CTNF 18/863,375 CTNF 88067 DETAILED ACTION Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao (ZHAO, LIYUE. "ACTIVE LEARNING WITH UNRELIABLE ANNOTATIONS." PhD diss., University of Central Florida, 2013.), and further in view of Mozafari et al (Mozafari, Barzan, Purna Sarkar, Michael Franklin, Michael Jordan, and Samuel Madden. "Scaling up crowd-sourcing to very large datasets: a case for active learning." (2014).) and Ishida et al (US 20210243374 A1) . RE claim 1, Zhao teaches A label histogram creating device that creates a label histogram by performing a sampling process of assigning a label for classifying a piece of data by using a crowdsourcing, the label histogram indicating a probability distribution of possible labels for the piece of data, the label histogram creating device comprising a hardware processor (Fig 4. 1, abstract, pages 33-34, method implemented by a typical computer comprising a hardware processor) configured to, for a data set including a plurality of pieces of data, perform a first sampling process by using the crowdsourcing to create a set of label histograms (Fig 4. 1, abstract, page 35, the set of initially labeled instances indicates a first sampling process), perform a pick out process of picking out pieces of data that are targets of a second sampling process from the data set on the basis of uncertainty of information included in the label histograms (Fig 4. 1, abstract, pages 35-36), and perform, by using the crowdsourcing, the second sampling process on the pieces of data picked out by the pick out part process (Fig 4. 1, abstract, pages 35-36). Zhao does not explicitly teach: set the number of times of sampling for each piece of data, and the second sampling process with the number of times of sampling increased compared to the number of times of sampling in the first sampling process. However he explicitly sets the number of times of sampling for the second sampling (page 38 first paragraph). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention that Zhao at least readily/implicitly teaches set the number of times of sampling for the first sample to balance the quality vs resource budget requirement, known in prior art. For example Mozafari teaches determine the number of times of sampling for each piece of data in page 3 cols 1-2, algorithm in Fig 3 as the allocated budget to control the number of sampling/degree of redundancy for the iterative process as the first sampling process. In addition Zhao teaches continuing the relabeling process until sufficient agreement is achieved or a predetermined annotation limit is reached, thereby increasing the number of annotations associated with selected samples (abstract, pages 2-3, 38). Mozafari also teaches starting from a small batch size and increasing the number of samples (page 7, col 2) improve the AL algorithm’s effectiveness by incorporating previously requested labels, while recognizing uncertain samples warrant additional annotation effort relative to samples having greater confidence (abstract, page 3 cols 1-2, algorithm in Fig 3). Furthermore Ishida teaches increasing reliability by by repeating the second sampling by a number of times the probability of acquiring a higher reliability level becomes equal to or higher than a threshold than a previous sampling and may be determined adaptively through statistical processing of a reliability level distribution in [0105]. Therefore it would have been obvious to one of ordinary skill in the art at the time of the invention to include in Zhao set the number of times of sampling for each piece of data, and the second sampling process with the number of times of sampling increased compared to the number of times of sampling in the first sampling process, as set forth above combining the teachings of Zhao, Mozafari and Ishida in order to improve accuracy of the uncertain labels and thereby increasing system effectiveness and user experience. RE claim 2, Zhao as modified by Mozafari and Ishida teaches wherein the hardware processor is configured to perform a sampling process a plurality of times by using the crowdsourcing after the first sampling process, and each time the sampling process is performed, perform a pick out process of picking out pieces of data that are targets of a next sampling process with the number of times of picking out a piece of data reduced compared to the number of times of picking out a piece of data for a previous sampling process, and the hardware processor is configured to perform the next sampling process on the pieces of data picked out by the pick out process with the number of times of sampling increased compared to the number of times of sampling in the previous sampling process (Zhao, Fig 4. 1, abstract, pages 33-34, in addition Mozafari abstract, page 7, col 2 as set forth in rejection of claim 1). RE claim 3, Zhao as modified by Mozafari and Ishida teaches wherein the hardware processor is configured to calculate, for a piece of data for which the label histogram has been created through the sampling process, an information entropy of the label histogram (Zhao, Fig 4. 1, abstract, pages 36-37, in addition Mozafari page 4 col 2). RE claim 4, Zhao as modified by Mozafari and Ishida teaches wherein the hardware processor is configured to pick out, as the pick out process, pieces of data whose information entropies are dispersed from one another (Zhao, Fig 4. 1, abstract, pages 36-37, in addition Mozafari page 4 col 2). RE claim 5, Zhao as modified by Mozafari and Ishida teaches wherein, as the pick out process, the hardware processor is configured to: divide a section between a minimum value and a maximum value of the information entropies into subsections according to the number of pieces of data to be picked out; and pick out a piece of data including an information entropy that is closest to a boundary position of each subsection (Zhao, Fig 4. 1, abstract, pages 29-31, 36-37, 71 etc. In addition Mozafari Figs 4-5, page 5 cols 1-2). Claim 6 recites limitations similar in scope with limitations of claim 1 as method and therefore rejected under the same rationale. Claim 7 recites limitations similar in scope with limitations of claim 1 and therefore rejected under the same rationale. In addition Zhao teaches A non-transitory storage medium storing a label histogram creating program (Fig 4. 1, abstract, pages 33-34, method is typically stored as a computer program in a CRM) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See attached 892) . Any inquiry concerning this communication or earlier communications from the examiner should be directed to SULTANA MARCIA ZALALEE whose telephone number is (571)270-1411. The examiner can normally be reached Monday- Friday 8:00am-4:30pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kent Chang can be reached at (571)272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Sultana M Zalalee/ Primary Examiner, Art Unit 2614 Application/Control Number: 18/863,375 Page 2 Art Unit: 2614 Application/Control Number: 18/863,375 Page 3 Art Unit: 2614 Application/Control Number: 18/863,375 Page 4 Art Unit: 2614 Application/Control Number: 18/863,375 Page 5 Art Unit: 2614 Application/Control Number: 18/863,375 Page 6 Art Unit: 2614 Application/Control Number: 18/863,375 Page 7 Art Unit: 2614
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