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 Amendment filed on 6/10/2026. Claims 7-18 are currently pending and have been examined. Claims 1-6 and 19-20 stand withdrawn. Claim 7 has been amended.
Examiner recommends that Applicant cancel the withdrawn, unelected claims.
Election
Applicant’s remarks regarding the restriction requirement have been considered but are not persuasive.
Applicant “continues to assert that the inventions of Groups I, II, and III are not independent or distinct, that they represent a single unified inventive concept directed to an integrated AI-powered online shopping system, that they are designed to and must operate together, that they overlap in scope and are obvious variants of one another, and that no serious search or examination burden has been adequately established.”
Examiner respectfully disagrees, and refers to the Response provided to the arguments in the Non-Final Rejection. The arguments are not found persuasive because the three 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. The three groups provide three alternative embodiments 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. 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.
The requirement is still deemed proper and is therefore maintained as 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 by inspecting the natural language input for key terms or phrases associated with conditions set by the user within the natural language input;
querying a system using the relevant criteria and modifying the relevant criteria prior to querying the chat bot based on the extracted key terms or phrases to generate a preliminary list of suggested items;
comparing the preliminary list against an inventory system to verify item availability;
processing the preliminary list using a matching technique that weights and scores items of the preliminary list based on a combination of store preferences, user preferences, and user transaction history 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
A fuzzy matching algorithm
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
A fuzzy matching algorithm
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.
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 7-9, 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (US 20240320063 A1), hereinafter Wang, in view of Rennison (US 7827125 B1), hereinafter Rennison.
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 by inspecting the natural language input for characteristics associated with conditions set by the user within the natural language input (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] – “The prompt may include a task request and additional contextual information that is useful for responding to the query.” [0035]);
querying a chat bot using the relevant criteria and modifying [embedding] the relevant criteria prior to querying the chatbot based on the extracted characteristics to generate a preliminary list of suggested items (Wang: “ the content presentation module 210 may … to generate a search query representation (e.g., an embedding) that represents characteristics of the search query. 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 using an algorithm that weights and scores items of the preliminary list based on a combination of store preferences, user preferences, and user transaction history to generate a customized list for the user (Wang: The content presentation module 210 may weight the score for an item based on the predicted availability of the item …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] – “The content presentation module 210 may use an item selection model to score items for presentation to a customer. 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 …shopping preferences, favorite items, … default retailer/retailer location, payment instrument, delivery location, or delivery timeframe. … collect the customer data … based on the customer's interactions with the online system” [0048] – “item data may include … a time that the item was last found, a time that the item was last not found (a picker looked for the item but could not find it), the rate at which the item is found, or the popularity of the item.” [ 0049] – See also [0079]); 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),
but does not specifically teach that the characteristics are key terms or phrases; or that the algorithm is a fuzzy matching algorithm.
However, Rennison teaches methods for delivering search results [Abstract], including that:
the characteristics are key terms or phrases (Rennison: “Extracting concepts from text using natural language parsing (NLP) techniques is another method commonly used. This approach uses semantic or lexical analysis to parse text into parts of speech. These lexical elements are then matched against grammar rules to extract entities from the text.” Col. 4, lines 45-55 – “The system can parse of documents into fields containing text strings and extract concepts from the fielded text strings, where the concepts are nodes in a semantic network.” Col. 12, lines 40-50 - “the term "concept" includes any type of information or representation of an idea, topic, category, classification, group, term, unit of meaning and so forth, expressed in …textual, or other forms. … A concept can also be represented by search terms” Col. 13, lines 3-15); and
the algorithm is a fuzzy matching algorithm (Rennison: “the system can use fuzzy search algorithms to compute ranked matches …and can weigh the information together to determine how "much" of the search criteria a document has” Col. 11, lines 25-35 – “The system 102 can also construct searches based on the user's input query by constructing a set of Search Criteria that can be organized into groups, and by using set of matching Concepts and a set of fuzzy search algorithms to determine a rank ordering of the matching Concepts based on a score for each matching Concept” Col. 14, lines 35-45 – “Methods for conducting fuzzy searches of the indexed semantic network include the following: 1) searching the network from criteria specified from outside the semantic network whose results are ranked and scored” Col. 12, lines 45-55 – “The weight of each ScoreCriteriaValue can be computed using either fuzzy frequency or fuzzy inverse frequency. ” Col. 49, lines 40-50).
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 processing the natural language input to extract relevant criteria by inspecting the natural language input for characteristics associated with conditions set by the user within the natural language input & processing the preliminary list using an algorithm that weights and scores items of the preliminary list based on a combination of store preferences, user preferences, and user transaction history to generate a customized list for the user, except that now it would also teach that the characteristics are key terms or phrases; and that the algorithm is a fuzzy matching algorithm, according to the teachings of Rennison. 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 accurate search results (Rennison: Col. 5).
Regarding claim 8, Wang/Rennison teach 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/Rennison teach 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/Rennison teach 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/Rennison teach 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/Rennison teach 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/Rennison teach 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/Rennison teach 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/Rennison teach 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/Rennison teach 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/Rennison teach 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).
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over Wang/Rennison, and further in view of Smith (US 20150099589 A1).
Regarding Claim 10, Wang/Rennison teach 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/Rennison 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]).
Response to Arguments
Applicant’s arguments filed 6/10/2026 have been fully considered but are not persuasive.
Claim Rejection – 35 §USC 101
Applicant argues that “claim 7 recites a specific and integrated technical process that improves the functioning of the AI-based shopping system itself.” Applicant argues that “processing the natural language input by inspecting it for key terms or phrases associated with conditions set by the user…is not a generic natural language processing step,” but “a specific technical inspection mechanism …that generic NLP or chatbot systems do not perform.”
Examiner disagrees. With reference to the rejection above, processing a user’s natural language input to identify key terms or phrases associated with user-set conditions is part of the abstract idea itself. This step is not recited in the claim to be performed by a computer, but merely that the input is received from a device and associated with an online request; these additional elements are invoked as mere instructions to apply the abstract idea to a technological environment, creating only a general linking between the data that is then examiner in the argued step and computer technology [MPEP 2106.05(f)].
Applicant further argues that “modifying the relevant criteria prior to querying the chatbot based on the extracted key terms or phrases …is a technical significant step that differentiates the claimed method from conventional chatbot-based shopping systems” because it “pre-processes and modifies the input before providing it to the chatbot,” which “is a specific technical operation that improves the accuracy and relevance of the chatbot’s response, thereby improving the operation of the AI system itself.
Examiner disagrees. Similar to the discussion above, the ability to modify input data, as recited at a high level of generality in the claim, is part of the abstract idea itself, such that any alleged improvement to the accuracy or relevance of data-processing capabilities from unspecified modification of an input to the data modification process is at best a business improvement rooted solely in the abstract idea. The additional elements, such as the data being processed by a chatbot, are invoked at a high level of generality as mere instructions to apply this abstract idea to a technological environment [MPEP 2106.05(f)].
Applicant further argues that “processing the preliminary list using a fuzzy matching algorithm that weights and scores items based on a combination of store preferences, user preferences, and user transaction history …is not a generic filtering step,” but “a specific algorithmic process -a fuzzy matching and weight scoring algorithm- that operates on the preliminary list to refine it into a customized result,” which “is a concrete, technical mechanism that improves the quality and personalization of the output in a specific way that is tied to the operation of the system.”
Examiner disagrees. Similar to the discussion above, the ability to process the preliminary list using a procedure that weights and scores items based on a combination of store preferences, user preferences, and user transaction history to generate a customized list for the user is part of the abstract idea, as identified in the rejection above. As such, any alleged improvements to quality or personalization of the list by weighting it based on preferences & history is at best a business improvement rooted solely in the abstract idea. The additional elements, such as the data being processed by fuzzy matching algorithm, are invoked at a high level of generality as mere instructions to apply this abstract idea to a technological environment [MPEP 2106.05(f)].
Applicant argues that “the specification identifies the technical problems addressed by the invention: Existing shopping technology …require users to manually scroll through extensive product lists and individually add items to their shopping carts …failing to offer personalized recommendations that fully consider individual preferences …do not interact directly with retail systems to check or access real-time inventory data, place orders, or integrate with the retailer’s checkout and scheduling systems, resulting in a fragmented and inefficient shopping experience.” Applicant argues that “the specification then identifies the technical solution: …by providing a more integrated, efficient, and user-friendly solution…[that] includes automating the assembly of ingredients and/or items based on user-input recipes, items, or events, offering intelligent substitutions and modifications, and directly interfacing with retail systems or platforms for real-time inventory and order processing.” Applicant concludes that “the inspection and modification of natural language input prior to chatbot querying, and the fuzzy matching algorithm applied to the preliminary list are the specific mechanisms by which the invention achieves this technical improvement.”
Examine disagrees. The argued problem is a business problem rather than a technological one, and, with reference to the rejection above, the alleged solution provided in the claims is rooted solely in the identified abstract idea. At best, the alleged improvements are a business solution to a business problem, with additional elements such as the data processing being performed by a generic “fuzzy matching algorithm” and data being received & presented via a generic “device” providing only a general linking to computer technology [MPEP 2106.05(f)]. In particular, the argued ability to inspect natural language for key words, modify data prior to data processing, and return a personalized list in the manner claimed and identified in the rejection above, are abstract steps that form part of a concept for personalized recommendations. The additional elements, including that data is processed by a chat bot, are invoked as mere instructions to apply this abstract idea to a technological environment [MPEP 2106.05(f)].
Applicant argues that “the Examiner’s analysis itself is conducted at a high level of generality,” which identifies additional elements “without analyzing the specific technical steps recited in the body of the claim.” Applicant argues that the rejection “reduc[es] a claim that recites specific pre-processing of natural language input, modification of query criteria, and fuzzy matching algorithmic scoring into a generic label of personalized shopping recommendations.” Applicant states that the claims “are directed to a specific technical method that uses particular algorithmic processes (inspection for key terms, modification of criteria prior to chatbot querying, and fuzzy matching with weighted scoring) to improve how the AI-based shopping system processes user input and generates output,” which is “an improvement to the technology itself, not merely an application of an abstract concept to a conventional computer environment.”
Examiner disagrees. With reference to the rejection above, the argued “technical steps” are in fact part of the abstract idea. Some of the argued steps, such as the processing of natural language input by extract key words, are entirely abstract with no connection to any additional elements. Steps such as executing a query using the criteria and modifying the criteria prior to the query, as well as processing the preliminary list using a technique that weights and scores items of the preliminary list based on a combination of store preferences, user preferences, and user transaction history to generate a customized list for the user, are similarly abstract, with additional elements such as the query being performed by a generic chat bot and the list-processing algorithm being a “fuzzy matching” algorithm, being invoked at a high level of generality as mere instructions to apply these abstract steps to a technological environment [MPEP 2106.05(f)]. Rather than improving the technology itself, the additional elements provide only a general linking of an abstract idea to a field of computer technology.
Applicant further argues with reference to Desjardins that “the specific technical steps of inspecting natural language input for user-defined conditions, modifying the query criteria before chatbot interaction, and applying a fuzzy matching weighted scoring algorithm represent improvements to how the AI processing system itself operates - not merely the use of a computer or chatbot as a tool to implement an abstract business concept.”
Examiner disagrees, and notes that the claims do not recite an “AI processing system,” but merely a method that is generally linked to a device, a chatbot, and a computer algorithm. Similar to the discussion above, the argument refers to abstract steps that form a portion of the abstract idea identified in the rejection above. Whereas Desjardins defines a specific problem unique to computer technology and claims a specific solution to that problem, the pending claims provide an abstract idea for a particular method of personalizing recommendation lists for a user, and invokes computer elements at a high level of generality as mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)]. In, particular, the argued ability for “inspecting natural language input for user-defined conditions, modifying the query criteria” before executing the query, and applying a “weighted scoring algorithm” are all limitations of the abstract idea.
Claim Rejection – 35 §USC 102
Applicant’s arguments with respect to the 102 rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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
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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
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