DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
In the reply filed 2026 August 25. The following changes have been made: amendments to claims 24, 28-30, and 34-35.
Claims 24-35 are currently pending and have been examined.
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 24-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process (claims 24-29) and a machine (claims 30-35).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 24 recites steps of
by automated computing machinery comprising a graph analyzer executing one or more trained machine-learning models on an enterprise knowledge graph comprising patient data structured as triples of nodes and relationships:
receiving a new patient for group therapy;
deriving, by the graph analyzer, a patient archetype of the new patient by clustering nodes of the enterprise knowledge graph;
selecting a potential group for the new patient in dependence upon patient
archetype and patient attributes;
calculating, by a trained cohesion machine-learning model of the graph analyzer, a group cohesion value for the potential group including the new
patient; and
determining whether the calculated group cohesion value meets group cohesion
requirements;
if the calculated group cohesion value meets inclusion group cohesion
requirements, adding the new group patient to the group; and
if the calculated group cohesion value does not meet group cohesion
requirements, selecting another potential group for the new patient; and
If there is not another potential group for the new patient, creating a new group
that meets group cohesion requirements.
Claim 30 recites similar limitations as claim 24.
These steps for managing patient flow for group therapy, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. That is, nothing in the claim element precludes the italicized portions from managing personal behavior or relationships or interactions between people through organizing the activity around a workflow for managing patient flow (receiving, placing, replacing, or creating new groups for group therapy). This could be analogized to managing organizational workflow that are essential practices in the healthcare industry. Also, nothing in the claim element precludes the italicized portions from performing fundamental economic practices or principles pertaining to selecting participants for a group, based on attributes and cohesion, which is a building block of service-based industries (i.e., psychotherapy). This could be analogized to organizing a team, forming a committee, or matching clients to services, which courts have found to be fundamental, non-patentable concepts. The italicized portions containing the recitation of deriving a patient archetype and calculating a group cohesion value have been treated as part of the abstract idea, specifically as mathematical calculations, which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance. If a claim limitation, under its broadest reasonable interpretation, covers performance as organizing human activity and mathematical calculations but for the recitation of generic computer components, then it falls within the “Methods of Organizing Human Activity” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements non-italicized portions identified above for claims 24 and 30, does 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 recitation of by automated computing machinery comprising a graph analyzer executing one or more trained machine-learning models on an enterprise knowledge graph; by the graph analyzer; and, by a trained cohesion machine-learning model of the graph analyzer amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of receiving a new patient which amounts to mere data gathering since it does not add meaningful limitations to the receiving action performed, see MPEP 2106.05(g))
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity and add insignificant extra-solution activity to the abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) 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 add insignificant extra-solution activity to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as through a therapeutic chat interface application; wherein the therapeutic chat interface application comprises an artificial intelligence agent chatbot; by the artificial intelligence agent chatbot; using a machine learning model comprising a natural language understanding model; using a generative artificial intelligence model by the artificial intelligence agent chatbot; through the therapeutic chat interface application through the artificial intelligence agent chatbot; through the therapeutic chat interface application; and, within the therapeutic chat interface application by the artificial intelligence agent chatbot, e.g., requiring the use of software to tailor information and provide it to the user on a generic computer, see Intellectual Ventures I LLC v. Capital One Bank, MPEP 2106.05(f)
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as recitation of receiving a new patient, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i).
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 generic computer implementation.
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 25-29 and 31-35 reciting particular aspects for managing patient flow for group therapy such as
[Claims 25 & 31] wherein calculating a group cohesion value for the potential group includes determining a time in group for each patient;
[Claims 26 & 32] wherein calculating a group cohesion value for the potential group includes determining, for each patient in the group, a time in group with every other patient in the group;
[Claims 27 & 33] wherein calculating a group cohesion value for the potential group includes evaluating compatibility among of the patient archetypes of patients in the group;
[Claims 28 & 34] wherein calculating a group cohesion value for the potential group includes evaluating compatibility among of the patient archetypes of patients in the group and a therapist archetype of the therapist of the group;
[Claims 29 & 35] wherein calculating a group cohesion value for the potential group includes assessing a size of the potential group;
these italicized portions are methods of organizing human activity since they merely describe types of data and determinations that can be performed by humans.
Step 2A Prong 2
Dependent claims do not recite additional subject matter which integrate the abstract idea into a practical application.
Step 2B
The dependent claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, in consideration of all the facts, the present invention is not a patent-eligible invention under USC 101. Additionally, it is evident that the present claims monopolize the judicial exception since it covers tasks that human therapists have performed mentally or manually for decades, effectively "tying up" a fundamental tool of psychology thus restricting further innovation in this area without offering a specific, technical improvement to a computing device. The Supreme Court forbids patents that preempt the use of such "building blocks of human ingenuity" (MPEP 2106); “monopolization of those tools through the grant of a patent might tend to impede innovation more than it would tend to promote it.” Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (quoting Myriad, 569 U.S. at 589, 106 USPQ2d at 1978 and Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012)).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35
U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148
USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 24-35 are rejected under 35 U.S.C. 103 as being unpatentable over Yalom et al. (Compatibility and Cohesiveness) in view of HHS (Criteria for the Placement of Clients in Groups) and further in view of Chowdhury et al. (Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction).
Regarding claim 24, Yalom discloses receiving a new patient for group therapy ([pg. 268] “focuses on the issue of the suitability of a particular patient for the modality of group therapy.”)
calculating a group cohesion value for the potential group including the new patient; and determining whether the calculated group cohesion value meets group cohesion requirements ([pg. 271] “Scores of all patients present in a given meeting were averaged separately for each question and used as an index of group "meeting to meeting" satisfaction.” [pg. 273] “the not-named group the highest Cohesiveness”)
if the calculated group cohesion value meets group cohesion requirements, adding the new patient to the group ([pg. 270] “approximately 75% of these patients were accepted for group therapy […] new members being added as members left the group, and there was no limit set on the group's life span”)
and if the calculated group cohesion value does not meet group cohesion requirements ([pg. 273] “the over-named group had the lowest cohesiveness”)
and if there is not another potential group for the new patient, creating a new group that meets group cohesion requirements ([pg. 272 to 273] “extreme incompatibility was defined as the most incompatible 5% of dyadic scores on interchange and originator compatibility. The persons named in these dyadic scores were then broken down into two groups, those who were incompatible with only one other person, and those who were incompatible with more than one. Three groups were thus generated: "not named," "named-once," and "over-named.")
Yalom does not explicitly disclose however Chowdhury teaches a graph analyzer executing one or more trained machine-learning models on an enterprise knowledge graph comprising patient data structured as triples of nodes and relationships ([pg. 1672] “This study addresses these gaps by introducing an innovative framework that combines the strengths of Graph Neural Network (GNN) and Transformer technologies. This framework is designed to achieve more precise outcome predictions based on a stratification of patients using phenotypical representations, which were captured from both the temporal dynamics and the heterogeneity presentation in EHR data, such as the variability and changing correlations among phenotypical features over time. [pg. 1673] “To model the process of patient's treatment in the clinical setting, we formulated a health network for each individual by converting patient-specific events from the EHR sequence P
into a concept graph G = (V, E, A, X), where V represents vertices, E the connecting edges, A the adjacency matrix, and X the node feature matrix. Nodes V are derived from three EHR data sources […] Refer to Supplementary Notes S3 and S4 for the model and training details, respectively.”)
deriving, by the graph analyzer, a patient archetype of the new patient by clustering nodes of the enterprise knowledge graph ([pg. 1672] “With the application of unsupervised cluster analysis (i.e., K-Means Clustering) to
the learned patient graph representations, we were subsequently able to derive phenotypically distinct patient subgroups (i.e., phenogroups).”)
selecting a potential group for the new patient in dependence upon the patient archetype and patient attributes ([pg. 1679] “The classifier was able to accurately assign new patients to phenogroups 1, 2, 3 and 4 with F1 scores of 0.91, 0.83, 0.81 and 0.94, respectively.”)
by a trained cohesion machine-learning model of the graph analyzer ([pg. 1679] “outcomes can be predicted using a more customized model tailored for these groups”)
It would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the compatibility and cohesiveness system of Yalom by automated computing machinery comprising a graph analyzer executing one or more trained machine-learning models on an enterprise knowledge graph comprising patient data structured as triples of nodes and relationships; deriving, by the graph analyzer, a patient archetype of the new patient by clustering nodes of the enterprise knowledge graph; selecting a potential group for the new patient in dependence upon patient archetype and patient attributes; and, by a trained cohesion machine-learning model of the graph analyzer as taught by Chowdhury since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Yalom in view of Chowdhury does not explicitly disclose however HHS teaches selecting another potential group for the new patient ([pg. 37] “facilitate the placement of people from minority cultures and ease such clients into existing groups […] Clients may need to move to different groups as they progress through treatment, encounter setbacks, and become more or less committed to recovery”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the compatibility and cohesiveness system of Yalom and stratification system of Chowdhury selecting another potential group for the new patient as taught by HHS since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 25, Yalom discloses wherein calculating a group cohesion value for the potential group includes determining a time in group for each patient ([pg. 270] “The five therapy groups all began at approximately the same time and were studied for their first 12 meetings.” [pg. 271] “Each patient's score was averaged separately on each of the three items over the first six meetings and over the next six meetings to give the person's "meeting-to-meeting" satisfaction.”)
Regarding claim 26, Yalom discloses wherein calculating a group cohesion value for the potential group includes determining, for each patient in the group, a time in group with every other patient in the group ([pg. 270] “The five therapy groups all began at approximately the same time and were studied for their first 12 meetings.”)
Regarding claim 27, Yalom discloses wherein calculating a group cohesion value for the potential group includes evaluating compatibility among of the patient archetypes of patients in the group ([pg. 270 to 271] “By averaging these dyadic compatibilities, the originator and the interchange compatibility of that person to the group may be found (individual-group compatibility). […] Finally, by averaging the compatibilities of all dyads, the total group originator and interchange compatibility may be found.”)
Regarding claim 28, Yalom discloses wherein calculating a group cohesion value for the potential group includes evaluating compatibility among of the patient archetypes of patients in the group and a therapist archetype of the therapist of the group ([pg. 271] “The questionnaire consists of these 11 questions, each answered on a 5-point scale. […] 9. How do you feel about the group therapist(s)?” [pg. 273] “patients were ranked within each group on their interchange and originator compatibility with each of the therapists. Rank-order correlations were done with cohesion at the 6th and 12th meetings, and with the scores on the post group items. The correlation coefficients were transformed and averaged as described above.”)
Regarding claim 29, Yalom discloses wherein calculating a group cohesion value for the potential group includes assessing the size of a potential group ([pg. 271] “A cohesiveness questionnaire (after Gross, 1957,3" modified by us for therapy groups) was administered to each group twice (at the end of the 6th and 12thmeetings). The questionnaire consists of these 11 questions, each answered on a 5-point scale. […] If you could replace members of your group with other "ideal group members," how many would you exchange? (Exclusive of group therapists).”)
Regarding claim 30, Yalom discloses receiving a new patient for group therapy ([pg. 268] “focuses on the issue of the suitability of a particular patient for the modality of group therapy.”)
calculating a group cohesion value for the potential group including the new patient; and determining whether the calculated group cohesion value meets group cohesion requirements ([pg. 271] “Scores of all patients present in a given meeting were averaged separately for each question and used as an index of group "meeting to meeting" satisfaction.” [pg. 273] “the not-named group the highest Cohesiveness”)
if the calculated group cohesion value meets inclusion group cohesion requirements, adding the new group patient to the group ([pg. 270] “approximately 75% of these patients were accepted for group therapy […] new members being added as members left the group, and there was no limit set on the group's life span”)
and if the calculated group cohesion value does not meet group cohesion requirements ([pg. 273] “the over-named group had the lowest cohesiveness”)
and If there is not another potential group for the new patient, creating a new group that meets group cohesion requirements ([pg. 272 to 273] “extreme incompatibility was defined as the most incompatible 5% of dyadic scores on interchange and originator compatibility. The persons named in these dyadic scores were then broken down into two groups, those who were incompatible with only one other person, and those who were incompatible with more than one. Three groups were thus generated: "not named," "named-once," and "over-named.")
Yalom does not explicitly disclose however Chowdhury teaches a graph analyzer that executes one or more trained machine-learning
models on an enterprise knowledge graph comprising patient data structured as triples of nodes and relationships, the automated computing machinery ([pg. 1672] “This study addresses these gaps by introducing an innovative framework that combines the strengths of Graph Neural Network (GNN) and Transformer technologies. This framework is designed to achieve more precise outcome predictions based on a stratification of patients using phenotypical representations, which were captured from both the temporal dynamics and the heterogeneity presentation in EHR data, such as the variability and changing correlations among phenotypical features over time. [pg. 1673] “To model the process of patient's treatment in the clinical setting, we formulated a health network for each individual by converting patient-specific events from the EHR sequence P
into a concept graph G = (V, E, A, X), where V represents vertices, E the connecting edges, A the adjacency matrix, and X the node feature matrix. Nodes V are derived from three EHR data sources […] Refer to Supplementary Notes S3 and S4 for the model and training details, respectively.”)
deriving, by the graph analyzer, a patient archetype of the new patient by clustering nodes of the enterprise knowledge graph ([pg. 1672] “With the application of unsupervised cluster analysis (i.e., K-Means Clustering) to the learned patient graph representations, we were subsequently able to derive phenotypically distinct patient subgroups (i.e., phenogroups).”)
selecting a potential group for the new patient in dependence upon the patient archetype and patient attributes ([pg. 1679] “The classifier was able to accurately assign new patients to phenogroups 1, 2, 3 and 4 with F1 scores of 0.91, 0.83, 0.81 and 0.94, respectively.”)
by a trained cohesion machine-learning model of the graph analyzer ([pg. 1679] “outcomes can be predicted using a more customized model tailored for these groups”)
It would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the compatibility and cohesiveness system of Yalom by automated computing machinery comprising a graph analyzer executing one or more trained machine-learning models on an enterprise knowledge graph comprising patient data structured as triples of nodes and relationships; deriving, by the graph analyzer, a patient archetype of the new patient by clustering nodes of the enterprise knowledge graph; selecting a potential group for the new patient in dependence upon patient archetype and patient attributes; and, by a trained cohesion machine-learning model of the graph analyzer as taught by Chowdhury since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Yalom in view of Chowdhury does not explicitly disclose however HHS teaches selecting another potential group for the new patient ([pg. 37] “facilitate the placement of people from minority cultures and ease such clients into existing groups […] Clients may need to move to different groups as they progress through treatment, encounter setbacks, and become more or less committed to recovery”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the compatibility and cohesiveness system of Yalom and stratification system of Chowdhury selecting another potential group for the new patient as taught by HHS since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 31, the limitations are rejected for the same reasons as stated above for claim 25.
Regarding claim 32, the limitations are rejected for the same reasons as stated above for claim 26.
Regarding claim 33, the limitations are rejected for the same reasons as stated above for claim 27.
Regarding claim 34, the limitations are rejected for the same reasons as stated above for claim 28.
Regarding claim 35, the limitations are rejected for the same reasons as stated above for claim 29.
Response to Arguments
The arguments filed on 2026 August 25 have been considered but are not fully persuasive.
Regarding the claim objections, applicant has amended claims 28-29 and 34-35 to overcome the claim objections. Therefore, the claim objections have been withdrawn.
Regarding the drawing objections, applicant has amended the specification to add in missing reference characters. Therefore, drawing objection has been withdrawn.
Regarding the USC 101 rejection, applicant argues on that the rejection overlooks important aspects of the claim and when those claim elements that give character to the claims are considered, the claim is no directed to an abstract idea. Applicant submits that the amended claims are not the recitation of a result to be achieved on a generic computer, but rather a specific disclosed technical mechanism. Multiple portions of the specification are cited with the assertion that the claims recite a particular improvement to a computerized process for administering and composing therapy groups - a technological solution effected by a particular machine applying particular computational techniques - and impose meaningful limits well beyond the alleged abstract idea and cannot be performed by humans. For step 2B, the applicant argues that the present office action doesn’t account for the present amendments which must be supported by factual evidence. Regarding the concern of preemption, applicant asserts that the amended claims are confined to a particular automated methodology tied to a graph analyzer, trained machine-learning models, and an enterprise knowledge graph of triples. That clinicians remain entirely free to compose therapy groups by professional judgment or by any technique outside this specific claimed implementation, and the claims do not preempt the general concept of group formation.
Examiner disagrees with the applicant’s arguments. While applicant is trying to clarify the claim language with amendments, examiner asserts that the claims are still directed to the judicial exception(s) as confirmed by multiple subject matter experts at the USPTO. Applicant’s invention describes a administrative method for managing group therapy placement. The claimed steps rely entirely on mathematical and logical functions like clustering and data parsing. Historically, a human administrator could evaluate patient attributes, look at existing groups, and determine compatibility using manual metrics. Translating this logic onto a computer does not change its abstract nature. MPEP 2106 is clear in that claims can recite an abstract idea even if they are claimed as being performed on a computer. Examiner points to the USPTO October 2019 Guidance (also incorporated in MPEP 2106) which states that claims can recite an abstract idea even if they are claimed as being performed on a computer. The USPTO October 2019 Guidance is clear in that the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite an abstract idea even though the limitations may not be entirely performed by humans. The computer components recited in the present application are still recited at a high level of generality. The computers in the claims are not used in a specific, inventive way. Again, the claims are very outcome-focused and do not detail how each of the outcomes are exactly reached. The machine learning model presently claimed is a black box model in that there is no clarity on the actual computer processing, algorithmic steps, or how the computer is programmed to achieve the results in a non-abstract way different from how humans analyze/process data. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to ‘implement[ing] the abstract idea of intermediated settlement on a generic computer’, it cannot save OIP's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). Even if the claims nominally recites computer components that are rooted in technology, there is no recitation of how the computer components are specifically programmed to distinguish from generic computer processes. Further analyzing the additional elements under Step 2A Prong 2, examiner asserts that the amendments do not move the needle on USC 101 since the specification provides a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art. The MPEP provides that improvements to the functioning of a computer or to any other technology or technical field can signal eligibility, see MPEP 2106.05(a), and provides examples of improvements to computer functionality, MPEP 2106.05(a)(I), and improvements to any other technology of technical field, MPEP 2106.05(a)(I). “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool”. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. Id. It was the specification' s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691. The claim was not simply the addition of general-purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts. 822 F.3d at 1339, 118 USPQ2d at 1691. Unlike Enfish, the instant claimed invention appears to improve upon a judicial exception rather than a problem in the software arts. Rather than improving a computer's algorithm (i.e., solving a technically based problem), the claimed invention purports to solve the non-technological problems of lack of specialization in-person and lack of consideration of patient factors by clinics ([0001]-[0002] of the specification) through automating placement of patients into group therapy. The fact remains that applicant’s main/glaring issue is that the specification does not show or describe a deficiency in computer technology. That is, the present claims do not improve how a computer or machine-learning model works on a hardware or software level. Instead, applicant is solving a business or medical management problem and improve upon a judicial exception. All the applicant is doing is applying known technology for their intended benefit(s) to a new data environment and calling it an improvement (see Customedia Techs., LLC v. Dish Network Corp., Case No.18-2239 (Fed. Cir. Mar. 6, 2020). Even if the claims provide a solution to a technical problem, examiner asserts that applicant is clearly using a standard, off-the-shelf machine learning primitive operating in its normal manner:
First, one of ordinary skill in the art would recognize that training a machine learning model to calculate a precise group cohesion value is incredibly difficult especially since cohesion is a highly subjective, dynamic psychological metric. Furthermore, human relationships and therapy outcomes are notoriously noisy data points. Feeding subjective clinical notes or demographic triples into an ML model to output a strict metric (i.e., meets group cohesion requirements) will likely suffer from high variance and poor generalization. The applicant’s specification does not explain/lacks detail on how hard-to-define or brittle machine learning targets are managed that is necessary for an improvement to be apparent to a person of ordinary skill in the art. Thus, it is not evident from the specification how the claims improve the operation of computers.
Second, one of ordinary skill in the art would recognize that the claimed workflow requires "deriving a patient archetype […] by clustering nodes of the enterprise knowledge graph" when a new patient is received. Global graph clustering (like Louvain or Infomap) or dynamic Node2Vec embeddings on an enterprise-scale graph are highly computationally expensive. Doing this synchronously upon receiving a patient will cause massive latency bottlenecks. The applicant’s specification does not explain/lacks detail on how high latency is accounted for that is necessary for an improvement to be apparent to a person of ordinary skill in the art. Thus, it is not evident from the specification how the claims improve the operation of computers.
Third, one of ordinary skill in the art would recognize that if the first group fails the cohesion check, the system must loop: selecting another potential group → calculating group cohesion → repeat. If an enterprise has hundreds of active groups, running a graph-based ML prediction repeatedly for every single candidate group creates a severe performance bottleneck. The applicant’s specification does not explain/lacks detail on how computational complexity is accounted for that is necessary for an improvement to be apparent to a person of ordinary skill in the art. Thus, it is not evident from the specification how the claims improve the operation of computers.
Fourth, one of ordinary skill in the art would recognize that relying on triples of nodes and relationships means a new patient with sparse historical data will have very few edges. The clustering algorithm will likely fail and group the patient into a generic unknown archetype or misplace them entirely due to a lack of topological context. The applicant’s specification does not explain/lacks detail on how graph topology vulnerabilities are handled that is necessary for an improvement to be apparent to a person of ordinary skill in the art. Thus, it is not evident from the specification how the claims improve the operation of computers.
The examiner also asserts the following facts which the applicant has been unable to dispute:
1) the invention does NOT involve a novel algorithm or data structure that significantly improves the computer's functionality,
2) the invention does NOT involve a new hardware component or configuration that works with the computer to achieve a specific technical benefit, and
3) the computer is NOT used in a completely new way demonstrating a significant technical advancement.
Improvement to the abstract idea is not an improvement to computer technology. Thus, examiner does not see the present claims improving the functioning of a computer or provide improvements to any other technology or technical field. Applicant’s amended claim glaringly still does not show any improvement to the additional element(s). The claimed invention appears similar to the example of improvements that are insufficient to show an improvement in computer-functionality such as arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). See MPEP 2106.05(a)(I)(viii). The asserted benefits from the applicant’s invention seem to come as a result of the use of general-purpose computers. The broad claims are lacking concrete limitations to integrate the abstract idea into a practical application. Examiner points out that the claimed limitations have no indication in the specification that the operations recited invoke any inventive programming, require any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is implemented using other than generic computer components to perform generic computer functions. See DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (fed Cir. 2014) (“[A]fter Alice, there can remain no doubt: recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.”). Most importantly, in DDR Holdings & unlike the present claims, the claims at issue specified how interactions with the Internet were manipulated to yield a desired result—a result that overrode the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink. 773 F.3d at 1258; 113 USPQ2d at 1106. The examiner also points out that there is no indication in the specification that the claimed invention affects a transformation or reduction of a particular article to a different state or thing. Examiner points to the recitation of machine learning in the claim(s) as generic. "[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice Corp. v. CLS Banklnt'l, 573 U.S. 208 223 (2014). Applicant does not and cannot contend they invented the concept of machine learning, nor does the specification disclose any new machine learning model. The alleged improvement of using the machine learning lies in the abstract idea itself, not to any technological improvement nor to any improvement to the functioning of a computer. See BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88 (Fed. Cir. 2018). The fact pattern of the applicant’s claims is congruent to the Recentive Analytics, Inc. v. Fox Corp., 2025 U.S.P.Q.2d 628 (Fed. Cir. 2025) decision by the Federal Circuit. Just like in Recentive, the present claims still do not delineate steps through which the machine learning achieves an improvement. See, e.g., IBM v. Zillow Grp., Inc., 50 F.4th 1371, 1381 (Fed. Cir. 2022) (holding abstract a claim that "d[id] not sufficiently describe how to achieve [its stated] results in a non-abstract way," because "[s]uch functional claim language, without more, is insufficient for patentability under our law." (quoting Two-Way Media Ltd v. Comcast Cable Commc'ns, LLC, 874 F.3d 1329, 1337 (Fed. Cir. 2017))); see also Intell. Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1342 (Fed. Cir. 2017) (similar); Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016) (similar). Claiming a mere concept or functional result without disclosing the implementation details does not overcome USC 101. Applying an established technique to a new field or data set is insufficient for patent eligibility. To show an involvement of a computer assists in improving technology, the claims must recite details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology (MPEP 2106.05(a)(II)). In Finjan, Inc. v. Blue Coat Systems the courts found that the claims were “directed to a non-abstract improvement in computer functionality…” (MPEP 2106.04(d)). The present invention does not meet the condition set forth by the courts and thus does not integrate the judicial exception into a practical application. The claims are clearly not eligible under Step 2A Prong 2. Examiner points out that the “apply it” and “well-understood, routine, and conventional” analysis was performed under Step 2B, with court case citations, which didn’t result in the claim being eligible under USC 101. In comparison to Bascom, examiner points out that Bascom is not similar to the present application because Bascom claimed a technical improvement in the art i.e., a technology-based solution to filter content on the internet while the present application is not presenting an improvement (as indicated above). Producing a practical result is not listed as a consideration for patentability in MPEP 2106. The use of a computer or other machinery in its ordinary capacity for economic or other tasks or simply adding a general-purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Applicant also made the assertion that the claim does not preempt the general concept of group
formation. If the applicant truly believes this, then the applicant can make the record on what would infringe this invention. This would help the examiner and the USPTO in its USC 101 analysis. The applicant has not demonstrated that their invention is inventive. There is absolutely no justification for withdrawing the USC 101. Therefore, the USC 101 rejection is strongly maintained.
Regarding the USC 103 rejection, applicant’s arguments regarding the amendments, combination of the Yalom and HHS reference, and deficiencies of Yalom and HHS have been considered but are moot since they do not apply to the newly cited reference of record: Chowdhury.
In response to applicant’s argument that a person of ordinary skill would not have modified Yalom because the modification would change Yalom's principle of operation; and, that Yalom teaches away from the proposed modification just like HHS teaches away which makes the combination improper, examiner disagrees and points out that the modification or change to Yalom is a predictable variation that falls within the normal skill in the art, rather than a fundamental redesign. A modification that merely changes a business process, such as automating a manual activity or reordering steps, does not necessarily constitute a change in the underlying principle of operation of a technology or system. Further, the proposed modification does not change the principle of operation, but merely optimizes the performance by applying a known technique to improve the applicant’s invention, which is a predictable variation within the skill of one of ordinary skill in the art. Applicant tries to cite portions of Yalom and HHS to assert teaches away, but examiner points out that Yalom and HHS merely describe a different approach rather than actively discouraging the approach taken by the applicant. A reference that suggests a certain path is better does not necessarily teach away. Teaching away is a high bar and is not met by mere disclosure of alternative methods or a preference for another approach. Especially important, which counters the applicant’s arguments, is the fact that the prior art does not contain explicit warnings, criticism, or even negative comments about the applicant’s claimed invention. The warning in HHS about automatic matchmaking is safety mechanism with respect to considering bias and prejudice, unless the applicant’s argument is another way of admitting on record that the presently claimed invention is indeed biased and prejudiced. Applicant’s “teaches away” argument is based on a narrow, irrelevant portion of the prior art. Rather, when viewed as a whole, the art provides a motivation to combine that yields predictable results, which weighs against a finding of teaches away. Lastly, it is evident that applicant did not present evidence of secondary considerations, such as long-felt but unsolved need, failure of others, or commercial success, which can overcome an obviousness rejection.
With respect to the motivation statement, while it may seem conclusory, the rationale used by the examiner is valid rationale that cleanly applies to the present invention. The motivation statement that ended mid-sentence is a typo and has been fixed.
In response to applicant's arguments that Yalom fails to show certain features of applicant’s invention, it is noted that the features upon which applicant relies (i.e., “calculating a group cohesion value for the potential group and determining meeting of group cohesion requirements” means a gate or gating in which a group is not randomly composed and has no cohesion requirement to evaluate against) is not recited in the rejected claims 24 and 30. Although the claims are interpreted in light of the specification, limitations from the specification [0054] are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The broadest reasonable interpretation of the claim language of group cohesion calculation and determination would not be limited to specifically a gate or gating in which a group is not randomly composed and has no cohesion requirement to evaluate against. The dependent claims are still rejected under USC 103 for the reasons indicated for the independent claims.
Prior Art Cited but Not Relied Upon
Fenkel, C. (2018). EXPLORING THE CLINICAL UTILITY OF MOBILE APPLICATIONS FOR PROMOTING AFFECT REGULATION AMONG CLIENTS WITH BEHAVIORAL HEALTH PROBLEMS (Doctoral dissertation, University of Pennsylvania).
This reference is relevant because it discloses the applicant’s invention as it pertains to group therapy.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WINSTON FURTADO whose telephone number is (571)272-5349. The examiner can normally be reached Monday-Friday 8:00 AM to 4:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WINSTON R FURTADO/Primary Examiner, Art Unit 3687
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