Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Election filed on 12/22/2025. Claims 7-18 are pending and have been examined. Claims 1-6 and 19-20 are withdrawn.
Election
Applicant’s election with traverse of Group II in the reply filed on 12/22/2025 is acknowledged.
The traversal is on the grounds that the “the three invention groups represent different aspects of a single inventive concept directed to an integrated AI-powered online shopping system,” arguing that “All three invention groups are directed to the same underlying inventive concept: utilizing an AI virtual shopping assistant integrated with a container application to process natural language user input and generate customized shopping lists within an existing online shopping platform.” Applicant argues that all three groups share “common, distinctive features that define the invention.”
This is not found persuasive because the two groups are directed to independent or distinct inventions. In particular, each of the groups is not directed to the argued concept: for instance, group II does not recite an AI virtual shopping assistant, a container application, much less their integration with an existing online shopping platform. With reference to the Restriction Requirement, each of the groups recites limitations not present in the other – each recites different steps and hardware to perform those steps. While the three distinct groups may recite some of the same general “common” features argued, they do so as part of distinct, independent inventions.
Applicant further argues that the three groups “must be used together to achieve the invention’s stated purpose,” arguing that each of the groups has “no independent utility” as standalone claims. Applicant argues that group I “requires the AI virtual shopping assistant …to function,” Group II “requires the container application…as the user interface,” and Group III “requires both the container application and processing method to operate.”
This is not found persuasive because the two groups are directed to independent or distinct inventions. As noted above, these three independent groups each recite distinct, self-contained methods and systems which do not rely on each other to function. There is not suggestion in Group II, for instance, that any form of AI virtual assistant or container application would need to be present to allow the method to be performed; similarly, Group I already recites an AI virtual shopping assistant, but does not require that the method rely on a specific assistant as described in another Group. These groups are not interdependent; rather, they provide 3 distinct methods and systems for performing operations in a particular technical field.
Applicant further argues that the inventions overlap in scope, arguing that “All three groups claim: processing natural language user input, utilizing an AI virtual shopping assistant, generating customized shopping lists, integration with online shopping application, and presenting lists to users for interaction/confirmation.” Applicant argues that “all three groups result in the identical product: a customized shopping list generated through AI processing of natural language input within an integrated container application.”
This is not found persuasive because the two groups are directed to independent or distinct inventions. As addressed above, only group I recites a container application, and Group II does not recite an AI virtual shopping assistant. The three groups provide, at best, alternative methods/systems for producing differently-generated and differently-configured lists; the mere allegation that that the claims recite different versions of similar types of operations does not establish that the claims are not mutually exclusive.
Applicant further argues that the inventions are obvious variants, arguing that “the differences between the groups are merely formal,” and represent “different claiming strategies for the same underlying technology.” Applicant argues that there is “no serious search or examination burden” from examining the three groups together, arguing that “the same prior art references are likely relevant to all groups, the field of search substantially overlaps, and the same search strategies would be employed.” Applicant argues that “with modern electronic search databases and AI-assisted search tools, examining related aspects of the same technology does not present the type of burden that restriction requirements were designed to address.” Applicant argues that “the inventions share common keywords, concepts, and technological domains” and thus do not present a search burden.
This is not found persuasive because the two groups are directed to independent or distinct inventions. While the three groups are directed to a same general field of technology, and may use some of the same “keywords” in their limitations, each invention requires a different field of search. For instance, Group I requires a container application interacting with an AI virtual shopping assistant, whereas Group II requires a chat bot generating a preliminary list and assessment of item availability. With reference to MPEP 808, the alleged capabilities of modern AI tools are not a consideration for establishing serious search burden. Rather, the groups are evaluated to determine if each of the intentions are independent; in this case, the three distinct groups provide three distinct system/methods for performing different operations to generate three different types of lists in three different ways, using three different combinations of hardware, in three different contexts. While general concepts such as generating a list, using a UI, and processing natural language may be present in all three groups, the recitations of each group are directed to distinct inventions, not merely variants of each other.
Applicant further argues that the legal standard for restriction has not been met for the reasons of independence/distinctness and serious search burden as argued above.
Examiner disagrees for the reasons addressed in the Restriction Requirement and those reasons addressed above.
Applicant further argues that the restriction between the two method inventions of Groups I & II and the system invention of Group III is improper as “the system claims encompass the method steps as functional limitations,” “the system claims require performance of the method to function,” and “the specification describes a single embodiment encompassing both method and system.”
This is not found persuasive because the two groups are directed to independent or distinct inventions. As addressed above, Groups I & II are directed to two distinct methods, one requiring specific AI and application hardware & software, the other performing steps such as checking item availability. The system claims do not encompass the steps of either of these unrelated methods; rather than requiring or reciting the hardware and software used to perform Group I or the method operations of Group II, Group III presents an independent, distinct system that performs different method steps, enumerated in the system claims, without recitation of the ability or requirement to perform the operations of either of the two distinct methods, or to incorporate the hardware/software that the two distinct methods rely upon. The groups were not restricted merely for their statutory category, but because they are directed to three distinct inventions, each requiring different hardware/software and performing different operations.
The requirement is still deemed proper and is therefore made FINAL.
Claim Rejection - 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 7-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 7-18 are directed to a process. Therefore, claims 7-18 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claim 7 recites at least the following limitations that are believed to recite an abstract idea:
receiving, from a user, a request for natural language input associated with a shopping request;
processing the natural language input to extract relevant criteria;
querying a system using the relevant criteria to generate a preliminary list of suggested items;
comparing the preliminary list against an inventory system to verify item availability;
processing the preliminary list to generate a customized list for the user; and
transmitting the customized list to the user for presentation to the user.
The above limitations recite the concept of a personalized shopping recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 7-18recite an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
A user-operated device
Online shopping
A chat bot
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. For example, claims 8-9, 13-15, 17 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. As for claims 10-12, 16, 18 these claims are similar to the independent claims except that they recite the further additional elements of APIs, a link, a cloud, a large model, a UI. These additional elements are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. Therefore, the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
A user-operated device
Online shopping
A chat bot
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejection – 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 7-9, 11-18 are rejected under 35 U.S.C. 102 as being anticipated by Wang et al (US 20240320063 A1).
Regarding Claim 7, Wang discloses a method, comprising:
receiving, from a user-operated device of a user, a request for natural language input associated with an online shopping request (Wang: “a user may wish to use the information obtained from the LLM application to request services in the application of the online system 140. For example, the user may prompt the LLM application “what is a recipe for lasagna?”” [0037] - “the user provides a prompt to the LLM application as a request … an example question is “spaghetti and meatballs recipe” … the question may be “what's an easy recipe for veggie stir-fry that I can make in less than 20 minutes?” ” [0039]);
processing the natural language input to extract relevant criteria (Wang: “apply natural language processing (NLP) techniques to the text in the search query to generate a search query representation (e.g., an embedding) that represents characteristics of the search query. ” [0055] – “The model serving system 150 receives a request including input data (e.g., text data, audio data, image data, or video data) and encodes the input data into a set of input tokens.” [0030]);
querying a chat bot using the relevant criteria to generate a preliminary list of suggested items (Wang: “The content presentation module 210 may use the search query representation to score candidate items for presentation to a customer (e.g., by comparing a search query embedding to an item embedding).” [0055] - “The model serving system 150 applies the machine-learned model to generate a set of output tokens.” [0030] – “A user may interact with an application (e.g., web or mobile application), such as a chatbot application, deployed by the model serving system 150 and powered by a large language model (LLM).” [0036] – “the response from the LLM may be a list of ingredients” [0039]);
comparing the preliminary list against an inventory system to verify item availability (Wang: “map the ingredients to actual items that match the description or fall under similar or same product category, and are available at the identified store locations.” [0042] – “scores items based on a predicted availability of an item.” [0056]);
processing the preliminary list to generate a customized list for the user (Wang: “the content presentation module 210 may filter out items from presentation to a customer based on whether the predicted availability of the item exceeds a threshold.” [0056] – “The content presentation module 210 also may identify items that the customer is most likely to order and present those items to the customer. For example, the content presentation module 210 may score items and rank the items based on their scores. The content presentation module 210 displays the items with scores that exceed some threshold (e.g., the top n items or the p percentile of items).” [0053]); and
transmitting the customized list to the user-operated device for presentation on the user-operated device (Wang: “The content presentation module 210 displays the items with scores that exceed some threshold (e.g., the top n items or the p percentile of items).” [0053] – “ an example question is “spaghetti and meatballs recipe” and the response from the LLM may be a list of ingredients and instructions for making the recipe. As another example, the question may be “what's an easy recipe for veggie stir-fry that I can make in less than 20 minutes?” and the response may be a list of ingredients and instructions for making the veggie stir-fry recipe.” [0039] – See Figure 3A).
Regarding claim 8, Wang discloses the method of claim 7, further comprising receiving user modifications to the customized list including at least one of additions, deletions, or substitutions of items in the customized list (Wang: “ the link (when clicked by the user) renders a landing page that displays one or more retailer stores …the items extracted from the conversation session to actual items for the retailer store to create a shopping list for the user. … The user can then simply add the items to an order by clicking a UI element (e.g., “add 17 ingredients to cart”)” [0076] - “The ordering interface allows a customer to update the shopping list, e.g., by changing the quantity of items, adding or removing items, or adding instructions for items that specify how the item should be collected.” [0015] – “The user can access the order page and modify the list of items (e.g., add or delete or update) and add the items in the user's order by clicking a button, for example, the “Add 9 items to cart” button in FIG. 5 .” [0043] See also “see alternatives” and quantity selectors, Figure 4.).
Regarding claim 9, Wang discloses the method of claim 8, further comprising updating prices and totals associated with the user modifications (Wang: “The online system 140 generates the order page including the list of ingredients, the items for each ingredient, an image of the items, as well as information like price for each item and an option to view alternatives to the particular item. … The user can access the order page and modify the list of items (e.g., add or delete or update) and add the items in the user's order by clicking a button, for example, the “Add 9 items to cart” button in FIG. 5 .” [0043] – See Figure 4. – “The order management module 220 computes a total cost for the order and charges the customer that cost.” [0066]).
Regarding claim 11, Wang discloses the method of claim 7, further comprising providing, a link to a recipe associated with one or more items in the customized shopping list (Wang: “The integration module 225 generates a link to a landing page that is provided to the user.” [0067] - “when the list of items in the API request are “recipe” shopping lists, the online system 140 may generate a landing page that is classified as a recipe page. The recipe page includes a title of the recipe, an image of the recipe, and the list of ingredients. This way, the user as a user of the online system 140 may have a dedicated datastore to store favorite recipes of the user. The recipe page may have a UI element (e.g., “Save recipe” element in FIG. 4 ) that when clicked, allows the user to save the recipe page in the datastore.” [0077] – See Figure 4, particularly share & save recipe buttons).
Regarding claim 12, Wang discloses the method of claim 7, further comprising hosting the method on a cloud and synchronizing a finalized customized list defined by a user with a transaction system to complete an online order with checkout payment and pickup or delivery instructions (Wang: “the LLM may be trained and hosted on a cloud infrastructure service. ” [0033] – “the user can then simply add the items to an order by clicking a UI element (e.g., “add 17 ingredients to cart”), such that the order can be fulfilled” [0076] – “ The order management module 220 uses payment information provided by the customer (e.g., a credit card number or a bank account) to receive payment for the order. ” [0066] – “The customer's order may specify which groceries they want delivered from the grocery store and the quantities of each of the groceries. The customer's client device 100 transmits the customer's order to the online system 140 and the online system 140 selects a picker to travel to the grocery store retailer location to collect the groceries ordered by the customer. ” [0028]).
Regarding claim 13, Wang discloses the method of claim 7, wherein receiving further includes identifying a request for ingredients necessary to prepare a specified recipe for a dish within the natural language input (Wang: “the user provides a prompt to the LLM application as a request … an example question is “spaghetti and meatballs recipe” … the question may be “what's an easy recipe for veggie stir-fry that I can make in less than 20 minutes?” ” [0039]).
Regarding claim 14, Wang discloses the method of claim 7, wherein receiving further includes identifying an event type for a user requested event within the natural language input (Wang: “the user provides a prompt to the LLM application as a request … an example question is “spaghetti and meatballs recipe” … the question may be “what's an easy recipe for veggie stir-fry that I can make in less than 20 minutes?” ” [0039] – “ For an example query processing task, the language model may receive a sequence of input tokens that represent a query and generate a sequence of output tokens that represent a response to the query. ” [0030]).
Regarding claim 15, Wang discloses the method of claim 7, wherein processing the natural language input to extract the relevant criteria further includes processing audio associated with the natural language input and converting the audio to text (Wang: “The model serving system 150 receives a request including input data (e.g., text data, audio data, image data, or video data) and encodes the input data into a set of input tokens. The model serving system 150 applies the machine-learned model to generate a set of output tokens. Each token in the set of input tokens or the set of output tokens may correspond to a text unit. For example, a token may correspond to a word” [0030]).
Regarding claim 16, Wang discloses the method of claim 7, wherein querying further includes querying a large natural language model to process the natural language input received from a user with modifications to the relevant criteria (Wang: “the language models are large language models (LLMs) that are trained on a large corpus of training data to generate outputs for the NLP tasks” [0032] – “The content presentation module 210 may use the search query representation to score candidate items for presentation to a customer (e.g., by comparing a search query embedding to an item embedding).” [0055] –“the content presentation module 210 may score items and rank the items based on their scores. The content presentation module 210 displays the items with scores that exceed some threshold (e.g., the top n items or the p percentile of items).” [0053]).
Regarding claim 17, Wang discloses the method of claim 7, wherein processing the preliminary list further includes filtering the preliminary list to align with store or user preferences and prioritizing items based on a combination of the store preferences, the user preferences, user transaction history, and promotional discounts available at a time of the online shopping request (Wang: “An item selection model is a machine learning model that is trained to score items for a customer based on item data for the items and customer data for the customer.” [0054] - “Customer data may include a customer's name, address, shopping preferences, favorite items, … default settings established by the customer, such as a default retailer/retailer location, payment instrument, delivery location, or delivery timeframe. ” [0048] – “the retailer computing system 120 may provide the online system 140 with updated item prices, sales, or availabilities.” [0025]).
Regarding claim 18, Wang discloses the method of claim 7, wherein transmitting further includes tagging items in the customized shopping list with user-selectable options within a user interface associated with an online shopping application (Wang: “ The ordering interface allows a customer to update the shopping list, e.g., by changing the quantity of items, adding or removing items, or adding instructions for items that specify how the item should be collected.” [0015] – “the landing page includes 17 items that are mapped to the list of ingredients in the request, including ground beef, breadcrumbs, Parmesan cheese, and marinara sauce. The user can then simply add the items to an order by clicking a UI element” [0076] – See selectable options, e.g. quantity, alternatives, add to cart, checkmarks, in Fig. 4).
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim Rejection – 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-
obviousness.
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Smith (US 20150099589 A1).
Regarding Claim 10, Wang discloses the method of claim 7, further comprising interfacing, via application programming interfaces (APIs), to access the inventory system, and a transaction system (Wang: “the retailer computing system 120 may provide the online system 140 with updated item prices, sales, or availabilities. Additionally, the retailer computing system 120 may receive payment information from the online system 140 for orders serviced by the online system 140.” [0025] – “the customer client device 100 executes a client application that uses an application programming interface (API) to communicate with the online system 140.” [0013]),
But does not specifically teach interfacing with a loyalty system.
However, Smith teaches a recommendation service [Abstract], including further interfacing with a loyalty system (Smith: “the number of impressions to a particular part of the catalog 344 may be tracked (e.g., the player likes to look at the Super Mario part of the catalog). There may also be an ability to link the player's account to a customer loyalty system.” [0062]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Wang would continue to teach interfacing, via application programming interfaces (APIs), to access the inventory system, and a transaction system, except that now it would also teach further interfacing with a loyalty system, according to the teachings of Smith. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to provide an effect, efficient recommender system (Smith: [0023]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Reference U (NPL – see attached) discusses ML techniques for recommending recipes, including determining alternative ingredients
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS J SULLIVAN whose telephone number is (571)272-9736. The examiner can normally be reached Mon - Fri 8-5 MT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached on (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689