not an additional element. See the rejection below where the additional elements are bolded. Additionally, evidence to conventionality is provided in Step 2B in the form of caselaw and specification support. Furthermore, the claims do not recite S-Bert embeddings, and the specification will not be read into the claim.
Applicant argues that the claims recite limitations analogous to Enfish and Bascom for improving functionality.
In response to Applicant’s arguments, see response to arguments above previously addressing this similar argument.
Applicant argues that the claims recite specific algorithms and stateful programmatic operations that improve machine behavior.
In response to Applicant’s arguments, the claims recite invoking the use of machine learning and computer components to carry out the abstract idea of patient engagement. See above for similar arguments addressing this argument.
Applicant argues that with regards to claim 14, the claim recites particular hardware and ordered operations that integrated it into a practical application. The claim recites technical features that produce computer improvements and therefore supply significantly more than the abstract idea.
In response to Applicant’s arguments, similar to the response given above with the exemplary claim 1 for the analysis, claim 14 recites additional elements that amount to merely applying the abstract idea in a computer environment and insignificant extrasolution activity. See the rejection below for further clarification.
The dependent claims 2-3, 5-13, 15-16, 18-26 recite patent-eligible limitations (see Remarks pgs. 37-43).
In response to Applicant’s argument, as discussed in the rejection below, the dependent claims further narrow or define the abstract idea. Beyond the limitations consistent with the independent claims, the additional limitations amount to nothing more than merely amount to invoking computers as a tool to perform the abstract idea and are generally linking the abstract idea to a particular field of environment.
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 1-3, 5-16, 18-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 of the Alice/Mayo Test
Claims 1-3, 5-13 are drawn to a method, which is within the four statutory categories (i.e. process). Claims 14-16, 18-26 are drawn to a system, which is within the four statutory categories (i.e. apparatus).
Step 2A of the Alice/Mayo Test - Prong One
The independent claims recite an abstract idea. For example, independent claim 1 (and substantially similar with independent claim 14) recites:
A computer-implemented patient engagement method executed by a networked patient engagement system (100) comprising a processor (150), a memory (120), a machine learning (ML) module (116), and a display (130), the method comprising:
receiving, by a patient engagement system (100), an input through the display (130), from a first patient from a plurality of patients, wherein the input indicates that the first patient has an Automatic Negative Thought (ANT);
displaying, by the patient engagement system (100) on the display (130), a first option to participate in a triple-column associated with the ANT of the first patient, a second option for assistance of at least one second patient of the plurality of patients, and a third option for assistance of an application;
detecting, by the patient engagement system (100), one of the first option, the second option and the third option selected by the first patient on the display (130); and
performing, by the patient engagement system (100), one of:
when the first option is selected by the first patient, displaying the triple-column, receiving an input corresponding the ANT, a cognitive distortion and a rational response in the triple-column from the first patient and allocating a score to the first patient based on the input provided by the first patient, wherein the patient engagement system (100) stores the cognitive distortion and rational response in a user-specific database (104) and stores an anonymized version in a generic database (106) and records the score in a user activity database (108) and memory (120) for leaderboard and analytics,
when the second option is selected by the first patient, sending a cognitive distortion and rational response request to the at least one second patient, receiving the cognitive distortion and the rational response for the ANT from the at least one second patient, upon acceptance by the first patient storing accepted rational responses in the user-specific database (104), storing anonymized version in the generic database (106), and allocating a score to the at least one second patient and the first patient, wherein the patient engagement system (100) decrements a crowd stipend of the first patient upon sending the cognitive distortion and rational response request and updates a record corresponding to the first patient in the user activity database (108), and
when the third option is selected by the first patient, invoking ML model (116) from the ML module (112) to generate pre-trained embeddings for the ANT using pre-learned embeddings (114), using the pre-trained embeddings to retrieve similar stored ANTs to predict the cognitive distortion and the rational response for the ANT, presenting the predicted cognitive distortion and rational response to the first patient for acceptance, and upon acceptance recording acceptance feedback for subsequent model updates, allocating a score to the first patient,
wherein sending a cognitive distortion and rational response request to the at least one second patient comprises:
determining whether the crowd stipend of the first patient meets a crowd stipend threshold;
sending the cognitive distortion and rational response request to the at least one second patient when the crowd stipend of the first patient meets the crowd stipend threshold; and
decrementing the crowd stipend of the first patient.
These underlined elements recite an abstract idea that can be categorized, under its broadest reasonable interpretation, to cover the management of personal behavior or interactions (i.e., following rules or instructions), but for the recitation of generic computer components. For example, but for the patient engagement system, machine learning model, application, display, network, databases, memory, processor, the limitations in the context of this claim encompass an automation of detecting a patient’s negative thoughts, presenting options for selection by the user and following rules or instructions to make response predictions, allocate scores, and provide appropriate crowd stipends to patients. If a claim limitation, under its broadest reasonable interpretation, covers management of personal behavior or interactions but for the recitation of generic computer components, then the limitations fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a).
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-3, 5-13, 15-16, 18-26 reciting particular aspects of the abstract idea).
Step 2A of the Alice/Mayo Test - Prong Two
For example, independent claim 1 (and substantially similar with independent claim 14) recites:
A computer-implemented (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) patient engagement method executed by a networked patient engagement system (100) comprising a processor (150), a memory (120), a machine learning (ML) module (116), and a display (130), (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))the method comprising:
receiving, by a patient engagement system (100), (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) an input through the display (130), (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))from a first patient from a plurality of patients, wherein the input indicates that the first patient has an Automatic Negative Thought (ANT);
displaying, by the patient engagement system (100) on the display (130), a first option to participate in a triple-column associated with the ANT of the first patient, a second option for assistance of at least one second patient of the plurality of patients, and a third option for assistance of an application; (merely insignificant extrasolution activity steps as noted below, see MPEP 2106.05(g))
detecting, by the patient engagement system (100), (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) one of the first option, the second option and the third option selected by the first patient on the display (130); and
performing, by the patient engagement system (100), (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) one of:
when the first option is selected by the first patient, displaying the triple-column, receiving an input corresponding the ANT, a cognitive distortion and a rational response in the triple-column from the first patient and allocating a score to the first patient based on the input provided by the first patient, wherein the patient engagement system (100) stores the cognitive distortion and rational response in a user-specific database (104) and stores an anonymized version in a generic database (106) and records the score in a user activity database (108) and memory (120) for leaderboard and analytics, (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) and (merely insignificant extrasolution activity steps as noted below, see MPEP 2106.05(g))
when the second option is selected by the first patient, sending a cognitive distortion and rational response request to the at least one second patient, receiving the cognitive distortion and the rational response for the ANT from the at least one second patient, upon acceptance by the first patient storing accepted rational responses in the user-specific database (104), storing anonymized version in the generic database (106), and allocating a score to the at least one second patient and the first patient, wherein the patient engagement system (100) decrements a crowd stipend of the first patient upon sending the cognitive distortion and rational response request and updates a record corresponding to the first patient in the user activity database (108), and
when the third option is selected by the first patient, invoking ML model (116) from the ML module (112) to generate pre-trained embeddings for the ANT using pre-learned embeddings (114), using the pre-trained embeddings to retrieve similar stored ANTs to predict the cognitive distortion and the rational response for the ANT, (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) presenting the predicted cognitive distortion and rational response to the first patient for acceptance, and upon acceptance recording acceptance feedback for subsequent model updates, allocating a score to the first patient,
wherein sending a cognitive distortion and rational response request to the at least one second patient comprises:
determining whether the crowd stipend of the first patient meets a crowd stipend threshold;
sending the cognitive distortion and rational response request to the at least one second patient when the crowd stipend of the first patient meets the crowd stipend threshold; and
decrementing the crowd stipend of the first patient.
The judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations, which:
amount to mere instructions to apply an exception (such as recitations the patient engagement system, machine learning model, application, display, network, databases, processor, thereby invoking computers as a tool to perform the abstract idea, see applicant’s specification [0044], [0048], [0059]-[0073], [0111]-[0112], see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of displaying the different options, storing information in databases, amounts to insignificant extrasolution activity, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-3, 5-13, 15-16, 18-26 recite additional limitations which amount to invoking computers as a tool to perform the abstract idea, and claims 2-3, 5-13, 15-16, 18-26 additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo Test for Claims
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and adding insignificant extra-solution activity to the abstract idea. Additionally, the additional elements, other than the abstract idea per se, amount to no more than elements which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as using the patient engagement system, machine learning model, application, display, network, databases, processor, e.g., Applicant’s spec describes the computer system with it being well-understood, routine, and conventional because it describes in a manner that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such elements to satisfy 112a. (See Applicant’s Spec. [0044], [0048], [0059]-[0073], [0111]-[0112]); using a processor, memory, display, and machine learning, e.g., merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014).
adding insignificant extrasolution activity to the abstract idea, for example mere data gathering, selecting a particular data source or type of data to be manipulated, and/or insignificant application. The following represent examples that courts have identified as insignificant extrasolution activities (e.g. see MPEP 2106.05(g)): displaying the different options, e.g., outputting or providing access to the information, Symantec, 838 F.3d at 1321 and MPEP 2106.05(g)(3)); storing information in databases, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv).
Dependent claims recite additional subject matter beyond the abstract idea which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea and are generally linking the abstract idea to a particular field of environment. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Therefore, the claims are not patent eligible, and are rejected under 35 U.S.C. § 101.
Allowable Subject Matter
Claims 1-3, 5-16, 18-26 are free of prior art over Bindler et al. (US 2003/0059750) in view of Howard (US 2017/0251985) and Ahmad et al. (US 10476821). The prior art references, or reasonable combination thereof, could not be found to disclose, or suggest all of the limitations found in the independent claims. The closest prior art is Bindler et al. (US 2003/0059750), which teaches learning to detect and identify negative thoughts for implementing customizable modules for patient engagement. Howard (US 2017/0251985) teaches using machine learning to analyze data for patterns in a user’s words. Ahmad et al. (US 10476821) teaches an engagement hub with a natural language processor that structures an unstructured messages and parses text. The references taken solely, or in combination, fail to provide the required limitations, and modification of any complementary combination of the references of record would be impermissible hindsight and not provide any advantages over their present application. Dependent claims are also free of prior art due to their corresponding dependency of the independent claims.
Conclusion
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/AMANDA R. COVINGTON/Examiner, Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686