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 .
The following is a Final Office Action in response to communications received on 3/30/2026. Claims 1-20 are currently pending and have been examined. Claims 1-6, 8, 10-15, 17, 19, and 20 have been 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 1-9 are a method, claims 10-18 are a computer product, and claims 19-20 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, 10 and 19, taking claim 1 as a representative claim) recite:
A method, performed at a computer system comprising a processor and a non-transitory computer readable medium, comprising:
receiving, an order status from a picker client device indicating a picker fulfilling an order for a customer from a retailer is in a checkout line at the retailer;
receiving, an image of items in the checkout line from the picker client device, the image captured by the picker client device;
identifying, one or more items included in the image;
generating, using a trained machine learning model, one or more probabilities of the customer performing a specific interaction with different identified items;
ranking the items based on the one or more probabilities of the customer performing a specific interaction with different identified items, wherein ranking the items comprises performing an embedding similarity match between the items in the image with items associated with the order;
selecting a subset of identified items based on ranking the items;
generating, a message including a carousel portion that includes interactive image slots presenting information describing one or more identified items of the subset of identified items, wherein the information presented by the interactive image slots of the carousel portion corresponds to a position associated with ranking the items; and
transmitting the message to a client device of the customer for presentation in a communication interface displaying messages between the customer and the picker.
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 determining items of potential interest to a user at the checkout from a picker completing the shopper for an online customer leading to obtaining revenue from the customer adding one or more items in the checkout line to an online order being completed in the store. (see paragraph 0007 of the instant 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, performed at a computer system comprising a processor and a non-transitory computer readable medium, comprising: (claim 1)
A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising: (claim 10)
A system comprising: a processor; and a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the processor, cause the processor to perform steps comprising: (claim 19)
receiving, an order status from a picker client device indicating a picker fulfilling an order for a customer from a retailer is in a checkout line at the retailer;
receiving, an image of items in the checkout line from the picker client device, the image captured by the picker client device;
identifying, one or more items included in the image;
generating, using a trained machine learning model, one or more probabilities of the customer performing a specific interaction with different identified items;
ranking the items based on the one or more probabilities of the customer performing a specific interaction with different identified items, wherein ranking the items comprises performing an embedding similarity match between the items in the image with items associated with the order;
selecting a subset of identified items based on ranking the items;
generating, a message including a carousel portion that includes interactive image slots presenting information describing one or more identified items of the subset of identified items, wherein the information presented by the interactive image slots of the carousel portion corresponds to a position associated with ranking the items; and
transmitting the message to a client device of the customer for presentation in a communication interface displaying messages between the customer and the picker.
The additional elements of 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 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).
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.
Even when considered as an ordered combination, the additional elements of claim 1, 10 and 19 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, 10, and 19 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, 10 and 19 are ineligible.
Claim 2 recites further comprising: receiving, at the computer system, a selection of an identified item via the communication interface from the client device; and transmitting an identification of the identified item and a request to include the identified item in the order to the picker client device for presentation to the picker. The limitation merely further limits the abstract idea and does not further integrate the judicial exception into a practical application.
Claim 3 recites wherein transmitting the message to the client device of the customer comprises: modifying the order to include the identified item; storing the order at the computer system; and transmitting an identification of the identified item and a request to pick the identified item to the picker client device. The limitation merely further limits the abstract idea and does not further integrate the judicial exception into a practical application.
Claim 4 recites further comprising: receiving a modified order status from the picker client device, the modified order status indicating the picker has completed checking out from the retailer; and transmitting an instruction to the client device preventing selection of one or more identified items of the subset of identified items via the communication interface. The limitation merely further limits the abstract idea and does not further integrate the judicial exception into a practical application.
Claim 5 recites wherein transmitting the instruction to the client device preventing selection of the one or more identified items comprises: transmitting an additional message to the client device indicating the customer is unable to select one or more identified items of the subset of identified items via the communication interface; and transmitting the instruction to the client device. The limitation merely further limits the abstract idea and does not further integrate the judicial exception into a practical application.
Claim 6 recites wherein generating, the message comprises: generating text content including a time interval; generating a carousel portion including a plurality of slots, each slot displaying information describing an identified item of the subset of identified items; and generating the message including the text content and the carousel portion. The limitation merely further limits the abstract idea and does not further integrate the judicial exception into a practical application.
Claim 7 recites wherein the text content is generated by applying a large language model to a prompt including: instruction to generate text indicating the picker is checking out, an identifier of the picker, and an estimated amount of time until the picker completes checking out of the retailer. The limitation merely further limits the abstract idea and does not further integrate the judicial exception into a practical application.
Claim 8 recites wherein the trained machine learning model is trained by: obtaining a training dataset including a plurality of training examples, a training example of the plurality of training examples including characteristics of a training customer and attributes of an item, the training example having a label indicating whether the training customer performed the specific interaction with the item; applying an untrained machine learning model to each training example of the training dataset to generate a predicted probability of the training customer performing the specific interaction with the item; scoring the untrained machine learning model using a loss function and the label of the training example; and updating one or more parameters of the untrained machine learning model by backpropagation based on scoring the untrained machine learning model until one or more criteria are satisfied to obtain the trained machine learning model. While the claim recites the additional elements of machine learning models such as backpropagation, the machine learning is recited at a high level of generality and does not integrate the judicial exception into a practical application.
Claim 9 recites wherein the specific interaction with an identified item comprises the customer including the identified item in the order. The limitation merely further limits the abstract idea and does not further integrate the judicial exception into a practical application.
Claims 11-18 and 20 recite parallel claim language and are rejected for the reasons set forth above.
For these reasons, claims 1-20 are rejected.
Subject Matter Free of Prior Art
Claims 1, 10 and 19 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.
Taking claim 1 as a representative claim, claim 1 recites the combination of steps including:
receiving, an order status from a picker client device indicating a picker fulfilling an order for a customer from a retailer is in a checkout line at the retailer;
receiving, an image of items in the checkout line from the picker client device, the image captured by the picker client device;
identifying, one or more items included in the image;
generating, using a trained machine learning model, one or more probabilities of the customer performing a specific interaction with different identified items;
ranking the items based on the one or more probabilities of the customer performing a specific interaction with different identified items, wherein ranking the items comprises performing an embedding similarity match between the items in the image with items associated with the order;
selecting a subset of identified items based on ranking the items;
generating, a message including a carousel portion that includes interactive image slots presenting information describing one or more identified items of the subset of identified items, wherein the information presented by the interactive image slots of the carousel portion corresponds to a position associated with ranking the items; and
transmitting the message to a client device of the customer for presentation in a communication interface displaying messages between the customer and the picker.
Claims 10 and 19 recite parallel claim language to include the combination determined to overcome the prior art.
The closest prior art was found to be as follows:
Page (US 20170287053) discloses updating a database and interface to show items of a shopping list have been collected by a remote shopper for an end client (abstract and paragraph 0014), however the reference does not disclose receiving, at an online system, an order status from a picker client device indicating a picker fulfilling an order for a customer from a retailer is in a checkout line at the retailer as recited in the claimed invention, nor the remaining limitations required in the claim.
Graube (US 20220051310) discloses the following citations:
[0116] In block 6b, in addition to the store picker collecting additional or alternative products not on the shopping list, the user mobile device of the store picker or the store management entity server may transmit images or other information of available alternative products to the ordering customer.
[0112] In block 6a, the store picker may select, collect, and scan additional or alternative products that are not on the shopping list. This may happen because the store picker could not find a product (e.g., the product is hard to find or is unavailable) or because the store picker has found a substitute product and coordinated the substitute selection with the ordering customers (e.g., via the user mobile device). As a further alternative, the ordering customer may have separately requested an add-on product not originally on the shopping list, which the store picker then collects from a shelf. The store picker may coordinate and/or communicate with the ordering customer through the user mobile device (e.g., through a phone call, messaging, or other communication function). Regardless, in block 6a the store picker may scan products not on the shopping list. While the reference makes mention to the picker sending relevant images of products to an end client and adding additional items relevant to the end client’s cart, the reference does not disclose “receiving, at the computer system, an image of items in the checkout line from the picker client device, the image captured by the picker client device; identifying, by the computer system, one or more items included in the image; generating, using a trained machine learning model, one or more probabilities of the customer performing a specific interaction with different identified items; ranking the identified items based on the generated probabilities of the customer performing a specific interaction with different identified items; selecting a subset of identified items based on the ranking; generating, by the computer system, a message including information describing one or more identified items of the subset” as required by the claimed invention.
Sivan (US 20210082024) discloses the following citations:
[0005] providing dynamic product suggestions to a customer in a checkout aisle, in addition to providing multiple different fulfillment options for dynamically suggested products, including in-store item fulfillment options. During a checkout process in a checkout aisle in a retail store, products can be dynamically suggested to a customer on a customer-facing device based on a variety of factors, such as products that have been scanned and/or are detected/identified as being queued-up for scanning during the checkout process, product purchasing behaviors relevant to the shopping experience (e.g., customer-specific shopping behaviors, store-specific shopping behaviors, time/day of week/seasonal shopping behaviors, and/or combinations thereof), and/or other factors. This technology can assist customers in a variety of ways during their checkout process, for example, by helping customers avoid forgetting to purchase items, by suggesting relevant products (e.g., avoiding suggesting products customer has placed on conveyor belt but have not yet been scanned), and by providing a variety of convenient fulfillment options (e.g., in-store fulfillment, shipping options).
[0039] The product signals 110 can include information that provides at least some identifying information for the products not yet scanned, such as partial and/or whole images of the products from one or more vantage points, RFID scans of RFID tags attached/connected to the products, and/or other information.
While the reference makes mention to recommending products to a shopper in the checkout line based on the scanned products at checkout, the images are received from the store’s sensors/camera and the recommendations are provided directly to the end client, there is not picker or picker device in the recited system/method. The reference does not disclose “receiving, at an online system, an order status from a picker client device indicating a picker fulfilling an order for a customer from a retailer is in a checkout line at the retailer; receiving, at the computer system, an image of items in the checkout line from the picker client device, the image captured by the picker client device; transmitting the message from the computer system to a client device of the customer for presentation in a communication interface displaying messages between the customer and the picker”
Cancro (US 20140214562) discloses the following citations:
[0022] It may be desirable to notify users of suggested items being offered by a retailer within a facility of the retailer prior to an upcoming transaction at a point of sale terminal. Notifying users of an item suggested by the retailer for the user to purchase at a most relevant time, e.g., when the user is waiting in line at a checkout lane prior to an upcoming transaction, are described herein. Accordingly, an item impulse device may be present at each checkout lane to notify the user of the item suggested by the retailer and allow the user to obtain the item prior to the upcoming transaction at the point of sale terminal.
While the reference makes mention to the need to address the problem of capitalizing on impulse buys by shoppers at a checkout line, the reference does not implement the solution as claimed in the claimed invention.
Dalal (US 20210342914) discloses the following citations:
[0094] A portable computing device 104 automatically activates one or more cameras 218 of the computing device to capture one or more images that include one or more products 702. The computing device control circuit 202 applies an OCR application 704 to identify text from the image. The identified text is evaluated relative to one or more limited dictionaries 211 of predefined text to extract a subset of key text that includes of some or all of the identified from the image and that is expected to be relevant in identifying a product.
[0016] cause at least a subset of the first product information to be displayed on the display;
[0037] In some embodiments, the rankings, additional ranking or other ordering, may further be dependent on one or more other factors, which may include for example, customer purchase history, customer preferences, recent popularity of products, customer location, inventory information, other factors, or a combination of such factors. In some embodiments, the product recommendation system 102 accesses one or more customer databases 110 that store thousands, and typically hundreds of thousands, of customer profiles. Each customer profile is associated with one of thousands, and typically hundreds of thousands, of different customers. As described above, each customer profile typically includes purchase history information, and in some instances customer preference information corresponding to the respective customer. The ranking can use the customer history information and/or preference information from the customer profile corresponding to the customer associated with the portable computing device 104 submitting the received product profile.
[0041] The product recommendation system 102 autonomously communicates the set of one or more product identifiers to the portable computing device 104 that communicated the query product vector.
While the reference discloses the determination of product recommendation to communicate via a device based on data identified from an image, the reference does not disclose “receiving, at an online system, an order status from a picker client device indicating a picker fulfilling an order for a customer from a retailer is in a checkout line at the retailer; receiving, at the computer system, an image of items in the checkout line from the picker client device, the image captured by the picker client device; transmitting the message from the computer system to a client device of the customer for presentation in a communication interface displaying messages between the customer and the picker.”
Wu (US20240289823) discloses the following citations:
shown in Figure 9 is the product pairs with probability of purchase in percentage
[0113] This allows for frequency of co-purchase to be incorporated into the training of the machine-learning model such that the model will assign a higher probability of products being purchased together for products that have been more frequently (e.g. more often) purchased together.
shown in Figure 12 an output of the co-pair items to a device
While the reference discloses determining the probability of purchase pairs based on past shopping data, the reference does not disclose receiving, at an online system, an order status from a picker client device indicating a picker fulfilling an order for a customer from a retailer is in a checkout line at the retailer; receiving, at the computer system, an image of items in the checkout line from the picker client device, the image captured by the picker client device; identifying, by the computer system, one or more items included in the image; generating, by the computer system, a message including information describing one or more identified items of the subset; and transmitting the message from the computer system to a client device of the customer for presentation in a communication interface displaying messages between the customer and the picker.”
“Real-Time Object Detection with IOT Using a Smart Cart” using cameras located on a shopping cart to collect cart related data in order to expedited the check out process.
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 (and parallel claims 9 and 16) in combination that overcome the prior art are:
receiving, an order status from a picker client device indicating a picker fulfilling an order for a customer from a retailer is in a checkout line at the retailer;
receiving, an image of items in the checkout line from the picker client device, the image captured by the picker client device;
identifying, one or more items included in the image;
generating, using a trained machine learning model, one or more probabilities of the customer performing a specific interaction with different identified items;
generating, a message including a carousel portion that includes interactive image slots presenting information describing one or more identified items of the subset of identified items, wherein the information presented by the interactive image slots of the carousel portion corresponds to a position associated with ranking the items; and
transmitting the message to a client device of the customer for presentation in a communication interface displaying messages between the customer and the picker.
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 claims 1-20 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 3/30/2026 have been fully considered but they are not persuasive.
With respect to the remarks directed to 35 USC 101, the examiner does not find the remarks to be persuasive. The rejection has been updated above to address the claims as amended. The image slots of the instant application, read in light of the specification, are no more than elements of an interface presenting resulting information. As described in [0089-90] these slots display information describing the item of the subset. The disclosure does not recite these slots to be more technical in nature than the presentation of the item information. As such, these slots are merely presenting the resulting information of the “ranking” and “selecting” steps which are part of the abstract idea. As such, the image slots are merely presenting the abstract idea and are not a technical solution to a technical problem. The implementation of the ranking and selecting being done by machine learning further does not integrate the judicial exception into a practical application. The machine learning is recited at a high level of generality as stated in [0063] of the specification, this machine learning can be any of a number of models to include neural networks, such as perceptrons, multilayer perceptrons, convolutional neural networks, recurrent neural networks, sequence-to-sequence models, generative adversarial networks, or transformers. The detail of the machine learning does not go beyond merely applying the machine learning to the abstract idea.
For at least these reasons, the claims remain rejected under 35 USC 101.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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).
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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 4/16/2026
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