DETAILED ACTION
This action is in response to a filing filed on November 11th, 2024. Claims 1-20 have been examined in this application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1: Claims 1-9 is/are drawn to method (i.e., a process), and claims 10-18 is/are drawn to computer readable medium (i.e., a manufacture), and claims 19-20 is/are drawn to system (i.e., a manufacture). (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Claim 1: A method comprising:
receiving, from a client device and via an interface, a user query;
identifying one or more featured products based on the user query;
generating a prompt for input to a machine-learned generative language model, the prompt specifying at least a request related to the user query and a request to suggest the one or more featured products in association with a response to the prompt;
providing the prompt to a model serving system for execution by the machine-learned generative language model;
receiving, from the model serving system, a response generated by executing the machine-learned generative language model on the prompt, the response including at least one of the one or more featured products;
generating a query response to the user query based on the response generated by executing the machine-learned generative language model on the prompt, the query response including at least a suggestion for the at least one of the one or more featured products;
transmitting instructions, to the client device, to cause display of the generated query response to the user;
receiving and collecting data on user interactions with the query response;
and fine-tuning the machine-learned generative language model based on the collected data on user interactions with the query response.
(Examiner notes: The underlined claim terms above are interpreted as additional elements beyond the abstract idea and are further analyzed under Step 2A - Prong Two)
Under their broadest reasonable interpretation, the independent claims is/are directed to the abstract idea of organizing human activity, specifically the claim recites a receiving a user query, identifying products responsive to the query, generating a request to obtain suggested products, receiving generated product information, presenting product recommendations to the user, collecting data regarding user interactions with those recommendations, and fine-tuning the recommendation system based on that interaction data. This constitutes a commercial recommendation and advertising practice involving organizing human activity, including marketing and sales optimization, as well as evaluating information and modifying recommendations based on user preferences. Thus, the claimed subject matter is directed to an abstract idea falling within the judicial exception category of “certain methods of organizing human activity”. From applicant’s specification, the claimed invention is implemented to “Once an LLM deployed on the model serving system 150 receives the prompt, it generates a response including at least two parts. The first part includes a response to the user request, and the second part includes a suggestion for the one or more featured products in association with the response to the user request. The one or more featured products may be sponsored products. In certain instances, the first part (including the response to the user request) and the second part (including suggestions) are intertwined in the response. Especially, when the featured products are relevant to the response, integrating them seamlessly can potentially offer value to the user, making advertising opportunities less intrusive or disruptive” (see 0090 of instant specification). The claims recite a method of organizing human activity, specifically steps of recites a high-level functional workflow of receiving a user query, identifying products responsive to the query, generating a request to obtain suggested products, receiving generated product information, presenting product recommendations to the user, collecting data regarding user interactions with those recommendations, and fine-tuning the recommendation system based on that interaction data. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
The dependent claims further elaborate on aspects of the same abstract idea by adding features relating to ranking, bidding, auction-based selection, hyperlink presentation, recipe generation, multiple-user handling, and catalog mapping. Claims 2, 11, and 20 recite generating relevance scores and selecting products above a threshold, which constitutes evaluating and ranking information, an abstract mental process and a fundamental practice in recommendation systems. Claims 3, 8, 12, 17 and related claims recite receiving bid values and performing auction-based selection of products, which constitutes a fundamental economic practice involving advertising placement and competitive bidding. Auction-based selection and sponsored ranking are longstanding commercial practices. Claims 4, 5, 6, 13, 14, and 18 recite presenting recommendations in textual form, generating recipe pages, and creating hyperlinks, which amount to formatting and presenting information for display, an activity that falls within organizing and presenting information. Claims 7, 15, and 16 recite handling multiple users and mapping products to catalogs, which reflect managing user-specific data and matching items within a commercial system, another form of organizing human activity and information processing. Accordingly, the dependent claims do not introduce a new technological concept but instead add further refinements to the underlying abstract idea of product recommendation, ranking, and advertising optimization based on user input and interaction data. Accordingly, claims 1-20 are directed to an abstract idea under 35 U.S.C. §101, using conventional communication and recordkeeping techniques. As such, the claims are directed to an abstract idea involving certain methods of organizing human activity and mental processes, which falls within a judicial exception under 35 U.S.C. §101.
Independent claim(s) 10 and 19 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
As such, the Examiner concludes that claims 1 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using a client device and via an interface, machine-learned generative language model, model serving system, processors, etc. (Claims 1, 10, and 19) is/are equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of using a client device and via an interface, machine-learned generative language model, model serving system, processors, etc. (Claims 1, 10, and 19, and dependent claims 2-9, 11-18, and 20) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computerized environments (e.g., receive, identify, generate, provide, transmit, collect, fine-tune, etc. steps performed by a client device and via an interface, machine-learned generative language model, model serving system, processors, etc.). This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The recited additional element(s) steps of such as receiving a user query from a client device, providing a prompt to a model serving system, receiving a generated response, transmitting instructions to cause display of the query response, and collecting user interaction data merely constitute data gathering and output presentation activities performed before or after the core abstract idea of generating and refining product recommendations based on user input. Such data reception, transmission, display, and post-solution collection steps are routine and conventional computer functions that do not meaningfully limit the abstract idea. The claim does not recite any specific improvement to computer functionality, network architecture, or machine-learning model architecture, but instead uses generic computing components (client device, interface, model serving system, machine-learned model) as tools to implement the abstract idea. Similarly, the step of fine-tuning the machine-learned generative language model based on collected interaction data merely reflects updating or adjusting a recommendation model using user feedback, which is part of the abstract idea itself (i.e., refining recommendations based on user behavior) rather than a technological improvement, which constitutes insignificant extra-solution activity without adding any technical improvement (Independent Claims 1, 10, and 19), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. (See MPEP 2106.05(g)).
Dependent claims 2-9, 11-18, and 20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed above in “Step 2A – Prong 2”, the identified additional elements in independent Claims 1, 10, and 19, and dependent claims 2-9, 11-18, and 20 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
The recited additional element(s) of outputting a plurality of tokens, presenting the communication record in a standardized form and displaying on a graphical user interface (Independent Claims 1, 10, and 19), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea), i.e. these steps merely perform the steps of receiving a user query from a client device, providing a prompt to a model serving system, receiving a generated response, transmitting instructions to cause display of the query response, and collecting user interaction data merely constitute data gathering and output presentation activities performed before or after the core abstract idea of generating and refining product recommendations based on user input, which is similar to “Receiving or transmitting data over a network, e.g., using the Internet to gather data”, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), “Storing and retrieving information in memory”, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; “Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price”, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93, Determining an estimated outcome and setting a price, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here) (See MPEP 2106.05(d) (II)).
This conclusion is based on a factual determination. Applicant’s own disclosure at paragraph [0071-0072] acknowledges that “the recommendations are in the form of a list of potential recipes the ingredients can fulfill, and a list of additional ingredients to fulfill each recipe. The content presentation module 210 may present each suggested recipe and the list of additional ingredients for fulfilling the recipe to the customer. The content presentation module 210 may allow the customer to automatically place one or more additional ingredients in the basket of the customer …The order management module 220 that manages orders for items from customers. The order management module 220 receives orders from a customer client device 100 and offers the orders to pickers for service based on picker data. For example, the order management module 220 offers an order to a picker based on the picker’s location and the location of the retailer from which the ordered items are to be collected” This additional element therefore do not ensure the claim amounts to significantly more than the abstract idea.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
The dependent claims 2-9, 11-18, and 20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
Specifically, claims 2, 11, and 20 recite generating relevance scores and selecting products based on a threshold. Assigning scores to items, ranking items according to those scores, and applying thresholds to filter results were conventional techniques in information retrieval systems, search engines, and recommendation platforms. Such ranking and filtering mechanisms merely apply conventional mathematical evaluation and comparison techniques to implement the abstract idea of recommending products. Claims 3, 8, 12, and 17 recite receiving bid values from product providers and performing an auction process to select products. Competitive bidding, sponsored search ranking, and auction-based advertisement placement were widely used commercial practices implemented on generic computer systems prior to the effective filing date. Selecting items for display based on bid amounts and competitive ranking constitutes a conventional monetization mechanism in online advertising and does not add a technological improvement to computer functionality. Claims 4, 5, 6, 13, 14, and 18 recite generating recipe pages, producing textual content incorporating product suggestions, creating hyperlinks, and formatting output for display. Presenting information in webpage form, embedding hyperlinks, and generating textual recommendation content are routine output presentation techniques performed by generic web servers and client devices. These limitations merely specify the format in which the abstract idea is communicated to the user and do not provide a technical improvement. Claims 7, 15, and 16 recite handling multiple user queries and mapping products to a catalog of an online system. Managing multiple users, associating items with entries in a product catalog, and filtering results based on stored user preferences were conventional database and e-commerce operations performed using generic computing infrastructure. When considered as an ordered combination, the dependent claims simply apply conventional scoring, bidding, ranking, catalog matching, and presentation techniques to implement the abstract idea of generating and refining product recommendations. The claims do not recite any specialized hardware, unconventional data structure, specific machine-learning architecture, or technical improvement to computer performance. Instead, they rely on generic computing components performing their expected functions. Accordingly, the additional limitations of the dependent claims do not amount to significantly more than the abstract idea and therefore fail to provide an inventive concept under Step 2B. Because these elements do not solve a specific technical problem or offer a technical improvement over existing systems, they are viewed as merely "applying" the abstract idea on a generic computer, thus failing to provide a practical application that would render the claims patent-eligible, and therefore do not add an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter.
When viewed as an ordered combination, the additional elements of claims 2-9, 11-18, and 20 merely instruct to implement the abstract idea using generic computer components to collect, store, represent, and display information. The claims do not recite any unconventional arrangement of elements, nor do they effect an improvement to computer functionality or another technical field and therefore fail to integrate the abstract concept into a practical application and it is recited at a high level of generality and does not integrate the judicial exception into a practical application.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-20 are not eligible subject matter under 35 USC 101.
Claim Rejections - 35 USC § 103
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:
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 of this title, 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.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4-7, 9-11, 13-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. 20180293644 (“Allen”) in view of U.S. Pub. 20240202452 (“Schillace”).
As per claims 1, 10, and 19, Allen discloses, receiving, from a client device and via an interface, a user query (Examiner interprets that the prior art discloses receiving a shopping list from a mobile device and processing the entries using NLP and taxonomy classification i.e. The shopping list entries represent natural language intent (e.g., “make chocolate chip cookies”) and therefore constitute a user query received via an interface) (“Shopping list analysis program 106 receives a shopping list from mobile device 110 and performs natural language processing techniques, including taxonomy classification, to classify the shopping list entries into a taxonomic hierarchy of categories, coupled with a confidence level for each category result”) (0021, 0019, Fig. 1);
identifying one or more featured products based on the user query (Examiner interprets that the prior art discloses identifying product categories, brands, stores, and specific ingredient items using taxonomy hierarchies and entity analysis i.e. determines ingredient-level products from high-level intent (e.g., identifying ingredients necessary for homemade chocolate chip cookies). These identified ingredients and products correspond to the claimed “featured products” which is again are reasonably interpreted as specific ingredients, brands, or SKUs mapped from the high-level intent. Allen uses taxonomic hierarchies and entity analysis to determine product categories, stores that carry the products, and specific product names, brands, or locations, and it leverages expert sources or user history to determine ingredients needed for recipes from the high-level concept (for example, “make chocolate chip cookies”)) (“hierarchies can be used to determine the category of products that the user wants to buy, and therefore identify the stores that carry the shopping list products, based on available inventory data from the stores. In other embodiments, entity analysis may be performed to identify product names, brands, stores, and/or locations contained in the shopping list. NLP and NLU techniques may also be used in cognitive capabilities to understand high level, result based shopping list entries, in which shopping list items are determined by use of expert sources and/or user history, such as determining ingredients for a recipe.”) (0012, 0023);
generating a prompt for input to a machine-learned generative language model (Examiner notes that a machine-learned generative language model is reasonably interpreted as any machine-learned model capable of generating textual output in response to structured input. Allen discloses use of supervised/unsupervised ML and neural network techniques for determining ingredients lists and generating shopping lists outputs, The generation of ingredient lists and store recommendations constitutes generation of textual content responsive to user input, which meets the BRI of generative language model) (“NLP and NLU techniques may also be used in cognitive capabilities to understand high level, result based shopping list entries, in which shopping list items are determined by use of expert sources and/or user history, such as determining ingredients for a recipe … using supervised and/or unsupervised machine learning techniques, as well as neural network techniques, and continue to learn as additional information becomes available, as well as when goals or requirements change. Cognitive computing applications may also continuously interact with users and other computers, devices, data sources, and services (e.g., social media). Cognitive computing also enables applications to understand contextual elements by extracting information from a variety of source”) (0012-0014), the prompt specifying at least a request related to the user query and a request to suggest the one or more featured products in association with a response to the prompt (Examiner interprets the structured query sent to the expertise source as a prompt because it is a structured input that specifies first a request related to user query (e.g., make chocolate chip cookies), and second request to generate specific product outputs (ingredient item). The claimed prompt therefore under BRI, any structured input provided to a machine-learned model to generate output as a prompt) (“In some embodiments of the present invention, shopping list analysis program 106 also queries subject expertise sources 122 to accurately determine individual items associated with high level shopping list entries. For example, a shopping list may include an entry of a high level, result based concept such as “make chocolate chip cookies.” In this example, shopping list analysis program 106 identifies that the user wishes to make homemade chocolate chip cookies instead of buying premade chocolate chip cookies. Shopping list analysis program 106 identifies the hierarchy of categories related to the entry and queries one of the plurality of subject expertise sources related to food recipes to identify the ingredients necessary for homemade chocolate chip cookie”) (0027);
providing the prompt to a model serving system for execution by the machine-learned generative language model (Examiner notes that the combination of shopping list analysis program executing ML techniques and interacting with external systems constitutes a model serving system because it receives model input, executes ML processing, and returns generated output to client devices i.e. receives model input, executes ML processing, and returns generated textual output) (“NLP and NLU techniques may also be used in cognitive capabilities to understand high level, result based shopping list entries, in which shopping list items are determined by use of expert sources and/or user history, such as determining ingredients for a recipe … using supervised and/or unsupervised machine learning techniques, as well as neural network techniques, and continue to learn as additional information becomes available, as well as when goals or requirements change. Cognitive computing applications may also continuously interact with users and other computers, devices, data sources, and services (e.g., social media). Cognitive computing also enables applications to understand contextual elements by extracting information from a variety of source”) (0012-0014);
receiving, from the model serving system, a response generated by executing the machine-learned generative language model on the prompt (Examiner interprets list of ingredients returned by the expert source is interpreted as the generated response, these ingredients constitute the featured products required by the prompt i.e. identifying ingredients necessary for a recipe and generating store recommendations based on those ingredients) (“Shopping list analysis program 106 identifies the hierarchy of categories related to the entry and queries one of the plurality of subject expertise sources related to food recipes to identify the ingredients necessary for homemade chocolate chip cookies. Shopping list analysis program 106 may then provide store recommendations based on the identified ingredients.”) (0027, Fig. 1), the response including at least one of the one or more featured products (Examiner interprets that this example, where shopping list analysis program identifies that the user wishes to make homemade chocolate chip cookies instead of buying premade chocolate chip cookies. Shopping list analysis program identifies the hierarchy of categories related to the entry and queries one of the plurality of subject expertise sources related to food recipes to identify the ingredients necessary for homemade chocolate chip cookies i.e. the ingredient list constitutes the response generated by execution of the machine-learned model, and includes the identified featured products) (“In some embodiments of the present invention, shopping list analysis program 106 also queries subject expertise sources 122 to accurately determine individual items associated with high level shopping list entries. For example, a shopping list may include an entry of a high level, result based concept such as “make chocolate chip cookies.” In this example, shopping list analysis program 106 identifies that the user wishes to make homemade chocolate chip cookies instead of buying premade chocolate chip cookies. Shopping list analysis program 106 identifies the hierarchy of categories related to the entry and queries one of the plurality of subject expertise sources related to food recipes to identify the ingredients necessary for homemade chocolate chip cookie”) (0027);
generating a query response to the user query based on the response generated by executing the machine-learned generative language model on the prompt, the query response including at least a suggestion for the at least one of the one or more featured products (Examiner interprets store recommendations based on identified ingredients are interpreted as the final query response containing product suggestions i.e. in this example, where shopping list analysis program identifies that the user wishes to make homemade chocolate chip cookies instead of buying premade chocolate chip cookies. Shopping list analysis program identifies the hierarchy of categories related to the entry and queries one of the plurality of subject expertise sources related to food recipes to identify the ingredients necessary for homemade chocolate chip cookies i.e. generates store recommendations and ordered product lists based on identified ingredients, then these output constitutes a query response including suggestions for featured products) (“In some embodiments of the present invention, shopping list analysis program 106 also queries subject expertise sources 122 to accurately determine individual items associated with high level shopping list entries. For example, a shopping list may include an entry of a high level, result based concept such as “make chocolate chip cookies.” In this example, shopping list analysis program 106 identifies that the user wishes to make homemade chocolate chip cookies instead of buying premade chocolate chip cookies. Shopping list analysis program 106 identifies the hierarchy of categories related to the entry and queries one of the plurality of subject expertise sources related to food recipes to identify the ingredients necessary for homemade chocolate chip cookie”) (0027, 0023, 0041, Fig. 1);
transmitting instructions, to the client device, to cause display of the generated query response to the user (Examiner interprets transmitting ordered product lists, alerts, hyperlinks, and store location information to a mobile device for display) (“shopping list analysis program 106 may send an ordered list of the available products, including the store, and store location, to a mobile device 110 to be displayed in user interface 112. In another embodiment, shopping list analysis program 106 may send an alert to the user, while providing means for the user to view the available products (such as a hyperlink). In yet another embodiment, shopping list analysis program 106 may use a mapping service on mobile device 110 to display a location of a store offering the available products. In still another embodiment, shopping list analysis program 106 may notify the user of the availability of a product in a store that is located along a user provided route”) (0041, 0039).
Allen specifically doesn’t disclose, receiving and collecting data on user interactions with the query response and fine-tuning the machine-learned generative language model based on the collected data on user interactions with the query response, however Schillace discloses, receiving and collecting data on user interactions with the query response (Examiner interprets that cited art discloses tracking chat history, browsing history and purchase history which is interpreted as collecting data on the user interactions with the AI’s response i.e. ML models trained on user-specific data including chat history, browsing history, purchase history, and previous inputs) (“Additionally, the model repository may contain one or more ML models that are user specific, meaning they have been trained on information specifically related to a certain user (e.g., chat history, browsing history, purchase history, previous inputs, user preferences from an online profile, other data relating specifically to the user, etc.)”) (0028);
and fine-tuning the machine-learned generative language model based on the collected data on user interactions with the query response (Examiner notes that tracking chat history, browsing history, and purchase behavior constitutes collecting data on user interactions with system responses and further the training or updating ML models using user-specific interaction data constitutes fine-tuning the machine-learned model) (“one or more prompts encompass the semantic context of the task objective and task request so that the ML model can generate model output responsive to the requested task and/or intent without requiring additional training or fine-tuning of the model prior to generating model output responsive to the task or intent. It will be appreciated that a prompt may be comprised of a plurality of prompt templates. A prompt template may include any of a variety of data, including, but not limited to, natural language, image data, audio data, video data, and/or binary data, among other examples. In examples, the type of data may depend on the type of ML model that will be leveraged to respond to the received input”) (0024-0028).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for receiving a user query, identifying one or more featured products based on the user query, generating a prompt for input to a machine-learned generative language model, the prompt specifying at least a request related to the user query and a request to suggest the one or more featured products in association with a response to the prompt, providing the prompt to a model serving system for execution by the machine-learned generative language model, receiving a response generated by executing the machine-learned generative language model on the prompt, the response including at least one of the one or more featured products, generating a query response to the user query based on the response generated by executing the machine-learned generative language model on the prompt, the query response including at least a suggestion for the at least one of the one or more featured products; transmitting instructions to cause display of the generated query response to the user, as taught by Allen, receiving and collecting data on user interactions with the query response and fine-tuning the machine-learned generative language model based on the collected data on user interactions with the query response, as taught by Schillace for the purpose to allows a general ML model to be applied to a plurality of applications without the need for expensive and time-consuming training to fine-tune the ML model to improve relevance of the featured products.
As per claims 2, 11, and 20, Allen discloses, generating a respective relevance score for each of a set of candidate featured products, the relevance score indicating a level of relevance of a respective featured product to the user query (“shopping list analysis program 106 searches the inventory of nearby stores for “soft drinks” products, as the inferior rank in the taxonomic hierarchy … business sources 114 may include the actual store website including product detail and inventory information. In still other embodiments, business sources 114 may obtain data from general purpose search engines, specialized search engines, and/or store websites to compile an inventory per store location to be stored in database 116. Database 116 can be implemented with a type of storage device capable of storing product inventory and description data, such as a hard disk drive or solid state drive on a database server, and configuration files that can be accessed and utilized by business sources 114”) (0023-0026);
and selecting the one or more featured products having relevance scores above a threshold for inclusion in the prompt (Examiner interprets determining a confidence level for each category result i.e. confidence level as a relevance score and the determination of whether the fit is poor, good, or best as applying a threshold) (“the confidence level must exceed a predefined threshold indicating a level of confidence that the category accurately aligns with the entry item of the shopping list. If shopping list analysis program 106 determines the confidence level to exceed the predefined threshold shopping list analysis program 106 initiates a search for nearby stores that include in their inventory, categories of items aligning with the shopping list entries. Therefore, if the confidence level of a determined category does not exceed the predefined threshold, the category is removed from consideration while searching for nearby stores. In some embodiments, the confidence level is calculated in accordance to the term frequency-inverse document frequency (TF-IDF) or any other statistical measure useful to evaluate the importance of a word in relation to a document in a collection or corpus …”) (0032-0034, 0021).
As per claims 4 and 13, Allen discloses, wherein generating the query response comprises generating a recipe page including a list of products for fulfilling the recipe, wherein the list of products includes the one or more featured products (Examiner interprets the combination of the identified recipe type intent, obtaining ingredient items, and generating recommendations/product lists is interpreted as generating recipe page including a list of products to fulfill the recipe with those products being the featured products) (“shopping list analysis program 106 identifies that the user wishes to make homemade chocolate chip cookies instead of buying premade chocolate chip cookies. Shopping list analysis program 106 identifies the hierarchy of categories related to the entry and queries one of the plurality of subject expertise sources related to food recipes to identify the ingredients necessary for homemade chocolate chip cookies. Shopping list analysis program 106 may then provide store recommendations based on the identified ingredients. Although this example is related to food recipes, shopping list analysis program 106 may also query other domain specific knowledge websites such as do it yourself websites, recipe websites, fashion websites, music websites, and any other website that shows or teaches subject expertise in an area related to high level shopping list entries”) (0027).
As per claims 5 and 14, Allen discloses, wherein generating the query response comprises generating textual content incorporating the suggestion for the one or more featured products in the textual content (Examiner interprets the display of product list is interpreted as textual content that incorporates suggestions for the featured products directly within the response presented to the user) (“To identify the nearby brick and mortar stores that carry the desired products, shopping list analysis program 106 receives the location of mobile device 110 and queries business sources 114 to determine the location and inventory of nearby brick and mortar stores that carry the categories of products described in the shopping list entries. Relating to a previous example, a shopping list including an item such as "Soft Drink A" or "Soft Drink B" may return the taxonomic hierarchy "/food and drink/nonalcoholic beverages/soft drinks" with a corresponding confidence level of 0.80. In an embodiment of the present invention, the taxonomic ranks in the hierarchy of categories may be used to narrow store recommendations”) (0023, Fig. 1).
As per claims 6 and 15, Allen discloses, receiving a second user query from a second client device (Examiner interprets the ability to receive multiple shopping lists or prompts from different users/devices is interpreted as receiving a “second user query” from second client device) (“Shopping list analysis program 106 may also provide store recommendations based on user preferences and/or the purchase history of the user.” and “shopping list analysis program 106 queries a plurality of user preference sources 118 to identify user provided preferences or a user preference that can be inferred by past user behavior. Shopping list analysis program 106 may then provide store recommendations based on the user preferences”) (0040 and 0048, fig. 1);
generating a second prompt for input to the machine-learned generative language model, the second prompt specifying at least a second request related to the second user query and a second request to include one or more consumer packaged good (CPG) products in a response (Examiner interprets new structured query for the second user, including the second intent (e.g., another recipe or shopping need) and a constraint that certain products(CPG items) be identified, is interpreted as a “second prompt” that requests inclusion of CPG products in the response) (“In some embodiments of the present invention, shopping list analysis program 106 also queries subject expertise sources 122 to accurately determine individual items associated with high level shopping list entries. For example, a shopping list may include an entry of a high level, result based concept such as “make chocolate chip cookies.” In this example, shopping list analysis program 106 identifies that the user wishes to make homemade chocolate chip cookies instead of buying premade chocolate chip cookies. Shopping list analysis program 106 identifies the hierarchy of categories related to the entry and queries one of the plurality of subject expertise sources related to food recipes to identify the ingredients necessary for homemade chocolate chip cookie”) (0027, 0023, 0041, Fig. 1);
and receiving a second response generated by executing the machine-learned generative language model on the second prompt, the second response including the one or more CPG products (Examiner interprets the ingredients/products which can be CPG items returned for the second query are interpreted as the one or more CPG products included in the second response generated by the model) (“In some embodiments of the present invention, shopping list analysis program 106 also queries subject expertise sources 122 to accurately determine individual items associated with high level shopping list entries. For example, a shopping list may include an entry of a high level, result based concept such as “make chocolate chip cookies.” In this example, shopping list analysis program 106 identifies that the user wishes to make homemade chocolate chip cookies instead of buying premade chocolate chip cookies. Shopping list analysis program 106 identifies the hierarchy of categories related to the entry and queries one of the plurality of subject expertise sources related to food recipes to identify the ingredients necessary for homemade chocolate chip cookie”) (0027, 0023, 0041, Fig. 1).
As per claims 7 and 16, Allen discloses, generating a second query response to the second user query by mapping the one or more CPG products to one or more products in a catalog of an online system (Examiner interprets) (“shopping list analysis program 106 may have identified multiple available products in stores nearby the user. Shopping list analysis program 106 retrieves the information for each of the available products and compares product attributes (such as, price, color, status, sizes, brands, among others) with respect to the user preferences. Referring to a previously presented example, if shopping list analysis program 106 identifies that the user routinely buys gluten free products, shopping list analysis program 106 may narrow the refined product list to gluten free hot dog buns. Shopping list analysis program 106 may also provide store recommendations based on user preferences and/or the purchase history of the user. For example, if the user routinely buys a category of products in a particular store, shopping list analysis program 106 may narrow product recommendations to only those products available at the user preferred store. Product attributes may also be used to remove an available product for failure to meet user preferences and/or previously provided feedback from the user.”) (0040);
and transmitting instructions to the second client device to cause presentation of the one or more mapped products to a second user (“shopping list analysis program 106 may send an ordered list of the available products, including the store, and store location, to a mobile device 110 to be displayed in user interface 112. In another embodiment, shopping list analysis program 106 may send an alert to the user, while providing means for the user to view the available products (such as a hyperlink). In yet another embodiment, shopping list analysis program 106 may use a mapping service on mobile device 110 to display a location of a store offering the available products. In still another embodiment, shopping list analysis program 106 may notify the user of the availability of a product in a store that is located along a user provided route”) (0041, 0039).
As per claims 9 and 18, Allen discloses, wherein generating the suggestion for the one or more featured products includes creating hyperlinks associated with the products, allowing direct interaction with the featured products within the response (Examiner interprets means for user to view the available products such as hyperlink is interpreted as creating hyperlinks associated with the products, allowing direct user interaction with the featured products within the response) (“shopping list analysis program 106 may send an alert to the user, while providing means for the user to view the available products (such as a hyperlink). In yet another embodiment, shopping list analysis program 106 may use a mapping service on mobile device 110 to display the nearby stores. In still another embodiment, shopping list analysis program 106 may notify that availability of a product along a predefined, user provided route”).
Claims 3, 8, 12, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. 20180293644 (“Allen”) in view of U.S. Pub. 20240202452 (“Schillace”) in view U.S. Pub. 20080249991 (“Valz”).
As per claims 3 and 12, Allen specifically doesn’t disclose, receiving a bid value from each of a plurality of product providers that offer a set of candidate featured products; and selecting the one or more featured products having bid values above a threshold for inclusion in the prompt, however Valz discloses, receiving a bid value from each of a plurality of product providers that offer a set of candidate featured products (Examiner interprets The advertiser bid amounts constitute the claimed “bid values” received from a plurality of product providers, because each advertiser provides a monetary value associated with a candidate product or listing that competes for presentation in response to a user query i.e. the bid/money associated with an item in the sponsored search system is interpreted as a bid value received from a product provider (advertiser) for candidate featured products corresponding to query keywords) (“when a search query is received at the information web server 106, the query is provided to the search engine 124 to identify matching search listings of the information database 108. The matching search listings are collected in a set of search results … the bid amount adjustment engine 126 obtains from the user preference and navigation information system 112 information about the current user interests within the online sponsored search system as developed by the user preference and navigation information system 112. This information may be used by the bid amount adjustment engine 126 to automatically adjust the bids or money amounts of items in the information database 108. When the search engine 124 then orders the search results into a search results listing, the conventional process of ordering according to bid amount may be maintained. Alternatively, the bid amount adjustment engine 126 may provide the information about the current user interests to the search engine … the set of search listings is ordered at least in part based on the information about current user interests within the sponsored search system 100. The result is a dynamic pricing model for the system 100. Instead of being fixed or varying only in response to advertiser bid adjustments, the system automatically and dynamically adjusts bid prices in the sponsored search system based on relative popularity or interest levels of users of the system”) (0044-0046);
and selecting the one or more featured products having bid values above a threshold for inclusion in the prompt (Examiner interprets a bid threshold because only listings with sufficiently high effective bid amounts are selected for inclusion and/or prominent placement in the sponsored results listing. Listings with bid amounts below competing levels are not selected for display or receive reduced visibility. Therefore, selecting listings based on sufficiently high bid amounts corresponds to selecting products having bid values above a threshold i.e. selecting items with sufficiently high effective bids to appear in the sponsored result list is interpreted as selecting featured products with bid values above a threshold for inclusion in the prompt or response content) (“selection of the subject matter listings to be included on the web page is based on money amounts bid by advertisers associated with the respective search listings. In general, larger money amounts or bids are rewarded with more prominent positioning, to increase the likelihood that the advertisement will be subsequently clicked by the user viewing the web page”) (0049, 0044-0046).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for receiving a user query, identifying one or more featured products based on the user query, generating a prompt for input to a machine-learned generative language model, the prompt specifying at least a request related to the user query and a request to suggest the one or more featured products in association with a response to the prompt, providing the prompt to a model serving system for execution by the machine-learned generative language model, receiving a response generated by executing the machine-learned generative language model on the prompt, the response including at least one of the one or more featured products, generating a query response to the user query based on the response generated by executing the machine-learned generative language model on the prompt, the query response including at least a suggestion for the at least one of the one or more featured products; transmitting instructions to cause display of the generated query response to the user, as taught by Allen, fine-tuning the machine-learned generative language model based on the collected data on user interactions with the query response, as taught by Valz for the purpose to adapt sponsored results based on observed user preferences and by matching search listings corresponding to a query and associated with a bid amount provided by the advertiser.
As per claims 8 and 17, Allen discloses, mapping a CPG product to a set of candidate products (Examiner interprets) (“shopping list analysis program 106 may have identified multiple available products in stores nearby the user. Shopping list analysis program 106 retrieves the information for each of the available products and compares product attributes (such as, price, color, status, sizes, brands, among others) with respect to the user preferences. Referring to a previously presented example, if shopping list analysis program 106 identifies that the user routinely buys gluten free products, shopping list analysis program 106 may narrow the refined product list to gluten free hot dog buns. Shopping list analysis program 106 may also provide store recommendations based on user preferences and/or the purchase history of the user. For example, if the user routinely buys a category of products in a particular store, shopping list analysis program 106 may narrow product recommendations to only those products available at the user preferred store. Product attributes may also be used to remove an available product for failure to meet user preferences and/or previously provided feedback from the user.”) (0040).
Allen specifically doesn’t disclose, receiving a bid value for each candidate product in the set of candidate products; and performing an auction process to select a product from the set of candidate products having a bid value above a threshold, however Valz discloses, receiving a bid value for each candidate product in the set of candidate products (Examiner interprets that each advertiser associated with a search listing submits a monetary bid amount corresponding to keywords, and these bid amounts are stored in the information database and used to determine ranking. The advertiser bid amounts constitute the claimed “bid values” received from a plurality of product providers, because each advertiser provides a monetary value associated with a candidate product or listing that competes for presentation in response to a user query) (“when a search query is received at the information web server 106, the query is provided to the search engine 124 to identify matching search listings of the information database 108. The matching search listings are collected in a set of search results … the bid amount adjustment engine 126 obtains from the user preference and navigation information system 112 information about the current user interests within the online sponsored search system as developed by the user preference and navigation information system 112. This information may be used by the bid amount adjustment engine 126 to automatically adjust the bids or money amounts of items in the information database 108. When the search engine 124 then orders the search results into a search results listing, the conventional process of ordering according to bid amount may be maintained. Alternatively, the bid amount adjustment engine 126 may provide the information about the current user interests to the search engine … the set of search listings is ordered at least in part based on the information about current user interests within the sponsored search system 100. The result is a dynamic pricing model for the system 100. Instead of being fixed or varying only in response to advertiser bid adjustments, the system automatically and dynamically adjusts bid prices in the sponsored search system based on relative popularity or interest levels of users of the system”) (0044-0046);
and performing an auction process to select a product from the set of candidate products having a bid value above a threshold (Examiner interprets sponsored search system constitutes an auction process because multiple advertisers compete by submitting bid amounts associated with candidate products, and the system selects and ranks products based on those competing bid amounts. The competitive ordering of listings based on bid magnitude inherently reflects an auction mechanism in which higher bids prevail over lower bids. Therefore, Valz discloses performing an auction process to select products having bid values above a competitive threshold for inclusion in the results listing) (“selection of the subject matter listings to be included on the web page is based on money amounts bid by advertisers associated with the respective search listings. In general, larger money amounts or bids are rewarded with more prominent positioning, to increase the likelihood that the advertisement will be subsequently clicked by the user viewing the web page”) (0049).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for receiving a user query, identifying one or more featured products based on the user query, generating a prompt for input to a machine-learned generative language model, the prompt specifying at least a request related to the user query and a request to suggest the one or more featured products in association with a response to the prompt, providing the prompt to a model serving system for execution by the machine-learned generative language model, receiving a response generated by executing the machine-learned generative language model on the prompt, the response including at least one of the one or more featured products, generating a query response to the user query based on the response generated by executing the machine-learned generative language model on the prompt, the query response including at least a suggestion for the at least one of the one or more featured products; transmitting instructions to cause display of the generated query response to the user, as taught by Allen, receiving a bid value for each candidate product in the set of candidate products; and performing an auction process to select a product from the set of candidate products having a bid value above a threshold, as taught by Valz for the purpose to adapt sponsored results based on observed user preferences and by matching search listings corresponding to a query and associated with a bid amount provided by the advertiser.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US. Pat. 11947912 (“Dong”).
Dong outlines system for determining named entity recognition tags. In various examples, first input data representing a natural language input may be determined. In some examples, a first machine learned model may determine first data comprising a first encoded representation of the first input data. In various examples, second data representing a grouping of text of the first input data may be determined based at least in part on the first data. In some examples, first entity data may be determined by searching a memory layer using the second data. In at least some examples, the first entity data and the first data may be combined to generate third data. In various examples, output data comprising a predicted named entity recognition tag may be generated for the grouping of text based at least in part on the third data.
26. Any inquiry concerning this communication or earlier communications from the examiner should be directed to GAUTAM UBALE whose telephone number is (571)272-9861. The examiner can normally be reached Mon-Fri. 7:00 AM- 6:30 PM PST.
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/GAUTAM UBALE/
Primary Examiner, Art Unit 3689