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 .
DETAILED ACTION
Response to Amendment
Applicant’s “Response to Amendment and Reconsideration” filed on 4/23/2026 has been considered.
Claims 1-2, 6, 8-10, 14-17 are pending in this application and an action on the merits follows.
Information Disclosure Statement
The information disclosure statements (IDS) submitted 2/10/2026, 5/29/2026 are being considered by the examiner.
Claim Rejections - 35 USC § 103
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.
Claims 1-2, 6, 8-10, 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Garner (U.S. Patent Publication No, 2021/0110371), in view of Park (U.S. Patent Publication No. 2024/0193933) and further in view of Kalra et.al. (U.S. Patent Publication No. 2021/0264607).
Regarding claims 1 and 9, Garner teaches a vision sensor configured to obtain and transmit image information of a product to an arithmetic processing unit; (fig 2)
recognize the image information of the product based on an image recognition model, to obtain and transmit category information of the product to a data transceiver, (transceiver 225, [34]) wherein the image recognition model is obtained by training a machine learning model based on image information of different products and actual category information corresponding thereto; (machine learning models, [41-43])
the data transceiver configured to transmit the category information of the product to a backend server of a seller, so that the backend server of the seller makes a settlement based on the category information of the product and preset price information of different categories of products, (identify one or more categories , [80], shopping cart, price, [117-118]) wherein the data transceiver comprises: a wireless network communication device; and an interactive touch screen, which is connected to the data transceiver, and configured to provide visual and clickable product category information, (wireless, touch screen, [34]).
Garner does not explicitly disclose perform a target detection on the image information of the product using a one-stage detector; However, Park teaches a step of extracting an object region from a thermal image data of the thermal image camera using an installed YOLO model and then creating an object region coordinate data by returning coordinates of the object region by means of an image processor, [14]. It would have been obvious to one with ordinary skill in the art before the effective filing date, to modify the product recognition system of Garner, to include one-stage detector, as taught by Park, in order to improve detection speed and improve real-time recognition performance, [4].
Garner does not explicitly disclose perform an image segmentation on the image information of the product using an instance segmentation algorithm or a semantic segmentation algorithm; However, Kalra further teaches Semantic segmentation refers to a computer vision process of capturing one or more two-dimensional (2-D) images of a scene and algorithmically classifying various regions of the image (e.g., each pixel of the image) as belonging to particular of classes of objects. For example, applying semantic segmentation to an image of people in a garden may assign classes to individual pixels of the input image, [3], The prediction may include a segmentation mask, [11], his Polarized CNN framework includes a backbone that is suitable for processing the particular texture of polarization and can be coupled with other computer vision architectures such as Mask R-CNN (e.g., to form a Polarized Mask R-CNN architecture) to produce a solution for accurate and robust instance segmentation of transparent objects, [71]. It would have been obvious to one with ordinary skill in the art before the effective filing date, to modify the product recognition system of Garner, to include AI-accelerated semantic segmentation technique, as taught by Kalra, in order to enhance visual understanding.
Garner does not explicitly recognize after the target detection and image segmentation. However, Garner recognizes product image information using trained machine learning models. Kalra teaches generating segmentation maps that isolate object regions from image content and Park teaches localizing objects within image using one stage detector. After modifying Garner to perform target detection as taught by Park and image segmentation as taught by Kalra, it would have been obvious to recognize the image information of the product ager the detecting and segmentation based on Garner’s image model because applying recognition to a detected and segmented product region reduces background interference and improves recognition accuracy. Such modification would have yielded predictable results.
Regarding claim 2, 10, Garner does not explicitly disclose, however Kalra teaches the arithmetic processing unit comprises an Al chip, (Kalra teaches types of electronic circuits may include a central processing unit (CPU), a graphics processing unit (GPU), an artificial intelligence (AI) accelerator (chip), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a digital signal processor (DSP), [66]).
Regarding claim 6, Garner teaches a storage medium, which is respectively connected to the vision sensor and the arithmetic processing unit, and configured to store the image information of the product, the category information of the product and configuration information of the image recognition model, (processing images and/or video content to identify one or more products captured in images and/or video content, [76]).
Regarding claims 8 and 15, Garner teaches receive an operation instruction issued by the backend server of the seller via the data transceiver, and perform a corresponding operation according to the operation instruction, wherein the operation instruction comprises re-recognizing a category of a product and simultaneously recognizing categories of a plurality of product, (retraining, [88-91]).
Regarding claim 14, Garner teaches the training and testing process of the image recognition model comprises: taking image information of different products and actual category information corresponding thereto as sample data to construct a training set and a testing set; training the machine learning model with the training set to obtain the image recognition model; and testing the image recognition model with the testing set, (the system 100 includes one or more model training systems 110 that train one or more machine learning models and/or update training of such models to be distributed to mobile devices and/or to update models operating on mobile devices, [28].
Regarding claim 16, Garner teaches a computer program which, when executed by a processor, implements the method according to claim 9, Fig. 3-8.
Regarding claim 17, Garner taking image information of different products and actual category information corresponding thereto as sample data to construct a training set and a testing set; training the machine learning model with the training set to obtain the image recognition model; and testing the image recognition model with the testing set, (customize training, [75], set, [81-84], he smaller model utilizes less storage space and processing power, and typically processes image data faster in use to get to identify a product that the customer is attempting to identify through the portable user device using the image and/or video data, [79]).
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 MILENA RACIC whose telephone number is (571)270-5933. The examiner can normally be reached M-F 7:30am-4pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Florian (Ryan) Zeender can be reached at (571)272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MILENA RACIC/Patent Examiner, Art Unit 3627
/FLORIAN M ZEENDER/Supervisory Patent Examiner, Art Unit 3627
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