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 .
Status of Claims
This action is in response to filings received 09/16/2025 and claims earliest priority from Japanese foreign application 2024-167349, filed 09/26/2024.
Claims 1, 7 and 13 are independent and claims 2-6, 8-12 and 14-18 are dependent.
Claims 1-18 are currently pending and have been examined.
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 1-18 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Under Step 1, claims 1-6 recite a non-transitory computer-readable recording medium and therefore fall within the statutory category of manufacture. Claims 7-12 recite an information processing apparatus and therefore fall within the statutory category of machine. Claims 13-18 recite an information processing system and therefore fall within the statutory category of machine. Accordingly, claims 1-18 satisfy Step 1 of the subject matter eligibility analysis.
Under Step 2A, Prong One, claims 1, 7, and 13 recite a judicial exception. Specifically, claims 1, 7, and 13 recite:
classifying a plurality of items to be determined into one or more categories based on a plurality of appearance images of the plurality of items;
calculating, for each of the one or more categories into which the items are classified, a statistical value of prices of a plurality of items in the category, based on the plurality of prices stored in the storage area;
changing a pattern for the classifying and performing the classifying and the calculating so that one or more of the statistical values satisfy a given condition; and
outputting category information indicating a category of each of the plurality of items when the given condition is satisfied.
These limitations recite mental processes, including observations, evaluations, judgments, and opinions, because they recite classifying items into categories, calculating a statistical value of prices, changing a classification pattern based on whether statistical values satisfy a given condition, and outputting the resulting category information. To the extent that the claimed “calculating” and determination of whether the statistical values “satisfy a given condition” require mathematical relationships or calculations, the limitations additionally recite mathematical concepts. Mental processes and mathematical concepts are recognized groupings of abstract ideas. Accordingly, independent claims 1, 7, and 13 recite an abstract idea.
Under Step 2A, Prong Two, claims 1 and 7 additionally recite a plurality of appearance images being stored in a storage area in association with corresponding prices of the plurality of items. Claim 1 further recites a non-transitory computer-readable recording medium having stored therein an information processing program that causes a computer to perform the claimed process, while claim 7 recites a memory and a processor coupled to the memory.
These additional elements do not integrate the abstract idea into a practical application. The computer-readable recording medium, computer, memory, processor, storage area, appearance images, and stored prices provide the computer environment and information upon which the recite classifying, calculating, changing, and outputting are performed. The additional elements merely use a computer as a tool to perform the abstract idea and do not impose a meaningful limit on the abstract idea. Accordingly, the judicial exception is not integrated into a practical application.
Claim 13 additionally recites a self-checkout apparatus, a fraud detection apparatus configured to perform fraud detection in the self-checkout apparatus using an image recognition model, a processor, and that the fraud detection apparatus is configured to perform the fraud detection by the image recognition model trained using the category information.
These additional elements do not integrate the abstract idea into a practical application. The self-checkout apparatus, fraud detection apparatus, processor, and image recognition model provide a technological environment in which the claimed classifying, calculating, changing, and outputting are performed and subsequently use the resulting category information. The claim does not recite an improvement to the functioning of the processor, self-checkout apparatus, fraud detection apparatus, or image recognition technology itself, but instead uses these elements to perform or apply the recited abstract idea. Thus, the additional elements amount to use of the judicial exception with computer components and within the particular technological environment of fraud detection in a self-checkout apparatus. Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, with respect to claims 1, 7, and 13, the additional elements, when considered individually and as an ordered combination, do not amount to significantly more than the judicial exception. The recited computer-readable recording medium, computer, memory, processor, storage area, self-checkout apparatus, fraud detection apparatus, and image recognition model perform the claimed abstract functions or provide the technological environment in which those functions are performed. Considered as an ordered combination, the additional elements do not add an inventive concept that transforms the abstract idea into a patent-eligible application. Rather, the claims use computer components to carry out or apply the recited classifying, calculating, changing, and outputting of information.
Furthermore, the recited generic computer components and their conventional computer functions are well-understood, routine, and conventional in the relevant field. The Specification describes the fraud detection apparatus as an information processing apparatus or computer and states that the fraud detection apparatus may be a computer such as an edge terminal or server (¶ [57]). The Specification further provides that the fraud detection apparatus and server may be embodied by a virtual server, physical server, single computer, two or more computers, or hardware and network resources provided by a cloud environment (¶ [64-65]). The Specification describes the computer as including a processor, graphics processing unit, memory, storing device, interface device, input-output device, and reader (¶ [68]), and provides conventional examples of processors, memory, and storing devices (¶¶ [69-72]). The Specification further describes the recited functions as being embodied by loading programs stored in the storing device into memory and executing the programs using the processor (¶ [73]). Thus, the Specification describes the recited computer components and their use to execute the claimed functions at a high-level of generality and in a manner indicating that such computer components and activities are sufficiently well-known that their particulars need not be described, and therefore constitute well-understood, routine, and conventional computer components and activities. Accordingly, the additional elements, considered individually and as an ordered combination, do not provide an inventive concept sufficient to amount to significantly more than the judicial exception. Therefore, independent claims 1, 7 and 13 do not amount to significantly more than the exception.
Dependent claims 2, 8 and 14 further recite calculating a false alert rate and changing the pattern based on whether the statistical values and false alert rate satisfy the given condition. The calculating and evaluation of whether values satisfy a condition further recite the abstract idea identified above, including mathematical concepts. The recitation of a machine learning model and captured image does not integrate the abstract idea into a practical application because these elements provide the technological environment and data used in performing the claimed calculation, classification, and evaluation. The claims do not recite a particular improvement to the operation of the machine learning model itself. Accordingly, the additional limitations do not integrate the judicial exception into a practical application and, individually and in combination with the limitations of the claims from which they depend, do not amount to significantly more than the judicial exception or provide an inventive concept.
Dependent claims 3, 9 and 15 recite a multi-objective optimization process that optimizes objective functions related to the statistical values and false alert rate, which further recites a mathematical concept and therefore constitutes additional recitation of the judicial exception rather than an additional element that integrates the exception into a practical application. Accordingly, the additional limitations do not integrate the judicial exception into a practical application and, individually and in combination with the limitations of the claims from which they depend, do not amount to significantly more than the judicial exception or provide an inventive concept.
Dependent claims 4, 10 and 16 recite obtaining information and classifying information based on similarity. The limitations therefore further recite the abstract idea identified above and/or constitute data gathering used in carrying out the claimed classification. The use of appearance images, image feature and storage area does not by itself improve the operation of a computer or image-processing technology, but instead provides the information upon which the claimed clustering and classification are performed. Accordingly, the additional limitations do not integrate the judicial exception into a practical application and, individually and in combination with the limitations of the claims from which they depend, do not amount to significantly more than the judicial exception or provide an inventive concept.
Dependent claims 5, 11 and 17 recite outputting of the appearance images and category information as training data for a machine learning model. The outputting of the appearance images and category information as training data constitutes use of the information resulting from the recited classification. Claims 5 and 11 further recite executing a machine learning process of the machine learning model using the training data. The claims do not recite a particular improvement to the machine learning model or the functioning of the computer itself, but instead use the claimed category information as training data for the machine learning model. Accordingly, the additional limitations do not integrate the judicial exception into a practical application and, individually and in combination with the limitations of the claims from which they depend, do not amount to significantly more than the judicial exception or provide an inventive concept.
Dependent claims 6, 12 and 18 recite obtaining category information, comparing the category obtained from the trained machine learning model with the category of the registered item, determining whether the categories match, and outputting an alert when they do not match. The obtaining and determining limitations further recite the abstract evaluation and comparison of information. The trained machine learning model, captured image, self-checkout apparatus/device, and outputting of an alert do not integrate the judicial exception into a practical application because the self-checkout apparatus/device provides the particular technological environment to the functioning of the machine learning model or self-checkout apparatus/device itself, but rather use these elements to apply the recited evaluation and comparison of information in the self-checkout environment. Accordingly, the additional limitations do not integrate the judicial exception into a practical application and, individually and in combination with the limitations of the claims from which they depend, do not amount to significantly more than the judicial exception or provide an inventive concept.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-7, 10-13, & 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Yuya Obinata et al. (US 2024/0211920 A1, herein Obinata).
As per claim 1, Obinata teaches a non-transitory computer-readable recording medium having stored therein an information processing program that causes a computer to perform a process comprising:
classifying a plurality of items to be determined into one or more categories based on a plurality of appearance images of the plurality of items […] (Obinata ¶¶ [98-103 & 174-177]);
calculating, for each of the one or more categories into which the items are classified, a statistical value of prices of a plurality of items in the category, based on the plurality of prices stored in the storage area (Obinata ¶¶ [177-179]);
changing a pattern for the classifying and performing the classifying and the calculating so that one or more of the statistical values satisfy a given condition (Obinata ¶¶ [179-182]); and
outputting category information indicating a category of each of the plurality of items when the given condition is satisfied (Obinata ¶¶ [114-116 & 182-183]).
Although Obinata does not expressly disclose that the appearance images are stored in association with the corresponding prices of the merchandise items, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to associate the stored appearance image of each merchandise item with the corresponding price information for that merchandise item. Obinata expressly processes merchandise based on image information and corresponding merchandise attributes, including price (Obinata ¶¶ [100-104 & 174-179]). Associating the image and price information with the same merchandise item would have predictably facilitated retrieval and processing of the corresponding merchandise information when generating the price-based classifications disclosed by Obinata. One of ordinary skill in the art would have had reason to make such an association because Obinata uses both merchandise image information and merchandise price information in processing the same merchandise items, and storing the information in association with the corresponding merchandise item would have predictably enabled the respective information to be retrieved and processed for that item.
Claims 7 and 13 recite analogous limitations as claim 1 above and are rejected under the same premise.
With respect to the additional limitations of claim 13, Obinata further teaches a self-checkout apparatus and an unauthorized activity detection apparatus configured to perform fraud detection at the self-checkout apparatus using an image recognition/machine learning model and generated hierarchical category information (Obinata ¶¶ [63-68, 81-83, 114-115 & 122-128]).
Although Obinata does not expressly disclose that the image recognition/machine learning model is trained using the generated hierarchical category information, Obinata teaches training an image recognition/machine learning model using corresponding image and textual information (Obinata ¶¶ [89-96]) and separately teaches generating hierarchical category information for use by the image recognition/machine learning model (Obinata ¶¶ [97-100 & 113-115]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the generated category information as corresponding category labels when training the image recognition/machine learning model. One of ordinary skill in the art would have been motivated to do so to train the model to recognize the generated merchandise categories from merchandise images, thereby predictably enabling the trained model to perform the category-based fraud detection taught by Obinata.
As per claim 4, Obinata teaches the non-transitory computer-readable recording medium according to claim 1, wherein the classifying comprises:
obtaining an image feature of each of the plurality of appearance images stored in the storage area (Obinata ¶¶ [102-103]);
and clustering the plurality of appearance images based on a similarity of the image features (Obinata ¶¶ [103 & 176-177]).
Claims 10 and 16 recite analogous limitations as claim 4 above and are rejected under the same premise.
As per claim 5, Obinata teaches the non-transitory computer-readable recording medium according to any claim 1, wherein the outputting of the category information comprises outputting, as training data for a machine learning model that outputs a category of the item to be determined based on a captured image of the item to be determined, each of the plurality of appearance images stored in the storage area and a category of each of the plurality of items indicated by the category information, and the process further comprises executing a machine learning process of the machine learning model using the training data (Obinata ¶¶ [89-100 & 113-115]).
Although Obinata does not expressly disclose using the generated category information together with the corresponding appearance images as the training data, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the generated category information as labels for the corresponding merchandise images when training the image classification model. Obinata generates category information for classifying merchandise and separately teaches training an image classification model using corresponding image and textual training data (Obinata ¶¶ [89-100 & 113-115]). Using the generated categories as labels for the corresponding training images would have predictably trained the model to output the generated categories when subsequently presented with merchandise images.
Claim 11 recites analogous limitations as claim 5 above and is rejected under the same premise. Claim 17 recites the corresponding training-data limitation and is rejected under the same premise with respect to that limitation.
As per claim 6, Obinata teaches the non-transitory computer-readable recording medium according to claim 5, wherein the process further comprises:
obtaining a category of an item registered in a self-checkout apparatus based on the category information (Obinata ¶¶ [70, 83 & 85]);
determining whether or not a category obtained by inputting a captured image of the item to be determined into the trained machine learning model matches the category of the registered item (Obinata ¶¶ [81-83, 114-115 & 122-128]); and
outputting an alert to the self-checkout apparatus when the category obtained by the inputting does not match the category of the registered item (Obinata ¶¶ [122-128]).
Claims 12 and 18 recite analogous limitations as claim 6 above and are rejected under the same premise.
Claims 2-3, 8-9 & 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Obinata in view of Lotfi A. Zadeh et al. (US 2022/0121884 A1, herein Zadeh).
As per claim 2, Obinata teaches the non-transitory computer-readable recording medium according to claim 1, wherein the process further comprises:
[…] when the machine learning model is trained based on the one or more classified categories, the machine learning model outputting a category of the item to be determined based on a captured image of the item to be determined (Obinata ¶¶ [114-116 & 122-128]); and
changing the pattern for the classifying and performing the classifying and the calculating so that the one or more of the statistical values [...] (Obinata ¶¶ [179-182]).
Although Obinata does not expressly disclose calculating a false alert rate of the machine learning model and changing the classification pattern additionally based on the false alert rate, Zadeh teaches evaluating classification performance using false-positive, false-negative, or error rates (Zadeh ¶ [2238]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply Zadeh’s known classifier-performance evaluation to Obinata’s classification pattern determination such that the classification pattern accounts for both the price based statistical value and the false-alert performance of the resulting image classifier. One of ordinary skill would have been motivated to do so to select a classification pattern that maintains Obinata’s desired price-based grouping while reducing erroneous classifications, thereby predicably improving classification reliability.
Claims 8 and 14 recite analogous limitations as claim 2 above and are rejected under the same premise.
As per claim 3, Obinata teaches the non-transitory computer-readable recording medium according to claim 2, wherein the changing the pattern for the classifying and the performing of the classifying and the calculating comprise a […] optimization process that optimizes an objective function related to the one or more of the statistical values […] (Obinata ¶¶ [179-182]).
Although Obinata does not expressly disclose a multi-objective optimization process that optimizes an objective function related to the statistical value and an objective function related to the false alert rate, Zadeh teaches optimizing classification/recognition based on error-related performance criteria, including false-positive and false-negative error rates, and selecting an optimal strategy based on a time and error-rate trade-off (Zadeh¶¶ [2238 & 2240]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply Zadeh’s optimization of multiple classification-performance criteria to Obinata’s classification-pattern determination such that the optimization accounts for both Obinata’s Price-based statistical value and the false-alert performance of the resulting classifier. One of ordinary skill would have been motivated to do so to select a classification pattern that balances the desired price-based grouping with classification-error performance, thereby predictably improving the suitability of the resulting classification pattern for image-based item recognition.
Claims 9 and 15 recite analogous limitations as claim 3 above and are rejected under the same premise.
Conclusion
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/T.P.K./Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
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