CTNF 18/660,708 CTNF 97268 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The information disclosure statements (IDS) submitted on 05/10/2024 and 11/22/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA 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. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: 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. 07-30-05 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph: (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. 07-30-06 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. Such claim limitation(s) is/are: “control unit” in claim 9. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) 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 § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-12-aia AIA (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. 07-15-aia AIA Claim(s) 7 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by us patent application publication no. 2022/0245523 to Yamamoto For claim 7, Yamamoto discloses a non-transitory computer-readable recording medium (see, e.g., pars. 70-75 and FIG.3) storing a determination program causing a computer to execute a process comprising: generating vector information based on a first feature of a target and a second feature of the target, and conversion parameters (see, e.g., pars. 41-43. 47, 88-101, 107-110, 130-137, 141-147, 151-158, 162-169, 173-179, 183-189, 193-200, 204-211, 218-223, and 241-248 and FIGS. 4,11-19 and 21-22, which teach generating a third/sixth feature vector based on the first/fourth feature vector and the second/fifth feature vector, which is generated by the converting unit) ; and inputting an image and the vector information to a machine learning model including a first machine learning model portion configured to identify the first feature and a second machine learning model portion configured to identify the second feature (see, e.g., pars. 47, 105-110, 137-138, 147-148, 158-159, 169-170, 179-180, 189-190, 200-201, 211-212, 218-224, and 241-248 and FIGS. 4,11-19 and 21-22, which teach inputting an image and the feature vectors to the machine learning model, wherein the machine learning device includes the first extracting unit that identifies the fourth feature vector and the second extracting unit that identifies the fifth feature vector) , and determining a correspondence relationship between the target and a subject in the image (see, e.g., pars.111-114, 224-225, 249-251, and 259 and FIGS. 4, 19 and 22, which teach determining a correspondence between the classifications of subject image and the target) . 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. 07-21-aia AIA Claim (s) 1-4 and 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yamamoto in view us patent application publication no. 2021/0056343 to Toizumi et al. (hereinafter Toizumi) . For claim 1, Yamamoto as applied teaches a non-transitory computer-readable recording medium (see, e.g., pars. 70-75 and FIG.3 ) storing a machine learning program causing a computer to execute a process comprising: generating vector information based on a first feature of a target included in an image, a second feature of the target, and conversion parameters (see, e.g., pars. 41-43, 88-101, 130-137, 141-147, 151-158, 162-169, 173-179, 183-189, 193-200, 204-211, and 218-223 and FIGS. 1, 4 and 11-19, which teach generating a third feature vector based on the first feature vector and the second feature vector, which is generated by the converting unit) ; and executing training of a machine learning model and update of the conversion parameters by inputting the image and the vector information to the machine learning model (see, e.g., pars. 105-111, 137-138, 147-148, 158-159, 169-170, 179-180, 189-190, 200-201, 211-212, and 223-224 and FIGS. 11-19, which teach training and updating a machine learning model by inputting the image and the feature vectors) , the machine learning model including a first machine learning model portion configured to identify the first feature (see, e.g., pars. 88, and 131 and FIGS. 4 and 11-19, which teach identifying the first feature using the first extracting unit) and a second machine learning model portion configured to identify the second feature (see, e.g., pars. 89-101 and 132-136 and FIGS. 4, 11-18, which teach identifying the second feature using the second extracting unit) . Yamamoto as applied does not explicitly teach updating the conversion parameters. Toizumi in the analogous art teaches updating the feature transformation parameters (see, e.g., pars. 82-95 and FIGS. 5-7 of Toizumi). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto to update the conversion parameters as taught by Toizumi because doing so would optimize the parameters for a respective transformation, e.g., dimension reduction (see pars. 82-84 of Toizumi). For claim 2, Yamamoto in view of Toizumi teaches that the generating of the vector information includes generating a first vector indicating the first feature and a second vector indicating the second feature (see, e.g., pars. 88-101, 131-136 and 141-146 and FIGS. 11-18, which teach generating a first feature vector indicating a feature of an entire image including the objects therein and a second feature vector indicating a partial feature, such as a candidate object probability, a size, color and etc.) . For claim 3, Yamamoto in view of Toizumi teaches that the generating of the vector information includes generating the first vector and the second vector that are corrected based on a relationship between the first feature and the second feature (see, e.g., pars. 43, 102-104 and 137 and FIGS. 1, 4 and 11-18, which teach generating the third feature vector by combining/coupling the first feature and second feature vectors, e.g., using the sum and/or product of elements/dimensions of the first and second feature vectors) . For claim 4, Yamamoto in view of Toizumi teaches that the inputting of the vector information to the machine learning model includes inputting the first vector to the first machine learning model portion and inputting the second vector to the second machine learning model portion (see, e.g., pars. 88-101, 131-136 and 141-146 and FIGS. 11-18, which teach inputting an entire image including the first feature vector to the first extracting unit and inputting object portions including the second feature vector to the second extracting unit). For claim 8, Yamamoto as applied teaches that the determining the correspondence relationship includes determining whether or not the first feature and the second feature of the target match a first feature identified in the first machine learning model portion and a second feature identified in the second machine learning model portion (see, e.g., pars.111-114, 224-225, 249-251, and 259 and FIGS. 4, 19 and 22, which teach determining a correspondence between the classifications of subject image and the target, wherein the classification of the target is based on the first and second feature vectors and the classification of the subject is based on the fourth and fifth feature vectors) . While Yamamoto as applied teaches comparing the first and second vectors with the fourth and fifth vectors, it does not explicitly teach determining whether the feature vectors match. Toizumi in the analogous art teaches determining cosine similarity between feature vectors to determine therebetween (see, e.g., pars. 56-62 of Toizumi). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto to determining similarity between the feature vectors as taught by Toizumi because doing so would yield a predictable result of indicating how similar the vectors are irrespective of their length and improve the recognition results of the class estimation unit (see MPEP 2143(I)(D) and par. 61 of Toizumi). For claim 9, Yamamoto as applied teaches a machine learning apparatus (see, e.g., pars. 32-33 and FIG. 1, which teach a machine learning device) comprising a control unit (see, e.g., pars. 71 and FIG. 3) configured to perform processing comprising: generating vector information based on a first feature of a target included in an image, a second feature of the target, and conversion parameters (see, e.g., pars. 41-43, 88-101, 130-137, 141-147, 151-158, 162-169, 173-179, 183-189, 193-200, 204-211, and 218-223 and FIGS. 1, 4 and 11-19, which teach generating a third feature vector based on the first feature vector and the second feature vector, which is generated by the converting unit) ; and executing training of a machine learning model and update of the conversion parameters by inputting the image and the vector information to the machine learning model (see, e.g., pars. 105-111, 137-138, 147-148, 158-159, 169-170, 179-180, 189-190, 200-201, 211-212, and 223-224 and FIGS. 11-19, which teach training and updating a machine learning model by inputting the image and the feature vectors) , the machine learning model including a first machine learning model portion configured to identify the first feature (see, e.g., pars. 88, and 131 and FIGS. 4 and 11-19, which teach identifying the first feature using the first extracting unit) and a second machine learning model portion configured to identify the second feature (see, e.g., pars. 89-101 and 132-136 and FIGS. 4, 11-18, which teach identifying the second feature using the second extracting unit) . Yamamoto as applied does not explicitly teach updating the conversion parameters. Toizumi in the analogous art teaches updating the feature transformation parameters (see, e.g., pars. 82-95 and FIGS. 5-7 of Toizumi). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto to update the conversion parameters as taught by Toizumi because doing so would optimize the parameters for a respective transformation, e.g., dimension reduction (see pars. 82-84 of Toizumi) . 07-21-aia AIA Claim (s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yamamoto in view of Toizumi and further in view of Us patent application publication no. 2018/0157938 to Wang et al. (hereinafter Wang) . For claim 5, while Yamamoto in view of Toizumi teaches inputting the second vector to the second machine learning model portion (see, e.g., pars. 88-101, 131-136 and 141-146 and FIGS. 11-18 of Yamamoto, which teach inputting object portions including the second feature vector to the second extracting unit), it does not explicitly teach inputting an output of the first machine learning model portion. Wang in the analogous art teaches that the machine learning model is configured such that an output of the first machine learning model portion and the second vector are input to the second machine learning model portion (see, e.g., pars. 188-195 and 234-246 and FIG. 9 of Wang, which teach inputting the output of the first sub-network to the second sub-network). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Toizumi to use parallel sub-neural networks of Wang because doing so would improve the performance of the neural network and reduce the number of neural networks and storage amount for the model (see, e.g., par. 232 and 242 of Wang). For claim 6, while Yamamoto does not explicitly teach, Wang in the analogous art teaches in a case where a third machine learning model portion configured to identify a third feature of the target included in the image is added to the machine learning model, the generating of the vector information includes generating the vector information based on the first feature, the second feature, the third feature, and the conversion parameters (see, e.g., pars. 10-12, 19-21, 69-74, 189-190, and 228 and FIG. 9 of Wang, which teach using a third sub-neural network for a third different attribute/feature) , and the executing of the training of a machine learning model and the update of the conversion parameters includes inputting the image and the vector information to the machine learning model in which parameters of both of the first machine learning model portion and the second machine learning model portion obtained by previous training are fixed (see, e.g., pars. 75-79, 189-190 and FIG. 9 of Wang, which teach that each of the sub-neural networks are independent on one another and have a parallel relationship; the examiner finds the above teaching to implicitly teach, if not suggest, that each sub-neural networks are trained separately and independently, and that parameters of one sub-neural network do not affect parameters of another) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto to use parallel sub-neural networks as taught by Wang because doing so would improve the performance of the neural network and reduce the number of neural networks and storage amount for the model (see, e.g., par. 232 and 242 of Wang). Yamamoto in view of Wang does not explicitly teach updating conversion parameters. Toizumi in the analogous art teaches updating the feature transformation parameters (see, e.g., pars. 82-95 and FIGS. 5-7 of Toizumi). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamamoto in view of Wang to update the conversion parameters as taught by Toizumi because doing so would optimize the parameters for a respective transformation, e.g., dimension reduction (see pars. 82-84 of Toizumi). Additional Citations The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action. Citation Relevance Suzuki et al. (us pat. pub. 2020/0117991) Describes a learning apparatus, detecting apparatus, learning method, and detecting method. In one embodiment, a feature model, which calculates a feature value of an input image, is trained on a plurality of first images. First feature values corresponding one-to-one with the first images are calculated using the feature model, and feature distribution information representing a relationship between a plurality of classes and the first feature values is generated. When a detection model which determines, in an input image, each region with an object and a class to which the object belongs is trained on a plurality of second images, second feature values corresponding to regions determined within the second images by the detection model are calculated using the feature model, an evaluation value, which indicates class determination accuracy of the detection model, is modified using the feature distribution information and the second feature values, and the detection model is updated based on the modified evaluation value. Jung et al. (us pat. pub. 2023/0095716) Describes an object classification method and apparatus. The object classification method includes receiving an input image, storing first feature data extracted by a first feature extraction layer of a neural network configured to extract features of the input image, receiving second feature data from a second feature extraction layer which is an upper layer of the first feature extraction layer, generating merged feature data by merging the first feature data and the second feature data, and classifying an object in the input image based on the merged feature data. Kobori et al. (us pat. pub. 2023/0419717) Describes a re-identification method for performing re-identification of a target object in image data using a machine learning model. The re-identification method comprises acquiring first image data and second image data in both of which the target object is, acquiring a plurality of first output data and a plurality of second output data by inputting the first image data and the second image data into the machine learning model, calculating a plurality of distances each of which is a distance in an embedding space between each of the plurality of first output data and each of the plurality of second output data, determining that the target object of the first image data and the target object of the second image data are similar when a predetermined number or more of the plurality of distances are less than a predetermined threshold. Table 1 Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Table 1 and form 892 . 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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. /WOO C RHIM/Examiner, Art Unit 2676 Application/Control Number: 18/660,708 Page 2 Art Unit: 2676 Application/Control Number: 18/660,708 Page 3 Art Unit: 2676 Application/Control Number: 18/660,708 Page 4 Art Unit: 2676 Application/Control Number: 18/660,708 Page 5 Art Unit: 2676 Application/Control Number: 18/660,708 Page 6 Art Unit: 2676 Application/Control Number: 18/660,708 Page 7 Art Unit: 2676 Application/Control Number: 18/660,708 Page 10 Art Unit: 2676 Application/Control Number: 18/660,708 Page 11 Art Unit: 2676 Application/Control Number: 18/660,708 Page 12 Art Unit: 2676
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