DETAILED ACTION
The present application is being examined under the pre-AIA first to invent provisions.
Status of Claims
This communication is a first office action non-final rejection on the merits. Claim(s) 1-20, as filed on 05/23/2025, are currently pending and have been fully considered below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/26/2025 and 06/26/2025 are being considered by the examiner.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and thus do not satisfy the criteria for subject matter eligibility.
Step 1
Claim(s) 1 and 12-13 fall(s) in two of the four statutory categories of invention.
Step 2A Prong One: Yes
The limitations of claim(s) 1 and 12-13 recite(s) concept(s) of items recommendations, which falls into the grouping of Certain Methods of Organizing Human.
Claims 1 and 13 “
Claims: 1 and 12: receiving,
accessing,
generating,
providing,
receiving,
outputting, h the food delivery system.
The limitations of claims 1, 12-13 recite concepts of items recommendations, which falls into the grouping of Certain Methods of Organizing Human Activity. More specifically, the claim language recites concepts that receives data (A, B, E), generate data (C), transmitting data (D, F), and thus are considered commercial practice known in the retail business.
Claims 1-20 recite an abstract idea.
Step 2A Prong Two: No
Claims 1, 12-13 additional elements are:
Claim(s) 1, 12-13: “by a computing system with one or more processors”, “machine-learned large language”;
Claim 12 “computing system, comprising: one or more processors; and one or more non-transitory, computer-readable media storing instructions that are executable by the one or more processors to cause the computing system to perform operations, the operations comprising”;
The claimed additional elements that perform limitations A, B, E are claimed at a high level of generality and are considered nothing more than merely data gathering data / data receive, and thus are considered nothing more than insignificant extra-solution activity; the additional elements that perform limitations C is claimed at a high level of generality and is considered data generated without the recitation of technological improvement, and thus are considered generality linking the use of the judicial exception to a particular technological environment and/or field of use; the additional elements that perform limitation D and F are claimed at a high level of generality and is considered nothing more than data transmitted, and thus are mere instructions to implement an abstract idea on a computer; When view in combination, the additional elements merely describe how to generally “apply” the abstract idea in a generic or general-purpose computer, and generality links the use of the judicial exception to a particular technological environment or field of use, and thus do not integrate the abstract idea into a practical application, and claim(s) 1 and 12-13 are directed to the judicial exception.
Claims 1-20 recite an abstract idea.
Step 2B: No
As discussed with respect to Step 2A Prong Two, the additional elements in the claims generally linking the use of the judicial exception to a particular technological environment or field of use (i.e., computer technology) such that they amount to no more than mere instructions to apply the judicial exception using generic computer components. The same analysis applies here in 2B, i.e., does not recite any additional element or combination of elements that amounts to significantly more than the selected exception.
Further, considered as an ordered combination, the additional elements of Applicants' claims add nothing that is not already present when the steps are considered separately. The claimed invention does not focus on an improvement in computers as tools, but rather certain independently abstract ideas of infrastructure management to collect data, receive data, and generate reports that use computers as tools. {Elec. Power, 830 F.3d at 1354). (Step 2B: NO).
Further, the Office have found that receiving and transmitting data over the network is not enough to be patent-eligible, see MPEP 2106.05(d), that gathering data is not enough is not enough to be patent-eligible, see MPEP2106.05(g). The processing data is not enough is not enough to be patent-eligible, 2106.05(f), 2106.05(g).
Even when the steps are considered in combination, did not amount to an inventive concept.
As for dependent claims 2-11, 14-20, the claims merely recite limitations that further narrow the abstract idea recited on claims 1 and 13, and thus fail to amount significantly more.
Therefore, claims 1-20 are ineligible.
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, 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.
Claim(s) 1-7, 12-16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Canberk et al. (US 20250299668 A1, hereinafter Canberk) in view of Boyd et al.(US 20250315875 A1, hereinafter Boyd).
Regarding claim(s) 1 and 12-13, Canberk discloses:
Claims 1 and 13 “A computer-implemented method, the method comprising:”, claim 12: “A computing system, comprising: one or more processors; and one or more non-transitory, computer-readable media storing instructions that are executable by the one or more processors to cause the computing system to perform operations, the operations comprising:”, Figure 1-9;
receiving, by a computing system with one or more processors, a user query, wherein the user query is associated with a food delivery system; (para. 70-71 audio is captured; para. 53-64 - order food delivery when the user expresses the intent to dine out)
accessing, by the computing system, contextual data for the user query; (Para. 23-25 - Convert speech to text by preserving the user context; The context, typically consisting of the converted text and potentially additional conversational history, provides the necessary background information for the LLM to make this determination; para. 53-64 - order food delivery when the user expresses the intent to dine out; Figure 3)
generating, by the computing system, model input, the model input including the user query and the contextual data for the user query; (para. 70-72 “an LLM prompt is created for use as input to an LLM 406. This prompt includes the converted text and an instruction directing the LLM to determine if the text represents a command or request intended for the domain-specific automated agent or if it is part of the ambient conversation. The prompt may also include additional context, such as the user's previous interactions or commands, to assist the LLM in making a more informed decision”;);
providing, by the computing system, model input as input to a machine-learned large language model; (para. 70-73 “The prompt is then transmitted to the LLM 408, which resides on a server that could be accessed over a network. The LLM analyzes the prompt and generates a structured output as a response”)
receiving, by the computing system, a query response as an output of the machine learned receive large language model processing the model input; and (para. 72-75 “The prompt is then transmitted to the LLM 408, which resides on a server that could be accessed over a network. The LLM analyzes the prompt and generates a structured output as a response”)
Canberk does not disclose “outputting, by the computing system, the query response to the user for display, the query response comprising a carousel of selectable options available.”, claim 13:”receive..suggestion”, And Even if argued that the query is not associated with the food delivery system, claim 13 “outputting, by the computing system, the suggestion for display to a user.”
Boyd discloses: Para. 13-15 - A user uses an online concierge system to order items to be delivery, and the online concierge system 140 to present to a user. coupons, recipes, or item suggestions”, para. 70-72 and 85-88 A LLM receives an input, and generate an output with infer items on recipe that would be compelling to the customer , and is presented as carousel to the user via the online concierge system 140, e.g., via the content presentation module 210 ; suggested items and recipes;
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify Canberk to include the above limitations as taught by Boyd, in order to generate a user interface of the online system with accurate and detailed multi-item content that is specific for a particular user, see Boyd para. 1.
Regarding claim(s) 2 and 3 and 14 and 15, Canberk does not disclose: claim 2“wherein the contextual data includes one or more of a user order history, user profile data, and data associated with food delivery system.” Claim 3 “wherein the data associated with the food delivery system can include data describing a plurality of vendors and food items provided by those vendors.”
Boyd discloses: Para. 70-71 - “ The prompt generation module 260 may generate a prompt for input into the LLM. The prompt may include data gathered by the data gathering module 250, i.e., the catalog data with recipe information and/or item information and the user sales data”, para.45, 68 -71 - gathering data items information, sale, promotion, catalog data, user sale data, user data such as delivery location and timeframe, default retailer, picker data such as retailers and retailers distance where items were picked; para. 46 collect the item data that also include information that is useful for predicting the availability of items in retailer locations. For example, the data collection module 200 may collect the item data that include, for each item-retailer combination (a particular item at a particular warehouse), 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. The data collection module 200 may collect the item data from the retailer computing system 120
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify Canberk to include the above limitations as taught by Boyd, in order to generate a user interface of the online system with accurate and detailed multi-item content that is specific for a particular user, see Boyd para. 1.
Regarding claim(s) 4 and 18, Canberk discloses:
wherein the model input is a prompt, and the prompt includes past queries and responses in an ongoing conversation. Para. 70-72 - The prompt may also include additional context, such as the user's previous interactions or commands; para. 72 – a prompt is created)
Regarding claim(s) 5, Canberk discloses:
wherein the model output includes data organized into a schema defined in the prompt. (para. 67 - structured input and structured output, para. 68 form of JSON, para. 69 – receives structured output; para. 116)
Regarding claim(s) 6, Canberk does not disclose: wherein the query response comprises a natural language textual response as part of a conversation with the user.
Boyd discloses: para. 40, 52 “The interface system 160 receives one or more queries from the online concierge system 140 on the external data. The interface system 160 constructs one or more prompts for input to the model serving system 150. A prompt may include the query of the user and context obtained from the structured index of the external data”, “the content presentation module 210 may 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. The content presentation module 210 may use the search query representation to score candidate items for presentation to a user (e.g., by comparing a search query embedding to an item embedding”, para. 45-46, 68 -71;
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify Canberk to include the above limitations as taught by Boyd, in order to generate a user interface of the online system with accurate and detailed multi-item content that is specific for a particular user, see Boyd para. 1.
Regarding claim(s) 7, Canberk discloses:
wherein the model output include search terms and filters. (para. 33-39 – input – search terms and intent: as filter)
Regarding claim 16, Canberk does not disclose: wherein the user order history includes one or more of: one or more items that were previously purchased by the user, one or more entities from which the one or more items were purchased, one or more times when the one or more items were purchased, and a frequency with which the one or more items are purchased.
Boyd discloses: para. 70-71 - the user sales data includes historical item quantity information for the user, order history for the user; para.45, 68 -71 - gathering data items information, sale, promotion, catalog data, user sale data, user data such as delivery location and timeframe, default retailer, picker data such as retailers and retailers distance where items were picked; para. 46 collect the item data that also include information that is useful for predicting the availability of items in retailer locations. For example, the data collection module 200 may collect the item data that include, for each item-retailer combination (a particular item at a particular warehouse), 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. The data collection module 200 may collect the item data from the retailer computing system 120
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify Canberk to include the above limitations as taught by Boyd, in order to generate a user interface of the online system with accurate and detailed multi-item content that is specific for a particular user, see Boyd para. 1.
Regarding claim(s) 19, Canberk does not disclose wherein the suggestion is displayed within a carousel of selectable options available through a food delivery system.
Boyd discloses: Para. 13-15 - A user uses an online concierge system to order items to be delivery, and the online concierge system 140 to present to a user. coupons, recipes, or item suggestions”; Para. 72 and 85-88 A LLM receives an input, and generate an output with infer items on recipe that would be compelling to the customer , and is presented as carousel to the user via the online concierge system 140, e.g., via the content presentation module 210 ; suggested items and recipes;
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify Canberk to include the above limitations as taught by Boyd, in order to generate a user interface of the online system with accurate and detailed multi-item content that is specific for a particular user, see Boyd para. 1.
Regarding claim(s) 20, Canberk discloses:
wherein the contextual data include previously submitted input queries. (Para. 23-25Convert speech to text by preserving the user context; The context, typically consisting of the converted text and potentially additional conversational history, provides the necessary background information for the LLM to make this determination; para. 53-64 - order food delivery when the user expresses the intent to dine out).
Claim(s) 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Canberk and Boyd combination as applied to claim(s) 7 and further in view of Yaqoob et al. (US 20250245731 A1, hereinafter Yaqoob).
Regarding claim(s) 8, the combination does not disclose: wherein the search terms and prompts are provided to a search system, the method further comprising: receiving, from the search system, a list of candidate items to recommend to the user.
Yaqoob discloses: para. 87 “the serving layer 420 may call the search engine 440 to search a database to identify a set of items based on each enhanced query. In some embodiments, the search engine 440 can use a search model, e.g. the search model 396 in the database 116, to search for product items in a product database of the retailer based on the enhanced query”; para 88 “The search engine 440 can search the database using the nearest neighbor index based on the enhanced query to identify the set of items.”; para. 118-125 - receive from search engine a list of suggest items would most likely be purchased (or interacted with) by customers based on historical customer transaction
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify the combination to include the above limitations as taught by Yaqoob, in order to provide accurate and helpful product suggestions, see Yaqoob para. 4.
Regarding claim(s) 9 and 10, Canberk does not disclose: claim 9 “ranking, by the computing system, the list of candidate items; and populating the carousel of selectable options available based on the ranked list of selectable items.”, claim 10: “wherein the selectable options represent food items available from merchants and wherein the selectable are organized in the carousel based on the merchant from which the food items are available.”;
Boyd discloses: Para. 70-72 “The content presentation module 210 (or some other module of the online concierge system 140) may rank the recipes output by the LLM (e.g., based on a price of each recipe, or some other metric) before displaying one or more recipes with the highest rank at the user interface of the user client device 100.”, para. 79 - the content presentation module 210 may rank recipes and/or items in the list… he selected user-specific recipes and/or items may be displayed in a carousel in; para. 13-52 – items available on retailer, The data collection module 200 may collect the item data that also include information that is useful for predicting the availability of items in retailer locations.
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify Canberk to include the above limitations as taught by Boyd, in order to generate a user interface of the online system with accurate and detailed multi-item content that is specific for a particular user, see Boyd para. 1.
Regarding claim(s) 11, Canberk discloses:
wherein the prompt includes a requested schema for the output produced by the model.
(para. 67 - structured input and structured output, para. 68 form of JSON, para. 69 – receives structured output; para. 116, 53 request would be formatted according to specific agent; 16 and 81; Figure 3)
Claim(s) 17 is rejected under 35 U.S.C. 103 as being unpatentable over Canberk and Boyd combination as applied to claim(s) 13 and further in view of Halimsaputera (US 20200342550 A1).
Regarding claim 17, the combination does not disclose wherein the suggestion includes a predicted next order date for a particular item and method further comprises: determining, by the computing system, a current date and a current time; and determining, by the computing system and based on the current date and the current time to display the suggestion to the user at the current time.
Halimsaputera discloses: para. 32“Upon determination of one or more recommended restaurants, the recommendations may be presented via notifications coordinated by the notification generator 132. For example, the notification generator may generate instructions to be sent to the user device(s) of the user(s) to pop up a notification of at least a highest-ranking restaurant (as determined by personalized recommendation logic 130). In one example, the notification generator 132 may transmit notifications to a user device at a pre-determined time, wherein the pre-determined time may be selected by a user as part of a user account setup, or may be inferred via user transaction behavior (e.g., a most common time of day when a user makes purchases via the restaurant recommendation system 100). The notification may be presented on a map (e.g., with a marker showing a location of the highest-ranking recommended restaurant(s)) and/or as a standalone notification.”
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention, to modify the combination to include the above limitations as taught by Halimsaputera, in order to a single user or a group of users may quickly identify restaurants of interest using a single resource that is tailored to the user(s) preferences and context, see Halimsaputera para. 6.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VANESSA DELIGI whose telephone number is (571)272-0503. The examiner can normally be reached on Monday-Friday 07:30AM-5PM.
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/VANESSA DELIGI/Patent Examiner, Art Unit 3627
/FLORIAN M ZEENDER/ Supervisory Patent Examiner, Art Unit 3627
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