DETAILED ACTION
1. 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
2. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/01/2026 has been entered.
3. Currently claims 1 and 11 have been amended; claims 2 and 12 are already canceled. Therefore, claims 1, 3-11 and 13-20 are pending in this application.
Response to Claim Amendment
4. The amendment to each of claims 1 and 11 is sufficient to overcome the rejection set forth in the previous office action under section §112(b). Accordingly, the Office withdraws the above rejection.
Claim Rejections - 35 USC § 101
5. Non-Statutory (Directed to a Judicial Exception without an Inventive Concept/Significantly More)
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, 3-11 and 13-20 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1)
The current claims fall within one of the four statutory categories of invention (MPEP 2106.03).
(Step 2A) [Wingdings font/0xE0] Prong One:
The claim(s) recite a judicial exception, namely an abstract idea, as shown below:
— Considering the independent claims (claims 1 and 11) as representative claims, the following claimed limitations recite an abstract idea (note that the “model”, as used below, is construed as a template or a rule that one uses to evaluate data):
calculate a score for an edible, comprising:
[collect] a performance profile comprising a plurality of logged performance metrics;
determine an edible of interest relating to a user;
receive nourishment information relating to the edible of interest to the user, wherein the nourishment information comprises a plurality of ingredients;
generate the score for the edible of interest using a scoring model:
[collect] data that correlates elements of the performance profile and the nourishment information to an edible score;
[update] the scoring model with feedback from previous iterations of the scoring model;
generate the score for the edible of interest as a function of the [updated] scoring model;
calculate one or more nutrient biodiversity scores as a function of the nourishment information comprising:
evaluating each ingredient of the plurality of ingredients, wherein evaluating each ingredient includes:
extracting at least a nutrient from each ingredient of the plurality of ingredients; and
calculating a nutrient biodiversity score for the at least a nutrient further comprises the use of a biodiversity model that comprises:
collect data that correlates nourishment entry data to nutrient biodiversity data;
[update] the biodiversity model with feedback from previous iterations of the biodiversity model; and
generate the nutrient biodiversity score as a function of the [updated] biodiversity model;
determine a nutritional requirement as a function of at least the nourishment information; and
[present] the nutritional requirement and the one or more nutrient biodiversity scores and the score of the edible of interest.
Accordingly, the limitations identified above recite an abstract idea since the limitations correspond to mental processes, which is part of the enumerated groupings of abstract ideas identified according to the eligibility standard (see MPEP 2106.04(a)).
For instance, the claims correspond to a mental process; such as, an evaluation, an observation and/or a judgement process, wherein (A) a score corresponding to an edible of interest related to a user is calculated using a scoring model based on (i) data collected from a performance profile that comprises a plurality of logged performance metrics, and (ii) nourishment information relating to the edible of interest of the user, the nourishment information comprises a plurality of ingredients; wherein the calculation above involves the use of: (a) data that correlates elements of the performance profile and the nourishment information to an edible score, and (b) feedback data from previous iterations of the scoring model; and furthermore, (B) a nutrient biodiversity score(s) for a nutrient(s)—extracted from an ingredient(s)—is generated using a biodiversity model based on (a) data that correlates nourishment entry data to nutrient biodiversity data, (b) feedback data from previous iteration of the model; and accordingly, once a nutritional requirement is determined as a function of the nourishment information, the user is presented with pertinent results—such as, the nutritional requirement, the one or more nutrient biodiversity scores, and the score of the edible of interest.
(Step 2A) [Wingdings font/0xE0] Prong Two
The claim(s) recite additional element(s), wherein a computing device that comprises a disapply interface is utilized to facilitate the recited functions/steps regarding: collecting logged data (e.g., retrieving a performance profile comprising a plurality of logged user performance metrics); determining interest relating to a user (e.g., determine an edible of interest relating to a user); collecting further information (e.g., receive nourishment information relating to the edible of interest to the user, wherein the nourishment information comprises a plurality of ingredients); performing calculations using one or more machine-learning algorithms/models in order to generate one or more scores (e.g., “generate the score for the edible of interest utilizing a score machine-learning model and comprises: receiving training data, wherein the training data correlates elements of the performance profile and the nourishment information correlated to an edible score training, iteratively, the score machine-learning model . . . calculate one or more nutrient biodiversity scores as a function of the nourishment information comprising: evaluating each ingredient of the plurality of ingredients, wherein evaluating each ingredient includes: extracting at least a nutrient from each ingredient . . . training, iteratively, the biodiversity machine-learning model using the biodiversity training data, wherein training the biodiversity machine-learning model includes retraining the biodiversity machine-learning model with feedback from previous iterations of the biodiversity machine-learning model; and generating the nutrient biodiversity score as a function of the trained biodiversity machine-learning model”); determining one or more results (e.g., “determine a nutritional requirement as a function of at least the nourishment information”); and displaying one or more results (e.g., “display the nutritional requirement and the one or more nutrient biodiversity scores and the score of the edible of interest through a display interface”), etc.
However, the claimed additional element(s) fail to integrate the abstract idea into a practical application since the additional element(s) are utilized merely as a tool to facilitate the abstract idea. Thus, when each claim is considered as a whole, the additional element(s) fail to integrate the abstract idea into a practical application since they fail to impose meaningful limits on practicing the abstract idea. For instance, when each of the claims is considered as a whole, none of the claims provides an improvement over the relevant existing technology.
The observations above confirm that the claims are indeed directed to an abstract idea.
(Step 2B)
Accordingly, when the claim(s) is considered as a whole (i.e., considering all claim elements both individually and in combination), the claimed additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to “significantly more” than the abstract idea itself (also see MPEP 2106). The claimed additional elements are directed to conventional computer elements, which are serving merely to perform conventional computer functions. Accordingly, none of the current claims, when considered as a whole, recites an element—or a combination of elements—directed to an inventive concept.
It is also worth note, per the original disclosure, that the claimed system/method is directed to a conventional and generic arrangement of the additional elements. For instance, the specification describes a system that utilizes one or more commercially available conventional computing devices (e.g., a smartphone, a laptop computer, a desktop computer, etc.) that communicate with an online server via the conventional communication network—such as, the Internet (e.g., see [0009]; [0014]); and wherein, based on the analysis of one or more parameters collected regarding a user and/or food items (e.g., performance metrics, sensor/medical data, one or more ingredients/meals), the system generates one or more results (e.g., one or more nutrient amounts, a biodiversity score(s) for a nutrient(s), etc.); and thereby, the system presents pertinent information to the user, etc. (see ([0014] to [0027]).
In addition, the utilization of the conventional computer/network technology to facilitate the presentation of pertinent information—such as nutritional information—to a user(s), based on the analysis of collected data related to one or more users and/or food items, etc., is directed to a well-understood, routine or conventional activity in the art (e.g., US 2014/0349256; US 2013/0216982; US 2013/0280681; US 2005/0113650, etc.).
Note also that the use of two or more machine learning models, including an arrangement in which the output(s) from a first machine-learning model is utilizes as the input(s) to the second machine-learning model, etc., is also part of the conventional computer network technology (e.g., US 2008/0154651; US 2007/0005568, etc.).
The above observation confirms that the current claimed invention fails to amount to “significantly more” than an abstract idea.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 3-10 and 13-20). Particularly, each of the dependent claims also fails to amount to “significantly more” than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea. Accordingly, none of the current claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology).
● Applicant’s arguments directed to section §101 have been fully considered (i.e., the arguments filed on 04/01/2026). However, the arguments are not persuasive at least for the following reasons:
Firstly, regarding the Office’s findings presented under Prong One of Step 2A, Applicant is asserting that “[c]laim 1 as amended recites a process to "receiving training data . . . training, iteratively, the score machine-learning model using the training data, wherein training the score machine-learning model includes retraining the score machine-learning model with feedback . . . the biodiversity machine-learning model with feedback from previous iterations of the biodiversity machine-learning model ...’ The process, just like in Synopsys, except in its most simplistic form, could not conceivably be performed in the human mind as the recitation discloses iterative training processes of a score machine-learning model and a biodiversity machine-learning model using feedback from previous iterations. This is not akin to an observation and/or a judgement process as the system may be configured to go through large volume of input data and training data to generate the score for the edible of interest as a function of the trained score machine-learning model and the nutrient biodiversity score as a function of the trained biodiversity machine-learning model, respectively. Even in its most simplistic form, no human judgment/observation could achieve and execute the aforementioned limitation” (emphasis added).
However, Applicant appears to fail to properly apply the inquiry set forth under Prong One of Step 2A. In particular, similar to the point made in the previous office action, Prong One does not require any of the additional elements to be considered when evaluating whether a given claim is reciting an abstract idea. Instead, it requires one to identify only the limitations that recite the abstract idea; see MPEP 2106.07(a), (emphasis added).
For Step 2A Prong One, the rejection should identify the judicial exception by referring to what is recited (i.e., set forth or described) in the claim and explain why it is considered an exception . . . the rejection should identify the abstract idea as it is recited (i.e., set forth or described) in the claim and explain why it is an abstract idea.
Accordingly, none of the claimed additional elements, which includes the recited machine-learning models (e.g., the claimed “score machine-learning model” and “biodiversity machine-learning model”), is considered to be part of the abstract idea. In contrast, Applicant is relying on the additional elements in an attempt to challenge the Office’s findings presented under Prong One of Step 2A; namely, the finding regarding mental processes. Consequently, Applicant’s arguments are not even relevant to challenge—much less negate—the Office’s analysis. This is because the eligibility test regarding a mental process does not necessarily require whether a human can execute any machine-learning algorithm in the mind. Instead, the test requires one to determine whether a human (e.g., a nutritionist or a dietician, etc.) can use one or more models/aids—such as one or more nutrition templates/rules—to estimate one or more scores (e.g., a score representing the user’s edible of interest; a score representing the biodiversity of a nutrient, etc.). Thus, unlike Applicant’s assertion, none of the computer elements, including the machine learning-models, is considered when evaluating whether the claim is reciting an abstract idea. Accordingly, Applicant’s alleged “large volume of input data and training data”, which the machine-learning model is supposedly processing, is irrelevant since the above is merely existing computer functions that the existing computer technology is performing (i.e., an issue relevant to Prong Two, but not to Prong One).
Note also that the inaccuracy of Applicant’s logic is further demonstrated when one considers the court’s decision regarding Electric Power Group. For instance, the claim that the court considered regarding Electric Power Group recites, at least in part, the following limitations (emphasis added),
12. A method of detecting events on an interconnected electric power grid in real time over a wide area and automatically analyzing the events on the interconnected electric power grid . . . receiving a plurality of data streams, each of the data streams comprising sub-second, time stamped synchronized phasor measurements . . . detecting and analyzing events in real-time from the plurality of data streams . . . measurements from the data streams including at least one of frequency instability, voltages, power flows, phase angles, damping, and oscillation modes . . .
Accordingly, if one applies Applicant’s theory to claim 12 above, one may be tempted to conclude that the claim above is not a mental process. In particular, one may be tempted to argue that that the claimed process of detecting and automatically analyzing events on an electric power grid in real-time, including: (a) receiving multiple data streams that include synchronized phasor measurements that are collected in real-time; (b) detecting and analyzing limits, sensitiveness or rate of changes of at least one of frequency instability, voltages, phase angles, etc., are computer implemented functions/steps that cannot be performed in the human mind (and/or using a pen and paper).
In contrast, despite the limitations above, the court concluded that the claim is reciting an abstract idea; namely, a mental process. This is because the claim is using the existing technology—merely as a tool—to facilitate an abstract idea; such as, collecting information, analyzing the information, and displaying certain results. Similarly, Applicant’s current claims (e.g., see current claim 1) are also using the existing
computer technology—merely as a tool—to facilitate an abstract idea; such as, the process of collecting and analyzing data related to the user and nourishment; and subsequently, generating relevant information to the user; such as: a score representing edible of interest; a score representing the biodiversity of a nutrient; nutritional requirement, etc.
The observation above confirms that the claims do recite an abstract idea; namely, a mental process, since the claims do contain limitations that can practically be performed in the human mind; see MPEP 2106.04(a)(2)(III)(A). Thus, Applicant’s attempt to challenge the Offices findings, while misapplying Synopsys, is not persuasive.
In addition, while referring to the USPTO’s recent memorandum, including the Examples 39 and 47 of the USPTO guidance, Applicant asserts that “claim 1 as amended recites limitation ‘[training, ] iteratively, the score machine-learning model using the training data, wherein training the score machine-learning model includes retraining the score machine-learning model with feedback . . . iterations of the biodiversity machine-learning model ... ’, which does not recite a judicial exception . . . Examiners must establish unpatentability under 35 U.S.C. § 101 by the ‘preponderance of the evidence.’ The August 2025 Memo . . . not when ‘an examiner is uncertain as to the claim's eligibility.’ The August 2025 Memo” (emphasis added).
However, except for repeating the same assertion, Applicant does not raise any further issue. In particular, here also Applicant is once again relying on the claimed additional elements (e.g., the machine-learning modes) in an attempt to challenge the Office’s findings presented under Prong One of Step 2A. However, as already pointed out above, Prong One does not require one to consider any of the additional elements; rather, Prong One of Step 2A requires one to consider only the limitations that recite the abstract idea (see the discussion presented above). Consequently, Applicant’s arguments are still not persuasive.
Moreover, the analysis already provides plenty of evidence, which confirms the fact that the current claims do recite an abstract idea. Accordingly, unlike Applicant’s theory, the Office’s finding is not based on “uncertain[ty]”; rather, based on sufficient evidence, which the claims themselves provide. Thus, Applicant’s assertion is not valid.
Secondly, regarding Prong Two of Step 2A, Applicant asserts that “[t]he Office asserts on page 6 of the Office Action that ‘the claimed additional element(s) fail to integrate the abstract idea into a practical application . . . ‘The claim itself does not need to explicitly recite the improvement described in the specification.’ . . . the July 2024 Guidelines teach that integration of a judicial exception into a practical application may be achieved when ‘[(1)] the specification ... set[s] forth an improvement in the technology[;] and[, (2)] the claim ... reflect[s] the disclosed improvement’ . . . claims that are eligible under 3 5 U.S. C. § 101 are found in at least Example 47 (Artificial Neural Network for Anomaly Detection - claim 1 and 3) . . . Analogous to Example 47 and claims 1 and 3, in the present application, claim 1 as amended also teaches technological improvement that is integrated into a practical application of calculating an edible score in the field of nourishment in order to deliver custom food suggestions . . . paragraphs [0002]-[0003] of the present specification” (emphasis added).
However, except for the attempt made to summarize some of the USPTO’s guidelines and/or some sections from the MPEP, Applicant fails to demonstrate whether any of the current claims implements an element—or a combination of elements—that provides a technological improvement over the relevant existing technology. In particular, given the fact that the claimed—and the disclosed—system/method is relying on the computer/network technology, an integration (if any) of the abstract idea is demonstrated if the claimed—or even the disclosed—system/method is implementing an element—or a combination of elements—that provides a technological improvement over the existing computer/network technology. In contrast, as evident from the current claims, including the original disclosure, none of the current claims is implementing any element—or any combination of elements—that provides a technological improvement over the existing computer/network technology. Instead, each of the current claims is utilizing the existing computer/network technology—merely as a tool—to facilitate an abstract idea; such as, providing, based on the analysis of collected user data and nourishment data, a user with relevant information in the form of one or more scores and/or nutritional requirement, etc.
The finding above confirms that none of the current claims, when considered as a whole, integrates the abstract idea into a patent-eligible practical application (i.e., the claims fail to impose meaningful limits on practicing the abstract idea). Consequently, Applicant’s conclusory assertion, namely the alleged “practical application of calculating an edible score in the field of nourishment in order to deliver custom food suggestions” (emphasis added), is not persuasive. Applicant is in fact mistaking the abstract idea, which the claimed existing technology is facilitating, for a technological improvement.
Applicant further asserts, “[the] limitation, ‘[training,] iteratively, the score machine-learning model using the training data, wherein training the score machine-learning model includes retraining the score machine-learning model with feedback . . . feedback from previous iterations of the biodiversity machine-learning model ...’ teaches an improvement to the efficiency of the process. For instance, paragraph [0032] of the present specification discloses ‘at least a processor is also configured to optimize the plurality of ingredients within edible of interest and/or nourishment information 132 as a function of each nutrient biodiversity score 134. As used herein, ‘optimize’ means to rearrange or rewrite to improve efficiency of processing.’ . . . Therefore, pursuant to Step 2A of the § 101 analysis prescribed in Alice, claim 1 as amended recites patentable subject matter” (emphasis added).
However, except for demonstrating the existing technology that the claimed (and the disclosed) system/method is utilizing, Applicant fails to demonstrate whether the claimed (or the disclosed) system/method is implementing a technological improvement. For instance, the use of one or more machine-learning models to analyze collected data, including generating one or more relevant results based on the analysis, etc., is already part of the existing computer/network technology. Moreover, as evident from the name itself, machine-learning models normally learn and update their parameters based on feedback gathered from one or more sources (e.g. a result(s) from a previous output; user provided parameter(s); newly collected or updated information, etc.). Accordingly, simply utilizing such features of the existing computer/network technology to facilitate an abstract idea in a desired filed or environment (e.g., dietetics or nutritional science, etc.) does not constitute a technological improvement.
Moreover, unlike Applicant’s assertion, the alleged optimization, which the claimed computer is supposedly performing with respect to the plurality of ingredients within edible of interest—and/or nourishment information—as a function of each nutrient biodiversity score, has nothing to do with a technological improvement. Instead, the above is merely referring to the calculation adjustments that the system is performing in order to improve the suitability of the result(s) being generated. Of course, such process of optimizing is already one of the basic features of existing machine-learning models since a machine-learning model learns and updates its parameters in order to improve or refine the accuracy of the result it is generating. Consequently, Applicant ‘s assertion is not persuasive since Applicant is once again mistaking the features of the existing computer technology for the alleged technological improvement.
Thirdly, regarding Step 2B, Applicant asserts, “the 2B analysis by the Office is moot in light of the amendments to claim 1 and the arguments above . . . Examiners are cautioned not to oversimplify claim limitations and expand the application of the ‘apply it’ consideration . . examiners are reminded that the ‘apply it’ consideration often overlaps with the improvements consideration . . . claim 1 as amended recites details of a particular way to iteratively train a score machine-learning model using training data that correlates elements of the performance profile and the nourishment information, and a biodiversity machine-learning model using training data that correlates nourishment entry data to nutrient biodiversity data . . . claim 1 as amended is allowable under 35 U.S.C. §101, at least for the reasons stated above . . . the rejection to claim 11 has been overcome for the same reasons as to claim 1 . . . claims 3-10 and 13-20 overcome these rejections for at least the same reasons as discussed above with reference to claims 1 and 11” (emphasis added).
However, except for describing the steps and/or functions being claimed, which includes the process of training each of the score machine-learning model and the biodiversity machine-learning model using their respective training data, Applicant does not demonstrate whether any of the claims—considered as a whole—is implementing
an inventive concept, which is the test that one must apply to show eligibility (if any) under Step 2B. Of course, an inventive concept (if any) is demonstrated if any of the current claims is directed to the non-conventional and non-generic arrangement of the additional elements. In contrast, given the fact that the claimed (and the originally disclosed) system/method is relying merely on the conventional computer/network technology, each of the current claims—when considered as a whole—is directed merely to the conventional and generic arrangement of the additional elements. In particular, the currently claimed (and the originally disclosed) system/method is utilizing the conventional computer/network technology—merely as a tool—to facilitate the abstract idea (e.g., see the finding under Prong One). Consequently, each of the current claims, when considered as a whole, fails to implement an inventive concept. Of course, the lack of technological improvement per the claimed—and disclosed—system/method also confirms the lack of an inventive concept; see MPEP 2106.05(a). Consequently, Applicant’s arguments directed to Step 2B are also not persuasive.
Thus, at least for the reasons discussed above, the Office concludes that none of the current claims, when considered as a whole, implements an inventive concept that amounts to “significantly more” than an abstract idea.
Prior Art
5. Considering each of claims 1 and 11 as a whole (including the respective dependent claims), the prior art does not teach or suggest the current claims (regarding the state of the prior art, see the office-action dated 03/13/2025).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUK A GEBREMICHAEL whose telephone number is (571) 270-3079. The examiner can normally be reached from 7:00 AM - 3:00 PM.
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/BRUK A GEBREMICHAEL/Primary Examiner, Art Unit 3715
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