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
This office action is in response to communication filed on 8/13/2024.
Claims 1-16 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted 8/13/2024, 6/11/2025, 9/23/2025 are being considered by the examiner.
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.
Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Regarding claims 1-16, under Step 2A claims 1-16 recite a judicial exception (abstract idea) that is not integrated into a practical application and does not provide significantly more.
Under Step 2A (prong 1), and taking claim 9 as representative, claim 9 recites a method for automatically identifying and creating a product catalog, comprising:
receiving image information of a product obtained by a vision sensor;
inputting the image information of the product into an image recognition model an
outputting category information of the product, 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; and
transmitting the category information of the product to a data transceiver 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.
The claims are directed to an abstract idea of identifying products, categorizing them and conducting commercial settlement based on categorization. These limitations recite organizing human activity, such as by performing commercial interactions and/or fundamental economic principals or practices (see: MPEP 2106.04(a)(2)(II)). This also describes concepts relating to the economy and commerce that represent fundamental economic practices, which also fall under organizing human activity.
The limitations recite mental processes such as recognizing, classifying and evaluating information. These steps correspond to information analysis and commercial decision making that can be performed mentally.
Accordingly, under step 2A (prong 1) claim 9 recites an abstract idea because claim 9 recites limitations that fall within the “Certain methods of organizing human activity” and “Mental processes” grouping of abstract ideas.
Under Step 2A (prong 2), the abstract idea is not integrated into a practical application. The Examiner acknowledges that claims 1 and 9 recite additional elements such as vision sensor, arithmetic processing unit, image recognition model, data transceiver, backend server, machine learning model.
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements of claim 9 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). This is most notably true for training steps, which rely on the use of generic machine learning technology in carrying out the claimed abstract idea (e.g., Specification, para 28, 65). This is also underscored by dependent claim 9, which expressly recites the use of known machine learning techniques. Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (prong 2), claim 9 does not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to representative claims, taken individually or as a whole the additional elements of claims 1 and 9 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claim 9 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(II)), including at least:
receiving or transmitting data over a network,
presenting offers
Even considered as an ordered combination (as a whole), the additional elements of claims 1 and 9 do not add anything further than when they are considered individually.
In view of the above, representative claims 1 and 9 do not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Regarding dependent claims 10-16, dependent claims 10-16 recite more complexities descriptive of the abstract idea itself, and at least inherit the abstract idea of claim 10-16. Furthermore, claims such as claims 11-12 merely extend the abstract idea by defining the training data set to be used, reciting further models. As such, claims 10-16 are understood to recite an abstract idea under step 2A (prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 10-16 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. This is because claims 10-16 rely on at least similar additional elements as recited in claims 1 and 9. That is, the machine learning limitations are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). The Examiner here again points to claim 9, as well as the discussion of generic machine learning techniques in the aforementioned paragraphs of Applicant’s specification. Lastly, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Lastly, under step 2B, claims 10-16 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
In view of the above, claims 10-16 do not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Regarding claims 1-8 (system), recite at least substantially similar concepts and elements as recited in claims 9-16 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. Furthermore, the mere recitation of generic computing components such as a processor, storage, and executable code does not remedy the deficiencies because similar logic applied under Step 2A (prong 2) and Step 2B is applicable. As such, claims 1-8 are rejected under at least similar rationale.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 5-7, 9, 11, 13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by WEI et.al. (U.S. Patent Publication No. 2024/0020672).
Regarding claims 1 and 9, WEI teaches a vision sensor configured to obtain and transmit image information of a product to an arithmetic processing unit, (a visual sensor, configured to collect visual information of goods placed on the weighing platform, [7];
the arithmetic processing unit configured to 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, 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 (modeling platform is configured to use a computer vision technology based on deep learning to train the identification model based on the collected visual information, the weight information, and the data of the user feedback in use [32]), and
the data transceiver configured to transmit the category information of the product to a backend server of a seller (updating one or more of goods information such as goods name, goods code, weighing apparatus code, price, pricing method, illustrative figure, [42], the confirmed goods information is transmitted to the POS system through a wired or wireless network, [30, 58]), 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, (price calculated based on goods code and pricing method, [71-74])..
Regarding claims 3, 11, WEI teaches before recognizing the image information of the product based on the image recognition model, the arithmetic processing unit is further configured to: perform a target detection on the image information of the product using a one-stage detector or a two-stage detector, (using a computer vision technology based on deep learning, by considering goods weight information, [24]),
Regarding claims 5, 13, WEI teaches the data transceiver comprises: USB wiring, TypeC wiring or a wireless network communication device, (the confirmed goods information is transmitted to the POS system through a wired or wireless network, [30]).
Regarding claims 6, WEI 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, ([18, 46]).
Regarding claims 7, WEI teaches an interactive touch screen, which is connected to the data transceiver, and configured to provide visual and clickable product category information, (see 93-94).
Regarding claim 16, WEI teaches a computer program product, comprising a computer program which, when executed by a processor, implements the method according to claim 9, [126].
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.
Claim 2, 4, 10, 12 are rejected under 35 U.S.C. 103 as being unpatentable over WEI in view of Kalra et.al. (U.S. Patent Publication No. 2021/0264607).
Regarding claims 2, 4, 10, 12, WEI does not explicitly teach the arithmetic processing unit comprises an AI chip; recognizing the image information of the product based on the image recognition model, the arithmetic processing unit is further configured to: perform an image segmentation on the image information of the product using an instance segmentation algorithm or a semantic segmentation algorithm;
However, 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]. 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 Wei’s visual goods identification system, to include AI-accelerated semantic segmentation technique, as taught by Kalra, in order to enhance visual understanding.
Claims 8, 15 are rejected under 35 U.S.C. 103 as being unpatentable over WEI in view of Yang et.al. (U.S. Patent Publication No. 2021/0401192).
Regarding claims 8, 15, WEI does not explicitly teach 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 products.
However, Yang teaches permitting multiple ones of the items associated with the transaction to be grouped and placed simultaneously together on the base for item recognition and item processing of each of the multiple items, claim 19, he cart is updated and connected to other endpoints via a web socket to transaction manager 143 of server 140 for purposes of obtaining pricing details of the items and obtaining/confirming payment at terminal 100 for the transaction/cart, [33], (backend server), iterating back to the detecting until a last item of the transaction is processed, claim 2.
It would have been obvious to one with ordinary skill in the art before the effective filing date, to modify the intelligent weighing system of WEI, to include the multi-item recognition capability, as taught by Yang, in order to improve checkout speed and reduce manual intervention. Wei emphasizes POS integration recognition and Yang provides a known solution for recognizing multiple items placed together during a transaction.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over WEI in view of Mavroeidis et.al. (U.S. Patent Publication No. 11,521,064)
Regarding claim 14, WEI 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.
However, Mavroeidis teaches training a neural network model comprises receiving training data comprising a first set of annotated images; training the neural network model using the training data, based on an initial regularization parameter; and iteratively performing steps of: testing the trained neural network model using the test data, Col.4 ln 8-24.
It would have been obvious to one with ordinary skill in the art before the effective filing date, to modify the intelligent weighing system of WEI, to include the training and testing workflow, as taught by Mavroeidis, in order to improve accuracy, reliability and validation of the image recognition model used for goods identification.
Conclusion
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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/MILENA RACIC/Patent Examiner, Art Unit 3627
/FLORIAN M ZEENDER/Supervisory Patent Examiner, Art Unit 3627