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 .
This nonfinal office action is in response to Applicant’s filing of application No. 18/837,216 on August 9, 2024. Claims 1-37 are pending and under examination.
Claim Objections
Claim 14 is objected to because of the following informalities: Claim 14 does not end with a period. Appropriate correction is required.
Claim Rejections - 35 USC§ 101
4. 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.
5. Claims 1-37 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 One:
Under Step one of an analysis, claim 1 does belong to a statutory category, namely it is a method claim. Likewise, claim 14 is a system. Claims 26 is a computer program product. Therefore, claims 1-37 fall under one of the four statutory classes of invention.
Step 2A. Prong 1:
Representative claim 1 recites:
identifying, via a computer, plant-based substances to replace an ingredient of the food item, wherein identifying plant-based substances to replace an ingredient of the food item comprises constructing a knowledge graph that includes nodes representing plant-based substances, a set of features associated with each node, and edges defining relationships between the nodes;
clustering the plant-based substances, via a machine learning model on a computer, into a plurality of clusters according to a desired objective and based on features of the plant- based substances;
classifying, via a machine learning classifier on a computer, the plant-based substances of a selected cluster into a plurality of classes based on the desired objective and the features of the plant-based substances of the selected cluster;
determining, via a computer, a score for each plant-based substance of a selected class based on metrics; and
determining, via a computer, a plant-based substance based on the score to produce a modified food item with the determined plant-based substance replacing the ingredient.
Claim 2 recites wherein the set of features associated with each node of the knowledge graph comprise respective functionalities of the plant-based substances.
Claim 3 further recites wherein the features of the plant-based substances used in the clustering step comprise features from the knowledge graph.
Claim 4 further recites wherein the features of the plant-based substances used in the clustering step comprise one or more selected from the group consisting of functionality, physicochemical characteristics, mechanical properties, chemical and molecular descriptors, sensorial characteristics, nutritional information, taxonomical information, bioactivity, and attributes from ancestral wisdom.
Claim 5 further recites wherein each cluster in the plurality of clusters is associated with a level of the desired objective.
Claim 6 further recites wherein the desired objective comprises a functionality of the ingredient to be replaced.
Claim 7 further recites wherein the machine learning model used in the clustering step comprises an unsupervised machine learning model trained with a set of features associated with plant-based substances as an input.
Claim 8 further recites wherein the classes correspond to a level of fitness for achieving the desired objective.
Claim 9 further recites wherein the machine learning classifier used in the classifying step comprises a supervised machine learning classifier trained using feature vectors of the plant-based substances as an input and known classes as an output.
Claim 10 further recites wherein the machine learning classifier is trained with new features from clusters resulting from unsupervised operation of the machine learning model.
Claim 11 recites calculating the metrics based on the properties of the plant-based substances meeting the desired objective.
Claim further recites wherein the features of the plant-based substances for the machine learning model comprise attributes of the plant-based substances obtained from ancestral wisdom.
Claim 13 further recites:
producing the modified food item by replacing the ingredient with the determined plant- based substance;
testing the modified food item with respect to characteristics for the food item;
obtaining feedback in response to the modified food item failing to satisfy the characteristics for the food item; and
training at least one of the machine learning model and the machine learning classifier using the feedback.
Claim 14 recites:
one or more memories; and at least one processor coupled to the one or more memories, the at least one processor configured to:
identify plant-based substances to replace an ingredient of the food item, wherein the at least one processor is configured to identify plant-based substances to replace an ingredient of the food item by constructing a knowledge graph that includes nodes representing plant-based substances, a set of features associated with each node, and edges defining relationships between the nodes; cluster, via a machine learning model, the plant-based substances into a plurality of clusters according to a desired objective based on features of the plant-based substances; classify, via a machine learning classifier, the plant-based substances of a selected cluster into a plurality of classes based on the desired objective and the features of the plant- based substances of the selected cluster;
determine a score for each plant-based substance of a selected class based on metrics; and
determine a plant-based substance based on the score to produce a modified food item with the determined plant-based substance replacing the ingredient.
Claim 15 further recites wherein the set of features associated with each node of the knowledge graph comprises respective functionalities of the plant-based substances.
Claim 16 further recites wherein the features of the plant-based substances used in the clustering step comprises features from the knowledge graph.
Claim 17 further recites wherein the features of the plant-based substances used by the at least one processor to cluster comprise one or more selected from the group consisting of functionality, physicochemical characteristics, mechanical properties, chemical and molecular descriptors, sensorial characteristics, nutritional information, taxonomical information, bioactivity, and attributes from ancestral wisdom.
Claim 18 further recites wherein each cluster in the plurality of clusters is associated with a level of the desired objective.
Claim 19 further recites wherein the desired objective comprises a functionality of the ingredient to be replaced.
Claim 20 further recites wherein the machine learning model is an unsupervised machine learning model trained with a set of features associated with plant-based substances as an input.
Claim 21 further recites wherein the classes correspond to a level of fitness for achieving the desired objective.
Claim 22 further recites wherein the machine learning classifier comprises a supervised machine learning classifier trained using feature vectors of the plant-based substances as an input and known classes as an output.
Claim 23 further recites wherein the machine learning classifier is trained with new features from clusters resulting from unsupervised operation of the machine learning model.
Claim 24 further recites wherein the at least one processor is further configured to calculate the metrics based on the properties of the plant-based substances meeting the desired objective.
Claim 25 further recites wherein the features of the plant-based substances for the machine learning model comprise attributes obtained from ancestral wisdom.
Claim 26 recites a computer program product for modifying a food item to contain plant-based ingredients, the computer program product comprising one or more computer readable media having instructions stored thereon, the instructions executable by at least one processor to cause the at least one processor to:
identify plant-based substances to replace an ingredient of the food item, wherein the instructions stored on the one or more computer readable media are executable by the at least one processor to cause the at least one processor to identify plant-based substances to replace an ingredient of the food item by constructing a knowledge graph that includes nodes representing plant-based substances, a set of features associated with each node, and edges defining relationships between the nodes;
cluster the plant-based substances, via a machine learning model, into a plurality of clusters according to a desired objective based on features of the plant-based substances;
classify, via a machine learning classifier, the plant-based substances of a selected cluster into a plurality of classes based on the desired objective and the features of the plant- based substances of the selected cluster;
determine a score for each plant-based substance of a selected class based on metrics; and
determine a plant-based substance based on the score to produce a modified food item with the determined plant-based substance replacing the ingredient.
Claim 27 further recites wherein the set of features associated with each node of the knowledge graph comprises respective functionalities of the plant-based substances.
Claim 28 further recites wherein the features of the plant-based substances used in the clustering step comprise features from the knowledge graph.
Claim 29 further recites wherein the features of the plant-based substances used by the at least one processor to cluster comprise one or more selected from the group consisting of functionality, physicochemical characteristics, mechanical properties, chemical and molecular descriptors, sensorial characteristics, nutritional information, taxonomical information, bioactivity, and attributes from ancestral wisdom.
Claim 30 further recites wherein each cluster in the plurality of clusters is associated with a level of the desired objective.
Claim 31 further recites wherein the desired objective comprises a functionality of the ingredient to be replaced.
Claim 32 further recites wherein the machine learning model comprises an unsupervised machine learning model trained with a set of features associated with plant-based substances as an input.
Claim 33 further recites wherein the classes correspond to a level of fitness for achieving the desired objective.
Claim 34 further recites wherein the machine learning classifier comprises a supervised machine learning classifier trained using feature vectors of the plant- based substances as an input and known classes as an output.
Claim 35 further recites wherein the machine learning classifier is trained with new features from clusters resulting from unsupervised operation of the machine learning model.
Claim 36 further recites wherein the instructions stored on the one or more computer readable media are executable by the at least one processor to cause the at least one processor to calculate the metrics based on the properties of the plant-based substances meeting the desired objective.
Claim 37 further recites wherein the features of the plant-based substances for the machine learning model comprise attributes obtained from ancestral wisdom.
.
The claims above recite the following limitations that are understood to recite an abstract idea being in nonbold, and with the additional limitations being in bold.
As per claim 1, the steps or functions of “identifying”, “clustering”, “classifying” and “determining” involve mental processes and/or generic computer functions.
Here, the claims fall into the category of functions of performing mental processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because it amounts to the functions of:
“identifying, via a computer, plant-based substances to replace an ingredient of the food item, wherein identifying plant-based substances to replace an ingredient of the food item comprises constructing a knowledge graph that includes nodes representing plant-based substances, a set of features associated with each node, and edges defining relationships between the nodes”. These functions are also viewed as mental/manual processes.
.
Step 2A, Prong Two of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.2019 PEG Section III(A)(2), 84 Fed. Reg. at 54-55.
In addition to the abstract ideas recited in the claims, the claims recite additional elements including a computer, a machine learning model on a computer, a machine learning classifier on a computer are similarly understood in light of applicant's specification as mere usage of any arrangement of computer software or hardware intermediate components potentially using networks to communicate with instructions are properly understood to be mere instructions to apply the abstraction using a computer processor. Performing steps or functions by a processor merely limits the abstraction to a computer field by execution by generic computers to process data (i.e., data for replacing an ingredient food item). The claimed limitations pertaining to the machine learning model amount merely to the very definition of the training aspect of the machine learning. As such, the independent claims do not reflect any improvement in machine learning (or in another technology/functioning of a computer), and the machine learning limitations are merely generic computer elements. Performing steps by a generic machine, or server computing device merely limit the abstraction to a computer field by execution by generic computers. See MPEP 2106.05 (1).
As noted in MPEP 2106.04(d), limitations which amount to instructions to implement an abstract idea on a computer or merely using a computer as a tool, limitations which amount to insignificant extra-solution activity, and limitations which amount to generally linking to a particular technological environment do not integrate a practical exception into a practical application.
Performance of the claimed steps or functions technologically may present a meaningful limit to the scope of the claims does not reasonably integrate the abstraction into a practical application.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus the claims are directed to an abstract idea.
Step 2B: The elements discussed above with respect to the practical application in Step 2A, prong 2 are equally applicable to consideration of whether the claims amount to significantly more. Accordingly, the clams fail to recite additional elements which, when considered individually and in combination, amount to significantly more.
Reconsideration of these elements identified as insignificant extra-solution activity as part of Step 2B does not change the analysis.
Positively reciting a “computer”, a “machine learning model on a computer”, and a “machine---- learning classifier on a computer”, does not change the analysis as these aspects are properly considered as additional elements which amount to instructions to apply it with a computer.
These claimed elements also as found in the dependent claims are also recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic component.
In processing the claims, it is noted that the recitation of these additional elements does not impact the analysis of the claims because these elements in combination are noted only to be one or more of a general purpose computer for performing basic or routine computer functions. The claimed computer, machine learning model, and a machine learning classifier on a computer are noted to a generic computer.
These additional elements do not overcome the analysis as these elements are merely considered as additional elements which amount to instructions to be applied to the generic computer.
The judicial exception is not integrated into a practical application. In particular, the claimed a computer, a machine learning model on a computer, a machine learning classifier on a computer are recited at a high level of generality such they amount to no more than mere instructions to apply the exception using generic components.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, claims 1, 14 and 126 are directed to an abstract idea.
Dependent claims 2-13 include additional elements beyond those recited by independent claim 1. Dependent claims 15-25 include additional elements beyond those recited by independent claim 14 and dependent claims 27-37 include additional elements beyond those recited by independent claim 26. The provision of additional details of a generic computer element does not render the element any less generic. The claimed steps do not amount to significantly more than the abstract idea, because they are well-understood, routine, and conventional computer functions in view of MPEP 2106 .05(d)(11). The recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 1-20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-37 are rejected under 35 USC. 101 as being directed to non-statutory subject matter.
6. NOTE: Currently there are no outstanding prior art rejections under 3 5 USC § 102 or 35 USC§ 103.
7. Prior art of record, individually or in combination, does not teach or fairly suggest:
“identifying, via a computer, plant-based substances to replace an ingredient of the food item, wherein identifying plant-based substances to replace an ingredient of the food item comprises constructing a knowledge graph that includes nodes representing plant-based substances, a set of features associated with each node, and edges defining relationships between the nodes; clustering the plant-based substances, via a machine learning model on the computer, into a plurality of clusters according to a desired objective and based on features of the plant-based substances; classifying, via a machine learning classifier on the computer, the plant-based substances of a selected cluster into a plurality of classes based on the desired objective and the features of the plant-based substances of the selected cluster; determining, via the computer, a score for each plant-based substance of a selected class based on metrics; and determining, via the computer, a plant-based substance based on the score to produce a modified food item with the determined plant-based substance replacing the ingredient” as recited in claim 1.
“identify plant-based substances to replace an ingredient of the food item, wherein the at least one processor is configured to identify plant-based substances to replace an ingredient of the food item by constructing a knowledge graph that includes nodes representing plant based substances, a set of features associated with each node, and edges defining relationships between the nodes; cluster, via a machine learning model, the plant-based substances into a plurality of clusters according to a desired objective based on features of the plant-based substances; classify, via a machine learning classifier, the plant-based substances of a selected cluster into a plurality of classes based on the desired objective and the features of the plant based substances of the selected cluster; determine a score for each plant-based substance of a selected class based on metrics; and determine a plant-based substance based on the score to produce a modified food item with the determined plant-based substance replacing the ingredient” as recited in claims 14 and 26.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. As per the attached PTO 892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROMAIN JEANTY whose telephone number is (571) 272-6732. The examiner can normally be reached M-F 9:00AM to 5:30PM.
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/RJ/
/ROMAIN JEANTY/Primary Examiner, Art Unit 3624