DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
For the purpose of prior art consideration, the effective filing date of the instant application is based on the application filed in Japan on December 23rd, 2022. However, priority to this date is not perfected until an English translation of the certified copy of the instant application is filed.
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, 3-8 and 10-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Whether a Claim is to a Statutory Category
In the instant case, claims 1 and 3-7 recite a non-transitory computer readable storage medium/machine; claims 8 and 10-14 recite a method/process and claim 15 recites an apparatus/machine that are performing a series of functions. Therefore, these claims fall within the four statutory categories of invention of a machine. Step 1 is satisfied.
Step2A – Prong 1: Does the Claim Recite a Judicial Exception
Exemplary claim 1 recites the following abstract concepts that are found to include an enumerated “abstract idea”:
A non-transitory computer-readable storage medium storing an alert generation program that causes at least one computer to execute a process, the process comprising:
acquiring a video of a person who holds a merchandise to be registered in a checkout machine;
specifying merchandise candidates corresponding to merchandises included in the video and a number of the merchandise candidates by inputting the acquired video to a trained machine learning model including a pair of an image encoder and a text encoder;
acquiring, through scanning or manually inputting, by the person, of merchandise codes printed or attached to the merchandises, items of merchandises registered by the person and a number of the items of the merchandises; and
generating an alert indicating an abnormality of merchandises registered in the checkout machine based on the acquired items of the merchandises and the number of the items of the merchandises not coinciding with the specified merchandise candidates and the number of the merchandise candidates, wherein the specifying includes:
inputting the video to the image encoder included in the machine learning model,
inputting a plurality of texts in accordance with a hierarchical structure including a plurality of hierarchies corresponding to attributes of merchandises and a number of merchandises to the text encoder included in the machine learning model, and
specifying the merchandise candidates corresponding to the merchandises included in the video and the number of the merchandise candidates based on maximizing, for each of the plurality of hierarchies, a similarity between a vector of the video output by the image encoder and a vector of the texts output by the text encoder.
[Emphasis added to show the abstract idea being executed by additional elements that do not meaningfully limit the abstract idea]
This non-transitory computer-readable storage medium claim is grouped within the "certain methods of organizing human activity” grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test because the claims involve a series of steps for sales activities of acquiring items of merchandises registered by the person, which is a process that is encompassed by the abstract idea of commercial or legal interactions. See e.g., MPEP 2106.04(a)(2) and Subject Matter Eligibility Example 47. Accordingly, claim 1 (and similarly claims 8 and 15) are found to recite abstract idea(s).
Step2A – Prong 2: Does the Claim Recite Additional Elements that Integrate the Judicial Exception into a Practical Application
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test, the additional elements of the claims such as non-transitory computer-readable storage medium, computer, machine learning model and checkout machine merely use a computer as a tool to perform an abstract idea and/or generally link the use of a judicial exception to a particular technological environment. Specifically, the non-transitory computer-readable storage medium, computer, machine learning model, image encoder, text encoder and checkout machine performs the steps or functions of sales activities of acquiring items of merchandises registered by the person. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer (or technical elements disclosed at a high level of generality such as non-transitory computer-readable storage medium, computer, machine learning model, image encoder, text encoder and checkout machine) performing functions of storing, acquiring, scanning, specifying, generating, inputting, maximizing and outputting that correspond to acts required to carry out the abstract idea (MPEP 2106.05(f) and (h)). Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
Step2B: Does the Claim Amount to Significantly More
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test, the additional elements of non-transitory computer-readable storage medium, computer, machine learning model, image encoder, text encoder and checkout machine being used to perform the steps of storing, acquiring, scanning, specifying, generating, inputting, maximizing and outputting amounts to no more than using a computer or processor to automate and/or implement the abstract idea of sales activities of acquiring items of merchandises registered by the person. As discussed above, taking the claim elements separately, non-transitory computer-readable storage medium, computer, machine learning model, image encoder, text encoder and checkout machine performs the steps or functions of commercial or legal interactions through sales activities of acquiring items of merchandises registered by the person. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of commercial or legal interactions through sales activities of acquiring items of merchandises registered by the person because said combination of elements remains disclosed at a high level of generality. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(l)(A)(f) & (h)). Therefore, the claims are not patent eligible.
Independent claim 8 describes a method to perform functions of acquiring, scanning, specifying, generating, inputting, maximizing and outputting relating to sales activities of acquiring items of merchandises registered by the person without additional elements beyond technical elements disclosed at a high level of generality such as an computer, machine learning model, image encoder, text encoder and checkout machine that provide significantly more than the abstract idea of commercial or legal interactions through sales activities of acquiring items of merchandises registered by the person as noted above regarding claim 1. Therefore, this independent claim is also not patent eligible.
Independent claim 15 describes an apparatus to perform functions of acquiring, scanning, specifying, generating, inputting, maximizing and outputting relating to sales activities of acquiring items of merchandises registered by the person without additional elements beyond technical elements disclosed at a high level of generality such as a memories, processors, computer, machine learning model, image encoder, text encoder and checkout machine that provide significantly more than the abstract idea of commercial or legal interactions through sales activities of acquiring items of merchandises registered by the person as noted above regarding claim 1. Therefore, this independent claim is also not patent eligible.
Dependent claims 3-7 and 10-14 further describes the abstract idea of commercial or legal interactions. Dependent claims 3-7 and 10-14 add inputting, specifying, outputting, referring, narrowing down, registering, acquiring and generating steps that are executed by a non-transitory computer readable medium, image encoder, machine learning model, text encoder, checkout machine, display and as disclosed in independent claims 1 and 8, however these additional steps remain disclosed at a high level of generality and do not amount to more than mere computer implementation of the abstract idea, which does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea. Therefore, dependent claims 3-7 and 10-14 are also not patent eligible. Further, the dependency of these claims on ineligible independent claims 1 and 8 also renders dependent claims 3-7 and 10-14 as not patent eligible.
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, 3-6, 8, 10-13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Krishnamurthy et al. (US 2022/0414374 A1) in view of Arroyo et al. (US 2023/0230408 A1).
Regarding Claims 1, 8 and 15, modified Krishnamurthy teaches:
A non-transitory computer-readable storage medium storing an alert generation program that causes at least one computer to execute a process/ An alert generation method for a computer to execute a process/ An information processing apparatus comprising: one or more memories; and one or more processors coupled to the one or more memories (See Krishnamurthy ¶ [0084] – outputting an alert based on item identification and [0092] – memory storing instructions to implement the functions of the system when executed by a processor, wherein said memory comprises one or more disks, tape drives, or solid-state drives, and may be used as an over-flow data storage device [non-transitory computer-readable storage medium by example]), the process comprising/ the one or more processors configured to:
acquiring a video of a person who holds a merchandise to be registered in a checkout machine (See Krishnamurthy ¶ [0054] – capturing images with video cameras, [0087] - the item tracking device may identify a digital cart that is associated with the user. In this example, the digital cart comprises information about items that the user has placed on the platform to purchase and [0095] – The item tracking system may employ process to detect a triggering event that corresponds with when a user puts their hand above the platform to place an item on the platform);
specifying merchandise candidates corresponding to merchandises included in the video and a number of the merchandise candidates by inputting the acquired video to a trained machine learning model (See Krishnamurthy ¶ [0078-0079] – identifying items with a machine learning model based on captured image data of said items, wherein the item tracking device may then compare the identified features of the item to a set of features that correspond with different items [merchandise candidates by example]) including a pair of an image encoder (See Krishnamurthy ¶ [0078-0079] – identifying items with a machine learning model based on captured image data of said items, wherein the item tracking device may then compare the identified features of the item to a set of features that correspond with different items [merchandise candidates by example]) …;
acquiring, through scanning or manually inputting, by the person, of merchandise codes printed or attached to the merchandises, items of merchandises registered by the person and a number of the items of the merchandises (See Krishnamurthy ¶ [0195] – the item tracking device may provide instructions for the user to scan a barcode of an item using a barcode scanner. In this case, the item tracking device may use the graphical user interface to display a combination of items that were detected on the platform as well as items that were manually scanned by the user [number of items by example]); and
generating an alert indicating an abnormality of merchandises registered in the checkout machine based on the acquired items of the merchandises and the number of the items of the merchandises not coinciding with the specified merchandise candidates and the number of the merchandise candidates (See Krishnamurthy ¶ [0081-0082] – the item tracking device may compare the number of identified items from the captured images to the number of items on the platform that was determined in operation… the item tracking device determines that at least one of the items has not been identified when the number of items identified items from the captured images does not match the determined number of items on the platform… the item tracking device may output a message [alert by example] on a graphical user interface that is located at the imaging device with instructions for the user to rearrange the position of the items on the platform) wherein the specifying includes:
inputting the video to the image encoder included in the machine learning model (See Krishnamurthy ¶ [0078-0079] – identifying items with a machine learning model based on captured image data of said items and [0146] – The item tracking system may employ process to filter the entries in the encoded vector library to reduce the amount of items that are considered when attempting to identify an item that is placed on the platform),
inputting a plurality of texts in accordance with a hierarchical structure including a plurality of hierarchies corresponding to attributes of merchandises and a number of merchandises … included in the machine learning model (See Krishnamurthy ¶ [0078-0079] – identifying items with a machine learning model based on captured image data of said items, wherein the item tracking device may then compare the identified features of the item to a set of features that correspond with different items [merchandise candidates by example] and [0146-0148] – The item tracking system may employ process to filter the entries in the encoded vector library [reference source data] to reduce the amount of items that are considered when attempting to identify an item that is placed on the platform… the item tracking device obtains feature descriptors for an item … describing the physical characteristics or attributes of an item, including an item type… an item type identifies a classification for the item, wherein said item classification is understood herein to define the limitation hierarchies based on the specification of the instant application), and
specifying the merchandise candidates corresponding to the merchandises included in the video and the number of the merchandise candidates based on maximizing, for each of the plurality of hierarchies, a similarity between a vector of the video output by the image encoder and a vector of the texts (See Krishnamurthy ¶ [0078-0079] – identifying items with a machine learning model based on captured image data of said items, wherein, the item tracking device may then compare the identified features of the item to a set of features that correspond with different items [merchandise candidates by example], [0146-0148] – The item tracking system may employ process to filter the entries, comprising text and barcodes [image/ video output] to identify items, in the encoded vector library [reference source data] to reduce the amount of items that are considered when attempting to identify an item that is placed on the platform… the item tracking device obtains feature descriptors for an item … describing the physical characteristics or attributes of an item, including an item type… an item type identifies a classification for the item [hierarchies] and [0158-0159] - the item tracking device may generate a similarity vector for a received encoded vector. A similarity vector comprises an array of numerical values where each numerical value indicates how similar the values in the received encoded vector are to the values in an encoded vector in the encoded vector library… After generating the similarity vector, the item tracking device can identify which entry, or entries, in the encoded vector library most closely matches the encoded vector for the identified item. In one embodiment, the entry that is associated with the highest numerical value [maximizing] in the similarity vector corresponds is the entry that closest matches the encoded vector for the item) ...
While Krishnamurthy teaches a machine learning based monitored checkout process that captures and analyzes image and text data to identify items for purchase (Krishnamurthy ¶ [0078-0082]), Krishnamurthy does not explicitly teach that said text data is input and output by a text encoder to process said text data. This is taught by Arroyo (See Arroyo ¶ [0109-0110] – the classification circuitry applies an example NLP-based model to classify the columns into the different types of facts using the detected column headers. For example, the columns can be classified into different product facts, such as code, description, quantity and price … the classification circuitry includes an example column classification model(s), which is a trained AI model that classifies text data output by the OCR circuitry. In some examples, the column classification model is based on a simple neural network that contains one layer, such as fastText, fastText is an open-source library for learning of word embeddings and text classification that can be used to train large datasets quickly and efficiently). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the machine learning based monitored checkout process that analyzes image and text data of Krishnamurthy the use of a text encoder model to analyze text data as taught by Arroyo to provide improvements in the efficiency and productivity of the decoding process by applying the ground truth annotations to the ML models (Arroyo ¶ [0050]), thereby increasing the accuracy of the machine learning based checkout monitoring process of Krishnamurthy.
Regarding Claim 3 and 10, modified Krishnamurthy teaches:
The non-transitory computer-readable storage medium/ alert generation method according to claim 1 and 8, wherein
the machine learning model refers to reference source data in which an attribute of a merchandise is associated with each of a plurality of hierarchies (See Krishnamurthy ¶ [0078-0079] – identifying items with a machine learning model based on captured image data of said items and [0146-0148] – The item tracking system may employ process to filter the entries in the encoded vector library [reference source data] to reduce the amount of items that are considered when attempting to identify an item that is placed on the platform… the item tracking device obtains feature descriptors for an item … describing the physical characteristics or attributes of an item, including an item type… an item type identifies a classification for the item, wherein said item classification is understood herein to define the limitation hierarchies based on the specification of the instant application), and
the specifying includes:
specifying the merchandise candidates and the number of the merchandise candidates by inputting the video to the image encoder (See Krishnamurthy ¶ [0147] – using text and barcodes [image/ video output] to identify items and [0158] - the item tracking device may generate a similarity vector for a received encoded vector. A similarity vector comprises an array of numerical values where each numerical value indicates how similar the values in the received encoded vector are to the values in an encoded vector in the encoded vector library),
inputting a text … for each of attributes of merchandises in a first hierarchy (See Krishnamurthy ¶ [0147] – using text and barcodes [image/ video output] to identify items and [0158] - the item tracking device may generate a similarity vector for a received encoded vector. A similarity vector comprises an array of numerical values where each numerical value indicates how similar the values in the received encoded vector are to the values in an encoded vector in the encoded vector library),
narrowing down attributes corresponding to the merchandises included in the video among the attributes of the merchandises in the first hierarchy based on a similarity between a vector of the video output by the image encoder and a vector of the text … (See Krishnamurthy ¶ [0146-0148] – The item tracking system may employ process to filter the entries, comprising text and barcodes [image/ video output] to identify items, in the encoded vector library [reference source data] to reduce the amount of items that are considered when attempting to identify an item that is placed on the platform… the item tracking device obtains feature descriptors for an item … describing the physical characteristics or attributes of an item, including an item type… an item type identifies a classification for the item [hierarchies] and [0158] - the item tracking device may generate a similarity vector for a received encoded vector. A similarity vector comprises an array of numerical values where each numerical value indicates how similar the values in the received encoded vector are to the values in an encoded vector in the encoded vector library),
inputting the video to the image encoder (See Krishnamurthy ¶ [0147] and [0158] as noted above),
inputting a text … for each merchandise in a second hierarchy narrowed down from among the attributes of the merchandises in the first hierarchy (See Krishnamurthy ¶ [0149-0150] - the item tracking device filters the encoded vector library based on the item type… the encoded vector library comprises a plurality of entries, each entry corresponds with a different item that can be identified by the item tracking device, each entry may comprise an encoded vector that is linked with an item identifier and a plurality of feature descriptors … Examples of item identifiers include, a product name [text], an SKU number [text], an alphanumeric code [text], a graphical code (e.g. a barcode) [video/ image], or any other suitable type of identifier… the item tracking device uses the item type to filter out or remove any entries in the encoded vector library that do not contain the same item type. This process reduces the number of entries in the encoded vector library that will be considered when identifying the item), and
specifying a number corresponding to the merchandises included in the video among the numbers of the merchandises in the second hierarchy based on a similarity between a vector of the video output by the image encoder and a vector of the text … (See Krishnamurthy ¶ [0147-0150] & [0158] as noted above and [0193] - the item tracking device determines whether the number of identified items matches the number of items that were detected on the platform in operation. The item tracking device determines that all of the items have been identified when the number of identified items matches the number of items that were detected on the platform).
While Krishnamurthy teaches a machine learning based monitored checkout process that captures and analyzes image and text data to identify items for purchase (Krishnamurthy ¶ [0078-0082]), Krishnamurthy does not explicitly teach that said text data is input and output by a text encoder to process said text data. This is taught by Arroyo (See Arroyo ¶ [0109-0110] – the classification circuitry applies an example NLP-based model to classify the columns into the different types of facts using the detected column headers. For example, the columns can be classified into different product facts, such as code, description, quantity and price … the classification circuitry includes an example column classification model(s), which is a trained AI model that classifies text data output by the OCR circuitry. In some examples, the column classification model is based on a simple neural network that contains one layer, such as fastText, fastText is an open-source library for learning of word embeddings and text classification that can be used to train large datasets quickly and efficiently). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the machine learning based monitored checkout process that analyzes image and text data of Krishnamurthy the use of a text encoder model to analyze text data as taught by Arroyo to provide improvements in the efficiency and productivity of the decoding process by applying the ground truth annotations to the ML models (Arroyo ¶ [0050]), thereby increasing the accuracy of the machine learning based checkout monitoring process of Krishnamurthy.
Regarding Claim 4 and 11, modified Krishnamurthy teaches:
The non-transitory computer-readable storage medium/ alert generation method according to claim 1 and 8, wherein
the machine learning model refers to reference source data in which an attribute of a merchandise is associated with each of a plurality of hierarchies (See Krishnamurthy ¶ [0078-0079] – identifying items with a machine learning model based on captured image data of said items and [0146-0148] – The item tracking system may employ process to filter the entries in the encoded vector library [reference source data] to reduce the amount of items that are considered when attempting to identify an item that is placed on the platform… the item tracking device obtains feature descriptors for an item … describing the physical characteristics or attributes of an item, including an item type… an item type identifies a classification for the item, wherein said item classification is understood herein to define the limitation hierarchies based on the specification of the instant application), and
the specifying includes:
specifying the merchandise candidates and the number of the merchandise candidates by inputting the video to the image encoder (See Krishnamurthy ¶ [0147] – using text and barcodes [image/ video output] to identify items and [0158] - the item tracking device may generate a similarity vector for a received encoded vector. A similarity vector comprises an array of numerical values where each numerical value indicates how similar the values in the received encoded vector are to the values in an encoded vector in the encoded vector library),
inputting a text … for each merchandise in a first hierarchy (See Krishnamurthy ¶ [0147] – using text and barcodes [image/ video output] to identify items and [0158] - the item tracking device may generate a similarity vector for a received encoded vector. A similarity vector comprises an array of numerical values where each numerical value indicates how similar the values in the received encoded vector are to the values in an encoded vector in the encoded vector library),
narrowing down numbers corresponding to the merchandises included in the video among the numbers of the merchandises in the first hierarchy based on a similarity between a vector of the video output by the image encoder and a vector of the text … (See Krishnamurthy ¶ [0146-0150] – The item tracking system may employ process to filter the entries, comprising text, SKU numbers and barcodes [image/ video output] to identify items, in the encoded vector library [reference source data] to reduce the amount of items that are considered when attempting to identify an item that is placed on the platform… the item tracking device obtains feature descriptors for an item … describing the physical characteristics or attributes of an item, including an item type… an item type identifies a classification for the item [hierarchies] and [0158] - the item tracking device may generate a similarity vector for a received encoded vector. A similarity vector comprises an array of numerical values where each numerical value indicates how similar the values in the received encoded vector are to the values in an encoded vector in the encoded vector library),
inputting the video to the image encoder (See Krishnamurthy ¶ [0147] and [0158] as noted above), inputting a text … for each of attributes of merchandises in a second hierarchy narrowed down from among the numbers of the merchandises in the first hierarchy (See Krishnamurthy ¶ [0149-0150] - the item tracking device filters the encoded vector library based on the item type… the encoded vector library comprises a plurality of entries, each entry corresponds with a different item that can be identified by the item tracking device, each entry may comprise an encoded vector that is linked with an item identifier and a plurality of feature descriptors … Examples of item identifiers include, a product name [text], an SKU number [text], an alphanumeric code [text], a graphical code (e.g. a barcode) [video/ image], or any other suitable type of identifier… the item tracking device uses the item type to filter out or remove any entries in the encoded vector library that do not contain the same item type. This process reduces the number of entries in the encoded vector library that will be considered when identifying the item), and
specifying attributes corresponding to the merchandises included in the video among the attributes of the merchandises in the second hierarchy based on a similarity between a vector of the video output by the image encoder and a vector of the text … (See Krishnamurthy ¶ [0149-0150] - the item tracking device filters the encoded vector library based on the item type… the encoded vector library comprises a plurality of entries, each entry corresponds with a different item that can be identified by the item tracking device, each entry may comprise an encoded vector that is linked with an item identifier and a plurality of feature descriptors … Examples of item identifiers include, a product name [text], an SKU number [text], an alphanumeric code [text], a graphical code (e.g. a barcode) [video/ image], or any other suitable type of identifier… the item tracking device uses the item type to filter out or remove any entries in the encoded vector library that do not contain the same item type. This process reduces the number of entries in the encoded vector library that will be considered when identifying the item and [0158] - the item tracking device may generate a similarity vector for a received encoded vector. A similarity vector comprises an array of numerical values where each numerical value indicates how similar the values in the received encoded vector are to the values in an encoded vector in the encoded vector library).
While Krishnamurthy teaches a machine learning based monitored checkout process that captures and analyzes image and text data to identify items for purchase (Krishnamurthy ¶ [0078-0082]), Krishnamurthy does not explicitly teach that said text data is input and output by a text encoder to process said text data. This is taught by Arroyo (See Arroyo ¶ [0109-0110] – the classification circuitry applies an example NLP-based model to classify the columns into the different types of facts using the detected column headers. For example, the columns can be classified into different product facts, such as code, description, quantity and price … the classification circuitry includes an example column classification model(s), which is a trained AI model that classifies text data output by the OCR circuitry. In some examples, the column classification model is based on a simple neural network that contains one layer, such as fastText, fastText is an open-source library for learning of word embeddings and text classification that can be used to train large datasets quickly and efficiently). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the machine learning based monitored checkout process that analyzes image and text data of Krishnamurthy the use of a text encoder model to analyze text data as taught by Arroyo to provide improvements in the efficiency and productivity of the decoding process by applying the ground truth annotations to the ML models (Arroyo ¶ [0050]), thereby increasing the accuracy of the machine learning based checkout monitoring process of Krishnamurthy.
Regarding Claim 5 and 18, modified Schuessler teaches:
The non-transitory computer-readable storage medium/ alert generation method according to claim 1 and 8, wherein
the checkout machine is configured to register items of merchandises selected by the person and numbers of the items of the merchandises from among a list of merchandises output in a display of the checkout machine (See Krishnamurthy ¶ [0087] - the item tracking device may identify a digital cart that is associated with the user. In this example, the digital cart comprises information about items that the user has placed on the platform to purchase [registers items] and [0192] – the item tracking device may output the identified item identifier for the identified item by adding the item identifier to a list of identified items that is on a graphical user interface), and
the acquiring includes acquiring the items of the merchandises and the numbers of the items of the merchandises from the checkout machine (See Krishnamurthy ¶ [0081-0082] – the item tracking device may compare the number of identified items from the captured images to the number of items on the platform that was determined in operation…).
Regarding Claim 6 and 13, modified Schuessler teaches:
The non-transitory computer-readable storage medium/ alert generation method according to claim 1 and 8, wherein
the generating includes generating an alert for warning that the specified number of the merchandise candidates and the number of the items of the merchandises acquired from the checkout machine do not coincide (See Krishnamurthy ¶ [0081-0082] – the item tracking device may compare the number of identified items from the captured images to the number of items on the platform that was determined in operation… the item tracking device determines that at least one of the items has not been identified when the number of items identified items from the captured images does not match the determined number of items on the platform… the item tracking device may output a message [alert by example] on a graphical user interface that is located at the imaging device with instructions for the user to rearrange the position of the items on the platform).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Krishnamurthy et al. (US 2022/0414374 A1) in view of Arroyo et al. (US 2023/0230408 A1) and Guo et al. (US 2020/0357045 A1).
Regarding Claim 7 and 14, modified Krishnamurthy teaches:
The non-transitory computer-readable storage medium according to claim 1 (See claim 1 and 8 as noted above), wherein
While Krishnamurthy teaches a monitored checkout process for registering items for purchase and generating an alert when an abnormality is detected (Krishnamurthy ¶ [0078-0082]), Krishnamurthy does not explicitly teach that said generating includes generating an alert including one selected from a difference of a purchase amount based on the specified number of the merchandise candidates and the number of the items of the merchandises acquired from the checkout machine, and identification information of the checkout machine. This is taught by Guo (See Guo ¶ [0036] – a computing device is a checkout machine or a POS terminal device [identification information by example] and [0089-0090] – the price difference between the and the selected product candidate and the top ranked product candidate will trigger the verification process. For example, if the price difference is greater than a threshold, e.g., 20%, the verification process may be triggered, and meanwhile, an alert may be generated. If a potential mismatch is detected, the system may temporally suspend the ongoing transaction and generate an alert for the potential mismatch). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the monitored checkout process of Krishnamurthy the use of purchase amount comparison between registered items and items expected to be registered through monitoring of the item registration process as taught by Guo to improve a computing device's ability to detect unpackaged, unlabeled, or mislabeled products (Guo ¶ [0006]), thereby increasing the accuracy of the checkout monitoring process of Krishnamurthy.
Response to Arguments
Applicant's arguments filed 12/17/2025 have been fully considered but they are not persuasive.
Rejection under 35 U.S.C. § 101:
The amendments to independent claims 1, 8 and 15 do not improve patent eligibility of the claimed invention of the instant application and the previous rejection under 35 U.S.C. § 101 in maintained.
Contrary to the applicant’s assertion that amended independent claims 1, 8 and 15 define a specific advancement in computer technology-a multimodal machine learning model specially trained to process images and text in a common embedding space through a hierarchical refinement process, said amendments leave the methods of said claims as executed by technical elements disclosed at a high level of generality such that said methods are not more than merely applying a computer to perform the functions required by said methods, which does not show integration into a practical application nor does it show significantly more than the abstract ideas discussed above in the current rejection under 35 U.S.C. § 101. The amended claims as they are currently amended, as a whole, merely show data comparison without any additional action beyond a computer processing data, which is required to show a practical application or significantly more than the abstract idea. Any improvement of a claimed invention must be clearly reflected by said claims. The specification of an instant application is not read into the claims during examination.
Rejection under 35 U.S.C. § 102:
Considering the applicant’s arguments and the amendments to independent claims 1, 8 and 15, the claims as they are currently limited overcome the Krishnamurthy prior art reference and the previous rejection under 35 U.S.C. § 102 is withdrawn. Any arguments solely against Krishnamurthy are herein rendered moot. However, the invention of the instant application remains unpatentable because it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate features from Arroyo in the invention of Krishnamurthy as described above in the current rejection under 35 U.S.C. § 103.
Rejection under 35 U.S.C. § 103:
No new arguments are made for dependent claims 7 and 14 other than those previously stated for their respective independent base claims 1 and 8. The previous rejection under 35 U.S.C. § 103 is maintained as it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate features from Arroyo and Guo in the invention of Krishnamurthy as described above in the current rejection of claims 7 and 14 under 35 U.S.C. § 103.
The applicant is generally reminded that prior art must be considered in its entirety (MPEP 2141.02 (VI)).
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.
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/MATTHEW S WERONSKI/Examiner, Art Unit 3627
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627