CTNF 18/799,518 CTNF 87262 DETAILED ACTION 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. Claims 1-20 are pending in the application. Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. 08-34 AIA Claim s 1, 8 and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1, 16 and 20 respectively of U.S. Patent No. 12,093,305 B2 . Although the claims at issue are not identical, they are not patentably distinct from each other because the following reasons . Listed in the following table is a limitation-to-limitation comparison of the examined claim and the conflicting claim. Application being examined 18/799,518 (hereafter ‘518 application) Conflicting Patent 12,093,305 B2 (hereafter ‘305 patent) Claim 1: 1. A computer system that trains a neural network to identify features of an item embodied in an image and to use the neural network to identify other items that have similar features to said features, the computer system comprising: one or more processors; and one or more hardware storage devices that store instructions that are executable by the one or more processors to cause the computer system to: access a plurality of images that provide different visualizations of a same item; use the plurality of images to train a neural network to identify a plurality of features of the item; generate a vector for each feature in the plurality of features, resulting in generation of a plurality of vectors, wherein the plurality of vectors includes an identity embedding vector; and use at least one vector included in the plurality of vectors to facilitate a search for a different item that is determined to meet a similarity requirement with regard to the item in the plurality of images. Claim 1: 1. A computing system that trains a neural network to identify machine recognizable features of an item that is embodied in an image and to use the neural network to identify other items that have similar features to the machine recognizable features, said computing system comprising: at least one processor; and at least one hardware storage device that stores instructions that are executable by the at least one processor to cause the computing system to: access a plurality of images, wherein each image in the plurality of images provides a different visualization of a same item; machine train on the plurality of images using a neural network to identify a plurality of features of the item; generate a plurality of vectors for each feature in the plurality of features such that the neural network is trained on multiple features of the item, wherein the plurality of vectors includes an identity embedding vector that provides a supposed identity for the item; and use at least one vector included in the plurality of vectors to facilitate a search for one or more different items that are determined to meet a similarity requirement with regard to the item in the plurality of images, wherein: a search definition for the search includes accessing a latent vector that describes a user-specified change to at least one feature included in the plurality of features, and the search is based on a combination of said at least one vector and the latent vector such that the search involves searching for different items that are identified as having the changed at least one feature. Claim 8 8. A method for training a neural network to identify features of an item embodied in an image and for using the neural network to identify other items that have similar features to said features, the method comprising: accessing a plurality of images that provide different visualizations of a same item; using the plurality of images to train a neural network to identify a plurality of features of the item; generating a vector for each feature in the plurality of features, resulting in generation of a plurality of vectors, wherein the plurality of vectors includes an identity embedding vector; and using at least one vector included in the plurality of vectors to facilitate a search for a different item that is determined to meet a similarity requirement with regard to the item in the plurality of images. Claim 16 16. A method for training a neural network to identify machine recognizable features of an item that is embodied in an image and to use the neural network to identify other items that have similar features to the machine recognizable features, the method comprising: accessing a plurality of images, wherein each image in the plurality of images provides a different visualization of a same item; machine training on the plurality of images using a neural network to identify a plurality of features of the item; generating a plurality of vectors for each feature in the plurality of features such that the neural network is trained on multiple features of the item, wherein the plurality of vectors includes an identity embedding vector that provides a supposed identity for the item; using at least one vector included in the plurality of vectors to facilitate a search for one or more different items that are determined to meet a similarity requirement with regard to the item in the plurality of images, wherein: a search definition for the search includes accessing a latent vector that describes a user-specified change to at least one feature included in the plurality of features, and the search is based on a combination of said at least one vector and the latent vector such that the search involves searching for different items that are identified as having the changed at least one feature. Claim 15 15. One or more hardware storage devices that store instructions that are executable by one or more processors to cause the one or more processors to: access a plurality of images that provide different visualizations of a same item; use the plurality of images to train a neural network to identify a plurality of features of the item; generate a vector for each feature in the plurality of features, resulting in generation of a plurality of vectors, wherein the plurality of vectors includes an identity embedding vector; and use at least one vector included in the plurality of vectors to facilitate a search for a different item that is determined to meet a similarity requirement with regard to the item in the plurality of images. Claim 20 20. At least one hardware storage device that stores instructions that are executable by at least one processor of a computer system to cause the computer system to access a plurality of images, wherein each image in the plurality of images provides a different visualization of a same item; machine train on the plurality of images using a neural network to identify a plurality of features of the item; generate a plurality of vectors for each feature in the plurality of features such that the neural network is trained on multiple features of the item, wherein the plurality of vectors includes an identity embedding vector that provides a supposed identity for the item; use at least one vector included in the plurality of vectors to facilitate a search for one or more different items that are determined to meet a similarity requirement with regard to the item in the plurality of images, wherein: a search definition for the search includes accessing a latent vector that describes a user-specified change to at least one feature included in the plurality of features, and the search is based on a combination of said at least one vector and the latent vector such that the search involves searching for different items that are identified as having the changed at least one feature. Therefore claims 1, 16 and 20 of ‘305 patent teach every limitation recited in claims 1, 8 and 15 respectively of the ‘518 application . 08-34 AIA Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 16 of U.S. Patent No. 11,720,622 B2, claim 8 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 17 and 16 of U.S. Patent No. 11,720,622 B2, and claim 15 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 21 and 16 of U.S. Patent No. 11,720,622 B2 . Although the claims at issue are not identical, they are not patentably distinct from each other because the following reasons . Listed in the following table is a limitation-to-limitation comparison of the examined claim and the conflicting claim. Application being examined 18/799,518 (hereafter ‘518 application) Conflicting Patent 11,720,622 B2 (hereafter ‘622 patent) Claim 1: 1. A computer system that trains a neural network to identify features of an item embodied in an image and to use the neural network to identify other items that have similar features to said features, the computer system comprising: one or more processors; and one or more hardware storage devices that store instructions that are executable by the one or more processors to cause the computer system to: access a plurality of images that provide different visualizations of a same item; use the plurality of images to train a neural network to identify a plurality of features of the item; generate a vector for each feature in the plurality of features, resulting in generation of a plurality of vectors, wherein the plurality of vectors includes an identity embedding vector; and use at least one vector included in the plurality of vectors to facilitate a search for a different item that is determined to meet a similarity requirement with regard to the item in the plurality of images Claim1 : 1. A computing system that trains a neural network to identify machine recognizable features of an item that is embodied in an image and to use the neural network to identify other items that have similar features to the machine recognizable features, said computing system comprising: at least one processor; and at least one hardware storage device that stores instructions that are executable by the at least one processor to cause the computing system to: access a plurality of images, wherein each image in the plurality of images provides a different visualization of a same item; machine train on the plurality of images using a neural network to identify a plurality of features of the item; generate a plurality of embedding vectors for each feature in the plurality of features such that the neural network is trained on multiple features of the item, wherein the plurality of embedding vectors includes an identity embedding vector that provides a supposed identity for the item; and use the identity embedding vector to generate a probability vector representing probabilities that the supposed identity of the item is of various values. 16. The computing system of claim 1, wherein execution of the instructions further causes the computing system to use the identity embedding vector to facilitate a search for a different item that is determined to meet a similarity requirement with regard to the item in the plurality of images. Claim 8 8. A method for training a neural network to identify features of an item embodied in an image and for using the neural network to identify other items that have similar features to said features, the method comprising: accessing a plurality of images that provide different visualizations of a same item; using the plurality of images to train a neural network to identify a plurality of features of the item; generating a vector for each feature in the plurality of features, resulting in generation of a plurality of vectors, wherein the plurality of vectors includes an identity embedding vector; and using at least one vector included in the plurality of vectors to facilitate a search for a different item that is determined to meet a similarity requirement with regard to the item in the plurality of images. Claim 17: 17. A method for training a neural network to identify machine recognizable features of an item that is embodied in an image and to use the neural network to identify other items that have similar features to the machine recognizable features, the method comprising: accessing a plurality of images, wherein each image in the plurality of images provides a different visualization of a same item; machine training on the plurality of images using a neural network to identify a plurality of features of the item; generating a plurality of embedding vectors for each feature in the plurality of features such that the neural network is trained on multiple features of the item, wherein the plurality of embedding vectors includes an identity embedding vector that provides a supposed identity for the item; and using the identity embedding vector to generate a probability vector representing probabilities that the supposed identity of the item is of various values. 16. The computing system of claim 1, wherein execution of the instructions further causes the computing system to use the identity embedding vector to facilitate a search for a different item that is determined to meet a similarity requirement with regard to the item in the plurality of images. Claim 15 15. One or more hardware storage devices that store instructions that are executable by one or more processors to cause the one or more processors to: access a plurality of images that provide different visualizations of a same item; use the plurality of images to train a neural network to identify a plurality of features of the item; generate a vector for each feature in the plurality of features, resulting in generation of a plurality of vectors, wherein the plurality of vectors includes an identity embedding vector; and use at least one vector included in the plurality of vectors to facilitate a search for a different item that is determined to meet a similarity requirement with regard to the item in the plurality of images. Claim 21 21. At least one hardware storage device that stores instructions that are executable by at least one processor of a computer system to cause the computer system to: access a plurality of images, wherein each image in the plurality of images provides a different visualization of a same item; machine train on the plurality of images using a neural network to identify a plurality of features of the item; generate a plurality of embedding vectors for each feature in the plurality of features such that the neural network is trained on multiple features of the item, wherein the plurality of embedding vectors includes an identity embedding vector that provides a supposed identity for the item; and use the identity embedding vector to generate a probability vector representing probabilities that the supposed identity of the item is of various values. 16. The computing system of claim 1, wherein execution of the instructions further causes the computing system to use the identity embedding vector to facilitate a search for a different item that is determined to meet a similarity requirement with regard to the item in the plurality of images. Therefore claims 1 and 16 of ‘622 patent teaches every limitation recited in claim 1 of the ‘518 application, claims 17 and 16 of ‘622 patent teaches every limitation recited in claim 8 of the ‘518 application, and claims 21 and 16 of ‘622 patent teaches every limitation recited in claim 15 of the ‘518 application . 08-34 AIA Claim s 1 and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 11,373,095 B2. Claim 8 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 16 of U.S. Patent No. 11,373,095 B2 . Although the claims at issue are not identical, they are not patentably distinct from each other because the following reasons . Listed in the following table is a limitation-to-limitation comparison of the examined claim and the conflicting claim. Application being examined 18/799,518 (hereafter ‘518 application) Conflicting Patent 11,373,095 B2 (hereafter ‘095 patent) Claim 1: 1. A computer system that trains a neural network to identify features of an item embodied in an image and to use the neural network to identify other items that have similar features to said features, the computer system comprising: one or more processors; and one or more hardware storage devices that store instructions that are executable by the one or more processors to cause the computer system to: access a plurality of images that provide different visualizations of a same item; use the plurality of images to train a neural network to identify a plurality of features of the item; generate a vector for each feature in the plurality of features, resulting in generation of a plurality of vectors, wherein the plurality of vectors includes an identity embedding vector; and use at least one vector included in the plurality of vectors to facilitate a search for a different item that is determined to meet a similarity requirement with regard to the item in the plurality of images. Claim 1: 1. A computing system configured to train a neural network to identify machine recognizable features of an item that is embodied in an image and to use the neural network to identify other items that have similar features to the machine recognizable features, said computing system comprising: one or more processors; and one or more computer-readable media that store instructions that are executable by the one or more processors to cause the computing system to perform machine learning, which includes: accessing a plurality of images, wherein each image in the plurality of images provides a different visualization of a same item; machine training on the plurality of images using a neural network to identify a plurality of features of the item; generating a plurality of embedding vectors for each feature in the plurality of features of the item, so that the neural network is trained on multiple features of the item and is thus more capable of later comparing similarity or differences of searched items across those multiple different features, wherein the plurality of embedding vectors includes an identity embedding vector that provides a supposed identity for the item; and causing an identity classifier neural network to use the identity embedding vector to generate a probability vector representing probabilities that the supposed identity of the item is of various values, wherein the identity classifier neural network is a single layer neural network. Claim 8 8. A method for training a neural network to identify features of an item embodied in an image and for using the neural network to identify other items that have similar features to said features, the method comprising: accessing a plurality of images that provide different visualizations of a same item; using the plurality of images to train a neural network to identify a plurality of features of the item; generating a vector for each feature in the plurality of features, resulting in generation of a plurality of vectors, wherein the plurality of vectors includes an identity embedding vector; and using at least one vector included in the plurality of vectors to facilitate a search for a different item that is determined to meet a similarity requirement with regard to the item in the plurality of images. Claim 16 16. A method for training a neural network to identify machine recognizable features of an item that is embodied in an image and to use the neural network to identify other items that have similar features to the machine recognizable features, the method comprising: accessing a plurality of images, wherein each image in the plurality of images provides a different visualization of a same item; machine training on the plurality of images using a neural network to identify a plurality of features of the item; generating a plurality of embedding vectors for each feature in the plurality of features of the item, so that the neural network is trained on multiple features of the item and is thus more capable of later comparing similarity or differences of searched items across those multiple different features, wherein the plurality of embedding vectors includes an identity embedding vector that provides a supposed identity for the item; and causing an identity classifier neural network to use the identity embedding vector to generate a probability vector representing probabilities that the supposed identity of the item is of various values, wherein the identity classifier neural network is a single layer neural network. Claim 15 15. One or more hardware storage devices that store instructions that are executable by one or more processors to cause the one or more processors to: access a plurality of images that provide different visualizations of a same item; use the plurality of images to train a neural network to identify a plurality of features of the item; generate a vector for each feature in the plurality of features, resulting in generation of a plurality of vectors, wherein the plurality of vectors includes an identity embedding vector; and use at least one vector included in the plurality of vectors to facilitate a search for a different item that is determined to meet a similarity requirement with regard to the item in the plurality of images. Claim 1: 1. A computing system configured to train a neural network to identify machine recognizable features of an item that is embodied in an image and to use the neural network to identify other items that have similar features to the machine recognizable features, said computing system comprising: one or more processors; and one or more computer-readable media that store instructions that are executable by the one or more processors to cause the computing system to perform machine learning, which includes: accessing a plurality of images, wherein each image in the plurality of images provides a different visualization of a same item; machine training on the plurality of images using a neural network to identify a plurality of features of the item; generating a plurality of embedding vectors for each feature in the plurality of features of the item, so that the neural network is trained on multiple features of the item and is thus more capable of later comparing similarity or differences of searched items across those multiple different features, wherein the plurality of embedding vectors includes an identity embedding vector that provides a supposed identity for the item; and causing an identity classifier neural network to use the identity embedding vector to generate a probability vector representing probabilities that the supposed identity of the item is of various values, wherein the identity classifier neural network is a single layer neural network. Therefore claim 1 of ‘095 patent teaches every limitation recited in claims 1 and 15 of ‘518 application, and claim 16 of ‘095 patent teaches every limitation recited in claim 8 of ‘518 application . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 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. 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 1-6, 8-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dal Mutto et al. (US Patent 10,691,979 B2, hereafter Dal Mutto), in view of Uchiyama (US Publication 2020/0272860 A1) . As per claim 1 , Dal Mutto teaches a computer system (ABSTRACT; FIG. 2A-2B) that trains a neural network to identify features of an item embodied in an image and to use the neural network to identify other items that have similar features to said features (Abstract; FIG. 1 shows a process of classifying a target object 10, in this case a boot, as an instance of a particular style of boot 22 in an inventory of shoes 20 (col. 4 ln 3-7). FIG. 4 shows a trained neural network (col. 2 ln 24-31)), the computer system comprising: one or more processors (FIG. 2B #108; col. 2 ln 38-41); and one or more hardware storage devices that store instructions that are executable by the one or more processors (FIG. 2B #110; col. 2 ln 38-41) to cause the computer system to: access a plurality of images that provide different visualizations of a same item (FIG. 2A #100, #150; col. 4 ln 34-47); use the plurality of images to train a neural network to identify a plurality of features of the item (FIG. 4 shows a convolutional neural network based classification system which comprises a first neural network CNN1 and a second neural network CNN2; col. 13 ln 65-col. 14 ln 5 “In some embodiments, the neural network is trained based on training data, which may include a set of 3D models of objects and their corresponding labels (e.g., the correct classifications of the objects). A portion of this training data may be reserved as cross-validation data to further adjust the parameters of during the training process, and a portion may also be reserved as a test data to confirm that the network is properly trained”; Dal Mutto further teaches using the trained CNN to extract a plurality of features (Abstract: “a multi-dimensional shape descriptor space representation of a 3D shape of the query object; a multi-dimensional color descriptor space representation of a texture of the query object; and a one-dimensional size descriptor space representation of a size of query object”); See FIG. 3B; col. 12 ln 61-col. 13 ln 2); generate a vector for each feature in the plurality of features, resulting in generation of a plurality of vectors (FIG. 4-5; col. 14 ln 19-25; col. 14 ln 28-34), wherein the plurality of vectors includes an identity embedding vector; and use at least one vector included in the plurality of vectors to facilitate a search for a different item that is determined to meet a similarity requirement with regard to the item in the plurality of images (FIG. 1A showing a different item #22 that is similar to the searched item #10 among a plurality of items #20 is recognized/classified. Col. 2 ln 27-31 “The computing the classification of the query object based on the descriptor may be performed by identifying a result object from the inventory of objects having a closest distance to the descriptor of the query object in shape descriptor space, color descriptor space, and size descriptor space”; col. 15 ln 22-25 “In some embodiments of the present invention, the classifier CNN.sub.2 classifies the target object 10 by using the descriptor F of the target object to retrieve a most similar shape in a data set”). Dal Mutto, however, does not teach that the plurality of vectors includes an identity embedding vector. Uchiyama teaches a system for training an appearance signature extractor using a training data set, the training data set including input images associated with a plurality of domains (Abstract). When training the appearance signature extractor, an input image of the training data set, an identity label, and a domain label are input into the appearance signature extractor. The identity label provides identity information of the input image. Uchiyama further teaches determining an identity score from the appearance signature. The identify score is an identity vector which represents a probability of identity. See Abstract; FIG. 1; FIG. 5-6; para. [0020]-[0021], [0083], para. [0090]-[0093]. Taking the combined teachings of Dal Mutto and Uchiyama as a whole, it would have been obvious for a person with ordinary skill in the art before the effective filing date of the claimed invention to consider generating an identity embedding vector. The identity embedding vector represents a probability of identity of an object. Generating identity embedding vector provides an efficient means for tracking an object as recognized by Uchiyama (para. [0003], [0131]). As per claim 2 , dependent upon claim 1, Dal Mutto in view of Uchiyama teaches the identity embedding vector provides a supposed identity for the image (Uchiyama teaches when training an appearance signature extractor, an input image of the training data set, an identity label, and a domain label are input into the appearance signature extractor. The identity label provides identity information of the input image. Uchiyama further teaches determining an identity score from the appearance signature. The identify score is an identity vector which represents a probability of identity (See Abstract; FIG. 1; FIG. 5-6; para. [0020]-[0021], [0083], para. [0090]-[0093]). Therefore the determined identity score from the appearance signature (i.e., the result output from the appearance signature extractor), which is an identity vector representing a probability of identity, is regarded as a supposed identity for the image). As per claim 3 , dependent upon claim 1, Dal Mutto in view of Uchiyama teaches the plurality of features includes an actual identity of the item and a category of the item (Uchiyama teaches when training an appearance signature extractor, an input image of the training data set, an identity label, and a domain label are input into the appearance signature extractor. The identity label provides identity information of the input image. Uchiyama further teaches determining an identity score from the appearance signature. The identify score is an identity vector which represents a probability of identity (See Abstract; FIG. 1; FIG. 5-6; para. [0020]-[0021], [0083], para. [0090]-[0093]). Therefore the identity label, which provides a ground truth for training the appearance signature extractor, is considered an actual identity of the item and a category of the item (para. [0019] “In machine learning, a “one-hot vector” is commonly used to represent a “class”, a “category” or an “identity” of object”; para. [0083] “The identity label provides information about an identity of an object of interest captured in the corresponding input image. The identity information of the identity label can distinguish instances or categories of the object. For example, if the object is a person, the identity label distinguishes individuals. The identity label could be an index number within a set. Assuming dth domain includes Id identities in the training data, the identity label could take from 0 to Id−1”). As per claim 4 , dependent upon claim 1, Dal Mutto in view of Uchiyama teaches wherein the plurality of features includes a shape of the item and a color of the item (Dal Mutto: Abstract “the descriptor including: a multi-dimensional shape descriptor space representation of a 3D shape of the query object; a multi-dimensional color descriptor space representation of a texture of the query object”). As per claim 5 , dependent upon claim 1, Dal Mutto in view of Uchiyama teaches the plurality of vectors includes an embedding vector and a probability vector (Dal Mutto teaches when training the CNN for feature extraction and classification, a plurality of feature vectors are generated via the first part of the CNN (i.e. CNN1) (FIG. 4-5; col. 13 ln 33-45; .col. 14 ln 19-27). These plurality of feature vectors are regarded as embedding vectors. The extracted feature vectors are then fed into the second part of the CNN (i.e., CNN2) for classification (FIG. 4-5). The output classification result is a probability vector representing the class-assignment probability distribution (col. 13 ln 48-55; col. 15 ln 10-20)). As per claim 6 , dependent upon claim 1, Dal Mutto in view of Uchiyama teaches the identity embedding vector is used to generate a probability vector (Uchiyama teaches when training an appearance signature extractor, an input image of the training data set, an identity label, and a domain label are input into the appearance signature extractor. The identity label provides identity information of the input image. Uchiyama further teaches determining an identity score from the appearance signature. The identify score is an identity vector which represents a probability of identity (See Abstract; FIG. 1; FIG. 5-6; para. [0020]-[0021], [0083], para. [0090]-[0093]). Therefore the determined identity score from the appearance signature (i.e., the result output from the appearance signature extractor), which is a probability of identity, is regarded as probability vector (FIG. 5; para. [0090] “The identity probability represents which identities the appearance signature belongs to. The identity probability is a vector, where more probable elements have a larger value. The length of the vector is determined by number of identities included in the domain”)). Regarding claim 8, claim 8 recites a method with elements corresponding to the elements recited in claim 1. Therefore, the recited elements of this claim are mapped to Dal Mutto in view of Uchiyama in the same manner as the corresponding elements in its corresponding system claim, claim 1. Additionally, the rationale and motivation to combine Dal Mutto and Uchiyama presented in rejection of claim 1 apply to this claim. Regarding claim 9, claim 9 recites a method with elements corresponding to the elements recited in claim 2. Therefore, the recited elements of this claim are mapped to Dal Mutto in view of Uchiyama in the same manner as the corresponding elements in its corresponding system claim, claim 2. Regarding claim 10, claim 10 recites a method with elements corresponding to the elements recited in claim 3. Therefore, the recited elements of this claim are mapped to Dal Mutto in view of Uchiyama in the same manner as the corresponding elements in its corresponding system claim, claim 3. Regarding claim 11, claim 11 recites a method with elements corresponding to the elements recited in claim 4. Therefore, the recited elements of this claim are mapped to Dal Mutto in view of Uchiyama in the same manner as the corresponding elements in its corresponding system claim, claim 4. Regarding claim 12, claim 12 recites a method with elements corresponding to the elements recited in claim 5. Therefore, the recited elements of this claim are mapped to Dal Mutto in view of Uchiyama in the same manner as the corresponding elements in its corresponding system claim, claim 5. Regarding claim 13, claim 13 recites a method with elements corresponding to the elements recited in claim 6. Therefore, the recited elements of this claim are mapped to Dal Mutto in view of Uchiyama in the same manner as the corresponding elements in its corresponding system claim, claim 6. Regarding claim 15, an independent storage device claim, claim 15 recites a storage device with elements corresponding to the elements recited in claim 1. Therefore, the recited elements of this claim are mapped to Dal Mutto in view of Uchiyama in the same manner as the corresponding elements in its corresponding system claim, claim 1. Additionally, the rationale and motivation to combine Dal Mutto and Uchiyama presented in rejection of claim 1 apply to this claim. Regarding claim 16, claim 16 recites a storage device with elements corresponding to the elements recited in claim 2. Therefore, the recited elements of this claim are mapped to Dal Mutto in view of Uchiyama in the same manner as the corresponding elements in its corresponding system claim, claim 2. Regarding claim 17, claim 17 recites a storage device with elements corresponding to the elements recited in claim 3. Therefore, the recited elements of this claim are mapped to Dal Mutto in view of Uchiyama in the same manner as the corresponding elements in its corresponding system claim, claim 3. Regarding claim 18, claim 18 recites a storage device with elements corresponding to the elements recited in claim 4. Therefore, the recited elements of this claim are mapped to Dal Mutto in view of Uchiyama in the same manner as the corresponding elements in its corresponding system claim, claim 4. Regarding claim 19, claim 19 recites a storage device with elements corresponding to the elements recited in claim 5. Therefore, the recited elements of this claim are mapped to Dal Mutto in view of Uchiyama in the same manner as the corresponding elements in its corresponding system claim, claim 5. Regarding claim 20, claim 20 recites a storage device with elements corresponding to the elements recited in claim 6. Therefore, the recited elements of this claim are mapped to Dal Mutto in view of Uchiyama in the same manner as the corresponding elements in its corresponding system claim, claim 6 . 07-21-aia AIA Claim s 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Dal Mutto et al. (US Patent 10,691,979 B2, hereafter Dal Mutto), in view of Uchiyama (US Publication 2020/0272860 A1), as applied above to claim 1 and claim 8 respectively, and further in view of Ho et al. (US Patent 7,218,759 B1, hereafter Ho) . As per claim 7, Dal Mutto in view of Uchiyama teaches a color vector (Dal Mutto Abstract “a multi-dimensional color descriptor space representation of a texture of the query object”; col. 10 ln 32-37), but does not teach a color probability vector. Ho is evidenced that representing a color vector using a color probability vector is well-known and practiced (col. 3 ln 55-58; col. 4 ln 12-15). It would have been obvious to one of ordinary skill in the art, before the effective filing date of this application, to modify teachings of Dal Mutto and Uchiyama to incorporate the teachings of Ho to represent a color vector as a probability vector. By expressing color vector in such a way, it is convenient to judge which pixel belongs to a color associated to a desired object as recognized by Ho (FIG. 7, col. 4 ln 12-15). Regarding claim 14, claim 14 recites a method with elements corresponding to the elements recited in claim 7. Therefore, the recited elements of this claim are mapped to Dal Mutto in view of Uchiyama and Ho in the same manner as the corresponding elements in its corresponding system claim, claim 7. Additionally, the rationale and motivation to combine Dal Mutto, Uchiyama and Ho presented in rejection of claim 7 apply to this claim . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is recorded in form PTO-892 . Contact Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUEMEI G CHEN whose telephone number is (571)270-3480. The examiner can normally be reached Monday-Friday 9am-6pm. 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, John M Villecco can be reached on (571) 272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /XUEMEI G CHEN/Primary Examiner, Art Unit 2661 Application/Control Number: 18/799,518 Page 2 Art Unit: 2661 Application/Control Number: 18/799,518 Page 3 Art Unit: 2661 Application/Control Number: 18/799,518 Page 4 Art Unit: 2661 Application/Control Number: 18/799,518 Page 5 Art Unit: 2661 Application/Control Number: 18/799,518 Page 6 Art Unit: 2661 Application/Control Number: 18/799,518 Page 7 Art Unit: 2661 Application/Control Number: 18/799,518 Page 8 Art Unit: 2661 Application/Control Number: 18/799,518 Page 9 Art Unit: 2661 Application/Control Number: 18/799,518 Page 10 Art Unit: 2661 Application/Control Number: 18/799,518 Page 11 Art Unit: 2661 Application/Control Number: 18/799,518 Page 12 Art Unit: 2661 Application/Control Number: 18/799,518 Page 13 Art Unit: 2661 Application/Control Number: 18/799,518 Page 14 Art Unit: 2661 Application/Control Number: 18/799,518 Page 15 Art Unit: 2661 Application/Control Number: 18/799,518 Page 16 Art Unit: 2661 Application/Control Number: 18/799,518 Page 17 Art Unit: 2661 Application/Control Number: 18/799,518 Page 18 Art Unit: 2661 Application/Control Number: 18/799,518 Page 19 Art Unit: 2661 Application/Control Number: 18/799,518 Page 20 Art Unit: 2661 Application/Control Number: 18/799,518 Page 21 Art Unit: 2661 Application/Control Number: 18/799,518 Page 22 Art Unit: 2661 Application/Control Number: 18/799,518 Page 23 Art Unit: 2661 Application/Control Number: 18/799,518 Page 24 Art Unit: 2661 Application/Control Number: 18/799,518 Page 25 Art Unit: 2661 Application/Control Number: 18/799,518 Page 26 Art Unit: 2661 Application/Control Number: 18/799,518 Page 28 Art Unit: 2661 Application/Control Number: 18/799,518 Page 29 Art Unit: 2661 Application/Control Number: 18/799,518 Page 30 Art Unit: 2661 Application/Control Number: 18/799,518 Page 31 Art Unit: 2661 Application/Control Number: 18/799,518 Page 32 Art Unit: 2661 Application/Control Number: 18/799,518 Page 33 Art Unit: 2661 Application/Control Number: 18/799,518 Page 34 Art Unit: 2661 Application/Control Number: 18/799,518 Page 35 Art Unit: 2661 Application/Control Number: 18/799,518 Page 36 Art Unit: 2661
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