DETAILED ACTION
This action is in reply to the Amendments filed on 12/04/2025.
Claims 6, 11, and 18 were previously cancelled.
Claims 1-5, 7-10, 12-17, 19 and 20 are currently pending and have been examined
Response to Amendment
Applicant’s amendment, filed 12/04/2025, has been entered. Claims 1, 9, 10, 12, 13, 17, 19 and 20 have been amended.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/04/2025 has been entered.
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 .
Claim Objections
Claims 1-5, 7-10, 12-17, 19 and 20 are objected to because of the following informalities:
-Claims 1, 13, and 20 read “responsive to the the user pressing the first user interface element …” but should likely read “responsive to the user pressing the first user interface element …”
Claims 2-5, 7-10, 12, 14-17, and 19 inherit the deficiencies noted in claims 1 and 13, and are therefore objected to on the same basis.
Appropriate correction is required.
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 1-5, 7-10, 12-17, 19 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories (see MPEP 2106.03). All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 1 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-A method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
-receiving a signal indicating that an item in a cart requested by a user of an online system is not available;
-accessing a replacement model, wherein the replacement model is a machine-learning model trained to identify a set of one or more replacement items for replacing the item;
-applying the replacement model to one or more features of the item to score each replacement item in the identified set of one or more replacement items;
-responsive to identifying a failure of the replacement model to identify one or more suitable replacement items according to defined criteria, accessing a recipe prediction model, wherein the recipe prediction model is a machine-learning model trained to infer a recipe that is potentially associated with the item;
-applying the recipe prediction model to the one or more features of the item and information about one or more ingredients in the cart to generate a name of the inferred recipe and a replacement item category associated with the inferred recipe;
-collecting, from a database of recipes of the online system and using the name of the inferred recipe, a set of one or more recipes;
-identifying, from the set of one or more recipes and using the replacement item category, a set of one or more candidate replacement items;
-applying the replacement model to the name of the inferred recipe, the replacement item category and information about the set of one or more candidate replacement items to identify one or more replacement items from the set of one or more candidate replacement items;
-responsive to inferring the name of the recipe and the replacement item category, generating an inquiry message for the user including the name of the recipe and the replacement item category;
-causing a device associated with the user to display a user interface with the one or more replacement items for recommendation to the user to be included in the cart instead of the item, the inquiry message prompting the user to confirm whether the inference of the recipe is correct,
-wherein the inquiry message includes a first user interface element having a first functionality that generates, when the user [selects] presses the first user interface element, a confirmation that the inference of the recipe is correct, and
-wherein the inquiry message further includes a second user interface element displayed adjacent to the first user interface element, the second user interface element having a second functionality that generates, when the user [selects] presses the second user interface element, a confirmation that the inference of the recipe is not correct; and
-responsive to the the user [selecting] pressing the first user interface element or the user [selecting] pressing the second user interface element, generating a user feedback signal with information that the inference of the recipe is correct or with information that the inference of the recipe is not correct; and
-updating a set of parameters of the recipe prediction model using the user feedback signal
The above limitations recite the concept of recommending replacement items to a user. The above limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a).
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations of applying the replacement model to one or more features of the item to score each replacement item in the identified set of one or more replacement items; applying the recipe prediction model to the one or more features of the item and information about one or more ingredients in the cart to generate a name of the inferred recipe and a replacement item category associated with the inferred recipe; identifying, from the set of one or more recipes and using the replacement item category, a set of one or more candidate replacement items; applying the replacement model to the name of the inferred recipe, the replacement item category and information about the set of one or more candidate replacement items to identify one or more replacement items from the set of one or more candidate replacement items; responsive to inferring the name of the recipe and the replacement item category, generating an inquiry message for the user including the name of the recipe and the replacement item category; and updating a set of parameters of the recipe prediction model using the user feedback signal are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “applying,” “applying,” “identifying,” “applying,” “generating,” and “updating” in the context of this claim encompass advertising, and marketing or sales activities.
Similarly, the limitations of a method, performed at a computer system comprising a processor and a computer-readable medium, comprising: receiving a signal indicating that an item in a cart requested by a user of an online system is not available; accessing a replacement model, wherein the replacement model is a machine-learning model trained to identify a set of one or more replacement items for replacing the item; responsive to identifying a failure of the replacement model to identify one or more suitable replacement items according to defined criteria, accessing a recipe prediction model, wherein the recipe prediction model is a machine-learning model trained to infer a recipe that is potentially associated with the item; collecting, from a database of recipes of the online system and using the name of the inferred recipe, a set of one or more recipes; causing a device associated with the user to display a user interface with the one or more replacement items for recommendation to the user to be included in the cart instead of the item, the inquiry message prompting the user to confirm whether the inference of the recipe is correct, wherein the inquiry message includes a first user interface element having a first functionality that generates, when the user [selects] presses the first user interface element, a confirmation that the inference of the recipe is correct, and wherein the inquiry message further includes a second user interface element displayed adjacent to the first user interface element, the second user interface element having a second functionality that generates, when the user [selects] presses the second user interface element, a confirmation that the inference of the recipe is not correct; and responsive to the the user [selecting] pressing the first user interface element or the user [selecting] pressing the second user interface element, generating a user feedback signal with information that the inference of the recipe is correct or with information that the inference of the recipe is not correct are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the system is a computer system comprising a processor and a computer-readable medium, that the system is an online system, that the replacement model is a machine-learning model that is trained, that the recipe prediction model is a machine-learning model that is trained, that the collecting is from a database of recipes of the online system, that the one or more replacement items are displayed on a user interface of a device associated with a user, that the first element is a first user interface element, that the user presses the first element, that the second element is a the second user interface element, and that the user presses the second element, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “computer system,” “a processor,” “a computer-readable medium,” “online system,” “the replacement model is a machine-learning model,” “the recipe prediction model is a machine-learning model,” “trained,” “a database,” “a device” “a user interface,” “first user interface element,” “second user interface element,” “press[ing] the first user interface element” and “press[ing] the second user interface element” language, “receiving,” “accessing,” “accessing,” “collecting,” “causing display,” “generates,” “generating,” and “updating” in the context of this claim encompasses advertising, and marketing or sales activities.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
-A method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
-receiving a signal indicating that an item in a cart requested by a user of an online system is not available;
-accessing a replacement model, wherein the replacement model is a machine-learning model trained to identify a set of one or more replacement items for replacing the item;
-applying the replacement model to one or more features of the item to score each replacement item in the identified set of one or more replacement items;
-responsive to identifying a failure of the replacement model to identify one or more suitable replacement items according to defined criteria, accessing a recipe prediction model, wherein the recipe prediction model is a machine-learning model trained to infer a recipe that is potentially associated with the item;
-applying the recipe prediction model to the one or more features of the item and information about one or more ingredients in the cart to generate a name of the inferred recipe and a replacement item category associated with the inferred recipe;
-collecting, from a database of recipes of the online system and using the name of the inferred recipe, a set of one or more recipes;
-identifying, from the set of one or more recipes and using the replacement item category, a set of one or more candidate replacement items;
-applying the replacement model to the name of the inferred recipe, the replacement item category and information about the set of one or more candidate replacement items to identify one or more replacement items from the set of one or more candidate replacement items;
-responsive to inferring the name of the recipe and the replacement item category, generating an inquiry message for the user including the name of the recipe and the replacement item category;
-causing a device associated with the user to display a user interface with the one or more replacement items for recommendation to the user to be included in the cart instead of the item, the inquiry message prompting the user to confirm whether the inference of the recipe is correct,
-wherein the inquiry message includes a first user interface element having a first functionality that generates, when the user presses the first user interface element, a confirmation that the inference of the recipe is correct, and
-wherein the inquiry message further includes a second user interface element displayed adjacent to the first user interface element, the second user interface element having a second functionality that generates, when the user presses the second user interface element, a confirmation that the inference of the recipe is not correct; and
-responsive to the the user pressing the first user interface element or the user pressing the second user interface element, generating a user feedback signal with information that the inference of the recipe is correct or with information that the inference of the recipe is not correct; and
-updating a set of parameters of the recipe prediction model using the user feedback signal
These limitations are not indicative of integration into a practical application because:
The additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) as supported by paragraph [0092] of Applicant’s specification – “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In some embodiments, a software module is implemented with a computer program product comprising one or more computer-readable media storing computer program code or instructions, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.” Specifically, the additional elements of a computer system, a processor, a computer-readable medium, an online system, the replacement model is a machine-learning model, the recipe prediction model is a machine-learning model, training, a database, a device, a user interface, a first user interface element, a second user interface element, pressing the first user interface element and pressing the second user interface element are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of receiving data, accessing data, applying data, collecting data, identifying data, generating data, causing display of data, and updating data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these 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. The claim is directed to an abstract idea. Further, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) 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.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Claim 13 is a computer program product reciting similar functions as claim 1. Examiner notes that claim 13 recites the additional elements of a non-transitory computer readable storage medium having instructions encoded thereon, a processor, an online system, the replacement model is a machine-learning model, the recipe prediction model is a machine-learning model, training, a database, a device, a user interface, a first user interface element, a second user interface, pressing the first user interface element and pressing the second user interface element, however, claim 13 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Claim 20 is a computer system reciting similar functions as claim 1. Examiner notes that claim 20 recites the additional elements of a computer system, a processor, a non-transitory computer-readable storage medium having instructions, an online system, the replacement model is a machine-learning model, the recipe prediction model is a machine-learning model, training, a database, a device, a user interface, a first user interface element, a second user interface, pressing the first user interface element and pressing the second user interface element, however, claim 20 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Therefore, claims 1, 13, and 20 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 2-5, 7-10, 12, 14-17 and 19, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. § 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-5, 7-10, 12, 14-17 and 19 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 2, 7, and 12 do not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. Dependent claims 3-5, 8-10, 14-17 and 19 recite the additional elements of the availability model being a machine-learning model that is trained, device associated with a picker, the database, the online system, training a set of parameters of the recipe prediction model, re-training at least one of the replacement model or the recipe prediction model, and the processor, but similar to the analysis under prong two of Step 2A these additional elements are used as a tool to perform the abstract idea. As such, under prong two of Step 2A, claims 2-5, 7-10, 12, 14-17 and 19 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-5, 7-10, 12, 14-17 and 19 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 13, and 20, dependent claims 2-5, 7-10, 12, 14-17 and 19 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. recommending replacement items to a user) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Subject Matter Allowable over the Prior Art
In the present application, claims 1-5, 7-10, 12-17, 19 and 20 would be allowable if rewritten or amended to overcome the rejections under 35 USC § 101 set forth in this Office action. The following is the Examiner's statement of reasons of allowance:
Regarding 35 U.S.C. §103, upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the applicant’s invention. Claims 1-5, 7-10, 12-17, 19 and 20 are allowable over the prior art as follows:
Claims 1-5, 7-10, 12-17, 19 and 20 are allowable over 35 U.S.C. §103 as follows:
Claims 1-5, 7-10, 12-17, 19 and 20 are allowable for the reasons detailed in the “Allowable Subject Matter” section of the Final Office Action dated 07/22/2025.
The most relevant prior art made of record includes over Guo et al. (US 2017/0193582 A1), Durazo Almeida et al. (US 11,257,049 B1), and Nigul et al. (US 2022/0215061 A1).
The most relevant NPL is cited NPL reference U (cited 07/18/2025, 10/10/2025, and 01/05/2025 on PTO-892) teaches providing recipe recommendations to a user based on the items in a user’s cart, but does not teach or suggest the recited limitations.
Response to Arguments
Rejections under 35 U.S.C. §101
Applicant argues that limitations of amended claim 1 integrate the alleged judicial exception into a practical application of a computer system that generates a specific a user interface based on an output of machine-learning model, and where the specific user's interaction with the user interface is used to retrain the machine-learning model. These limitations do not merely link the judicial exception to a technical field, but instead add a meaningful limitation in that they employ information provided by the judicial exception (i.e., the inferred name of the recipe, the inferred replacement item category, and the one or more replacement items for recommendation) to generate a user interface with specific components that allow for a specific and more efficient user's interaction with the user interface. Limitations of amended claim 1 associated with the user interface are not recited at a high level of generality. Instead, these limitations of amended claim 1 recite "what the interface elements are, how they are displayed, and what the user selects on an interface" as suggested in the Office Action. (Office Action, p. 15.) Moreover, "while an additional limitation ( or combination) that merely applies the judicial exception on a generic computer may not render a claim eligible on its own, an additional limitation ( or combination) that meaningfully limits the judicial exception can render it eligible." (See USPTO Memorandum, August 4, 2025, p. 4). For at least these reasons, amended claim 1 as a whole imposes meaningful limits on practicing the judicial exception, and therefore integrates the judicial exception into a practical application. Thus, claim 1, as amended herein, is eligible under Step 2A - Prong 2 (Remarks, pages 15-16).
Examiner respectfully disagrees. Merely utilizing user feedback to update a machine learning model does not improve the model itself and the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). While the claims recites an inquiry message that includes interface elements that can be pressed by a user to either confirm or reject the confirmation of the recipe, these recitations don’t include the technical aspects of how they are displayed (e.g. overlaid, embedded, in response to an interface element selected on the device of the picker, etc.) and what the user selects beforehand to trigger this message, as well as, how the interface is changed following the selection (e.g. does the confirmation enable selection of another interface element to add the recommended item to the cart?). Accordingly, the amended claims fail to recite specific interface features that would reflect an improvement in the functioning of a computer or an improvement to another technology or technical field 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. Accordingly, the amended claims are ineligible.
Applicant further argues that independent claims 13 and 20 are amended to recite similar limitations in claim 1, and, thus, independent claims 13 and 20, as amended herein, integrate the judicial exception into a practical application for at least the same reasons as amended claim 1. Each of the remaining pending claims depends on claim 1 or claim 13; thus, these claims also integrate the judicial exception into a practical application (Remarks, page 16).
Examiner respectfully disagrees. As detailed in response to the arguments above, claim 1 is not eligible. Accordingly, independent claims 13 and 20 and the dependent claims are ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Nelson et al. (US 2019/0361463 A1) teaches a user confirming replacement of an item and the replacement being suggested based on ingredients of a meal the use has previously had.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARIELLE E WEINER whose telephone number is (571)272-9007. The examiner can normally be reached M-F 8:30-5:00.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689
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