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 .
Priority
Acknowledgement is made of Applicant’s claim of priority as a continuation of U.S. Application No. 18340000, filed June 22, 2023, U.S. Application No. 17520152, filed November 5, 2021, U.S. Application No. 16864591, filed May 1, 2020, U.S. Application No. 16042877, filed July 23, 2018 and U.S. Application No. 15227612, filed August 3, 2016.
Information Disclosure Statement
The information disclosure statements (“IDS”) filed on September 27, 2024, August 25, 2025 and April 29, 2026 were reviewed and the listed references were noted.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 7 and 17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 7 and 17 recite “the representation of the 3D object includes a trained machine learning model”. The specification does not describe how the representation of the 3D object could include a trained machine learning model and only explains that the representation of the 3D object is a 3D model.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 4, 9, 10, 14 and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 3, 4, 9, 10, 14 and 19 recite the limitations “the environment of the robot”. There is insufficient antecedent basis for these limitations in the claim. The rejection could be overcome by amending the claims to recite “an environment of a robot”.
Claims 9 and 10 recite the limitations “the first background” and “the second background”. There is insufficient antecedent basis for these limitations in the claim. The rejection could be overcome by amending the claims to recite “a first background” and “a second background” or by amending claim 9 to depend from claim 8.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-6, 8-10, 12-16 and 18-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 2-4, 7-8, 10-14, 18 and 20-21 of U.S. Patent No. 12,103,178. Although the claims at issue are not identical, they are not patentably distinct from each other because the scopes of the claims are the same.
The following chart shows Claim 1 of the instant application compared to Claim 2 of U.S. Patent No. 12,103,178:
U.S. Application No. 18/899,829
U.S. Patent No. 12,103,178
A method implemented by one or more processors, the method comprising:
identifying a representation of a three-dimensional (3D) object;
generating a plurality of rendered images based on the representation of the 3D object,
wherein generating the rendered images based on the representation of the 3D object comprises:
rendering, using the representation of the 3D object, a first image that renders the 3D object and that includes first additional content; and
rendering, using the representation of the 3D object, a second image that renders the 3D object and that includes second additional content that is distinct from the first additional content;
generating training examples that each include a corresponding one of the rendered images as training example input and that each include training example output that is based on a feature of the 3D object in the corresponding one of the rendered images; and
providing the training examples for training of a machine learning model.
A method implemented by one or more processors, the method comprising:
identifying a three-dimensional (3D) object model of an object;
generating a plurality of rendered images based on the 3D object model, wherein the rendered images capture the 3D object model at a plurality of different poses relative to viewpoints of the rendered images, and
wherein generating the rendered images based on the object model comprises:
rendering a first image that renders the 3D object model at a first pose of the different poses and that includes first additional content; and
rendering a second image that renders the 3D object model at a second pose of the different poses and that includes second additional content that is distinct from the first additional content;
generating training examples that each include a corresponding one of the rendered images as training example input and that each include training example output that is based on a feature of the object in the corresponding one of the rendered images; and
providing the training examples for training of a machine learning model.
Claim 2 of the immediate application is rejected for being similar to claim 8 of the U.S. Patent.
Claims 3 and 4 of the immediate application is rejected for being similar to claims 3 and 4 of the U.S. Patent.
Claim 5 of the immediate application is rejected for being similar to claim 10 of the U.S. Patent.
Claim 6 of the immediate application is rejected for being similar to claim 11 of the U.S. Patent.
Claim 8 of the immediate application is rejected for being similar to claim 3 of the U.S. Patent.
Claim 9 of the immediate application is rejected for being similar to claim 4 of the U.S. Patent.
Claim 10 of the immediate application is rejected for being similar to claim 7 of the U.S. Patent.
Claim 12 of the immediate application is rejected for being similar to claim 12 of the U.S. Patent.
Claim 13 of the immediate application is rejected for being similar to claim 18 of the U.S. Patent.
Claim 14 of the immediate application is rejected for being similar to claims 13 and 14 of the U.S. Patent.
Claim 15 of the immediate application is rejected for being similar to claim 20 of the U.S. Patent.
Claim 16 of the immediate application is rejected for being similar to claim 21 of the U.S. Patent.
Claim 18 of the immediate application is rejected for being similar to claim 13 of the U.S. Patent.
Claim 19 of the immediate application is rejected for being similar to claim 14 of the U.S. Patent.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 7-8, 11-13, 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Su et al. (“Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views”) in view of Heisele et al. (US 8,422,797 B2).
Regarding claim 1, Su teaches a method implemented by one or more processors, the method comprising:
identifying a representation of a three-dimensional (3D) object (Su, p. 2688, To generate training data, we render 3D model views. To increase the diversity of object geometry, we create new 3D models by deforming existing ones downloaded from a modestly-sized online 3D model repository);
generating a plurality of rendered images based on the representation of the 3D object (Su, p. 2687, use 3D models to render images for training object detectors and viewpoint classifiers), wherein generating the rendered images based on the representation of the 3D object comprises:
rendering, using the representation of the 3D object, a first image that renders the 3D object and that includes first additional content (Su, p. 2688, to generate training data, we render 3D model views. To increase the diversity of object geometry, we create new 3D models by deforming existing ones downloaded from a modestly-sized online 3D model repository. To increase the diversity of object appearance and background clutterness, we design a synthesis pipeline by randomly sampling rendering parameters, applying truncation patterns and adding random backgrounds from scene images (i.e., includes first additional content); and
rendering, using the representation of the 3D object, a second image that renders the 3D object and that includes second additional content that is distinct from the first additional content (Su, p. 2688, to generate training data, we render 3D model views. To increase the diversity of object geometry, we create new 3D models by deforming existing ones downloaded from a modestly-sized online 3D model repository. To increase the diversity of object appearance and background clutterness, we design a synthesis pipeline by randomly sampling rendering parameters, applying truncation patterns and adding random backgrounds from scene images (i.e., second additional content that is distinct from the first additional content). P. 2692, higher diversity of background helps minimize overfitting); and
providing the training examples for training of a machine learning model (Su, p. 2688, training the CNN with the synthesized images).
Although Su teaches training a CNN with training examples of synthesized images (Su, p. 2688), Su does not explicitly teach “generating training examples that each include a corresponding one of the rendered images as training example input and that each include training example output that is based on a feature of the 3D object in the corresponding one of the rendered images”. However, in an analogous field of endeavor, Heisele teaches the training data set includes images that are positive examples (i.e., the target object is present) and images that are negative examples (i.e., the target object is absent). Each training image is labeled correctly regarding whether it is a positive example or a negative example (Heisele, Col. 4, lines 35-46).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the method of Su with the teachings of Heisele by including generating training examples that include rendered images as an input and an output based on a feature of the 3D object (i.e., whether the target object is present). One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for training a view-based object recognition system, as recognized by Heisele. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Regarding claim 2, Su in view of Heisele teaches the method of claim 1, further comprising:
generating a first scene using the representation of the 3D object (Su, p. 2688, adding random backgrounds from scene images),
generating a second scene using the representation of the 3D object (Su, p. 2688, adding random backgrounds from scene images);
wherein rendering the first image with the first additional content comprises rendering the first image using the first scene (Su, p. 2688, use alpha-composition to blend a rendered image as foreground and a scene image as background); and
wherein rendering the second image with the second additional content comprises rendering the second image using the second scene (Su, p. 2688, use alpha-composition to blend a rendered image as foreground and a scene image as background).
Regarding claim 7, Su in view of Heisele teaches the method of claim 1, wherein the representation of the 3D object includes a trained machine learning model (Su, p. 2689, CNN trained for viewpoint estimation).
Regarding claim 8, Su in view of Heisele teaches the method of claim 1, wherein rendering the first image with the first additional content comprises rendering the 3D object onto a first background, and wherein rendering the second image with second additional content comprises rendering the 3D object onto a second background that is distinct from the first background (Su, p. 2688, to generate training data, we render 3D model views. To increase the diversity of object geometry, we create new 3D models by deforming existing ones downloaded from a modestly-sized online 3D model repository. To increase the diversity of object appearance and background clutterness, we design a synthesis pipeline by randomly sampling rendering parameters, applying truncation patterns and adding random backgrounds from scene images (i.e., second background is distinct from the first background). P. 2692, higher diversity of background helps minimize overfitting).
Regarding claim 11, Su in view of Heisele teaches the method of claim 1, further comprising:
training the machine learning model using the training examples (Su, p. 2688, training the CNN with the synthesized images).
Claims 12-13, 17-18 and 20 recite systems with elements corresponding to the steps recited in Claims 1-2, 7-8 and 11, respectively. Therefore, the recited elements of these claims are mapped to the proposed combination in the same manner as the corresponding steps in their corresponding method claims. Additionally, the rationale and motivation to combine the Su and Heisele references, presented in rejection of Claim 1, apply to these claims. Finally, the combination of the Su and Heisele references discloses a memory storing instructions (Heisele, Col. 3 line 54 – Col. 4 line 4, memory can include any of the above and/or other devices that can store information/data/programs) and one or more processors (Heisele, Col. 3 line 54 – Col. 4 line 4, the computers may include a single processor or may be architectures employing multiple processor designs).
Claims 3-4, 9-10, 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Su et al. (“Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views”) in view of Heisele et al. (US 8,422,797 B2), as applied to claims 1-2, 7-8, 11-13, 17-18 and 20 above, and further in view of Rosen et al. (US 2017/0008174 A1, filed April 15, 2014).
Regarding claim 3, Su in view of Heisele teaches the method of claim 1, as described above.
Although Su in view of Heisele teaches rendering the image with backgrounds from scene images (Su, p. 2688), they do not explicitly teach “wherein rendering the first image with the first additional content comprises including the first additional content, in rendering the first image, based on an environment of the robot”. However, in an analogous field of endeavor, Rosen teaches generating a high fidelity 3D-photometric image of objects in the environment in which the robot is operating (Rosen, Para. [0076]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the method of Su in view of Heisele with the teachings of Rosen by including that rendering the first image with first additional content based on the environment of a robot. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for a robot with human-like intelligence, as recognized by Rosen. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Regarding claim 4, Su in view of Heisele further in view of Rosen teaches the method of claim 3, wherein rendering the second image with the second additional content comprises including the second additional content, in rendering the second image, based on the environment of the robot (Rosen, Para. [0076], generation of a high fidelity 3D-photometric image of objects in the environment in which the robot is operating. The 3D-photometric image, made up of electronic pixel-signals, must be a high fidelity representation of the objects/colors present in the FOV of the visual system).
The proposed combination as well as the motivation for combining the Su, Heisele and Rosen references presented in the rejection of Claim 3, apply to Claim 4 and are incorporated herein by reference. Thus, the method recited in Claim 4 is met by Su in view of Heisele further in view of Rosen.
Regarding claim 9, Su in view of Heisele teaches the method of claim 6, as described above.
Although Su in view of Heisele teaches rendering the 3D object in random backgrounds (Su, p. 2688), they do not explicitly teach “selecting the first background based on the environment of the robot”. However, in an analogous field of endeavor, Rosen teaches generating a high fidelity 3D-photometric image of objects in the environment in which the robot is operating (Rosen, Para. [0076]).
The proposed combination as well as the motivation for combining the Su, Heisele and Rosen references presented in the rejection of Claim 3, apply to Claim 9 and are incorporated herein by reference. Thus, the method recited in Claim 9 is met by Su in view of Heisele further in view of Rosen.
Regarding claim 10, Su in view of Heisele further in view of Rosen teaches the method of claim 9, further comprising:
selecting the second background based on the environment of the robot (Rosen, Para. [0076], generation of a high fidelity 3D-photometric image of objects in the environment in which the robot is operating. The 3D-photometric image, made up of electronic pixel-signals, must be a high fidelity representation of the objects/colors present in the FOV of the visual system).
The proposed combination as well as the motivation for combining the Su, Heisele and Rosen references presented in the rejection of Claim 3, apply to Claim 10 and are incorporated herein by reference. Thus, the method recited in Claim 10 is met by Su in view of Heisele further in view of Rosen.
Claims 14 and 19 recite systems with elements corresponding to the steps recited in Claims 3 and 9, respectively. Therefore, the recited elements of these claims are mapped to the proposed combination in the same manner as the corresponding steps in their corresponding method claims. Additionally, the rationale and motivation to combine the Su, Heisele and Rosen references, presented in rejection of Claim 3, apply to these claims. Finally, the combination of the Su, Heisele and Rosen references discloses a memory storing instructions (Heisele, Col. 3 line 54 – Col. 4 line 4, memory can include any of the above and/or other devices that can store information/data/programs) and one or more processors (Heisele, Col. 3 line 54 – Col. 4 line 4, the computers may include a single processor or may be architectures employing multiple processor designs).
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Su et al. (“Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views”) in view of Heisele et al. (US 8,422,797 B2), as applied to claims 1-2, 7-8, 11-13, 17-18 and 20 above, and further in view of Tosic et al. (US 2018/0005079 A1, filed July 1, 2016).
Regarding claim 5, Su in view of Heisele teaches the method of claim 1, as described above.
Although Su in view of Heisele teaches a training data set including images and labels (Heisele, Col. 4, lines 35-46), they do not explicitly teach “wherein the training example output of each of the training examples includes a corresponding pose of the object in the corresponding one of the rendered images”. However, in an analogous field of endeavor, Tosic teaches training a CNN using training data that includes object images (e.g., training images) and object orientation (pose) data (Tosic, Para. [0046]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Su in view of Heisele with the teachings of Tosic by including training the model with training data including the rendered image and the object orientation in the rendered image. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for training a model to predict motion based on object viewpoint recognition, as recognized by Tosic. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Claim 15 recites a system with elements corresponding to the steps recited in Claim 5. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Su, Heisele and Tosic references, presented in rejection of Claim 5, applies to this claim. Finally, the combination of the Su, Heisele and Tosic references discloses a memory storing instructions (Heisele, Col. 3 line 54 – Col. 4 line 4, memory can include any of the above and/or other devices that can store information/data/programs) and one or more processors (Heisele, Col. 3 line 54 – Col. 4 line 4, the computers may include a single processor or may be architectures employing multiple processor designs).
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Su et al. (“Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views”) in view of Heisele et al. (US 8,422,797 B2), as applied to claims 1-2, 7-8, 11-13, 17-18 and 20 above, and further in view of Vijayanarasimhan et al. (US 2017/0252924 A1, with priority to US Provisional Application No. 62/303,139, filed March 3, 2016, which provides sufficient teaching for the subject matter used herein).
Regarding claim 6, Su in view of Heisele teaches the method of claim 1, as described above.
Although Su in view of Heisele teaches using a 3D model to render images (Su, p. 2687), they do not explicitly teach “wherein the rendered images each include a plurality of color channels and a depth channel”. However, in an analogous field of endeavor, Vijayanarasimhan teaches a current image includes multiple channels, such as a red channel, a blue channel, a green channel and/or a depth channel (Vijayanarasimhan, Para. [0057]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the method of Su in view of Heisele with the teachings of Vijayanarasimhan by including that the rendered images include a plurality of color channels and a depth channel. One having ordinary skill in the art would have been motivated to combine the references because doing so would allow for training a network to control a robot in its environment, as recognized by Vijayanarasimhan. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Claim 16 recites a system with elements corresponding to the steps recited in Claim 6. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Su, Heisele and Vijayanarasimhan references, presented in rejection of Claim 6, applies to this claim. Finally, the combination of the Su, Heisele and Vijayanarasimhan references discloses a memory storing instructions (Heisele, Col. 3 line 54 – Col. 4 line 4, memory can include any of the above and/or other devices that can store information/data/programs) and one or more processors (Heisele, Col. 3 line 54 – Col. 4 line 4, the computers may include a single processor or may be architectures employing multiple processor designs).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emma Rose Goebel whose telephone number is (703)756-5582. 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, Amandeep Saini can be reached at (571) 272-3382. 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.
/Emma Rose Goebel/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
Prosecution Insights