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 action is in response to the amendment filed on 31st August, 2026. Claim 10 has been amended. Claims 1-9 and 14-24 have been cancelled. Claims 25-44 have been added. Claims 10-13 and 25-44 remain rejected in the application.
Response to Arguments
Applicant's arguments with respect to Claims 10, 33, and 39 filed on 31st August, 2026, with respect to the rejection under 35 U.S.C. § 103, regarding that the prior art does not teach the limitation(s): "determining, by the computing system, a plurality of skin deformations of the mesh model at a plurality of positions of the plurality of skeletal segments based on a compressibility of the plurality of medial volumes" and "generating, by the computing system, based on the plurality of skin deformations of the mesh model of the avatar and a type of material associated with the wearable asset, a deformable mesh model of the wearable asset" have been fully considered, but are moot because of new grounds for rejection. It has now been taught by the combination of Kanaujia, E, and Santesteban.
Regarding arguments to Claims 11-13, 25-32, 34-38, and 40-44, they directly/indirectly depend on independent Claims 10, 33, and 39 respectively. Applicant does not argue anything other than independent Claims 10, 33, and 39. The limitations in those claims, in conjunction with combination, was previously established as explained.
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 10-12, 25-35, 37-41, and 43-44 are rejected under 35 U.S.C. 103 as being unpatentable over Kanaujia et al. (US 20130250050 A1, previously cited), hereinafter referenced as Kanaujia, in view of E et al. (US 20240221287 A1, previously cited), hereinafter referenced as E, and further in view of Santesteban et al. (US 20210118239 A1), hereinafter referenced as Santesteban.
Regarding Claim 10, Kanaujia discloses a computer-implemented method of generating wearable assets for avatars (Kanaujia, [0091]: teaches a method for generating detailed 3D human models <read on avatars>; [0124]: teaches processing the shape and size of a carried accessory <read on wearable asset>), the method comprising:
receiving, by a computing system comprising one or more processors, a wearable asset associated with an avatar (Kanaujia, [0086]: teaches "module 108 may use the estimated pose and shape of the human object <read on avatar> to automatically identify disproportionate body parts, detecting accessories <read on wearable asset> (e.g., a backpack, suitcase, purse, etc.), the size of the detected accessories, and/or to infer attributes of the human object, such as gender, age and ethnicity"; [0080]: teaches modules 103-108 of the system, as well as their components, are implemented with hardware circuitry, such as one or more processors);
receiving, by the computing system, a mesh model of the avatar (Kanaujia, [0091]: teaches estimating a final pose and shape of a deformable 3D human model <read on mesh model>), wherein
the mesh model of the avatar is associated with a hierarchical skeleton comprising a plurality of skeletal segments and a plurality of medial volumes (Kanaujia, [0107]: teaches "a course 3D human shape model 320 comprised of a plurality of cylindrical body parts 322a <read on medial volumes> individually mapped to align with segments <read on skeletal segments> of a skeleton 324a" as shown in FIG. 3B; [0115]: teaches sampling angular priors of the joints in a skeletal hierarchy (such as shoulder and femur skeletal joints) of the 3D human shape model);
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determining, by the computing system, a plurality of skin deformations of the mesh model at a plurality of positions of the plurality of skeletal segments [[based on a compressibility of the plurality of medial volumes]] (Kanaujia, [0116]: teaches the 3D mesh surface undergoing deformation <read on skin deformation> only under the influence of the skeleton attached to it, where "the shape deformation due to pose may be obtained by first skinning the 3D mesh to the skeleton and transforming the vertices under the influence of associated skeletal joints <read on positions of skeletal segments>," which are associated to different body segments <read on plurality of positions>; Note: it should be noted that the shape deformation is being interpreted as being applied to each skeletal segment <read on plurality of skin deformations>); and
[[generating, by the computing system, based on the plurality of skin deformations of the mesh model of the avatar and a type of material associated with the wearable asset, a deformable mesh model of the wearable asset.]]
However, Kanaujia does not expressly disclose
determining, by the computing system, a plurality of skin deformations of the mesh model at a plurality of positions of the plurality of skeletal segments based on a compressibility of the plurality of medial volumes; and
generating, by the computing system, based on the plurality of skin deformations of the mesh model of the avatar and a type of material associated with the wearable asset, a deformable mesh model of the wearable asset.
E discloses
determining, by the computing system, a plurality of skin deformations of the mesh model at a plurality of positions of the plurality of skeletal segments based on a compressibility of the plurality of medial volumes (E, [0110]: teaches each mesh vertex of the virtual clothing being bound to at least one bone <read on medial volume> in the skeleton model <read on skeletal segments> of the target object, and the impact weight <read on compressibility> of the bone on each mesh vertex being configured so that the skin information for the one at least one bone in the skeleton model is generated); and
[[generating, by the computing system, based on the plurality of skin deformations of the mesh model of the avatar and a type of material associated with the wearable asset, a deformable mesh model of the wearable asset.]]
E is analogous art with respect to Kanaujia because they are from the same field of endeavor, namely deforming 3D meshes for humanoid figures. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to deform a virtual clothing model based on a target object's posture (i.e., the pose of a 3D humanoid mesh) as taught by E into the teaching of Kanaujia. The suggestion for doing so would allow for a more accurate alignment result due to the acquisition of target positions of each mesh vertex of the virtual clothing, thereby improving the generated output. Therefore, it would have been obvious to combine E with Kanaujia.
However, the combination of Kanaujia and E does not expressly disclose
generating, by the computing system, based on the plurality of skin deformations of the mesh model of the avatar and a type of material associated with the wearable asset, a deformable mesh model of the wearable asset.
Santesteban discloses
generating, by the computing system, based on the plurality of skin deformations of the mesh model of the avatar and a type of material associated with the wearable asset, a deformable mesh model of the wearable asset (Santesteban, [0071]: teaches several material parameters <read on type of material> for a garment mesh <read on wearable asset>; [0058]: teaches learning-based cloth deformation models that can be shaped based on the body shape, where "the shape of the body produces an overall deformation in the form of stretch or relaxation, caused by tight or oversized garments, respectively").
Santesteban is analogous art with respect to Kanaujia, in view of E because they are from the same field of endeavor, namely machine learning models that are trained to deform 3D human body models. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a material parameter attribute for garments as part of the learning process for the machine learning model as taught by Santesteban into the teaching of Kanaujia, in view of E. The suggestion for doing so would allow the neural network to determine a more natural look and fit for virtual garments/clothes on different types of bodies and body parts, such as the stretch and wrinkles of the virtual garment/clothes, thereby avoiding unrealistic output results. Therefore, it would have been obvious to combine Santesteban with Kanaujia, in view of E.
Regarding Claim 33, it recites the limitations that are similar in scope to Claim 10, but in one or more tangible non-transitory computer-readable media. As shown in the rejection, the combination of Kanaujia, E, and Santesteban discloses the limitations of Claim 10. Additionally, Kanaujia discloses one or more tangible, non-transitory computer-readable media storing computer-readable instructions that when executed by one or more processors cause the one or more processors to perform operations (Kanaujia, [0079]: teaches a storage module, such as a computer readable medium that is non-volatile <read on non-transitory computer-readable media>; [0080]: teaches one or more processors of a computer that performs various algorithms <read on operations> on received data and execute instructions <read on computer-readable instructions>), the operations comprising:…
Thus, Claim 33 is met by Kanaujia according to the mapping presented in the rejection of Claim 10, given the computer-implemented method corresponds to one or more tangible non-transitory computer-readable media.
Regarding Claim 39, it recites the limitations that are similar in scope to Claim 10, but in a computing system. As shown in the rejection, the combination of Kanaujia, E, and Santesteban discloses the limitations of Claim 10. Additionally, Kanaujia discloses a computing system (Kanaujia, [0080]: teaches a computer <read on computing system>) comprising:
one or more processors (Kanaujia, [0080]: teaches one or more processors of the computer);
one or more non-transitory computer-readable media storing instructions that when executed by the one or more processors cause the one or more processors to perform operations (Kanaujia, [0079]: teaches a storage module, such as a computer readable medium that is non-volatile <read on non-transitory computer-readable media>; [0080]: teaches one or more processors of the computer that performs various algorithms <read on operations> on received data and execute instructions <read on computer-readable instructions>) comprising:…
Thus, Claim 39 is met by Kanaujia according to the mapping presented in the rejection of Claim 10, given the computer-implemented method corresponds to a computing system.
Regarding Claims 11, 34, and 40, the combination of Kanaujia, E, and Santesteban discloses the computer-implemented method, the one or more tangible non-transitory computer-readable media, and the computing system of Claims 10, 33, and 39 respectively. The combination of Kanaujia and E does not expressly disclose the limitations of Claims 11, 34, and 40; however, Santesteban discloses wherein the generating, by the computing system, based on the plurality of skin deformations of the mesh model of the avatar, a deformable mesh model of the wearable asset comprises:
generating, by the computing system, the mesh model of the avatar based on inputting the wearable asset and the plurality of skin deformations of the avatar into one or more machine-learning models that are configured to generate the deformable mesh model of the wearable asset (Santesteban, [0071]: teaches several material parameters for a garment mesh <read on wearable asset>; [0058]: teaches learning-based <read on machine-learning model> cloth deformation models <read on deformable mesh model> that can be shaped based on the body shape, where "the shape of the body produces an overall deformation in the form of stretch or relaxation, caused by tight or oversized garments, respectively").
Santesteban is analogous art with respect to Kanaujia, in view of E because they are from the same field of endeavor, namely machine learning models that are trained to deform 3D human body models. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a material parameter attribute for garments as part of the learning process for the machine learning model as taught by Santesteban into the teaching of Kanaujia, in view of E. The suggestion for doing so would allow the neural network to determine a more natural look and fit for virtual garments/clothes on different types of bodies and body parts, such as the stretch and wrinkles of the virtual garment/clothes, thereby avoiding unrealistic output results. Therefore, it would have been obvious to combine Santesteban with Kanaujia, in view of E.
Regarding Claims 12, 35, and 41, the combination of Kanaujia, E, and Santesteban discloses the computer-implemented method, the one or more tangible non-transitory computer-readable media, and the computing system of Claims 10, 33, and 39 respectively. Additionally, Kanaujia further discloses wherein the determining, by the computing system, a plurality of skin deformations of the mesh model at a plurality of positions of the plurality of skeletal segments comprises:
determining, by the computing system, the plurality of skin deformations based on inputting the mesh model of the avatar at the plurality of positions into one or more machine-learning models that are configured to determine the plurality of skin deformations (Kanaujia, [0099]: teaches the system <read on machine-learning models> analyzing input images/video and identifying an anomalous shape of the target as an accessory (i.e., a backpack), where the accessory is "removed from estimations <read on determined skin deformations> in creating the coarse 3D human models and creating the detailed 3D human models"; [0116]: teaches the 3D mesh surface undergoing deformation <read on skin deformation> only under the influence of the skeleton attached to it, where "the shape deformation due to pose may be obtained by first skinning the 3D mesh to the skeleton and transforming the vertices under the influence of associated skeletal joints," which are associated to different body segments <read on plurality of positions>; Note: it should be noted that the shape deformation is being interpreted as being applied to each skeletal segment <read on plurality of skin deformations>).
Regarding Claim 25, the combination of Kanaujia, E, and Santesteban discloses the computer-implemented method of Claim 10. Additionally, Kanaujia further discloses wherein the determining, by the computing system, a plurality of skin deformations of the mesh model at a plurality of positions of the plurality of skeletal comprises:
inputting, by the computing system, the mesh model of the avatar at the plurality of positions into one or more machine-learning models that are configured to determine the plurality of skin deformations (Kanaujia, [0116]: teaches the 3D mesh surface undergoing deformation <read on skin deformation> only under the influence of the skeleton attached to it, where "the shape deformation due to pose may be obtained by first skinning the 3D mesh to the skeleton and transforming the vertices under the influence of associated skeletal joints," which are associated to different body segments <read on plurality of positions> and learned <read on machine-learning model>).
Regarding Claim 26, the combination of Kanaujia, E, and Santesteban discloses the computer-implemented method of Claim 10. The combination of Kanaujia and E does not expressly disclose the limitations of Claim 26; however, Santesteban discloses wherein the generating, by the computing system, based on the plurality of skin deformations of the mesh model of the avatar, a deformable mesh model of the wearable asset comprises:
determining, by the computing system, that dimensions of the deformable mesh model of the wearable asset are not greater than one or more size thresholds (Santesteban, [0052]: teaches approximating the deformation of a garment by scaling it <read on dimensions of deformable mesh model> such that garment 302a is smaller in size <read on size threshold> than garment 302b adjusted in size to fit the target shape).
Santesteban is analogous art with respect to Kanaujia, in view of E because they are from the same field of endeavor, namely machine learning models that are trained to deform 3D human body models. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a material parameter attribute for garments as part of the learning process for the machine learning model as taught by Santesteban into the teaching of Kanaujia, in view of E. The suggestion for doing so would allow the neural network to determine a more natural look and fit for virtual garments/clothes on different types of bodies and body parts, such as the stretch and wrinkles of the virtual garment/clothes, thereby avoiding unrealistic output results. Therefore, it would have been obvious to combine Santesteban with Kanaujia, in view of E.
Regarding Claim 27, the combination of Kanaujia, E, and Santesteban discloses the computer-implemented method of Claim 26. The combination of Kanaujia and E does not expressly disclose the limitations of Claim 27; however, Santesteban discloses wherein
the one or more size thresholds are based on sizes of the plurality of medial volumes (Santesteban, [0052]: teaches approximating the deformation of a garment by scaling it such that garment 302a is smaller in size <read on size threshold> than garment 302b adjusted in size to fit the target shape <read on medial volumes>).
Santesteban is analogous art with respect to Kanaujia, in view of E because they are from the same field of endeavor, namely machine learning models that are trained to deform 3D human body models. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a material parameter attribute for garments as part of the learning process for the machine learning model as taught by Santesteban into the teaching of Kanaujia, in view of E. The suggestion for doing so would allow the neural network to determine a more natural look and fit for virtual garments/clothes on different types of bodies and body parts, such as the stretch and wrinkles of the virtual garment/clothes, thereby avoiding unrealistic output results. Therefore, it would have been obvious to combine Santesteban with Kanaujia, in view of E.
Regarding Claims 28, 37, and 43, the combination of Kanaujia, E, and Santesteban discloses the computer-implemented method, the one or more tangible non-transitory computer-readable media, and the computing system of Claims 10, 33, and 39 respectively. The combination of Kanaujia and E does not expressly disclose the limitations of Claims 28, 37, and 43; however, Santesteban discloses wherein
the plurality of skin deformations of the mesh model at the plurality of positions of the plurality of skeletal segments are based on a stretchiness or firmness of the plurality of medial volumes (Santesteban, [0059]: teaches learning-based cloth deformation models applying corrective displacements on an unposed cloth state of a virtual garment, where "the shape of the body produces an overall deformation in the form of stretch or relaxation, caused by tight or oversized garments, respectively"; [0056]: teaches a skinning weight matrix is defined by projecting each vertex of the template cloth mesh onto the closest triangle of the template body mesh <read on positions of skeletal segments>, and interpolating the body skinning weights <read on medial volumes>).
Santesteban is analogous art with respect to Kanaujia, in view of E because they are from the same field of endeavor, namely machine learning models that are trained to deform 3D human body models. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a material parameter attribute for garments as part of the learning process for the machine learning model as taught by Santesteban into the teaching of Kanaujia, in view of E. The suggestion for doing so would allow the neural network to determine a more natural look and fit for virtual garments/clothes on different types of bodies and body parts, such as the stretch and wrinkles of the virtual garment/clothes, thereby avoiding unrealistic output results. Therefore, it would have been obvious to combine Santesteban with Kanaujia, in view of E.
Regarding Claim 29, the combination of Kanaujia, E, and Santesteban discloses the computer-implemented method of Claim 10. Additionally, Kanaujia further discloses wherein the wearable asset comprises
virtual clothing, a virtual accessory, or a virtual bag (Kanaujia, [0094]: teaches virtual accessories being a backpack <read on virtual bag>, a briefcase, a purse, a handheld suitcase, a wheeled suitcase, etc.).
Regarding Claims 30, 38, and 44, the combination of Kanaujia, E, and Santesteban discloses the computer-implemented method, the one or more tangible non-transitory computer-readable media, and the computing system of Claims 10, 33, and 39 respectively. The combination of Kanaujia and E does not expressly disclose the limitations of Claims 30, 38, and 44; however, Santesteban discloses wherein the generating, by the computing system, based on the plurality of skin deformations of the mesh model of the avatar, a deformable mesh model of the wearable asset comprises:
inputting, by the computing system, the wearable asset and the plurality of skin deformations of the avatar into one or more machine-learning models that are configured to generate the deformable mesh model of the wearable asset (Santesteban, [0054]: teaches a machine-learning model learning the complex non-linear deformations of clothing for modeling cloth deformations for given garments to generate said cloth deformations <read on generated deformable mesh model of wearable asset>, where the cloth mesh is deformed using a skinning function <read on skin deformations of avatar>).
Santesteban is analogous art with respect to Kanaujia, in view of E because they are from the same field of endeavor, namely machine learning models that are trained to deform 3D human body models. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a material parameter attribute for garments as part of the learning process for the machine learning model as taught by Santesteban into the teaching of Kanaujia, in view of E. The suggestion for doing so would allow the neural network to determine a more natural look and fit for virtual garments/clothes on different types of bodies and body parts, such as the stretch and wrinkles of the virtual garment/clothes, thereby avoiding unrealistic output results. Therefore, it would have been obvious to combine Santesteban with Kanaujia, in view of E.
Regarding Claim 31, the combination of Kanaujia, E, and Santesteban discloses the computer-implemented method of Claim 30. The combination of Kanaujia and E does not expressly disclose the limitations of Claim 31; however, Santesteban discloses wherein the one or more machine-learned models are configured to
perform one or more deformation prediction operations to determine shapes of the deformable mesh model of the wearable asset at the plurality of positions (Santesteban, [0085]: teaches predicting <read on deformation prediction operations> cloth deformation of a garment <read on determined shapes of deformable mesh model of wearable asset> directly as a function of a pose of the body shape).
Santesteban is analogous art with respect to Kanaujia, in view of E because they are from the same field of endeavor, namely machine learning models that are trained to deform 3D human body models. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a material parameter attribute for garments as part of the learning process for the machine learning model as taught by Santesteban into the teaching of Kanaujia, in view of E. The suggestion for doing so would allow the neural network to determine a more natural look and fit for virtual garments/clothes on different types of bodies and body parts, such as the stretch and wrinkles of the virtual garment/clothes, thereby avoiding unrealistic output results. Therefore, it would have been obvious to combine Santesteban with Kanaujia, in view of E.
Regarding Claim 32, the combination of Kanaujia, E, and Santesteban discloses the computer-implemented method of Claim 10. The combination of Kanaujia and E does not expressly disclose the limitations of Claim 32; however, Santesteban discloses
modifying, by the computing system, the wearable asset to conform to the plurality of skin deformations of the mesh model of the avatar (Santesteban, [0085]: teaches applying kinematic transformations of the underlying skeleton directly to the garment template mesh, which is interpreted to be conforming the wearable asset to the mesh model of the avatar).
Santesteban is analogous art with respect to Kanaujia, in view of E because they are from the same field of endeavor, namely machine learning models that are trained to deform 3D human body models. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a material parameter attribute for garments as part of the learning process for the machine learning model as taught by Santesteban into the teaching of Kanaujia, in view of E. The suggestion for doing so would allow the neural network to determine a more natural look and fit for virtual garments/clothes on different types of bodies and body parts, such as the stretch and wrinkles of the virtual garment/clothes, thereby avoiding unrealistic output results. Therefore, it would have been obvious to combine Santesteban with Kanaujia, in view of E.
Claims 13, 36, and 42 are rejected under 35 U.S.C. 103 as being unpatentable over Kanaujia et al. (US 20130250050 A1, previously cited), hereinafter referenced as Kanaujia, in view of E et al. (US 20240221287 A1, previously cited), hereinafter referenced as E, and further in view of Santesteban et al. (US 20210118239 A1), hereinafter referenced as Santesteban as applied to Claims 10, 33, and 39 above respectively and further in view of Villegas et al. (US 20220020199 A1, previously cited), hereinafter referenced as Villegas.
Regarding Claims 13, 36, and 42, the combination of Kanaujia, E, and Santesteban discloses the computer-implemented method, the one or more tangible non-transitory computer-readable media, and the computing system of Claims 10, 33, and 39 respectively. The combination of Kanaujia, E, and Santesteban does not expressly disclose the limitations of Claims 13, 36, and 42; however, Villegas discloses wherein the plurality of positions of the plurality of skeletal segments are based on
one or more range of motion parameters of the hierarchical skeleton (Villegas, [0041]: teaches performing motion retargeting with kinematic constraints <read on range of motion parameters> using a digital skeleton <read on hierarchical skeleton> that includes multiple joints 204 (204a-204g) that connects different structural members (e.g., limbs) of the digital skeleton; Note: it should be noted that kinematic constraints are mathematical, geometric, and/or physical restrictions placed on the motion of rigid bodies, which clamps their degrees of freedom in motion).
Villegas is analogous art with respect to the combination of Kanaujia, E, and Santesteban because they are from the same field of endeavor, namely deforming 3D humanoid models. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement kinematic constraints for each skeletal joint of a 3D humanoid model as taught by Villegas into the combined teaching of Kanaujia, E, and Santesteban. The suggestion for doing so would restrict the movement of each limb, thereby allowing for more natural movement and yielding predictable results. Therefore, it would have been obvious to combine Villegas with the combination of Kanaujia, E, and Santesteban.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Eisemann et al. (US 20170032579 A1) discloses skinning 3D meshes with reference to a skeleton utilizing statistical weight optimization techniques;
Ju (US 20210350619 A1) discloses displaying clothing patterns that determine attributes of bounding volumes for each body part;
Makeev et al. (US 20230120883 A1) discloses inferring skeletal structure for creating a practical 3D asset;
Makeev et al. (US 20220292791 A1) discloses an automated cage-to-cage fitting technique for fitting arbitrary 3D geometry to arbitrary target 3D geometry for 3D avatars in a 3D environment;
Oh (US 20130057544 A1) discloses automatic 3D clothing transfer for a given avatar;
Sminchisescu et al. (US 20190371080 A1) discloses generating an image of a person in a body pose and clothing;
Tamstorf et al. (US 20120281019 A1) discloses an algorithmic framework for simulating hyper-elastic soft tissue based on the kinematics of a computer-generated object; and
Tiwari et al. (US 20220368882 A1) discloses draping a 3D garment on a 3D human body.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 KARL TRUONG whose telephone number is (703)756-5915. The examiner can normally be reached 10:30 AM - 7:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kent Chang can be reached at (571) 272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.D.T./Examiner, Art Unit 2614
/KENT W CHANG/Supervisory Patent Examiner, Art Unit 2614
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