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
Claims 1-20, as recited in an RCE filed on October 15, 2025, were previously pending and subject to a non-final office action filed on November 12, 2025 (the “November 12, 2025 Non-Final Office Action”). On February 12, 2026, Applicant: (i) submitted amendments to claims 1, 13, and 16; (ii) canceled claim 20; and (iii) added new claim 21 (the “February 12, 2026 Amendment”). As such, claims 1-19 and 21, as recited in the February 12, 2026 Amendment, are currently pending and subject to the final office action below.
Response to Applicant’s Remarks
Response to Applicant’s Remarks Concerning Rejections under 35 U.S.C. § 101
Applicant’s arguments, see Applicant’s Remarks, pp. 7-11, V. Rejections under 35 U.S.C. § 101 Section, filed February 12, 2026, with respect to rejections of claim 1-20 under 35 U.S.C. § 101 have been fully considered, but they are not persuasive. Further, in light of the 2019 Revised Patent Subject Matter Eligibility Guidance, provided by the USPTO, effective January 7, 2019 (available at MPEP § 2106) (the “2019 Revised PEG”), the § 101 rejections of claims 1-19 are maintained and the § 101 rejection of new claim 21 is added in this final office action.
First, Applicant argues that the claims do not recite an abstract mental process, because “the claim requires a health platform to apply a machine learning model to the ingested first and second data sets and also requires generating a list of providers by processing the first and second data sets with a rules engine comprising a language model, and returning the list of providers”. See Applicant’s Remarks, at p. 7. Examiner respectfully disagrees. A similar argument was addressed in a previous office action. See the November 12, 2025 Non-Final Office Action, at pp. 3-4. Courts have long-recognized that claims can recite a mental process even if they are claimed as being performed on a computer. MPEP § 2106.04(a)(2)(III)(C). In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. Id. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process. Id.
An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC. See MPEP § 2106.04(a)(2)(III)(C). In Voter Verified, Inc., the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are "human cognitive actions" that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer.
Similarly, in the present case, Applicant’s specification demonstrates steps of ingesting the first and second data steps are steps that a humans are capable of performing mentally. For example, paragraph [0003] in Applicant’s specification as filed on April 12, 2023 discloses that the method includes ingesting the first and second data sets, which Applicant discloses as being the equivalent of processing, analyzing, etc. The Examiner asserts that a human is capable of processing and analyzing data mentally or with the aid of pen and paper, especially when the step of ingesting two data sets is claimed at a high level of generality. Therefore, Applicant’s specification demonstrates that the steps of ingesting the first and second data sets recited in Applicant’s claims are capable of being performed mentally by a human. Merely adding a machine learning model to perform abstract mental steps does not take the claim outside Mental Processes grouping of abstract ideas. Here, the machine learning model is an additional element, but it is claimed with a high-level of generality, because Applicant has not described the machine learning model with any particularity, nor has Applicant described the algorithm (e.g., process, steps, flowchart, etc.) that machine learning model goes through in order to generate the course of treatment. Similarly, Applicant has not described the language model with any particularity, nor has Applicant described the process/steps that the language model goes through in order to generate the list of providers. Accordingly, when read as a whole, Applicant’s claims are directed to an abstract idea within the Mental Processes grouping of abstract ideas, and this argument is not persuasive.
Next, Applicant argues that the claims “recite additional elements that meaningfully limit any alleged abstract idea by requiring a health platform to determine and load a selected medical engine, apply a machine learning model to the ingested first and second data sets, generate a course of medical treatment based on the model application, update a graphical user interface to display the course of medical treatment, generate a list of providers by processing the first and second data sets with a rules engine comprising a language model, and return the list of providers. See Applicant’s Remarks, at p. 8. Examiner respectfully disagrees. In order to show that a claim that is directed to a judicial exception be patent eligible under the “meaningful limit” line of reasoning, the claim must include “additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.” See MPEP § 2106.05(e). The claim should add meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment to transform the judicial exception into patent-eligible subject matter. Id.
Here, Applicant’s claims do not provide a specific and tangible method for generating the course of medical treatment and a list of providers. Unlike claims analyzed in: (1) the Diamond v. Diehr case, which were directed to the use of the Arrhenius equation (an abstract idea or law of nature) in an automated process for operating a rubber-molding press; and (2) the Classen Immunotherapies Inc. v. Biogen IDEC case, which were directed to methods that gathered and analyzed the effects of particular immunization schedules on the later development of chronic immune-mediated disorders in mammals in order to identify a lower risk immunization schedule, and then immunized mammalian subjects in accordance with the identified lower risk schedule (thereby lowering the risk that the immunized subject would later develop chronic immune-mediated diseases), Applicant’s steps of (i) applying the machine learning model to generate the course of medical treatment and (ii) generating the list of providers with a rules engine does not provide a specific and tangible method for generating the treatment and list of providers. For example, Applicant has not described the specific steps, flow-charts, or algorithm for the machine learning model or rules engine. Rather, Applicant is merely claiming the machine learning model and rules engine comprising the language model features as ideas of a solution without providing any description for how the machine learning model and the rules engine are able to perform their associated functions (i.e., how does the machine learning model generate the course of treatment and how does the language model generate the list of providers?). Further, Applicant’s arguments that the platform is designed to remove clinician bias is not persuasive, because Applicant’s specification does not provide any description for how the platform, machine learning model, or rules engine is able to remove human biases. Therefore, the additional elements recited in the claims are not indicative of integrating an abstract concept into a practical application under Prong Two of Step 2A, and this argument is not persuasive.
Lastly, Applicant argues that the claims recite significantly more than the abstract idea, because the claims require a specific platform workflow that combines model-based treatment generation with a separate model-based provider identification pipeline. See Applicant’s Remarks, at p. 10. Examiner respectfully disagrees with this argument. When making a determination of whether the additional elements in a claim amount to significantly more than a judicial exception, the examiner should evaluate whether the elements define only well-understood, routine, conventional activity. MPEP § 2106.05(d). In this respect, the well-understood, routine, conventional consideration overlaps with other Step 2B considerations, particularly the improvement consideration (see MPEP § 2106.05(a)), the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)), and the insignificant extra-solution activity consideration (see MPEP § 2106.05(g)). Id. Thus, evaluation of those other considerations may assist examiners in making a determination of whether a particular element or combination of elements is well-understood, routine, conventional activity. Id.
In the present case, the additional elements recited in the claims represent well-understood, routine, and conventional activity. Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., manually or by merely thinking). MPEP § 2106.05(d). Applicant’s claims were deemed to recite an abstract mental process, namely a method for recommending a course of medical treatment, comprising: ingesting a first data set from a first medical diagnostics assessment of a patient; ingesting a second data set of identifying factors associated with the patient; determining a selected medical engine to conduct a health assessment based on the ingested sets of data; generating a course of medical treatment; generating a list of providers; and returning the list of providers. All of these action steps represent concepts that are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper (including an observation, evaluation, judgment, and/or opinion). As described in the amended rejections under the Claim Rejections – 35 U.S.C. § 101 Section below, the additional elements represent generic computer components and functions for implementing the abstract idea or generally link the abstract idea to the field of machine learning and language models. For example, Applicant has not described the models recited in the claims with any specificity, nor do the claims recite the specific steps that the models go through in order to generate the course of treatment or the list of providers.
Therefore, Applicant’s claims do not recite any additional elements which are deemed to provide significantly more than the abstract idea. For these reasons, this argument is not persuasive. Therefore, the rejections of claims 1-19 under 35 U.S.C. § 101 are maintained and the rejections of new claim 21 are added in this office action. Please see the amended rejections under the Claim Rejections – 35 U.S.C. § 101 Section below, for further clarification and complete analysis.
Response to Applicant’s Remarks Concerning Rejections under 35 U.S.C. § 103
Applicant’s arguments, see Applicant’s Remarks, pp. 11-14, IV. Rejections under 35 U.S.C. § 103 Section, filed February 12, 2026, with respect to rejections of claims 1-20 under 35 U.S.C. § 103, have been fully considered, but they are moot in light of Applicant’s amendments to independent claims 1 and 13. Furthermore, the prior art search attached to this office action failed to generate closer prior art results. Accordingly, independent claims 1 and 13 are considered to be novel and non-obvious over the prior art and the prior art rejections of claims 1-19 are withdrawn.
Claim Objections
Claim 21 is objected to because of the following informalities:
- the term "mode" in line 5 of claim 21 should be "model" (i.e., this limitation should be "generating the list of providers by processing the enhanced assessment with practitioner embeddings using the language model"). Appropriate correction is required.
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 1-19 and 21 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more. See MPEP § 2106 (hereinafter referred to as the “2019 Revised PEG”).
Step 1 of the 2019 Revised PEG
Following Step 1 of the 2019 Revised PEG, claims 1-12, 19, and 21 are directed to a method of using a machine learning model to recommend a course of medical treatment, which is within one of the four statutory categories (i.e., a process). See MPEP § 2106.03. Claims 13-18 are directed to a computer readable medium tangibly encoded with a computer program to recommend a course of medical treatment, which is also within one of the four statutory categories (i.e., a manufacture). See id. NOTE – Claim Interpretation: The computer readable medium recited in claims 13-18 is interpreted to be limited to non-transitory, tangible computer readable media (i.e., interpreted as excluding transitory computer readable media, such as signals per se), in accordance with Applicant’s disclosure. See Applicant’s specification as filed on April 12, 2023, paragraph [0085], where Applicant discloses that “the computer readable medium is a non-transitory computer readable medium”.
Step 2A of the 2019 Revised PEG - Prong One
Following Prong One of Step 2A of the 2019 PEG, the claim limitations are to be analyzed to determine whether they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. See MPEP §2106.04. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: (1) Mathematical Concepts; (2) Certain Methods of Organizing Human Activity, and (3) Mental Processes. See MPEP § 2106.04(a).
Claims 1-19 and 21 are rejected under 35 U.S.C. § 101, because the claimed invention is directed to an abstract idea without significantly more. Representative independent claims 1 and 13 include limitations that recite an abstract idea. Note that independent claim 13 is a computer readable medium claim, while claim 1 covers the matching method claim. Specifically, independent claim 13 recites (and claim 1 substantially recites the following limitations):
A computer readable medium tangibly encoded with a computer program to recommend a course of medical treatment, the computer program executable by a processor to perform actions comprising:
ingesting a first data set from a medical diagnostics assessment of a patient;
ingesting a second data set of identifying factors associated with the patient;
determining a selected medical engine from a plurality of medical engines to conduct a health assessment based on the ingested first and second data sets;
loading onto the health platform the selected medical engine;
applying to the ingested first and second data sets, the machine learning model;
generating, based on the applying the machine learning model to the first and second data sets, the course of medical treatment;
updating a graphical user interface to display the generated course of medical treatment;
generating a list of providers by processing the first and second data sets with a rules engine comprising a language model; and
returning the list of providers.
However, the Examiner submits that the foregoing underlined limitations constitute a process that, under its broadest reasonable interpretation, falls within the “Mental Processes” grouping of abstract ideas. See 2019 Revised PEG. The Mental Processes category covers concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper (including an observation, evaluation, judgment, or opinion) (i.e., a method for recommending a course of medical treatment, comprising: ingesting a first data set from a first medical diagnostics assessment of a patient; ingesting a second data set of identifying factors associated with the patient; determining a selected medical engine to conduct a health assessment based on the ingested sets of data; generating a course of medical treatment; generating a list of providers; and returning the list of providers). That is, other than reciting some computer components and functions (the foregoing limitations in claim 1 and 13 which are not underlined), the context of claims 1 and 13 encompass concepts that are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper (including an observation, evaluation, judgment, and/or opinion) (i.e., a method for recommending a course of medical treatment, comprising: ingesting a first data set from a first medical diagnostics assessment of a patient; ingesting a second data set of identifying factors associated with the patient; determining a selected medical analytic engine to conduct a health assessment based on the ingested sets of data; generating a course of medical treatment; generating a list of providers; and returning the list of providers).
The aforementioned claim limitations described in claims 1 and 13 are analogous to claim limitations directed toward concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper, because they merely recite limitations which encompasses a person mentally and/or manually observing, evaluating, making judgments, opinions related to: (1) ingesting a first data set from a first medical diagnostics assessment of a patient (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally observe data associated with a medical diagnostics assessment of a patient); (2) ingesting a second data set of identifying factors associated with a patient (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally observe data related to identifying factors associated with a patient, such as observing the patient’s name or birthdate); (3) determining a selected engine to conduct a health assessment based on the ingested sets of data (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally and manually determine which software application/rules/protocol to use for conducting a health assessment based on the ingested sets of data); (4) generating a course of treatment for the patient (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally and manually come up with a treatment for a patient, such as mentally coming up with a prescription for a patient and writing it down on a piece of paper); (5) generating a list of providers by processing the first and second data sets (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could mentally and manually come up with a list of providers after analyzing the first and second data sets); and (6) returning the list of providers (i.e., a type of observation, evaluation, judgment, and/or opinion where a person could manually writing the list of providers down on a piece of paper). Therefore, the aforementioned underlined claim limitations may reasonably be interpreted as mental/manual observations, evaluations, judgments, and/or opinions made by a person, such as a healthcare professional. If a claim limitation, under its broadest reasonable interpretation, covers concepts which are capable of being performed in the human mind or encompasses a human performing the step(s) mentally with the aid of a pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. See 2019 Revised PEG. Accordingly, claims 1 and 13 recite an abstract idea.
Furthermore, Examiner notes that dependent claims 2-12 and 14-19 and 21 have limitations that further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below. Examiner notes that: (1) dependent claims 2, 7, 11, 12, 14, 17, 18, and 21 provide limitations that are deemed to be additional elements which require further analysis under Prong Two of Step 2A; and (2) dependent claims 3-6, 8-10, 15, 16, and 19 do not provide any limitations that are deemed to be additional elements which require further analysis under Prong Two of Step 2A. For example, claims 3-6, 8-10, 15, 16, and 19 further limit the abstract idea in narrowing the type of data that is used to generate the medical treatment or the type of information that is generated with the medical treatment (i.e., these steps are deemed to be reasonably performed mentally or manually using a pen and paper, because a person may reasonably generate a medical treatment based on certain data (i.e., an observation of a certain type of data)). Further, claim 21 further limits the abstract idea by adding additional mental/manual steps for generating an enhanced assessment by processing the health assessment; generating the list of providers by processing the enhanced assessment with practitioner embeddings; and generating a course of medical treatment.
Step 2A of the 2019 Revised PEG - Prong Two
Regarding Prong Two of Step 2A of the 2019 Revised PEG, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted in the 2019 Revised PEG, it must be determined whether any additional elements in the claims are indicative of integrating the abstract idea 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.” See MPEP §§ 2106.05 (f)-(h).
In the present case, for independent claim 13, the additional elements beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional elements” while the underlined portions continue to represent the at least one “abstract idea”):
A computer readable medium tangibly encoded with a computer program (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) to recommend a course of medical treatment, the computer program executable by a processor (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) to perform actions comprising:
ingesting a first data set form a medical diagnostics assessment of a patient;
ingesting a second data set of identifying factors associated with the patient;
determining a selected medical engine from a plurality of medical engines to conduct a health assessment based on the ingested first and second data sets;
loading onto the health platform the selected medical engine (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f));
applying to the ingested first and second data sets, the machine learning model (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h));
generating, based on the applying the machine learning model to the first and second data sets (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)), the course of medical treatment;
updating a graphical user interface (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)) to display the generated course of medical treatment (the Examiner submits that this additional element amounts to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d));
generating a list of providers by processing the first and second data sets with a rules engine comprising a language model (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)); and
returning the list of providers.
However, the recitation of these generic computer components and functions in claims 1 and 13 are recited at a high-level of generality (i.e., using generic computer devices and software to perform the method for recommending a course of medical treatment, comprising: ingesting a first data set from a first medical diagnostics assessment of a patient; ingesting a second data set of identifying factors associated with the patient; determining a selected medical engine to conduct a health assessment based on the ingested sets of data; and generating a course of medical treatment), such that it amounts to no more than: (1) adding the words “apply it” (or is the equivalent of) with the judicial exception; mere instructions to implement an abstract idea on a computer; or merely uses a computer as a tool to perform an abstract idea; (2) adding insignificant extra-solution activity to the judicial exception; and (3) generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.05(f)-(h). For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
- The following is an example of court decisions that demonstrate merely applying instructions by reciting the computer structure as a tool to implement the claimed limitations (e.g., see MPEP § 2106.05(f)):
- A commonplace business method or mathematical algorithm being applied on a general purpose computer, e.g., see Alice Corp. Pty. Ltd. v. CLS Bank Int’l – similarly, the current invention implements the commonplace medical business method of generating a course of medical treatment for a patient on a general purpose computer (i.e., the Examiner submits that the additional elements directed to the computer readable medium tangibly encoded with a computer program; the computer program executable by a processor; and the healthcare platform, which load and apply the machine learning model; and the graphical user interface, represent a generic computer device with generic software).
- Requiring the use of software to tailor information and provide it to the user on a generic computer, e.g., see Intellectual Ventures I LLC v. Capital One Bank (USA) – similarly, the current invention requires software components and the system (i.e., the computer readable medium tangibly encoded with a computer program; the computer program executable by a processor; and the healthcare platform, which load and apply the machine learning model and the rules engine comprising the language model) to perform the abstract idea.
- The following is an example of an insignificant extra-solution activity (e.g., see MPEP § 2106.05(g)):
- Example of Mere Data Gathering/Mere Data Outputting:
- Obtaining information about transactions using the Internet to verify credit card transactions, e.g., see CyberSource v. Retail Decisions, Inc. – similarly, the ultimate step directed to “updating a graphical user interface to display the generated course of medical treatment”, described in claims 1 and 13, is a necessary data outputting step (i.e., displaying the generated course of medical treatment is necessary in order to output the results of the mental process of generating the course of medical treatment).
- The following are examples of generally linking the use of a judicial exception to a particular technological environment or field of use (e.g., see MPEP § 2106.05(h)):
- (1) Specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, FairWarning v. Iatric Sys.; (2) Specifying that the abstract idea of using advertising as currency is used on the Internet, because this narrowing limitation is merely an attempt to limit the use of the abstract idea to a particular technological environment, Ultramercial, Inc. v. Hulu; and (3) Requiring that the abstract idea of creating a contractual relationship that guarantees performance of a transaction (a) be performed using a computer that receives and sends information over a network, or (b) be limited to guaranteeing online transactions, because these limitations simply attempted to limit the use of the abstract idea to computer environments, buySAFE Inc. v. Google, Inc. - similarly, the limitations directed to: the rules engine comprising a language model and the steps directed to: “loading onto the health platform the selected medical engine”; “applying to the ingested first and second data sets, the machine learning model”; “applying the machine learning model to the third data set and the data from the external data source”; and “the machine learning model utilizing one or more of a regression algorithm, a decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, and a support vector machine algorithm”, amounts to limiting the abstract idea to the field of machine learning and language models. See MPEP 2106.05(h).
Thus, the additional elements in independent claims 1 and 13 are not indicative of integrating the judicial exception into a practical application. Similarly, dependent claims 3-6, 8-10, 15, 16, and 19 do not recite any additional elements outside of those identified as being directed to the abstract idea described above. Examiner notes that dependent claims 2, 7, 11, 12, 14, 17, 18, and 21 recite the following additional elements (in bold font below with limitations deemed to be part of the above identified abstract idea identified in underlined font):
ingesting a third data set from a second medical diagnostics assessment of the patient; receiving from an external data source (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)), data related to the third data set; applying the machine learning model to the third data set and the data from the external data source (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)); and updating the generated course of medical treatment (as described in claims 2 and 14);
wherein the first data set is stored as vector embeddings (the Examiner submits that this additional element amounts to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of storing and retrieving information in memory, as evidenced by the Versata Dev. Group, Inc. v. SAP Am., Inc. case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)) (as described in claim 7);
wherein the second data set is stored as vector embeddings (the Examiner submits that this additional element amounts to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of storing and retrieving information in memory, as evidenced by the Versata Dev. Group, Inc. v. SAP Am., Inc. case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)) (as described in claim 11);
wherein displaying the generated course of medical treatment further comprises displaying reasons for generating the course of medical treatment (the Examiner submits that this additional element amounts to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)) (as described in claim 12 and 18);
wherein the first data set and the second data set are stored as vector embeddings (the Examiner submits that this additional element amounts to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of storing and retrieving information in memory, as evidenced by the Versata Dev. Group, Inc. v. SAP Am., Inc. case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)) (as described in claim 17); and
further comprising: generating an enhanced assessment by processing the health assessment with the language model (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)); generating the list of providers by processing the enhanced assessment with practitioner embeddings using the language mode (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)); and generating the course of medical treatment with the machine learning model utilizing one or more of a regression algorithm, a decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, and a support vector machine algorithm (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h)).
As such, the additional elements in dependent claims 2, 7, 11, 12, 14, 17, 18, and 21 are not indicative of integrating the judicial exception into a practical application. Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, unlike the claims that have been held as a whole to be directed to an improvement or otherwise directed to something more than the abstract idea, claims 1-19 and 21: (1) are not directed to improvements to the functioning of a computer, or to any other technology or technical field similar to the Enfish, LLC v. Microsoft Corp. case (see MPEP § 2106.05(a)); (2) do not apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see MPEP § 2106.04(d)(2)); (3) do not apply the judicial exception with, or by use of, a particular machine (see MPEP § 2106.05(b)); (4) do not effect a transformation or reduction of a particular article to a different state or thing (see MPEP § 2106.05(c)); nor do they (5) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as whole is more than a drafting effort designed to monopolize the exception (see MPEP § 2106.05(e) and MPEP § 2106.04(d)(2)). For these reasons, claims 1-19 and 21 do not recite additional elements that integrate the judicial exception into a practical application.
Step 2B of the 2019 Revised PEG
Regarding Step 2B of the 2019 Revised PEG, claims 1-19 and 21 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, with respect to integration of abstract idea into a practical application, the additional elements of claims 1-19 and 21 amount to no more than: (1) adding the words “apply it” (or is the equivalent of) with the judicial exception; mere instructions to implement an abstract idea on a computer; or merely uses a computer as a tool to perform an abstract idea; (2) adding insignificant extra-solution activity to the judicial exception; and (3) generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.05(f)-(h). Further the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than limitations consistent with what the courts recognize, or those having ordinary skill in the art would recognize, to be well-understood, routine, and conventional computer components. See MPEP § 2106.05 (d).
Specifically, the Examiner submits that the additional elements of claims 1-19 and 21, as recited, the computer readable medium tangibly encoded with a computer program; processor; health platform; machine learning model; graphical user interface; rules engine comprising a language model; and the steps of: “loading onto the health platform the selected medical engine”; “applying to the ingested first and second data sets, the machine learning model”; “updating a graphical user interface to display the generated course of medical treatment”; “applying the machine learning model to the third data set and the data from the external data source”; “wherein the first data set is stored as vector embeddings”; “wherein the second data set is stored as vector embeddings”; “wherein displaying the generated course of medical treatment further comprises displaying reasons for generating the course of medical treatment”; “wherein the first data set and the second data set are stored as vector embeddings”; and “the machine learning model utilizing one or more of a regression algorithm, a decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, and a support vector machine algorithm”, are generic computer components and functions. See MPEP § 2106.05(d)(II).
- In regard to the computer readable medium tangibly encoded with a computer program; processor; health platform; machine learning model; graphical user interface; rules engine comprising a language model; and the steps of: “loading onto the health platform the selected medical engine”; “applying to the ingested first and second data sets, the machine learning model”; “applying the machine learning model to the third data set and the data from the external data source”; and “the machine learning model utilizing one or more of a regression algorithm, a decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, and a support vector machine algorithm”, these additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than well-understood, routine, and conventional activities previously known to the industry, because:
- Applicant’s disclosure supports this assertion – for example, Applicant discloses that the “machine learning system may apply a rigorous and automated process to recommend a course of treatment based on historical data”. Applicant’s specification as filed on April 12, 2023, paragraph [0058]. Further, Applicant’s discloses that the “computer hardware 603 may include hardware from a single computing device (e.g., a single server) or from multiple computing devices (e.g., multiple servers), such as multiple computing devices in one or more data centers”, such as “one or more processors 607, one or more memories 608, one or more storage components 609 (e.g., big data 108), and/or one or more networking components 610”. Applicant’s specification as filed on April 12, 2023, paragraph [0061]. These paragraphs demonstrate that the computer readable medium tangibly encoded with a computer program; processor; health platform; machine learning model; graphical user interface; rules engine comprising a language model; and the steps of: loading onto the health platform the selected medical analytic engine”; “applying to the ingested first and second data sets, the machine learning model”; “applying the machine learning model to the third data set and the data from the external data source”; and “the machine learning model utilizing one or more of a regression algorithm, a decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, and a support vector machine algorithm”, comprise a plurality of general purpose computing devices and software, because they are claimed at high-level of generality/in a generic manner. Therefore, Applicant’s disclosure provides evidence that the above identified additional elements are well-understood, routine, and conventional devices previously known to the pertinent industry.
- The Examiner submits that these limitations amount to merely using a computer or other machinery as tools for performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f) and analysis of these limitations under Step 2A, Prong Two above).
- The Examiner submits that these limitations generally link the use of the judicial exception to a particular technological environment or field of use – for example, the limitations directed to the rules engine comprising a language model and the steps directed to: “loading onto the health platform the selected medical engine”; “applying to the ingested first and second data sets, the machine learning model”; “applying the machine learning model to the third data set and the data from the external data source”; and “the machine learning model utilizing one or more of a regression algorithm, a decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, and a support vector machine algorithm”, amount to generally linking the abstract idea to the field of machine learning and language models (see MPEP § 2106.05(h) and analysis of these limitations under Step 2A, Prong Two above).
Therefore, these limitations are also deemed to be well-understood, routine, and conventional under Step 2B for similar reasons since they are claimed in a generic manner.
- Regarding the steps and features of: “updating a graphical user interface to display the generated course of medical treatment”; “wherein the first data set is stored as vector embeddings”; “wherein the second data set is stored as vector embeddings”; “wherein displaying the generated course of medical treatment further comprises displaying reasons for generating the course of medical treatment”; and “wherein the first data set and the second data set are stored as vector embeddings” - The following represents examples that courts have identified to be well-understood, routine, and conventional activities (e.g., see MPEP § 2106.05(d)):
- Receiving or transmitting data over a network, e.g., see Intellectual Ventures v. Symantec – similarly the limitations directed to: “updating a graphical user interface to display the generated course of medical treatment”; and “wherein displaying the generated course of medical treatment further comprises displaying reasons for generating the course of medical treatment”, are similarly deemed to be well-understood, routine, and conventional activity in the medical field, because they also represent mere collection and transmission of data over a network (i.e., (i) “updating a graphical user interface to display the generated course of medical treatment”; and (ii) “displaying reasons for generating the course of medical treatment”, are each the equivalent of transmitting data over a network to a generic display device.
- Storing and retrieving information in memory, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly the limitations directed to: “wherein the first data set is stored as vector embeddings”; “wherein the second data set is stored as vector embeddings”; and “wherein the first data set and the second data set are stored as vector embeddings”, also merely represent storing information in a memory.
Therefore, the additional described in claims 1, 2, 7, 11-14, 17, 18, and 21 are deemed to be additional elements which do not amount to significantly more than the abstract idea identified above.
Thus, taken alone, the additional elements of claims 1, 2, 7, 11-14, 17, 18, and 21 do not amount to significantly more than the above-identified judicial exception (the abstract idea). Furthermore, 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 functionality of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an ordered combination, claims 1, 2, 7, 11-14, 17, 18, and 21 are nonetheless rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Additionally, dependent claims 3-6, 8-10, 15, 16, and 19 (which depend on claims 1 and 13 due to their respective chains of dependency), do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Examiner notes that 3-6, 8-10, 15, 16, and 19 do not include any additional elements beyond those identified as well-understood, routine, and conventional components as described above in the subject matter eligibility rejections of independent claims 1 and 13. Dependent claims 3-6, 8-10, 15, 16, and 19 merely add limitations that further narrow the abstract idea described in independent claims 1 and 13. Therefore, claims 1-19 and 21 are nonetheless rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. See PTO-892.
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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Examiner, Art Unit 3686
/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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