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 .
Formal Matters
Applicant's response, filed 11 March 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.
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.
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; iteratively updating model parameters of the at least a machine-learning process across a plurality of training iterations using machine-state persistence until convergence criterion associated with the nutritional output is satisfied; and 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 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.
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.
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 “iteratively updating model parameters of the at least a machine-learning process across a plurality of training iterations using machine-state persistence until a convergence criterion associated with the nutritional output is satisfied”. 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).
Subject Matter Free of the Prior Art
The following is an examiner’s statement of subject matter free of the prior art:
The ordered combination of limitations in independent claims 1 and 11 stating: 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; select at least one sorted training data set of the plurality of sorted training data sets as a function of the user psychological data using the natural language processing algorithm; determine, using at least a machine-learning process, a nutritional output comprising a plurality of meal possibilities; 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 is free of the prior art.
The most remarkable prior arts of record are as follows:
Minobe et al. (US Patent App Pub No US20210257079A1) teaching on considering the seasonality of the ingredients when making a meal plan generally in the Detailed Description in ¶ 0032 and ¶ 0079-82;
Bennett et al. (US Patent Application No. 2015/0194071) teaching on recommending a food/meal for the user based on the determined nutrient level category and the received user data including the nutrient breakdown of the food consumed in the Detailed Description in ¶ 0154-155, ¶ 0165, ¶ 0199, ¶ 0218, ¶ 0220-256, and in the Figures in fig. 12A;
Tome Eftimov, Statistical Data Analysis and Natural Language Processing for Nutrition Science, Doctoral Dissertation Jožef Stefan International Postgraduate School (Jan 2018) teaching on a natural language processing part-of-speech tagging probability weighted algorithm to extract word classes and cluster via a similarity metric related words to use a training data in a domain specific nutritional based machine learning algorithm from the nutritional health text corps in the § 5.3.1 Part-of-speech tagging probability weighted method on p. 95-97 and § 5.4.1 FoodEx2 on p. 97-98;
Izzah et al., Classification of nutritional status of toddlers using fuzzy k nearest neighbor in every class (FK-NNC), J OF PHYSICS: CONFERENCE SERIES (2019) teaching on utilizing fuzzy k-nearest neighbor determining similarity distances between a normalized input vector and a centroid to classify the nutritional status of a person based in part on collected data in the § 2.1 Proximity concept on p. 2, § 2.2 Data normalization, and § 4.4.3.Calculates the accumulated distance of K neighbors for each class on p 6
While the individual elements of the claim may be taught by the prior art references shown, one of ordinary skill in the art would not reasonably combine the reference without reliance on hindsight reasoning and a piecemeal analysis. Therefore, claims 1-5, 7-15, and 17-20 are free of the prior art.
Response to Arguments
Applicant's arguments filed 11 March 2026 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant first asserts that the instant claims recite limitations that cannot be practically performed mentally, citing AI-SME update where a hardware based RFID serial number data structure is considered too complex to perform mentally. Examiner is not persuaded. There is no evidence of a hardware RFID related data structure in the claim. A human is practically capable of considering sensor data, determining a metabolic state of a user, normalizing said data, implementing a similarity analysis to other people regarding their metabolic state via reading natural language of other people and sorting the data to individulas similar to themselves, generating some model (here, a linear relationship between weight and calories falls well within the broadest reasonable interpretation of the claim and supported by the instant specification’s disclosure), and updating the model when users update new data.
More importantly, Applicant admits on the record that these operations are mathematical transformations – that is, whether or not the math is “complex” enough to be performed mentally, the claims still recite an abstract idea.
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Applicant continues to assert that Examiner has misclassified the claims as a mental process as the claim does not merely recite “apply machine learning”, noting that Examiner has reductively applied the broadest interpretation embodied in the specification of linear regression. Applicant’s arguments that the classification algorithm is not linear regression and Examiner has oversimplified the claims is not based in Applicant’s own specification. From ¶ 0014 of Applicant’s own specification “Classification may be performed using, without limitations, linear classifiers…”. Applicant seems to assert that clustering limits the claim to a non-linear embodiment. Examiner notes that one of ordinary skill in the art would understand that clusting can be performed utilizing a linear similarity distance. While there are embodiments of clustering that compare multiple variables, this embodiment is not realized in the claim; instead the claim is comparing a single metabolic state value. The natural language processor has no structure outside a generic description on the known operations of natural language processors (see the instant specification in ¶ 0071). Secondly, the arguments are further not persuasive as even if considered too complex to perform mentally, the claims still recite a mathematical concept as admitted by Applicant. Applicant’s assertion that a human mind cannot receive sensor data is not persuasive. This limitation was treated as an additional element by Examiner.
Next, Applicant asserts that the claims amount to a practical application via an “concrete technological implementation, outlining each “step” that Applicant porports believes integrates the abstract idea into a particular technology solution. Examiner notes that a claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (see MPEP § 2106.04(d) - Integration of a Judicial Exception Into A Practical Application). The court has provided limitations that are indicative that an additional element (or combination of elements) may have integrated the exception into a practical application and limitations that did not integrate a judicial exception into a practical application (see MPEP § 2106.04(d)(I) – Relevant Considerations for Evaluating Whether Additional Elements integrate a Judicial Exception into a Practical Application) wherein the claims may amount to (1) improvements to the functioning of a computer, (2) improvements to a technological field, (3) applying the judicial exception to a particular machine (as evaluated above in ¶ ), (4) transforming or reducing a particular article ot a different state or thing, (5) unconventional activity or steps that confine the claim to a particular useful application, or (6) other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Here the instant claims seem more analogous to "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Finally Applicant asserts that the claims recite additional elements that are not merely well understood, routine, and conventional, stating that Examiner has not met the evidentiary standard required. Applicant then recites the following (Examiner’s response for each element is added in the brackets:
Claim 1 recites additional elements beyond any alleged abstract idea, including:
retrieving bio-physiological signals from a sensor related to muscle activity [treated as an additional element with MPEP § 2106.05(d)(II) evidence – if Applicant wishes to assert that sending sensor data over a network is not well understood, routine, and conventional, Examiner notes that the use of a wearable device to collect patient health data is well-understood, routine, and conventional activity. This position is supported by (1) Kakkar et al. (US Patent App. No. 2016/0089089) teaching on the system receiving biomarker data for the user from a wearable user device in the Detailed Description in ¶ 0062 and ¶ 0073; (2) An et al. (US Patent App. No. 2013/0116578) teaching on a patient wearable device for collecting biomarker data in the Background in ¶ 0004-5; and (3) Kaleal (US Patent App. No. 2016/0086500) teaching on a biomarker collection device worn by the user in the Detailed Description in ¶ 0049 and ¶ 0052];
generating normalized vectors from that sensor-derived data [an abstract idea as admitted by Applicant stating “these are structural mathematical transformations];
implementing similarity-based clustering across a plurality of metabolic-state vectors [an abstract idea as admitted by Applicant stating “these are structural mathematical transformations];
sorting training data using a natural language processing algorithm to generate structured training datasets; selecting datasets as a function of user psychological data [an abstract idea as admitted by Applicant stating “these are structural mathematical transformations];
iteratively updating machine-learning model parameters using machine-state persistence [an abstract idea as admitted by Applicant stating “these are structural mathematical transformations];
continuing training until a convergence criterion is satisfied [an abstract idea as admitted by Applicant stating “these are structural mathematical transformations];
receiving a user geolocation [treated as an additional element];
identifying meal possibilities as a function of geolocation and seasonal availability [Examiner sustains this is clearly a mental process];
generating a geographically adjusted nutritional output [Examiner sustains this is clearly a mental process];
calculating an optimization value; and [Examiner sustains this is clearly a mental process and a mathematical concept];
displaying the output as a function of that value [treated as an additional element].
The consideration under Step 2B is if the additional elements, alone or in combination, are well-understood, routine and conventional in the field – the novelty of the abstract idea is not considered relevant under the Step 2B analysis. Here, the additional elements, alone or in combination, amount 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).
Next, Applicant asserts that Examiner failed to prove that a computing device and a user device are generic computer components by not providing proper Berkheimer evidence. 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). If Applicant believes the user device or the computing device are note well understood, Examiner notes the elements would 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 had possession of the claimed invention.
Finally, Applicant asserts that Examiner did not consider the abstract performance of the additional elements with every limitation of the claim. The consideration under Step 2B is if the additional elements, alone or in combination, are well-understood, routine and conventional in the field – the novelty of the abstract idea is not considered relevant under the Step 2B analysis. Here, the additional elements, alone or in combination, amount 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).
Next, Applicant asserts that machine learning with machine-state persistence and convergence control is not conventional data processing. Without commenting on the validity of said statement, Examiner notes this is moot at the limitations are new matter and rejected accordingly.
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 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