DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/18/2026 has been entered.
Claims 1-20 are pending and claims 1, 9, and 17 are amended.
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.
Step 1: The claims 1-8 are a method, claims 9-16 are a computer readable medium, and claims 17-20 are a system. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the 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 2A Prong 1: The independent claims (1, 9 and 17, taking claim 1 as a representative claim) recite:
A method comprising:
receiving, at an online system from a device associated with a delivery agent of the online system, an image of an order delivered at a location of a user associated with the order, the device associated with a batch of orders associated with the delivery agent being simultaneously fulfilled by the delivery agent;
accessing a plurality of features about the order, the plurality of features comprising features associated with other orders of the batch of orders;
applying a machine learning model to automatically detect cross-order contamination errors in real-time, to the plurality of features and the received image, wherein the model is trained to predict a likelihood that an image includes an error in a delivered order based on the image comprising a feature associated with at least one of the other orders of the batch orders, wherein the machine learning model outputs a likelihood that the received image includes an error in the delivered order;
determining, from the output of the machine learning model, that the order delivered at the location is erroneous by identifying that the image depicts an item from a different order in the batch of orders; and
responsive to the determining, sending, in real time, a warning message to the device associated with the delivery agent, wherein the warning message causes the device associated with the delivery agent to display a message about a potential error comprising indicia that an incorrect item was delivered based on the image of the order including an item that is part of at least one of the other orders of the batch of orders.
These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as well as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claimed invention recites steps for receiving an image of a delivered order, accessing features about the delivered order, applying a model to predict a likelihood that an image includes an error regarding the delivered order, outputting a likelihood of the error, determining if the delivered location was an error, sending a warning message based on the determination. The invention is directed to improving the accuracy of deliveries as set forth in [0003] of the specification. The steps under its broadest reasonable interpretation specifically fall under sales activities. 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.
Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of
A method comprising: (claim 1)
A non-transitory computer-readable storage medium storing instructions that when executed by a computer processor cause the computer processor to perform steps comprising: (claim 9)
A computer system, the computer system comprising: a computer processor; and
a non-transitory computer-readable storage medium storing instructions that when executed by a computer processor cause the computer processor to perform steps comprising: (claim 17)
at an online system from a device; the device
applying a machine learning model to automatically detect cross-order contamination errors in real-time
wherein the model is trained
wherein the machine learning model outputs
the output of the machine learning model,
in real time
The additional elements emphasized above are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application – MPEP 2106.05(f).
The recitation of applying a machine learning model to automatically detect cross-order contamination errors in real-time, wherein the model is trained […], wherein the machine learning model outputs […], the output of the machine learning model, […] merely indicates a field of use or technological environment in which the judicial exception is performed. Although these additional elements limit the identified judicial exception, which involves applying a machine learning model that is trained to predict a likelihood that an image includes an error and the output of the machine learning model is the likelihood of the error being in an image, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. The specification sets forth in [0053] that any appropriate type of machine learning for the determination may be used to carrying out the “machine learning” implementation. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using a generic computer component and generally linking the judicial exception to a particular technological environment.
Even when considered as an ordered combination, the additional elements of claim 1, 9, and 17 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1, 9, and 17 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claims 1, 9, and 17 are ineligible.
Dependent claims 2-8, 10-16, and 18-20 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1, 9 and 17 without significantly more.
Claim 2 recites wherein accessing a plurality of features about the order comprises receiving one or more of: a batch of orders associated with the delivery agent, wherein the batch of orders comprises a set of orders being simultaneously fulfilled by the delivery agent; contextual history of the user, wherein the contextual history of the user comprises demographic attributes of the user, order history of the user, and browsing history of the user; data associated with the delivery agent, wherein the online system receives locational data of the device associated with the delivery agent; or item data of the order, wherein the item data comprises item identifiers and attributes of the items. The limitation merely further limits the abstract idea and therefore does not integrate the judicial exception into a practical application.
Claim 3 recites wherein accessing a plurality of features about the order comprises extracting, from the image of the order, text content depicted in the image, and wherein applying the machine learning model to the plurality of features and the received image comprises applying the machine learning model to the extracted text content. The limitation merely further limits the abstract idea and recites the applying of the machine learning at a high level of generality and therefore does not integrate the judicial exception into a practical application.
Claim 4 recites wherein applying the machine learning model to the plurality of features and the received image comprises applying a multi-class model to the plurality of features and the received image, wherein the multi-class model generates a probability value for each delivery error type in a multi-label classification. The limitation merely further limits the abstract idea and recites the applying of the machine learning at a high level of generality and therefore does not integrate the judicial exception into a practical application. As set forth in [0053] of the specification any appropriate type of machine learning may be used in the determination.
Claim 5 recites wherein sending the warning message to the device associated with the delivery agent comprises transmitting, to the device associated with the delivery agent, a user interface alerting the delivery agent of one or more delivery error types. The limitation merely further limits the abstract idea and recites the user interface at a high level of generality and therefore does not integrate the judicial exception into a practical application.
Claim 6 recites wherein determining, from the output of the machine learning model, that the order delivered at the location is erroneous comprises determining that the order has one or more of: an erroneous location; an erroneous number of bags selected for the order; an erroneous order from a batch of orders associated with the delivery agent; or an erroneous item in the order. The limitation merely further limits the abstract idea and therefore does not integrate the judicial exception into a practical application.
Claim 7 recites further comprising: generating a training dataset including a set of data instances, wherein a data instance includes inputs comprising the received image and additional features of the order and expected outputs comprising previous orders for which positive indication was received from the user; and re-training the machine learning model using the training dataset. The limitation merely further limits the abstract idea and recites the applying of the machine learning at a high level of generality and therefore does not integrate the judicial exception into a practical application. As set forth in [0053] of the specification any appropriate type of machine learning may be used in the determination.
Claim 8 recites wherein applying the machine learning model to the received image further comprises: identifying locational attributes of the image by applying text detection recognition methods, to extract attributes of the location of the image; identifying objects through object detection of the received image; and passing the identified locational attributes of the image and the identified objects to the machine learning model. The limitation merely further limits the abstract idea and recites the applying of the machine learning at a high level of generality and therefore does not integrate the judicial exception into a practical application. As set forth in [0053] of the specification any appropriate type of machine learning may be used in the determination.
Claims 10-16 and 18-20 recite parallel claim language and therefore are also rejected for the reasons set forth above. For these reasons claims 1-20 are rejected under 35 USC 101.
Subject Matter Free of Prior Art
Claims 1, 9 and 17 are determined to have overcome the prior art of rejection and are free of prior art, however the claims remain rejected under 35 USC 101, as set forth above. Dependent claims 2-8, 10-16, and 18-20 are also free of prior art by virtue of dependency.
Taking amended claim 1 as a representative claim, the claims as amended are found to overcome the prior art rejection for the reasons set forth below.
Claim 1 now recites the additional claimed features emphasized below of:
receiving, at an online system from a device associated with a delivery agent of the online system, an image of an order delivered at a location of a user associated with the order, the device associated with a batch of orders associated with the delivery agent being simultaneously fulfilled by the delivery agent;
accessing a plurality of features about the order, the plurality of features comprising features associated with other orders of the batch of orders;
applying a machine learning model to automatically detect cross-order contamination errors in real-time to the plurality of features and the received image, wherein the model is trained to predict a likelihood that an image includes an error in a delivered order based on the image comprising a feature associated with at least one of the other orders of the batch of orders, wherein the machine learning model outputs a likelihood that the received image includes an error in the delivered order;
determining, from the output of the machine learning model, that the order delivered at the location is erroneous by identifying that the image depicts an item from a different order in the batch of orders; and
responsive to the determining, sending, in real time, a warning message to the device associated with the delivery agent, wherein the warning message causes the device associated with the delivery agent to display a message about a potential error comprising indicia that an incorrect item was delivered based on the image of the order including an item that is part of at least one of the other orders of the batch of orders.
The closest prior art, in addition to the previously cited art, was found to be as follows:
US 20220083964 discloses (of particular note Figure 2) the process of receiving an image from a courier for a delivered package, feedback from the recipient, and the determination whether the delivery was successful from the processed image. The focus of the image processing is the location of the delivery made by the courier
US 20250140001- discloses image processing, shown in Figures 2-4, of trays of bakery items. The tray of items can be from batch orders and are matched to their respective orders to determine if an error has occurred for the plurality of orders. The imaging and error determination is made prior to the deliver of the items. The reference states “[0055] The method 100 of FIG. 10 repeats until all of the orders 26 are complete (at least as far as quantity, as there may still be some errors), as shown in FIG. 9 (again, note that there are six dollies 80 and six stacks 50, but only three are visible in FIG. 9). The at least one computer 12a has tallied five bakery trays 52 having a plurality of first bakery products 54a, three bakery trays 52 having a plurality of second bakery products 54b, and three bakery trays 52 having a plurality of third bakery products 54c, all in one stack 50d on one dolly 80, which would correspond to one order 26 or a portion of an order 26. [0056] The at least one computer 12a has tallied twelve bakery trays 52 having a plurality of second bakery products 54b, all in another stack 50b on another dolly 80, which would correspond to another order 26 or a portion of an order 26. The at least one computer 12a has tallied twelve bakery trays 52 having a plurality of plurality of first bakery products 54a, all in another stack 50a on another dolly 80, which would correspond to another order 26 or a portion of an order 26. Again, in this example, there would be three more stacks 50 (dollies 80), as shown in FIG. 6, that would also be tallied and would each correspond to an order 26 or portion of an order 26. [0057] In step 116, the at least one computer 12a then compares the tallies of bakery products 54 in each stack 50 to the orders 26. In step 118, the at least one computer 12a then indicates confirmation or indicates error, including which order(s) 26, which bakery product types, which stacks 50 and whether there are too many or too few, or whether there are empty bakery trays 52.
US 20230289707 discloses determining error for batches of orders using transaction data processing, not image data processing taken at the point of delivery (see Figures 5-6). The reference states “[0041] The error handling engine 322 checks for sorting errors in a batched order using the transaction data received from the PoS device and/or the shopper 208 (e.g., a shopper mobile application on the client device 110 of the shopper 208). In one embodiment, the error handling engine 322 compares the transaction data to stored batched order data to determine whether one or more picked items were incorrectly sorted by the shopper 208. As an example of a sorting error, a shopper 208 fulfilling a batched order including an order from a customer named Annie and an order from a customer named Brian places a loaf of bread ordered by Annie into Brian's bag, and uses the PoS device to charge the bread to Brian. [0040] Depending upon the embodiment, the correction instruction may be sent as soon as the error handling engine 322 determines a sorting error, upon confirmation by a shopper 208 of delivery of an order in the batched order to a customer at a destination location, and/or upon entry by the client device 110 of the shopper 208 into a geofence around a destination location or a current location of a client device 110 of a customer (e.g., as determined by a global positioning system (GPS) trace provided by the client device 110).
US 11900686 discloses using image analysis to determine if a delivery had been completed at the correct location. The reference states [Col. 4 lines 45-65] (30) The delivery driver takes an image, referred herein also as a photo-on-delivery (POD), of the item at the delivery location during drop-off. The image can be image data stored as a file (e.g., the image includes image data). During post-delivery 130, the computer system 102 process the image using one or more ML models. If the computer system 102 determines the item was delivered to an incorrect delivery location based on the image processing, the driver device 112 receives delivery notification data 134A indicating the item was delivered to an incorrect delivery location from the computer system 102. A customer device 122 associated with the correct delivery location (e.g., a device of a user associated with that location) or the incorrect delivery location (e.g., a device of another user associated with that location) may also receive the delivery notification data 134B. Although such processing is described as being performed post-delivery 130, the processing may be performed during the delivery 120 such that an indication of an incorrect delivery is provided in real-time. Additionally or alternatively, the image is processed to determine delivery image evaluation data 126. However, the reference does not disclose the process being completed on a per item basis across batches of orders.
US 10627244 discloses the comparison of image files to determine the correct items were delivered to the appropriate location (see Figure 7). The reference states [Col. 7 lines 45-65] In some embodiments, the courier client application 333 may also prompt the courier to confirm delivery of the items 319 at the delivery location 106. For example, the courier client application 333 may cause the courier to be prompted to take a photograph or video of the items 319 at the delivery location 106. The photograph or video may be sent to the delivery assistance application 313. The delivery assistance application 313 may compare, as further described herein, the photograph or video with one or more media files 322 to determine that the location photographed or videoed by the courier matches the delivery location 106. The delivery assistance application 313 may also analyze the photograph or video to determine whether the dimensions or characteristics of the package(s) of items 319 match the package information 320 for the order 316. This allows the delivery assistance application 313 to determine whether the correct items 319 were delivered by the courier. While the reference discloses image analysis of delivered items, the reference does not disclose the process being completed for batches of orders after the delivery of the items.
It was found that no references alone or in combination, neither anticipates, reasonable teaches, nor renders obvious the below noted features of Applicant’s invention. The features of claim 1 emphasized below (and parallel claims 9 and 17) in combination that overcome the prior art are:
receiving, at an online system from a device associated with a delivery agent of the online system, an image of an order delivered at a location of a user associated with the order, the device associated with a batch of orders associated with the delivery agent being simultaneously fulfilled by the delivery agent;
accessing a plurality of features about the order, the plurality of features comprising features associated with other orders of the batch of orders;
applying a machine learning model to the plurality of features and the received image, wherein the model is trained to predict a likelihood that an image includes an error in a delivered order based on the image comprising a feature associated with at least one of the other orders of the batch of orders, wherein the machine learning model outputs a likelihood that the received image includes an error in the delivered order;
determining, from the output of the machine learning model, that the order delivered at the location is erroneous; and
responsive to the determining, sending, in real time, a warning message to the device associated with the delivery agent, wherein the warning message causes the device associated with the delivery agent to display a message about a potential error comprising indicia that an incorrect item was delivered based on the image of the order including an item that is part of at least one of the other orders of the batch of orders.
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references 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 emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that the claims are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Response to Arguments
Applicant’s arguments, filed 3/18/2026 with respect to prior art have been fully considered and are persuasive.
With respect to the remarks directed to 35 USC 101, specifically Desjardins, Examiner notes that the fact patterns of the instant case are different from those set forth in Ex Parte Desjardins, and different fact patterns may have different eligibility outcomes. In Ex Parte Desjardins, the claimed invention was a method of training a machine learning model on a series of tasks, and technical improvements as a result of the model training were identified as reduced storage, reduced system complexity, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks. While the ARP in Ex Parte Desjardins determined that these improvements were sufficient to reverse the 101 rejection of the claims at hand in Ex Parte Desjardins, analogous improvements are not apparent in the instant claims. Furthermore, as discussed below, neither Applicant’s specification nor the instant claims set forth analogous improvements. Accordingly, under the analysis set forth according to the MPEP, discussed below, the amended claims stand as ineligible.
Here as discussed in the remarks, the alleged improvement lies in the determination of the error and then subsequently determines the location of where the erred item is located. This information is promptly provided to the end users so that the issue can be quickly resolved. That is the improvement lies in the abstract idea, and not a technical solution to a technical problem. The use of additional elements, such as the user interface and machine learning done in real time, are merely recited at a high level of generality for carrying out the abstract idea. Though applicant’s specification recites improvements over conventional systems, these improvements are to the business process, not the technology itself, as was the case in Desjardins.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTORIA E. FRUNZI whose telephone number is (571)270-1031. The examiner can normally be reached Monday- Friday 7-4 (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 4/13/2026
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