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
Priority
Not applicable (i.e., the filing date of 3/29/2021 is the effective filing date).
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.
Eligibility Analysis Step 1 (YES): Claims 1 and 11 fall into at least one of the statutory categories (i.e., process, machine).
Eligibility Analysis Step 2A1 (YES): The claims recite an abstract idea. The identified abstract idea is as underlined (claim 1 being representative):
a computing device, the computing device configured to:
obtain an arthritic element;
receive an arthritic training set, wherein the arthritic training set correlates the arthritic element to an arthritic batch, wherein the arthritic batch is produced by identifying an arthritic enumeration, wherein the arthritic enumeration comprises a measurable value associated with an effect of arthritis, and wherein the arthritic enumeration is identified as a function of at least a user range of motion;
train an arthritic machine-learning model using the arthritic training set;
generate the arthritic batch as a function of the trained arthritic machine-learning model;
produce a nourishment demand as a function of the arthritic batch, wherein the nourishment demand is determined as a function of receiving a nourishment goal;
determine an edible, using an edible machine-learning model, as a function of the arthritic batch and the nourishment demand, wherein the edible machine-learning model is training using an edible training set that correlates the nourishment demand to the edible; 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, along with a mathematical concept that includes mathematical relationships, mathematical formulas or equations, and mathematical calculations. That is, other than reciting generic computer component language, the claimed invention amounts to a human following a series of rules or steps to generate a nourishment program as a function of a determined edible. For example, but for the generic computer component language, the claims encompass a person obtaining, receiving, manipulating, generating, producing, or determining data in the manner described in the identified abstract idea, supra. These are steps for managing personal behavior or relationships 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 relationships or interactions between people but for recitation of generic computer component language, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Further, the claims recite “train an arthritic machine-learning model using the arthritic training set; generate the arthritic batch as a function of the trained arthritic machine-learning model” (claim 1 being representative). The specification at para. 0014 and 0051 describes the training as being performed by simple linear regression. Each of the training and use of the machine learning model are considered to be part of the abstract idea, since they fall under data manipulations that humans perform and thus are part of the rule following. The Examiner notes that the Applicant has described machine learning to encompass simplistic mathematical models such as simple linear regression (specification para. 0014 and 0051) and thus the machine learning is interpreted to be part of the abstract idea. Alternately, when given its broadest reasonable interpretation in light of the disclosure, the training and use of the arthritic machine-learning model represent the creation of mathematical interrelationships between data using a mathematical operation (see Specification again at para. 0014 and 0051). As such, the training and use of the arthritic machine-learning model covers a mathematical concept (that includes mathematical relationships, mathematical formulas or equations, and mathematical calculations) 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)). If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships but for the recitation of generic computer component language, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
Further, the claims recite “determine an edible, using an edible machine-learning model, as a function of the arthritic batch and the nourishment demand, wherein the edible machine-learning model is training using an edible training set that correlates the nourishment demand to the edible” (claim 1 being representative). The specification at para. 0031 describes the training as being performed by simple linear regression. Each of the training and use of the machine learning model are considered to be part of the abstract idea, since they fall under data manipulations that humans perform and thus are part of the rule following. The Examiner notes that the Applicant has described machine learning to encompass simplistic mathematical models such as simple linear regression (specification para. 0031) and thus the machine learning is interpreted to be part of the abstract idea. Alternately, when given its broadest reasonable interpretation in light of the disclosure, the training and use of the edible machine-learning model represents the creation of mathematical interrelationships between data using a mathematical operation (see again Specification at para. 0031). As such, the training and use of the edible machine-learning model covers a mathematical concept (that includes mathematical relationships, mathematical formulas or equations, and mathematical calculations) 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 (MPEP § 2106.04(a)(2)(I)). If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships but for the recitation of generic computer component language, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
Accordingly, the claims recite one or more multiple abstract ideas, which fall in different groupings. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes. See MPEP § 2106.
Eligibility Analysis 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.
Eligibility Analysis 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).
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, producing the arthritic batch, identifying the arthritic enumeration, 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), which can be a mathematical operation such as a simple linear regression, multiple linear regression, etc. (see Specification at para. 0036 and 0051). See analysis, supra.
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), Hadad et al. (US 2019/0244541 A1; “Hadad” herein), and McCaffrey (2014) (“Neural Network Training Using Simplex Optimization”).
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], [0010], [0028]), the computing device configured to:
obtain an arthritic element (see Specification at para. 0010: an "arthritic element" is an element of data associated with an individual's biological system that denotes an arthritic state. [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 with characteristics of a user (necessarily obtained)… 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 (an arthritic element).);
receive an arthritic training set, wherein the arthritic training set correlates the arthritic element to an arthritic batch (see Specification at para. 0012: an “arthritic batch” is a profile or estimation of an individual’s joints or 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 (arthritic element) with skills/skill limitations learned by engine 154 (necessarily receiving the arthritic training set), is unlikely to be able to perform operations requiring strong hand actions. 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 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. The former is a temporary condition while the latter is a more permanent condition. This information may be combined into a user profile (arthritic batch) to further associate skills and/or skill limitations with the user.), wherein the arthritic batch is produced by identifying an arthritic enumeration, wherein the arthritic enumeration comprises a measurable value associated with an effect of arthritis, and wherein the arthritic enumeration is identified as a function of at least a user range of motion (The Specification at para. 0012: an "arthritic batch" is a profile and/or estimation of an individual's joints and/or connective tissues. [0088] teaches a user profile engine 156 generates/updates a user profile (the arthritic batch) … that specifies the skills and skill limitations temporary or more permanent in nature (produced)… and may be dynamically updated based on periodic evaluation of user information. [0089] teaches a user profile engine 156 may obtain information from an activities of daily living (ADL) analysis engine 140… wearable sensors 135 measure ranges of motion of the user with regard to 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 actions that the user is able to perform… then correlated with skills and skill limitations based on the correlations determined by engine 152… These identified skills/skill limitations may be added to the user profile via the user profile engine 156.);
train […] using the arthritic training set ([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 (necessarily train an engine using the arthritic training set), is unlikely to be able to perform operations requiring strong hand actions. 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 may be associated with particular skills and/or skill limitations.);
generate the arthritic batch as a function of the trained […] ([0090] teaches this information may be combined into a user profile to further associate skills and/or skill limitations with the user (generate the arthritic batch as a function of the trained engine). Further, [0088] teaches the user profile engine 156 generates/updates a user profile… that specifies the skills and skill limitations… and may be dynamically updated based on periodic evaluation of user information.);
[…] as a function of the arthritic batch (user profile), […];
determine an edible, using an edible machine-learning model, as a function of the arthritic batch and […] (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 determining an edible) for which the user has the skills to successfully complete the operation by performing all of the actions in the operation. Figs. 1, 5 and [0078], [0145] teach providing candidate recipes to the cognitive system 100 / IBM Chef Watson™ pipeline 590 (using an edible machine-learning model), which performs evidence-based evaluation and confidence scoring using the user skill-based scores generated for the various recipes, to generate a ranked candidate set of recipe recommendations.); and
generate a nourishment program as a function of the edible (Figs. 3, 5, [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 (recipes).)
Greenberger may not teach producing the arthritic batch using an arthritic machine-learning model.
Apte teaches
using an arthritic machine-learning model (see Applicant’s disclosure at para. 0052. [0112] teaches variations of the method 100 can additionally or alternately utilize any other suitable algorithms in performing a characterization process. [0126] teaches a comparison can involve various machine learning techniques, such as supervised machine learning (e.g., naïve Bayes classifier) and unsupervised machine learning (e.g., principal component analysis).)
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 to perform a function using a supervised or unsupervised machine learning technique (e.g., a naïve Bayes classifier) 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 health / improving a state of a locomotor system condition (see Apte at para. 0003, 0015).
Greenberg/Apte does not teach
produce a nourishment demand… wherein the nourishment demand is determined as a function of receiving a nourishment goal; or
determine an edible, using an edible machine-learning model, as a function of… the nourishment demand, wherein the edible machine-learning model is training using an edible training set that correlates the nourishment demand to the edible.
Hadad teaches
produce a nourishment demand… wherein the nourishment demand is determined as a function of receiving a nourishment goal (see Specification para. 0030. [0085] teaches the solution vector x will contain the actual amounts of each food item the user is required to consume (nourishment demand)… Additionally, the problem of what to eat and how much is solved using an optimization function… Here the food items chosen are preferably closest to the user's known preferences (necessarily received). Figs. 3A-B and [0058]-[0059] teach the algorithm tests whether the user is infatuated with meat 321, or if they usually eat a smaller healthier lunch 322 (a nourishment goal), e.g., if the user chooses “lots” of food daily, the algorithm will try to create such meals… not only that, but the algorithm also tests the user’s willingness to commit to a healthier diet… If the user chooses the right-hand side image 332, the algorithm will try to create healthier meals, while if the image on the left-hand side is chosen, the user will receive meals more to their liking, regardless of their healthiness.)
determine an edible, using an edible […] model, as a function of […] and the nourishment demand (Fig. 2, [0064] teach, using this data, the first module (an edible model) creates a nutritional schedule--specifying which food items are to be consumed at each meal (determine an edible) and at what quantity (as a function of the nourishment demand) for the next week.), wherein the edible […] model is […] using an edible training set that correlates the nourishment demand to the edible ([0084]-[0086] teaches the algorithm combines all constraints listed below ([0119]-[0130]) with reference to Fig. 5 and creates a constraints matrix “A”, and a constraints vector “b”... “A” contains nutritional amounts of each food item and each food group (an edible training set)… the solution vector x will contain the actual amounts of each food item the user is required to consume (the nourishment demand is necessarily correlated to the edible)… This linear optimization problem may be solved using the Simplex algorithm.)
Greenberger/Apte/Hadad may not teach an edible machine-learning model is training using an edible training set.
McCaffrey teaches
training a machine learning model (The title teaches neural network training using simplex optimization. Below the title: “Simplex optimization is one of the simplest algorithms available to train a neural network”.)
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 mechanisms of Greenberger/Apte/Hadad to implement a neural network algorithm and train the neural network algorithm using the Simplex algorithm and to use this information as part of a method of neural network training as taught by McCaffrey, with the motivation of improving a complex mathematical function for a given set of training data (see McCaffrey at pg. 1, para. 1-3).
Re. CLAIM 2, Greenberger/Apte/Hadad/McCaffrey 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/Hadad/McCaffrey 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/Hadad/McCaffrey teaches the system of claim 1, wherein producing 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/Hadad/McCaffrey 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 (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/Hadad/McCaffrey teaches the system of claim 1, wherein producing 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 (necessarily determined)… 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/Hadad/McCaffrey teaches the system of claim 1, wherein producing 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 (necessarily determined)… 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/Hadad/McCaffrey teaches the system of claim 1, wherein producing 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 (producing the arthritic batch).)
Re. Claim 10, Greenberger/Apte/Hadad/McCaffrey 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 (receiving an arthritic outcome)… The user may then submit this feedback to the cognitive system by pressing the submit GUI element 432. Greenberger [0079] teaches providing this feedback to the skill based operation selection/modification engine 150 (a nourishment machine-learning model) 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.)
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, Hadad, McCaffrey, and Moturu et al. (US 2017/0235912 A1; “Moturu” herein).
Re. Claim 9, Greenberger/Apte/Hadad/McCaffrey teaches the system of claim 1, wherein producing the arthritic batch (the user profile) further comprises:
receiving an arthritic […] (see 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/Hadad/McCaffrey 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/Hadad/McCaffrey 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. §112
Regarding the rejections, the Applicant has amended the claims to overcome the previous written description rejection, hereby withdrawn. The amended claims as considered do not cause any new issues.
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. “Amended claim 1 recites, among other steps, obtaining "an arthritic element," receiving "an arthritic training set, wherein the arthritic training set correlates the arthritic element to an arthritic batch," and training "an arthritic machine-learning model using the arthritic training set." 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” (Remarks, pg. 8-9).
Regarding A1: The Examiner respectfully submits that obtaining data, receiving a correlated dataset, training, and use of the machine learning model are part of the abstract idea (at least data manipulations that humans perform as part of following rules or instructions to perform a data-driven computational process). The Examiner notes that the machine learning encompasses simplistic mathematical models such as simple linear regression and thus the machine learning is interpreted to be part of the abstract idea. 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 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 as an additional element in step 2A2 and 2B.
The Examiner submits that the examples of data analysis, modeling and automated output generation are forms of human interaction with a computer, which fall under certain methods of organizing human activity. Given the broadest reasonable interpretation, 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. “These steps illustrate a technical implementation involving the transformation of one set of data (the arthritic batch) into another (the nourishment demand) using trained computational models” (Remarks, pg. 8).
Re. argument A2: The Examiner respectfully submits the basis of rejection as necessitated by amendment or afforded by RCE. Also, given broadest reasonable interpretation, the training and use of the computational model is part of the identified abstract idea.
Additionally, the courts have held that a transformation of data is not a “transformation” sufficient to render a claim subject matter eligible (“For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).”) See MPEP 2106.05(c).
A3. “This recitation describes the use of two distinct trained machine learning models, which are one for arthritic data and one for edible data to compute an integrated output. Such a configuration demonstrates a non-conventional data architecture that uses the relationship between physiological and nutritional datasets to produce an optimized result. These steps are technological in nature and demonstrate a specific application of machine learning models to complex physiological and nutritional correlations… Each step in the claim contributes to a data processing workflow…” (Remarks, pg. 9).
Re. argument A3: The Examiner respectfully submits that neither machine learning model(s) nor the training thereof qualifies as more than a mathematical concept. Also, taking data as input, data manipulations, and producing an output using an optimization method are rules or instructions (i.e., data processing steps) a person could certainly do implement on a generic computer. Further, the arthritic machine-learning algorithm and edible machine-learning algorithm are broad enough to encompass “simple linear regression”, which a person could certainly do even without a computer, such that the training and application of the arthritic machine-learning model are basic tools of scientific work. While the abstract idea may be improved, an improved abstract idea is still an abstract idea. Only additional elements can provide an integration or significantly more. MPEP 2106. However, the additional elements as considered do not provide either of these, as is discussed below.
A4. “The claim language does not include, rely on, or resemble any concepts that courts have recognized as "methods of organizing human activity."” (remarks, pg. 9).
Re. argument A4: The Examiner respectfully submits that the court case examples 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.
A5. “Similarly, the limitations of claim 1, which include "training an arthritic machine-learning model using the arthritic training set, generating the arthritic batch as a function of the trained arthritic machine-learning model, and determining an edible using an edible machine-learning model" do not recite judicial exceptions even if they "may involve or rely upon mathematical concepts." These limitations describe 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… By contrast, claim 1 does not…” (remarks, pg. 10-11).
Re. argument A5: The Examiner respectfully submits that Example 39 differs from claim 1 in that machine learning technology is claimed as an additional element, whereas the “machine-learning algorithms” and the “training”, given the BRI in light of the specification, encompass the training of a simplistic mathematical model performed by simple linear regression. 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 respectfully asserts that “training” and applying data to the arthritic machine-learning model to generate the output data both cover a mathematical concept.
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”).
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 and the edible machine-learning model. The specification states that the training may encompass a simplistic mathematical algorithm of “simple linear regression”, which is 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.
“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, which is recited 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 and the use of the machine-learning algorithms encompasses a series of rules or instructions including data manipulations a person could do on a generic computer; alternately, the training and use of the machine-learning algorithms encompasses a mathematical concept that includes mathematical relationships, mathematical formulas or equations, and mathematical calculations.
A6. “This process reflects a technical improvement over traditional, rule-based or manually derived nutritional systems, which lack the ability to dynamically model physiological conditions and adapt nourishment recommendations based on machine-learned correlations” (remarks, pg. 12).
Re. argument A6: The Examiner respectfully disagrees. The computing device, as previously discussed, 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, producing, identifying, training, using/applying, 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. 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 described improvement to the computing device, and there is no other technology claimed that may or may not be improved. If the specification does not set forth or explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the Examiner should not determine the claim improves technology. MPEP 2106.04(d)(1).
A7. “the present claims improve the technological process of physiological data modeling and nourishment generation…” (Remarks, pg. 12).
Re. argument A7: The Examiner respectfully submits that the proposed problem with physiological data modeling and nourishment generation is not a technical problem; this is at most a medical problem. Since this is not a technical problem (i.e., caused by the computer to which the claims are confined), the claimed invention does not provide a practical application or significantly more under this measure or by any measure prov