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 .
Status of Claims
Applicant’s Amendments filed on 09/09/2025 has been entered and made of record.
Currently pending Claim(s)
1–8, 11–22
Independent Claim(s)
1, 22
Amended Claim(s)
1–8
Canceled Claim(s)
9, 10
New Claim(s)
11–22
Response to Arguments
This office action is responsive to Applicant’s Arguments/Remarks Made in an Amendment received on 09/09/2025.
In view of the specification amendments [Remarks] filed on 09/09/2025, the title objection has been withdrawn.
In view of the claim amendments [Remarks] filed on 09/09/2025 with respect to claims 1 and 4-7, U.S.C. 112(f) claim interpretation have been carefully considered and the claim interpretation to claims 1 and 4-7 under 35 U.S.C. 112(f) are withdrawn.
In view of the new claim amendments and applicant arguments [Remarks] filed on 09/09/2025 with respect to 35 U.S.C. 101 claim rejections have been carefully considered and the claim rejections under 35 U.S.C. 101 are withdrawn.
Regarding the rejections made under 35 USC 103, Applicant’s Arguments/Remarks with respect to independent claim 1 have been fully considered but are moot because the arguments do not apply to any of the references being used in the current rejection and the independent claims are now rejected by newly cited art Buttner (‘US 2020/0324205 A1’) as explained in the body of the rejection below. Furthermore, the dependent claims are rejected with 103 rejections based on the new 103 rejections from the independent claims.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 08/07/2025 and 09/17/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
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 22 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., abstract idea – mental process) without significantly more.
Step (1) Are the claims directed to a process, machine, manufacture, or composition of matter;
Step (2A) Prong One: Are the claims directed to a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims are directed to a judicial exception under Prong One, then is the judicial exception integrated into a practical application;
Step (2B) If the claims are directed to a judicial exception and do not integrate the judicial exception, do the claims provide an inventive concept.
Claim 22:
Step 1:
Claim 22 recites an information processing method. Therefore, the claims are directed to the statutory categories of process.
Step 2A:
Prong One:
Claim 22 recites “generating animation data connecting the first-time series data, the second time-series data, and the third time-series data.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating data to connect three different sections which is practically capable of being performed in the human mind with the assistance of pen and paper.
Prong Two:
This judicial exception is not integrated into a practical application. The additional elements “controlling a display to display a first time-series data and a second time-series data,” “receiving user operation to select the first time-series data and the second time-series data,” and “controlling the display to display a first section, a second section, and a third section; wherein the first section is a section that the first time-series data is inserted, the second section is a section that the second time-series data is inserted, the third section is a section to generate a third time-series data that connecting the first time-series data and the second time-series data” amount to no more than mere necessary data gathering and applying because it is simply using hardware, or a generic computer as a tool to perform the abstract idea. Thus, they are insignificant extra-solution activity. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application and the claims are thus directed to the abstract idea.
Step 2B:
Claim 22 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. “Controlling a display to display a first time-series data and a second time-series data,” “receiving user operation to select the first time-series data and the second time-series data,” and “controlling the display to display a first section, a second section, and a third section; wherein the first section is a section that the first time-series data is inserted, the second section is a section that the second time-series data is inserted, the third section is a section to generate a third time-series data that connecting the first time-series data and the second time-series data” amount to no more than mere necessary data gathering and applying because it is simply using hardware, or a generic computer as a tool to perform the abstract idea. These elements, individually and in combination, are well-understood, routine, conventional activity. As such, the claims are ineligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 22 is rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by Corazza et al. (US 2012/0038628 A1) (hereafter, “Corazza”).
Regarding claim 22, Corazza discloses an information processing method [methods for generating and concatenating 3D character animations ... in which recommendations are made by the animation system concerning motions that smoothly transition when concatenated ... and the user device is configured to render a 3D character, para 0007] comprising: controlling a display to display a first time-series data and a second time-series data [Figure 9; a user interface 1300 includes a viewer 1302 for motion concatenation ... different animations 1306 are selected by the user ... the user interface also present recommendations 1312 concerning motions that can be concatenated with the existing sequence (the examiner interprets different animations and additional motions to be first time-series data and second time-series data), para 0072]; receiving user operation to select the first time-series data and the second time-series data [different animations 1306 are selected by the user ... a user is able to select a first animation 1302 (the examiner interprets a first animation to be first time-series data) and concatenate additional motions 1312 (the examiner interprets additional motions to be second time-series data), para 0072, 0073]; controlling the display to display a first section, a second section, and a third section [Figure 9; different animations 1306 are selected by the user and played in a sequence along with their respective transitions 1308, para 0072]; wherein the first section is a section that the first time-series data is inserted [a user is able to select a first animation 1302, para 0073], the second section is a section that the second time-series data is inserted [and concatenate additional motions 1312 to that first animation to create a sequence of concatenated animations, para 0073], the third section is a section to generate a third time-series data that connecting the first time-series data and the second time-series data [In instances where the selected motions do not transition smoothly, a short “bridging” transition motion (the examiner interprets a transition motion to be third-time series data) 1308 can be selected by the animator, para 0073]; and generating animation data connecting the first time-series data, the second time-series data, and the third time-series data [a user is able to select a first animation 1302 and concatenate additional motions 1312 to that first animation to create a sequence of concatenated animations. In instances where the selected motions do not transition smoothly, a short “bridging” transition motion 1308 can be selected by the animator and/or recommended by the animation system to improve the smoothness of the transition, para 0073].
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 1–5, 14, 16, and 19–21 are rejected under 35 U.S.C. 103 as being unpatentable over Buttner et al. (US 2020/0324205 A1) (hereafter, “Buttner”) in view of Chen et al. (Chen, Songle, et al. "Partial similarity human motion retrieval based on relative geometry features." 2012 Fourth International Conference on Digital Home. IEEE, 2012) (hereafter, “Chen”).
Regarding claim 1, Buttner discloses an information processing method comprising: acquiring a processed feature amount that is a feature amount calculated by applying, to an unprocessed feature amount that is a feature amount of each time or each part of an object calculated from time-series data concerning motion of the object [the systems and methods use input animation data (e.g., mocap data), para 0028], a weight parameter prepared for each time or each part to emphasize a corresponding portion of the motion of the object [an atom in the list has a weight (e.g., between 0 and 1) that represents the contribution of the atom to the active pose, para 0045], the weight parameter to be applied to the unprocessed feature amount is determined [the motion synthesizer determines a weight value for each atom in the list (e.g., which is updated each frame) ... after an updating of the atom weights, the motion synthesizer generates an active pose (e.g., for the active frame) of a character using the list of atoms and associated weights, para 0045] by an estimator obtained by learning a relation between the unprocessed feature amount and the weight parameter using a machine learning model [Figure 4A; shown in FIG. 4A is a first part of a method 400 for machine learning animation generation, or MLAG method. The MLAG method 400 updates the state and trajectory and produces an active character pose ... the weights associated with the set of active atoms are changed based on a time function, the changing including increasing the weight of the target atom and decreasing the weight of all other atoms, para 0044, 0020].
Buttner fails to explicitly disclose searching for motion data by using the processed feature amount to identify a piece of motion data having a partial similarity to the motion of the object based on the emphasized portion.
However, Chen teaches searching for motion data by using the processed feature amount to identify a piece of motion data having a partial similarity to the motion of the object [Figure 1; DTW algorithm uses the predicted combined weights to calculate the similarity between the query and each action in the motion database ... candidate actions ranked by their similarity are returned to the user ... the relative geometry features of each action in the motion database are first extracted and stored in a database, pg. 299, section III, A. System overview, right column, third paragraph, second paragraph] based on the emphasized portion [in the on-line retrieval of human motion, the module of content match first extracts the global effective geometry features from the query action, and then the query features are inputted into the trained regression model to predict their initial combined weights, pg. 299, A. System overview, right column, third paragraph]
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Buttner to incorporate the teachings of Chen to optimize the weights for a query action, as recognized by Chen.
Further, one skilled in the art could have combined the elements as described above with 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 Chen with Buttner to obtain the invention as specified in claim 1.
Regarding claim 2, which claim 1 is incorporated, Buttner fails to explicitly disclose wherein learning includes learning a relation between the feature amount of each part and the weight parameter for each part.
However, Chen teaches wherein learning includes learning a relation between the feature amount of each part and the weight parameter for each part [we train a linear regression model to reflect the relationship between the global effective features and the combined weights of these features, pg. 299, section III, A. System overview, right column, second paragraph].
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Buttner to incorporate the teachings of Chen to improve the accuracy of search results, as recognized by Chen.
Further, one skilled in the art could have combined the elements as described above with 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 Chen with Buttner to obtain the invention as specified in claim 2.
Regarding claim 3, which claim 2 is incorporated, Buttner fails to explicitly disclose wherein learning includes learning a relation between the feature amount of each time and the weight parameter for each time.
However, Chen teaches learning includes learning a relation between the feature amount of each time and the weight parameter for each time [we train a linear regression model to reflect the relationship between the global effective features and the combined weights of these features ... each feature in an action is time series data, pg. 299, section III, A. System overview, right column, second paragraph; pg. 301, section III, D. Linear regression model training, left column, second paragraph].
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Buttner to incorporate the teachings of Chen to improve the accuracy of search results, as recognized by Chen.
Further, one skilled in the art could have combined the elements as described above with 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 Chen with Buttner to obtain the invention as specified in claim 3.
Regarding claim 4, which claim 3 is incorporated, Buttner discloses calculating similarity between the processed feature amount of the object acquired and a feature amount of each of multiple pieces of motion data, and searches for motion data in reference to a result of similarity calculation [Figure 4A-4B; a similarity between pairs of hash codes (e.g., a hash code from within the motion library and a hash code from the active modified motion fragment) is quantified as a cosine similarity between each hash code in the pair ... the frame for the closest match to the active modified motion fragment from the motion library becomes the new target atom, para 0058].
Regarding claim 5, which claim 4 is incorporated, Buttner fails to explicitly disclose acquiring, in reference to the result of similarity calculation, a predetermined number of pieces of motion data as search results in order of high feature amount similarity with the processed feature amount.
However, Chen teaches acquiring, in reference to the result of similarity calculation, a predetermined number of pieces of motion data as search results in order of high feature amount similarity with the processed feature amount [DTW algorithm uses the predicted combined weights to calculate the similarity between the query and each action in the motion database ... candidate actions ranked by their similarity are returned to the user ... calculate the similarity between the query and each action in the motion database by DTW, and return the top-N ranked actions to the user, pg. 299, section III, A. System overview, right column, third paragraph; pg. 301, section III, E. Relevance feedback, right column, second paragraph].
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Buttner to incorporate the teachings of Chen to optimize search results, as recognized by Chen.
Further, one skilled in the art could have combined the elements as described above with 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 Chen with Buttner to obtain the invention as specified in claim 5.
Regarding claim 14, which claim 1 is incorporated, Buttner discloses wherein the weight parameter for a part of the object having a velocity [a motion fragment is conveniently represented as a matrix where each entry contains the velocity of a joint at a time. For each joint, a maximum velocity is determined over the input data. This information is used to normalize the velocities when constructing a motion fragment, so that each entry in the matrix falls into the range {−1, +1}, para 0041] greater than a predetermined value is set to be larger than the weight parameter for a part of the object having a velocity less than the predetermined value [an atom in the list has a weight (e.g., between 0 and 1) that represents the contribution of the atom to the active pose ... the dominant atom is an atom in the list that has the largest value of weight, para 0045, 0046].
Regarding claim 16, which claim 4 is incorporated, Buttner discloses wherein the similarity is calculated using a correlation coefficient between the processed feature amount and the feature amount of each of the multiple pieces of motion data [the similarity can be quantified using a cosine distance or cosine similarity that uses the dot product of the pair of hash codes divided by the product of the magnitude of the two hash codes ... to find the closest match between the hash code for the active motion fragment and a hash code from within the motion library (e.g., that represents a motion fragment similar to the active motion fragment), para 0058, 0056].
Regarding claim 19, which claim 1 is incorporated, Buttner discloses modifying a section of existing animation data by replacing the section with motion data found by the search [the future trajectory of the active motion fragment (e.g., generated at operation 406), is replaced by the predicted future trajectory from the trajectory prediction neural network to create a modified active motion fragment ... based on a cosine similarity between an active modified motion fragment (e.g., as modified in operation 410 and possibly operation 416) and a closest match to the active modified motion fragment (e.g., as determined at operation 418) in the motion library being less than the cosine similarity between the active modified motion fragment and an unmodified motion fragment (e.g., as determined at operation 406), the frame for the closest match to the active modified motion fragment from the motion library becomes the new target atom, para 0050, 0058].
Regarding claim 20, which claim 1 is incorporated, Buttner fails to explicitly disclose wherein obtaining the estimator includes learning a relation between the feature amount of each part and the weight parameter for each part.
However, Chen teaches wherein obtaining the estimator includes learning a relation between the feature amount of each part and the weight parameter for each part [we train a linear regression model to reflect the relationship between the global effective features and the combined weights of these features, pg. 299, section III, A. System overview, right column, second paragraph].
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Buttner to incorporate the teachings of Chen to improve the accuracy of search results, as recognized by Chen.
Further, one skilled in the art could have combined the elements as described above with 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 Chen with Buttner to obtain the invention as specified in claim 20.
Regarding claim 21, which claim 2 is incorporated, Buttner fails to explicitly disclose wherein obtaining the estimator includes learning a relation between the feature amount of each time and the weight parameter for each time.
However, Chen teaches learning includes learning a relation between the feature amount of each time and the weight parameter for each time [we train a linear regression model to reflect the relationship between the global effective features and the combined weights of these features ... each feature in an action is time series data, pg. 299, section III, A. System overview, right column, second paragraph; pg. 301, section III, D. Linear regression model training, left column, second paragraph].
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Buttner to incorporate the teachings of Chen to improve the accuracy of search results, as recognized by Chen.
Further, one skilled in the art could have combined the elements as described above with 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 Chen with Buttner to obtain the invention as specified in claim 21.
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Buttner (US 2012/0004887 A1) in view of Chen ("Partial similarity human motion retrieval based on relative geometry features") as applied above, and further in view of Iwao (US 2020/0005670 A1) (hereafter, “Iwao”).
Regarding claim 6, which claim 5 is incorporated, neither Buttner nor Chen appears to explicitly disclose wherein searching for the motion data includes comparing a processed feature amount calculated from time-series data concerning motion of a reference object obtained by converting a skeleton of the object into a reference skeleton to a feature amount calculated from reference motion data obtained by converting a skeleton of skeleton data into the reference skeleton.
However, Iwao teaches wherein searching for the motion data includes comparing a processed feature amount calculated from time-series data concerning motion of a reference object obtained by converting a skeleton of the object into a reference skeleton to a feature amount calculated from reference motion data obtained by converting a skeleton of skeleton data into the reference skeleton [Fig 3 & 5; the information processing apparatus 100 displays information relating to the motion of the learner and the information relating to the motion of the model in the analyzed data display area 302 so that the information relating to the motion of the learner can be compared with the information relating to the motion of the model ... the shape data and the skeleton data about the model and the learner are displayed in the image display area 501, para 0031, 0035].
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Buttner in view of Chen to incorporate the teachings of Iwao to compare and correct the processed feature amount, if necessary, as recognized by Chen.
Further, one skilled in the art could have combined the elements as described above with 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 Iwao with Buttner and Chen to obtain the invention as specified in claim 6.
Regarding claim 7, which claim 6 is incorporated, neither Buttner nor Chen appears to explicitly disclose for at least one part, correcting a feature amount of the motion data by mixing the processed feature amount with the feature amount of the motion data at a set ratio.
However, Iwao teaches for at least one part, correcting a feature amount of the motion data by mixing the processed feature amount with the feature amount of the motion data at a set ratio [Fig 5; the position correction unit 1006 automatically performs the positioning based on the reference joint. In a case where the automatic check box is not checked but a specific joint is designated, the position correction unit 1006 performs the positioning based on the designated joint ... the positioning for display is performed such that a rotation axis and a position of the reference joint HL of the learner and a rotation axis and a position of the reference joint HE of the model are superimposed on each other, para 0056].
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Buttner in view of Chen to incorporate the teachings of Iwao to be able to correct specific feature amounts, as recognized by Chen.
Further, one skilled in the art could have combined the elements as described above with 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 Iwao with Buttner and Chen to obtain the invention as specified in claim 7.
Regarding claim 8, which claim 7 is incorporated, Buttner discloses wherein the feature amount of each part of the object includes at least one of velocity, position, or posture [posture (the examiner interprets the claim limitation to require only one feature amount): the motion synthesizer generates an active pose (e.g., for the active frame) of a character using the list of atoms and associated weights, para 0045].
Claims 11–13 are rejected under 35 U.S.C. 103 as being unpatentable over Buttner (US 2012/0004887 A1) in view of Chen ("Partial similarity human motion retrieval based on relative geometry features") as applied above, and further in view of Fukumoto et al. (WO 2019/203188 A1) (hereafter, “Fukumoto”).
Regarding claim 11, which claim 1 is incorporated, neither Buttner nor Chen appears to explicitly disclose wherein the time-series data is acquired from one or more inertial sensors attached to the object.
However, Fukumoto teaches wherein the time-series data is acquired from one or more inertial sensors attached to the object [acceleration and angular velocity information can be obtained from two or more inertial sensors attached to two or more parts of the body, para 0044].
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Buttner in view of Chen to incorporate the teachings of Fukumoto to obtain information with higher accuracy, as recognized by Fukumoto.
Further, one skilled in the art could have combined the elements as described above with 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 Fukumoto with Buttner and Chen to obtain the invention as specified in claim 11.
Regarding claim 12, which claim 11 is incorporated, neither Buttner nor Chen appears to explicitly disclose wherein the time-series data is acquired from one or more inertial sensors attached to the object.
However, Fukumoto teaches wherein the time-series data is acquired from one or more inertial sensors attached to the object [wherein the number of inertial sensors is six [Figure 1; six sensor devices 10A to 10F are attached to six parts of the body of a user, para 0020].
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Buttner in view of Chen to incorporate the teachings of Fukumoto to obtain information with higher accuracy, as recognized by Fukumoto.
Further, one skilled in the art could have combined the elements as described above with 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 Fukumoto with Buttner and Chen to obtain the invention as specified in claim 12.
Regarding claim 13, which claim 12 is incorporated, neither Buttner nor Chen appears to explicitly disclose wherein one motion sensor among the plurality of motion sensors is attached to a waist of the user, two motion sensors among the plurality of motion sensors are attached to respective wrists of the user, two motion sensors among the plurality of motion sensors are attached to respective ankles of the user, one motion sensor among the plurality of motion sensors is attached to a head of the user.
However, Fukumoto teaches wherein one motion sensor among the plurality of motion sensors is attached to a waist of the user [a user U1 wears a sensor device 10A on his/her waist, para 0020], two motion sensors among the plurality of motion sensors are attached to respective wrists of the user [sensor devices 10B and 10E on his/her wrists, para 0020], two motion sensors among the plurality of motion sensors are attached to respective ankles of the user [sensor devices 10C and 10D on his/her ankles, para 0020], one motion sensor among the plurality of motion sensors is attached to a head of the user [a sensor device 10F on his/her head, para 0020].
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Buttner in view of Chen to incorporate the teachings of Fukumoto to obtain information with higher accuracy, as recognized by Fukumoto.
Further, one skilled in the art could have combined the elements as described above with 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 Fukumoto with Buttner and Chen to obtain the invention as specified in claim 13.
Claims 15 is rejected under 35 U.S.C. 103 as being unpatentable over Buttner (US 2012/0004887 A1) in view of Chen ("Partial similarity human motion retrieval based on relative geometry features") as applied above, and further in view of Jung (EP 0720384 A1) (hereafter, “Jung”).
Regarding claim 15, which claim 1 is incorporated, neither Buttner nor Chen appears to explicitly disclose wherein the similarity is calculated using a mean squared error between the processed feature amount and the feature amount of each of the multiple pieces of motion data.
However, Jung teaches wherein the similarity is calculated using a mean squared error between the processed feature amount and the feature amount of each of the multiple pieces of motion data [at each of the block matching sections 41 to 49, an error function employing a MSE measurement is calculated, using the weight function, between the search block from the current frame block formation section 20 and the candidate block from each of the candidate block formation sections 21 to 29 ... the error function indicates the degree of similarity between the search block and the selected candidate block, para 0017].
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Buttner in view of Chen incorporate the teachings of Jung to prevent discontinuities in motion, as recognized by Jung.
Further, one skilled in the art could have combined the elements as described above with 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 Jung with Buttner and Chen to obtain the invention as specified in claim 15.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Buttner (US 2012/0004887 A1) in view of Chen ("Partial similarity human motion retrieval based on relative geometry features") and further in view of Iwao (US 2020/0005670 A1) (hereafter, “Iwao”), as applied above, Fukumoto (WO 2019/203188 A1).
Regarding claim 17, which claim 7 is incorporated, neither Buttner, Chen, nor Iwao appears to explicitly disclose wherein correcting is based on a relative position of the at least one part from a waist of the object.
However, Fukumoto teaches wherein correcting is based on a relative position [the correction is performed by referring to the output of the first process (hereinafter also referred to as the first output) and the output of the second process (hereinafter also referred to as the second output). The first output includes posture information and position information of the attachment part, and the second output includes position information, para 0060] of the at least one part from a waist of the object [the sensor devices 10A to 10F include inertial sensors (IMU: Inertial Measurement Unit) ... a user U1 wears a sensor device 10A on his/her waist ... the part of the body to which the sensor device 10 is attached may also be referred to as the attachment part, para 0020].
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Buttner in view of Chen and further in view of Iwao to incorporate the teachings of Fukumoto to obtain information with higher accuracy, as recognized by Fukumoto.
Further, one skilled in the art could have combined the elements as described above with 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 Fukumoto with Buttner, Chen, and Iwao to obtain the invention as specified in claim 17.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Buttner (US 2012/0004887 A1) in view of Chen ("Partial similarity human motion retrieval based on relative geometry features"), as applied above, and further in view of Corazza (US 2012/0038628 A1).
Regarding claim 18, which claim 1 is incorporated, neither Buttner nor Chen appears to explicitly disclose concatenating a plurality of pieces of motion data found by the search to generate a single piece of animation data.
However, Corazza teaches concatenating a plurality of pieces of motion data found by the search to generate a single piece of animation data [a set of motions for concatenation is generated (1104) based upon the similarity of the motions to the selected motion. After the animator has selected (1106) an additional motion, motion data is generated or obtained with respect to the motion and concatenated (1108) with the motion data of the initial motion sequence ... the process repeats until an animator has obtained a desired sequence of concatenated motions. The motion data can then be retargeted and used to animate a 3D character, para 0070].
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Buttner in view of Chen to incorporate the teachings of Corazza to create smooth transitions, as recognized by Corazza.
Further, one skilled in the art could have combined the elements as described above with 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 Corazza with Buttner and Chen to obtain the invention as specified in claim 18.
Conclusion
The art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2024/0119087 A1 to Yoshida discloses an image processing apparatus that acquires frame images in time series, detects a keypoint of an object, computes a feature value for each of the frame images, computes a direction of change in the feature value, and searches for a moving image by using the computed direction of change in the feature value.
US 6,414,684 B1 to Mochizuki et al. discloses a method for generating animation data with time-series motion data for generating a motion of an object.
An Eigen-based motion retrieval method for real-time animation to Wang et al. discloses a motion retrieval method for real-time animation.
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/TOLUWANI MARY-JANE IJASEUN/Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676