DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is in response to the amendment received on 12/03/2025. Claims 1-20 remain pending in this application.
The claim objections to claims 7 and 12 has been withdrawn in light of the amendments made to these claims.
The 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejection to claim 12 has been withdrawn in light of the amendment made to this claim.
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 an abstract idea without significantly more.
Step 1:
Claims 1-10 are drawn to an apparatus (a system) which is within the four statutory categories (i.e. machine). Claims 11-20 are drawn to a method which is within the four statutory categories (i.e. process).
Step 2A, Prong 1:
Claims 1 and 11 have been amended to recite:
“receive user data comprising phenotypic data and at least one biological extraction related to a user from at least a remote device, wherein the user data further comprises a physical attribute and a nutritional history of the user based on the phenotypic data and at least one biological extraction;
generating, by a computing device, a cluster machine learning model by training the cluster machine learning model using training data configured to correlate phenotypic data inputs and biological extraction inputs to phenotype cluster identifiers;
classifying, by the computing device, the user data to one or more phenotypic clusters as a function of the user data using the cluster machine learning model;
storing, in a memory, the one or more phenotype clusters as a phenotype profile associated with the user;
assigning, by the computing device, the classified user data one or more cohort labels as a function of the one or more phenotypic clusters;
generating, by the computing device, alimentary data by retrieving cohort-specific alimentary records correlated to nutritional requirements as a function of the one or more cohort labels; and
outputting, by the computing device, an alimentary program to the user as a function of the alimentary data”
The limitations of “generating, by a computing device, a cluster machine learning model by training the cluster machine learning model using training data configured to correlate phenotypic data inputs and biological extraction inputs to phenotype cluster identifiers; classifying, by the computing device, the user data to one or more phenotypic clusters as a function of the user data using the cluster machine learning model;… assigning, by the computing device, the classified user data one or more cohort labels as a function of the one or more phenotypic clusters; generating, by the computing device, alimentary data by retrieving cohort-specific alimentary records correlated to nutritional requirements as a function of the one or more cohort labels” correspond to mathematical relationships, which falls within the “mathematical concept” grouping of abstract ideas.
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Claims 2-10 and 12-20 are ultimately dependent from claims 1, 11 and include all the limitations of claims 1, 11. Therefore, 2-10 and 12-20 recite the same abstract idea. 2-10 and 12-20 describe a further limitation regarding the basis for generating alimentary program for the user. These are all just further describing the abstract idea recited in claims 1,11, without adding significantly more.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements of at least a processor; and a memory communicatively connected to the processor, wherein the memory contains instructions configuring the at least a processor to perform receiving, generating a model, classifying, assigning, generating and outputting steps. Claims also recite the additional elements of “ receiving the user data from the at least a remote device further comprises: receiving a user instruction; querying the remote device as a function of the user instruction; and receiving the user data from the remote device in response to the query”, “receiving the user data further comprises: iteratively querying the at least a remote device; and iteratively receiving the user data in response to the iterative querying”, “plurality of remote devices”, “a smart scale”, “memory further contains instructions configuring the at least one processor to generate one or more preparation instructions of the ingredient combination”, “the plurality of phenotypic clusters includes an activity multiplier”, “the energy band machine learning model is trained using energy band training data”, which are hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
The limitations of “receive user data…”, “storing, in a memory, the one or more phenotypic clusters as a phenotype profile associated with the user” and “outputting…program data to the user” correspond to mere data gathering, which are insignificant extra-solution activities (see MPEP 2106.05 (g)). Therefore, they do not provide a practical application for the abstract idea.
Accordingly, these 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. The claims are directed to an abstract idea.
Step 2B:
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 using a processor to perform receiving data and generating a model steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claims are not patent eligible.
Response to Arguments
Applicant's arguments filed 12/03/2025 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in the order in which they appear.
Argument about Step 2A, prong one: Applicant argues that the present claim recites “generate a cluster machine learning model by training the cluster machine learning model using training data configured to correlate phenotypic data inputs and biological extraction inputs to phenotype cluster identifiers” and “store the one or more phenotypic clusters as a phenotype profile associated with the user”, which are system-operation limitations, not mathematical assertions.
In response, Examiner submits that the limitation of storing, in a memory, the one or more phenotypic clusters as a phenotype profile associated with the user” corresponds to mere data gathering, which are insignificant extra-solution activities, as indicated in the rejection above. The limitation of “generating a cluster machine learning model…configured to correlate…data inputs…to phenotype cluster identifier” corresponds to mathematical relationships, since the current specification recites “…processor may be configured to generate a machine learning model, such as phenotype classifier, using a Naive Bayes classification algorithm. Naive Bayes classification algorithm generates classifiers by assigning class labels to problem instances, represented as vectors of element values.” And the Naive Bayes classification algorithm corresponds to mathematical relationships. Therefore, claim limitations correspond to “mathematical concepts”.
Applicant argues that claim limitations are similar to the claim limitations of Example 39 in the USPTO Guidance. In response, Examiner submits that the claim of Example 39 recites “applying one or more transformations to each digital facial image…; creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non-facial images; training the neural network in a first stage using the first training set; creating a second training set for a second stage of training comprising the first training set and digital non-facial images that are incorrectly detected as facial images after the first stage of training; and training the neural network in a second stage using the second training set”, and these claim limitations are not directed to mathematical concepts/an abstract idea. On the other hand, the current claims recite “generate a cluster machine learning model…”, which is described in the specification as applying the Naive Bayes classification algorithm, which corresponds to mathematical concepts.
Argument about Step 2A, prong two: Applicant argues that claim 1 recite a machine learning model by “generate a cluster machine learning model by training the cluster machine learning model using training data configured to correlate phenotypic data inputs and biological extraction inputs to phenotype cluster identifiers” and classifying user data into phenotype clusters using the trained model, which requires iterative computational learning based on structured datasets, similar to the claim in Example 47 of the USPTO’s Guidance.
In response, Examiner submits that claim 3 of the Example 47 is found to be eligible, not because of using the trained machine learning model, but the system detecting network intrusions. The Guidance recites “In limitation (a), the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). In limitation (b), use of a trained ANN does not integrate the abstract idea of limitation (b) into a practical application for similar reasons as explained above in limitation (d) of Claim 2. Additionally, the recitation of “network traffic” generally links the abstract idea recited in limitation (b) to a particular field of use. See MPEP 2106.05(h).” on page 12, lines 18-24. The current claims are directed to classifying user data into phenotype clusters using the trained model (cluster machine learning model), which corresponds to mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea.
Applicant argues that the current claims improve existing approaches to phenotype-based nutrition systems by integrating a trained machine-learning model into a technical workflow that operates on biological data, similar to the Example 48 of the guidance.
In response, Examiner submits that the current claims are directed to using a processor to perform generating a model, classifying the user data…generating and outputting an alimentary program to the user, that are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea. The claim 3 of Example 48, on the other hand, provides a technical improvement, since “The disclosure states that this invention offers an improvement over existing speech-separation methods by providing a particular speech-separation technique that solves the problem of separating speech from different speech sources belonging to the same class, while not requiring prior knowledge of the number of speakers or speaker-specific training.” (page 24, lines 26-30). There is no indication of an improved technique for classifying the user data recited in the current claims nor in the current specification. The current specification recites “processor 104 may classify user data 108 to activity multiplier using a classifier. Classifier may be trained using training data. Training data may include phenotypic data correlated to an activity multiplier. Classifier may receive inputs from a user. Inputs may include user data 108, or the like, as described herein. Classifier may output at least an activity multiplier corresponding to an input received. Classifying user data 108 to activity multiplier may be done by any classification process described in this disclosure.” in [0021].
Argument about Step 2B: Applicant argues that claims recite significantly more than routine and conventional activity and provides a technical improvement in automated physiological classification and nutrition-data processing.
In response, Examiner submits that, as indicated in the section above, the current claims are directed to using a processor to perform generating a model, classifying the user data…generating and outputting an alimentary program to the user, that are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Therefore, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILEK B COBANOGLU whose telephone number is (571)272-8295. The examiner can normally be reached 8:30-5:00 ET.
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/DILEK B COBANOGLU/Primary Examiner, Art Unit 3687