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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/03/2025 has been entered.
Response to Arguments
Regarding the arguments against the rejection of claims under 35 USC 101, the Examiner respectfully disagrees. Applicant argues that the present claims are not directed to a mental process under Step 2A Prong 1 because the operations recited in the claim cannot be practically performed in the human mind as training a first machine learning model using first training data of a vector form and includes biological extraction data correlated with target nutrition quantity data. Examiner asserts that the use of this specific training data for the model is not interpreted to be a part of the abstract idea; the use of this training data for the model recites insignificant additional elements as noted in the Step 2A Prong 2 section of this Office Action. As noted in the below rejection, there is no indication of a technological improvement beyond generic training of a machine learning model or classifier using the specific training data. Further, the generation of the alimentary instruction set as a function of the extraction using the first trained model recites using the trained model as merely a generic tool to carry out the abstract idea related to the generation of the instruction set. Under broadest reasonable interpretation, generation of the instruction set can be performed in the human mind without the explicit need of a computer to perform this generation. Further, using the structured datastore recites the use of additional elements as part of the insignificant pre-solution activities analyzed under Step 2A Prong 2 and does not recite an abstract idea and where the use of a “scheduling table” recites an abstract construct of data for a user to analyze as part of the abstract idea of a mental process. Further, “filtering” the ingredient combinations according to goal parameters recites filtering of the output of the model, where the output has been analyzed to be a part of the abstract idea. Use of the alimentary instruction set is also interpreted as being abstract. Use of the beneficial ingredient classifier is further an additional element that is merely a tool to carry out the abstract idea. Overall, the use of the “layered architecture” recites no more than the use of generic machine learning models to carry out the abstract idea, as there is no indication of a specific, technological improvement to the system.
Applicant further argues that Example 47 is similar to that of the instant application in that the amended claims recite concrete machine learning training and execution steps as the claims are not directed to mathematical model in isolation. Examiner further asserts that the additional elements recite the use of generic computing components such as the trained machine learning model with the vectorized biological training data with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere computer implementation. Example 47 recites a specific technological improvement related to network security rather than using generic computing components as a tool to carry out the abstract idea as recited in the instant application. In the instant application, the use of these generic computing components to detect anomalies, improve nutritional personalization, ingredient selection accuracy and resource utilization does not recite a technical improvement, but an improvement of the abstract idea, see MPEP 2106.05(a)II, particularly “Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.” Further, the improvement related to “automatically” generating target nutrient quantities and selecting ingredients in “real time” is merely the result of the computer implementation, see MPEP 2106.05(f), specifically” "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).”
Applicant further argues that Example 48 is similar to that of the instant application in that there is a practical application with the use of the trained models and outputting of the nutritional targets from the biological systems. Examiner asserts that Example 48 recites a practical application related to speech signal technology and improves this technical process. The instant application on the other hand uses the trained machine learning models as merely a tool where the output recites part of the abstract idea. There is no indication of the “nested architecture” being a technical improvement over merely using multiple generically trained machine learning models to carry out the abstract idea. Again, merely automating the process related to metabolic guidance, meeting nutritional needs and classification using the biological states to eliminate the need for static rules or manual interpretation does not integrate a judicial exception into a practical application.
Applicant further argues that the claims contain limitations amounting to an inventive concept representing “significantly more” than the alleged judicial exceptions under step 2B in that there is a non-conventional and ordered and combination of features does not merely recite generic automation of a known process, but instead defines a specific, technical architectures in which biological extraction data is vectorized and proceed by a trained model to produce nutritional targets which are used by a second classifier to dynamically generate ingredient combinations. Examiner further asserts that as previously described and as noted in the updated rejection of this Office Action, the use of the correlated vector data as training data and generically applying it to the machine learning model to generate the recommendations for provisioning is not an improvement to a technology that does not demonstrate advanced computation techniques, and the combination of additional elements recites well understood, routine and conventional activities that does not demonstrate a technological improvement and is not significantly more than the judicial exception. Further, as described in the analysis under Step 2B of this Office Action, factual evidence from the Applicant’s Specification and the use of recognized court cases does not support a conclusion that the combination of the additional elements of the instant application does recites well understood, routine, and conventional activity and is significantly more than the judicial exception, see MPEP 210605(d)(II).
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 1-3, 5-13, and 15-20 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
It is appropriate for the Examiner to determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance to the Subject Matter Eligibility Test as recited in the following Steps: 1, 2A, and 2B, see MPEP 2106(III.).
Patent Subject Matter Eligibility Test: Step 1:
First, the Examiner is to establish whether the claim falls within any statutory category including a process, a machine, manufacture, or composition of matter, see MPEP 2106.03(II.) and MPEP 2106.03(I).
Claims 1-3, 5-10 are related to a system, and claims 11-13, 15-20 are also related to a method (i.e., a process). Accordingly, these claims are all within at least one of the four statutory categories.
Patent Subject Matter Eligibility Test: Step 2A- Prong One:
Step 2A of the Subject Matter Eligibility Test demonstrates whether a clam is directed to a judicial exception, see MPEP 2106.04(I.). Step 2A is a two-prong inquiry, where Prong One establishes the judicial exception. Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes, see MPEP 2106.04(II.)(A.)(1.) and 2106.04(a)(2).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A system for alimentary provisioning, wherein the system comprises a computing device configured to:
record at least a biological extraction from a user;
generate an alimentary instruction set for the user as a function of the at least a biological extraction, wherein the alimentary instruction set comprises a plurality of target nutrient quantities, and generating the alimentary instruction set comprises:
training a first machine-learning model using first training data, wherein the first training data is represented in vector form and includes biological extraction data correlated with target nutrition quantity data; and
generating the alimentary instruction set as a function of the biological extraction using the trained machine-learning model;
receive a goal parameter from the user;
generate a plurality of ingredient combinations, wherein each ingredient combination is a combination of at least two ingredients of a plurality of ingredients, wherein generating the plurality of ingredient combinations comprises receiving the plurality of ingredients from a provider ingredient datastore configured to group provider ingredients according to a schedule table indicating when ingredients are available based on at least production;
filter the plurality of ingredient combinations according to the goal parameter; and
select a plurality of beneficial ingredient combinations for the user, using a beneficial ingredient classifier, from the plurality of ingredient combinations, wherein the plurality of beneficial ingredient combinations is selected as a function of the alimentary instruction set, and wherein the beneficial ingredient classifier is a machine learning process configured to generate a plurality of categories of ingredient combinations matching the alimentary instruction set generated using the trained first machine-learning model.
The Examiner submits that the foregoing underlined limitations constitute a “mental process” as the following abstract limitations are related to alimentary provisioning:
“generate” an alimentary instruction set for the user as a function of recorded biological extraction, where the set comprises target nutrient quantities, which is an abstract limitation related to an observation and analysis of the biological extraction to make a judgement on the instruction set,
“generate” a plurality of ingredient combinations which is a combination of at least two ingredients, where this is a consideration of “grouping” provider ingredients according to a schedule table indicating when ingredients are available based on production, which is an abstract limitation related to a judgment of the combinations based on analysis from a abstract construction of a schedule table of the availability of the produced based on production,
“filter” the combinations according to the received goal parameter, which is an abstract limitation related to a judgment on the combinations,
“select” a plurality of beneficial ingredient combinations for the user which is selected as a function of the instruction set, which is an abstract limitation related to an analysis and judgment related to the beneficial ingredient combinations for the alimentary provisioning,
“generating” a plurality of categories of ingredient combinations matching the alimentary instruction set, which is an abstract limitation of analysis of the ingredients to match with the instruction sets and to make decisions of where to put the ingredient combinations into categories.
The claim as a whole recites abstract steps for alimentary provisioning which can be performed in the human mind with or without the aid of pen and paper.
The abstract idea recited in claim 11 is similar to that of claim 1.
Any limitations not identified above as part of the abstract idea are deemed “additional elements” (i.e., processor) and will be discussed in further detail below.
Accordingly, the claim as a whole recites at least one abstract idea.
Furthermore, dependent claims further define the at least one abstract idea, and thus fails to make the abstract idea any less abstract as noted below:
Claims 6 and 16 recite abstract limitations of filtering the ingredient combination according to the received user specific proscription, which further describes the abstract idea. Claims 7 and 17 recite abstract limitations further describing the selection of combinations as comprising determining a list, determining a distance metric from the list to the instruction set and selecting the combination as a function of the distance metric, thus further describing the abstract idea. Claims 8 and 18 recite abstract limitations related to further describing the selection of beneficial ingredient combination comprising selecting the combination that minimizes the distance metric, thus further describing the abstract idea. Claims 9 and 19 recite further abstract limitations of describing the generating of plurality of combinations as categorizing the ingredients according to time availability, thus further describing the abstract idea. Claims 10 and 20 recite abstract limitations of further describing the generating of combinations as comprising categorizing the ingredients according to geographic availability, thus further describing the abstract idea.
Patent Subject Matter Eligibility Test: Step 2A- Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrates the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exceptions into a “practical application,” see MPEP 2106.04(II.)(A.)(2.) and 2106.04(d)(I.).
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A system for alimentary provisioning, wherein the system comprises a computing device configured to (merely invokes use of computer and computer components as a tool as noted below, see MPEP 2106.05(f)):
record at least a biological extraction from a user (merely data gathering steps as noted below, see MPEP 2106.05(g) and buySAFE, Inc. v. Google, Inc.);
generate an alimentary instruction set for the user as a function of the at least a biological extraction wherein the alimentary instruction set comprises a plurality of target nutrient quantities, and generating the alimentary instruction set comprises:
training a first machine-learning model using first training data, wherein the first training data is represented in vector form and includes biological extraction data correlated with target nutrition quantity data; and (merely invokes use of computer and computer components as a tool as noted below, see MPEP 2106.05(f))
generating the alimentary instruction set as a function of the biological extraction using the trained machine-learning model (merely invokes use of computer and computer components as a tool as noted below, see MPEP 2106.05(f));
receive a goal parameter from the user (merely data gathering steps as noted below, see MPEP 2106.05(g) and buySAFE, Inc. v. Google, Inc.);
generate a plurality of ingredient combinations, wherein each ingredient combination is a combination of at least two ingredients of a plurality of ingredients wherein generating the plurality of ingredient combinations comprises receiving the plurality of ingredients from a provider ingredient datastore configured to (merely data gathering steps as noted below, see MPEP 2106.05(g) and buySAFE, Inc. v. Google, Inc.) group provider ingredients according to a schedule table indicating when ingredients are available based on at least production;
filter the plurality of ingredient combinations according to the goal parameter; and
select a plurality of beneficial ingredient combinations for the user, using a beneficial ingredient classifier (merely invokes use of computer and computer components as a tool as noted below, see MPEP 2106.05(f)), from the plurality of ingredient combinations, wherein the plurality of beneficial ingredient combinations is selected as a function of the alimentary instruction set, and wherein the beneficial ingredient classifier is a machine learning process configured to generate a plurality of categories of ingredient combinations matching the alimentary instruction set generated using the trained first machine-learning model (merely invokes use of computer and computer components as a tool as noted below, see MPEP 2106.05(f)).
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitation of the system comprising a computing device configured to perform steps, training a first machine-learning model using first training data, wherein the first training data is represented in vector form and includes biological extraction data correlated with target nutrition quantity data, the use of the trained model, the use of a beneficial ingredient classifier that is a machine learning process, and further use of the trained first machine-learning model, the Examiner submits that these limitations amount to merely using software to tailor information and provide it to the user on a generic computer (see MPEP § 2106.05(f)). [0009, 0010] of Applicant’s Specification recites the generic configuration of the computing device. [0012, 0020] recites the use of the correlated biological extraction data with the target nutrition quantity data as training data, however the vectorized data is trained using generic training processes with the generic machine learning model. [0023, 0024] recites further the classifier and use of the vectorized data as training data, however there is no indication of a technological improvement beyond generic training of a machine learning model or classifier. [0022, 0025] further recites the use of the beneficial ingredient classifier, however the description merely recites generic machine learning construction with generic training of the model. [0022] recites further use of the first trained machine learning model for generating the categories, however this generic model is used merely as a tool to carry out the abstract idea. Claim 11 additionally recites the use of a processor to carry out the steps of the abstract idea as further recited in [0010]. The additional elements recite the use of generic computing components such as the computing device with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere computer implementation.
Regarding the additional limitations of record at least a biological extraction from a user, receive a goal parameter from the user, and receiving the plurality of ingredients from a provider ingredient datastore, these are merely pre-solution activities. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)). [0041, 0029] of the Applicant’s Specification recites the action of recording the biological extraction and receiving the goal parameters from the user. The recording of biological instruction is used to generate the alimentary instruction set which is received from remote devices as described in [0014]. The received goal parameters into the computing device are used to generate the ingredient combinations which is received from the alimentary provider device as described in [0018]. [0015] recites the use of a provider ingredient datastore that is connected in a device in communication with the system to merely store and retrieve the data that is used for the abstract idea. The action of recording and receiving data are steps for data gathering for the abstract idea, and thus the use of the units recites insignificant pre-solution activities.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception for alimentary provisioning, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception, see MPEP 2106.04(d), 2106.05(a), 2106.05(b).
For these reasons, the independent claims do not recite additional elements that integrate the judicial exception into a practical application.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set below:
Claims 2 and 12 recite additional elements further describing the received goal parameters as comprising an amount of time to prepare the ingredient combinations, however this receiving of data merely recites insignificant pre-solution activity. Claims 3 and 13 recite additional elements further describing the generating of combinations as including receiving the ingredients from a datastore, where the retrieval of information from storage still merely recites insignificant pre-solution activity. Claims 5 and 15 recite additional elements describing the generating of combinations as including receiving the ingredients from alimentary provider devices, where the retrieval of information from these devices still merely recites insignificant pre-solution activity. Claims 6 and 16 recite additional elements further describing the computing device as receiving a user specific prescription from the user, however the receiving of this data merely recites insignificant pre-solution activity.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Patent Subject Matter Eligibility Test: Step 2B:
Regarding Step 2B of the Subject Matter Eligibility Test, the independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application, see additionally MPEP 2106.05(II.). Further, it may need to be established, when determining whether a claim recites significantly more than a judicial exception, that the additional elements recite well understood, routine, and conventional activities, see MPEP 2106.05(d).
Regarding the additional limitation of the system comprising a computing device configured to perform steps, training a first machine-learning model using first training data, wherein the first training data is represented in vector form and includes biological extraction data correlated with target nutrition quantity data, and the use of the trained model, the Examiner submits that these limitations amount to merely using software to tailor information and provide it to the user on a generic computer (see MPEP § 2106.05(f)). [0009, 0010] of Applicant’s Specification recites the generic configuration of the computing device. [0012, 0020] recites the use of the correlated biological extraction data with the target nutrition quantity data as training data, however the vectorized data is trained using generic training processes with the generic machine learning model. [0022, 0025] further recites the use of the beneficial ingredient classifier, however the description merely recites generic machine learning construction with generic training of the model. [0023, 0024] recites further the classifier and use of the vectorized data as training data, however there is no indication of a technological improvement beyond generic training of a machine learning model or classifier. [0022] recites further use of the first trained machine learning model for generating the categories, however this generic model is used merely as a tool to carry out the abstract idea. Claim 11 additionally recites the use of a processor to carry out the steps of the abstract idea as further recited in [0010]. The additional elements recite the use of generic computing components such as the computing device with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere computer implementation and does not recite significantly more than the judicial exception.
Regarding the additional limitations of record at least a biological extraction from a user, receive a goal parameter from the user, and receiving the plurality of ingredients from a provider ingredient datastore, these are merely pre-solution activities. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g) and MPEP § 2106.05(d)(II), specifically “buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)”). [0041, 0029] of the Applicant’s Specification recites the action of recording the biological extraction and receiving the goal parameters from the user. The recording of biological instruction is used to generate the alimentary instruction set which is received from remote devices as described in [0014]. The received goal parameters into the computing device are used to generate the ingredient combinations which is received from the alimentary provider device as described in [0018]. [0015] recites the use of a provider ingredient datastore that is connected in a device in communication with the system to merely store and retrieve the data that is used for the abstract idea. The action of recording and receiving data are steps for data gathering for the abstract idea, and thus the use of the units recites insignificant pre-solution activities. The computing device receiving the biological extraction and goal parameters from the user from other devices via a network and receiving of data from the provider ingredient datastore from another device recite well understood, routine and conventional activities.
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
For the reasons stated, the claims fail the Subject Matter Eligibility Test and therefore claims 1-3, 5-13, and 15-20 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Conclusion
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/CONSTANTINE SIOZOPOULOS/
Examiner
Art Unit 3686