Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Amendment filed on 4/22/2026. Claims 1, 6-7, 10-14, 16-18, and 22-28 are currently pending and have been examined. Claims 2-5, 8-9, 15, 19-21 stand cancelled. Claims 1, 6, 10-14, 16, 18, 22-23 have been amended. Claims 26-28 have been newly entered.
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/22/2026 has been entered.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 112(a) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Provisional Application No. 63294457, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. The provisional application fails to provide support for at least the limitations of training & retraining as claimed, processing the selection by facilitating delivery of the one or more items in the cart as claimed, initiating communication with a transporter user or autonomous vehicle, or transmitting a status message, as recited in the independent claims.
Claim Objections
Claim 1 is objected to for the following informality: “initiating, via a network interface communication with a service provider computer” should include a comma after “interface.” Appropriate correction is required.
Claim Rejection - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 6-7, 10-14, 16-18, and 22-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 1, 6-7, 10-11, 16-18, 22, 24-28 are directed to a process, and claims 12-14 and 23 are directed to a machine. Therefore, claims 1, 6-7, 10-14, 16-18, and 22-25 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Taking Claim 1 as representative, Claim 1 recites at least the following limitations that are believed to recite an abstract idea:
obtaining a plurality of user features of a plurality of users from a user features storage, a plurality of service provider features of a plurality of service providers from a service provider features storage, and a procedure from a procedure storage;
preparing the procedure by inputting into the procedure the plurality of user features and the plurality of service provider features into the procedure, the preparing comprising iteratively adjusting biases and weights until, when provided an input and an output, the procedure accepts the input and generates an output similar to the output, and updating procedure parameters to generate a prepared procedure;
generating a plurality of candidate carts for each end user of the plurality of users, by:
iterating through the plurality of service providers and generating each of the plurality of candidate carts for a corresponding service provider among the plurality of service providers, wherein the prepared procedure is input, for each service provider, service provider features corresponding to the service provider and user features to generate a cart including one or more items associated with the service provider;
storing in a candidate cart storage, for each end user, the plurality of candidate carts as a queue, wherein the candidate cart storage is a scalable candidate cart storage system configured to support the plurality of users, wherein generation of the plurality of candidate carts is performed asynchronously from cart provisioning;
in response to a cart request message received from an end user, a set of candidate carts associated with the end user from the candidate cart storage;
validating the candidate carts of the set of candidate carts to obtain validated carts by:
accessing up-to-date menu information from a menus storage associated with the plurality of service providers, and removing any cart in which one or more items are unavailable based on the up-to-date menu information;
sorting the validated carts based on a relevance score for each cart, wherein the relevance score is determined using a process that factors in user features of the end user, service provider features, and user events including user interaction data obtained from a user events storage;
storing, in the candidate carts storage, the sorted carts in a second queue in an order related to the relevance score;
providing the sorted carts in an order of the second queue to the end user, wherein the end user is enabled to:
view the provided carts one at a time,
request a new cart to be displayed,
add or remove items from a displayed cart prior to cart selection;
receiving from the end user, a selection signal of a selected cart that is received, the selected cart being associated with a particular service provider;
processing the selected cart by:
generating a fulfillment request including cart contents of the selected cart and user and service provider information, and
initiating via a communication with the particular service provider for preparation of one or more ordered items contained in the selected cart and with a transporter user or delivery means for delivering the one or more ordered items using navigation data from a navigation means including positioning data;
transmitting at least one status update message to the end user during fulfillment related to the selected cart, the at least one status update message including information on a location of the transporter user or delivery means and estimated delivery time;
receiving a delivery confirmation message from the transporter user or delivery means indicating successful delivery; and
updating the prepared procedure by updating the prepared procedure using data related to the selected cart, the data including user features and service provider features associated with completed delivery, to refine procedure parameters for future cart generation.
The above limitations recite the concept of personalized purchase recommendations and delivery support . These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 1, 6-7, 10-14, 16-18, and 22-28 recite an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
A server computer comprising a processor and a computer-readable medium
Databases, one of which is fault-tolerant
A machine learning (ML) model
Training & re-training the ML model
User devices
A machine learning process
A graphical user interface
electronic signals
autonomous vehicle
a navigation network including global positioning system (GPS) data
A server computer comprising: a processor; and a computer-readable medium coupled to the processor, the computer- readable medium comprising code executable by the processor for implementing a method
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. For example, claims 6-7,17-18, 22-23, 26 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. As for claims 10-11, 13-14, 24-25, & 27-28 these claims are similar to the independent claims except that they recite the further additional elements of a further databases, further autonomous vehicles, a central server computer, a hybrid ML model, and a relational database. These additional elements are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. Therefore, the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
A server computer comprising a processor and a computer-readable medium
Databases, one of which is fault-tolerant
A machine learning (ML) model
Training & re-training the ML model
User devices
A machine learning process
A graphical user interface
electronic signals
autonomous vehicle
a navigation network including global positioning system (GPS) data
A server computer comprising: a processor; and a computer-readable medium coupled to the processor, the computer- readable medium comprising code executable by the processor for implementing a method
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Allowable over Prior Art of Record
Claims 1, 6-7, 10-14, 16-18, and 22-28 are allowable over prior art though rejected on other grounds [e.g. 35 USC §101] as discussed above. The combination of elements of the claim as a whole are not found in the prior art.
Claims 1, 6-7, 10-14, 16-18, and 22-28 would be allowable if rewritten to overcome the rejections under 35 USC §101 as set forth in this Office Action, and to include all of the limitations of the base claim and any intervening claims.
Upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the Applicant’s invention.
In the present application, claims 1, 6-7, 10-14, 16-18, and 22-28 are allowable over prior art. The most related prior art patent of record is Kysta et al (US 20210035188 A1), hereinafter Kysta, Obaidi (US 20170090484 A1), hereinafter Obaidi, and Reference U (NPL – see attached).
Kysta teaches a method for product recommendation, in which a trained ML model is used [0054, 0135] after being trained on product information and user histories [0049]. Based on user information, such as their current order, the system determines one or more products to recommend based on product similarities [0136]. Upon navigating to checkout, the system examines the items in the user’s cart and determines products similar thereto to recommend [0053]. These are provided to the user on a webpage screen and can be added to their cart directly [0079]. The system facilitates the checkout and supports delivery arrangements for fulfilling the order [0077-0080]. The ML model is re-trained after a period of time to be updated to accommodate changes in user behavior [0050-0051].
Obaidi teaches order delivery systems [Abstract] in which instructions for the pickup and drop-off of an order can be communicated to a drone [0036], which can be directed to the pick-up location [0028]. This can be one of a plurality of drones to be selected from & scheduled to handle the delivery [0048]. Using GPS data [0026], the system can be navigated to the relevant locations [0027-0037]. Upon arrival, the drone provides a notification indicating its arrival to the customer [0030].
Reference U discusses personalized ranking of product options for on-demand food delivery, including a machine learning algorithm that can rank the options from a plurality of restaurants based on a variety of factors.
Other relevant prior art includes Beauchamp (US 20200320607 A1), which teaches systems for product recommendations using a trained machine learning algorithm to generate bundles of items that can be added to a cart together; Mane et al (US 20210056385 A1), which teaches a machine learning recommendation technique for items that can be added to a shopping cart trained on item data. Items are ranked based on relevance by AI; and Fotso et al (US 20220327583 A1), which uses machine learning to generate a plurality of pre-set service offering recommendations for each merchant in a database.
However, each of these references fail to disclose or render obvious at least the limitations of: generating, by the server computer, a plurality of candidate carts for each end user of the plurality of users, by: iterating through the plurality of service providers and generating each of the plurality of candidate carts for a corresponding service provider among the plurality of service providers, wherein the trained ML model is input, for each service provider, service provider features corresponding to the service provider and user features to generate a cart including one or more items associated with the service provider; storing, by the server computer in a candidate cart database, for each end user, the plurality of candidate carts as a queue, wherein the candidate cart database is a fault-tolerant, scalable candidate cart database system configured to support the plurality of users, wherein generation of the plurality of candidate carts is performed asynchronously from cart provisioning; in response to a cart request message received from an end user device operated by an end user, retrieving, by the server computer, a set of candidate carts associated with the end user from the candidate cart database; validating, by the server computer, the candidate carts of the set of candidate carts to obtain validated carts by accessing up-to-date menu information from a menus database associated with the plurality of service providers, and removing any cart in which one or more items unavailable based on the up-to-date menu information.
Ultimately, the particular combination of limitations as claimed, is not anticipated nor rendered obvious in view of the cited references, and the totality of the prior art. While certain references may disclose more general concepts and parts of the claim, the prior art available does not specifically disclose the particular combination of these limitations.
The references, however, do not teach or suggest, alone or in combination the claimed invention. Examiner emphasizes that the prior art/additional art would only be combined and deemed obvious based on knowledge gleaned from the applicant’s disclosure. Such a reconstruction is improper (i.e. hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
The Examiner further emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for further modification of the evidence at hand to arrive at the claimed invention. The combination of features as claimed would not be obvious to one of ordinary skill in the art as combining various references from the totality of evidence to reach the combination of features as claimed would be a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
It is thereby asserted by Examiner that, in light of the above and further deliberation over all of the evidence at hand, that the claims are allowable over prior art (though rejected under 35 USC §101) as the evidence at hand does not anticipate the claims and does not render obvious any further modification of the references to a person of ordinary skill in the art.
Response to Arguments
Applicant's arguments filed 4/22/2026 have been fully considered but are not persuasive.
Claim Rejections – 35 USC § 101
Applicant characterizes the Rejection as “identif[ying] the claims as directed to an abstract idea,” and states that the claims recite “technological elements” that the Rejection determines “are recited at a high level of generality and do not amount to a practical application or an inventive concept.” Applicant notes that the 101 “rejection is not based on prior art, but on the claims failing to present ‘significantly more’ than the abstract idea itself, and lacking a concrete technical improvement in computer technology.”
Examiner notes with reference to the Rejection above that the claims have been evaluated and determined to be directed to an abstract idea under Step 2A Prong 1. Under Step 2A Prong 2, the computer-related additional elements of the claims, as identified in the rejection, are determined, alone or in combination, not to integrate the abstract idea into a practical application. Instead, they are invoked as mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)]. Similarly, in Step 2B, the claims are evaluated as a whole and determined not to amount to significantly more than the abstract idea itself; not just for “lacking a … technological improvement,” but for failing to amount to significantly more than the abstract idea for any of the rationales covered in MPEP 2106.05. Instead, as noted above, the additional elements provide only a general linking to computer technology. With respect to Applicant’s note that the 101 rejection “is not based on prior art,” it is noted that “lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101.” [MPEP 2106.05(I)].
Applicant argues that the claims are directed “to a technical solution that is necessarily rooted in computer technology,” arguing that the claims require “asynchronous generation and queuing of carts in a fault-tolerant, scalable database,” “real-time validation using up-to-date menu data,” “ML-based relevance scoring and user interaction data,” an “interactive graphical user interface with specific functionality,” and “automated fulfillment and end-to-end transaction processing.”
Examiner disagrees. The argued limitations are part of the abstract idea itself, such that the alleged solution is at most a business solution to a business problem stemming solely from the abstract idea, with the additional elements being invoked as mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)], resulting in only a general linking to computer technology. Asynchronous generation and quieting of carts in a scalable storage system, i.e. ability to generate recommendable carts & store them prior to the time when they are presented to a user is part of the abstract idea itself, with the additional elements that the step is performed by a computer and that the storage is a “fault-tolerant” database without further detail amounting to mere instructions to apply this ability to determine information in advance & store it for later use to a generic computer. Similarly, validating that the carts are still available using up-to-date menu data prior to presenting them to the user is also part of the abstract idea, except for similar recitations that steps be performed by a computer and that storage means should be a generic database. Examiner notes that the claims do not recite real-time validation as argued. Similarly, scoring cart relevance and making use of interaction data, and allowing user selecting and modification of recommended carts, are part of the abstract idea, with additional elements that a generic ML model should perform the abstract calculations and that user actions should be made through a GUI merely provide a general linking to computer technology, without integrating these abstract limitations into a practical application. The claims do not recite transaction or payment processing capabilities as argued. The claimed fulfillment support steps are part of the abstract idea itself, except for the recitation of computer-related additional elements, recited at a high level of generality, which are invoked as mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)].
Applicant further argues that “each of these steps is recited with particularity and is supported in the specification as a technological solution, not as field-of-use or post-solution activity,” specifically arguing that “the combination of asynchronous cart generation, real-time validation, machine-learning-driven relevance scoring, advanced database architecture, and user-interactive GUI is not generic, nor is it found in cited prior art.”
Examiner disagrees, and notes, similar to above, that “real-time” validation and any detail on database architecture beyond one being stated to be “fault-tolerant,” are not present in the claims, and do not appear to be supported by the Specification. The combination of asynchronous cart generation, validation, relevance scoring, storage, and user-interactive cart options is part of the abstract idea itself, except for the recitation of additional elements which are invoked as mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)] With respect to Applicant’s argument that claims limitations are not “found in cited prior art,” it is noted that “lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101.” [MPEP 2106.05(I)]. In the instant case, it is noted that a claim for a new abstract idea is still an abstract idea; the argued abstract steps, while not rejected over prior art, are still an abstract idea as addressed in the rejection above.
Applicant further argus that “the claimed subject matter does not unduly preempt all forms of cart recommendation or order delivery,” stating that “the scope is tied to the described system and method, as implemented in the technical context of distributed databases, ML-driven scoring, and GUI-based user interaction.” Applicant concludes that “claim 1 amounts to significantly more than the abstract idea.”
Examiner disagrees, and notes that neither the claims nor specification recite or suggest “distributed databases” as argued. With reference to the rejection above, the claims are not rejected as “preempt[ing] all forms of cart recommendation or order delivery,” but as reciting a concept for personalized purchase recommendations and delivery support which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106. The additional elements of the claims, rather than integrating this abstract idea into a practical application or amounting to significantly more than the abstract idea itself, are invoked as mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)], such that they offer at best the increased speed or efficiency inherent to a general purpose computer [MPEP 2106.05(a)].
Applicant argues that new claims 26-28 “are patentable by virtue of their dependencies and additional features recited therein.”
Examiner disagrees for the reasons addressed in the Rejection and Response above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS J SULLIVAN whose telephone number is (571)272-9736. The examiner can normally be reached Mon - Fri 8-5 PT.
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/T.J.S./
Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
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