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 .
Notice to Applicant
This communication is in response to the Request for Continued Examination (RCE) filed 3/4/26. Claims 1, 3, 11, 13, 15, 19, and 20 have been amended. Claims 2, 4, 10, 12, and 14 are canceled. Claims 1, 3, 5-9, 11, 13, and 15-20 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/4/26 has been entered.
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, 5-9, 11, 13, and 15-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1, 3, 5-9 & 20 are directed to a non-transitory computer-readable medium (i.e., a machine) and claims 11, 13, and 15-19 are directed to a method (i.e., a process). Accordingly, claims 1, 3, 5-9, 11, 13, and 15-20 are all within at least one of the four statutory categories.
Step 2A - Prong One:
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) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts.
Representative independent claim 11 includes limitations that recite at least one abstract idea. Specifically, independent claim 11 recites:
11. A method for training a machine-learning model comprising: generating a set of training examples for a nutritional replacement model based on a set of training examples for a replacement model, wherein generating a training example in the set of training examples comprises: accessing item data describing a target item and item data describing a candidate item; generating a replacement score based on the item data for the target item and the candidate item, wherein the replacement score indicates a likelihood that a scored user would approve the candidate item as a replacement for the target item, wherein generating the replacement score comprises: using a label of the training example for the replacement model as the replacement score, wherein the replacement model is a machine-learning model trained to predict a likelihood that a user will approve a first item as a replacement for a second item, wherein the label indicates whether a training user selected the candidate item as a replacement for the target item; generating a nutrition score based on the item data for the candidate item, wherein the nutrition score represents a nutritional value of the candidate item; generating a nutritional replacement score by computing a linear combination or a product of the replacement score and the nutrition score the replacement score and the nutrition score; and storing a training example for the nutritional replacement model that comprises the item data for the candidate item, the item data for the target item, and a label based on the nutritional replacement score; initializing the nutritional replacement model; training the nutritional replacement model by iteratively updating a set of parameters for the nutritional replacement model based on each training example of the generated set of training examples for the nutritional replacement model; storing a final set of parameters for the trained nutritional replacement model to a computer-readable medium as the parameters for the nutritional replacement model; receiving a selection of an item through a graphical user interface displayed on a client device of a requesting user; accessing a set of candidate items based on item data associated with the item; applying the trained nutritional replacement model to item data associated with each of the set of candidate items to generate a nutritional replacement score for each of the candidate items; and updating the graphical user interface displayed on the client device of the requesting user to include the item and a subset of the set of candidate items, wherein the subset of the set of candidate items are arranged on the graphical user interface based on the nutritional replacement score for each of the candidate items.
The Examiner submits that the foregoing underlined limitations constitute “a mental process” because generating a set of training examples for a nutritional replacement model based on a set of training examples for a replacement model, wherein generating a training example in the set of training examples comprises: generating a replacement score based on the item data for the target item and the candidate item, wherein the replacement score indicates a likelihood that a scored user would approve the candidate item as a replacement for the target item, wherein generating the replacement score comprises: using a label of the training example for the replacement model as the replacement score, wherein the replacement model is to predict a likelihood that a user will approve a first item as a replacement for a second item, wherein the label indicates whether a training user selected the candidate item as a replacement for the target item; generating a nutrition score based on the item data for the candidate item, wherein the nutrition score represents a nutritional value of the candidate item; generating a nutritional replacement score by computing a linear combination or a product of the replacement score and the nutrition score the replacement score and the nutrition score; initializing the nutritional replacement model; training the nutritional replacement model by iteratively updating a set of parameters for the nutritional replacement model based on each training example of the generated set of training examples for the nutritional replacement model; and applying the model to item data associated with each of the set of candidate items to generate a nutritional replacement score for each of the candidate items amount to observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind or via pen and paper.
The Examiner submits that the foregoing underlined limitations constitute “certain methods of organizing human activity” because accessing item data describing a target item and item data describing a candidate item; receiving a selection of an item; accessing a set of candidate items based on item data associated with the item; wherein the subset of the set of candidate items are arranged based on the nutritional replacement score for each of the candidate items amount to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), at the currently claimed high level of generality.
Accordingly, the claim recites at least one abstract idea.
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 integrate 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 exception into a “practical application.”
The limitations of claims 1, 11, and 20, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and certain methods of organizing human activity but for the recitation of generic computer components. That is, other than reciting a computer system, a graphical user interface, a client device, a processor, a non-transitory computer readable storage medium, and a non-transitory computer-readable medium to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind or from being certain methods of organizing human activity. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind and certain methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Mental Processes” and “certain methods of organizing human activity” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the computer system, graphical user interface, client device, processor, non-transitory computer readable storage medium, and non-transitory computer-readable medium are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of generating data, accessing data, performing calculations, storing data, updating data, receiving a selection, and displaying data) such that it amounts no more than mere instructions to apply the exception using generic computer components. The limitations regarding “training” and “machine-learning” are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the 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 to effect a particular treatment or prophylaxis for a disease or medical condition, 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 § 2106.05). Their collective functions merely provide conventional computer implementation.
Claims 3, 5-9, 13, and 15-19 are ultimately dependent from Claim(s) 1 and 11 and include all the limitations of Claim(s) 1 and 11. Therefore, claim(s) 3, 5-9, 13, and 15-19 recite the same abstract idea. Claims 3, 5-9, 13, and 15-19 describe further limitations regarding accessing and storing user data, applying a nutrition scoring model to the item data for the candidate item, wherein the nutrition scoring model is trained, identifying recipes, comparing items, applying a natural language process or a large language model, and generating the nutrition score based on the item data for the target item. These are all just further describing the abstract ideas recited in claims 1 and 11, without adding significantly more.
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 into a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, independent claims 1, 11, and 20 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 reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations directed to storing an example and storing parameters to a computer-readable medium, all of which the Examiner submits merely add insignificant extra-solution activity to the abstract idea or are claimed in a merely generic manner (e.g., at a high level of generality), the Examiner further submits that such steps are not unconventional as they merely consist of storing and retrieving information in memory and/or electronic recordkeeping. See MPEP 2106.05(d)(II).
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.
Therefore, claims 1, 3, 5-9, 11, 13, and 15-20 are ineligible under 35 USC §101.
Claim Objections
Claim 1 is objected to because of the following informalities: change “generating nutritional replacement score“ to “generating a nutritional replacement score….“ Appropriate correction is required.
Claims 11 and 20 are objected to because of the following informalities: delete the duplicate “the replacement score and the nutrition score“ in the “generating a nutritional replacement score…“ step of the claims. Appropriate correction is required.
Claims 1, 11, and 20 are objected to because of the following informalities: change “generate a nutritional replacement score for each of the candidate items…“ to “generate the nutritional replacement score for each of the candidate items….“ Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3, and 5-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 is rejected for reciting unclear language. Note the limitations of “wherein generating a training example in the set of training examples comprises…generating a replacement score based on a training example in the set of training examples for the replacement model.” Examiner requests clarifying whether or not the second mention of “a training example” is a different “training example” than the first mention of “a training example” and also clarifying how a training example is generated based on a training example.
Claims 3 and 5-9 incorporate the deficiencies of claim 1, through dependency, and are therefore also rejected.
Subject Matter Free of Prior Art
Regarding independent claims 1, 11, and 20, the closest prior art of record, Avery, JR et al. (US 2020/0364662 A1), Pawar (US 2022/0114640 A1), and Catalano (US 7,680,690 B1), do not teach or fairly suggest: generating a nutritional replacement score by computing a linear combination or a product of the replacement score (wherein generating the replacement score comprises: using a label of the training example for the replacement model as the replacement score, wherein the replacement model is a machine-learning model trained to predict a likelihood that a user will approve a first item as a replacement for a second item, wherein the label indicates whether a training user selected the candidate item as a replacement for the target item) and the nutrition score (wherein the nutrition score represents a nutritional value of the candidate item).
Response to Arguments
Applicant's arguments filed 3/4/26 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 3/4/26.
(1) Applicant argues that the 101 rejection should be withdrawn.
(A) As per the first argument, see 101 rejection above. The computer system, graphical user interface, client device, processor, non-transitory computer readable storage medium, and non-transitory computer-readable medium are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of generating data, accessing data, performing calculations, storing data, updating data, receiving a selection, and displaying data) such that it amounts no more than mere instructions to apply the exception using generic computer components. The limitations regarding “training” and “machine-learning” are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. Furthermore, Applicant’s arguments are not persuasive because optimizing recommendations does not solve a technical problem. In addition, it is unclear how the “field of recommendation engines” is improved since the claims do not even recite a “recommendation engine.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The cited but not applied prior art teaches a system for individualized customer interaction (CA-2556778-C).
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/LENA NAJARIAN/Primary Examiner, Art Unit 3687