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 .
1. Applicant’s amendment filed 03/18/2026 is entered. Claims 1, 3, 9, 12, 14, 18, and 20 are amended. Claims 1, 12, and 20 are independent claims. Claims 2-11 depend from claim 1, claims 13-19 depend from claim 12. Claims 1-20 are pending for examination.
2. Telephone interview:
A telephone interview was conducted on 03/05/2026 at the request of the Applicant’s representative. The Examiner’s interview summary is reproduced below for ready reference:
“Issues Discussed:
35 U.S.C. 101
Applicant's interview agenda is attached for ready reference. Suggested amendments were discussed by the Examiner and he indicated that they do not overcome the 35 USC 101 rejection, because the models relate to mathematical calculations and the computer functions based upon the results of models are generic computer functions and also amount to applying a computer as a tool for executing mental processes. No agreement was reached. Further amendments and subject matter from Fig.4 were discussed. Examiner assured that any formal amendment would be fully reconsidered and subject to search.”
Note; The currently amended independent claims 1, 12 and 20 were discussed during the telephone interview.
Claim Rejections - 35 USC § 101
3. 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--20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, when analyzed as per MPEP 2106.
Step 1 analysis:
Claims 1-11 are to a process comprising a series of steps, claims 12-19 to manufacture, and clam 20 to a system /apparatus, which are statutory (Step 1: Yes).
Step 2A Analysis:
Claim 1 recites:
1. (Currently Amended) A method, performed at a computer system comprising a processor and a non-transitory computer readable medium, comprising:
(i) receiving a query from a user at the computer system, the query indicating a user intent;
(ii) responsive to receiving the query, executing a plurality of machine learning models to satisfy the query, wherein the plurality of machine learning models comprising an ingredient identification model, an item identification model, and a candidate order form creation model;
(iii) generating, by the ingredient identification model, a set of item categories based on the query based on recipes obtained by the computer system, each recipe including a combination of item categories, wherein the ingredient identification model transmits a first prompt including the set of item categories to the item identification model;
(iv) generating, by the item identification model, a list of items based on the set of item categories and a retailer associated with the list of items, the list of items including at least one item corresponding to each item category of the set of item categories, wherein the item identification model receives the first prompt including the set of item categories from the ingredient identification model, wherein the item identification model transmits a second prompt including the list of items to the candidate order form creation model;
(v) generating, by the candidate order form creation model, an order form including characteristics of an order for the query, the order form based on the list of items and the retailer by applying [[a]] the candidate order form creation model to the list of items and to the retailer, the candidate order form creation model comprising a generative model tuned based on prior orders previously fulfilled for the user, wherein the candidate order form creation model receives the second prompt including the list of items from the item identification model;
(vi) transmitting the order form from the computer system to a client device of the user for display; and
(vii) generating an order having the characteristics included in the order form.
Step 2A Prong 1 analysis: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claims 1-20 recite abstract idea.
The highlighted limitations in steps (i), (iii), (iv), (v) , (vi) and (vii) comprising, “ (i) receiving a query from a user, the query indicating a user intent; (iii) generating, a set of item categories based on the query; (iv) generating a list of items based on the set of item categories and a retailer associated with the list of items, the list of items including at least one item corresponding to each item category of the set of item categories;(v) generating an order form including characteristics of an order for the query, the order form based on the list of items and the retailer; (vi) transmitting the order form to a user for display; and (vii) generating an order having the characteristics included in the order form. ”, under their broadest reasonable interpretation, relate to a commercial activity of preparing an order for a query received from a user and the order including a list of items prepared as per the query and providing the order to the user for his display and then generate the order for fulfillment falling within “Certain Methods of Organizing Human Activity” including sales and purchase activities or behaviors.
The highlighted limitations comprising, “ (iii) generating a set of item categories based on the query, based on recipes obtained, each recipe including a combination of item categories, (iv) generating a list of items based on the set of item categories and a retailer associated with the list of items, the list of items including at least one item corresponding to each item category of the set of item categories, (v) generating an order form including characteristics of an order for the query, the order form based on the list of items and the retailer; and (vii) generating an order having the characteristics included in the order form.”; under their broadest reasonable interpretation, fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion and making simple decisions using a pen and paper. See MPEP 2106.04(a)(2), subsection III. That is, other than reciting “by a processor” nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the “by a processor” language, the claim encompasses a person analyzing the received query to determine the item categories related to recipes and considering the ingredients related to the recipes, based on the determined item categories preparing a list of items with which a supplier/retailer is associated and then using an order template [candidate order form creation model] preparing an order based on the user’s prior order history using a pen and paper and finally generate an order including the characteristics of the template used . The mere nominal recitation of by a processor does not take the claim limitations out of the mental process grouping. Thus, the claim 1 recites a mental process.
Since the limitations of the other two independent claims 12 and 20 recite similar limitations as claim 1, they are analyzed on the same basis reciting “Certain Methods of Organizing Human Activity” and “Mental Processes”
Since each of the claims 1, 12, and 20 recite limitations falling under two separate groupings of abstract ideas, the Supreme Court (discussing Bilski v. Kappos, 561 U.S. 593 (2010)) has treated such claims in the same manner as claims reciting a single judicial exception. Accordingly, limitations considered under Certain Methods of Organizing Human Activity” and “Mental Processes” are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
Thus, claims 1, 12, and 20 with the dependent claims 2-11 from base claim 1 and dependent claims 13-19 from base claim 12 recite an abstract idea.
Step 2A Prong 2 analysis: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claims 1-20: The judicial exception is not integrated into a practical application.
Claim 1 recites the additional limitations of using generic computer components comprising a generic processor executing the steps , “ (i) receiving a query from a user at the computer system, the query indicating a user intent; (ii) responsive to receiving the query, executing a plurality of machine learning models to satisfy the query, wherein the plurality of machine learning models comprising an ingredient identification model, an item identification model, and a candidate order form creation model; (iii) generating, by the ingredient identification model, a set of item categories based on the query, the ingredient identification model comprising a generative model tuned based on recipes obtained by the computer system, each recipe including a combination of item categories, wherein the ingredient identification model transmits a first prompt including the set of item categories to the item identification model; (iv) generating, by the item identification model, a list of items based on the set of item categories and a retailer associated with the list of items, the list of items including at least one item corresponding to each item category of the set of item categories, wherein the item identification model receives the first prompt including the set of item categories from the ingredient identification model, wherein the item identification model transmits a second prompt including the list of items to the candidate order form creation model; (v) generating, by the candidate order form creation model, an order form including characteristics of an order for the query, the order form based on the list of items and the retailer by applying the candidate order form creation model to the list of items and to the retailer, the candidate order form creation model comprising a generative model tuned based on prior orders previously fulfilled for the user, wherein the candidate order form creation model receives the second prompt including the list of items from the item identification model; (vi) transmitting the order form from the computer system to a client device of the user for display; and (vii) generating an order having the characteristics included in the order form.”.
The limitations “ (i)receiving a query ……; and (vi)transmitting the order ….” are mere data gathering and output/transmitting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output/transmit, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting/transmitting. See MPEP 2106.05. Further, these limitations are recited as being performed by a computer. The computer is recited at a high level of generality and is used as a tool to perform the generic computer function of receiving data and transmitting data. See MPEP 2106.05(f).
The limitations in step (ii) recite a number of machine learning models comprising an ingredient identification model, an item identification model, and a candidate order form creation model for executing the steps (iii), (iv), and (v) which have been analyzed as mental processes in the analysis of Step 2A< Prong One above. The limitations in steps “(iii) generating, by the ingredient identification model, a set of item categories based on the query, the ingredient identification model comprising a generative model tuned based on recipes obtained by the computer system, each recipe including a combination of item categories, wherein the ingredient identification model transmits a first prompt including the set of item categories to the item identification model; (iv) generating, by the item identification model, a list of items based on the set of item categories and a retailer associated with the list of items, the list of items including at least one item corresponding to each item category of the set of item categories, wherein the item identification model receives the first prompt including the set of item categories from the ingredient identification model, wherein the item identification model transmits a second prompt including the list of items to the candidate order form creation model; (v) generating, by the candidate order form creation model, an order form including characteristics of an order for the query, the order form based on the list of items and the retailer by applying the candidate order form creation model to the list of items and to the retailer, the candidate order form creation model comprising a generative model tuned based on prior orders previously fulfilled for the user, wherein the candidate order form creation model receives the second prompt including the list of items from the item identification model; and (vii) generating an order having the characteristics included in the order form.”, are recited as being performed by a computer. The computer is recited at a high level of generality. In these limitations the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
The limitations in steps (iii), (iv) and (v) recite using machine learning models comprising an ingredient identification model, an item identification model, and a candidate order form creation model for executing the steps of generating a set of item categories…, a list of items based on the set of item categories and an order form…. These machine learning models are used to generally apply the abstract idea without placing any limits on how the ingredient identification model, an item identification model, and a candidate order form creation model function. Rather, these limitations only recite the outcome of “generating a set of item categories, a list of items, and an order and do not include any details about how the “generating steps “ are accomplished. See MPEP 2106.05(f). The recitation of “using these machine learning models” in steps (iii), (iv), and (v) also merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional element “using machine learning models” limit the identified judicial exceptions , these type of limitation merely confine the use of the abstract idea to a particular technological environment (machine learning models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly, when considered individually and in combination, these additional elements in the claim 1 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 1 is directed to an abstract idea. Since the limitations of the other two independent claims 12 and 20 recite similar limitations as claim 1, they are analyzed on the same basis as directed to an abstract idea.
Dependent claims 2-11 and 13-19 from base claims 1 and 12 respectively have been reviewed by the examine. They merely expand the scope of the limitations discussed above for claims 1 and 12 merely reciting gathering data or executing mental processes, which do not integrate the abstract idea into a practical application because they do not add any meaningful limits on practicing the abstract idea.
Even when viewed individually and in combination, the additional elements in claims 1-20 do not integrate the recited judicial exception into a practical application because they do not add any meaningful limits on practicing the abstract idea (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). Step 2A=Yes. Claims 1-20 are directed to abstract ideas.
Step 2B analysis: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Since claims are as per Step 2A are directed to an abstract idea, they have to be analyzed per Step 2B, if they recite an inventive step, i.e., the claim recite additional elements or a combination of elements that amount to “Significantly More” than the judicial exception in the claim. As discussed above with respect to Step 2A Prong Two, the additional elements in the claims 1-20 amount to no more than mere instructions to apply the exception using a generic computer components, and generally linking the judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B, i.e., mere instructions to apply the exception using a generic computer components, and generally linking the judicial exception to a particular technological environment or field of use using a generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Additional elements of receiving data and transmitting data were both found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering/transmitting/outputting/ displaying/ presenting data . However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). ). The background of the example does not provide any indication that the computer components are anything other than a generic, off the shelf computer component and the Symantec, TLI, OIP Techs, Versata court decisions cited in MPEP 2106.05(d) (ii) indicate that mere data gathering/ transmitting/ outputting/displaying/ presenting/ data steps using a generic computer are well-understood, routine, conventional function when they are claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the receiving, acquiring, transmitting, and displaying steps are well-understood, routine conventional activities are supported under Berkheimer Option 2. See MPEP 2106.05 (f) 2: Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Even when considered individually and in combination, the additional elements in claims 1-20 represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
4. Prior art discussion:
With regards to the currently amended independent claims 1, 12, and 20, the prior art of record including the cited references below, alone or combined, neither teaches nor renders obvious at least the limitations comprising, as a whole, “generating a set of item categories based on a received query by a computer system applying machine learning models comprising: an ingredient identification model , an item identification model, and a candidate order form creation model, wherein the ingredient identification model comprising a generative model tuned based on recipes including a combination of categories obtained is applied to generate a set of item categories based on the query, the item identification model generates a list of items based on the set of item categories and a retailer associated with the list of items, and the candidate order form creation model generates an order form including characteristics of an order for the query, the order form based on the list of items and the retailer by applying the candidate order form creation model to the list of items and to the retailer, and the candidate order form creation model comprising a generative model tuned based on prior orders previously fulfilled for the user”, in combination with the rest of the limitations recited in the independent claims 1, 12, and 20. Claims 2-11 depend from claim 1, and claims 13-19 depend from claim 12.
5. Best prior art of record relevant to the claimed invention but not considered :
(i) Tate et al. cited in the Non-Final Rejection mailed 12/19/2025 [US 2022/0044299 having the same applicant but different inventive entity; see Abstract, para 0004, ] describes an online concierge system and method for determining generic items for orders in response to received queries from users, assigns each item to a product category based on stored data in a database, uses a trained prediction model on the availability of the item and then sends a list of items for display to the user, wherein the prediction model is a machine learning model.
(ii) Nigul Tate et al. cited in the Non-Final Rejection mailed 12/19/2025 [US 2022021506 A1 published 2022-07-07 in reference to the Application# 17/142038 now US Patent# 11907313 B2 has the same Applicant as the instant application but different inventive entity. See Abstract, paras: 0030, 0032, 0036, 0039--0040] teaches a method and system for recommending recipes using time-horizon based user ingredient pool and machine learning model. The system comprise an order module responding to user’s queries by mapping items to item categories describing items with same categories and comprises a recipe database to recommend to customers recipes with a list of ingredients wherein the list includes a category identifier and a quantity of the item needed for the recipe.
(iii) Gudla et al. Tate et al. cited in the Non-Final Rejection mailed 12/19/2025 [US US20240104622 A1 B2 has the same Applicant as the instant application but different inventive entity. See Abstract] describes an online system and method queries a database in response to a received query and determines a set of items matching the query, wherein a machine learning model is applied to determine the set of items which includes a retailer location associated with a retailer type and each item is associated with an item category.
(iv) Allen et al. Tate et al. cited in the Non-Final Rejection mailed 12/19/2025 [US 20180293644 A1; see paras 0045- 0047] describes a shopping list analysis program 106 which identifies the hierarchy of categories from a received shopping list of a user by using NLP [Natural language Processing] techniques and identifies the brands and locations also. The program determines the component requirements and provides recommendations if an expertise is required to identify items required for a recipe such as “make chocolate chip cookies.”.
(v) Faurot et al. Tate et al. cited in the Non-Final Rejection mailed 12/19/2025 [US 20220292568 A1;See para 0025 and Fig.4] describes that an order interface engine 216 determines a set of items/products for ingredients of a recipe received and/or selected by using natural language processing, mappings between ingredients, generic items, and products, and a machine-learned conversion model to determine products for the recipe.
Foreign reference:
(vi) JP 7282247 B1, see page 10 describes an ingredient identification unit 32 identifying ingredients to be cooked using a machine learning model learned in advance by machine learning, wherein the machine learning model is a model that has been learned to output information indicating ingredients used to cook an arbitrary menu in response to input of information indicating an arbitrary menu.
(vii) EP 3735880 Tate et al. cited in the Non-Final Rejection mailed 12/19/2025 [See Abstract and para 0048] describes a food processing device adapted to identify an ingredient and an associated recipe and a processor is adapted to identify the ingredients and the associated recipe by way of a machine learning algorithm trained to recognize ingredients based on the sensed ingredient characteristics, see Fig.4.
NPL reference:
(viii) Md Shafat Jamil Rokon. Md Kishor Morol, Ishra Binte Hasan, A. M. Saif, Rafid Hussain Khan ; “ Food Recipe Recommendation Based on Ingredients Detection Using Deep Learning “; ; Department of Computer Science American International University-Bangladesh (AIUB), (or arXiv:2203.06721v1 [cs.CV] for this version); Publication Date: 2022-03-13, retrieved from IP. Com on 04/15/2026; see pages 2 and 4; describes making a model that can identify ingredients and then recommend recipes based on those ingredients and recommend cooking recipes from recognized food ingredients. The generated recipe database and algorithm ate used for recipe recommendations.
(ix) Equihua et al. “ Sequence-aware item recommendations for multiply repeated user-item interactions”; published arXiv:2304.00578v1 [cs.IR] 2 Apr 2023 and retrieved from IP. Com on 12/15/2025 Tate et al. cited in the Non-Final Rejection mailed 12/19/2025 describes Recommender systems with applications of machine learning and data science in the fields of e-commerce, media streaming content, email marketing, and virtually every industry where personalization facilitates better user experience or boosts sales and customer engagement by analyzing past user behavior to predict which items are of most interest to users. Further the use of the Natural Language Processing techniques results in compressing, processing, and analyzing sequences of text and helps in the task of item recommendation with highly accurate predictions of user-items interactions for all users in a retail environment, without explicit feedback, and also helps in total sales by 5% and individual customer expenditure by over 50% in an A/B live test.
6. Allowability:
If the independent claims 1, 12, and 20 are amended to overcome 101 rejection, the Application can be placed in condition for allowance. Any further amendment will be subject to reconsideration and search.
Response to Arguments
7 Applicant's arguments filed 03/18/2026, see pages 11-14, against the rejection of all pending claims under 35 USC 101 have been fully considered but they are not persuasive.
Examiner disagrees with the applicant’s statement, “ The claims as amended cover a practical application elevating user experience by implementing a multi-modal agentic system, with the machine learning models generating prompts and querying other models with the generated prompts.”, because, as detailed above in paragraph 3, the currently amended claims are directed to judicial exception [Step 2A: YES] , when claim limitations are analyzed per Step 2A, Prong One and Step 2A, Prong Two, the additional elements when viewed individually and in combination , do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO).
Examiner respectfully disagrees with the Applicant’s arguments, “ The claims recite a specific arrangement of steps that integrate the content described as the technical solutions into a practical application. For example, the claims as amended describe "responsive to receiving the query, executing a plurality of machine learning models to satisfy the query, wherein the plurality of machine learning models comprising an ingredient identification model, an item identification model, and a candidate order form creation model; “generating, by the ingredient identification model, a set of item categories based on the query, the ingredient identification model comprising a generative model tuned based on recipes obtained by the computer system, each recipe including a combination of item categories, wherein the ingredient identification model transmits a first prompt including the set of item categories to the item identification model; “generating, by the item identification model, a list of items based on the set of item categories and a retailer associated with the list of items, the list of items including at least one item corresponding to each item category of the set of item categories, wherein the item identification model receives the first prompt including the set of item categories from the ingredient identification model, wherein the item identification model transmits a second prompt including the list of items to the candidate order form creation model;" and "generating, by the candidate order form creation model, an order form including characteristics of an order for the query, the order form based on the list of items and the retailer by applying the candidate order form creation model to the list of items and to the retailer, the candidate order form creation model comprising a generative model tuned based on prior orders previously fulfilled for the user, wherein the candidate order form creation model receives the second prompt including the list of items from the item identification model." Accordingly, the
claims do not cover an abstract idea alone, but to a specific and practical application, providing a technical improvement in predictive analytics to personalize conversion value curves according to individual user feature information. In view of the above, this rejection should be withdrawn. “ because the subject matter eligibility of claims has to be analyzed per Steps 2A, Prong One, Step 2A, Prong Two and Step 2B, as per the details provided in paragraph 3 above.
Step 2A, Prong One part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. As detailed in paragraph 3 above, claims do “set forth” or “describe” judicial exception. The limitations, “ in steps (i), (iii), (iv), (v) , (vi) and (vii) comprising, “ (i) receiving a query from a user, …. (iii) generating, a set of item categories ….; (iv) generating a list of items based on the set of item categories …….;(v) generating an order form including characteristics of an order for the query, ……….; (vi) transmitting the order form ……; and (vii) generating an order …. ”, under their broadest reasonable interpretation, relate to a commercial activity of preparing an order for a query received from a user and the order including a list of items prepared as per the query and providing the order to the user for his display and then generate the order for fulfillment falling within “Certain Methods of Organizing Human Activity” including sales and purchase activities or behaviors. Also, the limitations comprising, “ (iii) generating a set of item categories based on the query, ….. (iv) generating a list of items based on the set of item categories and a retailer associated with the list of items, ……, (v) generating an order form including characteristics of an order for the query….., and (vii) generating an order having the characteristics included in the order form.”; under their broadest reasonable interpretation, fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion and making simple decisions using a pen and paper, as analyzed above in paragraph 3 . See MPEP 2106.04(a)(2), subsection III.
When analyzed per Step 2A, Prong Two, the additional elements referred to by the applicant in his argument relate to a machine learning model which includes an ingredient identification model, an item identification model, and a candidate order form creation model,
is used to generally apply the abstract idea falling within “Mental Process” without placing any limits on how the Machine Learning Model including the ingredient identification model functions. Rather, these limitations only recite the outcome of generating a set of item categories based on the query, ….. (iv) generating a list of items based on the set of item categories and a retailer associated with the list of items, ……, (v) generating an order form including characteristics of an order for the query….., and (vii) generating an order having the characteristics included in the order form.”; and do not include any details about how the “generating steps” are accomplished. See MPEP 2106.05(f).
The recitation of “using a Machine Learning model including an ingredient identification model, an item identification model, and a candidate order form creation model, in steps “ (iii) generating a set of item categories based on the query, ….. (iv) generating a list of items based on the set of item categories and a retailer associated with the list of items, ……, (v) generating an order form including characteristics of an order for the query….., and (vii) generating an order having the characteristics included in the order form.”; also, merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a Machine Learning model including an ingredient identification model, an item identification model, and a candidate order form creation model” limits the identified judicial exceptions “ “ (iii) generating a set of item categories based on the query, ….. (iv) generating a list of items based on the set of item categories and a retailer associated with the list of items, ……, (v) generating an order form including characteristics of an order for the query….., and (vii) generating an order having the characteristics included in the order form”, these type of limitation merely confine the use of the abstract idea to a particular technological environment (Machine learning model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
In view of the foregoing, the Applicant’s arguments are not persuasive.
On pages 11-12 refers to the Applicant’s disclosure and states that it describes a technical problem and the specific arrangement of steps relates to technical solution which is a practical application. Examiner disagrees because the claims , using generic computer components, relate to a commercial activity of preparing an order for a query received from a user and the order including a list of items prepared as per the query and providing the order to the user for his display and then generate the order for fulfillment and as detailed in paragraph 3 above that the when considered individually and in combination, the additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which neither integrate the abstract idea into a practical application [Step 2A, Prong Two :NO] nor provide an inventive concept. [Step 2B: NO].
In view of the foregoing, rejection of currently amended claims 1-20 under 35 USC 101 is sustainable and maintained.
Conclusion
Final Rejection:
8. 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 YOGESH C GARG whose telephone number is (571)272-6756. The examiner can normally be reached Max-Flex.
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/YOGESH C GARG/Primary Examiner, Art Unit 3688
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