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 .
DETAILED ACTION
The present Office Action is in response to the Request for Continued Examination filed 18 May 2026.
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed 18 May 2026 has been entered.
Response to Amendment
In the amendment dated 05/18/2026, the following occurred: Claims 1, 4, 6-9, 11, 14 and 16-19 have been amended.
Claims 1-20 are pending and have been examined.
Priority
The effective filing date is 3/29/2021.
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 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 11 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (YES)
Claims 1 and 11 fall into at least one of the statutory categories (i.e., process, machine).
Step 2A1 (YES)
The claims recite an abstract idea. The identified abstract idea is as underlined (claim 1 being representative):
a computing device, wherein the computing device is configured to:
obtain an arthritic element from one or more detection devices by determining data associated with an individual’s biological system that denotes an arthritic state, wherein an arthritic state is a measure of a level of physical well-being of one or more of the individual’s joints and connective tissues;
receive an arthritic training set, wherein the arthritic training set correlates a plurality of arthritic elements to a plurality of arthritic batches;
train an arthritic machine-learning model comprising a neural network using the arthritic training set;
generate an arthritic batch as a function of the obtained arthritic element using the trained arthritic machine-learning model, wherein the arthritic batch is an estimated profile of the one or more of the individual’s joints and connective tissues;
receive, from an external device, an updated arthritic machine-learning model incorporating one or more of a firmware update, a software update and an arthritic machine-learning process correction;
generate an updated arthritic batch as a function of the arthritic element using the updated arthritic machine-learning model;
determine an edible as a function of the arthritic batch; and
generate a nourishment program as a function of the edible.
The identified limitations, as drafted, is a process that, under its broadest reasonable interpretation (BRI), covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions), along with a mathematical concept (discussed infra), but for the recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to generate a nourishment program (see Spec. Para. 0021 describing arthritic enumeration as human activity; Spec. Para. 0032 describing edible determination and receiving training data correlations as human activity; Spec. Para. 0012 and 0014-0015 also describing data entry and receiving training data as human activity; and Spec. Para. 0036 describing producing a nourishment program as human activity) in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “obtain… by determining… receive… generate… receive… generate… determine… generate…” as indicated supra.
Other than reciting generic computer components (discussed infra), i.e., a system implemented by a data processor (computer), the claimed invention amounts to managing personal behavior or interactions between people. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The claims further recite “training… using the arthritic training set” (claim 1 being representative). When given its broadest reasonable interpretation in light of the disclosure, the training for a machine learning model to generate “an arthritic batch” using a suitable training algorithm, e.g., Levenberg-Marquardt, conjugate gradient, simulated annealing or other algorithms, represents the creation of mathematical interrelationships between data (see Spec. Para. 0048). As such, the training for the machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The Examiner notes that the mathematical concept need not be expressed in mathematical symbols but not merely limitations that are based on or involve a mathematical concept (MPEP § 2106.04(a)(2)(I)). The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Alternately and for completeness, the type of training utilized by the claimed invention (i.e., “other algorithms”) is not described by the Applicant (see again Spec. Para. 0048). As such, the Examiner is required to analyze the training step given the broadest reasonable interpretation. The training of the model is considered in the alternative to be part of the abstract idea because it falls under data manipulations that humans perform (i.e., fitting a model to data) as the claim does not recite a specific training algorithm and the disclosure includes a non-specific example of a suitable training algorithm for the neural network. Thus, the training is also interpreted to be part of the abstraction--the rules or instructions that fall under Certain Methods of Organizing Human Activity. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 12 (Fed. Cir. April 18, 2025) (finding that “[i]terative training using selected training material…are incident to the very nature of machine learning.”).
Step 2A2 (NO)
The judicial exception, the above-identified abstract idea, is not integrated into a practical application. In particular, the claims recite the additional element of a computing device that implements the identified abstract idea. The additional element aforementioned is not described by the applicant and is recited at a high-level of generality (i.e., a generic computer or computer component performing a generic computer or computer component function that facilitates the identified abstract idea) such that this amounts no more than mere instructions to apply the exception on a generic computer (see Specification, e.g., at para. 0060). See MPEP § 2106.04(d)(I). Accordingly, the additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims further recite the additional elements of one or more detection devices as collecting, transmitting or outputting data. The additional elements are recited at a high-level of generality (i.e., each as a general means of collecting, transmitting or outputting data) and amount to location(s) from which data is received or to which data is transmitted or outputted, each of which represents an extra-solution activity (e.g., mere data gathering and data output). The additional elements further amount to location(s) from which a clinical test is performed to obtain input, e.g., the level of a biomarker in blood (see Spec. Para. 0010). MPEP § 2106.04(d)(I) indicates that extra-solution data gathering and data output activity cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
The claims further recite the additional element of an external device as collecting, transmitting or outputting data. The additional element is recited at a high-level of generality (i.e., general means of collecting, transmitting or outputting data) and amounts to a location from which data is received or to which data is transmitted or outputted, each of which represents an extra-solution activity (e.g., mere data gathering and data output). MPEP § 2106.04(d)(I) indicates that extra-solution data gathering and data output activity cannot provide a practical application. Accordingly, even in combination, the additional element does not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
The claims further recite the additional elements of a (trained) arthritic machine-learning model comprising a neural network and/or an updated arthritic machine-learning model that implement the identified abstract idea (e.g., to generate “an arthritic batch” or “an updated arthritic batch”). The additional elements are not described by the Applicant, are recited at a high-level of generality and are merely invoked as tools to perform an existing process (MPEP § 2106.05(f)(2), see case involving a commonplace business method or mathematical algorithm being applied on a general-purpose computer within the “Other examples”), such that this amounts no more than mere instructions to apply the abstract idea using a general-purpose computer (see Spec. Para. 0048: “the process of “training” the network… elements from a training data 504 set are applied” using a suitable training algorithm; and Spec. Para. 0016 and 0027). See MPEP § 2106.04(d)(I); and Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014). Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See also, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). The claims are directed to an abstract idea. Alternatively, or in addition, the implementation of the trained arthritic machine-learning model and the updated arthritic machine-learning model to generate data merely confines the use of the abstract idea (i.e., the trained model, the received updated model) to a particular technological environment or field of use (e.g., neural networks) and thus fails to add an inventive concept to the claims.
Step 2B (NO)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a computing device to perform the method (represented by claim 1) amounts no more than mere instructions to apply the exception using a generic computer or generic computer component. Mere instructions to apply an exception using generic computer(s) and/or generic computer component(s) cannot provide an inventive concept (“significantly more”). See MPEP § 2106.05(f).
Also discussed above with respect to integration of the abstract idea into a practical application, the additional elements of one or more detection devices (i.e., each a device that collects, transmits or outputs data) are each considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. MPEP 2106.05(d)(II) indicates that receiving, transmitting or outputting data over a network has been held by the courts to be well-understood, routine, conventional activity (citing TLI Communications, Symantec, OIP Techs., and buySAFE). See also MPEP 2106.05(g) (citing Cybersource, Mayo, OIP Techs.) Further, MPEP 2106.05(d)(II) indicates that determining the level of a biomarker in blood and performing clinical tests on individuals to obtain input is well-understood, routine, conventional activity (citing Mayo, Cleveland Clinic Foundation). See also MPEP 2106.05(g) (citing also In re Grams). Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). As such, the claims are not patent eligible.
Also discussed above with respect to integration of the abstract idea into a practical application, the additional element of an external device (i.e., a device that collects, transmits or outputs data) is considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. MPEP 2106.05(d)(II) indicates that receiving, transmitting or outputting data over a network has been held by the courts to be well-understood, routine, conventional activity (citing TLI Communications, Symantec, OIP Techs., and buySAFE). See also MPEP 2106.05(g) (citing Cybersource, Mayo, OIP Techs.) Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). As such, the claims are not patent eligible.
Also discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a (trained) arthritic machine-learning model comprising a neural network and/or an updated arthritic machine-learning model to generate data was found to (1) represent mere instructions to implement the abstract idea on a generic computer by invoking algorithm(s) merely as tool(s) to perform an existing process (i.e., only recites each algorithm as a tool to apply data to an algorithm and report the results), in each case to receive input data and output output data, and/or (2) confine the use of the abstract idea (i.e., the trained model, the received updated model) to a particular technological environment or field of use (i.e., neural networks). This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. The use of a trained or updated machine learning algorithm in its ordinary capacity to perform tasks in the identified abstract idea does not provide an inventive concept (“significantly more”). See MPEP § 2106.05(f). Also, MPEP § 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more). Accordingly, even in combination, the additional elements do not provide significantly more. Thus, the claims are not patent eligible.
Dependent claims 2-10 and 12-20, when analyzed as a whole, are similarly rejected under 35 U.S.C. §101 because the additional limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. The claims, when considered alone or as an ordered combination, either (1) merely further define the abstract idea, (2) do not further limit the claim to a practical application, or (3) do not provide an inventive concept such that the claims are subject matter eligible.
Claims 2-9 and 12-19 merely further describe the abstract idea (e.g. the arthritic element, obtaining the arthritic element, generating the arthritic batch, identifying the arthritic enumeration, generating/producing the arthritic batch). See analysis, supra.
Claims 10 and 20 recite the abstract idea of generating the nourishment program as a function of the arthritic outcome using a nourishment machine-learning model (claim 10 being representative). The Applicant has described machine learning to encompass simplistic mathematical models such as simple linear regression, multiple linear regression, etc. (see Spec. Para. 0036 and 0051) and thus the machine learning is interpreted to be part of the abstract idea, since it falls under data manipulations that humans perform and thus is part of the rule following. See analysis, supra. Alternately, when given its broadest reasonable interpretation in light of the disclosure, the use of the nourishment machine-learning model represents the creation of mathematical interrelationships between data using a mathematical operation (see again Spec. Para. 0036 and 0051). As such, the use of the nourishment machine-learning model represents a mathematical concept that is interpreted to be par of the identified abstract idea, supra. The Examiner notes that the mathematical concept need not be expressed in mathematical symbols but not merely limitations that are based on or involve a mathematical concept (MPEP § 2106.04(a)(2)(I)). The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claim 1-8, 10-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Greenberger et al. (US 2018/0158010 A1; “Greenberger” herein) in view of Apte et al. (US 2017/0372027 A1; “Apte” herein) and Neumann (US 2020/0321120 A1).
Re. Claim 1, Green teaches a system for generating an arthritic disorder nourishment program, the system comprising: a computing device (see Fig. 2, [0006]-[0007], [0028]), the computing device configured to:
obtain an arthritic element from one or more detection devices (104A-D) by determining data associated with an individual’s biological system that denotes an arthritic state, […] (Fig. 2, [0072], [0109] teach the cognitive system is configured to receive (obtain) inputs from various sources in the network 102, e.g., one or more computing device 104A-D including devices for a database storing the corpus or corpora 106. [0085], [0090] teach the skill/skill limit analysis engine 154 may… perform natural language processing (NLP) of the corpus or corpora 106 of medical information about the user, to associate skills and skill limits (arthritic batches) with characteristics of a user (data/arthritic elements)… For example, a patient electronic medical record (EMR) may indicate that one of the effects of rheumatoid arthritis is that the user will not be able to open medication or may have significant joint pain in the hands and feet (denotes an arthritic state).);
receive an arthritic training set, wherein the arthritic training set correlates a plurality of arthritic elements to a plurality of arthritic batches (see Specification at para. 0012: an “arthritic batch” is a profile or estimation of an individual’s joints or connective tissues. [0085], [0090] teach the skill/skill limit analysis engine 154 may… perform natural language processing (NLP) of the corpus or corpora 106 of medical information about the user, to associate (correlate) skills and skill limits (arthritic batches) with characteristics of a user (data/arthritic elements)… the patient EMR information may indicate that the patient has recently been treated for a wrist sprain and thus, based on an association of this user characteristic with skills/skill limitations learned by engine 154 (necessarily received), is unlikely to be able to perform operations requiring strong hand actions… Based on the association of user skills/skill limitations with user characteristics, as learned by engine 154, the medical condition may be associated with particular skills and/or skill limitations, e.g., a user with rheumatoid arthritics has weak fine motion skills and thus, is unlikely to perform actions such as slicing carrots or the like... This information may be combined into a user profile to further associate skills and/or skill limitations with the user.);
train […] using the arthritic training set ([0085], [0090] teach the skill limit analysis engine 154 may… associate skills and skill limits with characteristics of the user, as learned by engine 154.);
generate an arthritic batch as a function of the obtained arthritic element using the trained […], wherein the arthritic batch is an estimated profile of the one or more of the individual’s joints and connective tissues ([0090] teaches the patient EMR information may indicate that the patient has recently been treated for a wrist sprain and thus, based on an association of this user characteristic with skills/skill limitations learned by engine 154 (as a function of the obtained arthritic element using the trained engine), is unlikely to be able to perform operations requiring strong hand actions (an estimation). Moreover, the patient EMR information may indicate that the patient has been diagnosed with rheumatoid arthritis. Based on the association of user skills/skill limitations with user characteristics, as learned by engine 154, the medical condition user characteristic may be associated with particular skills and/or skill limitations (estimations)… This information may be combined into a user profile (an estimated profile) to further associate skills and/or skill limitations (of the individual’s joints and connective tissues) with the user.);
receive, from an external device, […] (Fig. 1, [0072] teach the cognitive system 100 receives data from various computing devices 104A-D on the network including access points for content creators. [0034] teaches the computer readable program instructions may execute entirely on the user’s computer, as a stand-alone software package… partly on a remote computer or entirely on the remote computer or server.);
generate an updated arthritic batch as a function of the arthritic element using the […] ([0088] teaches the user profile engine 156 generates/updates the user profile… that specifies the skills and skill limitations… and may be dynamically updated based on periodic evaluation of user information (the arthritic element).);
determine an edible as a function of the arthritic batch (Fig. 5, [0080], [0135] teach, in response to a request, retrieving the user profile (arthritic batch)… and applying the skills and/or skill limitations specified in the user profile to operations (e.g., recipes) specified in the operations knowledge base 166, to select/generate and/or modify one or more operations (e.g., recipes having a food or drink item) (necessarily determine an edible) for which the user has the skills to successfully complete the operation by performing all of the actions in the operation.); and
generate a nourishment program as a function of the edible (Figs. 3, 5, [0136], [0138] teach a final "answer" or recipe recommendation (necessarily generated) may be selected for output back to the user as a response to their original request and evaluation of either all or a subset of the operations, e.g., recipes.)
Greenberger may not teach
obtain an arthritic element (i.e., the user characteristics) by determining data associated with an individual’s biological system that denotes an arthritic state (i.e., the user characteristics associated with rheumatoid arthritis), wherein the arthritic state is a measure of a level of physical well-being of one or more of the individual’s joints and connective tissues;
train an arthritic machine-learning model comprising a neural network; or
an updated arthritic machine-learning model incorporating one or more of a firmware update, a software update and an arthritic machine-learning process correction.
Apte teaches
determining data associated with an individual’s biological system that denotes an arthritic state, wherein the arthritic state is a measure of a level of physical well-being of one or more of the individual’s joints and connective tissues (See Applicant’s disclosure at Fig. 2. [0017] teaches a locomotor system condition… produces physiological effects in relation to, e.g., range of motion, activity level, pain… diagnostics associated with the locomotor system condition can be typically assessed using one or more of: imaging-based method, stress testing, motion testing, biopsy, joint fluid assessment… the method 100 can be used for characterization… of rheumatoid arthritis.); and
train an arthritic machine-learning model comprising a neural network ([0112] teaches variations of the method 100 can additionally or alternately utilize any other suitable algorithms in performing the characterization process… by a learning style including… back propagation neural networks… Furthermore, the algorithm(s) can implement… an artificial neural network model.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the mechanisms for selecting user operations and/or modifying user operations based on determined user skills and/or skill limitations of Greenberger to receive patient diagnostics data and utilize machine learning (e.g., machine learning algorithms implementing an artificial neural network) and to use this information as part of a method and system for microbiome-derived diagnostics and therapeutics for locomotor system conditions as taught by Apte, with the motivation of improving locomotor system characterization/modeling and the locomotor system health condition (see Apte at Fig. 1A and para. 0003, 0015, 0020).
Greenberger/Apte may not teach an updated arthritic machine-learning model incorporating one or more of a firmware update, a software update and an arthritic machine-learning process correction.
Neumann teaches
an updated arthritic machine-learning model incorporating one or more of a firmware update, a software update and an arthritic machine-learning process correction ([0122] teaches advisory input (machine-learning process correction) may be fed back into system 100, including without limitation insertion into user database 936, inclusion in or use to update diagnostic engine 108, for instance by augmenting machine-learning models (incorporating) and/or modifying machine-learning outputs… Advisory input may be transmitted to user client device 180 utilizing any network methodology as described herein. See also [0153].)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the mechanisms for selecting user operations and/or modifying user operations based on determined user skills and/or skill limitations of Greenberger/Apte to receive advisory input for inclusion in / for updating machine-learning models, such as a machine-learning model relating physiological state data to prognostic labels that include presence of joint pain and generating prognostic output as a function of a classification of the prognostic label (see Neumann para. 0153), and to use this information as part of methods and systems for an artificial intelligence fitness professional support network for vibrant constitutional guidance as taught by Neumann, with the motivation of improving diagnostic support, treatment planning and patient care (see Neumann at para. 0153, 0159, and 0160-0161).
Re. CLAIM 2, Greenberger/Apte/Neumann teaches the system of claim 1, wherein the arthritic element includes a genetic element (Apte Fig. 1B, [0139]-[0140] teaches receiving a biological test sample from a subject S210 and characterizing the microbiome of the individual by deep sequencing bacterial DNAs from diseased and healthy subjects.)
Note: The limitation claims information/labels (“a genetic element”) that constitute nonfunctional descriptive information that is/are not functionally involved in the recited system (see MPEP §2111.05). The function described by the system would be performed the same regardless of whether the claimed information/labels was substituted with nothing. Because Greenberger teaches a system that is capable of obtaining information having data labels (the recited “arthritic element”), substituting the information/labels of the claimed invention for the information/labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to have substituted the information/labels applied to the obtained data of the prior art with any other information/labels because the results would have been predictable. MPEP 2112.01, Section III (see also In re Ngai, Ex Parte Breslow).
Re. Claim 3, Greenberger/Apte/Neumann teaches the system of claim 1, wherein obtaining the arthritic element includes receiving an arthritic questionnaire and obtaining the arthritic element as a function of the arthritic questionnaire (Greenberger [0088]-[0089] teaches the user profile obtains information (the arthritic element) including measurements of ranges of motion and generate a user profile… that specifies the skills and skill limitations associated with the user. Greenberger [0026] teaches the skills and skill limitations may be manually input to the user profile (arthritic batch), e.g., a questionnaire may be presented to the user (received) whereby the user may specify their skills and skill limitations… corresponding identifiers of skills and/or skill limitations may be added to the user's profile data structure based on the user’s response to the questionnaire.)
Re. Claim 4, Greenberger/Apte/Neumann teaches the system of claim 1, wherein generating the arthritic batch further comprises identifying an arthritic enumeration and producing the arthritic batch as a function of the arthritic enumeration (see Applicant’s disclosure at para. 0021. Greenberger [0088]-[0089] teaches the user profile obtains information including measurements of ranges of motion of various parts of the user’s body (identifying an arthritic enumeration) with this information being provided to the ADL analysis engine 140 which determines corresponding domain specific actions, each action having a skill strength requirement (see [0082]-[0083]), that the user is able to perform and actions that the user is not able to perform… the actions associated with the user may be provided to the skill based operation selection/modification engine 150 which then correlates the actions with skills and skill limitations… these identified skills and skill limitations are added to the user profile (the arthritic batch).)
Re. Claim 5, Greenberger/Apte/Neumann teaches the system of claim 4, wherein identifying the arthritic enumeration (see Applicant’s disclosure at para. 0021) further comprises:
receiving a user range of motion (Greenberger [0088]-[0090] teaches the user profile obtains information including measurements of ranges of motion of various parts of the user’s body, e.g., wrist (a user range of motion) with this information being provided to the ADL analysis engine 140);
determining a joint range of motion (The Examiner interprets the measured of range of motion for the wrist as a joint range of motion. Note: Each measured range of motion is a user’s best effort for that range of motion.); and
determining the arthritic enumeration as a function of the user range of motion, the joint range of motion, and an enumeration threshold (Greenberger [0088]-[0090] teaches this information (the user/joint range of motion) is provided to the ADL analysis engine 140, which determines corresponding domain specific actions, each action having a skill strength requirement (an enumeration threshold) (see [0082]-[0083]), that the user is able to perform and actions that the user is not able to perform (necessarily determining the arthritic enumerations)… the actions associated with the user may be provided to the skill based operation selection/modification engine 150 which then correlates the actions with skills and skill limitations… these identified skills and skill limitations are added to the user profile (the arthritic batch).)
Re. Claim 6, Greenberger/Apte/Neumann teaches the system of claim 1, wherein generating the arthritic batch includes determining an arthritic disorder and producing the arthritic batch as a function of the arthritic disorder (Greenberger [0085], [0134] teaches the user skill/skill limit analysis engine 154 may perform natural language processing of the corpora 106 to associate skills and skill limits with characteristics of a user (determining)… a corpus may comprise medical knowledge documents that describe a medical condition… From this information and the features extracted, the engine 154 may, e.g., associate rheumatoid arthritis and its effects with skills and skill limitations in the pre-defined skill listing data structure. Greenberger [0088]-[0089] teaches generating/updating the user profile (the arthritic batch) with skills and skill limitations associated with the user.)
Re. Claim 7, Greenberger/Apte/Neumann teaches the system of claim 1, wherein generating the arthritic batch includes determining an autoimmune disorder and producing the arthritic batch as a function of the autoimmune disorder (Greenberger [0085], [0134] teaches the user skill/skill limit analysis engine 154 may perform natural language processing of the corpora 106 to associate skills and skill limits with characteristics of a user (determining)… a corpus may comprise medical knowledge documents that describe a medical condition… From this information and the features extracted, the engine 154 may, e.g., associate rheumatoid arthritis and its effects with skills and skill limitations in the pre-defined skill listing data structure. Greenberger [0088]-[0089] teaches generating/updating the user profile (the arthritic batch) with skills and skill limitations associated with the user.)
Re. Claim 8, Greenberger/Apte/Neumann teaches the system of claim 1, wherein generating the arthritic batch further comprises: identifying a development vector; and producing the arthritic batch as a function of the development vector (see Applicant’s disclosure at para. 0023. Greenberger [0084] teaches the skill-domain action correlation engine 152 generates one or more data structures correlating domain specific actions with skills in the pre-defined skill listing and/or skill limitations associated with the predefined skill listing (development vectors). Greenberger [0017] teaches skill sets and skill limitations, i.e. restrictions on a user's ability to perform particular tasks… For example, a user may have weak motor skills and/or may not be able to chop ingredients or twist a bottle due to an arthritis condition… recipes that require ingredients that requiring chopping or opening of bottles that have twist tops may be beyond the skills available (necessarily identified) to the user due to their medical condition. Greenberger [0089] teaches that these identified skills and skill limitations may be added to the user’s user profile (the arthritic batch).)
Re. Claim 10, Greenberger/Apte/Neumann teaches the system of claim 1, wherein generating the nourishment program further comprises: receiving an arthritic outcome; and generating the nourishment program as a function of the arthritic outcome using a nourishment machine-learning model (Greenberger Fig. 4, [0141] teaches a user may have rheumatoid arthritis and thus, may have found the certain actions to be difficult and may select the GUI elements 432 corresponding to those actions, or groups of related actions, to indicate that those actions/groups of actions were difficult to achieve… The user may then submit this feedback to the cognitive system by pressing the submit GUI element 432. Greenberger [0079] teaches providing this feedback (received) to the skill-based operation selection/modification engine 150 to machine learn the association of skills and/or skill limitations in the user profile with the actions for which feedback is provided. Greenberger Fig. 5, [0093] teaches for subsequent requests, the user profile may then be used in conjunction with the operation selection/modification engine 158 of the skill-based operation selection/modification engine 150 to select and/or modify operations/recipes for consideration for returning to the user as recommended operations/recipes that the user may prepare to provide food/drink items for consumption… and subsequent output of the recipe recommendation (generated).)
Re. Claim 11, the subject matter of claim 11 is essentially defined in terms of a method, which is technically corresponding to system claim 1. Since claim 11 is analogous to claim 1, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 1.
Re. Claim 12, the subject matter of claim 12 is essentially defined in terms of a method, which is technically corresponding to system claim 2. Since claim 12 is analogous to claim 2, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 12.
Re. Claim 13, the subject matter of claim 13 is essentially defined in terms of a method, which is technically corresponding to system claim 3. Since claim 13 is analogous to claim 3, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 3.
Re. Claim 14, the subject matter of claim 14 is essentially defined in terms of a method, which is technically corresponding to system claim 4. Since claim 14 is analogous to claim 4, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 4.
Re. Claim 15, the subject matter of claim 15 is essentially defined in terms of a method, which is technically corresponding to system claim 5. Since claim 15 is analogous to claim 5, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 5.
Re. Claim 16, the subject matter of claim 16 is essentially defined in terms of a method, which is technically corresponding to system claim 6. Since claim 16 is analogous to claim 6, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 6.
Re. Claim 17, the subject matter of claim 17 is essentially defined in terms of a method, which is technically corresponding to system claim 7. Since claim 17 is analogous to claim 7, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 7.
Re. Claim 18, the subject matter of claim 18 is essentially defined in terms of a method, which is technically corresponding to system claim 8. Since claim 18 is analogous to claim 8, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 8.
Re. Claim 20, the subject matter of claim 20 is essentially defined in terms of a method, which is technically corresponding to system claim 10. Since claim 20 is analogous to claim 10, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 10.
Claim 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Greenberger in view of Apte, Neumann and Moturu et al. (US 2017/0235912 A1; “Moturu” herein).
Re. Claim 9, Greenberger/Apte/Neumann teaches the system of claim 1, wherein generating the arthritic batch (the user profile) further comprises:
receiving an arthritic […] (see Greenberger in claim 1 prior art rejection);
[…] and arthritic element; and
producing the arthritic batch as a function of […] (Greenberger [0088]-[0089] teaches the user profile… may be dynamically updated based on periodic evaluation of user information (the arthritic element).)
Greenberger/Apte may not teach
receiving an arthritic timeline;
determining a progression parameter as a function of the arthritic timeline
producing… as a function of the progression parameter.
Moturu teaches
receiving an arthritic timeline (see Applicant’s disclosure at para. 0022: “a list and/or linear representation of events associated with arthritis during a time period”. Fig. 13 shows a timeline of progression of a medical condition.);
determining a progression parameter as a function of the arthritic timeline (see Applicant’s disclosure at para. 0022: “a parameter that denotes a location on the timeline at which the user may be placed”. Fig. 13 shows the severity (progression parameter) of the medical condition over time.);
producing… as a function of the progression parameter (see previous citations. Additionally, [0052] teaches generating medical status analyses with greater frequency for conditions of greater severity.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the user operation selection and/or modification based on determined user skills and/or skill limitations of Greenberger/Apte/Neumann to receive/determine a timeline and a progression parameter and to produce other data as a function of the severity of the medical condition and to use this information as part of a method and system for improving care determination as taught by Moturu, with the motivation of improving care determination, care provider decision support and patient health (see Moturu, e.g., at Abstract and para. 0003, 0016).
Re. Claim 19, the subject matter of claim 19 is essentially defined in terms of a method, which is technically corresponding to system claim 9. Since claim 19 is analogous to claim 9, it is similarly analyzed and rejected in a manner consistent with the rejection of claim 9.
Response to Arguments
Rejections under 35 U.S.C. §101
Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments but does not find them persuasive for at least the following reasons. Applicant argues:
A1. “Claim 1 recites, among other steps… These steps describe the collection and processing of structured data and the use of a machine learning model to establish correlations within the data. This is a data-driven computational process, not a method involving managing or controlling interpersonal or economic activity. The language of the claim centers on data analysis and modeling, which are activities that are performed by computing systems according to programmed logic and statistical training, not by organizational frameworks or administrative processes…” (Remarks, pg. 3-4).
Regarding A1: The Examiner respectfully submits the basis of rejection as afforded by RCE and necessitated by amendment. Obtaining data, receiving a training dataset, training by a non-specific training algorithm, generating results, receiving an update, updating results, and determining a food recommendation based on the new results are part of the abstract idea (i.e., represent data manipulations that humans perform as part of following rules or instructions to perform a data-driven computational process alone or along with a mathematical concept). The Examiner notes that the machine learning model is now recited as a neural network and is treated like an additional element (discussed infra). While the abstract idea may be improved, an abstract idea is still an abstract idea. Only additional elements can provide an integration or an inventive concept. The Examiner also notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). The computational tool, i.e., the computing device, is considered an additional element (also discussed infra).
The examples of data analysis, modeling and automated output generation are forms of human interaction with a computer and represent rule following, which given the broadest reasonable interpretation, falls under certain methods of organizing human activity. Likewise, the claims recite Certain Methods of Organizing Human Activity (i.e., managing personal behavior or interactions between people, which includes one or more persons following a series of rules or instructions and human interaction with a computer). MPEP 2106.04(a)(2)(II).
A2. “Further, the amended claim 1 recites… These technical features describe the technical interaction which results in enhanced accuracy and efficiency of the computing device…” (remarks, pg. 4).
Re. argument A2: The Examiner respectfully submits that there are no technical details that would remove the claims from being directed to an abstract idea. For example, the training is recited in abstraction. The level of detail of the training process and the other steps does not remove the claims from reciting training or any other step in abstraction. Regardless, Applicant does not appear to be arguing that the technical detail is removing any portion of the claim from the identified abstract idea. Applicant instead appears to be leading into discussion of the additional elements (including the trained neural network, the external device, and the received updated machine-learning model) by alleging a technical solution. Further analysis will be discussed in response to remarks related to Steps 2A2 and 2B.
A3. “The claimed process, therefore, falls squarely within the realm of computer-implemented data analysis and modeling; and the interaction, control and communication between distributed hardware components of, and associated with, the system, and not within the judicial exception category of "methods of organizing human activity." Accordingly…” (remarks, pg. 4).
Re. argument A3: Additionally, the Examiner respectfully submits that the court case examples of abstract ideas provided in the MPEP are a non-exhaustive list. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners' focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. MPEP 2106. None of the steps of the claimed invention recite the required level of technical detail to remove the claims from reciting an abstract idea (e.g., rule following to perform data analysis and modeling along with data communication). Also, the steps when viewed together do not invent a new form of machine learning or any other technology. The claims utilize the medical condition reporting to subsequently determine a nutrition plan for the patient. Discussion shall now focus on the additional elements.
A4. “the Examiner is directed to the PTAB's decision in Ex parte Hannun… its analysis under Step 2A, Prong One, states (p. 10): The mathematical algorithm or formula, however, is not recited in the claims. As such, under the recent Memorandum, the claims do not recite a mathematical concept. See, e.g., Subject Matter Eligibility Examples: Abstract Ideas, at 7 (Jan. 7, 2019) (discussing Example 38 and noting that "The claim does not recite a mathematical relationship, formula, or calculation. While
some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claims."). Freeman, 573 F.2d at 1246” (remarks, pg. 5) (emphasis added).
Re. argument A4: The ex parte decision has no bearing on the facts as the instant claims do recite “training” and the model is a neural network now treated as an additional element.
Here, the Applicant has generically claimed the training of the neural network. The disclosure in the as-filed Specification or in the claims does not actually specify the type of math used to perform the training (i.e., the mathematical algorithm) and thus the Examiner must assume that the training is performed using a mathematical operation (otherwise a Written Description issue in view of 35 U.S.C. § 112(a) may be present). The training of the machine learning model / neural network is considered to be part of the CMOHA abstract idea because, given the BRI, it falls under data manipulations that humans perform and is thus part of the rules or instructions. Assuming arguendo that the training algorithm is narrowed to one of the examples given in the Applicant’s disclosure (i.e., assuming the math is not simple enough that a person could readily perform it as part of CMOHA), the training would still represent a mathematical concept as shown in the basis of rejection. See, e.g., Example 47 rather than Example 38.
The machine learning model is amended to be a neural network and is considered an additional element that does not integrate the abstract idea or provide significantly more. See basis of rejection.
A5. “Rather, it is directed to a specific technological process involving the application of trained machine learning models to process specific input data and generate data-driven outputs, rather than setting forth or defining any mathematical formulas, relationships, or equations. The focus of the claim is on the implementation and use of trained models within a structured computational workflow, not on performing mathematical calculations themselves…” (remarks, pg. 5).
Re. argument A5: The Examiner respectfully submits that the “training”, given the BRI in light of the specification, encompass the training of a simplistic mathematical model performed by simple linear regression or other algorithms. Not only are these data manipulations that a person could do as part of following rules or instructions, the training and use of each machine-learning model represents the creation of mathematical interrelationships between data using a mathematical operation. MPEP 2106.04(a)(2)(I) (“claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas”). Also citing MPEP 2106.04(a)(2)(I)(A), example iv. (“organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form”.)
The Examiner notes that the mathematical concept is also covered as a mathematical calculation for use of an algorithm to generate a result (citing MPEP 2106.04(a)(2)(I)(C), example v. (“using an algorithm for determining the optimal number of visits by a business representative to a client”).
The trained neural network and the received updated machine-learning model are additional elements and are not discussed in Step 2A1.
Like Example 47 claim 2, Applicant’s claims are ineligible. Example 47 claim 2 involves training of the machine learning model using conventional algorithm(s).
“Step (c) recites training an ANN using a selected algorithm. The training algorithm is a backpropagation algorithm and a gradient descent algorithm. When given their broadest reasonable interpretation in light of the background, the backpropagation algorithm and gradient descent algorithm are mathematical calculations.” Similarly, Applicant’s claim 1 recites “training”, i.e., a training algorithm (in accordance with the most recent USPTO best practices information session). Because the specific training algorithm is not recited by name, the Examiner looks to the specification to disclose what algorithms may be used to train the arthritic machine-learning model. The specification states that the training may encompass a simplistic mathematical algorithm of “simple linear regression” or “other algorithms”, i.e., a mathematical calculation.
“The plain meaning of these terms are optimization algorithms, which compute neural network parameters using a series of mathematical calculations”. The plain meaning of Applicant’s “simple linear regression” is a simple deterministic mathematical relationship between two variables x and y. The plain meaning of “other algorithms” is a series of mathematical calculations.
“Steps (a), (b), and (c) are all recited as being performed by a computer. The recited computer is recited at a high level of generality, i.e., as a generic computer performing generic computer functions”. Likewise, Applicant’s claim 1 steps are recited as being performed on a computer using a commonplace business algorithm at a high level of generality.
“[T]he claimed discretizing and training using a backpropagation algorithm and gradient descent algorithm encompasses performing mathematical calculations”. For Applicant’s claim 1, the training encompasses a series of rules or instructions including data manipulations or a mathematical calculations.
A6. “The claim does not recite or rely on any abstract mathematical formulas or algorithms. Nor does it describe manipulating numerical values in a way that could be considered mathematical in nature. Rather, it describes a specific technical application that involves real world interactions and data acquisitions rooted in a computer-implemented technical improvement of providing a physiological framework and executing technical operations using to improve system accuracy and efficiency” (remarks, pg. 7).
Re. argument A6: The Examiner notes that training is at least a mathematical concept and is identified as part of the abstract idea. Also, real world interactions and data acquisitions between people to report the data and produce a nourishment program fall under certain methods of human activity. See basis of rejection and response to arguments A1-A5.
The Examiner respectfully submits that the Applicant’s claimed invention recites the additional element(s) of the computing device implementing a trained neural network along with an external device and a received updated machine-learning model. While these additional elements implement the results of the abstract idea, there is no indication that these additional elements operate in a manner different than they normally operate. For example, applying training data and a suitable training algorithm to a neural network does not improve the neural network. The neural network operates as it normally operates. Also, the implementation of conventional algorithms on the computing device does not improve the computing device. Operating the neural network in the manner it normally operates is insufficient to improve that other technology. As such, these additional elements are not improved through implementation of the abstract idea and a practical application is not present.
Additionally, as discussed in previous Office action(s), the computing device is recited at a high level of generality (i.e., a generic computer performing generic computer functions). The use of the computing device for obtaining, receiving, training, generating and determining data, as drafted, does not provide an improvement within the meaning of that word; the computing device is not made to physically run faster, utilize fewer resources, or run more efficiently. (Applicant has now asserted that the “system” accuracy and efficiency have been improved, which is not the same as alleging the computing device has been improved.) If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. MPEP 2106.05(a). There is no disclosed improvement to the computing device, and there is no other technology claimed that may or may not be improved. As such, the Examiner should not determine the claim improves technology. MPEP 2106.04(d)(1).
A7. “analogously to Example 47, at least the limitations of currently amended claim 1 integrate the judicial exception into a practical application because (1) the specification teaches a technological improvement… in the field of providing more accurate frameworks…” (remarks, pg. 7) (emphasis omitted).
Re. argument A7: To add to response to argument A6, the Examiner respectfully submits that MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicate a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Applicant’s argued problem is not a technological problem caused by the computing device, neural networks, the external device, etc. The problem of providing inaccurate physiological frameworks was not a problem caused by the computing device or any other additional element; is it a problem that existed and/or exists regardless of whether a computing device is involved in the process. At best, Applicant’s identified problem is a scientific or medical problem. Because no technological problem is present, the claims do not provide a practical application. Further, although the abstract idea may be complex, an improved abstract idea is still an abstract idea. Only additional elements can provide a practical application or significantly more.
A8. “the disclosed system has tangible operational impacts in association with an external computing device… These technical operations reflect an accurate and performance-driven system that would not be practically achievable by human effort alone. The claimed system uses machine learning and tangible hardware to drive system-level decisions that are adaptable to e.g., heterogenous and dynamic inputs; and is scalable. The claimed system provides a technical improvement that uses an updated arthritic machine-learning model to generate an updated arthritic batch, which enhances predictive accuracy of the system and results in better health outcomes for arthritic patients” (remarks, pg. 8) (emphasis added).
Re. argument A8: The Examiner respectfully submits the basis of rejection including the following determinations as afforded by RCE:
The computing device amounts no more than mere instructions to apply the exception on a generic computer.
The one or more detection devices amount to locations from which data is received, which represents an extra-solution activity.
The external computing device that transmits data at a high level of generality also amounts to a location from which data is received and represents insignificant extra-solution activity.
The trained neural network and the received updated machine-learning model are merely invoked as tools to perform an existing process on a general-purpose computer, i.e., amount no more than mere instructions to apply the abstract idea on a computer.
Note: Performing an abstract idea with the aid of a computer (by more than “human effort alone”) is an incorrect standard.
Note: The specification does not disclose a technical problem of prediction accuracy caused by the computing device, the neural network, or any other additional element. It is unclear whether Applicant has identified any other problems here.
A9. “At least the following limitations of currently amended claim 1 reflect the technical improvements detailed above in the technical field of machine learning” (remarks, pg. 8-9).
Re. argument A9: This argument appears to assert that machine learning technology (e.g., the neural network) is improved. The Examiner respectfully disagrees. MPEP 2106.04(d) sates that one way in which a claimed abstract idea may be subject matter eligible under prong 2A2 is if the claimed invention provides an improvement to the computer or an improvement another technology or technological field. Example 47, Claim 3 is an illustration of this. The Specification of Example 47 describes how current network security systems have difficulty detecting the difference between ordinary and anomalous data. The additional elements of (d)-(f) of Example 47, Claim 3 provide a solution to this problem. Additionally, the claims represent an improvement as to how computers operate. According to the Examiner’s previous responses to arguments A5-A8, Applicant has not identified nor can the Examiner locate any improvement to either machine learning algorithm or to any other additional element.
Note: The Recentive Analytics, Inc. v. Fox Corp. decision can be used to discuss why the instant claims are ineligible. Recentive is directed to an ineligibility analysis. Recentive held that non-specifically claimed training of a machine learning algorithm is insufficient to provide a practical application or significantly more because it does not result in “improving the mathematical algorithm or making machine learning better.” Recentive at 12. The decision further instructed that “[i]terative training using selected training material…are incident to the very nature of machine learning” and thus does not provide for an improvement. Recentive at 12.
Here, the Applicant has generically claimed the training of the neural network type of machine learning. There is no disclosure in the as-filed Specification or in the claims that states how the training occurs (i.e., the mathematical algorithm) and thus the Examiner must assume that the training is performed in its normal manner (otherwise a Written Description issue in view of 35 U.S.C. § 112(a) may be present). Applicant’s claim merely describes the data (i.e., the arthritic training set correlating a plurality of arthritic elements to a plurality of arthritic batches) used to train the neural network. There is no improvement to the mathematical training algorithm because no description as to how the algorithm is trained is claimed or described in the claims and/or Specification.
Further, the Specification at Para. 0017-0019 and 0046 describes the particular type of machine learning as classifier algorithms including neural network, Naïve Bayes, K-nearest neighbors, support vector machines, etc. algorithms, which are known algorithms. Applicant did not invent these machine learning algorithms. Applicant’s claims are not directed towards “making machine learning better” because the claims are utilizing admittedly known/generic machine learning algorithms and the claims do not delineate steps through which the machine learning technology achieves any alleged improvement. Again, merely training known machine learning algorithms with selected training data is not an improvement to how the machine learning algorithms operate. And Applicant has not identified nor can the Examiner locate any improvement to the machine learning algorithm. The Examiner respectfully submits the Recentive decision in support of the Office’s position.
Note: The received updated machine-learning model is not positively recited as being trained, retrained, fine-tuned, or updated and is merely received in its most current form by the computing device.
Going back to discussion of Example 47, the use of the trained machine learning model in Example 47, Cl. 2 represents the application of the abstract idea (“apply it”) on a generic computer. This is because the use of the trained machine learning model in the claims does not place any limits on how the trained machine learning model functions. Where “the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished” (see MPEP 2106. 05(f)). Put another way, where the use of trained machine learning in a claim does not recite how its functions are actually performed and are merely recited at a high, non-inventive level, the machine learning itself represents the application of a mathematical concept because no improvement to the machine learning is claimed. Applicant’s argument that the claims recite improvement to machine learning technology is unpersuasive because the argued details, given the broadest reasonable interpretation in light of the Specification, represent generic machine learning functionality. See basis of rejection and response to arguments A5-A8.
Note: Improvement to health outcomes in arthritic patients is not an improvement within the meaning of the word. The alleged solution corresponds to a medical problem or other non-technical problem. Please refer to previous response to argument A7.
A10. “at least the limitations of claim 1 as amended recite meaningful limits on practicing the abstract idea… evidenced at least by the "practical application" analysis presented above in connection with Prong 2 of Step 2A” (remarks, pg. 10).
Re. argument A10: The Examiner respectfully disagrees and references responses to arguments A5-A9.
A11. “at least the limitations of claim 1 as amended amount to an inventive concept, and thus "significantly more" than any alleged abstract idea… Here, at least the limitations of claim 1 as amended recite the use of technical features associated with a system for providing a physiological framework using artificial intelligence and providing resilience in downstream technical operations in association with an external device” (remarks, pg. 10).
Re. argument A11: The Examiner respectfully submits the basis of rejection. Re. the computing device, the additional element implementing the abstract idea amounts no more than mere instructions to apply the exception using a generic computer, which cannot provide significantly more. Re. the one or more detection devices and the external device, these represent well-understood, routine, conventional activity in the field, which cannot provide significantly more. Re. the trained arthritic machine-learning model / neural network and the updated arthritic machine-learning model, these additional elements also represent mere instructions to implement the abstract idea on a generic computer, which is insufficient to provide significantly more.
Note: As for the WURC analysis, the Examiner respectfully submits that only the one or more detection devices and the external device require WURC analysis at Step 2B, since these limitations have been identified as reciting well-understood, routine, and conventional activity. See MPEP 2106.05(II). The Examiner has performed the required WURC analysis in the basis of rejection, re-evaluating the one or more detection devices and the external device that were considered to be insignificant extra-solution activity per MPEP 2106.05(g). These additional elements were found to represent well-understood, routine, conventional activity in the field and as such do not provide significantly more.
A12. “The above recited limitations, including at least the limitations claim 1 as amended, are not generic and instead recite a novel approach…” (remarks, pg. 10-11).
Re. argument A12: The Examiner respectfully submits that the computing device and other additional elements are recited at a high-level of generality and as performing conventional computer implementation and insignificant extra-solution activity. Considering the additional elements, alone or in combination, there is no specific limitation other than what is well-understood, routine and conventional activity in the field (see basis of rejection and response to argument A11), such that no unconventional steps confine the claim to a particular useful application. See MPEP § 2106.05(I)(A). Rather, the limitations simply append well-understood, routine and conventional activities, specified at a high-level of generality, to the judicial exception. See again MPEP § 2106.05(I)(A).
Further, while the process may be detailed, only additional elements can provide a practical application or an inventive concept (“significantly more”). MPEP 2106.05(I) states: “As made clear by the courts, the novelty of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter (internal quotations omitted, emphasis original).”
A13. “Applicant respectfully submits that no court cases, literature, or references are of record indicating that the above-described limitations are "well-understood, routine, [and] conventional," and furthermore asserts that neither the instant application nor the prosecution history in this matter contains any admission thereof” (remarks, pg. 11).
Re. argument A13: The Examiner respectfully submits the basis of rejection as afforded by RCE and as necessitated by amendment. Several court cases are evidenced in the required WURC analysis.
A14. “Further, claim 1 has features that amount to significantly more than the abstract idea, because such features provide a technical contribution to the field of machine learning and distributed architecture, which differs from conventional systems that do not achieve the desired level of accuracy in generating physiological frameworks (see paragraph [0002])…” (remarks, pg. 11).
Re. argument A14: Per response to argument A12, the computing device and other additional elements are recited at a high-level of generality and as performing conventional computer implementation and insignificant extra-solution activity. Considering the additional elements, alone or in combination, there is no specific limitation other than what is well-understood, routine and conventional activity in the field (see basis of rejection and response to argument A11), such that no unconventional steps confine the claim to a particular useful application (i.e., “technical contribution”). See MPEP § 2106.05(I)(A). Rather, the limitations simply append well-understood, routine and conventional activities, specified at a high-level of generality, to the judicial exception. See again MPEP § 2106.05(I)(A).
A15. “amended claim 1 contains limitations amounting to a non-conventional and non-generic arrangement of process steps. See BASCOM… limitations to claims 1 amount to a non-conventional and non-generic arrangement of computer and functions and other technical limitations” (remarks, pg. 11-12).
Re. argument A15: As previously stated in response to argument A12, while the process (steps) may be detailed, only additional elements can provide a practical application or an inventive concept (“significantly more”). MPEP 2106.05(I) states: “As made clear by the courts, the novelty of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter (internal quotations omitted, emphasis original).”
Additionally and for completeness, regarding BASCOM, the court agreed that the additional elements were generic computer, network and Internet components that did not amount to significantly more when considered individually, but explained that the district court erred by failing to recognize that an inventive concept may be found in the combination of the additional elements in a non-conventional and non-generic arrangement, i.e., the installation of a filtering tool at a specific location, which was remote from end-users, with customizable filtering features specific to each end-user. As previously shown, it is unclear what is non-conventional and non-generic about the arrangement of the additional elements in the Applicant’s case.
Regarding the rejection of Claims 2-20, the Applicant has not offered any or any additional arguments with respect to these claims other than to reiterate the argument(s) present for the claim(s) from which they depend or are analogous to. As such, the rejection of these claims is also maintained.
Rejection under 35 U.S.C. §103
Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments but does not find them persuasive for at least the following reasons. Applicant argues:
B1. “Applicant respectfully submits that Greenberger does not teach, suggest, or motivate… Accordingly, Applicant submits that claim 1 as amended is patentably distinguishable over Greenberger, Apte, Hadad and McCaffrey, alone or in combination, for at least the reasons discussed above.” (Remarks, pg. 12-16).
Regarding B1: The Examiner respectfully submits the basis of rejection as afforded by RCE and as necessitated by amendment. Greenberger in view of Apte and Neumann teaches or renders obvious the claim limitations.
Regarding the rejection of Claims 2-20, the Applicant has not offered any or any additional arguments with respect to these claims other than to reiterate the argument(s) present for the claim(s) from which they depend or are analogous to. As such, the rejection of these claims is also maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Groß et al. (US 2022/0108561 A1) for teaching a system for automated adjustment: a machine learning method and/or neural networks can be used, for example, to determine the node weight for the training plan. The input variables used for the determination of the node weight are the evaluation of the exercise and, optionally, the components of the training plan, with configurations of the training plan and/or training plan changes being used as output parameters. In one aspect, the node weights determined here are transmitted to a rule set. There they can replace, for example, already stored node weights for the training plan adjustment. See para. 0221 and Applicant’s Spec. Para. 0016 (“replace the arthritic machine-learning model with the updated machine-learning model ”).
Neuman (US 11,152,103 B1) for teaching systems and methods for generating an alimentary plan for managing musculoskeletal system disorders.
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/J.M.W./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
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