DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This office action is responsive to the amendment received 12/08/2025.
In the response to the Non-Final Office Action, the applicant states that claims 1, 3, 4, 6-9, 11, 12, 15-17, 19, and 20 are amended. No claims are cancelled.
Claims 1, 3, 4, 6-9, 11, 12, 15-17, 19, and 20 have been amended. In summary, claims 1-20 are pending in current application.
Response to Arguments
Applicant's arguments filed 12/08/2025 have been fully considered but they are not persuasive.
Regarding to claim objections, the amendments have been cured the basis of claim objections. Therefore, the claim objections of claim 16 is hereby withdrawn.
Regarding to 35 U.S.C 112 (b) rejection, the amendments have cured the basis of 35 U.S.C 112 (b) rejection. Therefore, the 45 U.S.C 112 (b) rejection of claims 3, 4, 7, 11, 12, 15, and 19- 20 is hereby withdrawn.
Regarding to claim 1, the applicant argues that Brookshire discloses "computing features for the skeleton corresponding to the user-supplied character” as recited in claim 1. The arguments have been fully considered, but they are not persuasive. The examiner cannot concur with the applicant for following reasons:
Brookshire discloses “computing features for the skeleton corresponding to the user-supplied character”. For example, in paragraph [0049], Brookshire teaches the final output is the 3D position of each joint in the skeleton; Brookshire further teaches converting the skeleton to a skinned multi-person linear; Brookshire further more teaches computing and refining the skeleton against 3D point cloud data from Dense Stereo reconstruction. In Fig. 3 and paragraph [0061], Brookshire teaches determining a stacked hourglass skeleton 300 with features for an image of a human body;
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. In paragraph [0067], Brookshire teaches the resulting joint positions assume that the maximum of the heat map is the best joint location in 2D. In paragraph [0068], Brookshire teaches altering the resulting 3D estimate of the joint position. In paragraph [0070], Brookshire teaches jointly optimizing the position of each joint; Brookshire further teaches bi,j is the bone length from joint i to joint j. In paragraph [0071], Brookshire teaches optimizing and computing multi-view 3D skeleton using Skinned Multi-Person Linear techniques; Brookshire further teaches optimizing and computing multi-view 3D skeleton by the skeleton fitting module. In paragraph [0074], Brookshire teaches three orientation angles for each of the 16 skeleton joints. In paragraph [0077], Brookshire teaches an SMPL model mesh 504 is rigged to an SMPL skeleton 506. In paragraph [0080], Brookshire teaches adjusting the rigid body transform at the f-th frame and the joint angles at the f-th frame. In paragraph [0081], Brookshire teaches the smoothing is done without affecting the bone length; Brookshire further teaches computing the bone length. In paragraph [0092], Brookshire teaches identifying the pixel locations of the bounding box.
Regarding to claim 1, the applicant further argues that Sun does not teach or suggest "determining, from the computed features, a set of betas and a scale value that correspond to a skinned multi-person linear (SMPL) model of the skeleton corresponding to the computed features” as recited in claim 1. The arguments have been fully considered, but they are not persuasive. The examiner cannot concur with the applicant for following reasons:
Sun discloses “determining, from the computed features, a set of betas and a scale value that correspond to a skinned multi-person linear (SMPL) model of the skeleton corresponding to the features”. For example, in paragraph [0056], Sun teaches accurately predicting the shape parameters and pose parameters. In paragraph [0057], Sun teaches extracting multi-dimensional human body information such as two-dimensional joints, three-dimensional joints, a two-dimensional human body segmentation map, and three-dimensional voxels by using the human body information extraction model. In paragraph [0066], Sun teaches indicating a human body pose in predicted SMPL parameters corresponding to the SMPL parameter prediction model. In paragraph [0067], Sun teaches the pose parameter prediction model is a 72-dimensional parameter; Sun further teaches indicating rotation vectors of 24 joints of a human body. In paragraph [0069], Sun teaches obtaining a predicted shape parameter. In paragraph [0073], Sun teaches substituting the predicted pose parameter and the predicted shape parameter into the SMPL model; Sun further teaches subsequently evaluating the parameter prediction performance of the three-dimensional human body model. In paragraph [0081], Sun teaches obtaining a predicted pose parameter and a predicted shape parameter in the predicted SMPL parameters; Sun further teaches a three-dimensional human body model is constructed based on the predicted parameters. In paragraph [0082], Sun teaches measuring differences between the predicted pose parameter and predicted shape parameter and annotated SMPL parameters. In paragraph [0094], Sun teaches the predicted SMPL parameters are determined. In paragraph [0100], Sun teaches β is the predicted shape parameter. In Fig. 2 and paragraph [0111], Sun teaches the server generates a three-dimensional joint map 25 according to the three-dimensional human body model 24; Sun further teaches the three-dimensional joint map 25 contains three-dimensional predicted joint coordinates of each joint.
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; Sun further more teaches legs and arms in the skeleton as illstrated in Fig. 2. In Fig. 11, Fig. 12, and paragraph [0196], Sun teaches an improved accuracy and an increased recall rate of the reconstruction result, a higher degree of fitness.
Regarding to claim 1, the applicant further more argues that there is no teaching, suggestion, or motivation to combine the Brookshire and Sun. The arguments have been fully considered, but they are not persuasive. The examiner cannot concur with the applicant for following reasons:
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the motivation for combining the Brookshire and Sun would have been to accurately predict the shape parameters and pose parameters; to improve the accuracy of a reconstructed three-dimensional human body in the aspects of the human body pose and shape; to obtain a predicted pose parameter and a predicted shape parameter in the predicted SMPL parameters as taught by Sun in paragraphs [0056], [0058], and [0081]. The law does not require that the references be combined for the reasons contemplated by the inventor” {In re Beattie, 974 F.2d 1309, 1314 (Fed. Cir. 1992)(prior citations omitted)). In summary, the Examiner’s articulated reasoning provides a rational underpinning to support the legal conclusion of obviousness. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398,418 (2007).
Regarding to claim 1, the applicant argues that the combination as proposed by the Examiner appears to teach away from the claimed invention. The arguments have been fully considered, but they are not persuasive. The examiner cannot concur with the applicant for following reasons:
In response to applicant's argument that the combination as proposed by the Examiner appears to teach away from the claimed invention, the examiner recognizes that the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). In this case, the motivation for combining the Brookshire and Sun would have been to accurately predict the shape parameters and pose parameters; to improve the accuracy of a reconstructed three-dimensional human body in the aspects of the human body pose and shape; to obtain a predicted pose parameter and a predicted shape parameter in the predicted SMPL parameters as taught by Sun in paragraphs [0056], [0058], and [0081]. The law does not require that the references be combined for the reasons contemplated by the inventor” {In re Beattie, 974 F.2d 1309, 1314 (Fed. Cir. 1992)(prior citations omitted)). In summary, the Examiner’s articulated reasoning provides a rational underpinning to support the legal conclusion of obviousness. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398,418 (2007).
Claims 9 and 17 are similar to claim 1. Claims 9 and 17 are not allowable due to the similar reasons as discussed above.
Claims 2-8, 10-16, and 18-20 are not allowable due to the similar reasons as discussed above.
Claim Rejections - 35 USC § 103
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 1-7, 9-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Brookshire (US 20210019507 A1) and in view of Sun (US 20210232924 A1).
Regarding to claim 1 (Currently Amended), Brookshire discloses a system ([0049]: a skinned multi-person linear model; SMPL; Fig. 1a: [0050]: a human skeleton pose estimation system; [0070]: the skeleton fitting module 160 performs an optimization to the first, multi-view 3D skeleton; [0071]: the optimized 3D skeleton from the skeleton fitting module 160 is converted to a Skinned Multi-Person Linear, SMPL, representation; [0073]: the shape parameters determine height, and weight) comprising:
one or more computer processors ([0021]: a processor; Fig. 12; [0098]: one or more processors);
one or more computer memories ([0021]: a memory is coupled to the processor; the memory stored one of programs or instructions executable by the processor; [0098]: memory; [0101]: static random-access memory);
a set of instructions stored in the one or more computer memories, the set of instructions configuring the one or more computer processors to perform operations, the operations comprising ([0021]: a memory is coupled to the processor; the memory stored one of programs or instructions executable by the processor; [0100-0101]: processors 1210 execute instructions; system memory 1220 is configured to store program instructions):
accessing a skeleton corresponding to a user-supplied character ([0049]: extract an initial skeleton which represents the joints, e.g., ankle, knee, and hip, in 3D pixel coordinates; all 2D and single-view 3D skeletons are then processed using a multi-view fusion technique; Fig. 1a; [0060]: extract an initial skeleton; extract a 2D skeleton based on a stacked hourglass method; [0093]: determine a single-view 3D skeleton from the identified pixels; extract a 2D skeleton);
computing features for the skeleton corresponding to the user-supplied character ([0049]: the final output is the 3D position of each joint in the skeleton; Fig. 3; [0061]: determine a stacked hourglass skeleton 300 with features for an image of a human body;
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; [0067]: the resulting joint positions assume that the maximum of the heat map is the best joint location in 2D; [0068]: alter the resulting 3D estimate of the joint position; [0074]: three orientation angles for each of the 16 skeleton joints; [0077]: an SMPL model mesh 504 is rigged to an SMPL skeleton 506; [0092]: identify the pixel locations of the bounding box ), the features including a set of effectors ([0049]: the final output is the 3D position of each joint in the skeleton; Fig. 3; [0061]: determine a stacked hourglass skeleton 300 with features for an image of a human body;
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; [0067]: the resulting joint positions assume that the maximum of the heat map is the best joint location in 2D; [0074]: three orientation angles for each of the 16 skeleton joints; a position and orientation angles are effectors);
a skinned multi-person linear (SMPL) model ([0049]: a skinned multi-person linear model; SMPL; [0072]: SMPL is typically defined using at least three types of parameters); and
estimating a pose of a skeleton of a custom character using the SMPL model and the set of effectors ([0047]: estimate the pose of a human's skeleton; [0078]: the SMPL mesh from the skeleton conversion module 170 is refined against 3D point cloud data from the dense stereo module 140 in the 3D model fit module 180; [0081]: the smoothing is performed in the joint angle space of the SMPL model; [0085]: quantify the performance of determined skeletal poses of a human skeleton pose estimation system; Fig. 11; [0095]: multi-view 3D skeleton is optimized to determine a final 3D skeleton pose estimation for the human; determines an SMPL mesh and an SMPL skeleton for optimizing the first, multi-view 3D skeleton).
Brookshire fails to explicitly disclose:
determining, from the computed features, a set of betas and a scale value that correspond to a skinned multi-person linear (SMPL) model of the skeleton corresponding to the computed features.
In same field of endeavor, Sun teaches:
determining, from the computed features, a set of betas and a scale value that correspond to a skinned multi-person linear (SMPL) model of the skeleton corresponding to the features ([0056]: accurately predict the shape parameters and pose parameters; [0069]: obtain a predicted shape parameter; [0073]: substitute the predicted pose parameter and the predicted shape parameter into the SMPL model; [0081]: obtain a predicted pose parameter and a predicted shape parameter in the predicted SMPL parameters; [0100]: β is the predicted shape parameter; Fig. 11; Fig. 12; [0196]: an improved accuracy and an increased recall rate of the reconstruction result, a higher degree of fitness; [0201]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brookshire to include “determining, from the computed features, a set of betas and a scale value that correspond to a skinned multi-person linear (SMPL) model of the skeleton corresponding to the features” as taught by Sun. The motivation for doing so would have been to accurately predict the shape parameters and pose parameters; to improve the accuracy of a reconstructed three-dimensional human body in the aspects of the human body pose and shape; obtain a predicted pose parameter and a predicted shape parameter in the predicted SMPL parameters as taught by Sun in paragraphs [0056], [0058], and [0081].
Regarding to claim 2 (Original), Brookshire in view of Sun discloses the system of claim 1, wherein the determining of the set of betas and scale value includes using a machine learning model trained to infer the beta values and the scale value from the SMPL model (Sun; [0052]: pose parameter prediction model is a neural network model for predicting a human body pose in a picture; [0053]: shape parameter prediction model is a neural network model for predicting a human body shape; [0054]: machine learning; [0081]: a pose parameter prediction model and a shape parameter prediction model respectively to obtain a predicted pose parameter and a predicted shape parameter in the predicted SMPL parameters).
Same motivation of claim 1 is applied here.
Regarding to claim 3 (Currently Amended), Brookshire in view of Sun discloses the system of claim 2, wherein the inferred beta values and the scale value match computed features of the pose of the skeleton of the custom character (Sun; Fig. 11; Fig. 12; [0196]: an improved accuracy and an increased recall rate of the reconstruction result, a higher degree of fitness between the reconstruction result and the human body image in the original picture; Fig. 13; [0203]: the predicted SMPL parameters match the three-dimensional human body model; [0206]: the predicted SMPL parameters match annotated SMPL parameters in the annotation information).
Same motivation of claim 1 is applied here.
Regarding to claim 4 (Currently Amended), Brookshire in view of Sun discloses the system of claim 2, the operations further comprising the training the machine learning model, the training including using pairs of skeleton features extracted from a tuple along with corresponding supervision samples (Sun; [0054]: in the field of machine learning, information used for indicating a key parameter in a training sample is referred to as annotation information; the annotation information includes at least one of SMPL parameters, two-dimensional joint coordinates, three-dimensional joint coordinates, or a two-dimensional human body contour; [0061]: perform model training based on several sample picture sets; Fig. 3; [0093]: calculate a first model prediction loss according to predicted SMPL parameters and annotated SMPL parameters in annotation information; [0105]: calculate a second model prediction loss according to predicted joint coordinates of joints in the three-dimensional human body model and annotated joint coordinates of joints in the annotation information; Fig. 3; [0173]: reversely train the pose parameter prediction model and the shape parameter prediction model according to the model prediction losses).
Same motivation of claim 1 is applied here.
Regarding to claim 5 (Original), Brookshire in view of Sun discloses the system of claim 1, the operations further comprising accessing or generating a plurality of SMPL models having varying beta and scale values (Sun; [0048]: the SMPL parameters include shape parameters β and pose parameters θ; the shape parameters β∈custom-character.sup.10 include 10 parameters such as a height, size, and head-to-body ratio that characterize a human body; [0049-0050]: adjust the shape of the average human body model according to the shape parameters; [0056]: finally obtain SMPL parameters outputted by the parameter prediction model; Fig. 11; Fig. 12; [0196]: different shapes and sizes as illustrated in Fig. 11 and Fig. 12; an improved accuracy and an increased recall rate of the reconstruction result, a higher degree of fitness).
Regarding to claim 6 (Currently Amended), Brookshire in view of Sun discloses the system of claim 5, the operations further comprising computing joint positions for each of the accessed or generated plurality of SMPL models using the varying beta and scale values (Sun; [0048]: 72 parameters corresponding to 24 joints; [0049-0050]: adjust the shape of the average human body model according to the shape parameters; [0050]: calculate positions of joints of the human body; [0057]: perform joint training on the trained models; Fig. 11; Fig. 12; [0196]).
Same motivation of claim 1 is applied here.
Regarding to claim 7 (Currently Amended), Brookshire in view of Sun discloses the system of claim 6, the operations further comprising computing skeleton features for each of the plurality of SMPL models (Brookshire; [0049]: the final output is the 3D position of each joint in the skeleton; Fig. 3; [0061]: determine a stacked hourglass skeleton 300 with features for an image of a human body;
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; [0067]: the resulting joint positions assume that the maximum of the heat map is the best joint location in 2D; [0068]: alter the resulting 3D estimate of the joint position; [0074]: three orientation angles for each of the 16 skeleton joints; [0077]: an SMPL model mesh 504 is rigged to an SMPL skeleton 506; [0092]: identify the pixel locations of the bounding box ), the features including a set of effectors ([0049]: the final output is the 3D position of each joint in the skeleton; Fig. 3; [0061]: determine a stacked hourglass skeleton 300 with features for an image of a human body;
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; [0067]: the resulting joint positions assume that the maximum of the heat map is the best joint location in 2D; [0074]: three orientation angles for each of the 16 skeleton joints; a position and orientation angles are effectors) and training a machine learning model, the training including matching the computed skeleton features with the pose of the skeleton of the custom character (Sun; Sun; Fig. 11; Fig. 12; [0196]: an improved accuracy and an increased recall rate of the reconstruction result, a higher degree of fitness between the reconstruction result and the human body image in the original picture; Fig. 13; [0203]: the predicted SMPL parameters match the three-dimensional human body model; [0206]: the predicted SMPL parameters match annotated SMPL parameters in the annotation information).
Regarding to claim 9 (Currently Amended), Brookshire discloses a method ([0049]: a skinned multi-person linear model; SMPL; Fig. 1a: [0050]: a human skeleton pose estimation system; [0070]: the skeleton fitting module 160 performs an optimization to the first, multi-view 3D skeleton; [0071]: the optimized 3D skeleton from the skeleton fitting module 160 is converted to a Skinned Multi-Person Linear, SMPL, representation; [0073]: the shape parameters determine height, and weigh) comprising:
The rest claim limitations are similar to claim limitations recited in claim 1. Therefore, same rational used to reject claim 1 is also used to reject claim 9.
Regarding to claim 10 (Original), Brookshire in view of Sun discloses the method of claim 9,
The rest claim limitations are similar to claim limitations recited in claim 2. Therefore, same rational used to reject claim 2 is also used to reject claim 10.
Regarding to claim 11 (Currently Amended), Brookshire in view of Sun discloses the method of claim 9,
The rest claim limitations are similar to claim limitations recited in claim 3. Therefore, same rational used to reject claim 3 is also used to reject claim 11.
Regarding to claim 12 (Currently Amended), Brookshire in view of Sun discloses the method of claim 9,
the rest claim limitations are similar to claim limitations recited in claim 4. Therefore, same rational used to reject claim 4 is also used to reject claim 12.
Regarding to claim 13 (Original), Brookshire in view of Sun discloses the method of claim 9,
the rest claim limitations are similar to claim limitations recited in claim 5. Therefore, same rational used to reject claim 5 is also used to reject claim 13.
Regarding to claim 14 (Original), Brookshire in view of Sun discloses the method of claim 13,
the rest claim limitations are similar to claim limitations recited in claim 6. Therefore, same rational used to reject claim 6 is also used to reject claim 14.
Regarding to claim 15 (Currently Amended), Brookshire in view of Sun discloses the method of claim 14,
the rest claim limitations are similar to claim limitations recited in claim 7. Therefore, same rational used to reject claim 7 is also used to reject claim 15.
Regarding to claim 17 (Currently Amended), Brookshire discloses a non-transitory computer-readable storage medium storing a set of instructions that, when executed by one or more computer processors, causes the one or more computer processors to perform operations, the operations comprising ([0021]: a memory is coupled to the processor; the memory stored one of programs or instructions executable by the processor; [0049]: a skinned multi-person linear model; SMPL; Fig. 1a: [0050]: a human skeleton pose estimation system; [0070]: the skeleton fitting module 160 performs an optimization to the first, multi-view 3D skeleton; [0071]: the optimized 3D skeleton from the skeleton fitting module 160 is converted to a Skinned Multi-Person Linear, SMPL, representation; [0073]: the shape parameters determine height, and weight; [0100-0101]: processors 1210 execute instructions; system memory 1220 is configured to store program instructions):
The rest claim limitations are similar to claim limitations recited in claim 1. Therefore, same rational used to reject claim 1 is also used to reject claim 17.
Regarding to claim 18 (Original), Brookshire in view of Sun discloses the non-transitory computer-readable storage medium of claim 17,
The rest claim limitations are similar to claim limitations recited in claim 2. Therefore, same rational used to reject claim 2 is also used to reject claim 18.
Regarding to claim 19 (Currently Amended), Brookshire in view of Sun discloses the non-transitory computer-readable storage medium of claim 17,
The rest claim limitations are similar to claim limitations recited in claim 3. Therefore, same rational used to reject claim 3 is also used to reject claim 19.
Regarding to claim 20 (Currently Amended), Brookshire in view of Sun discloses the non-transitory computer-readable storage medium of claim 17,
The rest claim limitations are similar to claim limitations recited in claim 4. Therefore, same rational used to reject claim 4 is also used to reject claim 20.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Brookshire (US 20210019507 A1) in view of Sun (US 20210232924 A1), and Ostadabbas (US 20220051437 A1).
Regarding to claim 8 (Currently Amended), Brookshire in view of Sun discloses the system of claim 1,
Brookshire in view of Sun further discloses generating effectors for inclusion in the set of effectors (Brookshire; [0049]: the final output is the 3D position of each joint in the skeleton; Fig. 3; [0061]: determine a stacked hourglass skeleton 300 with features for an image of a human body;
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; [0067]: the resulting joint positions assume that the maximum of the heat map is the best joint location in 2D; [0068]: alter the resulting 3D estimate of the joint position; [0074]: three orientation angles for each of the 16 skeleton joints; [0077]: an SMPL model mesh 504 is rigged to an SMPL skeleton 506; [0092]: identify the pixel locations of the bounding box).
Brookshire in view of Sun fails to explicitly disclose: the operations further comprising using an iterative effector recovery process to generate a minimum number of effectors.
In same field of endeavor, Ostadabbas teaches:
the operations further comprising using an iterative effector recovery process to generate a minimum number of effectors ([0090]: update the regression model by minimizing an error in the updated estimate from the two-dimensional pose annotation data and the joint depth-based proxy data; [0091]: repeat steps (f) through (g) for a determined number of iterations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Brookshire in view of Sun to include the operations further comprising using an iterative effector recovery process to generate a minimum number of effectors as taught by Ostadabbas. The motivation for doing so would have been to update the regression model by minimizing an error in the updated estimate from the two-dimensional pose annotation data and the joint depth-based proxy data; to achieve improved accuracy on existing benchmarks by 3D human pose estimation as taught by Ostadabbas in paragraphs [0090] and [0199].
Regarding to claim 16 (Currently Amended), Brookshire in view of Sun discloses the method of claim 8,
The rest claim limitations are similar to claim limitations recited in claim 8. Therefore, same rational used to reject claim 8 is also used to reject claim 16.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hai Tao Sun whose telephone number is (571)272-5630. The examiner can normally be reached 9:00AM-6:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Hajnik can be reached at 5712727642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAI TAO SUN/Primary Examiner, Art Unit 2616