DETAILED ACTION
Notice of AIA Status
The present application is being examined under the AIA the first inventor to file provisions.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/08/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Claims 1 and 14-18 recites limitation that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 1; recites the limitation, “captured by an image capturing device” [Line 2]
Claim 1; recites the limitation, “detectable by the image capturing device” [Line 3]
Claim 14; recites the limitation, “captured by the image capturing device” [Line 3]
Claim 15; recites the limitation, “captured by the image capturing device” [Line 3]
Claim 16; recites the limitation, “captured by the image capturing device” [Line 3]
Claim 17; recites the limitation, “an acquisition part that acquires” [Line 2]
Claim 17; recites the limitation, “captured by an image capturing device” [Line 2]
Claim 17; recites the limitation, “an estimation part that estimates” [Line 5]
Claim 17; recites the limitation, “an extraction part that extracts” [Line 5]
Claim 17; recites the limitation, “detectable by the image capturing device” [Line 3]
Claim 17; recites the limitation, “a determination part that determines” [Line 3]
Claim 17; recites the limitation, “an output part that outputs” [Line 3]
Claim 18; recites the limitation, “captured by an image capturing device” [Line 2]
Claim 18; recites the limitation, “detectable by the image capturing device” [Line 3]
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 1 and 14-18:
(i) “image capturing device” (Fig. 1, #4 Paragraph [0053]- The camera 4 is an example of an image capturing device. The camera 4 is a fixed camera arranged in a house where a user to be recognized for an action thereof lives. The camera 4 captures an image of the user at a predetermined frame rate, and inputs the captured image to the action recognition device 1 at a predetermined frame rate. (wherein the image acquisition device is a camera.).).
(ii) “an acquisition part” (Fig. 1 #21 Paragraph [0056]- The processor 2 has an acquisition part 21, an estimation part 22, an extraction part 23, a determination part 24, and an output part 25. Each of the acquisition part 21 to the output part 25 may come into effect when the central processing unit executes the action recognition program, or may be established in the form of a dedicated hardware circuit, such as an ASIC. (wherein acquisition part is a processor or dedicated circuit).).
(iii) “an estimation part” (Fig. 1 #22 Paragraph [0056]- The processor 2 has an acquisition part 21, an estimation part 22, an extraction part 23, a determination part 24, and an output part 25. Each of the acquisition part 21 to the output part 25 may come into effect when the central processing unit executes the action recognition program, or may be established in the form of a dedicated hardware circuit, such as an ASIC. (wherein the estimation part is a processor or dedicated circuit).).
(iv) “an extraction part” (Fig. 1 #23 Paragraph [0056]- The processor 2 has an acquisition part 21, an estimation part 22, an extraction part 23, a determination part 24, and an output part 25. Each of the acquisition part 21 to the output part 25 may come into effect when the central processing unit executes the action recognition program, or may be established in the form of a dedicated hardware circuit, such as an ASIC. (wherein the extraction part is a processor or dedicated circuit).).
(v) “a determination part” (Fig. 1 #24 Paragraph [0056]- The processor 2 has an acquisition part 21, an estimation part 22, an extraction part 23, a determination part 24, and an output part 25. Each of the acquisition part 21 to the output part 25 may come into effect when the central processing unit executes the action recognition program, or may be established in the form of a dedicated hardware circuit, such as an ASIC. (wherein the determination part is a processor or dedicated circuit).).
(vi) “an output part” (Fig. 1 #25 Paragraph [0056]- The processor 2 has an acquisition part 21, an estimation part 22, an extraction part 23, a determination part 24, and an output part 25. Each of the acquisition part 21 to the output part 25 may come into effect when the central processing unit executes the action recognition program, or may be established in the form of a dedicated hardware circuit, such as an ASIC. (wherein the output part is a processor or dedicated circuit).).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 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 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 of this title, 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, 5-8, 10, and 17-18 are rejected under 35 U.S.C 103 as being unpatentable over Luo et al. (US 20210281744 A1) hereafter referenced as Luo in view of Ng et al. (US 20200211154 A1) hereafter referenced as Ng.
Regarding claim 1, Luo explicitly teaches an action recognition method for an action recognition device that recognizes an action of a user (Fig. 8, Paragraph [0031]- Luo discloses a method and a device for recognizing an action of a target object and an electronic apparatus are provided according to the present disclosure.),
by a processor included in the action recognition device (Fig. 8, Paragraph [0143]- Luo discloses as shown in FIG. 8, the electronic apparatus 800 may include a processing device (for example, a central processing unit, a graphics processing unit and the like) 801.),
comprising: acquiring an image of the user captured by an image capturing device (Fig. 1, Paragraph [0018]- Luo discloses the original image acquiring module is configured to acquire an original image from an image source. The original image includes a target object.);
estimating a plurality of nodes of the user and reliability of each of the nodes from the image (Fig. 1, Paragraph [0022]- Luo discloses the visibility probability determining module is configured to output, by using the visibility determining model, a visibility probability of each of the multiple key points.);
extracting, from the estimated nodes, a predetermined detectable node which is detectable by the image capturing device (Fig. 4, Paragraph [0075]- Luo discloses the outputs of the model are probabilities each indicating whether a key point is visible. If the probability is greater than a threshold, it is determined that a visibility of the key point indicated by the probability is consistent with the mark of the key point on the image. (wherein the predetermined node is a node above a threshold value));
determining one or more candidate actions from a plurality of target actions by comparing reference reliability of a detectable node predetermined for each of the target actions with reliability of the extracted detectable node (Fig. 7, Paragraph [0128]- Luo discloses the first recognizing module is configured to output, if the combined value matches the reference value, the action corresponding to the reference value as the recognized action of the target object.);
determining the action of the user from the one or more candidate actions (Fig. 7, Paragraph [0128]- Luo discloses the first recognizing module is configured to output, if the combined value matches the reference value, the action corresponding to the reference value as the recognized action of the target object.);
Luo fails to explicitly teach outputting an action label indicating the determined action.
However, Ng explicitly teaches outputting an action label indicating the determined action (Fig. 1, Paragraph [0097]- Ng discloses that action-recognition module 108 is followed by fall-detection module 110, which receives the outputs from both pose-estimation module 106 (i.e., human keypoints 122) and action-recognition module 108 (i.e., the action labels/classifications 124)… action-recognition module 108 can then use cropped image 132 and/or keypoints 122 of a detected person to generate frame-by-frame action labels/classifications 124 for the detected person.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Luo of a an action recognition method for an action recognition device that recognizes an action of a user, by a processor included in the action recognition device, comprising: acquiring an image of the user captured by an image capturing device views with the teachings of Ng outputting an action label indicating the determined action.
Wherein having Luo’s system for action recognition wherein outputting an action label indicating the determined action.
The motivation behind the modification would have been to allow for more accurate detection and classification of actions to be obtained, since both Luo and Ng are systems that use images to determine actions. Wherein Luo’s system wherein improved accuracy of complex action recognition, while Ng’s system wherein further improved accuracy and robustness of system. Please see Luo et al. (US 20210281744 A1), Paragraph [0031] and Ng et al. (US 20200211154 A1) Paragraph [0004-7].
Regarding claim 5, Luo in view of Ng explicitly teaches the action recognition method according to claim 1, Luo further teaches wherein, in the determining of the action, each of the one or more candidate actions is determined to be the action (Fig. 7, Paragraph [0128]- Luo discloses the first recognizing module is configured to output, if the combined value matches the reference value, the action corresponding to the reference value as the recognized action of the target object.).
Regarding claim 6, Luo in view of Ng explicitly teaches The action recognition method according to claim 1, Luo further teaches wherein, in the determining of the one or more candidate actions, a similarity between a distribution of reliability of a plurality of detectable nodes and a distribution of reference reliability of the detectable nodes is calculated for each of the target actions (Fig. 1, Paragraph [0082]- Luo discloses Then the combined value of visibility attributes is compared with each of the reference values. In the present disclosure, the comparison may be performed by calculating a similarity between two vectors. The similarity between two vectors may be calculated by using the Pearson correlation coefficient, the Euclidean distance, the cosine similarity, the Manhattan distance and the like.),
and the one or more candidate actions are determined on the basis of the similarity calculated for each of the target actions (Fig. 1, Paragraph [0082]- Luo discloses if a similarity is greater than a predetermined second threshold, it is determined that the combined value matches the reference value. The action corresponding to the reference value is outputted as a recognized action of the target object.).
Regarding claim 7, Luo in view of Ng explicitly teaches the action recognition method according to claim 6, Luo further teaches wherein the similarity represents a total value of respective differences between the reliability and the reference reliability calculated for each of the detectable nodes (Fig. 1, Paragraph [0082]- Luo discloses Then the combined value of visibility attributes is compared with each of the reference values. In the present disclosure, the comparison may be performed by calculating a similarity between two vectors. The similarity between two vectors may be calculated by using the Pearson correlation coefficient, the Euclidean distance, the cosine similarity, the Manhattan distance and the like.).
Regarding claim 8, Luo in view of Ng explicitly teaches the action recognition method according to claim 6, Luo further teaches wherein the reference reliability includes true reliability given to a detectable node having preliminarily estimated reliability exceeding a threshold (Fig. 5, Paragraph [0082]- Luo discloses the reference value may also be a vector, for example, a 1*N vector. Futher in Fig. 4, Paragraph [0076]- Luo discloses the vector is a 1*N vector where N represents the number of the key points. A value of an element in the vector is equal to 0 or 1. 0 represents that a corresponding key point is invisible and 1 represents that a corresponding key point is visible. (Wherein 1 or visible is considered true)),
and false reliability given to a detectable node having preliminarily estimated reliability falling below the threshold (Fig. 5, Paragraph [0082]- Luo discloses the reference value may also be a vector, for example, a 1*N vector. Futher in Fig. 4, Paragraph [0076]- Luo discloses the vector is a 1*N vector where N represents the number of the key points. A value of an element in the vector is equal to 0 or 1. 0 represents that a corresponding key point is invisible and 1 represents that a corresponding key point is visible. (Wherein 0 or invisible is considered false)),
the action recognition method further comprising: giving the true reliability to a detectable node whose reliability estimated from the image exceeds the threshold (Fig. 1, Paragraph [0076]- Luo discloses the visibility probability is compared with the predetermined first threshold. The threshold may be equal to 0.8. That is, in a case that an outputted probability of a key point is greater than 0.8, it is determined that the key point is visible. In a case that an outputted probability of a key point is less than 0.8, it is determined that the key point is invisible. Then the probability is outputted through an activation function, where 1 is outputted for a visible key point, and 0 is outputted for an invisible key point. (Wherein 1 or visible is considered true)),
and giving the false reliability to the detectable node whose reliability estimated from the image falls below the threshold (Fig. 1, Paragraph [0076]- Luo discloses the visibility probability is compared with the predetermined first threshold. The threshold may be equal to 0.8. That is, in a case that an outputted probability of a key point is greater than 0.8, it is determined that the key point is visible. In a case that an outputted probability of a key point is less than 0.8, it is determined that the key point is invisible. Then the probability is outputted through an activation function, where 1 is outputted for a visible key point, and 0 is outputted for an invisible key point. (Wherein 0 or invisible is considered false)),
wherein the similarity is based on the number of values of reliability where truth of the reliability and truth of the reference reliability agree with each other, and false of the reliability and false of the reference reliability agree with each other on each of the detectable nodes (Fig. 5, Paragraph [0080-81]- Luo discloses the combined value of the visibility attributes is compared with the reference value. In step S503, if the combined value matches the reference value, the action corresponding to the reference value is outputted as the recognized action of the target object.).
Regarding claim 10, Luo in view of Ng explicitly teaches the action recognition method according to claim 1, Luo fails to explicitly teach wherein the node and the reliability are estimated by inputting the image into a learned model obtained through machine learning of a relation between the image and the node.
However, Ng explicitly teaches wherein the node and the reliability are estimated by inputting the image into a learned model obtained through machine learning of a relation between the image and the node (Fig. 1, Paragraph [0071]- Ng discloses here the probability of a detected keypoint represents a confidence score assigned to the detected keypoint by the pose-estimation model.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Luo of a an action recognition method for an action recognition device that recognizes an action of a user, by a processor included in the action recognition device, comprising: acquiring an image of the user captured by an image capturing device views with the teachings of Ng wherein the node and the reliability are estimated by inputting the image into a learned model obtained through machine learning of a relation between the image and the node.
Wherein having Luo’s system for action recognition wherein the node and the reliability are estimated by inputting the image into a learned model obtained through machine learning of a relation between the image and the node.
The motivation behind the modification would have been to allow for more accurate detection and classification of actions to be obtained, since both Luo and Ng are systems that use images to determine actions. Wherein Luo’s system wherein improved accuracy of complex action recognition, while Ng’s system wherein further improved accuracy and robustness of system. Please see Luo et al. (US 20210281744 A1), Paragraph [0031] and Ng et al. (US 20200211154 A1) Paragraph [0004-7].
Regarding claim 17, Luo explicitly teaches an action recognition device for recognizing an action of a user (Fig. 8, Paragraph [0031]- Luo discloses a method and a device for recognizing an action of a target object and an electronic apparatus are provided according to the present disclosure.),
comprising: an acquisition part that acquires an image of the user captured by an image capturing device (Fig. 1, Paragraph [0018]- Luo discloses the original image acquiring module is configured to acquire an original image from an image source. The original image includes a target object.);
an estimation part that estimates a plurality of nodes of the user and reliability of each of the nodes from the image (Fig. 1, Paragraph [0022]- Luo discloses the visibility probability determining module is configured to output, by using the visibility determining model, a visibility probability of each of the multiple key points.);
an extraction part that extracts, from the estimated nodes, a predetermined detectable node which is detectable by the image capturing device (Fig. 4, Paragraph [0075]- Luo discloses the outputs of the model are probabilities each indicating whether a key point is visible. If the probability is greater than a threshold, it is determined that a visibility of the key point indicated by the probability is consistent with the mark of the key point on the image. (wherein the predetermined node is a node above a threshold value));
a determination part that determines one or more candidate actions from a plurality of target actions by comparing reference reliability of a detectable node predetermined for each of the target actions with reliability of the extracted detectable node (Fig. 7, Paragraph [0128]- Luo discloses the first recognizing module is configured to output, if the combined value matches the reference value, the action corresponding to the reference value as the recognized action of the target object.);
Luo fails to explicitly teach an output part that outputs an action label indicating the determined action.
However, Ng explicitly teaches an output part that outputs an action label indicating the determined action (Fig. 1, Paragraph [0097]- Ng discloses that action-recognition module 108 is followed by fall-detection module 110, which receives the outputs from both pose-estimation module 106 (i.e., human keypoints 122) and action-recognition module 108 (i.e., the action labels/classifications 124)… action-recognition module 108 can then use cropped image 132 and/or keypoints 122 of a detected person to generate frame-by-frame action labels/classifications 124 for the detected person.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Luo of an action recognition device for recognizing an action of a user, comprising: an acquisition part that acquires an image of the user captured by an image capturing device with the teachings of Ng an output part that outputs an action label indicating the determined action.
Wherein having Luo’s system for action recognition wherein an output part that outputs an action label indicating the determined action.
The motivation behind the modification would have been to allow for more accurate detection and classification of actions to be obtained, since both Luo and Ng are systems that use images to determine actions. Wherein Luo’s system wherein improved accuracy of complex action recognition, while Ng’s system wherein further improved accuracy and robustness of system. Please see Luo et al. (US 20210281744 A1), Paragraph [0031] and Ng et al. (US 20200211154 A1) Paragraph [0004-7].
Regarding claim 18, Luo explicitly teaches a non-transitory computer readable recording medium storing an action recognition program for causing a computer to execute an action recognition method for recognizing an action of a user, by the computer (Fig. 8, Paragraph [0030]- Luo discloses a computer readable storage media is provided. The computer readable storage media is configured to store non-transient computer readable instructions that when being executed by a computer, cause the computer to perform steps in any one of the above methods.),
comprising: acquiring an image of the user captured by an image capturing device (Fig. 1, Paragraph [0018]- Luo discloses the original image acquiring module is configured to acquire an original image from an image source. The original image includes a target object.);
estimating a plurality of nodes of the user and reliability of each of the nodes from the image (Fig. 1, Paragraph [0022]- Luo discloses the visibility probability determining module is configured to output, by using the visibility determining model, a visibility probability of each of the multiple key points.);
extracting, from the estimated nodes, a predetermined detectable node which is detectable by the image capturing device (Fig. 4, Paragraph [0075]- Luo discloses the outputs of the model are probabilities each indicating whether a key point is visible. If the probability is greater than a threshold, it is determined that a visibility of the key point indicated by the probability is consistent with the mark of the key point on the image. (wherein the predetermined node is a node above a threshold value));
determining one or more candidate actions from a plurality of target actions by comparing reference reliability of a detectable node predetermined for each of the target actions with reliability of the extracted detectable node (Fig. 7, Paragraph [0128]- Luo discloses the first recognizing module is configured to output, if the combined value matches the reference value, the action corresponding to the reference value as the recognized action of the target object.);
determining the action of the user from the one or more candidate actions (Fig. 7, Paragraph [0128]- Luo discloses the first recognizing module is configured to output, if the combined value matches the reference value, the action corresponding to the reference value as the recognized action of the target object.);
Luo fails to explicitly teach outputting an action label indicating the determined action.
However, Ng explicitly teaches outputting an action label indicating the determined action (Fig. 1, Paragraph [0097]- Ng discloses that action-recognition module 108 is followed by fall-detection module 110, which receives the outputs from both pose-estimation module 106 (i.e., human keypoints 122) and action-recognition module 108 (i.e., the action labels/classifications 124)… action-recognition module 108 can then use cropped image 132 and/or keypoints 122 of a detected person to generate frame-by-frame action labels/classifications 124 for the detected person.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Luo of an a non-transitory computer readable recording medium storing an action recognition program for causing a computer to execute an action recognition method for recognizing an action of a user, by the computer, comprising: acquiring an image of the user captured by an image capturing device with the teachings of Ng an outputting an action label indicating the determined action.
Wherein having Luo’s system for action recognition wherein an outputting an action label indicating the determined action.
The motivation behind the modification would have been to allow for more accurate detection and classification of actions to be obtained, since both Luo and Ng are systems that use images to determine actions. Wherein Luo’s system wherein improved accuracy of complex action recognition, while Ng’s system wherein further improved accuracy and robustness of system. Please see Luo et al. (US 20210281744 A1), Paragraph [0031] and Ng et al. (US 20200211154 A1) Paragraph [0004-7].
Claims 2-3 are rejected under 35 U.S.C 103 as being unpatentable over Luo et al. (US 20210281744 A1) hereafter referenced as Luo in view of Ng et al. (US 20200211154 A1) hereafter referenced as Ng and Kechichian et al. (US 20210349122 A1) hereafter referenced as Kechichian.
Regarding claim 2, Luo in view of Ng explicitly teaches the action recognition method according to claim 1, Luo in view of Ng fails to explicitly teach wherein the action includes an action of the user using an appliance or equipment arranged in a facility.
However, Kechichian explicitly teaches wherein the action includes an action of the user using an appliance or equipment arranged in a facility (Fig. 1, Paragraph [0067]- Kechichian discloses some users make use of walking aids, including walkers, canes and wheelchairs. Using mobility aids changes the direction of the wrist during walking and may result in lower trigger rates for the detection algorithm, leading to delays in correctly estimating the wearing location.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Luo in view of Ng of an a an action recognition method for an action recognition device that recognizes an action of a user, by a processor included in the action recognition device, comprising: acquiring an image of the user captured by an image capturing device views with the teachings of Kechichian wherein the action includes an action of the user using an appliance or equipment arranged in a facility.
Wherein having Luo’s system for action recognition wherein the action includes an action of the user using an appliance or equipment arranged in a facility.
The motivation behind the modification would have been to allow for more information to be obtained, since both Luo and Kechichian are systems that determine location of joints. Wherein Luo’s system wherein improved accuracy of complex action recognition, while Kechichian’s system wherein further improved accuracy and efficiency of system. Please see Luo et al. (US 20210281744 A1), Paragraph [0031] and Kechichian et al. (US 20210349122 A1) Paragraph [0025].
Regarding claim 3, Luo in view of Ng and Kechichian explicitly teaches the action recognition method according to claim 2,
Luo in view of Ng fails to explicitly teach wherein the equipment includes a rod for assisting a motion of the user, and the appliance includes a stand or a chair for assisting a motion of the user.
However, Kechichian explicitly teaches wherein the equipment includes a rod for assisting a motion of the user (Fig. 1, Paragraph [0067]- Kechichian discloses some users make use of walking aids, including walkers, canes and wheelchairs. Using mobility aids changes the direction of the wrist during walking and may result in lower trigger rates for the detection algorithm, leading to delays in correctly estimating the wearing location. (Wherein a Cane is considered a rod for assisting a motion of the user)),
and the appliance includes a stand or a chair for assisting a motion of the user (Fig. 1, Paragraph [0067]- Keechichian discloses some users make use of walking aids, including walkers, canes and wheelchairs. Using mobility aids changes the direction of the wrist during walking and may result in lower trigger rates for the detection algorithm, leading to delays in correctly estimating the wearing location. (Wherein a Walker is considered a stand for assisting a motion of the user and a Wheelchair a chair for assisting user motion)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Luo in view of Ng of an a an action recognition method for an action recognition device that recognizes an action of a user, by a processor included in the action recognition device, comprising: acquiring an image of the user captured by an image capturing device views with the teachings of Kechichian wherein the equipment includes a rod for assisting a motion of the user, and the appliance includes a stand or a chair for assisting a motion of the user.
Wherein having Luo’s system for action recognition wherein the equipment includes a rod for assisting a motion of the user, and the appliance includes a stand or a chair for assisting a motion of the user.
The motivation behind the modification would have been to allow for more information to be obtained, since both Luo and Kechichian are systems that determine location of joints. Wherein Luo’s system wherein improved accuracy of complex action recognition, while Kechichian’s system wherein further improved accuracy and efficiency of system. Please see Luo et al. (US 20210281744 A1), Paragraph [0031] and Kechichian et al. (US 20210349122 A1) Paragraph [0025].
Claim 4 is rejected under 35 U.S.C 103 as being unpatentable over Luo et al. (US 20210281744 A1) hereafter referenced as Luo in view of Ng et al. (US 20200211154 A1) hereafter referenced as Ng and Hu et al. (US 20210097101 A1) hereafter referenced as Hu.
Regarding claim 4, Luo in view of Ng explicitly teaches the action recognition method according to claim 1, Luo in view of Ng fails to explicitly teach wherein, in the determining of the action, in connection with each of the one or more candidate actions, a distance between a coordinate of the extracted detectable node and a reference coordinate of the detectable node is calculated for each of the target actions, and the action is determined on the basis of the distance calculated for each of the target actions.
However, Hu explicitly teaches wherein, in the determining of the action, in connection with each of the one or more candidate actions, a distance between a coordinate of the extracted detectable node and a reference coordinate of the detectable node is calculated for each of the target actions (Fig. 6, Pargraph [0049]- Hu discloses FIG. 6 shows a schematic flowchart of a process for calculating the pose similarity between the candidate person and the reference person according to an embodiment of the present disclosure. As shown in FIG. 6, at block 610, based on the reference keypoint data of the reference person and the candidate keypoint data of the candidate person, the pose distance L between the candidate person and the reference person is calculated according to Equation (1)),
and the action is determined on the basis of the distance calculated for each of the target actions (Fig. 5, Paragraph [0053]- Hu discloses it is determined whether the pose similarity is greater than the predetermined threshold. If the pose similarity is greater than or equal to the predetermined threshold, at block 520, it is determined that the candidate person has the pose similar to the reference person.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Luo in view of Ng of an a an action recognition method for an action recognition device that recognizes an action of a user, by a processor included in the action recognition device, comprising: acquiring an image of the user captured by an image capturing device views with the teachings of Hu wherein, in the determining of the action, in connection with each of the one or more candidate actions, a distance between a coordinate of the extracted detectable node and a reference coordinate of the detectable node is calculated for each of the target actions, and the action is determined on the basis of the distance calculated for each of the target actions.
Wherein having Luo’s system for action recognition wherein, in the determining of the action, in connection with each of the one or more candidate actions, a distance between a coordinate of the extracted detectable node and a reference coordinate of the detectable node is calculated for each of the target actions, and the action is determined on the basis of the distance calculated for each of the target actions.
The motivation behind the modification would have been to allow for a more accurate system, since both Luo and Hu are systems that determine a pose using location of joints. Wherein Luo’s system wherein improved accuracy of complex action recognition, while Hu’s system wherein further improved accuracy and speed of system. Please see Luo et al. (US 20210281744 A1), Paragraph [0031] and Hu et al. (US 20210097101 A1) Paragraph [0003-4].
Claim 9 is rejected under 35 U.S.C 103 as being unpatentable over Luo et al. (US 20210281744 A1) hereafter referenced as Luo in view of Ng et al. (US 20200211154 A1) hereafter referenced as Ng and Luo et al. (US 20210271892 A1) hereafter referenced as Luo2.
Regarding claim 9, Luo in view of Ng explicitly teaches the action recognition method according to claim 6, Luo in view of Ng fails to explicitly teach wherein, in the determining of the one or more candidate actions, a target action in a top N-th similarity, where “N” is an integer equal to or greater than one, is determined to be each of the one or more candidate actions.
However, Luo2 explicitly teaches wherein, in the determining of the one or more candidate actions, a target action in a top N-th similarity, where “N” is an integer equal to or greater than one, is determined to be each of the one or more candidate actions (Fig. 1, Paragraph [0179]- Luo2 discloses the electronic device obtains a maximum similarity among the plurality of similarities as a first similarity (e.g., a highest first similarity). When the first similarity is greater than a first preset threshold, it indicates that a similarity between a dynamic action included in a first target window and the preset dynamic action is relatively high, and the dynamic action included in the first target window corresponding to the first similarity can be considered as the preset dynamic action.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Luo in view of Ng of an a an action recognition method for an action recognition device that recognizes an action of a user, by a processor included in the action recognition device, comprising: acquiring an image of the user captured by an image capturing device views with the teachings of Luo2 wherein, in the determining of the one or more candidate actions, a target action in a top N-th similarity, where “N” is an integer equal to or greater than one, is determined to be each of the one or more candidate actions.
Wherein having Luo’s system for action recognition wherein, in the determining of the one or more candidate actions, a target action in a top N-th similarity, where “N” is an integer equal to or greater than one, is determined to be each of the one or more candidate actions.
The motivation behind the modification would have been to allow for more accurate and flexible system, since both Luo and Luo2 are systems for action recognition. Wherein Luo’s system wherein improved accuracy of complex action recognition, while Luo2’s system wherein further improved accuracy and flexibility of prediction results. Please see Luo et al. (US 20210281744 A1), Paragraph [0031] and Luo2 et al. (US 20210271892 A1) Paragraph [0230].
Claims 11-13 are rejected under 35 U.S.C 103 as being unpatentable over Luo et al. (US 20210281744 A1) hereafter referenced as Luo in view of Ng et al. (US 20200211154 A1) hereafter referenced as Ng and Gu et al. (Multi-Person Pose Estimation using an Orientation and Occlusion Aware Deep Learning Network) hereafter referenced as Gu.
Regarding claim 11, Luo in view of Ng explicitly teaches the action recognition method according to claim 1, Luo in view of Ng fails to explicitly teach wherein, in the extracting of the detectable node, the detectable node is extracted with reference to a first database defining information indicating whether each of the nodes represents the detectable node.
However, Gu explicitly teaches wherein, in the extracting of the detectable node, the detectable node is extracted with reference to a first database defining information indicating whether each of the nodes represents the detectable node (Fig. 12, Section 3.1 Paragraph [0001]- Gu discloses the images of the COCO keypoint dataset were captured in human’s daily life, such as a party, meeting room and sport field. The images include multi-person with different scales, orientations, occlusions and postures. Figure 12 gives a demonstration of this task, each person in the image has a set of annotations including segmentation, class name (in this task it is “person”), bounding box position, the number of labeled keypoints and a list of body joint information (labeled in the format (x,y,v) with 17 joints, where x,y is the location of joint and v is the visibility of joint that v = 0 means not labeled, v = 1 means labeled but not visible and v = 2 means labeled and visible) (wherein the visible label represents if the node is detectable).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Luo in view of Ng of an a an action recognition method for an action recognition device that recognizes an action of a user, by a processor included in the action recognition device, comprising: acquiring an image of the user captured by an image capturing device views with the teachings of Gu wherein, in the extracting of the detectable node, the detectable node is extracted with reference to a first database defining information indicating whether each of the nodes represents the detectable node.
Wherein having Luo’s system for action recognition wherein, in the extracting of the detectable node, the detectable node is extracted with reference to a first database defining information indicating whether each of the nodes represents the detectable node.
The motivation behind the modification would have been to allow for more accurate predictions to be obtained, since both Luo and Gu are systems that determine joint locations in images. Wherein Luo’s system wherein improved accuracy of complex action recognition, while Gu’s system wherein further improved accuracy of joint estimation. Please see Fu et al. Luo et al. (US 20210281744 A1), Paragraph [0031] and Gu et al. (Multi-Person Pose Estimation using an Orientation and Occlusion Aware Deep Learning Network) Section 3.6 Paragraph [0005].
Regarding claim 12, Luo in view of Ng explicitly teaches the action recognition method according to claim 1, Luo in view of Ng fails to explicitly teach wherein, in the determining of the one or more candidate actions, the one or more candidate actions are determined with reference to a second database defining the reference reliability of the detectable node for each of the target actions.
However, Gu explicitly teaches wherein, in the determining of the one or more candidate actions, the one or more candidate actions are determined with reference to a second database defining the reference reliability of the detectable node for each of the target actions (Fig. 12, Section 3.1 Paragraph [0001]- Gu discloses the images of the COCO keypoint dataset were captured in human’s daily life, such as a party, meeting room and sport field. The images include multi-person with different scales, orientations, occlusions and postures. Figure 12 gives a demonstration of this task, each person in the image has a set of annotations including segmentation, class name (in this task it is “person”), bounding box position, the number of labeled keypoints and a list of body joint information (labeled in the format (x,y,v) with 17 joints, where x,y is the location of joint and v is the visibility of joint that v = 0 means not labeled, v = 1 means labeled but not visible and v = 2 means labeled and visible)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Luo in view of Ng of an a an action recognition method for an action recognition device that recognizes an action of a user, by a processor included in the action recognition device, comprising: acquiring an image of the user captured by an image capturing device views with the teachings of Gu wherein, in the determining of the one or more candidate actions, the one or more candidate actions are determined with reference to a second database defining the reference reliability of the detectable node for each of the target actions.
Wherein having Luo’s system for action recognition wherein, in the determining of the one or more candidate actions, the one or more candidate actions are determined with reference to a second database defining the reference reliability of the detectable node for each of the target actions.
The motivation behind the modification would have been to allow for more accurate predictions to be obtained, since both Luo and Gu are systems that determine joint locations in images. Wherein Luo’s system wherein improved accuracy of complex action recognition, while Gu’s system wherein further improved accuracy of joint estimation. Please see Luo et al. (US 20210281744 A1), Paragraph [0031] and Gu et al. (Multi-Person Pose Estimation using an Orientation and Occlusion Aware Deep Learning Network) Section 3.6 Paragraph [0005].
Regarding claim 13, Luo in view of Ng explicitly teaches the action recognition method according to claim 1, Luo in view of Ng fails to explicitly teach wherein, in the determining of the action, the action is determined with reference to a third database defining a reference coordinate of the detectable node for each of the target actions.
However, Gu explicitly teaches wherein, in the determining of the action, the action is determined with reference to a third database defining a reference coordinate of the detectable node for each of the target actions (Fig. 12, Section 3.1 Paragraph [0001]- Gu discloses the images of the COCO keypoint dataset were captured in human’s daily life, such as a party, meeting room and sport field. The images include multi-person with different scales, orientations, occlusions and postures. Figure 12 gives a demonstration of this task, each person in the image has a set of annotations including segmentation, class name (in this task it is “person”), bounding box position, the number of labeled keypoints and a list of body joint information (labeled in the format (x,y,v) with 17 joints, where x,y is the location of joint and v is the visibility of joint that v = 0 means not labeled, v = 1 means labeled but not visible and v = 2 means labeled and visible). (wherein the visible label represents if the node is detectable)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Luo in view of Ng of an a an action recognition method for an action recognition device that recognizes an action of a user, by a processor included in the action recognition device, comprising: acquiring an image of the user captured by an image capturing device views with the teachings of Gu wherein, in the determining of the action, the action is determined with reference to a third database defining a reference coordinate of the detectable node for each of the target actions.
Wherein having Luo’s system for action recognition wherein, in the determining of the action, the action is determined with reference to a third database defining a reference coordinate of the detectable node for each of the target actions.
The motivation behind the modification would have been to allow for more accurate predictions to be obtained, since both Luo and Gu are systems that determine joint locations in images. Wherein Luo’s system wherein improved accuracy of complex action recognition, while Gu’s system wherein further improved accuracy of joint estimation. Please see Luo et al. (US 20210281744 A1), Paragraph [0031] and Gu et al. (Multi-Person Pose Estimation using an Orientation and Occlusion Aware Deep Learning Network) Section 3.6 Paragraph [0005].
Claims 14-15 are rejected under 35 U.S.C 103 as being unpatentable over Luo et al. (US 20210281744 A1) hereafter referenced as Luo in view of Ng et al. (US 20200211154 A1) hereafter referenced as Ng and Fu et al. (US 20220254157 A1) hereafter referenced as Fu.
Regarding claim 14, Luo in view of Ng explicitly teaches the action recognition method according to claim 1, Luo in view of Ng fails to explicitly teach wherein the detectable node is preliminarily determined on the basis of a result of analysis of the image of the user captured by the image capturing device at an initial setting.
However, Fu explicitly teaches wherein the detectable node is preliminarily determined on the basis of a result of analysis of the image of the user captured by the image capturing device at an initial setting (Fig. 2, Paragraph [0038]- Fu discloses generating the indications of the joint and limb locations in the current frame 222 includes processing the initial prediction of joint locations in the current frame and the indications of joint locations from the previous frame with a first deep convolutional neural network to generate the indication of joint locations in the current frame. (wherein the previous frame is considered the initial setting)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Luo in view of Ng of an a an action recognition method for an action recognition device that recognizes an action of a user, by a processor included in the action recognition device, comprising: acquiring an image of the user captured by an image capturing device views with the teachings of Fu wherein the detectable node is preliminarily determined on the basis of a result of analysis of the image of the user captured by the image capturing device at an initial setting.
Wherein having Luo’s system for action recognition wherein the detectable node is preliminarily determined on the basis of a result of analysis of the image of the user captured by the image capturing device at an initial setting.
The motivation behind the modification would have been to allow for a more consistent system prediction, since both Luo and Fu are systems that determine location of joints in an image. Wherein Luo’s system wherein improved accuracy of complex action recognition, while Fu’s system wherein further improved accuracy and consistency of prediction results. Please see Luo et al. (US 20210281744 A1), Paragraph [0031] and Fu et al. (US 20220254157 A1) Paragraph [0083].
Regarding claim 15, Luo in view of Ng explicitly teaches the action recognition method according to claim 1, Luo in view of Ng fails to explicitly teach wherein the reference reliability is preliminarily calculated on the basis of the reliability of each node estimated from an image of the user having made each of the target actions, the image being captured by the image capturing device at an initial setting.
However, Fu explicitly teaches wherein the reference reliability is preliminarily calculated on the basis of the reliability of each node estimated from an image of the user having made each of the target actions, the image being captured by the image capturing device at an initial setting (Fig. 4, Paragraph [0058]- Fu discloses the prediction can also be seen as a confidence map with size H×W×2q, where q is the number of limbs defined. To prepare the ground-truth confidence map for limb prediction, e.g., the ground truth predictions 441b and 442b discussed hereinbelow in relation to FIG. 4, an embodiment first defines q limbs between a pair of joints indicating meaningful human limbs (or limbs of any object being detected) such as head, neck, body, trunk and forearm, which will form a skeleton of a human body in the pose association part.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Luo in view of Ng of an a an action recognition method for an action recognition device that recognizes an action of a user, by a processor included in the action recognition device, comprising: acquiring an image of the user captured by an image capturing device views with the teachings of Fu wherein the reference reliability is preliminarily calculated on the basis of the reliability of each node estimated from an image of the user having made each of the target actions, the image being captured by the image capturing device at an initial setting.
Wherein having Luo’s system for action recognition wherein the reference reliability is preliminarily calculated on the basis of the reliability of each node estimated from an image of the user having made each of the target actions, the image being captured by the image capturing device at an initial setting.
The motivation behind the modification would have been to allow for a more consistent system prediction, since both Luo and Fu are systems that determine location of joints in an image. Wherein Luo’s system wherein improved accuracy of complex action recognition, while Fu’s system wherein further improved accuracy and consistency of prediction results. Please see Luo et al. (US 20210281744 A1), Paragraph [0031] and Fu et al. (US 20220254157 A1) Paragraph [0083].
Claim 16 is rejected under 35 U.S.C 103 as being unpatentable over Luo et al. (US 20210281744 A1) hereafter referenced as Luo in view of Ng et al. (US 20200211154 A1) hereafter referenced as Ng, Hu et al. (US 20210097101 A1) hereafter referenced as Hu, and Fu et al. (US 20220254157 A1) hereafter referenced as Fu.
Regarding claim 16, Luo in view of Ng and Hu explicitly teaches the action recognition method according to claim 4, Luo in view of Ng and Hu fails to explicitly teach wherein, in the determining of the action, the action is determined with reference to a third database defining a reference coordinate of the detectable node for each of the target actions.
However, Fu explicitly teaches wherein the reference coordinate is preliminarily calculated on the basis of a coordinate of each node estimated from an image of the user having taken each of the target actions, the image being captured by the image capturing device at an initial setting (Fig. 2, Paragraph [0038]- Fu discloses generating the indications of the joint and limb locations in the current frame 222 includes processing the initial prediction of joint locations in the current frame and the indications of joint locations from the previous frame with a first deep convolutional neural network to generate the indication of joint locations in the current frame. (wherein the previous frame is considered the initial setting)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Luo in view of Ng and Hu of an a an action recognition method for an action recognition device that recognizes an action of a user, by a processor included in the action recognition device, comprising: acquiring an image of the user captured by an image capturing device views with the teachings of Fu wherein, in the determining of the action, the action is determined with reference to a third database defining a reference coordinate of the detectable node for each of the target actions.
Wherein having Luo’s system for action recognition wherein, in the determining of the action, the action is determined with reference to a third database defining a reference coordinate of the detectable node for each of the target actions.
The motivation behind the modification would have been to allow for a more consistent system prediction, since both Luo and Fu are systems that determine location of joints in an image. Wherein Luo’s system wherein improved accuracy of complex action recognition, while Fu’s system wherein further improved accuracy and consistency of prediction results. Please see Luo et al. (US 20210281744 A1), Paragraph [0031] and Fu et al. (US 20220254157 A1) Paragraph [0083].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered
pertinent to applicant`s disclosure.
Shen et al. (US 20100303303 A1)- The invention comprises an improved system, method, and computer-readable instructions for recognizing pose and action of articulated objects with collection of planes in motion. The method starts with a video sequence and a database of reference sequences corresponding to different known actions. The method identifies the sequence from the reference sequences such that the subject in performs the closest action to that observed. The method compares actions by comparing pose transitions. The cross-homography invariant may be used for view-invariant recognition of human body pose transition and actions.......................Please see Fig. 1. Abstract.
Ghafoor et al. (US 20220012478 A1)- An apparatus for performing image analysis to identify human actions represented in an image, comprising: a joint-determination module configured to analyse an image depicting one or more people using a first computational neural network to determine a set of joint candidates for the one or more people depicted in the image; a pose-estimation module configured to derive pose estimates from the set of joint candidates that estimate a body configuration for the one or more people depicted in the image; and an action-identification module configured to analyse a region of interest within the image identified from the derived pose estimates using a second computational neural network to identify an action performed by a person depicted in the image.....................Please see Fig. 1. Abstract.
QIAO et al. (US 20200237266 A1)- Action recognition methods are disclosed. An embodiment of the methods includes: identifying a video that comprises images of a human body to be processed; identifying at least one image to be processed, wherein the at least one image is at least one of an optical flow image generated based on a plurality of frames of images in the video, or a composite image of one or more frames of images in the video; performing convolution on the at least one image to obtain a plurality of eigenvectors, wherein the plurality of eigenvectors indicate a plurality of features of different locations in the at least one image; determining a weight coefficient set of each of a plurality of human joints of the human body based on the plurality of eigenvectors, wherein the weight coefficient set comprises a weight coefficient of each of the plurality of eigenvectors for the human joint; weighting the plurality of eigenvectors based on the weight coefficient set to obtain an action feature of each of the plurality of human joints; determining an action feature of the human body based on the action feature of each of the human joints; and determining an action type of the human body based on the action feature of the human body.......................Please see Fig. 1. Abstract.
Ning et al. (US 20210090284 A1)- A system and a method for pose tracking, particularly for top-down, online, multi-person pose tracking. The system includes a computing device having a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: provide a plurality of sequential frames of a video, the sequential frames comprising at least one keyframe and a plurality of non-keyframes; for each of the non-keyframes: receive a previous inference bounding box of an object inferred from a previous frame; estimate keypoints from the non-keyframe in an area defined by the previous inference bounding box to obtain estimated keypoints; determine object state based on the estimated keypoints, wherein the object state comprise a “tracked” state and a “lost” state; and when the object state is “tracked,” infer an inference bounding box based on the estimated keypoints to process a frame next to the non-keyframe.........................Please see Fig. 1. Abstract.
YAO et al. (US 20230274580 A1)- A method and system of image processing for action classification uses fine-grained motion-attributes..........................Please see Fig. 1. Abstract.
YAO et al. (US 20240078832 A1)- A learning-model generation apparatus 10 includes: an all-feature-amount-outputting unit that output, from image data of an object and for each joint of the object, a feature amount representing the joint; a feature-amount-generating unit that generates, from the feature amounts of the individual joints of the object and as training feature amounts, feature amounts in a case in which the feature amount of a certain joint is missing; and a learning-model-generating unit that, by using training data including the generated training feature amounts, generates a machine learning model by machine-learning positional relationships between the other joints in the case in which the feature amount of the certain joint is missing..........................Please see Fig. 1. Abstract.
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/LUCIUS CAMERON GREEN ALLEN/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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