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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/28/2026 has been entered.
Formal Matters
Applicant's response, filed 09/28/2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Examiner notes to Applicant that, under MPEP § 714, all claims being currently amended must be presented with markings to indicate the changes that have been made relative to the immediate prior version. Applicant has apparently left amendment marks from the claim changes filed 03/11/2026 in the amendments entered 09/28/2026. As the amendments made to the instant application are readily apparent and consistent with Applicant’s response entered, Examiner has hereinafter examined the claims presented in pursuit of compact prosecution.
Status of Claims
Claims 1-5, 7-15, and 17-20 are currently pending and have been examined.
Claims 1 and 11 have been amended.
Claims 6 and 16 have been canceled.
Claims 1-5, 7-15, and 17-20 have been rejected.
Priority
The instant application does not claim the benefit of priority under 35 U.S.C 119(e) or under 35 U.S.C. § 120, 121, or 365(c) to any prior applications. Accordingly, the effective filing date for the instant application is 31 March 2020.
Claim Interpretation
Independent claims 1 and 11 recite perform a clustering analysis by generating a first vector comprising the at least a retrieved element of user data and a plurality of vectors of metabolic states and training data; sort elements of the training data using a natural language processing algorithm; select at least one sorted training data set of the plurality of sorted training data sets; and training the at least a machine-learning process using the selected at least one sorted training data set. Examiner notes that one of ordinary skill in the art would recognize that the training data utilized by the first clustering analysis of bio-physiological signal data would have to include numeric data types (integers, floats, doubles, bytes, etc.) while the training data utilized by the natural language sorter of psychological data would be alphanumeric (strings, characters, arrays, vectors, etc.). As the antecedent basis for the training data attempts to conflate the two different training sets as one, Examiner is interpreting the claim to read on a “training set” that encompasses the totality of the training data used for each algorithm and that the classification algorithm and natural language sorter are trained on appropriate data types. Therefore, the sorted training data utilized to train “machine-learning process” would only include the psychology data. As the instant specification fails to define the bounds of what is or is not included in training data, application specific detail of the training process (only generic descriptions of the algorithms are describe at any length in the disclosure), or how the two algorithms work coextensively to generate the nutritional output, Examiner has no recommendations to Applicant for an alternative interpretation that is consistent with the claim language as drafted.
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 1-5, 7-15, and 17-20 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. Neural network classification algorithm configured to perform clustering
Step 1 – Statutory Categories of Invention:
Claims 1-5, 7-15, and 17-20 are drawn to a system or method, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites an artificial intelligence system for multi-factor selection process in part performing the steps of determine a user metabolic state utilizing the at least a retrieved element of user data and a classification algorithm configured to perform a clustering analysis by generating a first vector comprising the at least a retrieved element of use data and a plurality of vectors of metabolic states and training data normalizing each vector to make a vector comparison independent of absolute quantities of attributes, and implementing a measure of vector similarity of the first vector and the plurality of vectors to determine the user metabolic state, wherein the at least an element of user data further comprises user psychological data; sort elements of the training data using a natural language processing algorithm to generate a plurality of sorted training data sets each containing a plurality of data entries categorizing training data elements to categories of user data; select at least one sorted training data set of the plurality of sorted training data sets as a function of the user psychology data using the natural language processing algorithm; determine, using at least a machine-learning process, a nutritional output comprising a plurality of meal possibilities optimized as a function of the equivalency request, the at least an element of user data, and the user metabolic state; wherein determining the nutritional output further comprises: training the at least a machine-learning process using the selected at least one sorted training data set; determining the nutritional output utilizing the trained at least a machine-learning process; identify one or more meal possibilities from the plurality of meal possibilities available as a function of the user geolocation and seasonal availability; adjust the nutritional output to generate a geographically adjusted nutritional output comprising available elements, and calculate an optimization value utilizing the nutritional output. Independent claim 11 recites the same abstract idea.
These steps amount to functions performable in the mind or with pen and paper and are only concepts relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work (MPEP § 2106.04(a)(2)(III)(c)(1) citing the abstract idea grouping for mental processes on a generic computer). Examiner notes that consistent with Example 47 claim 2, the use of a generic machine learning process with no further detail regarding the application or process of using the algorithm amounts to an application of mental process on a generic computer. Here the specification provides that the machine learning process include any possible type of mathematical analysis of data to determine a relationship (see the instant specification in ¶ 0070 and ¶ 0072-73) including a linear regression model that can be reasonably performed mentally.
Dependent claims 2 recite, in part, utilizes the equivalency request to select the at least an element of user data relating to the equivalency request.
Dependent claims 3 recite, in part, wherein the at least an element of user data contains a user reported element of user data.
Dependent claims 4 recite, in part, wherein the at least an element of user data contains a biological extraction.
Dependent claims 5 recite, in part, generate, the classification algorithm, wherein the classification algorithm utilizes the at least an element of user data as an input and outputs a user metabolic state; and identify, using the classification algorithm and the at least an element of user data, a user metabolic state.
Dependent claims 7 recite, in part, assess the nutritional output to identify available elements; and adjust the nutritional output utilizing the available elements.
Dependent claims 8 recite, in part, compare the optimization value to the maximum user optimization value; and minimize the optimization value.
Dependent claims 9 recite, in part, subtract the optimization value from the maximum user optimization value to calculate a surplus; and utilize the surplus to suggest a lifestyle output.
Dependent claims 10 recite, in part, calculate the optimization value for a specified period of time.
Dependent claims 12 recite, in part, utilizing the equivalency request to select the at least an element of user data relating to the equivalency request.
Dependent claims 13 recite, in part, retrieving a user reported element of user data.
Dependent claims 14 recite, in part, retrieving the at least an element of user data further comprises retrieving a biological extraction.
Dependent claims 15 recite, in part, generating the classification algorithm, wherein the classification algorithm utilizes the at least an element of user data as an input and outputs a user metabolic state; and identifying, using the classification algorithm and the at least an element of user data, a user metabolic state.
Dependent claims 17 recite, in part, assessing the nutritional output to identify available elements; and adjusting the nutritional output utilizing the available elements.
Dependent claims 18 recite, in part, comparing the optimization value to the maximum user optimization value; and minimizing the optimization value.
Dependent claims 19 recite, in part, subtracting the optimization value from the maximum user optimization value to calculate a surplus; and utilizing the surplus to suggest a lifestyle output.
Dependent claims 20 recite, in part, calculating the optimization value for a specified period of time.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1 or 11 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements already analyzed in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claims 1 and 11 recite a neural network classification algorithm for clustering. The specification provides no detail regarding the utilization of a neural network classification algorithm as it related to a clustering analysis (see the Detailed Description in ¶ 0090 wherein clustering machine-learning process is disclosed as combinable with any of the machine-learning processes disclosed). The use of a neural network, in this case as a classification algorithm for clustering, only recites the neural network as a tool to apply data to an algorithm and report the results (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.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claims 1 and 11 recite a computing device and a user device. The specification defines the computing device and user device as any computing device (see the instant specification in ¶ 0008 and ¶ 0065). The use of a computing device and user device are recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Claims 1 and 11 recite receive an equivalency request, wherein the equivalency request contains a user specified individualized level. Claims 1 and 11 recite retrieve, from a sensor, at least an element of user data comprising a bio-physiological signal related to at least muscle activity. Claims 1 and 11 recite receive, from a user device by the computing device, a user geolocation associated with the user. Claims 8 and 18 recite receive, from a remote device, a maximum user optimization value. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Claims 1 and 11 recite display to the user, at the user device, the nutritional output as a function of the optimized value. The limitations are only recited as a tool which only serves as display/output of the data determined from the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to post-solution output on a well-known display device) and is therefore not a practical application of the recited judicial exception.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claims 1 and 11 recite a computing device and a user device. Claims 1 and 11 recite a neural network classification algorithm for clustering.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the computer and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claims 1 and 11 recite receive an equivalency request, wherein the equivalency request contains a user specified individualized level. Claims 1 and 11 recite retrieve, from a sensor, at least an element of user data comprising a bio-physiological signal related to at least muscle activity. Claims 1 and 11 recite receive, from a user device by the computing device, a user geolocation associated with the user. Claims 8 and 18 recite receive, from a remote device, a maximum user optimization value. The courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claims 1 and 11 recite display to the user, at the user device, the nutritional output as a function of the optimized value. The courts have decided that presenting generated data as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example iv. presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-5, 7-15, and 17-20 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. § 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-5, 7-15, and 17-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention. Independent claims 1 and 11 have been amended to include “a neural network classification algorithm configured to perform a clustering analysis”. While the specification discloses a clustering machine-learning process as combinable with any of the machine-learning processes (see the Detailed Description in ¶ 0090), one of ordinary skill would not reasonably ascertain that Applicant disclosed the algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention. The instant disclosure only discusses generic embodiments of neural networks (see the Detailed Description in ¶ 0073-74 disclosing convolutional neural network embodiment which do not require clustering to function or learn features and in ¶ 0014 wherein the clustering algorithm “k-nearest neighbor” is notably distinct from a neural-network based classifier) and provides no details on how the neural network would be implemented as a clustering algorithm. Furthermore, Examiner notes Applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitations in the application as filed (see 2163.04(I) regarding the burden on Examiner with regard to the written description requirement).
Claims 2-5, 7-10, 12-15, and 17-20 depend on claims 1 or 11 and do not remedy the written description requirement issues of claims 1 and 11. As dependent claims inherit the deficiencies of the claims they depend on, they are also rejected.
Response to Arguments
Examiner acknowledges that appropriate corrections to the claims for the previous 35 U.S.C. 112(a) rejections has been made and withdraws the rejections accordingly.
Applicant's arguments filed with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant asserts that the utilization of a [Examiner assumes a neural network and not the Naïve Bayes] particular classification algorithm to determine a user’s metabolic state and output a nutritional recommendation amounts to more than applying machine learning as a generic tool. Examiner disagrees. There is no evidence of an improvement to the operation of the machine learning itself in the claim or instant disclosure, instead the instant application relies on the application of a new abstract idea to a known machine learning algorithm to generate a result more similar to Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Fed. Cir. Apr. 18, 2025) wherein the mere use of machine learning to complete a specific task does not automatically quality the claim as non-abstract under Step 2A Prong 1, stating “the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved” (id. p. 15).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kohn et al. (US Patent App No 2022/0336109) teaching on utilizing a trained deep neural networks to cluster nutrition, medication, lifestyle, symptoms, pain, and user defined metrics together over time in ¶ 0086-88
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
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