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 .
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.
Election/Restriction
In response to an office restriction requirement, applicant elected Group III, claims 1-2, and 11-20, without traverse on July 6, 2026. As a result, claims 3-10 were not elected, and withdrawn from consideration. In response to the instant office action, applicant should cancel claims 3-10 to proceed with prosecution of the pending claims.
Response to Amendment
Applicant has amended claims 1-2, 11-13, 15, 18, 19 and 20 to overcome the rejection made pursuant to 35 U.S.C. 101 and as such the rejection has been withdrawn.
Response to Arguments
Applicant's arguments filed 8/31/2026 have been fully considered but they are not persuasive.
First, applicant argues Agarwal fails to teach “continuously processing one or more inputs received by a user device, while the one or more digital effects […] are presented on the user device, along with the set of instructions in real time by the generative machine learning model to update presentation of the digital effects” (see page 9 of applicant’s correspondence filed 8/31/2026). Examiner respectfully disagrees.
Agarwal teaches continuously processing one or more inputs received by a user device, while the one or more digital effects […] are presented on the user device, (Agarwal, ¶23 discloses that after an initial generation of the virtual world, the VR world generator can be used to iteratively add or remove objects from the generated world, explicitly stating that “a user may inspect a generated virtual environment to assess whether the VR world generated created an accurate representation of the virtual environment that the user intended to create [and if] an aspect of the virtual environment was either inaccurately generated, or if the user wishes to otherwise alter aspects of the generated virtual environment, the user may issue subsequent natural language commands to remove objects, add new objects, change details about various objects, and/or change details about the skybox” – in other words, the device continues to process natural language commands by a user while the user is inspecting the presented digital effects) along with the set of instructions in real time by the generative machine learning model to update presentation of the digital effects (Agarwal, ¶23 further discloses that the user can issue secondary plain language commands to tune the environment to achieve a particular environment or aesthetic using the VR world generator that iteratively adds or removes objects from the generated world – note ¶17 discloses the VR world generator includes natural language command processor and generative virtual environment builder, which indicates the user inputs are used by VR world generator to update digital effects presented on the user display; As for real-time processing, Agarwal discloses Fig 4 and ¶36, the process 400 for generating the virtual environment based on plain language commands “can be performed multiple times, repeatedly, iteratively, consecutively, concurrently, in parallel, etc., as requests to generate 3D environments and/or modify aspects of those 3D environments”, where the process includes receiving the plain language commands and generating the objects).
Accordingly, applicant’s argument is not persuasive.
Second, applicant argues Agarwal and McGill fail to teach “the digital effects experience is presented on the user device as a video by passing one or more graphics processing engines of the user device” as McGill does not use generative machine learning, and further the by pass of McGill “does not cause a video to be presented in lieu of device processing” as applicant argues the specification requires (see pages 11-12 of applicant’s correspondence filed 8/31/2026). Examiner respectfully disagrees.
Regarding applicant’s argument directed to McGill not teaching the use of generative machine learning, applicant's arguments are directed against the references individually, and one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). In particular, Agarwal is relied upon for teaching the generative machine learning processing, and McGill is only relied upon for showing that by-passing at least one graphics processing engine of the user device is a known technique for presenting video on a user device. Accordingly, applicant’s argument ignores the combination of references and is not persuasive.
Regarding applicant’s argument that McGill does not teach bypassing one or more graphics processing engines for the digital effects experience, it is noted that the features upon which applicant relies (i.e., exemplary embodiments of the by-passing that are provided in the applicant’s specfication) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The claim language merely recites “wherein the digital effects experience is presented on the user device as a video by bypassing one or more graphics processing engines of the user device.” As currently drafted, the only requirement is that at least one graphics processing engine of the user device is bypassed. This does not require all graphics processing engines are bypassed, nor does it require “by passing traditional graphics processing engines” as a whole, as applicant argues. Instead, applicant is reading in limitations that are not claimed. If applicant wants to narrow the claim language to explicitly require bypassing all graphics processing engines, then applicant should amend the claims to recite as such. As for the current state of the claim, McGill teaches bypassing one or more graphics processing engines of the user device (McGill, ¶40: server provides access to interactive video 136 by a separate computer or computer component, such as user computing device or player module; ¶41: player module directly access video card to bypass browsers video rendering engine and provide video output data directly to hardware video card).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention and with a reasonable expectation of success, to modify the technique for generating a virtual experience based on user text input as provided by Agarwal, by further including the technique by passing the graphics processing engine as provided by McGill, using known electronic interfacing and programming techniques. The modification merely substitutes one known type of graphics processing for another, namely server-side for client side, yielding predictable results of producing video in a known distributed system architecture. The modification allows for an improved rendering system with multiple rendering techniques by ensuring conflicting rendering engines are not utilized when a second desired technique is used, resulting in faster processing (i.e. not utilizing both unnecessarily) and preventing visual conflicts from overlapping processes, while also allowing offloading of the rendering to a server that delivers the video for presentation to a user on a client device that may not be able to process the machine learning as quickly, while still allowing interactive dynamic effects response to user interactions.
Accordingly, applicant’s arguments are not persuasive.
Applicant’s remaining arguments are based on the same rationale as claim 1 and not persuasive for the same reasons as provided above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 18, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over:
Agarwal et al. (US 2024/0212265 A1) in view of
McGill et al. (US 2016/0196044 A1).
Regarding claim 1, Agarwal discloses:
A system (Agarwal, Abstract and ¶45: computer system 500; ¶70: AR system) comprising:
at least one processor (Agarwal, ¶46: processor 510);
at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: (Agarwal, ¶50: processor accessing memory, memory 550 including programs and software; Claim 18 of Agarwal)
accessing a set of instructions that defines a digital effects experience (Note Applicant’s Spec. ¶39, filed 10/18/24: instructions include text prompts – Agarwal: ¶17-18: text data, e.g. “scuba diving in Hawaii”);
processing the set of instructions by a generative machine learning model to generate one or more digital effects comprising the digital effects experience (Agarwal: ¶17-18: text data, e.g. “scuba diving in Hawaii” user command processed t, with generative virtual environment builder generating effects; ¶35: the pipeline 300 may leverage one or more deep learning models to enable users to naturally describe 3D environments (i.e., without memorizing a specialized set of commands) and generate 3D environments based on those descriptions); and
continuously processing one or more inputs, received by a user device, while the one or more digital effects comprising the digital effects experience are presented on the user device, along with the set of instructions in real time by the generative machine learning model to update presentation of the one or more digital effects comprising the digital effects experience (Agarwal, ¶23; after generating initial world, VR world generator iteratively adds/removes objects and modifies, based on user commands; ¶¶26-28; Fig 4 and ¶36: process 400 can be performed multiple times, repeatedly, iteratively, consecutively, concurrently, in parallel, etc., as requests to generate 3D environments and/or modify aspects of those 3D environments, including e.g. generating skyboxes; ¶40: real-time rendering of skybox).
Agarwal does not explicitly teach bypassing one or more graphics processing engines of the user device for presenting video. It was known at the time, however, that interactive video can be provided to a client device in a form that does not require the user device graphics processing engine.
McGill discloses:
wherein the digital effects experience is presented on the user device as a video by bypassing one or more graphics processing engines of the user device (McGill, ¶40: server provides access to interactive video 136 by a separate computer or computer component, such as user computing device or player module; ¶41: player module directly access video card to bypass browsers video rendering engine and provide video output data directly to hardware video card)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention and with a reasonable expectation of success, to modify the technique for generating a virtual experience based on user text input as provided by Agarwal, by further including the technique by passing the graphics processing engine as provided by McGill, using known electronic interfacing and programming techniques. The modification merely substitutes one known type of graphics processing for another, namely server-side for client side, yielding predictable results of producing video in a known distributed system architecture. The modification allows for an improved rendering system with multiple rendering techniques by ensuring conflicting rendering engines are not utilized when a second desired technique is used, resulting in faster processing (i.e. not utilizing both unnecessarily) and preventing visual conflicts from overlapping processes, while also allowing offloading of the rendering to a server that delivers the video for presentation to a user on a client device that may not be able to process the machine learning as quickly, while still allowing interactive dynamic effects response to user interactions.
Regarding claim 19, the system of claim 1 performs the method of claim 19, and as such the claim is rejected based on the same rationale as claim 1 set forth above.
Regarding claim 20, Agarwal discloses:
A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations (Agarwal, ¶50: processor accessing memory, memory 550 including programs and software; Claim 18 of Agarwal)
Further regarding claim 20, the operations perform the method of claim 19 and as such claim 20 is rejected based on the same rationale as claim 19 set forth above.
Regarding claim 2, Agarwal further discloses:
wherein the digital effects experience comprises an augmented reality (AR) or virtual reality (VR) experience (Agarwal, ¶3 and ¶70: XR, VR, AR)
Regarding claim 18, Agarwal further discloses:
wherein the generative machine learning model comprises a video render trained to generate video based on a prompt comprising instructions (Agarwal, Abstract: The inferred location and experience can be provided as inputs to a generative virtual environment builder trained on real-world data—such as photos and videos captured by users engaging in various activities at various locations—which generates a navigable 3D virtual environment that can include a skybox, virtual objects (and their respective locations), or some combination thereof; ¶17: combine NLP and generative or autoregressive models to convert plain language words or commands into an automatically generated 3D environment, where generative virtual environment builder can receive the location and activity as inputs (along with embeddings, metadata, etc.) and generate a virtual environment (e.g., a skybox) and/or 3D objects within that virtual environment, the natural language command processor and the generative virtual environment builder can be combined to form a VR world generator, which can generate interactive 3D virtual environments and/or objects therein based on plain language description of a location and/or an activity; ¶70: Artificial reality content may include completely generated content or generated content combined with captured content (e.g., real-world photographs), where the artificial reality content may include video; Also claim 18 generative virtual environment using machine learning model)
Claim(s) 11 and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over:
Agarwal et al. (US 2024/0212265 A1) in view of
McGill et al. (US 2016/0196044 A1) and
Manesh et al. (Manesh, Setareh Aghel, et al., “How people prompt generative ai to create interactive VR scenes”, Proceedings of the 2024 ACM Designing Interactive Systems Conference. 2319–2340, July 2024).
Claim 11, the limitations included from claim 1 are rejected based on the same rationale as claim 1 set forth above. Further regarding claim 11, Agarwal further discloses:
wherein the set of instructions comprise a textual prompt, the textual prompt defining a scene and describing the one or more digital effects (Agarwal, ¶29: “attending a sports car exhibition at a convention center”, qualifying location and objects – e.g. exhibition at convention center is scene, and sport car describes objects shown; also ¶42: For example, after generating a tropical scuba diving-related 3D environment, second plain languages such as “add starfish,” “make the reef shallower,” or “remove clownfish” can cause the VR world generator to add 3D objects, remove 3D objects, and/or adjust the terrain or skybox.)
The only limitation not explicitly taught by Agarwal is the textual prompt describing movement of the one or more digital effects and positioning of the one or more digital effects.
Manesh discloses:
the textual prompt describing movement of the one or more digital effects and positioning of the one or more digital effects (Manesh, Abstract, “To explore how these lessons could be applied, we designed and built Ostaad, a conversational programming agent that allows non-programmers to design interactive VR experiences that they inhabit”; Fig. 1 – “Create a door on that wall” and “Please make the door open on its hinges”; p. 2322, Section 2.3: ¶1: generative models generat4e text conditioned on language input – also note Agarwal teaches prompt in form of text; p. 2325 right column discloses prompts, including “Place a brown rectangle coffee table in the middle of the room.”; p. 2326, section 4.1, ¶1, prompt ““Move the chair near the table.”; p. 2328, ¶4.2, ¶¶1-2 discloses prompting agent, and response of agent – i.e. prompt is used by generative model; p. 2330, section 5: “Ostaad is a working prototype of a programming agent that accepts embodied prompts to design interactive VR scenes” and pp. 2330-2331, Section 5.2: Transcriber translates to written text provided to Tasker and Coder and to Executor that executes generated code to implement user-requested changes to the scene, where Tasker and Coder rely on general-purpose large language model operating on prompts; Section 5.3 on p. 2331 further discloses creating interactive virtual environments in VR scene using prompts and feedback)
Agarwal and Manesh are both directed to systems and methods for utilizing language prompting of machine learning to generate image data in virtual or augmented reality. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention and with a reasonable expectation of success, to modify the technique for generating a virtual experience based on user text input as provided by Agarwal, including the technique by passing the graphics processing engine as provided by McGill, by further including the technique for in-situ prompting to augment a virtual experience using machine learning, as provided by Manesh, using known electronic interfacing and programming techniques. The modification results in an improved generative virtual experience by allowing easier user use for generating the virtual environment by using natural language and allowing more flexibility in the generation of the environment by utilizing more natural language inputs that non-engineer users better understand.
Regarding claim 14, Agarwal further discloses:
wherein the textual prompt defines interactivity of the digital effects experience, the interactivity describing modifications to be made by the generative machine learning model in real time (Agarwal, ¶23; after generating initial world, VR world generator iteratively adds/removes objects and modifies, based on user commands; ¶¶26-28; Fig 4 and ¶36: process 400 can be performed multiple times, repeatedly, iteratively, consecutively, concurrently, in parallel, etc., as requests to generate 3D environments and/or modify aspects of those 3D environments, including e.g. generating skyboxes; ¶40: real-time rendering of skybox)
Agarwal modified by McGill and Manesh further discloses:
wherein the textual prompt defines interactivity of the digital effects experience, the interactivity describing modifications to be made by the generative machine learning model in real time responsive to the one or more inputs received by a user device (Manesh, Fig. 1 – “Turn on this light when I touch it, and turn it off if I touch it again” prompt; p. 2322, Section 2.3: ¶1: generative models generate text conditioned on language input – also note Agarwal teaches prompt in form of text; p. 2328 ¶4.2, ¶¶1-2, p. 2330 and pp. 2330-2331, Section 5.2 further discusses prompting agent to generate effects)
Agarwal and Manesh are both directed to systems and methods for utilizing language prompting of machine learning to generate image data in virtual or augmented reality. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention and with a reasonable expectation of success, to modify the technique for generating a virtual experience based on user text input as provided by Agarwal, including the technique by passing the graphics processing engine as provided by McGill, by further including the technique for in-situ prompting to augment a virtual experience with response actions using machine learning, as provided by Manesh, using known electronic interfacing and programming techniques. The modification results in an improved generative virtual experience by allowing easier user use for generating the virtual environment by using natural language and allowing more flexibility in the generation of the environment by utilizing more natural language inputs that non-engineer users better understand, while also allowing for creation of a more engaging interactive experience.
Regarding claim 15, Agarwal modified by McGill and Manesh further discloses:
wherein the textual prompt includes a goal associated with the digital effects experience (Manesh, p. 2321, Fig. 2: participant issues a prompt of what agent should understand to move the world to the goal state; p. 2324, ¶2: “Each referent was shown to the participant, which they were to interpret as their goal (given the current state of the environment). Participants would then press a button to begin the audio recording, and then they would prompt the system with instructions to move toward their goal.”; p. 2322, Section 2.3: ¶1: generative models generate text conditioned on language input – also note Agarwal teaches prompt in form of text; p. 2328 ¶4.2, ¶¶1-2, p. 2330 and pp. 2330-2331, Section 5.2 further discusses prompting agent to generate effects).
Agarwal and Manesh are both directed to systems and methods for utilizing language prompting of machine learning to generate image data in virtual or augmented reality. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention and with a reasonable expectation of success, to modify the technique for generating a virtual experience based on user text input as provided by Agarwal, including the technique by passing the graphics processing engine as provided by McGill, by further including the technique for in-situ prompting to augment a virtual experience using machine learning, as provided by Manesh, using known electronic interfacing and programming techniques. The modification results in an improved generative virtual experience by allowing easier user use for generating the virtual environment by using natural language and allowing more flexibility in the generation of the environment by utilizing more natural language inputs that non-engineer users better understand.
Regarding claim 16, Agarwal further discloses:
wherein the textual prompt comprises a digital object that is used as the one or more digital effects (Agarwal, ¶29: “attending a sports car exhibition at a convention center”, qualifying location and objects – e.g. exhibition at convention center is scene, and sport car describes objects shown; also ¶42: For example, after generating a tropical scuba diving-related 3D environment, second plain languages such as “add starfish,” “make the reef shallower,” or “remove clownfish” can cause the VR world generator to add 3D objects, remove 3D objects, and/or adjust the terrain or skybox.)
Agarwal modified by McGill and Manesh further discloses:
wherein the textual prompt comprises a digital object that is used as the one or more digital effects and wherein the textual prompt comprises a set of conditions associated with presentation of the one or more digital effects (Manesh, Fig. 1 – “Turn on this light when I touch it, and turn it off if I touch it again” prompt; p. 2322, Section 2.3: ¶1: generative models generat4e text conditioned on language input – also note Agarwal teaches prompt in form of text; p. 2325 right column discloses prompts, including “Place a brown rectangle coffee table in the middle of the room.”; p. 2326, section 4.1, ¶1, prompt ““Move the chair near the table.”; p. 2328¶4.2, ¶¶1-2 discloses prompting agent, and response of agent – i.e. prompt is used by generative model; p. 2330 and pp. 2330-2331, Section 5.2 further discusses prompting agent to generate effects)
Agarwal and Manesh are both directed to systems and methods for utilizing language prompting of machine learning to generate image data in virtual or augmented reality. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention and with a reasonable expectation of success, to modify the technique for generating a virtual experience based on user text input as provided by Agarwal, including the technique by passing the graphics processing engine as provided by McGill, by further including the technique for in-situ prompting to augment a virtual experience using machine learning, as provided by Manesh, using known electronic interfacing and programming techniques. The modification results in an improved generative virtual experience by allowing easier user use for generating the virtual environment by using natural language and allowing more flexibility in the generation of the environment by utilizing more natural language inputs that non-engineer users better understand.
Claim(s) 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over:
Agarwal et al. (US 2024/0212265 A1) in view of
McGill et al. (US 2016/0196044 A1) and
Manesh et al. (Manesh, Setareh Aghel, et al., “How people prompt generative ai to create interactive VR scenes”, Proceedings of the 2024 ACM Designing Interactive Systems Conference. 2319–2340, July 2024) and in further view of
Swaminathan et al. (US 2021/0272341 A1)
Regarding claim 12, the limitations included from claim 11 are rejected based on the same rationale as claim 11 set forth above. Further regarding claim 12, Swaminathan discloses:
wherein the positioning of the one or more digital effects is described in relation to one or more real-world objects that are depicted in an input image or video (Swaminathan, ¶27: reference image 108 of human subject; ¶28: text vector used to identify which portions of reference image 108 are to be modified according to text description 110 – e.g. “raspberry short sleeve top”, identify text description 110 corresponds to upper body portion; ¶29 – image generation using generative model with text description inputs 110; ¶36: text description representative of at least one word of text describing one or more of clothing or an accessory to be depicted in the output image 106 as worn by the human subject of the reference image 108; ¶48: generative video; ¶76: in response to determining, from the text vector 204, that the text description 110 describes a “raspberry short sleeve top,” the masking module 116 may identify that the text vector 204 indicates an upper body region of the segmentation map 202 is to be masked in the masked image 206; ¶78: generative model causes output)
Agarwal and Swaminathan are both directed to systems and methods for utilizing text prompting of machine learning to generate image data. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention and with a reasonable expectation of success, to modify the technique for generating a virtual experience based on user text input as provided by Agarwal, including the technique by passing the graphics processing engine as provided by McGill and the technique for in-situ prompting to augment a virtual experience using machine learning, as provided by Manesh, by further including the technique for processing text to present virtual elements on an image or video image of a person as provided by Swaminathan, using known electronic interfacing and programming techniques. The modification results in an improved generative virtual experience by allowing greater flexibility with presentation of virtual objects, better tailored to the user’s preferences and allowing for more immersive experiences by allowing augmentation of different common real-world objects.
Regarding claim 13, Agarwal modified by McGill, Manesh and Swaminathan further discloses:
wherein the one or more real-world objects comprise a body part of a person depicted in the input image or video (Swaminathan: ¶27: reference image 108 of human subject; ¶28: text vector used to identify which portions of reference image 108 are to be modified according to text description 110 – e.g. “raspberry short sleeve top”, identify text description 110 corresponds to upper body portion; ¶29 – image generation using generative model with text description inputs 110; ¶75 discusses identifying different body parts, e.g. left foot, for including clothing over corresponding part)
Agarwal and Swaminathan are both directed to systems and methods for utilizing text prompting of machine learning to generate image data. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention and with a reasonable expectation of success, to modify the technique for generating a virtual experience based on user text input as provided by Agarwal, including the technique by passing the graphics processing engine as provided by McGill and the technique for in-situ prompting to augment a virtual experience using machine learning, as provided by Manesh, by further including the technique for processing text to present virtual elements on an image or video image of a person as provided by Swaminathan, using known electronic interfacing and programming techniques. The modification results in an improved generative virtual experience by allowing greater flexibility with presentation of virtual objects, better tailored to the user’s preferences and allowing for more immersive experiences by allowing augmentation of different common real-world objects.
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 WILLIAM A BEUTEL whose telephone number is (571)272-3132. The examiner can normally be reached Monday-Friday 9:00 AM - 5:00 PM (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DANIEL HAJNIK can be reached at 571-272-7642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLIAM A BEUTEL/Primary Examiner, Art Unit 2616
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