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. Claim Rejections - 35 USC § 102 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 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. Claim s 1-7 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Takehara, US Pub. No. 2020/0387756. Regarding claim 1, Takehara teaches a label accuracy improvement device (fig. 1) comprising: a controller configured to execute unit processing including learning processing of performing learning that uses labeled data as learning data, sets a mathematical model for estimating a label to be assigned to non-label data which is data to which a label is to be assigned in the learning data based on the non-label data to obtain a result of the estimation and a likelihood of the result of the estimation as a learning target, and updates the mathematical model to reduce a first label error, which is a difference between an estimated label of the learning target and a label included in the learning data (fig. 1, learning data generation apparatus 10, learning apparatus 12; fig. 10 , steps S10, S12, S16, S18 ) , determination processing of determining whether determination conditions that are predetermined conditions related to a second label error, which is a difference between a result estimated by the learned mathematical model based on the non-label data in the learning data and a label included in the learning data, and an inference score, which is a value that is greater as a magnitude of a likelihood obtained by the mathematical model is greater, are satisfied (fig. 1, classification evaluation unit 36, classification determination unit 38; fig. 10 , step S22 ) , and label update processing of updating a label included in the learning data according to a predetermined rule based on the non-label data when it is determined in the determination processing that the determination conditions are satisfied, wherein the determination conditions are conditions including a condition that both a condition that the second label error is greater than a predetermined magnitude and a condition that the inference score is greater than a predetermined magnitude are satisfied (fig. 1, label assigning unit 40; fig. 10, steps S22, S24, S26, S28, S30). Regarding claim 2, Takehara teaches wherein the predetermined rule is a rule for updating a label included in the learning data to a label indicating a result of classifying non-label data included in the learning data by a classifier that is a pre-learned classifier and classifies a classification target with a predetermined accuracy or higher (fig. 1, classification evaluation unit 36, classification determination unit 38; fig. 10, steps S18, S22, S26). Regarding claim 3, Takehara teaches wherein each piece of learning data used in the learning processing of first unit processing is data to which a label estimated using a predetermined classifier is assigned for each piece of data in a set of non-label data (fig. 1, imager 20, object extraction unit 32, classification evaluation unit 36). Regarding claim 4, Takehara teaches wherein each piece of learning data used in the learning processing of first unit processing is, for each piece of data in a set of non-label data, data to which information, indicating to which of classifications resulting from predetermined clustering of the set each piece of the data belongs, is assigned as a label (figs. 8, 9, [0055]). Regarding claim 5, Takehara teaches wherein the controller further executes the unit processing of using the learning data and the mathematical model that have been updated in the unit processing instead of the learning data and the mathematical model that have not yet been updated in the unit processing after the unit processing is executed (fig. 1, learning data generation unit 42, learning data transmission controller 44). Regarding claim 6, it is a method of claim 1 and is rejected on the same grounds presented above. Regarding claim 7, it is a non-transitory storage medium of claim 1 and is rejected on the same grounds presented above. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Kawaguchi et al. (US Pub. No. 2023/0040784) teaches utilizing a classification system to classify non-labeled data. Tsutsumi (US Pub. No. 2021/0256252) teaches a document classification server including a classification inference model and performing clustering. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT KENNETH B LEE JR whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-3147 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Mon - Fri 9am-5pm . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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