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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 on 09/02/2026 has been entered.
Claims 1-20 remain pending in this application.
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, wherein outputting the alimentary program comprises identifying a nutritional value of an ingredient of the alimentary program as a function of weight of the ingredient, and generating a representation of a percentage of compliance of the alimentary program with 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”, “output an alimentary program to the user as a function of the alimentary data, wherein outputting the alimentary program comprises identifying a nutritional value of an ingredient of the alimentary program as a function of weight of the ingredient, and generating a representation of a percentage of compliance of the alimentary program with the alimentary 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 09/02/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in the order in which they appear.
Applicant argues that the limitation of “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” does not correspond to mathematical relationships , since the claim does not recite any algorithm, formula or equation.
In response, Examiner submits that the MPEP recites “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.” in 2106.04(a)(2)IA, and 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.” in [0040], And the Naive Bayes classification algorithm corresponds to mathematical relationships. Therefore, claim limitations correspond to “mathematical concepts”.
Applicant argues that the specification recites other alternatives for the classification model (liner classifiers) not only Naïve Bayes classification algorithm. In response, Examiner submits that all of these examples of classification models correspond to mathematical relationships and therefore, the claim limitation correspond to mathematical concepts.
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 newly amended claim 1 and 11 recite “wherein outputting the alimentary program comprises identifying a nutritional value of an ingredient of the alimentary program as a function of weight of the ingredient, and generating a representation of a percentage of compliance of the alimentary program with the alimentary data”, which requires the claimed system to compute a new, derived output representing the program’s percentage of compliance with the user’s alimentary data, an output that does not exist an any dataset the system receives or retrieves.
In response, Examiner submits that the current specification recites “With continued reference to FIG. 1, a processor 104 may output alimentary program 140as a function of the alimentary data 136 and phenotype clusters 124. As used in the current disclosure, a "alimentary program" comprises instructions for preparing a meal that fulfills the nutritional requirements of a demographic category. An alimentary program 140 may include both an ingredient combination and preparation instructions. Generating alimentary program 140 may include generating an ingredient combination. As used in the current disclosure, an "ingredient combination" comprises one or more foods, spices, and/or condiments used to make a meal. An ingredient combination may include an ingredient and a quantity for those ingredients. The quantity of ingredients may be configured to be scaled as a function of the number of desired portions. In embodiments, processor 104 may identify the nutritional value of each ingredient as a function of the ingredients weight. This may be displayed as a function of percentage of compliance of the alimentary data 136. In some embodiments, processor 104 may be configured to identify one or more replacement ingredients within the ingredient combination. As used in the current disclosure, a "replacement ingredient" is an ingredient that is nutritionally similar to the ingredients prescribed within an ingredient combination. In embodiment, replacement ingredients may be used to adjust for allergies, religious beliefs, personal preferences, and/or taste preferences. In embodiments, breakfast, lunch, dinner, snack, may have a nutrient target assigned to them as a function of the alimentary data 136. In a non-limiting example, alimentary data 136 may prescribe that a demographic group intake lOg of protein for breakfast. An alimentary program 140 may be configured to accomplish this goal.” in [0043]. Therefore, the processor provide an output alimentary program with adjustable values. There is no indication in the current specification that this feature requires an improved technology or showing any improvement to the technology. The feature of outputting the alimentary program comprising identifying a nutritional value of an ingredient of the alimentary program 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 (MPEP 2106.05(f) & (h)).
Therefore, the arguments are not persuasive and claims are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
Conclusion
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
