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 Arguments
Applicant's arguments filed 10/15/2025 have been fully considered but they are not persuasive.
The applicant argues, “It is most respectfully submitted that, contrary to the Examiner's suggestion, it would not have been obvious to one of ordinary skill in the art to combine the system of Roberts with the text to 3D synthesis techniques of Poole. Roberts describes prompt-based display of 3D models which are pulled from a database of existing 3D assets. In fact, Roberts very clearly states: "[w]hile Web extraction tools have perfected algorithms for harvesting 2D media like images and video, for our game we needed a database with a sizable number of 3D assets." Page 6. Accordingly, Roberts is not generating 2D images or 3D models. Roberts is simply pulling from a pre-existing database of 3D models. Further, since Roberts is concerned with providing models during game play, latency is a critical issue: We dynamically loaded models at runtime via the Sketchfab API (which contains over 3 million models). All the models that contain the requested object get ordered by their number of likes and t n a random object with the lowest vertex count gets selected as the model to download. This way, download speed and model quality are being optimized. If one model takes more than 5 seconds to load, the next model in the list will start downloading to avoid failed downloads and extended loading times. Id (emphasis added); see also, pages 7-8 (5 Technical challenges & considerations; 5.1 Latency).” (See Applicant’s Remarks, page 5)
The examiner respectfully disagrees. Roberts is utilized to teach the communication components not the generation components. It is clear that the received components were generated at some point in time.
The applicant argues, “Poole does not disclose or suggest voice prompting or application of their 3D asset/scene generation in the field of game play, augmented reality or virtual reality. Further, Poole's generation timeframe is magnitudes longer than the simple downloading of pre- existing assets required in Roberts: 4. Optimization. Our 3D scenes are optimized on a TPUv4 machine with 4 chips. Each chip renders a separate view and evaluates the diffusion U-Net with per-device batch size of 1. We optimize for 15,000 iterations which takes around 1.5 hours. Compute time is split evenly between rendering the NeRF and evaluating the diffusion model. Page 7 (emphasis added). One skilled in the art would most certainly not combine Roberts with Poole as it would render the game play in Roberts inoperable for its intended purpose. MPEP 2143.01(V) (If a proposed modification would render the prior art invention being modified unsatisfactory for its intended purpose, there may be no suggestion or motivation to make the proposed modification. In re Gordon, 733 F.2d 900, 221 USPQ 1125 (Fed. Cir. 1984)).” (See Applicant’s Remarks, page 6)
The examiner respectfully disagrees. The processing speed is merely an experiment utilizing a minimum number of TPUs and it is clear that processing can scale based on demand and the number of TPUs can be increased exponentially to increase speed. The examiner is combining the 3D synthesis techniques of Pool.
The applicant argues, the references utilized in the dependent claims fail to cure the deficiencies of the independent claims. (See Applicant’s Remarks, pages 6-7)
The examiner respectfully disagrees as there are no deficiencies as described detailed above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 1-4, 6, 8-13, 15, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roberts et al. (“Steps Towards Prompt-Based Creation of Virtual Worlds” IEEE, 2022)(Hereinafter referred to as Roberts) in view of Poole et al. (“DreamFusion: Text-to-3D Using 2D Disffusion”, 2022)(Hereinafter referred to as Poole).
Regarding claim 1, Roberts teaches
A system for generating and providing an object in the field of view (FOV) of a user's augmented reality or virtual reality (AR/VR) session responsive to a verbal request for the object, the system (In this work we show that prompt-based methods can both accelerate in-VR level editing, as well as can become part of gameplay rather than just part of game development. See abstract) comprising:
an AR/VR communication component for receiving the verbal request for the object and producing a text request for the object based on the verbal request (Consultants in an ideation session could use voice prompts to generate multiple interactive 3D assets with the intent to inspire more prompts with a novel output. The process would be repeated until there was convergence of agreement for a critical path and further discovery. See page 4, right col., second to last paragraph);
and a communications component for providing the generated 3D model of the object within the FOV of the user's AR/VR session (Figure 1: Codex VR Pong as an example of prompt-based gameplay mechanics: the ball and the paddle can be transformed arbitrarily and can interact in unscripted ways. Here, a ball was transformed into a salmon steak, the paddle into a knife, and upon their collision the salmon turns into sushi. See figure 1) (Roberts; We dynamically loaded models at runtime via the Sketchfab API (which contains over 3 million models). All the models that contain the requested object get ordered by their number of likes and then a random object with the lowest vertex count gets selected as the model to download. See page6, left col., first paragraph) ( The overall model loading latency can be impacted by multiple technical factors such as network speed, system resource utilization, and complexity of the instantiated 3D model. While it is apparent that low network speeds and large file sizes increase latency, the trade-offs between user comfort and request completion times are not so obvious. Downloading the model requires significant CPU resources which are already strained by the high system requirements needed to render virtual reality. Once the model is downloaded, the CPU resources are strained again when the model is loaded into memory during instantiation. See section 4.1 Latency), but is silent to an image generation component for receiving the text request for the object and generating a two-dimensional (2D) image of the object; a model generation component for receiving the 2D image of the object and generating a three-dimensional (3D) model of the object;
Poole teaches that text to image synthesis using diffusion models is well known (Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. See abstract) and utilizing text to generate 3D models based on 2D diffusion from text prompts (In this work, we circumvent these limitations by using a pretrained 2D text-to-image diffusion model to perform text-to-3D synthesis. See abstract).
Roberts and Poole perform image processing to generate virtual 3d data and Poole teaches that by utilizing text to 2d diffusion to create 3d models the system can avoid an overly complicated large data sets of labeled 3d data and efficient architectures for denoising 3D data (Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D data and efficient architectures for denoising 3D data, neither of which currently exist. In this work, we circumvent these limitations by using a pretrained 2D text-to-image diffusion model to perform text-to-3D synthesis. See abstract), therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system of Roberts with text to 3D synthesis techniques of Poole such that the user could generate 3D models without using overly complicated datasets of labeled 3D data.
Regarding claim 2, Roberts in view of Poole teaches the system of claim 1, wherein the image generation component includes a generative model (Poole; For the diffusion model, we use the Imagen model from Saharia et al. (2022), which has been trained to synthesize images from text. See page 5, section 3).
Regarding claim 3, Roberts in view of Poole teaches The system of claim 2, wherein the generative model is a diffusion model (Poole; For the diffusion model, we use the Imagen model from Saharia et al. (2022), which has been trained to synthesize images from text. See page 5, section 3).
Regarding claim 4, Roberts in view of Poole teaches The system of claim 3, wherein the diffusion model is a latent diffusion model (Poole; Diffusion models are latent-variable generative models that learn to gradually transform a sample from a tractable noise distribution towards a data distribution (Sohl-Dickstein et al., 2015; Ho et al., 2020). See section 2 page 3, first paragraph).
Regarding claim 6, Roberts in view of Pool teaches the system of claim 1, wherein the model generation component includes a Neural Radiance Field (NeRF) model (Poole; See figure 3).
Regarding claim 8, Roberts in view of Poole teaches The system of claim 1, wherein the communications component is an asynchronous microservice (Roberts; We dynamically loaded models at runtime via the Sketchfab API (which contains over 3 million models). All the models that contain the requested object get ordered by their number of likes and then a random object with the lowest vertex count gets selected as the model to download. See page6, left col., first paragraph).
Regarding claim 9, Roberts in view of Poole teaches The system of claim 1, wherein the AR/VR communication component is a headset (In particular, this has the potential for allowing authoring Virtual Reality (VR) experiences from within the headset, as well as allow completely novel modes of gameplay. See page 1, right col., first paragraph).
Regarding claim 10, Roberts teaches A process for generating and providing an object in the field of view (FOV) of a user's augmented reality or virtual reality (AR/VR) session responsive to a verbal request for the object (In this work we show that prompt-based methods can both accelerate in-VR level editing, as well as can become part of gameplay rather than just part of game development. See abstract), the process comprising:
receiving, by an AR/VR communication component, a verbal request for the object (Consultants in an ideation session could use voice prompts to generate multiple interactive 3D assets with the intent to inspire more prompts with a novel output. The process would be repeated until there was convergence of agreement for a critical path and further discovery. See page 4, right col., second to last paragraph);
producing, by the AR/VR communication component, a text request for the object based on the verbal request (Consultants in an ideation session could use voice prompts to generate multiple interactive 3D assets with the intent to inspire more prompts with a novel output. The process would be repeated until there was convergence of agreement for a critical path and further discovery. See page 4, right col., second to last paragraph)( For user input we used voice commands via Azure Cognitive Services Speech to Text [37]. See page 6, left col., last paragraph);
receiving, by an image generation component, the text request for the object and generating a two-dimensional (2D) image of the object (Consultants in an ideation session could use voice prompts to generate multiple interactive 3D assets with the intent to inspire more prompts with a novel output. The process would be repeated until there was convergence of agreement for a critical path and further discovery. See page 4, right col., second to last paragraph) ( For user input we used voice commands via Azure Cognitive Services Speech to Text [37]. See page 6, left col., last paragraph);
and providing, by a communications component, the generated 3D model of the object within the FOV of the user's AR/VR session (Figure 1: Codex VR Pong as an example of prompt-based gameplay mechanics: the ball and the paddle can be transformed arbitrarily and can interact in unscripted ways. Here, a ball was transformed into a salmon steak, the paddle into a knife, and upon their collision the salmon turns into sushi. See figure 1) (Roberts; We dynamically loaded models at runtime via the Sketchfab API (which contains over 3 million models). All the models that contain the requested object get ordered by their number of likes and then a random object with the lowest vertex count gets selected as the model to download. See page6, left col., first paragraph) ( The overall model loading latency can be impacted by multiple technical factors such as network speed, system resource utilization, and complexity of the instantiated 3D model. While it is apparent that low network speeds and large file sizes increase latency, the trade-offs between user comfort and request completion times are not so obvious. Downloading the model requires significant CPU resources which are already strained by the high system requirements needed to render virtual reality. Once the model is downloaded, the CPU resources are strained again when the model is loaded into memory during instantiation. See section 4.1 Latency), but is silent to receiving, by a model generation component, the 2D image of the object and generating a three-dimensional (3D) model of the object.
Poole teaches that text to image synthesis using diffusion models is well known (Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. See abstract) and utilizing text to generate 3D models based on 2D diffusion from text prompts (In this work, we circumvent these limitations by using a pretrained 2D text-to-image diffusion model to perform text-to-3D synthesis. See abstract).
Roberts and Poole perform image processing to generate virtual 3d data and Poole teaches that by utilizing text to 2d diffusion to create 3d models the system can avoid an overly complicated large data sets of labeled 3d data and efficient architectures for denoising 3D data (Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D data and efficient architectures for denoising 3D data, neither of which currently exist. In this work, we circumvent these limitations by using a pretrained 2D text-to-image diffusion model to perform text-to-3D synthesis. See abstract), therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system of Roberts with text to 3D synthesis techniques of Poole such that the user could generate 3D models without using overly complicated datasets of labeled 3D data.
Regarding claim 11, Roberts in view of Poole teaches The process of claim 10, wherein generating a 2D image of the object includes application of a generative model to the generated 2D image (Poole; For the diffusion model, we use the Imagen model from Saharia et al. (2022), which has been trained to synthesize images from text. See page 5, section 3).
Regarding claim 12, Roberts in view of Poole teaches The process of claim 11, wherein the generative model is a diffusion model (Poole; For the diffusion model, we use the Imagen model from Saharia et al. (2022), which has been trained to synthesize images from text. See page 5, section 3).
Regarding claim 13, Roberts in view of Poole teaches The process of claim 12, wherein the diffusion model is a latent diffusion model (Poole; Diffusion models are latent-variable generative models that learn to gradually transform a sample from a tractable noise distribution towards a data distribution (Sohl-Dickstein et al., 2015; Ho et al., 2020). See section 2 page 3, first paragraph).
Regarding claim 15, Roberts in view of Poole teaches The process of claim 10, wherein the model generation component includes a Neural Radiance Field (NeRF) model (Poole; See figure 3).
Regarding claim 17, Roberts in view of Poole teaches The process of claim 10, wherein the communications component is an asynchronous microservice (Roberts; We dynamically loaded models at runtime via the Sketchfab API (which contains over 3 million models). All the models that contain the requested object get ordered by their number of likes and then a random object with the lowest vertex count gets selected as the model to download. See page6, left col., first paragraph).
Regarding claim 18, Roberts in view of Poole teaches the process of claim 10, further comprising: storing, by the image generation component, the generated 2D image in a first database and communicating a file location for the generated 2D image in the first database to the communications component; communicating, by the communications component, the file location of the 2D image to the model generation component; accessing, by the model generation component, the 2D image, generating the 3D model and storing the generated 3D model in a second database and communicating a file location for the generated 3D model in the second database to the communications component; and communicating, by the communications component, the 3D model file location to the AR/VR communication component for access thereby to provide the 3D model of the object in the field of view (FOV) of the user's augmented reality or virtual reality (AR/VR) session (Roberts; The overall model loading latency can be impacted by multiple technical factors such as network speed, system resource utilization, and complexity of the instantiated 3D model. While it is apparent that low network speeds and large file sizes increase latency, the trade-offs between user comfort and request completion times are not so obvious. Downloading the model requires significant CPU resources which are already strained by the high system requirements needed to render virtual reality. Once the model is downloaded, the CPU resources are strained again when the model is loaded into memory during instantiation. See section 5.1 latency)(The claim amounts to mere general storage and access of data and not significantly more. It is clear the data is stored and utilized by the combination of Roberts in view of Poole).
Regarding claim 19, Roberts teaches A computer readable non-transitory medium comprising a plurality of executable programmatic instructions that, when executed in a computer system, enables generating and providing an object in the field of view (FOV) of a user's augmented reality or virtual reality(AR/VR) session responsive to a verbal request for the object (The overall model loading latency can be impacted by multiple technical factors such as network speed, system resource utilization, and complexity of the instantiated 3D model. While it is apparent that low network speeds and large file sizes increase latency, the trade-offs between user comfort and request completion times are not so obvious. Downloading the model requires significant CPU resources which are already strained by the high system requirements needed to render virtual reality. Once the model is downloaded, the CPU resources are strained again when the model is loaded into memory during instantiation. See section 5.1 and figure 1), wherein the plurality of executable programmatic instructions, when executed:
produce a text request for the object based on the verbal request (Consultants in an ideation session could use voice prompts to generate multiple interactive 3D assets with the intent to inspire more prompts with a novel output. The process would be repeated until there was convergence of agreement for a critical path and further discovery. See page 4, right col., second to last paragraph)( For user input we used voice commands via Azure Cognitive Services Speech to Text [37]. See page 6, left col., last paragraph);
and provide the generated 3D model of the object within the FOV of the user's AR/VR session (Figure 1: Codex VR Pong as an example of prompt-based gameplay mechanics: the ball and the paddle can be transformed arbitrarily and can interact in unscripted ways. Here, a ball was transformed into a salmon steak, the paddle into a knife, and upon their collision the salmon turns into sushi. See figure 1) (Roberts; We dynamically loaded models at runtime via the Sketchfab API (which contains over 3 million models). All the models that contain the requested object get ordered by their number of likes and then a random object with the lowest vertex count gets selected as the model to download. See page6, left col., first paragraph) ( The overall model loading latency can be impacted by multiple technical factors such as network speed, system resource utilization, and complexity of the instantiated 3D model. While it is apparent that low network speeds and large file sizes increase latency, the trade-offs between user comfort and request completion times are not so obvious. Downloading the model requires significant CPU resources which are already strained by the high system requirements needed to render virtual reality. Once the model is downloaded, the CPU resources are strained again when the model is loaded into memory during instantiation. See section 4.1 Latency), but is silent to generate a two-dimensional (2D) image of the object from the text request; generate a three-dimensional (3D) model of the object from the 2D image.
Poole teaches that text to image synthesis using diffusion models is well known (Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. See abstract) and utilizing text to generate 3D models based on 2D diffusion from text prompts (In this work, we circumvent these limitations by using a pretrained 2D text-to-image diffusion model to perform text-to-3D synthesis. See abstract).
Roberts and Poole perform image processing to generate virtual 3d data and Poole teaches that by utilizing text to 2d diffusion to create 3d models the system can avoid an overly complicated large data sets of labeled 3d data and efficient architectures for denoising 3D data (Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D data and efficient architectures for denoising 3D data, neither of which currently exist. In this work, we circumvent these limitations by using a pretrained 2D text-to-image diffusion model to perform text-to-3D synthesis. See abstract), therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system of Roberts with text to 3D synthesis techniques of Poole such that the user could generate 3D models without using overly complicated datasets of labeled 3D data.
Regarding claim 20, Roberts in view of Poole teaches The computer readable non-transitory medium of claim 19, wherein the 2D image of the object is generated by a diffusion model (Poole; For the diffusion model, we use the Imagen model from Saharia et al. (2022), which has been trained to synthesize images from text. See page 5, section 3) and the 3D model is generated by a model generation component, selected from the group consisting of a conditional GAN and a NeRF model (Poole; See figure 3).
Claim(s) 5, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Roberts et al. (“Steps Towards Prompt-Based Creation of Virtual Worlds” IEEE, 2022)(Hereinafter referred to as Roberts) in view of Poole et al. (“DreamFusion: Text-to-3D Using 2D Disffusion”, 2022)(Hereinafter referred to as Poole) in view of Batzolis et al. (“Non-Uniform Diffusion Models”, 2022.)(Hereinafter referred to as Batzolis).
Regarding claim 5, Roberts in view of Poole teaches The system of claim 3, but is silent to wherein the diffusion model is pixel diffusion model.
Batzolis teaches utilizing a non-uniform pixel diffusion to lead to multi-scale diffusion models which require the same or less training and have a better FID score than the standard form (We show that non-uniform diffusion leads to multi-scale diffusion models which have similar structure to this of multi-scale normalizing flows. We experimentally find that in the same or less training time, the multi-scale diffusion model achieves better FID score than the standard uniform diffusion model. See abstract)( In this part of the experimental section, we compare the performance of the multiscale model that depends on nonuniform pixel diffusion to the performance of the standard model that depends on uniform diffusion. See section 4.1 paragraph 1).
Roberts in view of Poole and Batzolis teach of diffusion models and Batzolis teaches that by utilizing a non-uniform pixel diffusion model the system can achieve a better FID score, therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system of Roberts in view of Pool with the non-uniform pixel diffusion techniques of Batzolis such that the system could achieve a better FID score.
Regarding claim 14, Roberts in view of Poole teaches The process of claim 12, but is silent to wherein the diffusion model is pixel diffusion model.
Batzolis teaches utilizing a non-uniform pixel diffusion to lead to multi-scale diffusion models which require the same or less training and have a better FID score than the standard form (We show that non-uniform diffusion leads to multi-scale diffusion models which have similar structure to this of multi-scale normalizing flows. We experimentally find that in the same or less training time, the multi-scale diffusion model achieves better FID score than the standard uniform diffusion model. See abstract)( In this part of the experimental section, we compare the performance of the multiscale model that depends on nonuniform pixel diffusion to the performance of the standard model that depends on uniform diffusion. See section 4.1 paragraph 1).
Roberts in view of Poole and Batzolis teach of diffusion models and Batzolis teaches that by utilizing a non-uniform pixel diffusion model the system can achieve a better FID score, therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system of Roberts in view of Pool with the non-uniform pixel diffusion techniques of Batzolis such that the system could achieve a better FID score.
Claim(s) 7, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roberts et al. (“Steps Towards Prompt-Based Creation of Virtual Worlds” IEEE, 2022)(Hereinafter referred to as Roberts) in view of Poole et al. (“DreamFusion: Text-to-3D Using 2D Disffusion”, 2022)(Hereinafter referred to as Poole) in view of Li et al. (“StoryGAN: A Sequential Conditional GAN for Story Visualization”, 2019)(Hereinafter referred to as Li)
Regarding claim 7, Roberts in view of Poole teaches The system of claim 1, but is silent to wherein the model generation component includes a conditional Generative Adversarial Network (GAN).
Li teaches A conditional GAN system for story visualization (In this work, we propose a new task called Story Visualization. Given a multi-sentence paragraph, the story is visualized by generating a sequence of images, one for each sentence. In contrast to video generation, story visualization focuses less on the continuity in generated images (frames), but more on the global consistency across dynamic scenes and characters– a challenge that has not been addressed by any single-image or video generation methods. See abstract).
Roberts in view of Poole and Li teach of generating image data and Li teaches that a conditional GAN system can be used for story visualization, therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system of Roberts in view of Poole with the Conditional GAN techniques of Li such that the system could provide an element with story visualization characteristics.
Regarding claim 16, Roberts in view of Poole teaches The process of claim 10, but is silent to wherein the model generation component includes a conditional Generative Adversarial Network (GAN).
Li teaches A conditional GAN system for story visualization (In this work, we propose a new task called Story Visualization. Given a multi-sentence paragraph, the story is visualized by generating a sequence of images, one for each sentence. In contrast to video generation, story visualization focuses less on the continuity in generated images (frames), but more on the global consistency across dynamic scenes and characters– a challenge that has not been addressed by any single-image or video generation methods. See abstract).
Roberts in view of Poole and Li teach of generating image data and Li teaches that a conditional GAN system can be used for story visualization, therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the system of Roberts in view of Poole with the Conditional GAN techniques of Li such that the system could provide an element with story visualization characteristics.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS R WILSON whose telephone number is (571)272-0936. The examiner can normally be reached M-F 7:30-5:00PM.
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/NICHOLAS R WILSON/Primary Examiner, Art Unit 2611