DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation - § 112 (f) Notification
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
2. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
3. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Claim 1 contains the following limitation: image acquisition unit (“generic placeholder”) configured to (“transition word”) acquire an image (“functional language”) in which the customer is registering a commodity to be purchased…” This claim limitation does not use the word "means," but is nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Paragraph [0035] of the Applicant’s specification states that the “The image acquisition unit 46a is a processing unit for acquiring an image captured by the camera 10. The image acquisition unit 46a acquires, via the communication circuitry, image data transmitted from the camera 10 installed above the self-checkout machine 20.” Hence, the image acquisition unit is the specific structure that encompasses a processing unit for acquiring an image captured by the camera 10 and carries out the functions of this limitation.
Claim 1 also contains the limitation: “a commodity image extraction unit (“generic placeholder”) configured to (“transition word”) to extract a commodity image (“functional language”) from the image acquired by the image acquisition unit…” This claim limitation does not use the word "means," but is nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Paragraph [0036] of the Applicant’s specification states that the “The commodity image extraction unit 46b is a processing unit for extracting a commodity image from the image data acquired by the image acquisition unit 46a. The commodity image extraction unit 46b detects commodities shown in the image data acquired by the image acquisition unit 46a, acquires the commodity image, and stores the commodity image into the commodity image data 45a.” Hence, the commodity image extraction unit is the specific structure that encompasses a processing unit that carries out the functions of this limitation.
Claim 1 also contains the limitation: “a registered commodity specification unit (“generic placeholder”) configured to (“transition word”) specify commodity information (“functional language”) registered in the self-checkout system…” This claim limitation does not use the word "means," but is nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Paragraph [0038] of the Applicant’s specification states that the “The registered commodity specification unit 46c is a processing unit for acquiring checkout commodity data. Upon receiving the checkout commodity data from the self-checkout machine 20, the registered commodity specification unit 46c associates the checkout machine ID, the monetary amount, and the commodity information, included in the checkout commodity data, with each other, and stores them in the checkout commodity data 45b.” Hence, the registered commodity specification unit is the specific structure that encompasses a processing unit that carries out the functions of this limitation.
Claim 1 also contains the limitation: “a matching judgement unit (“generic placeholder”) configured to (“transition word”) input the commodity image and the commodity information (“functional language”) into a large language model, and judge matching between the commodity image and the commodity information, based on a result outputted from the large language model…” This claim limitation does not use the word "means," but is nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Paragraph [0039] of the Applicant’s specification states that the “The matching judgement unit 46d is a processing unit for judging presence/absence of an erroneous operation during checkout for commodities. When the checkout commodity data 45b has been updated by the registered commodity specification unit 46c, the matching judgement unit 46d specifies the checkout machine ID corresponding to the updated data, and extracts, from the commodity image data 45a, the most recent commodity image data corresponding to the checkout machine ID. Then, using the extracted commodity image data and the commodity names corresponding to the updated data in the checkout commodity data 45b, a similarity therebetween is calculated with a learned multimodal foundation model.” Hence, the matching judgement unit is the specific structure that encompasses a processing unit that carries out the functions of this limitation.
Claim 1 also contains the limitation: “a notification unit (“generic placeholder”) configured to (“transition word”) notify that the degree of matching is low (“functional language”) when the matching judgement unit has judged that a degree of matching is low…” This claim limitation does not use the word "means," but is nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Paragraph [0041] of the Applicant’s specification states that the “The notification unit 46e is a processing unit for notifying the clerk terminal 50 of the erroneous operation warning. Upon receiving the erroneous operation notification from the matching judgement unit 46d, the notification unit 46e notifies the clerk terminal 50 of this erroneous operation notification as an erroneous operation warning.” Hence, the notification unit is the specific structure that encompasses a processing unit that carries out the functions of this limitation.
Claim 8 contains these same generic placeholders as claim 1 so the same analysis applies to claim 8.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof (see analysis of the claims above).
If Applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, Applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
4. 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.
5. Claims 1-15 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In sum, claims 1-15 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea) and do not include an inventive concept that is something “significantly more” than the judicial exception under the January 2019 patentable subject matter eligibility guidance (2019 PEG) analysis which follows.
Under the 2019 PEG step 1 analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying step 1 of the analysis for patentable subject matter to the claims, it is determined that the claims are directed to the statutory category of a process, (claims 12-13), a machine (claims 1-11), and a manufacture (claims 14-15), where the machine and manufacture are substantially directed to the subject matter of the process. (See, e.g., MPEP §2106.03). Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability. Here, the claims recite the abstract idea of carrying out a transaction fraudulent analysis by:
acquiring an image in which the customer is registering a commodity to be purchased;
extracting a commodity image from the image acquired in the acquiring;
specifying commodity information registered in the self-checkout,…;
inputting the commodity image and the commodity information into a large language model, and judging matching between the commodity image and the commodity information, based on a result outputted from the large language model; and
notifying, when the matching judgement step has judged that a degree of matching is low, that the degree of matching is low.
Here, the recited abstract idea falls within one or more of the three enumerated 2019 PEG categories of patent ineligible subject matter, to wit: the category of certain methods of organizing human activity, which includes fundamental economic practices or principles and commercial or legal interactions (e.g., carrying out a transaction fraudulent analysis).
Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which the claim is directed does not include limitations that integrate the abstract idea into a practical application, since the recited features of the abstract idea are being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See, e.g., MPEP §2106.05(f)). Therefore, the claim is directed to an abstract idea.
Under the 2019 PEG step 2B analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the additional elements, such as: a “system” do not amount to an innovative concept since, as stated above in the step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming. (See, e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality to simply implement the abstract idea and are not themselves being technologically improved. (See, e.g., MPEP §2106.05 I.A.); (see also, paragraph [0002] of the specification). Claims 1, 8, 13, 14, and 15 are nearly identical to claim 12 so the same analysis applies to these claims as well. Claims 8, 13, and 15 contain a different term (“fusion model”) instead of a “large language model” that is contained in the other claims. This is a slightly different model but it is a generic machine learning model nonetheless.
Dependent claims 2-7 and 9-11 have all been considered and do not integrate the abstract idea into a practical application. Dependent claim 2 recites limitations that further define the abstract idea noted in claim 12 in that it describes that the matching judgement unit calculates a similarity between the commodity image and text. Dependent claim 3 recites limitations that further define the abstract idea noted in claim 12 in that it describes that the registered commodity specification unit acquires the commodity information from the checkout screen interface. Dependent claim 4 recites limitations that further define the abstract idea noted in claim 12 in that it describes that the registered commodity specification unit acquires the commodity information from the checkout screen interface. Dependent claim 5 recites limitations that further define the abstract idea noted in claim 12 in that it describes that when the commodity image has not been registered as the commodity information, there is a mismatch. Dependent claim 6 recites limitations that further define the abstract idea noted in claim 12 in that it describes a mismatch being found when the number of commodities is less than the number of commodities registered. Dependent claim 7 recites limitations that further define the abstract idea noted in claim 12 in that it describes a mismatch being found when the commodity recognized in the commodity image is different than the commodity whose name is in the information. Dependent claim 9 recites limitations that further define the abstract idea noted in claim 12 in that it describes matching the result outputted from the model and the commodity information. Dependent claim 10 recites limitations that further define the abstract idea noted in claim 12 in that it describes matching the result outputted from the model and the commodity information. Dependent claim 11 recites limitations that further define the abstract idea noted in claim 12 in that it describes matching the result outputted from the model and the commodity information.
The elements of the instant process steps when taken in combination do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself because the claims do not effect an improvement to another technology or technical field (e.g., the field of computer coding technology is not being improved); the claims do not amount to an improvement to the functioning of an electronic device itself which implements the abstract idea (e.g., the general purpose computer and/or the computer system which implements the process are not made more efficient or technologically improved); the claims do not perform a transformation or reduction of a particular article to a different state or thing (i.e., the claims do not use the abstract idea in the claimed process to bring about a physical change. See, e.g., Diamond v. Diehr, 450 U.S. 175 (1981), where a physical change, and thus patentability, was imparted by the claimed process; contrast, Parker v. Flook, 437 U.S. 584 (1978), where a physical change, and thus patentability, was not imparted by the claimed process); and the claims do not move beyond a general link of the use of the abstract idea to a particular technological environment (e.g., simply claiming the use of a computer and/or computer system to implement the abstract idea).
Claim Rejections - 35 USC § 102
6. 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 person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
7. Claims 1-15 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Scott et al. (WO 2020/228437) (hereinafter “Scott”).
Regarding claims 1, 8, 12, 13, 14, and 15, Scott discloses an image acquisition unit configured to acquire an image in which the customer is registering a commodity to be purchased. Scott states that “In general, aspects of this disclosure include an apparatus that may be conveniently installed next to a checkout machine without modifying the existing components or interrupting the regular functions of the checkout machine. In some embodiments, the apparatus includes one or more imaging devices and a display. The apparatus is configured to crosscheck product information derived from multi-sourced image data generated from the one or more imaging devices, further, to present corresponding information on the display based on the verification result. Advantageously, the apparatus may serve as a general-purpose loss prevention
measure for many types of checkout machines.” (See paragraph [0006]). Scott discloses a commodity image extraction unit configured to extract a commodity image from the image acquired by the image acquisition unit. Scott states that “In some embodiments, the disclosed apparatus has two cameras. The first camera is adapted to capture one or more images of the display of the checkout machine. The display of the checkout machine contains product information recognized by the checkout machine, e.g., based on a machine reading of an MRL. The second camera is adapted to capture one or more images of the product being checked out directly. Based on various computer vision technologies, the product in the image may be recognized, and corresponding product information may be retrieved from a product database based on such product recognition technologies. Accordingly, the computing process associated with the apparatus can determine the respective product information associated with the product based on the images from the first camera and the second camera respectively. Further, the computing process can crosscheck the product information from the two independent sources. A positive verification code may be generated if the product information derived from the two independent sources are consistent. Conversely, a negative verification code may be generated if the product information derived from the two independent sources are inconsistent.” (See paragraph [0021]). Scott discloses a registered commodity specification unit configured to specify commodity information registered in the self-checkout system. Scott states that “In some embodiments, the disclosed apparatus has two cameras. The first camera is adapted to capture one or more images of the display of the checkout machine. The display of the checkout machine contains product information recognized by the checkout machine, e.g., based on a machine reading of an MRL. The second camera is adapted to capture one or more images of the product being checked out directly. Based on various computer vision technologies, the product in the image may be recognized, and corresponding product information may be retrieved from a product database based on such product recognition technologies. Accordingly, the computing process associated with the apparatus can determine the respective product information associated with the product based on the images from the first camera and the second camera respectively. Further, the computing process can crosscheck the product information from the two independent sources. A positive verification code may be generated if the product information derived from the two independent sources are consistent. Conversely, a negative verification code may be generated if the product information derived from the two independent sources are inconsistent.” (See paragraph [0021]). Scott discloses a matching judgement unit configured to input the commodity image and a question text on an erroneous operation into a vision-language fusion model and large language model, and judge matching between the commodity image and the commodity information, based on a result outputted from the fusion model and large language model. Scott states that “Recognizer 176 is configured to recognize products from images and retrieve
corresponding product information (e.g., product identifier, name, unit price, representative images, etc.). In some embodiments, recognizer 176 is to compare the image features of a query product with image features of known products for similarity, e.g., via one or more machine learning models (MLMs) in MLM 180, so that the known products may be ranked based on their respective similarity measures against the query product. Such rank information may be used by verifier 178 to determine whether the product information obtained by the checkout machine (e.g., via the product's MRL) is consistent with the product information obtained by apparatus 120 from another independent source (e.g., product images captured by camera 124a).” (See paragraph [0040]). Scott also states that “In general, recognizer 176 may use technologies of object detection, image segmentation, or instance segmentation to determine the quantity of product 140. Different from semantic segmentation, instance segmentation would identify each instance of each product in an image. In some embodiments, neural networks (e.g., in MLM 180) are trained to perform such instance segmentation tasks. In other embodiments, other technologies may be used to determine the quantity of product 140, e.g., based on thresholding (using one or more specified threshold values to separate pixels into different levels to isolate objects), K-means clustering, Histogram-based image segmentation, edge detection, etc.” (See paragraph [0042]).
Scott discloses a notification unit configured to, when the matching judgement unit has judged that a degree of matching is low, notify that the degree of matching is low. Scott states that “Based on the verification code, corresponding messages can be generated and communicated to various recipients or devices. By way of example, the disclosed apparatus may take necessary measures, e.g., by displaying a message, playing an audible message, activating a warning light, etc., to remind the customer to rescan the product if the checkout machine did not receive the correct product information, e.g., due to a failure of the MRL reading, a mismatch between the physical product and the MRL, etc. As another example, the disclosed apparatus may send an electronic message (e.g., including the nature of the event, the relevant images, the product information, etc.) to a remote device, such as a server or a wireless device, which could be monitored by a loss prevention staff member. In other embodiments, different messages or loss prevention actions may be configured based on the specific implementation of the disclosed technologies.” (See paragraph [0022]).
Regarding claim 2, Scott discloses that the matching judgement unit calculates a similarity between the commodity image and a text that is the commodity information, and when the similarity is equal to or lower than a predetermined threshold value, the matching judgement unit judges that there is a mismatch. Scott states that “At block 450, the verification system determines whether the product information from the multiple sources are consistent. In some embodiments, the respective product identifiers from the multiple sources are required to match to be consistent. In some embodiments, the product identifier derived from one source is required to fall into a ranked list derived from another source. Other criteria for measuring consistency may be devised for other implementations of the disclosed technologies. At block 460, the verification system generates a negative verification code if the product information from the multiple sources is inconsistent. It may be noted that the verification system is designed to generate a negative verification code in some embodiments if one source provided product information and another source did not, such as in a non-scan or miss-scan case.” (See paragraphs [0083-0084]).
Regarding claim 3, Scott discloses the registered commodity specification unit acquires the registered commodity information from the self-checkout
system via an interface. Scott states that “At a high level, CV system 170 is configured to crosscheck product information as obtained by checkout machine 110 against product information obtained from another source independent from the checkout machine. In this embodiment, images from camera 124b cover
display 114, and such images contain product information as obtained by checkout machine 110. Meanwhile, images from camera 124a cover product 140 and scanner 112, and CV system 170 may directly derive product information of product 140 from these images, e.g., using computer vision technologies.” (See paragraph [0032]).
Regarding claim 4, Scott discloses the registered commodity specification unit acquires the commodity information displayed on a screen of the self-checkout system, through character recognition of a captured image of the screen. Scott states that “In some embodiments, the disclosed apparatus has two cameras. The first camera is adapted to capture one or more images of the display of the checkout machine. The display of the checkout machine contains product information recognized by the checkout machine, e.g., based on a machine reading of an MRL. The second camera is adapted to capture one or more images of the product being checked out directly. Based on various computer vision technologies, the product in the image may be recognized, and corresponding product information may be retrieved from a product database based on such product recognition technologies. Accordingly, the computing process associated with the apparatus can determine the respective product information associated with the product based on the images from the first camera and the second camera respectively. Further, the computing process can crosscheck the product information from the two independent sources. A positive verification code may be generated if the product information derived from the two independent sources are consistent. Conversely, a negative verification code may be generated if the product information derived from the two independent sources are inconsistent.” (See paragraph [0021]).
Regarding claim 5, Scott discloses when a commodity recognized in the commodity image has not been registered as the commodity information, the matching judgement unit judges that there is a mismatch, and the notification unit notifies that an omission of registration has occurred. Scott states that “At block 450, the verification system determines whether the product information from the multiple sources are consistent. In some embodiments, the respective product identifiers from the multiple sources are required to match to be consistent. In some embodiments, the product identifier derived from one source is required to fall into a ranked list derived from another source. Other criteria for measuring consistency may be devised for other implementations of the disclosed technologies. At block 460, the verification system generates a negative verification code if the product information from the multiple sources is inconsistent. It may be noted that the verification system is designed to generate a negative verification code in some embodiments if one source provided product information and another source did not, such as in a non-scan or miss-scan case.” (See paragraphs [0083-0084]).
Regarding claim 6, Scott discloses when the number of commodities recognized in the commodity image is less than the number of commodities registered as the commodity information, the matching judgement unit judges that there is a mismatch, and the notification unit notifies that duplicated registration has occurred. Scott states that “At block 450, the verification system determines whether the product information from the multiple sources are consistent. In some embodiments, the respective product identifiers from the multiple sources are required to match to be consistent. In some embodiments, the product identifier derived from one source is required to fall into a ranked list derived from another source. Other criteria for measuring consistency may be devised for other implementations of the disclosed technologies. At block 460, the verification system generates a negative verification code if the product information from the multiple sources is inconsistent. It may be noted that the verification system is designed to generate a negative verification code in some embodiments if one source provided product information and another source did not, such as in a non-scan or miss-scan case.” (See paragraphs [0083-0084]).
Regarding claim 7, Scott discloses when a commodity recognized in the commodity image is different from a commodity whose name or a commodity category is in the commodity information, the matching judgement unit judges
that there is a mismatch, and the notification unit notifies that a commodity different from an actual commodity has been registered. Scott states that “At block 450, the verification system determines whether the product information from the multiple sources are consistent. In some embodiments, the respective product identifiers from the multiple sources are required to match to be consistent. In some embodiments, the product identifier derived from one source is required to fall into a ranked list derived from another source. Other criteria for measuring consistency may be devised for other implementations of the disclosed technologies. At block 460, the verification system generates a negative verification code if the product information from the multiple sources is inconsistent. It may be noted that the verification system is designed to generate a negative verification code in some embodiments if one source provided product information and another source did not, such as in a non-scan or miss-scan case.” (See paragraphs [0083-0084]).
Regarding claim 9, Scott discloses the question text is a text asking what commodity a person holds in his/her hand, or what commodity is being scanned, and the matching judgement unit judges matching between the result outputted from the fusion model and the commodity information. Scott states that “Recognizer 176 is configured to recognize products from images and retrieve corresponding product information (e.g., product identifier, name, unit price, representative images, etc.). In some embodiments, recognizer 176 is to compare the image features of a query product with image features of known products for similarity, e.g., via one or more machine learning models (MLMs) in MLM 180, so that the known products may be ranked based on their respective similarity measures against the query product. Such rank information may be used by verifier 178 to determine whether the product information obtained by the checkout machine (e.g., via the product's MRL) is consistent with the product information obtained by apparatus 120 from another independent source (e.g., product images captured by camera 124a).” (See paragraph [0040]).
Regarding claim 10, Scott discloses the question text is a text asking what commodity is in a lower part of a shopping cart, and the matching judgement unit judges matching between the result outputted from the fusion model and the commodity information. Scott states that “Recognizer 176 is configured to recognize products from images and retrieve corresponding product information (e.g., product identifier, name, unit price, representative images, etc.). In some embodiments, recognizer 176 is to compare the image features of a query product with image features of known products for similarity, e.g., via one or more machine learning models (MLMs) in MLM 180, so that the known products may be ranked based on their respective similarity measures against the query product. Such rank information may be used by verifier 178 to determine whether the product information obtained by the checkout machine (e.g., via the product's MRL) is consistent with the product information obtained by apparatus 120 from another independent source (e.g., product images captured by camera 124a).” (See paragraph [0040]).
Regarding claim 11, Scott discloses the question text is a text asking whether a new commodity has been added to a screen, and the matching judgement unit judges matching between the result outputted from the fusion model and the commodity information. Scott states that “Recognizer 176 is configured to recognize products from images and retrieve corresponding product information (e.g., product identifier, name, unit price, representative images, etc.). In some embodiments, recognizer 176 is to compare the image features of a query product with image features of known products for similarity, e.g., via one or more machine learning models (MLMs) in MLM 180, so that the known products may be ranked based on their respective similarity measures against the query product. Such rank information may be used by verifier 178 to determine whether the product information obtained by the checkout machine (e.g., via the product's MRL) is consistent with the product information obtained by apparatus 120 from another independent source (e.g., product images captured by camera 124a).” (See paragraph [0040]).
Conclusion
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to AMIT PATEL whose telephone number is (313) 446-4902. The Examiner can normally be reached on Monday thru Thursday, 7:30 AM - 5:30 PM EST.
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/Amit Patel/
Examiner
Art Unit 3696
Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
Prosecution Insights