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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/01/2026 has been entered.
Status of Claims
In the amendment filed 06/01/2026 the following occurred: Claim 15 was amended; Claims 16 and 18 were cancelled; and Claim 26 was added as new. Claims 1-15, 17, and 19-26 are presented for examination.
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 1-15, 17, and 19-26 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 1-15, 17, and 19-26 are drawn to methods, systems, and non-transitory computer readable storage mediums, which is/are statutory categories of invention (Step 1: YES).
Independent claim 1 recites accessing a record of food items previously recorded by the patient, the record of food items comprising a current classification of each of a plurality of food items describing an effect of the food item on a metabolic state of the patient; retrieving a current metabolic profile of the patient, the current metabolic profile comprising 1) biosignal measurements collected during a current time period and 2) a current metabolic state of the patient determined for the current time period; encoding, into a vector representation, the record of food items, the biosignal measurements of the current metabolic profile, and the current metabolic state of the patient; for each of a plurality of food items of the record of food items, determining an updated classification of the food item by inputting the vector representation; and identifying a subset of the record of food items comprising food items for which the updated classification differs from the current classification; and generating, for display to the patient, a notification comprising a graphic representation of the identified subset and a recommendation for the patient to consume food items of the identified subset classified as improving the metabolic state of the patient.
Independent claim 11 recites accessing a record of activities previously recorded by a patient, each of a plurality of entries in the record of activities comprising 1) a duration of the activity and 2) biosignal measurements collected for the patient during the activity; retrieving a current metabolic profile of the patient, the current metabolic profile comprising 1) biosignal measurements collected during a preceding time period and 2) a metabolic state of the patient determined for the preceding time period; encoding, into a vector representation, the record of activities, the biosignal measurements of the current metabolic profile, and the current metabolic state of the patient; for each activity in the record of activities, determining an effect of the activity on the metabolic state of the patient by inputting the vector representation; and identifying a subset of activities that improve the metabolic state of the patient based on the effects determined by the patient-specific metabolic model; and generating, for display to the patient , a notification comprising a graphic representation of the identified subset and a recommendation for the patient to perform activities of the identified subset.
Independent claim 21 recites access a record of food items previously recorded by the patient, the record of food items comprising a current classification of each of a plurality of food items describing an effect of the food item on a metabolic state of the patient; retrieve a current metabolic profile of the patient, the current metabolic profile comprising 1) biosignal measurements collected during a current time period and 2) a current metabolic state of the patient determined for the current time period; encode, into a vector representation, the record of food items, the biosignal measurements of the current metabolic profile, and the current metabolic state of the patient; for each of a plurality of food items of the record of food items, determine an updated classification of the food item by inputting the vector representation; and identify a subset of the record of food items comprising food items for which the updated classification differs from the current classification; and generate, for display to the patient , a notification comprising a graphic representation of the identified subset and a recommendation for the patient to consume food items of the identified subset classified as improving the metabolic state of the patient.
Independent claim 22 recites a access a record of food items previously recorded by the patient, the record of food items comprising a current classification of each of a plurality of food items describing an effect of the food item on a metabolic state of the patient; retrieve a current metabolic profile of the patient, the current metabolic profile comprising 1) biosignal measurements collected during a current time period and 2) a current metabolic state of the patient determined for the current time period; encode, into a vector representation, the record of food items, the biosignal measurements of the current metabolic profile, and the current metabolic state of the patient; for each of a plurality of food items of the record of food items, determine an updated classification of the food item by inputting the vector representation; and identify a subset of the record of food items comprising food items for which the updated classification differs from the current classification; and generate, for display to the patient , a notification comprising a graphic representation of the identified subset and a recommendation for the patient to consume food items of the identified subset classified as improving the metabolic state of the patient
Independent claim 23 recites access a record of activities previously recorded by a patient, each of a plurality of entries in the record of activities comprising 1) a duration of the activity and 2) biosignal measurements collected for the patient during the activity; retrieve a current metabolic profile of the patient, the current metabolic profile comprising 1) biosignal measurements collected during a preceding time period and 2) a metabolic state of the patient determined for the preceding time period; encode, into a vector representation, the record of activities, the biosignal measurements of the current metabolic profile, and the current metabolic state of the patient; for each activity in the record of activities, determine an effect of the activity on the metabolic state of the patient by inputting the vector representation; and identify a subset of activities that improve the metabolic state of the patient based on the effects determined by the patient-specific metabolic model; and generate, for display to the patient , a notification comprising a graphic representation of the identified subset and a recommendation for the patient to perform activities of the identified subset
Independent claim 24 recites access a record of activities previously recorded by a patient, each of a plurality of entries in the record of activities comprising 1) a duration of the activity and 2) biosignal measurements collected for the patient during the activity; retrieve a current metabolic profile of the patient, the current metabolic profile comprising 1) biosignal measurements collected during a preceding time period and 2) a metabolic state of the patient determined for the preceding time period; encode, into a vector representation, the record of activities, the biosignal measurements of the current metabolic profile, and the current metabolic state of the patient; for each activity in the record of activities, determine an effect of the activity on the metabolic state of the patient by inputting the vector representation; and identify a subset of activities that improve the metabolic state of the patient based on the effects determined by the patient-specific metabolic model; and generate, for display to the patient, a notification comprising a graphic representation of the identified subset and a recommendation for the patient to perform activities of the identified subset.
The respective dependent claims 2-10, 12-15, 17, 19-20, and 25-26, but for the inclusion of the additional elements specifically addressed below, provide recitations further limiting the invention of the independent claim(s).
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that the invention is to “monitoring a patient's metabolic health, for performing analytics on metabolic health data recorded for the patient, and for generating a patient-specific recommendation for treating any metabolic health-related concerns” (see: specification paragraph 28). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address a problem where “[c]onventional disease management platforms or techniques either ignore or fail to fully understand important markers…these platforms are designed to treat symptoms as they arise rather than treating the root cause of the disease - the deterioration of a patient's metabolic health…conventional disease management platforms are incapable of generating patient-specific recommendations for improving metabolic health by consuming foods or participating in activities” (see: specification paragraph 3-4), a problem which the invention address by providing, for example, “a personalized metabolic state program that provides tailored nutrition and exercise recommendations aligned with individual preferences” (see: specification paragraph 10). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including an “by one or more wearable sensors worn by the patient…to a patient-specific metabolic food model, wherein the patient-specific metabolic food model is iteratively trained to classify the food item based on a training dataset of previously classified food items labeled with a corresponding effect on a metabolic state of a patient…on a computing device…” (claim 1), “on the computing device” (claim 2), “via the computing device…” (claim 9), “by one or more wearable sensors worn by the patient…to a patient-specific metabolic activity model, wherein the metabolic model is iteratively trained to predict the effect of the activity based on a training dataset of previously recorded activities labeled with a corresponding effect on a metabolic state of the patient…on a computing device…” (claim 11), “on the computing device” (claim 12), “one or more computer processors; and one or more computer-readable mediums storing instructions that, when executed by the one or more computer processors, cause the system to:…by one or more wearable sensors worn by the patient…to a patient-specific metabolic food model, wherein the patient-specific metabolic food model is iteratively trained to classify the food item based on a training dataset of previously classified food items labeled with a corresponding effect on a metabolic state of a patient…on a computing device…” (claim 21), “non-transitory computer readable storage medium comprising stored instructions that when executed by one or more processors of one or more computing devices, cause the one or more computing devices to:…by one or more wearable sensors worn by the patient…to a patient-specific metabolic food model, wherein the patient-specific metabolic food model is iteratively trained to classify the food item based on a training dataset of previously classified food items labeled with a corresponding effect on a metabolic state of a patient…on a computing device…” (claim 22), “one or more computer processors; and one or more computer-readable mediums storing instructions that, when executed by the one or more computer processors, cause the system to:…by one or more wearable sensors worn by the patient…to a patient-specific metabolic activity model, wherein the metabolic model is iteratively trained to predict the effect of the activity based on a training dataset of previously recorded activities labeled with a corresponding effect on a metabolic state of the patient…on a computing device…” (claim 23), “a non-transitory computer readable storage medium comprising stored instructions that when executed by one or more processors of one or more computing devices, cause the one or more computing devices to:…by one or more wearable sensors worn by the patient…to a patient-specific metabolic activity model, wherein the metabolic model is iteratively trained to predict the effect of the activity based on a training dataset of previously recorded activities labeled with a corresponding effect on a metabolic state of the patient…on a computing device…” (claim 24), and “by the one or more wearable sensors…” (claim 26), which are additional elements that are recited at a high level of generality (e.g., “one or more wearable sensors worn by the patient” is configured though no more than a statement than that data is collected “by” said sensor(s); the “computing device” is configured through no more than a statement than that data is displayed and recorded “on” said computing device; the “one or more computer processors; and one or more computer-readable mediums” are configured though no more than a statement than that instructions “stor[ed]” on said computer-readable medium(s) are “executed by” said processor(s); and the “non-transitory computer readable storage medium” is configured though no more than a statement than that instructions “stored” on said non-transitory computer-readable medium are to be “executed by” one or more processors of one or more computing devices; the “iteratively trained” model is employed though no more than a statement than that an updated classification is determined by inputting a vector representation “to” said iteratively trained model, which itself is merely iteratively trained to classify “based on” a training dataset) such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s). Accordingly, the claims are directed to an abstract idea(s) (Step 2A Prong Two: 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(s) into a practical application, using the additional elements to perform the abstract idea(s) amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea(s) with routine, conventional activity specified at a high level of generality in a particular technological environment. Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea(s) (Step 2B: NO).
Dependent claim(s) 2-10, 12-15, 17, 19-20, and 25-26, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea(s) without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Response to Arguments
Applicant’s arguments from the response filed on 12/09/2025 have been fully considered and will be addressed below in the order in which they appeared.
In the remarks, Applicant argues in substance that (1) the 35 U.S.C. 101 rejections should be withdrawn because “the specification discloses an improved way the model learns and operates. The specification identifies that "every person is unique in their metabolic health" and that "the same foods or activities may have different effects on the metabolic health of different patients," rendering "conventional disease management platforms ... incapable of generating patient-specific recommendations." Specification, i,i 3-4. The disclosed solution is a patient-specific metabolic model that is "iteratively trained such that the food classification model 720 continues to learn and refine its parameter values based on the new and updated data" to "more accurately predict the metabolic effects of a food item." Specification, i 99. Notably, the model dynamically reclassifies food items as the patient's physiology evolves, stating "[o]ver time, a patient's metabolic state may change (e.g., improve or worsen) causing the effect of some food items on their metabolic state to also change." Specification, i 67. This is directly analogous to Desjardins, where the ARP credited the improvement of "training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of 'catastrophic forgetting."' Dejardins Memo. Here, the model adapts to the patient's changing metabolic state while preserving its learned classification structure, representing an improvement to how the model itself operates, not merely to the result it produces.”
The Examiner respectfully disagrees. Applicant’s arguments are not persuasive.
As per Dejardins, the claims do not address a problem in machine learning concerning catastrophic forgetting, or anything similar. It is unclear as to which claimed features represent the “preserving its learned classification structure” as argued, but regardless, this is not a similar issue per Dejardins in the machine learning arts addressed by the claims. According to the specification, the invention addresses a problem where “[c]onventional disease management platforms or techniques either ignore or fail to fully understand important markers…these platforms are designed to treat symptoms as they arise rather than treating the root cause of the disease - the deterioration of a patient's metabolic health…conventional disease management platforms are incapable of generating patient-specific recommendations for improving metabolic health by consuming foods or participating in activities” (see: specification paragraph 3-4). This is a problem which the invention address by providing, for example, “a personalized metabolic state program that provides tailored nutrition and exercise recommendations aligned with individual preferences” (see: specification paragraph 10). These problems and solutions are abstract, and while the invention employs an iteratively trained model such that “the model dynamically reclassifies food items as the patient's physiology evolves,” as argued, such a model is not trained within the scope of invention – the claims are to a determination made by inputting a vector representation to a patient-specific metabolic food model, followed by a “wherein” limitation that describes the model as “iteratively trained to classify the food item based on a training dataset of previously classified food items labeled with a corresponding effect on a metabolic state of a patient” (e.g., claim 1), but such training is not an actively claimed step/function and lies outside the scope of invention.
As argued, per Dejardins, the improvement need not be explicitly set forth, “but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art.” There is no description of how the model operates to compare with Dejardins in order to identify an improvement in the way the model operates. The claimed model employed covers a broad range of machine learning including “mathematical models trained to learn the relationships” (see: specification paragraph 72), where the model is described as one “such as a mathematical function” (see: specification paragraph 73) and “may be a mathematical function” (see: specification paragraph 96, 132, and 139). The specification further states that: “As described herein, the term “model” refers to the result of a machine learning training process” (see: specification paragraph 73). If there are improvements to the way in which the invention iteratively trains such a model, they should be reflected as actively claimed steps/functions of the invention even if the improvement itself is not explicitly stated. An Office provided example relevant to the current limitations argued is Example 39, which concerns the actual manner in which the training of a neural network is conducted for facial detection in order to limit the number of false positives, and provides limitations to this effect beyond stating that it is “iteratively trained”.
In the remarks, Applicant argues in substance that (2) the 35 U.S.C. 101 rejections should be withdrawn because “the specification discloses parameter adjustments that improve model performance. The model "adjusts the model's parameters (e.g., weights in neural networks) to minimize the loss function" by "compar[ing] the predicted effect of a food item in the training data store 730 with the labeled effect for the food item." Specification, i 99. Parameter values are determined "for each feature of the patient's metabolic profile" by "analyzing and recognizing correlations between the features associated with the metabolic profile and the metabolic effect of the food item." Id This maps directly to new MPEP § 2106.0S(a)(I), where improvements sufficient for eligibility a include "adjustments to parameters of a machine learning model associated with tasks or workstreams." Third, the specification identifies a specific technical problem that the claimed invention solves. The specification describes the problem of metabolic response drift, representing a patient's physiological response to the same food changes as their metabolic health evolves. Specification iJ69; FIG. IO (illustrating changed glucose response curves for the same meal across time periods). Unfortunately, static classification systems produce stale, inaccurate predictions that diverge from current physiological reality, which the disclosed system aims to remedy. To that end, like "catastrophic forgetting" in Desjardins, metabolic response drift is a specific technical problem in personalized health monitoring systems that the claimed invention solves through a defined technical mechanism which compares current classifications against updated classifications to detect when metabolic responses have changed…Together, these limitations parallel the Desjardins claims that recited "adjustments in values to [a] plurality of performance parameters while preserving prior values" as part of an improved ML training methodology. Dejardins Memo…This comparison mechanism is analogous to how the Desjardins claims "protect[ed] performance of the machine learning model on the first machine learning task" while optimizing for the second – both systems preserve and compare information across temporal boundaries to maintain system accuracy as conditions change. Dejardins Memo. Taken together, this ordered combination is not merely "collect data, run a model, display results." It is a specific technical pipeline in which each step serves a defined role in detecting and responding to evolving physiological conditions…”
The Examiner respectfully disagrees. Applicant’s arguments are not persuasive.
As per the model weights, this is similar to ineligible claim 2 of Example 47, where in that example “requires specific mathematical calculations (a backpropagation algorithm and a gradient descent algorithm) to perform the training of the ANN and therefore encompasses mathematical concepts.” Hence, while the training of the model is an additional element, present claim 7 is treated as abstract for describing the argued weights utilized in said training: “determining a set of parameter values based on labels assigned to food items in the training dataset, each parameter value describing a weight associated with biosignal measurements and metabolic states of the training dataset”.
As per the following, it cannot be found in the MPEP § 2106.05(a)(I): "adjustments to parameters of a machine learning model associated with tasks or workstreams." The MPEP does not appear to contain the word “workstreams” – is this an LLM hallucination?
As per “the problem of metabolic response drift, representing a patient's physiological response to the same food changes as their metabolic health evolves”, the corresponding claimed functions are representative of the abstract idea and do not represent technical problems – as per the arguments, the claims concern “inaccurate predictions”, which is not similar to the “catastrophic forgetting” in machine learning. The claims here are not directed to a specific improvement to computer functionality that amount to a practical application. Rather, they are directed to the use of conventional or generic technology in a well-known environment, without any claim that the invention reflects an inventive solution to a technical problem presented by combining the two. In the present case, the claims fail to recite any elements that individually or as an ordered combination transform the identified abstract idea(s) in the rejection into a patent-eligible application of that idea.
There is no description of how the model operates to compare with Dejardins in order to identify an improvement in the way the model operates. That the model is iteratively trained does not concern the manner of training being performed. Per the December 5, 2025, Office memo with the subject Advance notice of change to the MPEP in light of Ex Parte Desjardins: “the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.” As has already been argued, the claims here are concerned not with a problem in machine learning, but according to the specification, the present invention addresses a problem where “[c]onventional disease management platforms or techniques either ignore or fail to fully understand important markers…these platforms are designed to treat symptoms as they arise rather than treating the root cause of the disease - the deterioration of a patient's metabolic health…conventional disease management platforms are incapable of generating patient-specific recommendations for improving metabolic health by consuming foods or participating in activities” (see: specification paragraph 3-4). This is a problem which the invention address by providing, for example, “a personalized metabolic state program that provides tailored nutrition and exercise recommendations aligned with individual preferences” (see: specification paragraph 10). These problems and solutions are abstract. Note again that in Desjardins “the claims reflected the improvement identified in the specification”, but here a “vector” is input to a “model” trained outside the scope of invention “to classify the food item based on a training dataset of previously classified food items labeled with a corresponding effect on a metabolic state of a patient”. This “is not merely "collect data, run a model, display results."” But it’s not significantly more than stated – a model (not even a machine learning model – as shown in arguments above, the specification makes clear this “model” could just be a mathematical formula) is trained “based on” a training dataset such that a “vector” of data can be input to the “model” to return a result in a situation in which the specification states that the problem is one of generating patient-specific recommendations for improving metabolic health by consuming foods or participating in activities” (see: specification paragraph 3-4), which is not “analogues”, as argued, to the catastrophic forgetting of Desjardins.
In the remarks, Applicant argues in substance that (3) the 35 U.S.C. 101 rejections should be withdrawn because, “[a]dditionally, the Examiner treated all training limitations as "mathematical concepts" because the specification broadly defines "model" as "the result of a machine learning training process." OA at 7. But Desjardins demonstrates that claims reciting ML model training can reflect technological improvements when they address "how the machine learning model itself operates." Dejardins Memo. The Examiner did not consider whether the specific characteristics of the claimed model ( e.g., patient-specific, iteratively trained on metabolic-state-labeled food data, producing updated classifications compared against current classifications) confer a technological improvement. Under revised MPEP § 2106.0S(a), "examiners should not dismiss additional elements as mere 'generic computer components' without considering whether such elements confer a technological improvement to a technical problem."”
The Examiner respectfully disagrees. Applicant’s arguments are not persuasive.
As desired, the “model” has been treated not as a “mathematical formula” the specification would allow, but as a trained machine learning model additional element, and yet, for the reasons found in the rejection and response to arguments above, this does not provide a practical application that is significantly more.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT A SOREY whose telephone number is (571)270-3606. The examiner can normally be reached Monday through Friday, 8am to 5pm.
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/ROBERT A SOREY/Primary Examiner, Art Unit 3682
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