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 .
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/9/25 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is 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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-20 are within the four statutory categories. Claims 1-10 are directed to a system and claims 11-20 to method, which are within the statutory categories of invention. (STEP 1: YES).
Step 2A1
Claim 1, which is representative of the inventive concept, recites a processor and non-transitory memory storing instructions for:
obtaining a query submitted by a user,
identifying, in the query, at least one predetermined keyword indicating a request for an expedited transaction by the user,
determining, based on the query and a transaction history of the user, a product item,
generating contract data related to a purchase contract of the product item, and
transmitting, as a response to the query, the contract data with an option for the user to select to directly place an order of the product item.
The underlined limitations as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., fundamental economic principles or practices, commercial or legal interactions, managing personal behavior) but for recitation of generic computer components. That is, other than reciting a system implemented by a data processor (computer), the claimed invention amounts to commercial interactions between a user (customer) and merchant by using contract data to facilitate a user to place an order for a product.
The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II))]. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional elements that implements the identified abstract idea: processor and non-transitory memory.
Neither the processor or non-transitory memory are described by the applicant and is recited at a high-level of generality (i.e., a generic computer performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites a step of transmitting, as a response to the query, the contract data with an option for the user to select to directly place an order of the product item. This step is considered to be part of the abstract idea, as part of the commercial interaction of facilitating purchases orders for a user. However, even if this step were to be considered an additional element instead of being part of the abstract idea, the transmitting step is recited at a high level of generality (i.e., as a general means of transmitting data) and amounts to the mere transmission of data, which is a form of extra-solution activity. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor and non-transitory memory to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the limitation of transmitting, as a response to the query, the contract data with an option for the user to select to directly place an order of the product item is considered to be part of the abstract idea, and even if it were not, would be considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. MPEP 2016.05(d)(II) indicates that receiving and/or transmitting data over a network has been held by the courts to be well-understood, routine, conventional activity (citing Symantec, TLI Communications, OIP Techs., and buySAFE). Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). As such the claim is not patent eligible.
Dependent claims 2-10 and 12-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claims 2 and 12 describe details on what the query entails, which further defines the abstract idea. Claims 2 and 12 also recite an additional element not previously cited in the independent claims, a “chatbox”. However, this chatbox only generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) and MPEP 2106.05(A) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more.
Claims 3 and 13 describe details on what the predetermined keywords represent or signify, which further defines the abstract idea. Claims 3 and 13 also recite an additional element not previously cited in the independent claims, a “natural language model” used to identify the predetermined keywords. However, this “natural language model” only generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) and MPEP 2106.05(A) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more. Further, the “natural language model” is deemed to represent mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to identify a predetermined keyword merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims.
Claims 4 and 14 describe details on how the query and transaction history are used to determine a product item, which further defines the abstract idea.
Claims 5 and 15 describe details on how/why the determined product item is eligible, which further defines the abstract idea.
Claims 6 and 16 describe details on how/why the determined product item is eligible, which further defines the abstract idea.
Claims 7 and 17 describe details on how the contract data is used to place an order and how it is generated, which further defines the abstract idea.
Claims 8 and 18 describe details on how eligible items are determined and how the selected product item is selected from the plurality of eligible items, which further defines the abstract idea.
Claims 9 and 19 describe details on how the contract data is used to place an order and how it is generated, which further defines the abstract idea.
Claims 10 and 20 describe details on determining whether users are eligible to have a purchase contract for expedited transactions and notifying eligible users of their eligibility, which further defines the abstract idea.
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 (i.e., changing from AIA to pre-AIA ) 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.
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 nonobviousness.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over D’Souza et al. (US Patent 11,176,598) in view of Mueller et al. (US PGPub 20170278062).
As per claim 1, D’Souza et al. teaches a system, comprising:
a processor, a non-transitory memory storing instructions [col. 3, lines 21-35: non-transitory computer readable media having stored thereon machine readable instructions to implement an artificial intelligence and machine learning based conversational agent; col. 4, lines 32-38: hardware processor], that when executed, cause the processor to:
obtain a query submitted by a user, identify, in the query, at least one predetermined keyword indicating a request for a transaction by the user, determine, based on the query and a transaction history of the user, a product item [col. 4, lines 39-59: the user request analyzer may convert, for the chat conducted with the conversational chatbot… to ascertain the request by the user to purchase the product…. Purchase request controller may generate, based on an analysis of the request by the user to purchase the product and the attribute associated with the user, an intent associated with the user to purchase the product…. Purchase request controller may generate.. a catalog that includes a plurality of products that match the request by the user to purchase the product],
generate contract data related to a purchase contract of the product item [col. 4, line 66 – col. 5, line 3: purchase request controller may generate based on the received selection of the product from the plurality of products and the received identification of the quantity associated with the selected product, a purchase request], and
transmit, as a response to the query, the contract data with an option for the user to select to directly place an order of the product item [col. 5, lines 29-36: a purchase order controller… may generate, based on the purchase request a purchase order associated with the selected product] {the purchase order implying that the user has placed an order for the product item(s) from the generated purchase request}.
D’Souza et al. does not teach that the user request is for an expedited transaction. However, Mueller et al. teaches a customized item search and browsing experienced based on delivery speed, where a listing of items is generated based at least in part on item search criterion and item availability via the particular delivery speed option [see abstract].
Both D’Souza et al. and Mueller et al. generates a list of items meeting particular criterion and parameters to facilitate their purchase; thus, they are deemed to be analogous references as directed towards solving similar problems. It would have been obvious before the effective filing date to modify the teachings of D’Souza et al. to be applied for expedited transactions, as taught by Mueller et al. because providing the desired products within a desired timeframe as specified by the customer would better satisfy the needs of the customer and increase customer satisfaction and likelihood of repeat business, and improve the user experience, which is a goal of Mueller et al. [paragraphs 8, 12]
Claim 11 recites limitations substantially to those recited in claim 1 above; thus, the same rejection and rationale applies. Further, the teachings of D’Souza et al. are computer-implemented [col. 3, lines 21-35: non-transitory computer readable media having stored thereon machine readable instructions to implement an artificial intelligence and machine learning based conversational agent; col. 4, lines 32-38: hardware processor].
Claim 20 recites limitations substantially to those recited in claim 1 above; thus, the same rejection and rationale applies. Further, the teachings of D’Souza et al. are computer-implemented including a non-transitory computer readable medium having instructions stored thereon [col. 3, lines 21-35: non-transitory computer readable media having stored thereon machine readable instructions to implement an artificial intelligence and machine learning based conversational agent; col. 4, lines 32-38: hardware processor].
As per claim 2, D’Souza teaches the system of claim 1, wherein:
the query is obtained via a chatbot the query includes a text or an utterance input by the user via the chatbot [col. 4, lines 39-59: the user request analyzer may convert, for the chat conducted with the conversational chatbot… to ascertain the request by the user to purchase the product.. the user request analyzer may convert, for the chat conducted with the conversation chatbot, a speech input to text or a text input to speech, to ascertain the request by the user to purchase the product.
Claim 12 recites limitations substantially to those recited in claim 2 above; thus, the same rejection and rationale applies.
As per claim 3, D’Souza teaches the system of claim 1, wherein:
the at least one predetermined keyword is identified based on a natural language model that is trained based on historical customer queries and historical transaction data [col. 13, lines 33-37: the chatbot functionality for the conversational chatbot may be developed, for example, by using a MICROSOFT Bot framework and LUIS for natural language processing; col. 5, lines 4-17: the purchase request controller may train, based on historical data, a convolutional neural network; col. 9, lines 35-41: a feature map may be created using historical purchase request data;].
Although not taught by D’Souza, Mueller et al. teaches at least one predetermined input that signifies speed or time for a shopping process [paragraph 12: the user may specify a delivery speed preference before searching or browsing for items; paragraph 14: delivery speed selection tool may include a dynamically generated listing of a plurality of delivery speed options that are available for a particular address given the current time of day. The delivery speed options may pertain to fixed speeds (e.g. within an hour) and/or fixed windows for delivery].
D’Souza et al. does not teach that the user request is for an expedited transaction. However, Mueller et al. teaches a customized item search and browsing experienced based on delivery speed, where a listing of items is generated based at least in part on item search criterion and item availability via the particular delivery speed option [see abstract].
Both D’Souza et al. and Mueller et al. generates a list of items meeting particular criterion and parameters to facilitate their purchase; thus, they are deemed to be analogous references as directed towards solving similar problems. It would have been obvious before the effective filing date to modify the teachings of D’Souza et al. to allow the user to specify expedited speed or time for transactions, as taught by Mueller et al. because providing the desired products within a desired timeframe as specified by the customer would better satisfy the needs of the customer and increase customer satisfaction and likelihood of repeat business, and improve the user experience, which is a goal of Mueller et al. [paragraphs 8, 12]
Claim 13 recites limitations substantially to those recited in claim 3 above; thus, the same rejection and rationale applies.
As per claim 4, D’Souza et al. teaches the system of claim 1, wherein the product item is determined based on:
searching for at least one eligible item regarding the requested transaction based on one or more criteria and selecting the product item from the at least one eligible item [col. 3, lines 51-57: initiation of a purchase requisition… In this regard, a user may select a product from a list of matching products and specify information such as, quantity information; col. 5, lines 13-18: purchase request controller may generate, based on an analysis of the intent associated with the user to purchase the product using the trained machine learning classifier, the catalog that includes the plurality of products that match the request by the user to purchase the product; col. 7, lines 52-60: a user may create a new purchase request.. the user may enter a product quantity… based on identification of the product, and based on identification of a product quantity, products that match the user’s search request may be displayed for the user to select, a product from a catalog of products… an order may be submitted with respect to the selected product].
D’Souza et al. does not teach that the user request is for an expedited transaction. However, Mueller et al. teaches a customized item search and browsing experienced based on delivery speed, where a listing of items is generated based at least in part on item search criterion and item availability via the particular delivery speed option [see abstract].
Both D’Souza et al. and Mueller et al. generates a list of items meeting particular criterion and parameters to facilitate their purchase; thus, they are deemed to be analogous references as directed towards solving similar problems. It would have been obvious before the effective filing date to modify the teachings of D’Souza et al. to be applied for expedited transactions, as taught by Mueller et al. because providing the desired products within a desired timeframe as specified by the customer would better satisfy the needs of the customer and increase customer satisfaction and likelihood of repeat business, and improve the user experience, which is a goal of Mueller et al. [paragraphs 8, 12].
Claim 14 recites limitations substantially to those recited in claim 4 above; thus, the same rejection and rationale applies.
As per claim 5, D’Souza et al. teaches the system of claim 4, and D’Souza et al. further teaches the use of a user profile that includes the address associated with the user [col. 6, lines 26-28].
Although not explicitly taught by D’Souza et al., Mueller et al. teaches
wherein an item is an eligible item regarding the expedited transaction when:
the item offers a shipping delivery option for the user’s previously saved address [paragraph 11: The availability of a particular delivery speed may depend upon the time that an order is placed, the delivery address of the user, a subscription status of the user, the location of item inventory, and/or the availability of a delivery service, among other factors. For example, same-day delivery may be available for a given delivery location until a certain cut-off time; paragraph 14: The delivery speed selection tool 106 may include a dynamically generated listing of a plurality of delivery speed options that are available for a particular address given the current time of day];
the item is in the user’s transaction history [paragraph 26: The user data may include various data about users of the electronic marketplace, including profile information, personalization information, demographic information, browsing history, order history, previous purchasing habits, and so on]; and
the item matches the query [paragraph 22: generated search results may be included within a search result listing that is returned to the client device for rendering in a user interface].
Both D’Souza et al. and Mueller et al. generates a list of items meeting particular criterion and parameters to facilitate their purchase; thus, they are deemed to be analogous references as directed towards solving similar problems. It would have been obvious before the effective filing date to modify the teachings of D’Souza et al. to offer expedited transactions for items previously purchased that also match or meet the needs specified by the user, as taught by Mueller et al. because providing the desired products within a desired timeframe as specified by the customer would better satisfy the needs of the customer and increase customer satisfaction and likelihood of repeat business, and improve the user experience, which is a goal of Mueller et al. [paragraphs 8, 12].
Claim 15 recites limitations substantially to those recited in claim 5 above; thus, the same rejection and rationale applies.
As per claim 6, D’Souza et al. teaches the system of claim 4, wherein:
the product item is identified to be eligible based on the searching with the one or more criteria [col. 7, lines 52-60: a user may create a new purchase request.. the user may enter a product quantity… based on identification of the product, and based on identification of a product quantity, products that match the user’s search request may be displayed for the user to select, a product from a catalog of products… an order may be submitted with respect to the selected product]; and
the contract data is provided to the user as a direct response to the query [col. 4, line 66 – col. 5, line 3: purchase request controller may generate based on the received selection of the product from the plurality of products and the received identification of the quantity associated with the selected product, a purchase request; col. 5, lines 29-36: a purchase order controller… may generate, based on the purchase request a purchase order associated with the selected product] {the purchase order implying that the user has placed an order for the product item(s) from the generated purchase request presented to them}.
Mueller et al. teaches allowing the user to specify additional criteria to act as a filtering mechanism after the initial results of matching items are presented, which could be applied in combination until a specific number of results remain, whether it is a single item or product [paragraph 22: The item search and navigation application may apply one or more refinements received from the client device or stored in connection with a user profile in order to filter or limit the search results; paragraph 8: One approach to improving the user experience may involve allowing the user to define additional criteria for sorting or filtering the search results after the results are presented].
Both D’Souza et al. and Mueller et al. generates a list of items meeting particular criterion and parameters to facilitate their purchase; thus, they are deemed to be analogous references as directed towards solving similar problems. It would have been obvious before the effective filing date to modify the teachings of D’Souza et al. to define or provide additional criteria to continue the initial search results, as taught by Mueller et al. until the desired number of results remain, because doing so would improve the user experience, which is a goal of Mueller et al. [paragraphs 8, 12], as is avoiding scenarios where the result set is too large to display within a single screen or page on a client device where the user has to scan through many undesired results [paragraph 1].
Claim 16 recites limitations substantially to those recited in claim 6 above; thus, the same rejection and rationale applies.
As per claim 7, although not explicitly taught by D’Souza et al., Mueller et al. teaches the system of claim 6, wherein:
the contract data is generated based on the user’s voluntarily saved information via a website or a chatbot application [paragraph 26: The user data may include various data about users of the electronic marketplace, including profile information, personalization information, demographic information, browsing history, order history, previous purchasing habits, and so on. In particular, the user data may include location data, one or more delivery addresses, a delivery speed preference, and/or other data]; and
the contract data is provided for the user to place an order of the product item directly by a single click [Fig 3C, ‘Buy Now’ button].
Both D’Souza et al. and Mueller et al. generates a list of items meeting particular criterion and parameters to facilitate their purchase; thus, they are deemed to be analogous references as directed towards solving similar problems. It would have been obvious before the effective filing date to modify the teachings of D’Souza et al. to use saved information about the user to create a contract for purchasing an item, as taught by Mueller et al. because providing the desired products within a desired timeframe as specified by the customer at or to a specified delivery location would better satisfy the needs of the customer and increase customer satisfaction and likelihood of repeat business, and improve the user experience, which is a goal of Mueller et al. [paragraphs 8, 12].
Claim 17 recites limitations substantially to those recited in claim 7 above; thus, the same rejection and rationale applies.
As per claim 8, D’Souza et al. teaches the system of claim 4, wherein:
selecting the product item based on the selection from the user [col. 5, lines 29-36: a purchase order controller… may generate, based on the purchase request a purchase order associated with the selected product] {the purchase order implying that the user has placed an order for the selected product item(s) from the generated purchase request}.
Although not explicitly taught by D’Souza et al., Mueller et al. teaches:
a plurality of eligible items are determined based on the searching with the one or more criteria [paragraph 22: generated search results 219 may be included within a search result listing that is returned to the client device 206 for rendering in a user interface]; and the product item is selected from the plurality of eligible items based on:
ranking the plurality of eligible items based on at least one of:
a matching score identifying a matching between each eligible item and the query, a recall score identifying whether each eligible item was purchased by the user before, a frequency score identifying how often each eligible item was purchased by the user before [paragraph 26: The user data may include various data about users of the electronic marketplace, including profile information, personalization information, demographic information, browsing history, order history, previous purchasing habits, and so on; paragraph 37: the items (FIG. 2) and results that are presented are customized for this delivery speed preference. Each of the results that are shown are available for delivery within one hour to the specified address of “822 Lexington Ave.” The results that are shown or their relative ranking may also be customized based at least in part on other personalization factors, such as a subscription status of the user and purchase history], and
providing a predetermined number of top-ranked eligible items to the user, obtaining a selection from the user regarding the predetermined number of top-ranked eligible items [paragraph 22: The item search and navigation application may apply one or more refinements received from the client device or stored in connection with a user profile in order to filter or limit the search results; paragraph 8: One approach to improving the user experience may involve allowing the user to define additional criteria for sorting or filtering the search results after the results are presented].
Both D’Souza et al. and Mueller et al. generates a list of items meeting particular criterion and parameters to facilitate their purchase; thus, they are deemed to be analogous references as directed towards solving similar problems. It would have been obvious before the effective filing date to modify the teachings of D’Souza et al. to rank the plurality of matching items based on prior purchase history and providing a subset (e.g., reduced number) of matching items to the user, as taught by Mueller et al. because providing the desired products within a desired timeframe as specified by the customer at or to a specified delivery location would better satisfy the needs of the customer and increase customer satisfaction and likelihood of repeat business, and improve the user experience, which is a goal of Mueller et al. [paragraphs 8, 12].
Claim 18 recites limitations substantially to those recited in claim 8 above; thus, the same rejection and rationale applies.
As per claim 9, although not explicitly taught by D’Souza et al., Mueller et al. teaches the system of claim 8, wherein:
the contract data is generated based on the user’s voluntarily saved information via a website or a chatbot application [paragraph 16: A delivery address selection tool may be provided in the user interface in order to facilitate a user selection of a particular delivery address. The account of the user may be associated with a set of prior addresses that have been used for previous deliveries. These prior addresses may be used to populate the delivery address selection tool with selectable address components]; and
the contract data is provided for the user to place an order of the product item directly by a single click [Fig 3C, ‘Buy Now’ button].
Both D’Souza et al. and Mueller et al. generates a list of items meeting particular criterion and parameters to facilitate their purchase; thus, they are deemed to be analogous references as directed towards solving similar problems. It would have been obvious before the effective filing date to modify the teachings of D’Souza et al. to use saved information about the user to create a contract for purchasing an item, as taught by Mueller et al. because providing the desired products within a desired timeframe as specified by the customer at or to a specified delivery location would better satisfy the needs of the customer and increase customer satisfaction and likelihood of repeat business, and improve the user experience, which is a goal of Mueller et al. [paragraphs 8, 12].
Claim 19 recites limitations substantially to those recited in claim 9 above; thus, the same rejection and rationale applies.
As per claim 10, although not explicitly taught by D’Souza et al., Mueller et al. teaches the system of claim 1, wherein the instructions, when executed, further cause the processor to:
determine, for a plurality of users, whether each user is eligible for having a purchase contract for an expedited transaction based on at least one of:
a number of grocery shopping experiences of the user, whether the user has a retail membership with free shipping feature, or a frequency for the user to add an item to cart and go to checkout immediately after receiving a search result [paragraph 31: The program information 228 may include parameters and criteria applicable to various delivery programs. For example, some delivery programs and associated delivery methods may be available only to users having a certain status, such as a subscription status. Some programs may be made available based upon spending thresholds, quantity thresholds, loyalty thresholds, and/or other factors]; and
transmit notifications to eligible users to indicate their eligibility, possibility and manner of requesting an expedited transaction [paragraph 41: The user interface shows the detail page (FIG. 3C) and the primary result (FIG. 3C) of the user interface (FIG. 3C) with the addition of a threshold indicator. The threshold indicator shows that the user is eligible for a delivery promotion (e.g., free expedited shipping) by spending a certain additional amount].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Driscoll et al. (US PGPub 20180330364) teaches a vendor utilizing a digital wallet service to complete an online transaction with a user with a chat channel, to generate and cache a payment request to the user, who can specify shipping address and shipping options, which include regular/standard shipping, and expedited shipping.
Gregory et al. (US PGPub 20200151659) teaches receiving a selection of an item by the user to display information, which includes determining shipping speed based on the fulfillment nodes that can currently delivery to the zip code of the user and availability/inventory level of the items at said nodes.
Prayagi et al. (US PGPub 20200151666) teaches systems and methods for determining the availability of a shipping option on a per-item basis. The retail user interface includes a plurality of selectable items available for delivery to a customer, and the shipping options are dependent based on transit time and availability.
Andrizzi et al. (US Patent 10,922,743) teaches a system that allows a customer to process recurring actions, such as a recurring purchase. The user can view information such as changes in price and availability from items previously purchased, as well as changes in shipping options for items.
Lohan et al. (US PGPub 20230177451) teaches a system for enabling purchase fulfillment options, such as expedited or reduced-cost shipping benefits to be used for the purchase of particular items.
Van Grootel et al. (US PGPub 2020/0311639) teaches a system for determining product fulfillment options for products, which are then communicated to the customer.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER H CHOI whose telephone number is (469)295-9171. The examiner can normally be reached M-Th 9am-7pm.
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/PETER H CHOI/ Supervisory Patent Examiner, Art Unit 3681