DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
A Preliminary Amendment was received 11/12/2024 to amend the specification, abstract, drawings and claims. Claims 1-20 are pending with amendments to claims 4, 6, 11, 13, 18, 20. Claim 21 is cancelled.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is considered by examiner.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 8, 15 each claim “quality of pose” in the limitation “compute, for each one of the persons, a pose score that represents quality of pose of the person” as “quality” may be interpreted as a subjective term and not a quantitative term when interpreted with a BRI and given its plain meaning. The specification was reviewed but no description was readily found to describe with detail the meaning for interpreting the limitation (example specification paragraphs describing the “quality of pose” include ¶ [0005]-[0006], [0017]-[0019]). See MPEP § 2173.05(b)(IV). It is also noted quality may be interpreted to either mean a particular pose such as an aesthetic or a technical measure of the captured image such as clarity of imaging. Therefore the claim limitation is indefinite for not including an objective standard for interpretation of the quality of pose. Thus, Applicant has failed to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 2-7 are rejected based on their dependency to claim 1.
Claims 9-14 are rejected based on their dependency to claim 8.
Claims 16-20 are rejected based on their dependency to claim 15.
Claims 5, 12, 19 each claim “for each person, computing the pose score based on a size of the cluster to which the person belongs” and it is unclear if the “size” refers to a number or a dimension, which may or may not be correlated. See MPEP § 2173.05(b)(IV).
No claims are dependent on claims 5, 12, 19.
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.
Claims 1-4, 8-11, 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a pose analyzing apparatus comprising: at least one memory that is configured to store instructions; and at least one processor that is configured to execute the instructions (memory and processor are generic computer components performing a task that is an abstract idea mental process) to:
acquire a target image on which two or more persons are captured (a person can visually see a field of view with people);
acquire target person information that indicates a target person (a person can identify a particular person);
estimate a pose for each one of the persons captured on the target image (a person can mentally identify a given pose of a target person within the field of view of people);
compute, for each one of the persons, a pose score that represents quality of pose of the person (a person can mentally determine a quality of the pose of each person viewed in the field of view);
detect one or more reference persons whose quality of pose is higher than quality of pose of the target person (the person mentally analyzing the people with the given pose can identify a person with a higher quality score of the given pose compared to a target person); and
output reference information that indicates the reference person (the person mentally analyzing the target person compared to the other persons in the field of view can have an given determination regarding the quality of the target person’s pose with a given score).
Claim 2 recites the pose analyzing apparatus according to claim 1 (as described above), wherein the computation of the pose score includes: acquiring a sample pose (a person may view an intended pose of a person (such as knowledge as to a template pose position in a synchronized motion); and for each one of the persons, computing a degree of similarity between the pose of the person and the sample pose to compute the pose score (a person can assess a given pose for multiple people observed to determine how accurate and similar each person is to the ideal pose).
Claim 3 recites the pose analyzing apparatus according to claim 2 (as described above), wherein the computation of the pose score includes: for each one of the persons, computing a first score and a second score and aggregating the first score and the second score into the pose score, the first score representing a degree of similarity between the pose of the person and the sample pose, the second score representing a degree of similarity between a trajectory of a representative key-point of the person and a trajectory of a representative key-point of the sample pose (a person analyzing a given pose and the quality of the pose of a target observed person can use multiple criteria to determine the quality of the pose and determine an aggregated score, such as the degree of similarity and a trajectory of the given key points (joints in movement)).
Claim 4 recites the pose analyzing apparatus according to claim 2 (as described above), wherein the acquisition of the sample pose includes: for each one of candidate sample poses, computing a candidate score that represents a degree of similarity between the candidate sample pose and the pose of the target person (the person observing can determine how similar a target person’s pose is to the ideal reference pose and associate a given similarity score based on the pose); and choosing the candidate sample pose with the greatest candidate score as the sample pose (a person can observe a target perform the pose multiple times to determine which version of the pose was most closely mimicking the ideal pose).
Claim 8 recites a pose analyzing method performed by a computer (generic computer components (memory and processor) performing a task that is an abstract idea mental process), comprising: steps identical to claim 1 (as described above).
Claim 9 recites the pose analyzing method according to claim 8, with further limitations identical to claim 2 (as described above).
Claim 10 recites the pose analyzing method according to claim 9, with further limitations identical to claim 3 (as described above).
Claim 11 recites the pose analyzing method according to claim 9, with further limitations identical to claim 4 (as described above).
Claim 15 recites a non-transitory computer-readable storage medium storing a program that causes a computer (generic computer components (memory and processor) performing a task that is an abstract idea mental process) to execute: perform steps identical to claim 1 (as described above).
Claim 16 recites the storage medium according to claim 15, with further limitations identical to claim 2 (as described above).
Claim 17 recites the storage medium according to claim 16, with further limitations identical to claim 3 (as described above).
Claim 18 recites the storage medium according to claim 16, with further limitations identical to claim 4 (as described above).
The limitations of analyzing a pose and comparing the pose to a reference are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, regarding the apparatus, other than reciting generic placeholder-related computer components, such as a memory and processor, nothing in the claim elements precludes the steps from practically being performed in the mind. For example in the independent claims, language of “acquire” is generic pre-processing data gathering of visual information (see MPEP 2106.05(g)); “estimate” and “compute” are broadly claimed to evaluate the visual pose; “detect” is to compare the visual poses; and “output” is generic post-processing data output (see MPEP 2106.05(g)). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, these claims each recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the method claims do not recite any elements which could not be performed in the mind and the claims only recite generic placeholder-related computer components, including a memory and processor. The computer components are recited at a high-level of generality (i.e., generic memory and processor for performing pose evaluation, which is described with a high level of generality of automating a manual operation) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, the computer components do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the aforementioned claims are directed to abstract ideas.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic placeholder-related computer components, the memory and processor, used to evaluate pose positions amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an invention concept. The claims are not patent eligible.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sivan et al (US 2021/0034843) in view of Rogez et al (LCR-Net: Localization-Classification-Regression for Human Pose).
Regarding Claim 1, Sivan et al teach a pose analyzing apparatus (computer system 206 to execute process 100 for target person analysis; Fig 1, 2 and ¶ [0071]-[0082]) comprising: at least one memory that is configured to store instructions (non-transitory storage 234 storing program instructions; Fig 2 and ¶ [0080]); and at least one processor that is configured to execute the instructions (processor232 executes instructions; Fig 2 and ¶ [0080]) to:
acquire a target image on which two or more persons are captured (a plurality of persons are captured in an image of drone 202, including a particular target person 204 identified within a plurality of persons by using face recognition app 220; ¶ [0085]-[0087]);
acquire target person information that indicates a target person (the identity of the target person 204 are compared to a face recognition system 240 to identify the target person 204, including identifying the head pose of the target person; ¶ [0083], [0089]); and
estimate a pose for each one of the persons captured on the target image (face or head pose of persons are analyzed to identify the target person 204 in the image and a confidence score is used to identify the target person 204; ¶ [0087]-[0089], [0100]-[0103]).
Sivan et al does not teach to compute, for each one of the persons, a pose score that represents quality of pose of the person; detect one or more reference persons whose quality of pose is higher than quality of pose of the target person; and output reference information that indicates the reference person.
Rogez et al is analogous art pertinent to the technological problem addressed in the current application and teaches to
compute, for each one of the persons, a pose score that represents quality of pose of the person (pose proposals are obtained for each person with a determination of the best representation based on the pose (error) score with multiple people included in the analysis; Fig 31-7 and 3.1 Localization; pose proposals network);
detect one or more reference persons whose quality of pose is higher than quality of pose of the target person (the regression aims at refining the anchor-pose to match the ground-truth (reference) of the individual and is used at refining to match the pose (thereby the ground truth is the higher quality pose); Fig 3 and 3.3 Regression); and
output reference information that indicates the reference person (the output regression data is output for refining the anchor-pose; Fig 3, 4 and 3.3 Regression).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Sivan et al with Rogez et al including to compute, for each one of the persons, a pose score that represents quality of pose of the person; detect one or more reference persons whose quality of pose is higher than quality of pose of the target person; and output reference information that indicates the reference person. By analyzing the pose of the individual as compared to a ground-truth reference, body parts may be localized to understand the typical poses, which provides for standardization of 3D pose estimation, which may be applied to a controlled (computer vision) system, as recognized by Rogez et al (Abstract, 1. Introduction).
Regarding Claim 2, Sivan et al in view of Rogez et al teach the pose analyzing apparatus according to claim 1 (as described above), wherein the computation of the pose score includes: acquiring a sample pose (Rogez et al, anchor pose samples are obtained for individuals in a given image; Fig 1, 2 and 1. Introduction ¶ 3); and for each one of the persons, computing a degree of similarity between the pose of the person and the sample pose to compute the pose score (Rogez et al, pose proposals are obtained for each person with a determination of the best representation based on the pose (error) score with multiple people included in the analysis; Fig 2-7 and 3.1 Localization; pose proposals network).
Regarding Claim 3, Sivan et al in view of Rogez et al teach the pose analyzing apparatus according to claim 2 (as described above), wherein the computation of the pose score (Rogez et al, anchor pose samples are scored for individuals in a given image; Fig 1-4 and 1. Introduction ¶ 3, 3.5 Pose Proposals) includes: for each one of the persons, computing a first score and a second score and aggregating the first score and the second score into the pose score (Rogez et al, for a given person, the pose proposals are each scored, which are averaged to determine the 2D pose of the person; Fig 3-4 and 3.5 Pose proposals integration), the first score representing a degree of similarity between the pose of the person and the sample pose (Rogez et al, pose proposal scores include a top scoring proposal for an estimated pose; 3.5 Pose proposal integration ¶ 1), the second score representing a degree of similarity between a trajectory of a representative key-point of the person and a trajectory of a representative key-point of the sample pose (Rogez et al, a regression is used for refining the anchor-pose to match based on the key points with a regression loss representing the similarity score between the anchor-pose and the ground-truth; Fig 3 and 3.3 Regression).
Regarding Claim 4, Sivan et al in view of Rogez et al teach the pose analyzing apparatus according to claim 2 (as described above), wherein the acquisition of the sample pose includes: for each one of candidate sample poses, computing a candidate score that represents a degree of similarity between the candidate sample pose and the pose of the target person (Rogez et al, a regression is used for refining the anchor-pose to match based on the key points with a regression loss representing the similarity score between the anchor-pose and the ground-truth; Fig 3 and 3.3 Regression); and choosing the candidate sample pose with the greatest candidate score as the sample pose (Rogez et al, grouping and mode finding is used to identify the person with an integration and thresholding applied to determine the final pose estimate; Fig 4 and 3.5 Pose proposal integration).
Regarding Claim 5, Sivan et al in view of Rogez et al teach the pose analyzing apparatus according to claim 1 (as described above), wherein the computation of the pose score includes: performing clustering on the persons based on the poses of the persons to divide the persons into two or more clusters (Rogez et al, the regression network includes clusters in estimation of the body parts and reduces truncation by the image boundary; 1. Introduction ¶ 2, 4.2 2D and 3D pose detection, Single person pose estimation); and for each person, computing the pose score based on a size of the cluster to which the person belongs (Rogez et al, pose clusters are used with a given number of clusters with the number of clusters doubled to deal with truncations, thereby influencing the output score associated with the anchor-pose to ground-truth score; 4.2 2D and 3D pose detection, Dealing with truncation).
Regarding Claim 6, Sivan et al in view of Rogez et al teach the pose analyzing apparatus according to claim 1 (as described above), wherein the reference information includes an output image that is generated by modifying the target image to show a mark that indicates the reference person (Rogez et al, the regression output includes an arrow (mark) that indicates the difference between the Anchor-Pose and the Ground-Truth pose data; Fig 3 and 3.3 LCR-Net Regression).
Regarding Claim 7, Sivan et al in view of Rogez et al teach the pose analyzing apparatus according to claim 6 (as described above), wherein the pose of the reference person is superimposed on the target person in the output image (Rogez et al, the pose proposal regression estimated is integrated into the final pose estimate of the person in the output image; Fig 4 and 3.5 Pose proposals integration ¶ 1-2).
Regarding Claim 8, Sivan et al in view of Rogez et al teach a pose analyzing method performed by a computer (Sivan et al, computer system 206 to execute process 100 for target person analysis; Fig 1, 2 and ¶ [0071]-[0082]), comprising: steps identical to claim 1, as described above.
Regarding Claim 9, Sivan et al in view of Rogez et al teach the pose analyzing method according to claim 8 (as described above), with further steps identical to claim 2, as described above.
Regarding Claim 10, Sivan et al in view of Rogez et al teach the pose analyzing method according to claim 9 (as described above), with further steps identical to claim 3, as described above.
Regarding Claim 11, Sivan et al in view of Rogez et al teach the pose analyzing method according to claim 9 (as described above), with further steps identical to claim 4, as described above.
Regarding Claim 12, Sivan et al in view of Rogez et al teach the pose analyzing method according to claim 8 (as described above), with further steps identical to claim 5, as described above.
Regarding Claim 13, Sivan et al in view of Rogez et al teach the pose analyzing method according to claim 8 (as described above), with further steps identical to claim 6, as described above.
Regarding Claim 14, Sivan et al in view of Rogez et al teach the pose analyzing method according to claim 8 (as described above), with further steps identical to claim 7, as described above.
Regarding Claim 15, Sivan et al in view of Rogez et al teach a non-transitory computer-readable storage medium (Sivan et al, non-transitory storage 234 storing program instructions; Fig 2 and ¶ [0080]) storing a program that causes a computer (Sivan et al, computer system 206 to execute process 100 for target person analysis; Fig 1, 2 and ¶ [0071]-[0082]) to execute:
Regarding Claim 16, Sivan et al in view of Rogez et al teach the storage medium according to claim 15 (as described above), with further steps identical to claim 2, as described above.
Regarding Claim 17, Sivan et al in view of Rogez et al teach the storage medium according to claim 16 (as described above), with further steps identical to claim 3, as described above.
Regarding Claim 18, Sivan et al in view of Rogez et al teach the storage medium according to claim 16 (as described above), with further steps identical to claim 4, as described above.
Regarding Claim 19, Sivan et al in view of Rogez et al teach the storage medium according to claim 16 (as described above), with further steps identical to claim 5, as described above.
Regarding Claim 20, Sivan et al in view of Rogez et al teach the storage medium according to claim 15 (as described above), with further steps identical to claim 6, as described above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kawai et al (US 2025/0355925, application 18/854,600), from the same applicant and co-inventors, teach a system for analyzing pose information of a single person using a model that analyzes elements of the person’s pose, which is distinct from the current application that claims identification and analysis of multiple individuals and estimating pose and associated quality metrics for each person.
Zhang et al (US 2022/0080260) teach a method and system for pose comparison including receiving a frame of a user video and comparing the pose of the user in the frame to a reference pose to determine quality of the pose.
Zhao et al (Learning to Acquire the Quality of Human Pose Estimation) teach a pose regression analysis for the quality analysis of keypoint image data to identify human pose and the quality of the pose.
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/KATHLEEN M BROUGHTON/ Primary Examiner, Art Unit 2661
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