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 .
Response to Arguments
Applicant's arguments filed in the Amendment A (“Response”) on 7 November 2025 have been fully considered but they are not persuasive. A new ground of rejection is made in view of United States Patent Application Publication No. 2019/0163710 A1 to Haghighat Kashani et al. (“Haghighat”) to cover the added features which were already present in the rejection to cancelled claim 7 in the Non-Final Rejection mailed 9 May 2025.
Applicant argues in the Response that Berg and Hurst do not teach a custom presentation of each dish. Initially, Examiner points out that the claims recite “dynamically generating a menu presentation…, the menu presentation including at least one of…. a custom presentation of each dish” and “dish configuration items provided for each one of the dish configuration choices having a custom presentation”. The claim feature is not necessarily limiting because if the menu presentation were to include and the prior art taught at least one of a list of categories or a defined rank/sequence for the presentation of the categories, instead of a custom presentation of each dish, then the dish configuration items provided for each one of the dish configuration choices having a custom presentation would not exist and therefore not be limiting.
Applicant further argues that the custom presentation is “rather clearly directed to the dish configuration items provided for each one of the dish configuration choices having a custom visual representation (e.g. customization of the image associated to the dish, the description of the dish and the rank/sequence for the presentation of dish in the associated category” (Response: pg. 16), however this interpretation is much narrower than what is claimed of “a custom presentation” and is even narrower than claim 2 which again is “at least one of a selection of an image associated with the dish, a selection of a description of the dish and a rank/sequence for the presentation of the dish in the associated category”. Berg clearly teaches “dynamically generating a dish configuration presentation to be presented on the user interface, the dish configuration presentation being generated based on the user inputs and including a list of dish configuration choices for building a selected dish and a defined rank/sequence for the presentation of the configuration choices” (Berg: ¶¶ 0098 and 0120 and Fig. 7D) while Hurst clearly teaches “dish configuration items provided for each one of the dish configuration choices having a custom presentation” (Hurst: ¶¶ 0041-0043 and Fig. 3). Berg teaches a system and method for dynamically generating menus and dish presentations. Hurst teaches a comparable system and method for dynamically generating menus and dish presentations that was improved in the same way as the claimed invention. Hurst offers the embodiment of dish configuration items provided for each one of the dish configuration choices having a custom presentation. One of ordinary skill in the art before the effective filing date of the claimed invention would have recognized the adaptation of the custom presentations as disclosed by Hurst to the system and method for dynamically generating menus and dish presentations as taught by Berg for the predicted result of improved systems and methods for dynamically generating menus and dish presentations. No additional findings are seen to be necessary.
Example 47, claim 2 of the July 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, we are shown that simply training a machine learning model and utilizing the machine learning model is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention. Therefore, Examiner has fully considered Applicant’s arguments towards the rejection under 35 USC 101, but they are not persuasive.
The added claim features presented in the Response were already present in the now cancelled claim 7, for which Examiner had recited Haghighat to teach. Applicant has not provided any arguments in the Response with regard to Haghighat, therefore Examiner maintains that Haghighat teaches the features added to the independent claims.
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, 8-11, 13-15, 17, 20, 22-25 and 27-29 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without “significantly more.” Claims 1-3, 5, 8-11, 13-15, 17, 20, 22-25 and 27-29 are directed to receiving menu data inputs, generating and transmitting a menu presentation, receiving user inputs and generating and transmitting dish configuration presentation based on the user inputs which is considered an abstract idea. Further, the claim(s) as a whole, when examined on a limitation-by-limitation basis and in ordered combination do not include an inventive concept.
Step 1 – Statutory Categories
As indicated in the preamble of the claims, the examiner finds the claims are directed to a process and a machine.
Step 2A – Prong One - Abstract Idea Analysis
Exemplary claim 1 (and similarly claim 13) recites the following abstract concepts, in italics below, which are found to include an “abstract idea”:
A computer implemented method for automatic and dynamic menu and dish presentation, the method comprising the steps of:
receiving menu data inputs from a plurality of data sources;
dynamically generating a menu presentation to be presented on a user interface using the received menu data inputs, the menu presentation including at least one of a list of categories, a defined rank/sequence for the presentation of the categories, a list of dish for each category and a custom presentation of each dish;
transmitting the menu presentation to the user interface and displaying the menu presentation on the user interface, the user interface being accessible by a user on a user computing device;
receiving user inputs from the computing device, the user inputs being relative to user selections of menu items displayed on the user interface according to the menu presentation;
dynamically generating a dish configuration presentation to be presented on the user interface, the dish configuration presentation being generated based on the user inputs and including a list of dish configuration choices for building a selected dish and a defined rank/sequence for the presentation of the configuration choices, with dish configuration items provided for each one of the dish configuration choices having a custom presentation; and
transmitting the dish configuration presentation to the user interface and displaying the dish configuration presentation on the user interface,
wherein the method further comprises generating specific menu or dish configuration item recommendations for an item of at least one of the menu presentation and the dish configuration presentation using a recommender system including at least one machine learning recommendation model trained using a labelled dataset.
The claim features in italics above as drafted, under its broadest reasonable interpretation, are mental processes and/or certain methods of organizing human activity performed by generic computer components. That is, other than reciting “a computing device” and “recommender system including at least one machine learning recommendation model trained using a labelled dataset” nothing in the claim element precludes the step from practically being performed in the mind or a method of organized human activity. For example, but for the “computing device” and “recommender system including at least one machine learning recommendation model trained using a labelled dataset” language, “dynamically generating a menu presentation to be presented on a user interface using the received menu data inputs, the menu presentation including at least one of a list of categories, a defined rank/sequence for the presentation of the categories, a list of dish for each category and a custom presentation of each dish… dynamically generating a dish configuration presentation to be presented on the user interface, the dish configuration presentation being generated based on the user inputs and including a list of dish configuration choices for building a selected dish and a defined rank/sequence for the presentation of the configuration choices, with dish configuration items provided for each one of the dish configuration choices having a custom presentation… wherein the method further comprises generating specific menu or dish configuration item recommendations for an item of at least one of the menu presentation and the dish configuration presentation using.. a… model” in the context of this claim encompass mental processes. If the claim limitations, under its broadest reasonable interpretation, covers steps which could be performed in the human mind including an observation, evaluation, judgement of opinion but for the recitation of generic computer components, then it falls within the “mental process” grouping of abstract ideas. Further, “receiving menu data inputs from a plurality of data sources… transmitting the menu presentation to the user interface and displaying the menu presentation on the user interface, the user interface being accessible by a user …; receiving user inputs …, the user inputs being relative to user selections of menu items displayed on the user interface according to the menu presentation;… transmitting the dish configuration presentation to the user interface and displaying the dish configuration presentation on the user interface” in the context of this claim encompasses certain methods of organizing human activity. If the claim limitations, under its broadest reasonable interpretation, covers fundamental economic practice, commercial or legal interaction or managing personal behavior or relationships or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two - Abstract Idea Analysis
This judicial exception is not integrated into a practical application. In particular, the claims only recite three additional elements – “a display media of a user computing device” and “recommender system including at least one machine learning recommendation model trained using a labelled dataset”. The “display media of a user computing device” and “recommender system including at least one machine learning recommendation model trained using a labelled dataset” are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f), i.e. the generating and transmitting steps) and data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g), i.e. the receiving steps). 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.
Step 2B - Significantly More Analysis
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 of “a display media of a user computing device” and “recommender system including at least one machine learning recommendation model trained using a labelled dataset” amount to no more than mere instructions to apply the exception using a generic computer component and insignificant extra-solution activity. Mere instructions to apply the exception using a generic computer component and insignificant extra-solution activity cannot provide an inventive concept. Further, the background does not provide any indication that the “display media of a user computing device” are anything other than a generic, off-the-shelf computer component. For these reasons, there is no inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 8-11, 13-14, 20, 22-25 and 27-29, are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication No. 2017/0109843 A1 to Berg et al. (“Berg”) in view of United States Patent Application Publication No. 2014/0279092 A1 to Hurst et al. (“Hurst”) and United States Patent Application Publication No. 2019/0163710 A1 to Haghighat Kashani et al. (“Haghighat”).
As per claims 1 and 13, the claimed subject matter that is met by Berg includes
a computer implemented method for automatic and dynamic menu and dish presentation, the method comprising the steps of (Berg: ¶¶ 0008-0009):
receiving menu data inputs from a plurality of data sources (Berg: ¶¶ 0068 and 0092 and Fig. 4);
dynamically generating a menu presentation to be presented on a user interface using the received menu data inputs, the menu presentation including at least one of a list of categories, a defined rank/sequence for the presentation of the categories, a list of dish for each category and a custom presentation of each dish (Berg: ¶¶ 0092-0095 and 0118 and Fig. 7A);
transmitting the menu presentation to the user interface and displaying the menu presentation on the user interface, the user interface being accessible by a user on a user computing device (Berg: ¶¶ 0092-0095 and 0118 and Fig. 7A);
receiving user inputs from the computing device, the user inputs being relative to user selections of menu items displayed on the user interface according to the menu presentation (Berg: ¶¶ 0096 and 0120 and Fig. 7C);
dynamically generating a dish configuration presentation to be presented on the user interface, the dish configuration presentation being generated based on the user inputs and including a list of dish configuration choices for building a selected dish and a defined rank/sequence for the presentation of the configuration choices (Berg: ¶¶ 0098 and 0120 and Fig. 7D); and
transmitting the dish configuration presentation to the user interface and displaying the dish configuration presentation on the user interface (Berg: ¶¶ 0098 and 0120 and Fig. 7D).
Berg fails to specifically teach 1.) dish configuration items provided for each one of the dish configuration choices having a custom presentation and 2.) wherein the method further comprises generating specific menu or dish configuration item recommendations for an item of at least one of the menu presentation and the dish configuration presentation using a recommender system including at least one machine learning recommendation model trained using a labelled dataset. The Examiner provides Hurst to teach and disclose claimed feature 1.
The claimed subject matter that is met by Hurst includes:
dynamically generating a dish configuration presentation to be presented on the user interface, the dish configuration presentation being generated based on the user inputs and including a list of dish configuration choices for building a selected dish and a defined rank/sequence for the presentation of the configuration choices, with dish configuration items provided for each one of the dish configuration choices having a custom presentation (Hurst: ¶¶ 0041-0043 and Fig. 3)
Berg teaches a system and method for dynamically generating menus and dish presentations. Hurst teaches a comparable system and method for dynamically generating menus and dish presentations that was improved in the same way as the claimed invention. Hurst offers the embodiment of dish configuration items provided for each one of the dish configuration choices having a custom presentation. One of ordinary skill in the art before the effective filing date of the claimed invention would have recognized the adaptation of the custom presentations as disclosed by Hurst to the system and method for dynamically generating menus and dish presentations as taught by Berg for the predicted result of improved systems and methods for dynamically generating menus and dish presentations. No additional findings are seen to be necessary.
Berg and Hurst fail to specifically teach 2.) wherein the method further comprises generating specific menu or dish configuration item recommendations for an item of at least one of the menu presentation and the dish configuration presentation using a recommender system including at least one machine learning recommendation model trained using a labelled dataset. The Examiner provides Haghighat to teach and disclose this claimed feature.
The claimed subject matter that is met by Haghighat includes:
wherein the method further comprises generating specific menu or dish configuration item recommendations for an item of at least one of the menu presentation and the dish configuration presentation using a recommender system including at least one machine learning recommendation model trained using a labelled dataset (Haghighat: ¶¶ 0033-0034, 0058, 0061-0063 and 0066-0067).
Berg and Hurst teach systems and methods for dynamically generating menus and dish presentations. Haghighat teaches a comparable system and method for dynamically generating menus and dish presentations that was improved in the same way as the claimed invention. Haghighat offers the embodiment of wherein the method further comprises generating specific menu or dish configuration item recommendations for an item of at least one of the menu presentation and the dish configuration presentation using a recommender system including at least one machine learning recommendation model trained using a labelled dataset. One of ordinary skill in the art before the effective filing date of the claimed invention would have recognized the adaptation of the machine learning recommendation model as disclosed by Haghighat to the systems and methods for dynamically generating menus and dish presentations as taught by Berg and Hurst for the predicted result of improved systems and methods for dynamically generating menus and dish presentations. No additional findings are seen to be necessary.
As per claims 2 and 14, the claimed subject matter that is met by Berg, Hurst and Haghighat includes:
wherein the custom presentation of each dish includes at least one of a selection of an image associated with the dish, a selection of a description of the dish and a rank/sequence for the presentation of the dish in the associated category (Hurst: ¶ 0037 and Fig. 1).
The motivation for combining the teachings of Berg, Hurst and Haghighat are discussed in the rejection of claims 1 and 13, and are incorporated herein.
As per claim 8, the claimed subject matter that is met by Berg, Hurst and Haghighat includes:
wherein the labelled dataset comprises data labelled using at least one of dish/food attribute data, historical user data, historical purchase data and contextual data regarding purchases (Haghighat: ¶¶ 0033-0034, 0058, 0061-0063 and 0066-0067).
The motivation for combining the teachings of Berg, Hurst and Haghighat are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 9, the claimed subject matter that is met by Berg, Hurst and Haghighat includes:
wherein the step of generating specific menu or dish configuration item recommendations for an aspect of at least one of the menu presentation and the dish configuration presentation using a recommender system comprises selecting at least one recommended system from a plurality of available recommender systems (Haghighat: ¶¶ 0033-0034, 0058, 0061-0063 and 0066-0067).
The motivation for combining the teachings of Berg, Hurst and Haghighat are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 10, the claimed subject matter that is met by Berg, Hurst and Haghighat includes:
further comprising receiving feedback data, processing the feedback data to generate an updated dataset and using the updated dataset to train a corresponding one of the at least one machine learning recommendation model (Haghighat: ¶¶ 0078 and 0081).
The motivation for combining the teachings of Berg, Hurst and Haghighat are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 11, the claimed subject matter that is met by Berg, Hurst and Haghighat includes:
wherein at least one of the steps of dynamically generating a menu presentation and dynamically generating a dish configuration presentation comprises generating a recommendation vector including a plurality of menu or dish configuration item recommendations and filtering the entries of the recommendation vector based on business rules associated to a corresponding restaurant (Haghighat: ¶¶ 0034-0043, 0062 and 0118).
The motivation for combining the teachings of Berg, Hurst and Haghighat are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 20, the claimed subject matter that is met by Berg, Hurst and Haghighat includes:
wherein the machine learning recommendation module includes a plurality of recommender systems, each configured to provide specific menu or dish configuration item recommendations for a corresponding one of an item of the menu presentation or an item of the dish configuration presentation and wherein each one of the recommender systems includes a recommender algorithm and at least one machine learning recommendation model trained using a labelled dataset (Haghighat: ¶¶ 0033-0034, 0058, 0061-0063 and 0066-0067).
The motivation for combining the teachings of Berg, Hurst and Haghighat are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 22, the claimed subject matter that is met by Berg, Hurst and Haghighat includes:
wherein the labelled dataset comprises data labelled using at least one of dish/food attribute data, historical user data, historical purchase data and contextual data regarding purchases (Haghighat: ¶¶ 0033-0034, 0058, 0061-0063 and 0066-0067).
The motivation for combining the teachings of Berg, Hurst and Haghighat are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 23, the claimed subject matter that is met by Berg, Hurst and Haghighat includes:
further comprising a business rule module filtering the entries of the recommendation vector based on business rules associated to a corresponding restaurant (Haghighat: ¶¶ 0034-0043, 0062 and 0118).
The motivation for combining the teachings of Berg, Hurst and Haghighat are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 24, the claimed subject matter that is met by Berg, Hurst and Haghighat includes:
wherein the machine learning recommendation module is in data communication with an AI data service connected to an AI database and a feature store connected to an online serving datastore, the system further comprising a feedback module receiving raw feedback data from the back end module and storing the raw feedback data in a feedback datastore (Haghighat: ¶¶ 0078 and 0081).
The motivation for combining the teachings of Berg, Hurst and Haghighat are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 25, the claimed subject matter that is met by Berg, Hurst and Haghighat includes:
wherein the feedback module comprises a batch processor repeatedly processing raw feedback data from the feedback datastore in batch and storing the processed data in an intermediate database and a service processing module processing the semi-processed processed data from the intermediate database and updating the AI database and the AI data service to reflect changes to the intermediate database (Haghighat: ¶¶ 0078 and 0081).
The motivation for combining the teachings of Berg, Hurst and Haghighat are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 27, the claimed subject matter that is met by Berg, Hurst and Haghighat includes:
wherein the feedback module further comprises a dataset creation module configured to process the semi-processed data from the intermediate database and to generate a training dataset adapted to a corresponding machine learning recommendation model (Haghighat: ¶¶ 0078 and 0081).
The motivation for combining the teachings of Berg, Hurst and Haghighat are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 28, the claimed subject matter that is met by Berg, Hurst and Haghighat includes:
wherein the feedback module further comprises a feature update module configured to generate features for the corresponding machine learning recommendation model and to update the feature store and online serving datastore accordingly (Haghighat: ¶¶ 0078 and 0081).
The motivation for combining the teachings of Berg, Hurst and Haghighat are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 29, the claimed subject matter that is met by Berg, Hurst and Haghighat includes:
wherein the feedback module further comprises a model training module configured to perform training of the corresponding machine learning recommendation model using the generated dataset (Haghighat: ¶¶ 0078 and 0081).
The motivation for combining the teachings of Berg, Hurst and Haghighat are discussed in the rejection of claim 1, and are incorporated herein.
Claims 3, 5, 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Berg in view of Hurst and Haghighat as applied in claims 1 and 13, and further in view of United States Patent Application Publication No. 2013/0130208 A1 to Riscalla (“Riscalla”).
As per claims 3 and 15, Berg, Hurst and Haghighat fail to specifically teach wherein the dish configuration items of the list of dish configuration choices for building a selected dish includes possible side dishes and wherein the custom presentation of side dishes includes a selection of an image associated to the corresponding one of the side dishes, a selection of a description of the corresponding one of the side dishes and a rank/sequence for the presentation of the corresponding one of the side dishes. The Examiner provides Riscalla to teach and disclose this claimed feature.
The claimed subject matter that is met by Riscalla includes:
wherein the dish configuration items of the list of dish configuration choices for building a selected dish includes possible side dishes and wherein the custom presentation of side dishes includes a selection of an image associated to the corresponding one of the side dishes, a selection of a description of the corresponding one of the side dishes and a rank/sequence for the presentation of the corresponding one of the side dishes (Riscalla: ¶¶ 0104-0106 and Fig. 7R)
Berg, Hurst and Haghighat teach systems and methods for dynamically generating menus and dish presentations. Riscalla teaches a comparable system and method for dynamically generating menus and dish presentations that was improved in the same way as the claimed invention. Riscalla offers the embodiment of wherein the dish configuration items of the list of dish configuration choices for building a selected dish includes possible side dishes and wherein the custom presentation of side dishes includes a selection of an image associated to the corresponding one of the side dishes, a selection of a description of the corresponding one of the side dishes and a rank/sequence for the presentation of the corresponding one of the side dishes. One of ordinary skill in the art before the effective filing date of the claimed invention would have recognized the adaptation of the side dishes as disclosed by Riscalla to the systems and methods for dynamically generating menus and dish presentations as taught by Berg, Hurst and Haghighat for the predicted result of improved systems and methods for dynamically generating menus and dish presentations. No additional findings are seen to be necessary.
As per claims 5 and 17, the claimed subject matter that is met by Berg, Hurst, Haghighat and Riscalla includes:
wherein the dish configuration items of the list of dish configuration choices for building a selected dish further includes at least one of possible beverages, possible complementary dishes and possible options/extras and wherein the custom presentation of at least one of the possible beverages, possible complementary dishes and possible options/extras includes a selection of an image associated to the corresponding one of the possible beverages, possible complementary dishes and possible options/extras, a selection of a description of the corresponding one of the possible beverages, possible complementary dishes and possible options/extras and a rank/sequence for the presentation of the corresponding one of the possible beverages, possible complementary dishes and possible options/extras (Hurst: ¶ 0037 and Fig. 1).
The motivation for combining the teachings of Berg, Hurst, Haghighat and Riscalla are discussed in the rejection of claims 3 and 15, and are incorporated herein.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hunter Wilder whose telephone number is (571)270-7948. The examiner can normally be reached Monday-Friday 8:30AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Florian Zeender can be reached at (571)272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A. Hunter Wilder/Primary Examiner, Art Unit 3627