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 .
Response to Amendment
Applicant’s Amendments filed on 09/30/2025 has been entered and made of record.
Currently pending Claim(s):
Independent Claim(s):
Amended Claim(s):
Cancelled Claim(s):
1, 2, 5–10, 13–18
1, 9 and 17
1, 2, 5, 6, 8–10, 13, 14 and 16–18
3, 4, 11, 12 and 19–24
Response to Applicant’s Arguments
This office action is responsive to Applicant’s Arguments/Remarks Made in an Amendment received on 09/30/2025.
In view of the amendments filed on 09/30/2025 on page 8 to the title, the specification objections is withdrawn.
In view of applicant Arguments/Remarks and amendment filed on 09/30/2025 with respect to independent claims 1, 9 and 17 under 35 U.S.C 101, claim rejection has been fully considered and the arguments are found to be not persuasive (See Page 8 and 9), therefore the claim rejection with respect to 35 U.S.C. 101 still applies.
Applicant argues, in summary that the application:
“Specifically, the claims are directed to processing a specific data structure and applying specific rules, to thereby improve the technical field of the image retrieval system by enhancing their accuracy and utility”
The Examiner respectfully disagrees with this line of reasoning. The Examiner have reviewed the specification and has found no indication of the improvement in the accuracy and utility of the field. Applicant has not directly stated where in the specification the improvement is made. With regards to the MPEP in light of Ex Parte Desjardins. The machine learning model ¶ [0101], “Further, since at least a skeleton from a head to a foot may be able to be detected by a skeleton estimation technique using machine learning “ does not reflect the improvement disclosed in the specification. Accordingly, the claims as a whole integrated a judicial exception. The Examiner has not found in the specification the improvement the machine learning model has provided in learning the new tasks.
In view of applicant Arguments/Remarks and amendment filed on 09/30/2025 with respect to independent claims 1, 9 and 17 under 35 U.S.C 103, claim rejection has been fully considered and the arguments are found to be not persuasive (See Page 9–12), therefore the claim rejection with respect to 35 U.S.C. 103 still applies.
Applicant argues, in summary that applied prior art (Watanabe, US 20190147292 A1) does not disclose or suggest (see page(s) 11):
“a weight is set for each of a plurality of portions of the person, and the operations further comprise classifying the plurality of subject images or selecting at least one target image from the plurality of subject images, by using the weight”
However, the Examiner respectfully disagrees with the Applicant’s line of reasoning. The Examiner has thoroughly reviewed the Applicant arguments but respectfully believes that the cited reference to reasonably and properly meet the claimed limitations.
Watanabe cites in the reference: ¶ [0120], “A features extracting unit 107 according to the present embodiment extracts local pose features from each feature point or a subset of feature points, not from overall pose information. By clustering the local pose features relative to a large number of images, a codebook 1901 of the features is generated as illustrated in FIG. 19. By retrieving a cluster to which new local pose features belong in the codebook, vector data can be converted into a code (vw: Visual Word). A histogram 1903 can be obtained by counting frequencies of the codes regarding all the local pose features in the image, the histogram can be used as the features of the entire image in which the pose information is reflected. In FIG. 19, for example, it is indicated that a features component of “pointing a finger” corresponds to a code vw5 of the histogram. Regarding images 1902 and 1904 of “some people point their fingers at a squatting person”, histograms 1903 and 1905 having similar features are extracted”. ¶ [0123], “The features extracting unit 107 extracts features from the feature point (S2003). The features of the feature point may be, for example, image features around the feature point, a distance and an angle with respect to an adjacent feature point may be used as pose features. Furthermore, instead of extracting the features for each feature point, pose features may be extracted for each subset of poses. For example, subsets of poses can be used, such as “head shoulder={head, neck, right shoulder, left shoulder}”, “right upper body={right shoulder, right elbow, right wrist}”, “left upper body={left shoulder, left elbow, left wrist}”, “pose={neck, left waist, right waist}”, “right lower body={right waist, right knee, right ankle}”, and “left lower body={left waist, left knee, left ankle}”. In addition, image features may be extracted for each subset of images. The features extracting unit 107 converts the features obtained in step S2003 into a code (S2004)”. Watanabe uses local features that are portions of the body and represents them as code which the frequency is represented in a histogram as the weight. The frequency is being interpreted as the weight of the portion. ¶ [0125], When the features extracting unit 107 has completed execution of steps S2003 to S2005 for all the features in the image, the features extracting unit 107 changes the histogram into the features, registers the changed features in the image database 108, and terminates the processing (S2007). At this time, values may be normalized according to the total number of the feature point”. Watanabe then stores the histogram as features for image retrieval
Therefore, with this broad interpretation, Watanabe US 2019/0147292 A1 in view of Daniel US 10,872,424 B2 teaches, discloses or suggests the Applicant’s invention, generating pose and color information from an image and a weight is set for each of the plurality of body portions of the person and selecting an image from a plurality of image using the pose information and the weights. Thus, due to the Applicant’s broad claim language, Applicant’s invention is not far removed from the art of record. As a result, it is respectfully submitted that the present application is not in condition for allowance.
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.
Claim(s) 1, 2, 5–10 and 13–18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, opinion, organizing human activity and mathematical concepts and calculations). The independent claim(s) 1, 9 and 17 recite(s) an apparatus, a method and a non-transitory CRM respectively. This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved .The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally and no additional features in the claims would preclude them from being performed as such except for the generic computer elements at high level of generality (i.e., processor, memory).
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that the independent claims 1, 9 and 17 are directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories? YES. Independent claims 1, 9 and 17 are directed to a an apparatus, a method and a non-transitory CRM respectively
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? YES, the claims are directed toward a mental process (i.e. abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Independent claims 1, 9 and 17 comprise a mental process that can be practicably performed in the human mind (or generic computers or components configured to perform the method) and, therefore, an abstract idea.
Regarding independent claim(s) 1: the limitations recite:
An image selection apparatus comprising: at least one memory configured to store instructions and at least one processor configured to execute the instruction to perform operations, the operations comprising (generic computer components/insignificant activity):
generating, from each of a plurality of subject images, pose information about a person included in the subject image and other information about the person; and (mental process including observation and evaluation, and can be done mentally in the human mind)
classifying the plurality of subject images or selecting at least one target image from the plurality of subject images, by using the pose information and the other information (mental process including observation and evaluation, and can be done mentally in the human mind), wherein:
the other information comprises color information about at least one of the person and an accessory of the person (mental process including observation and evaluation, and can be done mentally in the human mind),
a weight is set for each of a plurality of portions of the person (mental process including observation and evaluation, and can be done mentally in the human mind), and
the operations further comprise classifying the plurality of subject images or selecting at least one target image from the plurality of subject images, by using the weight (mental process including observation and evaluation, and can be done mentally in the human mind).
Regarding independent claim(s) 9: the limitations recite:
An image selection method comprising, by a computer (mental process including observation and evaluation, and can be done mentally in the human mind):
information generation processing of generating, from each of a plurality of subject images, pose information about a person included in the subject image and other information about the person (mental process including observation and evaluation, and can be done mentally in the human mind); and
image selection processing of classifying the plurality of subject images or selecting at least one target image from the plurality of subject images, by using the pose information and the other information (mental process including observation and evaluation, and can be done mentally in the human mind), wherein:
the other information comprises color information about at least one of the person and an accessory of the person (mental process including observation and evaluation, and can be done mentally in the human mind),
a weight is set for each of a plurality of portions of the person (mental process including observation and evaluation, and can be done mentally in the human mind), and
the method further comprises:
by the computer, in the image selection processing, classifying the plurality of subject images or selecting at least one target image from the plurality of subject images, by using the weight (mental process including observation and evaluation, and can be done mentally in the human mind).
Regarding independent claim(s) 17: the limitations recite:
A non-transitory computer-readable medium storing a program for causing a computer to include perform operations (generic computer components/insignificant activity), the operations comprising:
an information generation function of generating, from each of a plurality of subject images, pose information about a person included in the subject image and other information about the person (mental process including observation and evaluation, and can be done mentally in the human mind); and
an image selection function of classifying the plurality of subject images or selecting at least one target image from the plurality of subject images, by using the pose information and the other information (mental process including observation and evaluation, and can be done mentally in the human mind), wherein:
the other information comprises color information about at least one of the person and an accessory of the person (mental process including observation and evaluation, and can be done mentally in the human mind),
a weight is set for each of a plurality of portions of the person (mental process including observation and evaluation, and can be done mentally in the human mind), and
the operations further comprise classifying the plurality of subject images or selecting at least one target image from the plurality of subject images, by using the weight (mental process including observation and evaluation, and can be done mentally in the human mind).
These limitations, as drafted, is a simple process that, under their broadest reasonable interpretation, covers performance of the limitations in the mind or by a human. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
As such, a person could mentally observe the pose of a person and look through images to with the same person in the same pose looking at their accessories and deciding which body portion is more important for selecting an image. The mere nominal recitation that the various steps are being executed by a the generic computer component(s), for example, processor, device, memory does not take the limitations out of the mental process grouping. Thus, the claims recite a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? NO, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Independent claims 1, 9 and 17 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. Independent claims 1, 9 and 17 discloses an apparatus memory and a non-transitory computer-readable storage medium, which are generic computer components and/or insignificant pre/post-solution extra activity that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea in a system.
These limitations are recited at a high level of generality (i.e. as a general action or change being taken based on the results of the acquiring step) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity. Further, the claims are claimed generically and are operating in their ordinary capacity such that they do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Independent claim(s) 1, 9 and 17 do not recite any additional elements that are not well-understood, routine or conventional. The use of a generic computer elements are routine, well-understood and conventional process that is performed by computers.
Thus, since independent claims 1, 9 and 17 are: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, it is clear that independent claims 1, 9 and 17 are not eligible subject matter under 35 U.S.C 101.
Regarding claim 2, 5–8, 10, 13–16 and 18: the additional limitations do not integrate the mental process into practical application or add significantly more to the mental process. They are a mental processes including mental process including observation and evaluation, and can be done mentally in the human mind OR mathematical concepts.
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 non-obviousness.
Claim(s) 1, 2, 6, 7, 9, 10, 14, 15, 17, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al. (US 2019/0147292 A1, hereafter, "Watanabe") in view of Daniel et al. (US 10,872,424 B2, hereafter, "Daniel").
Regarding claim 1, Watanabe discloses an image selection apparatus comprising: at least one memory configured to store instructions and at least one processor configured to execute the instruction to perform (See Watanabe, ¶ [0051], FIG. 2 is a block diagram of an exemplary hardware configuration of the image retrieving system 100 according to the present embodiment. In FIG. 2, the image retrieving apparatus 104 includes a processor 201 and a storage apparatus 202 which are mutually connected. The storage apparatus 202 may include an arbitrary kind of storage medium. The storage apparatus 202 includes a combination of a semiconductor memory and a hard disk drive) operations comprising:
generating, from each of a plurality of subject images, pose information about a person included in the subject image and other information about the person (See Watanabe, ¶ [0039], The pose estimating unit 106 recognizes the pose information included in the input image. Pose estimating processing is executed in object units defined by the system. For example, a system assuming a person as one object detects a person included in the image, executes region detecting processing, and executes pose recognizing processing for each detected region); and classifying the plurality of subject images or selecting at least one target image from the plurality of subject images, by using the pose information and the other information (See Watanabe, ¶ [0037], The user can obtain an image including an object which matches the specified pose information through the image retrieval using the pose information. That is, when the user retrieves a desired image, the user can find an image including a similar pose by inputting the pose information of the object in addition to metadata such as a place and a time and the features of the appearance of the image. ¶ [0043], In addition, the features extracting unit 107 extracts the image features indicating the appearance of the image, in addition to the pose features. Thus, retrieval can be made by using not only the pose information but also the appearance of the image as a condition. Note: Examiner is interpreting the appearance of the image and other information), [the other information comprises color information about at least one of the person and an accessory of the person], a weight is set for each of a plurality of portions of the person (See Watanabe, ¶ [0120], A features extracting unit 107 according to the present embodiment extracts local pose features from each feature point or a subset of feature points, not from overall pose information. By clustering the local pose features relative to a large number of images, a codebook 1901 of the features is generated as illustrated in FIG. 19. By retrieving a cluster to which new local pose features belong in the codebook, vector data can be converted into a code (vw: Visual Word). A histogram 1903 can be obtained by counting frequencies of the codes regarding all the local pose features in the image, the histogram can be used as the features of the entire image in which the pose information is reflected. In FIG. 19, for example, it is indicated that a features component of “pointing a finger” corresponds to a code vw5 of the histogram. Regarding images 1902 and 1904 of “some people point their fingers at a squatting person”, histograms 1903 and 1905 having similar features are extracted. ¶ [0123], The features extracting unit 107 extracts features from the feature point (S2003). The features of the feature point may be, for example, image features around the feature point, a distance and an angle with respect to an adjacent feature point may be used as pose features. Furthermore, instead of extracting the features for each feature point, pose features may be extracted for each subset of poses. For example, subsets of poses can be used, such as “head shoulder={head, neck, right shoulder, left shoulder}”, “right upper body={right shoulder, right elbow, right wrist}”, “left upper body={left shoulder, left elbow, left wrist}”, “pose={neck, left waist, right waist}”, “right lower body={right waist, right knee, right ankle}”, and “left lower body={left waist, left knee, left ankle}”. In addition, image features may be extracted for each subset of images. The features extracting unit 107 converts the features obtained in step S2003 into a code (S2004). Note: Examiner is interpreting the histogram frequencies as the weight of the portions), and the operations further comprise classifying the plurality of subject images or selecting at least one target image from the plurality of subject images, by using the weight (See Watanabe, ¶ [0125], When the features extracting unit 107 has completed execution of steps S2003 to S2005 for all the features in the image, the features extracting unit 107 changes the histogram into the features, registers the changed features in the image database 108, and terminates the processing (S2007). At this time, values may be normalized according to the total number of the feature point. ¶ [0126], As described above, according to the present embodiment, by specifying the image to be a query, the user can compare features of the entire image accumulated in the image database and retrieve similar scenes).
However, Watanabe fail(s) to teach the other information includes color information about at least one of the person and an accessory of the person.
Daniel, working in the same field of endeavor, teaches: the other information includes color information about at least one of the person and an accessory of the person (See Daniel, [Pg. 5, ln. 21–29], An object tracking system can determine whether an object detected in a current image is the same object as a tracked object detected in one or more previous images in the sequence based on attributes of the detected object and attributes of the tracked object. The attributes can include various attributes such as the location of the object in the image, the color(s) of the object, patterns of the object, text content, physical size, and other visual attributes as described below. [Pg. 7, ln. 9–17], The attributes can include visual attributes that can be The attribute matching module 140 can then determine, extracted from the image or determined using image analysis, such as the color(s) of the object (e.g., a color profile for the object), patterns of the object, text content of the object, the physical size of the object, a make and/or model of a car, clothes being worn on a person, accessories worn by a person, a hair style or hair color of a person, facial features of a person, the pose of a person, and/or other appropriate visual attributes. [Pg. 9, ln. 30–34], To determine the overall similarity score for a detected 30 object and a tracked object, the attribute matching module 140 can identify one or more attribute modules 144 based on the attributes of the detected object and/or the attributes of the tracked object. Note: Examiner is interpreting the matching as selecting an object from an image from the cache of a confirmed tracked object to the detected object).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Watanabe’s reference the other information includes color information about at least one of the person and an accessory of the person based on the method of Daniel’s reference. The suggestion/motivation would have been to the tracking efficiency and accuracy (See Daniel, [Pg. 4, ln. 47–56]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Daniel with Watanabe to obtain the invention as specified in claim 1.
Regarding claim 2, Watanabe discloses the image selection apparatus according to claim 1, wherein the operations further comprise: acquiring a query image including a person; generating the pose information about the person included in the query image and the other information (See Watanabe, ¶ [0039], The pose estimating unit 106 recognizes the pose information included in the input image. Pose estimating processing is executed in object units defined by the system. For example, a system assuming a person as one object detects a person included in the image, executes region detecting processing, and executes pose recognizing processing for each detected region); and selecting the at least one target image by using the pose information and the other information of the query image and each of the plurality of subject images (See Watanabe e, ¶ [0037], The user can obtain an image including an object which matches the specified pose information through the image retrieval using the pose information. That is, when the user retrieves a desired image, the user can find an image including a similar pose by inputting the pose information of the object in addition to metadata such as a place and a time and the features of the appearance of the image. ¶ [0043], In addition, the features extracting unit 107 extracts the image features indicating the appearance of the image, in addition to the pose features. Thus, retrieval can be made by using not only the pose information but also the appearance of the image as a condition. Note: Examiner is interpreting the appearance of the image and other information).
Regarding claim 6, Watanabe discloses the image selection apparatus according to claim 1, wherein the other information comprises at least one of a face, a gender, an age group, and a body shape of the person (See Watanabe, ¶ [0037], The user can obtain an image including an object which matches the specified pose information through the image retrieval using the pose information. That is, when the user retrieves a desired image, the user can find an image including a similar pose by inputting the pose information of the object in addition to metadata such as a place and a time and the features of the appearance of the image. Therefore, the image retrieval accuracy is improved. Furthermore, by adding the image feature and the attribute to the conditions in addition to the pose information, a retrieval result which is close to a retrieval intention of the user can be efficiently presented. ¶ [0041], That is, the image features are value, which can be compared between images, a color, of the features such as a shape, and the like of the image. Both features are values with which similarity between the images can be compared, and, for example, expressed by fixed-length vectors).
Regarding claim 7, Watanabe discloses the image selection apparatus according to claim 1, wherein the other information is a position of the person in the subject image (See Watanabe, ¶ [0072], Therefore, the image retrieving apparatus 104 obtains similar images (603, 604, and 605) from the image database 108 and complements position information on lacking feature points from the pose information of the similar images (pose information 606). For calculation of the degree of similarity, for example, image features of a person image may be used, or pose features calculated from feature points of parts other than defect parts may be used. In addition, by narrowing a range of images by a time, a place, a position in the image, an attribute of the person, a tracking ID, and the like, appropriate similar images can be obtained. Note: Pose implies position and orientation so selecting by pose inherently selects by position, but this reference also selects by a position in the image as well).
Regarding claim 9, claim 9 is rejected the same as claim 1 and the arguments similar to that presented above for claim 1 are equally applicable to the claim 9, and all of the other limitations similar to claim 1 are not repeated herein, but incorporated by reference.
Regarding claim 10, claim 10 is rejected the same as claim 2 and the arguments similar to that presented above for claim 2 are equally applicable to the claim 10, and all of the other limitations similar to claim 2 are not repeated herein, but incorporated by reference.
Regarding claim 14, claim 14 is rejected the same as claim 6 and the arguments similar to that presented above for claim 6 are equally applicable to the claim 14, and all of the other limitations similar to claim 6 are not repeated herein, but incorporated by reference.
Regarding claim 15, claim 15 is rejected the same as claim 7 and the arguments similar to that presented above for claim 7 are equally applicable to the claim 15, and all of the other limitations similar to claim 7 are not repeated herein, but incorporated by reference.
Regarding claim 17, claim 17 is rejected the same as claim 1 and the arguments similar to that presented above for claim 1 are equally applicable to the claim 17, and all of the other limitations similar to claim 1 are not repeated herein, but incorporated by reference. Furthermore, Watanabe teaches a non-transitory computer-readable medium storing a program for causing a computer to include perform operations, the operations comprising (See Watanabe, [FIG. 2], 201 Processor, 202 Storage Apparatus, 203 Processing Program).
Regarding claim 18, claim 18 is rejected the same as claim 2 and the arguments similar to that presented above for claim 2 are equally applicable to the claim 18, and all of the other limitations similar to claim 2 are not repeated herein, but incorporated by reference.
Claim(s) 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al. (US 2019/0147292 A1, hereafter, "Watanabe") in view of Daniel et al. (US 10,872,424 B2, hereafter, "Daniel") further in view of Tsuji et al. (US 2020/0401616 A1, hereafter, “Tsuji”).
Regarding claim 5, Watanabe in view of Daniel teaches the image selection apparatus according to claim 1, [wherein the other information comprises color information about a target region including both of the person and surroundings of the person].
However, Watanabe and Daniel fail(s) to teach wherein the other information comprises color information about a target region including both of the person and surroundings of the person.
Tsuji, working in the same field of endeavor, teaches: wherein the other information comprises color information about a target region including both of the person and surroundings of the person (See Tsuji, ¶ [0070], Note that the similarity used here may be the same index as the similarity used in the creation of an entry in the image database 100, or may be a different index. Further, the index of similarity may be designated by the user. For example, by allowing the user to select a perspective of similarity such as similarity of a composition, a posture or pose of a person, and color or brightness, it is possible to provide the user with an image that is more likely to interest the user. ¶ [0062], Any algorithm may be used to calculate the similarity. For example, similarity is demonstrated [evaluated? Rated?] through comparisons of feature values (color, brightness, etc.), scenes, subjects' postures and poses, image processing methods (a filter, a stamp, etc.) and the like. Note: Examiner is interpreting comparing color of scenes and subjects as comparing the color information of a target object and the surroundings).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Watanabe’s reference to wherein the other information comprises color information about a target region including both of the person and surroundings of the person based on the method of Tsuji’s reference. The suggestion/motivation would have been to provide more accurate recommendation of images more relevant to the search (See Tsuji, ¶ [0070]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Tsuji with Watanabe and Daniel to obtain the invention as specified in claim 5.
Regarding claim 13, claim 13 is rejected the same as claim 5 and the arguments similar to that presented above for claim 5 are equally applicable to the claim 13, and all of the other limitations similar to claim 5 are not repeated herein, but incorporated by reference.
Claim(s) 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al. (US 2019/0147292 A1, hereafter, "Watanabe") in view of Daniel et al. (US 10,872,424 B2, hereafter, "Daniel") further in view of Bandara (US 2017/0139759 A1, hereafter, “Bandara”).
Regarding claim 8, Watanabe in view of Daniel teaches the image selection apparatus according to claim 1, wherein the operations further comprise displaying, on a terminal, an input screen for a user to input a weight of the pose information and the other information; and classifying the plurality of subject images or selecting the at least one target image (See Watanabe, ¶ [0046], The pose inputting unit 109 receives the pose information which is input by a user via the input apparatus 102. As described above, the pose information includes a set of a plurality of feature points, and the feature point has the coordinates and the reliability), by using the [weight input to the input screen]
However, Watanabe and Daniel fail(s) to teach weight input to the input screen.
Bandara, working in the same field of endeavor, teaches: weight input to the input screen (See Bandara, ¶ [0035], In certain embodiments, method 300 may include receiving, from an external source, expert knowledge. Method 300 may include determining, based on the expert knowledge, whether to weight any points or correlations in candidate pattern list 42 or data associated with the known significant pattern. Such expert knowledge may come from, for example, system administrator 2 or a developer. In certain other embodiments, a user may input expert knowledge used to weight candidate pattern list 42).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Watanabe’s reference to weight input to the input screen based on the method of Bandara’s reference. The suggestion/motivation would have been to increase the accuracy of searching for a pattern (See Bandara, ¶ [0002–0004]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Bandara with Watanabe and Daniel to obtain the invention as specified in claim 8.
Regarding claim 16, claim 16 is rejected the same as claim 8 and the arguments similar to that presented above for claim 8 are equally applicable to the claim 16, and all of the other limitations similar to claim 8 are not repeated herein, but incorporated by reference.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Adamek et al. (US 20170185857 A1) teaches methods and systems of identification of objects in query images are disclosed. Keypoints in the query images are identified corresponding to objects to be identified. Visual words are identified in a dictionary of visual words for the identified keypoints. A set of hits is identified corresponding to reference images comprising the identified keypoints. Reference images corresponding to the identified set of hits are ranked using clustering of matches in a limited pose space. The limited pose space comprises a one-dimensional table corresponding to the rotation between the object to be identified with respect to the reference image. A first subset of M reference images that obtained a rank above a predetermined threshold is then selected. Offline, hybrid and combined offline and hybrid systems for performing the proposed methods are disclosed.
De Campos et al. (US 20100189354 A1) teaches an apparatus, method, and computer program product are provided for generating an image representation. The method includes receiving an input digital image, extracting features from the image which are representative of patches of the image, generating weighting factors for the features based on location relevance data for the image, and weighting the extracted features with the weighting factors to form a representation of the image.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DION J SATCHER whose telephone number is (703)756-5849. The examiner can normally be reached Monday - Thursday 5:30 am - 2:30 pm, Friday 5:30 am - 9:30 am PST.
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/DION J SATCHER/ Patent Examiner, Art Unit 2676
/Henok Shiferaw/ Supervisory Patent Examiner, Art Unit 2676
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