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
Claims 1, 3, 7-8, 11, 16 and 20 are amended.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/06/2024 has been being considered by the examiner.
Drawings
The drawings were received on 05/11/2026. These drawings are acceptable.
Specification
The specification corrections were received on 05/11/2026. The specification is acceptable.
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.
Claim(s) 1-8, 11-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mora (MORA (No. US-20150178988-A1 “Mora”) in view of CHENG (No. JP-2019126705-A “Cheng”) and in further view of SUN (No. CN-115769259-A “Sun”).
Regarding claim 1, Mora teaches “A computer-implemented method for generating an articulation model, the method comprising:” (computer-implemented method for determining a shape of a 3D object from imagery);
(a method for generating a realistic 3D reconstruction model for an object; 0001);
“performing one or more operations to generate first three-dimensional (3D) geometry based on the first set of images;” (generating a mesh of said an object or being from said sequence of images captured; 0049);
“performing one or more operations to generate second 3D geometry based on the second set of images; and” (generating a mesh of said an object or being from said sequence of images captured; 0049);
While, Mora does not teach “receiving a first set of images of an object in a first articulation and a second set of images of the object in a second articulation;”.
Cheng teaches “receiving a first set of images of an object in a first articulation and a second set of images of the object in a second articulation;” (obtaining image information of a captured first set of images and an Nth set of images … N is an integer of 2 or more; 0005);
(the first set of images and the Nth set of Superimposing the image on the image; 0005);
The motivation for the above is to have accessible images for a more efficient and accurate 3D generation of articulation model.
However, Mora and Chen do not teach “performing one or more training operations to update one or more parameters of an articulation model of the object based on the first 3D geometry and the second 3D geometry.”
Sun teaches “performing one or more training operations to update one or more parameters of an articulation model of the object based on the first 3D geometry and the second 3D geometry.”
(Training computing system 150 may include model trainer 160 that trains machine-learned model 120 .... a loss function can be backpropagated through the model to update one or more parameters of the model; Pg. 6, Para 9); (used to update ... articulation parameters; Pg. 8, Para 7);
(the mesh model may include various shapes.... the mesh model can be a polygonal mesh... may include a collection of vertices.... defining the shape of the polyhedral object; Pg. 4, Para 4); (Given certain parameters (eg, predicted articulation parameters Dt 608 , skin weights W 606 , etc.), the linear blend skinning process 614 may output an articulated rest shape 612; Pg. 8 Para 5); (rendering may output a rendering 618 which may be input 620 into a loss function 628 . Ground-truth pixels, ground-truth optical flow 624 , ground-truth segmentation {St} 622 are also input 626 into loss function 628; Pg. 8, Para 6);
Sun discloses a model trainer that trains a function to update one or more parameters which teaches the claimed subject matter of one or more training operations to update one or more parameters of an articulation model. Sun also discloses a mesh model of various shapes and ground truth that is input into the function. This teaches the 3D geometry of the object model, thus teaching the claimed subject matter.
The motivation for the above is to have an efficient training operation for accurate generation of articulation model.
Mora, Cheng and Sun are analogous art as they are related to image processing for 3D articulation model generation.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Mora by receiving a first set of images of an object in a first articulation and a second set of images of the object in a second articulation as taught by Cheng. And modified Mora modified by Cheng by performing one or more training operations to update one or more parameters of an articulation model of the object based on the first 3D geometry and the second 3D geometry as taught by Sun.
Regarding claim 2, Cheng fails to teach all of claim 2. However, Mora teaches “The computer-implemented method of claim 1, wherein performing one or more operations to generate the first 3D geometry comprises:” (generating a mesh of said an object or being from said sequence of images captured; 0049);
“performing one or more operations to generate a first model of the object in the first articulation based on the first set of images; and” (generating a complete 3D object model from a set of images; 0005);
“performing one or more operations to generate the first 3D geometry based on the first model.” (generating a realistic 3D reconstruction model for an object; 0047);
The motivation for the above is to have an efficient and accurate generation of an articulated object when the operations are performed with the images inputted.
Regarding claim 3, Mora and Cheng fail to teach all of claim 3. However, Sun teaches “The computer-implemented method of claim 2, wherein performing one or more operations to generate the first model comprises performing one or more iterative operations to update parameters of at least one machine learning model included in the first model based on the first set of images.” (model trainer 160 that trains machine-learned model; Pg.6, Para 8);
(update parameters iteratively over multiple training iterations; Pg.6, Para 8);
The motivation for the above is to combine for an efficient and accurate generation of an articulated object when the operations are performed with machine learning model.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Mora modified by Cheng by modifying the performing of one or more operations to generate first model comprises performing one or more iterative operations to update parameters of at least one machine learning model included in the first model based on the first set of images as taught by Sun.
Regarding claim 4, Mora and Cheng fail to teach all of claim 4. However, Sun teaches “The computer-implemented method of claim 2, wherein the first model comprises a first machine learning model associated with geometry of the object and a second machine learning model associated with an appearance of the object.” (each application can communicate with the central intelligence layer (and the models stored therein); Pg.7, Para 10);
(The central intelligence layer includes many machine learning models. For example, as shown in FIG. 1C, a corresponding machine learning model may be provided for each application; Pg.7, Para 11);
The models stored in the central intelligence layer can be the geometry of the object. The central intelligence layer also includes machine leaning models, that can include a first and second learning model associated with the object.
The motivation for the above is to combine for an efficient and accurate generation of an articulated object when utilizing machine learning.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Mora modified by Cheng by modifying the first model comprises a first machine learning model associated with geometry of the object and a second machine learning model associated with an appearance of the object as taught by Sun.
Regarding claim 5, Mora and Cheng fail to teach all of claim 5. However, Sun teaches “The computer-implemented method of claim 2, wherein performing one or more operations to generate the first 3D geometry based on the first model comprises performing one or more operations of a reconstruction technique.” (Fig 2. Depicts 3D reconstruction technique);
(as a result of receiving the images 204, provide The reconstructed 3D model 206 of the object of interest; Pg.7, Para 2);
The motivation for the above is to combine for an efficient and accurate generation of an articulated object, utilizing a reconstruction technique.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Mora modified by Cheng by modifying the performing of one or more operations to generate the first 3D geometry based on the first model comprises performing one or more operations of a reconstruction technique as taught by Sun.
Regarding claim 6, Mora and Cheng fail to teach all of claim 6. However, Sun teaches “The computer-implemented method of claim 1, wherein the articulation model comprises a segmentation model that segments a plurality of parts of the object and a set of motion parameters defining one or more motions of each part included in the plurality of parts.” (an analysis-by-synthesis strategy and forward render contour, optical flow, and/or color images that are compared to video observations to adjust the model's camera, shape, and/or motion parameters. The proposed technique is able to accurately reconstruct rigid and non-rigid 3D shapes; Pg.5, Para 1);
The utilization of motion parameters lets the object be reconstructed with rigid/non-rigid parts of the object which relate to the plurality of parts of the object and its motion.
The motivation for the above is to combine for an efficient and accurate generation of an articulated object.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Mora modified by Cheng by modifying wherein the articulation model comprises a segmentation model that segments a plurality of parts of the object and a set of motion parameters defining one or more motions of each part included in the plurality of parts as taught by Sun.
Regarding claim 7, Mora and Cheng fail to teach all of claim 7. However, Sun teaches “The computer-implemented method of claim 6, wherein performing one or more training operations to update the one or more parameters of the articulation model comprises performing one or more backpropagation operations to update the set of motion parameters and one or more parameters of the segmentation model.” (Training computing system 150 may include model trainer 160 that trains machine-learned model 120 .... a loss function can be backpropagated through the model to update one or more parameters of the model; Pg. 6, Para 9); (used to update ... articulation parameters; Pg. 8, Para 7);
(can be backpropagated through the model to update one or more parameters of the model; Pg.6, Para 9); (performing backpropagation of errors; Pg.6, Para 10);
Updating the parameters of the model can be the motion and segmentation model parameters that are backpropagated.
The motivation for the above is to combine for an efficient and accurate generation of an articulated object with the backpropagation.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Mora modified by Cheng by modifying the performing of one or more operations to generate the articulation model comprises performing one or more backpropagation operations to update the set of motion parameters and one or more parameters of the segmentation model as taught by Sun.
Regarding claim 8, Mora and Cheng fail to teach all of claim 8. However, Sun teaches “The computer-implemented method of claim 7, wherein the one or more backpropagation operations minimize a loss function that comprises at least one of a consistency loss term that penalizes inconsistencies between corresponding points in the first articulation and the second articulation, a matching loss term that penalizes unmatching image features between pixel pairs associated with the first articulation and the second articulation, and a collision loss term that penalizes collisions between one or more parts included in the plurality of parts after applying a predicted forward motion from the first articulation to the second articulation.” (machine-learned mesh model of an object can be learned jointly with a machine-learned camera model by minimizing a loss function that evaluates one or more aspects of the object; Pg.2, Para 17);
(evaluating the loss function may include determining a first contour (eg, using one or more segmentation techniques, etc.) and a second contour (eg, based on a known location of an object within the rendered image). …. The loss function can be evaluated based at least in part on a comparison of the first profile and the second profile; Pg.3, Para 8);
(evaluating the loss function may include determining first texture data (e.g., using raw pixel data and/or various feature extraction techniques) and second texture data (e.g., using known texture data from the rendered image). …. The loss function may be evaluated based at least in part on a comparison of the first texture data and the second texture data; Pg. 4, Para 1); (a gradient signal may be generated for the loss function by comparing first texture data associated with the input image with second texture data associated with the rendered image; Pg. 4, Para 2);
(evaluating the loss function can include determining a first flow (eg, using one or more optical flow techniques, etc.) and a second flow (eg, based on known variations across image rendering). …. A loss function can be evaluated based at least in part on a comparison of the first stream and the second stream; Pg.3, Para 7);
(the motion regularizers used in evaluating the loss function may include a temporal smoothness term, a minimum motion term, and a as rigid as possible term; Pg.3, Para 6);
The loss function for determining a contour, texture and flow relates to the loss function of the consistency loss, matching loss and collision loss.
The motivation for the above is to combine for an efficient and accurate generation of an articulated object with the backpropagation operations.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Mora modified by Cheng by modifying the one or more backpropagation operations minimize a loss function that comprises at least one of a consistency loss term that penalizes inconsistencies between corresponding points in the first articulation and the second articulation, a matching loss term that penalizes unmatching image features between pixel pairs the first articulation and the second articulation, and a collision loss term that penalizes collisions between one or more parts included in the plurality of parts after applying a predicted forward motion from the first articulation to the second articulation as taught by Sun.
Regarding claim 11, Mora and Cheng fail to teach “One or more non-transitory computer-readable storage media including instructions that, when executed by at least one processor, cause the at least one processor to perform steps for generating an articulation model, the steps comprising:”.
However, Sun teaches “One or more non-transitory computer-readable storage media including instructions that, when executed by at least one processor, cause the at least one processor to perform steps for generating an articulation model, the steps comprising:” (non-transitory computer readable media; Pg.1, Para 11);
(stored on a storage device, loaded into memory, and executed by one or more processors; Pg.7, Para 3);
(includes one or more sets of computer-executable instructions stored in a tangible computer-readable storage medium; Pg.7, Para 3);
Claim 11 is directed to a non-transitory computer-readable storage media and its limitations are similar in scope and functions performed by the computer-implemented method of claim 1. Therefore, claim 11 limitations are also rejected with the same rationale as regarding claim 1.
The motivation for the above is to a computer readable storage media that is compatible with performing operations to generate an articulated object.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Mora modified by Cheng by modifying the One or more non-transitory computer-readable storage media including instructions that, when executed by at least one processor, cause the at least one processor to perform steps for generating an articulation model as taught by Sun.
Regarding claim 12, Cheng and Sun fail to teach all of claim 12. However, Mora teaches “The one or more non-transitory computer-readable storage media of claim 11, wherein performing one or more operations to generate the first 3D geometry comprises:” (generating a mesh of said an object or being from said sequence of images captured; 0049);
“performing one or more operations to generate a first model of the object in the first articulation based on the first set of images; and” (generating a complete 3D object model from a set of images; 0005);
“performing one or more operations to generate the first 3D geometry based on the first model.” (generating a realistic 3D reconstruction model for an object; 0047);
Claim 12 is directed to a non-transitory computer-readable storage media and its limitations are similar in scope and functions performed by the computer-implemented method of claim 2. Therefore, claim 12 limitations are also rejected with the same rationale as regarding claim 2.
Regarding claim 13, Mora and Cheng fail to teach all of claim 13. However, Sun teaches “The one or more non-transitory computer-readable storage media of claim 12, wherein performing one or more operations to generate the first model comprises performing one or more iterative operations to update parameters of at least one machine learning model included in the first model based on the first set of images.” (model trainer 160 that trains machine-learned model; Pg.6, Para 8);
(update parameters iteratively over multiple training iterations; Pg.6, Para 8);
Claim 13 is directed to a non-transitory computer-readable storage media and its limitations are similar in scope and functions performed by the computer-implemented method of claim 3. Therefore, claim 13 limitations are also rejected with the same rationale as regarding claim 3.
Regarding claim 14, Mora and Cheng fail to teach all of claim 14. However, Sun teaches “The one or more non-transitory computer-readable storage media of claim 12, wherein the first model comprises a first machine learning model associated with geometry of the object and a second machine learning model associated with an appearance of the object.” (each application can communicate with the central intelligence layer (and the models stored therein); Pg.7, Para 10);
(The central intelligence layer includes many machine learning models. For example, as shown in FIG. 1C, a corresponding machine learning model may be provided for each application; Pg.7, Para 11);
Claim 14 is directed to a non-transitory computer-readable storage media and its limitations are similar in scope and functions performed by the computer-implemented method of claim 4. Therefore, claim 14 limitations are also rejected with the same rationale as regarding claim 4.
Regarding claim 15, Mora and Cheng fail to teach all of claim 15. However, Sun teaches “The one or more non-transitory computer-readable storage media of claim 11, wherein the articulation model comprises a segmentation model that segments a plurality of parts of the object and a set of motion parameters defining one or more motions of each part included in the plurality of parts.” (an analysis-by-synthesis strategy and forward render contour, optical flow, and/or color images that are compared to video observations to adjust the model's camera, shape, and/or motion parameters. The proposed technique is able to accurately reconstruct rigid and non-rigid 3D shapes; Pg.5, Para 1);
Claim 15 is directed to a non-transitory computer-readable storage media and its limitations are similar in scope and functions performed by the computer-implemented method of claim 6. Therefore, claim 15 limitations are also rejected with the same rationale as regarding claim 6.
Regarding claim 16, Mora and Cheng fail to teach all of claim 16. However, Sun teaches “The one or more non-transitory computer-readable storage media of claim 15, wherein performing one or more operations to generate the articulation model comprises performing one or more backpropagation operations to update the set of motion parameters and one or more parameters of the segmentation model.” (can be backpropagated through the model to update one or more parameters of the model; Pg.6, Para 9);
(performing backpropagation of errors; Pg.6, Para 10);
Claim 16 is directed to a non-transitory computer-readable storage media and its limitations are similar in scope and functions performed by the computer-implemented method of claim 7. Therefore, claim 16 limitations are also rejected with the same rationale as regarding claim 7.
Regarding claim 17, Mora and Cheng fail to teach all of claim 17. However, Sun teaches “The one or more non-transitory computer-readable storage media of claim 16, wherein the segmentation model comprises a probability distribution associated with the plurality of parts.” (a mixture of Gaussian models can also guarantee smoothness. The number of shape and motion parameters can now be expressed as: It can scale linearly with the number of frames and bones; Pg.9 Para 10);
Gaussian models fall under the umbrella of probability distribution and the number of shape and motion parameters relates to the plurality of parts.
The motivation for the above is to have an accurate and efficient usage of probability distribution for smoother generation of the parts of the articulated object.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Mora modified by Cheng by modifying the segmentation model comprises a probability distribution associated with the plurality of parts as taught by Sun.
Regarding claim 18, Mora and Cheng fail to teach all of claim 18. However, Sun teaches “The one or more non-transitory computer-readable storage media of claim 11, wherein the first set of images includes a plurality of RGB-D (red, green, blue, depth) images of the object in the first articulation captured from different viewpoints.” (imagery (eg, an RGB input image); Pg.1, Para 1);
(different views of a 3D object can be created; Pg.4, Para 5);
The motivation for the above is to generate an accurate articulated object based on the RGB-D images inputted.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Mora modified by Cheng by modifying the first set of images includes a plurality of RGB-D (red, green, blue, depth) images of the object in the first articulation captured from different viewpoints as taught by Sun.
Regarding claim 20, Mora and Cheng fail to teach “A system, comprising: one or more memories storing instructions; and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to:”.
However, Sun teaches “A system, comprising: one or more memories storing instructions; and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to:” (Server computing system 130 includes one or more processors 132 and memory 134; Pg.6, Para 4);
(Memory 134 may store data 136 and instructions 138 executed by processor 132; Pg.6, Para 4);
Claim 20 is directed to a system and its limitations are similar in scope and functions performed by the computer-implemented method of claim 1. Therefore, claim 20 limitations are also rejected with the same rationale as regarding claim 1.
Claim(s) 9, 10, and 19 are rejected under 35 U.S.C. 103 as being unpatentable under Mora in view of Cheng and Sun and in further view of MA et al (Ma, Liqian, et al. "Sim2Real^ 2: Actively Building Explicit Physics Model for Precise Articulated Object Manipulation." 02/21/2023, arXiv preprint arXiv:2302.10693 (2023). (Year: 2023)).
Regarding claim 9, Mora and Cheng fail to teach all of claim 9. However, Ma teaches “The computer-implemented method of claim 1, further comprising performing one or more operations to simulate the articulation model in an extended reality (XR) environment.” (method to construct the explicit physics model of the single object instance in the simulation; Col Intro, Para 2);
The objects in the simulation are considered an object in an XR environment since simulations can fall in the categories of Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR).
The motivation for the above is to have a more compatible environment to perform the operations to generate an articulated object.
Mora, Cheng, Sun and Ma are analogous art as they are related to image processing for 3D articulation model generation.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Mora modified by Cheng and Sun by modifying the performing of one or more operations to simulate the articulation model in an extended reality (XR) environment as taught by Ma.
Regarding claim 10, Mora and Cheng fail to teach all of claim 10. However, Ma teaches “The computer-implemented method of claim 1, further comprising performing one or more operations to control a robot based on the articulation model.” (control the robot to actively interact with the object with a one-step action; Abstract);
The motivation for the above is to have an integral part of the generation of an articulated object.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Mora modified by Cheng and Sun by modifying the comprising performing of one or more operations to control a robot based on the articulation model as taught by Ma.
Regarding claim 19, Mora and Cheng fail to teach all of claim 19. However, Ma teaches “The one or more non-transitory computer-readable storage media of claim 11, wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform the step of performing one or more operations to at least one of simulate the articulation model in an extended reality (XR) environment or control a robot based on the articulation model.” (method to construct the explicit physics model of the single object instance in the simulation; Col Intro, Para 2);
(control the robot to actively interact with the object with a one-step action; Abstract);
The motivation for the above is to a combination of the simulation environment of the articulated object work with e robot for an accurate generation of an articulated object in and XR environment.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Mora modified by Cheng and Sun by modifying the instructions, when executed by the at least one processor, further cause the at least one processor to perform the step of performing one or more operations to at least one of simulate the articulation model in an extended reality (XR) environment or control a robot based on the articulation model as taught by Ma.
Response to Arguments
Applicant’s arguments, see pg. 11, filed 05/11/2026, with respect to Specification have been fully considered and are persuasive. The Objection of 02/12/2026 has been withdrawn. The Applicant address the objections to the Specification raised by the Examiner. Applicant argues that communication paths 106 and 113 refer to different communication paths shown in Figure 1.
The Examiner replies that the arguments are persuasive. The Examiner accepts the amendments to the Specification that address the “Computing device 140”, “CPUs 102” and reference numbers “402” and “404” corrections from Specification. Therefore, Objections to Specification is withdrawn.
Applicant’s arguments, see pg, filed 05/11/2026, with respect to Drawings have been fully considered and are persuasive. The Objection of 02/12/2026 has been withdrawn. The Applicant address the objections to the Drawings raised by the Examiner. Applicant argues that reference characters 106 and 113 designate different communication paths in Figure 1, and reference characters 120 and 121 designate different add-in cards in Figure 1.
The Examiner replies that the arguments are persuasive. The Examiner accepts the corrections to the Drawings that address the objections to reference number “112” used to designate Processor and Parallel Processing Subsystem and to reference number “116” used to designate Switch and 3D representation application. As well as the removal of reference numbers “402” and “404” from Drawings due to failure of mention within the Specification. Therefore, Objections to Drawings is withdrawn.
Applicant’s arguments, see pg. 12-17, filed 05/11/2026, with respect to Claims 1-10 have been fully considered and are persuasive. The Rejection of 02/12/2026 has been withdrawn. The Applicant argues that claims 1-10 rejections are traverse with respect to amended claims. That the amended claims are not directed towards non- statutory subject matter for at least two reasons based on the 2019 Revised Patent Subject Matter Eligibility Guidance issued by the United States Patent and Trademark Office ("2019 Guidance").
The Examiner replies that the amended claims overcome rejection and the Applicant’s arguments are persuasive. Therefore, the Examiner withdraws 101 Rejection with respect to the amended claims.
Applicant’s arguments, see pg. 17, filed 05/11/2026, with respect to the rejection(s) of claim(s) 1, 11, 20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of SUN CN-115769259-A. The Applicant argues that none of the cited references teaches or suggests these amended limitations. Therefore, no combination of the cited references can teach or suggest each and every limitation of amended claim 1. In addition, because the Examiner did not consider all of the limitations recited in the independent claims in the rejections, a prima facie case of obviousness has not been made with respect to claim 1. Amended claim 1 and all claims dependent thereon are in condition for allowance in view of the cited references. Further, each of amended independent claims 11 and 20 recites limitations similar to those discussed above with respect to allowable amended claim 1.
The Examiner replies that in light of the amended claim 1, due to the amended limitation “performing one or more training operations to update one or more parameters of an articulation model of the object based on the first 3D geometry and the second 3D geometry” the scope of the limitation has changed thus allowing for a further search and examination of the limitations of claim 1. The examiner cites CN-115769259-A Sun to teach the limitation of claim 1. Sun discloses a model trainer that trains a function to update one or more parameters which teaches the claimed subject matter of one or more training operations to update one or more parameters of an articulation model. Sun also discloses a mesh model of various shapes and ground truth that is input into the function. This teaches the 3D geometry of the object model, thus teaching the claimed subject matter. Therefore, the Examiner rejects amended claim 1, 11 and 20 limitations with prior art Sun under 103. All dependent claims are rejected under virtue of dependency of claims they depend on, claims 1, 11, and 20.
Conclusion
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 BRIGITER D PROTAZI whose telephone number is (571)272-7995. The examiner can normally be reached Monday - Friday 7:30-5.
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, Said A Broome can be reached at 5712722931. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/B.D.P./Examiner, Art Unit 2612
/Said Broome/Supervisory Patent Examiner, Art Unit 2612
Prosecution Insights