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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/20/2024 and 7/23/2026 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 § 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, 4, 6, 8-9, 11-13, 15-16, 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over K. Rajappan et. al. (United States Patent Application Publication US 2025/0232360 A1) in view of Xiao et. al. (United States Patent Application Publication US 20210019725 A1), and Glazer et. al. (United States Patent Application Publication US 20190114488 A1).
The applied reference Xiao et. al. has a common applicant (Walmart Apollo, LLC) with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2).
This rejection under 35 U.S.C. 103 might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C.102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B); or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement. See generally MPEP § 717.02.
Regarding claim 1, K. Rajappan et. al. discloses a system for zero-friction unpaid item identification via computer vision, the system comprising: a processor (K. Rajappan et. al. Abstract: system and method for automated product validation, identification, and transaction management in shopping environments. The system integrates a smart shopping cart equipped with at least one camera, a load cell, and a user interface. Further, discrepancies, such as unscanned products or mismatches, are flagged, and payment is blocked until resolved), a computer-readable medium storing instructions that are operative upon execution by the processor to: select an image of a selected cart from a plurality of images of the selected cart using a set of anchor points associated with a field of view of an image capture device (K. Rajappan et. al. [0020]: the at least one camera is configured to provide high-resolution images of the interior of the smart shopping cart from at least three predefined angels for improved product identification accuracy.); identify a plurality of items associated with the selected cart using the selected image (K. Rajappan et. al. [0021]: product validation from processing the background image and the foreground image to identify and isolate the new product; extracting an image embedding of the new product from the foreground image using an Artificial Intelligence, AI model); predict an item identifier (ID) associated with each item in the plurality of items associated with the selected cart, a set of identified items comprising a plurality of item IDs associated with the plurality of items (K. Rajappan et. al. [0052]-[0054] the barcode scanner captures product-identifying information, such as product type, weight, and price, and transmits this data to a processing unit for further validation. The scanning step ensures that every product intended to be added to the smart shopping cart is recorded in the system.); and send the notification to a user interface (UI) device associated with the scan device (K. Rajappan et. al. Fig. 3: displaying an alert to the user interface when the new product is validated or invalidated in real-time. Fig. 4a-4c, 5).
PNG
media_image1.png
1016
594
media_image1.png
Greyscale
However, K. Rajappan et. al. fails to disclose select a e-receipt associated with the selected cart from a plurality of active e-receipts using a fuzzy matching of a set of paid items included in the selected e-receipt and the set of identified items generated using the selected image in real time, the set of paid items comprising a receipt item ID associated with each item scanned during a transaction associated with the selected e-receipt; map each receipt item ID in the set of paid items to an identified item ID in the set of identified items, wherein an unmapped item in the set of identified items is a predicted unpaid item; upon receiving a verification request signal associated with the selected e-receipt from a scan device indicating a user is ready to exit, generate a notification including a verification result, wherein the verification result includes a set of unpaid items, wherein each predicted unpaid item in the set of unpaid items is associated with a predicted item ID in the set of identified items that fails to map to a corresponding receipt item ID in the set of paid items; the notification comprises a first list of items in the set of paid items and a second list of items in the set of unpaid items.
Xiao et. al. teaches select a e-receipt associated with the selected cart from a plurality of active e-receipts using a fuzzy matching of a set of paid items included in the selected e-receipt and the set of identified items generated using the selected image in real time (Xiao et. al. Fig. 3, [0084]: Computer vision software is used to create a list of the detected items (e.g., in the form of a CV profile or receipt) and the list is sent to a processing device in the cloud. In aspects, the CV profile includes transaction time information (e.g., when the transaction occurred such as a timestamp). [0087]-[0093]), the set of paid items comprising a receipt item ID associated with each item scanned during a transaction associated with the selected e-receipt (Xiao et. al. [0093]-[0098]); upon receiving a verification request signal associated with the selected e-receipt from a scan device indicating a user is ready to exit, generate a notification including a verification result, wherein the verification result includes a set of unpaid items, the notification comprises a first list of items in the set of paid items and a second list of items in the set of unpaid items (Xiao et. al. [0063]-[0073]: The local control circuit is configured to: receive the one or more images from the one or more cameras, analyze the images to determine items in the cart, responsively create a computer vision (CV) profile listing the items in the cart, the CV profile being associated with and identified by the transaction identifier. For example, images of known products are compared to the images of items in the cart. [0074]-[0078]: items on the CV profile are compared to the items on the transaction record. At step 216 and when the comparison indicates a discrepancy (the items in the CV profile do not match items in the transaction record), an action to take is determined. An action can be performed at self-checkout point-of-sale register. The action is taken and the action is one or more of: sending an electronic alert to the store employee, sending electronic information to the electronics device showing the discrepancy, sending a control signal to activate a warning indicator at the exit of the store, or sending a control signal to instruct an automated vehicle to retrieve an unpaid item from a customer and return the item to the retail store. For example, a message may be sent to a self-checkout monitoring employee at a self-checkout area.).
PNG
media_image2.png
974
552
media_image2.png
Greyscale
PNG
media_image3.png
756
546
media_image3.png
Greyscale
Glazer et. al. teaches to map each receipt item ID in the set of paid items to an identified item ID in the set of identified items, wherein an unmapped item in the set of identified items is a predicted unpaid item wherein each predicted unpaid item in the set of unpaid items is associated with a predicted item ID in the set of identified items that fails to map to a corresponding receipt item ID in the set of paid items (Glazer et. al. [0034]: a primary monitoring system can still be evaluated for alignment by mapping the records of the secondary monitoring system to the corresponding records of the primary monitoring system. This relates to the automating checkout process where two monitoring systems can each be configured to generate a monitoring system output related to selected items and/or user activity associated with item selection.).
PNG
media_image4.png
610
522
media_image4.png
Greyscale
The system and method for frictionless unpaid item identification is drawn from K. Rajappan et. al. because of the zero-friction shopping experience set-up. In a frictionless store, an individual is allowed to check in with a store electronically, browse for items to purchase, place items in bags/carts, and exit the store without any interaction with a store agent or with a checkout station, which allows for quick and easy shopping because no interaction with store agents are necessary. However, proper surveillance of unpaid items and inventory of the customer selected items are necessary to protect the store from loss stemming from theft. The system of K. Rajappan et. al. is advantageous because it incorporates several technical components and mechanisms that collectively ensure accurate validation and monitoring of the products while preventing fraudulent activities. Thus, each element is important to the claimed invention to improve security of the retail store. For these reasons, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of K. Rajappan et. al. with the disclosure of Xiao et. al. and Glazer et. al. so that the system for monitoring unpaid items through computer vision is robust and optimized for verification through the availability of e-receipts and mapping to the unpaid items.
Regarding claim 8, which is a method for zero-friction unpaid item detection, the method comprising the system of claim 1, the rejection analysis of claim 1 is incorporated herein.
Regarding claim 15, which is one or more computer storage devices having computer-executable instructions stored thereon, which, upon execution by a computer, cause the computer to perform operations comprising the method of claim 8, which the rejection analysis of claim 1 is incorporated herein.
Regarding claim 4, Xiao et. al. further discloses the system of claim 1, wherein the instructions are further operative to: generate a set of indicators within the selected image of the selected cart associated with the set of unpaid items, wherein each unpaid item is associated with an indicator in the set of indicators (Xiao et. al. Fig. 2, [0070]-[0078]: discrepancy between unpaid and paid items initiates an action or warning indicator).
PNG
media_image5.png
528
642
media_image5.png
Greyscale
Regarding claim 6, K. Rajappan et. al. further discloses the system of claim 1, wherein the instructions are further operative to: display a set of images of the set of unpaid items, wherein an image of each unpaid item is included in the set of images displayed on the UI device (K. Rajappan et. al. [0055]-[0056]: a user interface is also integrated with the smart shopping cart. Fig. 8: shopping list and payment summary).
PNG
media_image6.png
832
676
media_image6.png
Greyscale
Regarding claim 9, K. Rajappan et. al. further discloses the method of claim 8, further comprising: receiving first scan data associated with a first receipt from the scan device, the first scan data comprising a first receipt ID associated with the first receipt; retrieving a first result including a first set of unpaid items and a first set of paid items associated with a first basket of items; generating a first notification including the first result, wherein the first notification is transmitted to the UI device for presentation to the user in real-time; receiving second scan data associated with a second receipt from the scan device, the second scan data comprising a second receipt ID; retrieving a second result including a second set of unpaid items and a second set of paid items associated with a second basket of items; and generating a second notification including the second result, wherein the second notification is transmitted to the UI device for presentation to the user in real-time (K. Rajappan et. al. [0055]-[0056]: Additionally, the user interface generates alerts to notify the customer when a product is either successfully validated or invalidated due to discrepancies. The user interface is configured to display real-time alerts pertaining to the validation status of products, whether valid or invalid, based on the comparison of the scanned barcode, image embeddings, and weight data.).
Regarding claim 11, Xiao et. al. further discloses the method of claim 8, further comprising: generate a list of predicted universal product code (UPC) values associated with each identified item in the set of identified items, wherein each predicted UPC in the list of predicted UPC values is mapped to a UPC associated with an item in the set of paid items obtained from the selected receipt (Xiao et. al. [0072]: there is a barcode for each merchandise purchased).
PNG
media_image7.png
486
646
media_image7.png
Greyscale
Regarding claim 12, K. Rajappan et. al. further discloses the method of claim 8, further comprising: displaying an image of each unpaid item in the set of unpaid items on the UI device one at a time, wherein the user is instructed to scan each item as it is displayed on the UI device, wherein an image of a first unpaid item is displayed with a first instruction to scan the first unpaid item; and upon receiving scan data for the first unpaid item, displaying an image of a second unpaid item with a second instruction to scan the second unpaid item (K. Rajappan et. al. [0127]: In case the customer missed scanning of any product, the backend system may display a notification to the customer to scan the product first.).
Regarding claim 13, K. Rajappan et. al. further discloses the method of claim 8, further comprising: mapping a receipt item ID to a group of potential item IDs associated with a sub-set of items in the set of identified items (K. Rajappan et. al. [0058]: The vector database stores a plurality of reference embeddings associated with various products. Each reference embedding in the vector database includes data such as visual embeddings derived from product images, weight information, and barcode data. This comprehensive dataset serves as the basis for validating new products added to the smart shopping cart.).
Regarding claim 16, K. Rajappan et. al. further discloses the one or more computer storage devices of claim 15, wherein the operations further comprise: display an image of the selected cart including a set of indicators associated with the set of unpaid items; and display an image of each unpaid item in the set of unpaid items on the UI device one at a time (K. Rajappan et. al. [0060]-[0064]: The processing unit compares the extracted image embeddings of the new product and the scanned barcode against retrieved reference embeddings to ensure accuracy. If the extracted embedding and scanned barcode match the retrieved reference embeddings and associated data, the processing unit tags the new product as valid. Otherwise, the product is invalidated, and an alert is generated on the user interface.).
Regarding claim 18, Xiao et. al. further discloses the one or more computer storage devices of claim 15, wherein the receipt item ID is a universal product code (UPC) (Xiao et. al. [0012]: The customer additionally receives a paper receipt with the transaction number (e.g., a barcode), the timing information (such as a timestamp), and/or some other identifier (such as a QR code).).
Regarding claim 19, Xiao et. al. further discloses the one or more computer storage devices of claim 15, wherein a selected e-receipt is associated with a transaction completed at an unstaffed checkout lane (Xiao et. al. [0013]: From the cloud, a central processor contacts the store to get the transaction information (an electronic transaction record that may also include the start and end time information) and uses the timing information to access the CV profile. Fig. 3: 308-Customer completes checkout and eReceipt is created.).
Claim(s) 2, 3, 7, 10, 14, 17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over K. Rajappan et. al. (United States Patent Application Publication US 2025/0232360 A1) in view of Xiao et. al. (United States Patent Application Publication US 20210019725 A1), and Glazer et. al. (United States Patent Application Publication US 20190114488 A1) as applied to claim 1 and 15 above, and further in view of Thi et. al. (United States Patent US 12,100,218 B1).
Regarding claim 2 and 20, K. Rajappan et. al., Xiao et. al., and Glazer et. al. disclose the system of claim 1, and the one or more computer storage devices of claim 15. However, K. Rajappan et. al., Xiao et. al., and Glazer et. al. fail to disclose wherein the instructions are further operative to: select a first image of the selected cart in which the selected cart is located in proximity to a first anchor point within the field of view of the image capture device; and select a second image of the selected cart in which the selected cart is located in proximity to a second anchor point within the field of view of the image capture device, wherein a trained object detection model analyzes the first image and the second image to detect the plurality of items within the selected cart.
Thi et. al. teaches wherein the instructions are further operative to: select a first image of the selected cart in which the selected cart is located in proximity to a first anchor point within the field of view of the image capture device; and select a second image of the selected cart in which the selected cart is located in proximity to a second anchor point within the field of view of the image capture device, wherein a trained object detection model analyzes the first image and the second image to detect the plurality of items within the selected cart (Thi et. al. col. 6 lines 45-67, col. 7 lines 1-8, col. 8 lines 1-40, col. 10 lines 10-40, Fig. 11). This is important to the claimed invention because keeping track of each cart and user, which is construed as an anchor point, reduces the number of fraudulent transactions that occur within a hybrid retail environment. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of K. Rajappan et. al., Xiao et. al., Glazer et. al. and Thi et. al. so that these features are included in the solution of the claimed invention.
PNG
media_image8.png
676
694
media_image8.png
Greyscale
PNG
media_image9.png
946
552
media_image9.png
Greyscale
PNG
media_image10.png
948
538
media_image10.png
Greyscale
PNG
media_image11.png
920
610
media_image11.png
Greyscale
Regarding claim 3, K. Rajappan et. al., Xiao et. al., and Glazer et. al. disclose the system of claim 1. However, K. Rajappan et. al., Xiao et. al. and Glazer et. al. fail to disclose wherein the instructions are further operative to: obtain the plurality of images comprising a plurality of carts; track the selected cart through the plurality of images using object tracking; and place a bounding box around the selected cart in each image in the plurality of images.
Thi et. al. teaches wherein the instructions are further operative to obtain the plurality of images comprising a plurality of carts; track the selected cart through the plurality of images using object tracking; and place a bounding box around the selected cart in each image in the plurality of images (Thi et. al. col. 6 lines 45-67, col. 7 lines 1-8, col. 8 lines 1-40, col. 10 lines 10-40, Fig. 4). This is important to the claimed invention because keeping track of each cart and user, which is construed as an anchor point, reduces the number of fraudulent transactions that occur within a hybrid retail environment. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of K. Rajappan et. al., Xiao et. al., Glazer et. al. and Thi et. al. so that these features are included in the solution of the claimed invention.
Regarding claim 10, the rejection analysis of claim 3 is incorporated herein. Thi et. al. also teaches cropping each image to isolate the selected cart from other objects based on the bounding box around the selected cart in each image (Thi et. al. col 15, lines 13-67, col 16, lines 1-15: the segment-selection component selects certain segments of images based on time to acquire image data that captures for the cart-location component that uses this image data for further operations.).
PNG
media_image12.png
912
414
media_image12.png
Greyscale
Regarding claim 7 and 14, K. Rajappan et. al., Xiao et. al., and Glazer et. al. disclose the system of claim 1, and the method of claim 8. However, K. Rajappan et. al., Xiao et. al., and Glazer et. al. fail to disclose further comprising: generating the plurality of images of the selected cart by a set of cameras, the set of cameras comprising a ceiling mounted camera capturing a set of images associated with a top view of the selected cart, wherein the set of cameras transmits the plurality of images of the selected cart to a computing device via a network.
Thi et. al. teaches further comprising generating the plurality of images of the selected cart by a set of cameras, the set of cameras comprising a ceiling mounted camera capturing a set of images associated with a top view of the selected cart, wherein the set of cameras transmits the plurality of images of the selected cart to a computing device via a network (Thi et. al. col. 8 lines 1-40, Fig. 11). This is important to the claimed invention because keeping track of each cart and user, which is construed as an anchor point, via overhead cameras reduces the number of fraudulent transactions that occur within a hybrid retail environment by improving security and surveillance. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of K. Rajappan et. al., Xiao et. al., Glazer et. al. and Thi et. al. so that these features are included in the solution of the claimed invention.
Regarding claim 17, K. Rajappan et. al., Xiao et. al., and Glazer et. al. disclose the one or more computer storage devices of claim 15. However, K. Rajappan et. al., Xiao et. al., and Glazer et. al. fail to disclose wherein the operations further comprise: estimate, by a depth model, a plurality of depth values associated with a plurality of carts within an image; and select a cart closest to an anchor point based on the plurality of depth values, wherein the depth value indicates a proximity of the selected cart to the anchor point.
Thi et. al. teaches wherein the operations further comprise: estimate, by a depth model, a plurality of depth values associated with a plurality of carts within an image; and select a cart closest to an anchor point based on the plurality of depth values, wherein the depth value indicates a proximity of the selected cart to the anchor point (Thi et. al. col. 6 lines 45-67, col. 7 lines 1-8, col. 8 lines 1-40, col. 10 lines 10-40, the location of the cart was identified, along with the trajectory of a cart over the time interval, Fig. 11). This is important to the claimed invention because keeping track of each cart and user, which is construed as an anchor point, reduces the number of fraudulent transactions that occur within a hybrid retail environment. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of K. Rajappan et. al., Xiao et. al., Glazer et. al. and Thi et. al. so that these features are included in the solution of the claimed invention.
.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over K. Rajappan et. al. (United States Patent Application Publication US 2025/0232360 A1) in view of Xiao et. al. (United States Patent Application Publication US 20210019725 A1), and Glazer et. al. (United States Patent Application Publication US 20190114488 A1) as applied to claim 1 above, and further in view of Xiao et. al. (United States Patent Application Publication US 2023/0245535 A1).
The applied reference Xiao et. al. has a common applicant (Walmart Apollo, LLC) with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2).
This rejection under 35 U.S.C. 103 might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C.102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B); or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement. See generally MPEP § 717.02.
Regarding claim 5, K. Rajappan et. al., Xiao et. al., and Glazer et. al. disclose the system of claim 1. However, K. Rajappan et. al., Xiao et. al., and Glazer et. al. fail to disclose wherein the set of indicators comprises a bounding box associated with each unpaid item in the set of unpaid items.
Xiao et. al. US 2023/0245535 A1 teaches wherein the set of indicators comprises a bounding box associated with each unpaid item in the set of unpaid items. (Xiao et. al. US 2023/0245535 A1, [0021]: the control circuit may find or detect all the possible items in a cart (e.g., the container) and draw bounding boxes on those found/detected items. By one approach, if there is only one item found/detected, the control circuit may draw one bounding box. By another approach, if there are ten items found/detected, the control circuit may draw ten bounding boxes.). This is important to the claimed invention because the bounding box increases the accuracy of item identification based on images captured. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of K. Rajappan et. al., Xiao et. al., Glazer et. al., and Xiao et. al. US 2023/0245535 A1 so that a bounding box is used to identify the selected items.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Maxilom et. al. (US 20220076322 A1) is relevant to the claimed invention because it discloses a frictionless shopping experience. Ceccon et. al. is relevant to the claimed invention because it discloses a universally unique item identifier of an item and information device that allows user interaction.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA YIFANG LIN whose telephone number is (571)272-6435. The examiner can normally be reached M-F 7:00am-6:15pm, with optional day off.
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, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JESSICA YIFANG LIN/Examiner, Art Unit 2668 September 3, 2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
Prosecution Insights