CTNF 18/948,151 CTNF 101495 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claims 1, 1 3, 21-22, 24-27, 30-34, and 36-38 are rej ected under 35 U.S.C. 103 as being unpatentable over Ben edetto (US 20210121781 A1; hereinafter Benedetto) in view of Goyal et al. (US 20220245901 A1; hereinafter Goyal). Reg arding claim 1, Benedetto teaches a content generation system ( “FIG. 5 illustrates a system for generating interiors based on external 3D map data, and providing a virtual environment for gameplay, in accordance with implementations of the disclosure. It will be appreciated that various components of the system can be implemented over one or more server computers and/or other hardware having processors and memory,” (page 5, para [0060]). ), comprising: receiving circuitry configured to receive environment geometry data that defines a surface geometry of at least part of a three-dimensional (3D) virtual environment ( “ The map import logic 510 is configured to obtain 3D map data from the 3D mapping service 500, and import or process the 3D map data for use in a game engine of a video game, which is stored as part of the virtual environment map data 514. It will be appreciated that this portion of the virtual environment map data constitutes exterior data 515 representing the external 3D geometry and appearance (e.g. surface imagery) of real-world locations,” (page 5, para [0063]). “…a 3D map can include 3D geometry information that describes the topography of the land and the 3D exterior structure of objects…,” (page 2, para [0034]-[0035]). The environment geometry data includes Benedetto’s 3D map data, exterior data and / or external 3D geometry. The receiving circuitry includes the map import logic 510. ); selecting inferring circuitry comprising a n selecting inferring model trained to select infer , based on the received environment geometry data that defines the surface geometry of the at least part of the 3D virtual environment, one or more virtual elements ( “An interior generator 511 is configured to generate interiors of objects based on the exterior data. As described herein, a machine learning model 512 is trained to infer interior structure, related objects, etc ., using training data 513. The trained machine learning model 512 is then applied to infer the interior data 516 based on the exterior data 515 . It will be appreciated that the interior data 516 describes the internal 3D geometry of buildings and other structures/objects in the virtual environment and defines the placement of related items within the interiors. In some implementations, the interior data may reference art assets from an asset library, which may be used to define the structure and appearance of items, textures, surfaces or other rendered portions in the virtual environment,” (page 5, para [0064]). “By way of example without limitation, various interior features which can be inferred include: …positions and types of various fixtures and furnishings in the interior can be inferred (e.g. table, chair, stool, sofa/couch, desk, bed, cabinet, dresser, bookshelf, media stand, television, speaker, lamp, picture frames, etc. ), positions and types of articles typically found in the given object/building (e.g. in the case of a house—clothing, books, dining ware, etc.), or any other aspect of the interior which can be inferred and rendered,” (page 4, para [0048]). Benedetto’s machine learning model 512 reads on the trained selecting model. The virtual elements include fixtures, furnishings, articles, and / or objects such as table, chair, stool, sofa/couch, etc. The environment geometry data includes Benedetto’s exterior data. Inferring circuitry includes machine learning model 512. ); and generating circuitry configured to instantiate the selected virtual elements within the at least part of the 3D virtual environment ( “…the virtual environment is generated using the exterior data and the interior data …” (pages 4-5, para [0056]-[0057]; Fig 4). “An interior generator 511 is configured to generate interiors of objects based on the exterior data.…the interior data 516 describes the internal 3D geometry of buildings and other structures/objects in the virtual environment and defines the placement of related items within the interiors ... The virtual environment map data 514, including the exterior data 515 and the interior data 516, are utilized to render a virtual environment for gameplay ,” (page 5, para [0064] – [0065]). “Furthermore, the furnishings in the rooms of the house can also be inferred by a trained machine learning model. For example, the family room 220 may be likely to have a couch 226, and the dining room 218 can be expected to have a dining table 224. The specific placement of furnishings within rooms can also be inferred so that such furnishings are appropriately placed …,” (page 3, para [0042]). Instantiating the selected virtual elements includes generating, placing and / or rendering inferred fixtures, furnishings, and objects. Generating circuitry includes the interior generator 511. ). Benedetto does not explicitly teach selecting objects/ virtual elements via selecting circuitry comprising a selecting model. Goyal teaches selecting objects/ virtual elements via selecting circuitry comprising a selecting model ( “ Machine learning model 520 calculates a confidence for each input candidate object. The candidate object having the highest confidence is then selected and transmitted 532 to virtual environment generation circuitry 506, which incorporates the selected candidate object into the virtual environment. Virtual environment generation circuitry 506 transmits 534 the virtual environment frame or frames containing the candidate object comprising the supplemental content to output circuitry 510, where it is transmitted for display,” (page 5, para [0038]; Fig 2). Machine learning model 520 reads on selecting model. Selecting circuitry includes the machine learning model 520. ) . Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Goyal to Benedetto. The motivation would have been to “[allow] highly accurate predictions of suitable virtual objects that can be added to the virtual environment,” (para [0003]). Additional motivation would have been to improve efficiency, reduce computational load, and / or reduce the complexity of implementation. Regarding claims 13 and 32, they are rejected using the same citations and rationales described in the rejection of claim 1. Claim 32 additionally recites a system comprising: one or more computer processors, and one or more non-transitory computer readable media that store instructions which, when executed by the one or more computer processors, cause the one or more computer processors to perform operations . Benedetto teaches A system comprising: one or more computer processors ( “FIG. 5 illustrates a system for generating interiors based on external 3D map data, and providing a virtual environment for gameplay, in accordance with implementations of the disclosure. It will be appreciated that various components of the system can be implemented over one or more server computers and/or other hardware having processors and memory,” (Benedetto; page 5, para [0060]). ), and one or more non-transitory computer readable media that store instructions which, when executed by the one or more computer processors, cause the one or more computer processors to perform operations ( “In some implementations, a non-transitory computer-readable medium is provided having program instructions embodied thereon that, when executed by at least one computing device , cause said computing device to perform a method including the following operations: … using a trained machine learning model to infer interior data for the physical objects based on the exterior data, the interior data describing inferred internal structures of the physical objects; generating the virtual environment using the exterior data and the interior data...,” (Benedetto; page 1, para [0016]; pages 11-12, para [0126]). ) Regarding claim 38, it is rejected using the same citations and rationales described in the rejection of claim 32. Regarding claim 33, Benedetto in view of Goyal teaches the system of claim 32, wherein: the environment geometry data includes texture data that comprises one or more textures that are to be applied to the at least part of the 3D virtual environment ( Benedetto; “The 3D map can include surface textures/imagery that are applied to the visible surfaces of objects in accordance with their 3D geometry . For example, portions of the aforementioned captured images can be applied to the corresponding surfaces of a 3D geometric representation of an object. By way of example without limitation, in the illustrated implementation, the houses (e.g. 102, 104, 106) have 3D geometry/structure, that may have been determined using photogrammetry. For a given house, the 3D geometry can define a 3D model of the house, and further defines various surfaces of the house. To these surfaces, corresponding image portions can be applied or “painted” as surface textures , thereby yielding a 3D model that appears realistic,” (Benedetto; page 2, para [0035]; page 5, para [0063]). The texture data includes the surface textures/imagery. ); and the one or more virtual elements are selected further based on the texture data ( Benedetto; “… a machine learning model is employed to infer the interior structures of buildings and other objects, based on known information about the buildings/objects such as their 3D geometry and appearance ,” (page 2, para [0032]). “…this portion of the virtual environment map data constitutes exterior data 515 representing the external 3D geometry and appearance (e.g. surface imagery) of real-world locations,” (Benedetto; page 5, para [0063]). Appearance includes surface texture/ imagery. ). Regarding claim 21, it is rejected using the same citations and rationales described in the rejection of claim 33. Regarding claim 34, Benedetto in view of Goyal teaches the system of claim 32, wherein: the environment geometry data includes environment metadata that comprises: i. an indication of a type or genre of content for which the environment geometry data has been generated ( Benedetto; “It will be appreciated that the 3D map is correlated to specific location information, which can be in the form of coordinates or other geo-positioning identifiers which can accurately identify a particular location in the real world. The structures of the 3D map are correlated to real-world positions which they depict,” (page 2, para [0036]). “By way of example without limitation, various features which can be used as predictive factors for inferring an interior of an object, such as a building, include: geographic location (e.g. country, state, city, neighborhood, coordinates, etc.), local terrain, exterior dimensions, exterior shape, color, architectural style (e.g. tudor, colonial, mission, farmhouse, modern, etc.), age or date of construction, type of property (e.g. residential, … mall, retail, office, dining/restaurant, grocery, entertainment, educational, school, university, public, government, judicial, library, museum, movie theater…,” (Benedetto; pages 3-4, para [0044]-[0045]). Environment metadata includes Benedetto’s features: geographic location, architectural style, and / or type of property. An indication of a type of content for which the environment geometry data has been generated includes geographic location, architectural style, age or date of construction, and / or type of property. ), and ii. an indication of a type of environment that the environment geometry data represents ( (Benedetto; page 2, para [0036]; pages 3-4, para [0044]-[0045]). An indication of a type of environment that the environment geometry data represents includes: residential, office, dining/restaurant, grocery, museum, and movie theater. ); and the one or more virtual elements are selected further based on the environment metadata ( Benedetto; “In some implementations, the trained machine learning model further infers the interior data based on geolocation data associated to the real-world location,” (page 1, para [0013]). “Furthermore, the furnishings in the rooms of the house can also be inferred by a trained machine learning model. For example, the family room 220 may be likely to have a couch 226, and the dining room 218 can be expected to have a dining table 224. The specific placement of furnishings within rooms can also be inferred so that such furnishings are appropriately placed. It will be appreciated that the selection of furnishings by the machine learning model can be configured to have a cohesive style, and this may be based on factors such as a determined architectural style of the house, the geographical location of the house , the time era (as discussed further below) of the rendering of the interior, etc. It will be appreciated that the foregoing is merely one example of how furnishings in a building may be inferred,” (Benedetto; page 3, para [0041]-[0042]). ). Regarding claim 22, it is rejected using the same citations and rationales described in the rejection of claim 34. Regarding claim 36, Benedetto in view of Goyal teaches the system of claim 32, wherein the operations comprise: receiving input data indicative of a user prompt ( Benedetto; “FIG. 4 illustrates a method for generating a virtual environment for gameplay based on a real-world 3D mapped location, in accordance with implementations of the disclosure. At method operation 400, a request to initiate gameplay of a video game is received, wherein the gameplay is configured to occur in a virtual environment resembling a requested real-world location. For example, the user may request to play the video game in their neighborhood or town, a specific city, etc . In some implementations, the user may identify a particular address or landmark or other geographic location identifier , such that gameplay is to occur in the local vicinity,” (page 4, para [0052]). The input data indicative of a user prompt includes the user request and / or location selection. ), wherein the one or more of the virtual elements are selected further based on the received input data ( Benedetto; “ Responsive to the request, at method operation 402, exterior data associated to the requested real-world location is obtained , … At method operation 404, a trained machine learning model is used to infer interior data for the physical objects based on the exterior data , wherein the interior data describes inferred internal structures of the physical objects. For example, the interior data can define the interior layout or floorplan of a building, as well as furnishings and their positions within the building. (page 4, para [0053]-[0054]). After combination Benedetto’s inference becomes Goyal’s selection. The exterior data is based on the user request, and the interior data / objects / furnishings / virtual elements are based on the exterior data; therefore, the interior data / objects / furnishings / virtual elements are also based on the user request. ). Regarding claim 24, it is rejected using the same citations and rationales described in the rejection of claim 36. Regarding claim 37, Benedetto in view of Goyal teaches the system of claim 32, wherein the selecting model is trained using one or more sets of image data of one or more real-world environments ( Benedetto; “A 3D map such as that shown can be generated from images taken of the real-world location, such as satellite images, survey images taken from aircraft, images captured from the ground locally such as by mapping vehicles driven on the roads, images captured by individual persons, or any other images of the real-world location which can be correlated to their perspective locations …,” (page 2, para [0034]). “In some implementations, additional sources of related information about a real-world location can be utilized for purposes of inferring interior environments. For example, property information, pictures (exterior and interior) , transaction history, etc. could be obtained from additional source such as a real-estate information website. Information about a locale could be obtained from a government website, social media site, local news site, etc,” (Benedetto; page 4, para [0047]). “ The result of pre-processing and feature extraction is a set of training data which can be applied to train the machine learning model … By training the machine learning model using the training data, then the machine learning model becomes trained to infer the interiors of objects in a 3D map based on external features and related information ,” (Benedetto; page 4, para [0049] – [0050]; Fig 3). Pictures / images can be included in the training data. Pictures / images comprise image data. ). Regarding claim 25, it is rejected using the same citations and rationales described in the rejection of claim 37. Regarding claim 26, Benedetto in view of Goyal teaches the method of claim 13, wherein the selecting model is trained using one or more other 3D virtual environments that each include instantiated virtual elements ( Benedetto; “FIG. 3 illustrates a method for training a machine learning model to infer the interior of an object such as a building from a 3D map, in accordance with implementations of the disclosure. At method operation 300, a data collection process is performed. That is, a corpus of data is obtained that includes 3D structures from 3D maps and data describing their known interior structures, including interior layouts and furnishings . While the 3D structures referenced herein primarily include buildings, they may also include any other objects that may appear on a 3D map and for which an interior can be inferred, such as vehicles (e.g. cars, trucks, motorhomes/RV's, trailers, planes, boats, etc.), tunnels, tents, covered structures, etc. It will be appreciated that corresponding data about the interiors of such objects is included in the corpus of data , as this will be used to train the machine learning model ,” (page 3, para [0043]). The one or more other 3D virtual environments includes 3D maps and / or 3D structures included in the corpus of collected data. The instantiated virtual elements include interior furnishings / data. ). Regarding claim 27, Benedetto in view of Goyal teaches the method of claim 13, comprising: receiving one or more control signals for performing one or more changes to the instantiated virtual elements ( Benedetto; “It will be appreciated that a video game is responsive to user inputs, … As shown, a user operating controller device 956 may generate input data 958... The input data 962 is fed to the gaming system 918, which processes the input data 962 to update the game state of the video game,” (page 10, para [0112]). One or more control signals includes the user input data. ); and performing the one or more changes to the instantiated virtual elements based at least on the received control signals ( Benedetto; “…enabling users to view and interact with both exterior and interior portions of buildings/objects in a virtual environment that simulates a real-world location. It will be appreciated that the particular interactions available to the users will depend upon the player mechanics of the video game. For example, players can parachute into a particular neighborhood, walk/run or drive vehicles (e.g. cars, trucks, etc.) on the streets, fly aircraft through the neighborhood, etc. It will be appreciated that objects in the virtual environment can be destructible, alterable or otherwise changed, and that such changes can be persistent so that later players interacting in the same vicinity will experience the same changes...,” (page 5, para [0058]). “As noted herein, in some implementations, the virtual environment can be alterable, such as portions or items being destructible or moveable or otherwise changeable in some manner . In some implementations, the particular state of the virtual environment can be stored as virtual environment state data 517, which may define the states of objects in the virtual environment ... In some implementations, there is a global starting state, and a personalized delta indicating how the state has changed based on user actions in the virtual environment . For example, the environment can be destructible so that it is possible to break the glass in a building. In the initial starting state, the glass is not broken, but if it does get broken, then it will persist for that player...” (Benedetto; page 6, para [0068] – [0071]). Changes to the instantiated virtual elements includes changes to the states of objects, alterations, destructions of portions or items, and / or movement of portions or items. User actions / interactions are performed via user input data / control signals. The input data is used to update the game state of the video game which can include the states of objects. ). Regarding claim 30, Benedetto in view of Goyal teaches the method of claim 13, comprising: determining, based on the received environment geometry data, a location within the at least part of the virtual environment for a given selected virtual element or an orientation for the given selected virtual element ( Benedetto; “…The trained machine learning model 512 is then applied to infer the interior data 516 based on the exterior data 515 . It will be appreciated that the interior data 516 describes the internal 3D geometry of buildings and other structures/objects in the virtual environment and defines the placement of related items within the interiors ,” (page 5, para [0064]). “By way of example without limitation, various interior features which can be inferred include: … positions and types of various fixtures and furnishings in the interior can be inferred (e.g. table, chair, stool, sofa/couch, desk, bed, cabinet, dresser, bookshelf, media stand, television, speaker, lamp, picture frames, etc.), positions and types of articles typically found in the given object/building (e.g. in the case of a house—clothing, books, dining ware, etc.), or any other aspect of the interior which can be inferred and rendered,” (Benedetto; page 4, para [0048]). The location for a given selected virtual element includes placement/ positions of items, fixtures, furnishings, and / or articles. ), wherein the selected virtual element is instantiated within the at least part of the virtual environment at the determined location or in the determined orientation ( Benedetto; “…the virtual environment is generated using the exterior data and the interior data …” (pages 4-5, para [0056]-[0057]; Fig 4). “An interior generator 511 is configured to generate interiors of objects based on the exterior data.…the interior data 516 describes the internal 3D geometry of buildings and other structures/objects in the virtual environment and defines the placement of related items within the interiors ... The virtual environment map data 514, including the exterior data 515 and the interior data 516, are utilized to render a virtual environment for gameplay ,” (Benedetto; page 5, para [0064] – [0065]). “Furthermore, the furnishings in the rooms of the house can also be inferred by a trained machine learning model. For example, the family room 220 may be likely to have a couch 226, and the dining room 218 can be expected to have a dining table 224. The specific placement of furnishings within rooms can also be inferred so that such furnishings are appropriately placed …,” (Benedetto; page 3, para [0042]). Instantiating the selected virtual elements at the determined location includes generating, placing, and / or rendering inferred fixtures, furnishings, and objects. ). Regarding claim 31, Benedetto in view of Goyal teaches the method of claim 13, wherein one or more of the virtual elements comprise: i. a virtual character; ii. a virtual object; or iii. a texture ( Benedetto; “By way of example without limitation, various interior features which can be inferred include: …positions and types of various fixtures and furnishings in the interior can be inferred (e.g. table, chair, stool, sofa/couch, desk, bed, cabinet, dresser, bookshelf, media stand, television, speaker, lamp, picture frames, etc. ), positions and types of articles typically found in the given object/building (e.g. in the case of a house—clothing, books, dining ware, etc.), or any other aspect of the interior which can be inferred and rendered,” (page 4, para [0048]). The virtual elements include fixtures, furnishings, articles, and / or objects such as table, chair, stool, sofa/couch, etc. A virtual object includes any of the fixtures, furnishings, articles, and / or objects such as table, chair, stool, sofa/couch, etc. ) . 07-21-aia AIA Claim s 23, 29 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Benedetto in view of Goyal in further view of Maeder et al. (US 20240062490 A1; hereinafter Maeder) . Regarding claim 35, Benedetto in view of Goyal is not relied upon teaching but Maeder teaches the system of claim 32, wherein the selecting model is constrained to select less than or equal to a predetermined number of virtual elements ( “The context object information matching procedure 338 has access to trained matching ML model 260. The context object information matching procedure 338 uses the trained matching ML model 260 to match contextual information, placement space and objects belonging to the most relevant categories predicted by the object category selection procedure 336 to obtain a set of relevant objects for display on a given placement space. The set of relevant objects includes at least one relevant object,” (page 12, para [0206]). “In one or more embodiments, the context object information matching procedure 338 filters the objects based on the respective object relevance scores to obtain the set of relevant objects. As a non-limiting example, the context object information matching procedure 338 may only select objects to be included in the set of relevant objects if their relevance score is above a threshold. Further, in some embodiments, the context object information matching procedure 338 may only select one relevant object for the potential placement space ,” (page 12, para [0210]). The model is restricted to select one object. One object reads on a predetermined number of virtual elements. ). Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Maeder to Benedetto in view of Goyal. The motivation would have been to improve performance / efficiency, save computational resources, and / or improve render times. Additional motivation would be to reduce visual clutter and / or visually please the user. Additional motivation would have been to increase the relevance of the displayed content. Regarding claim 23, it is rejected using the same citations and rationales described in the rejection of claim 35. Regarding claim 29, Benedetto in view of Goyal is not relied upon teaching but Maeder teaches the method of claim 13, comprising: modifying, based on the received environment geometry data, one or more of the selected virtual elements ( “The image processing procedure 320 generates an augmented view 440 comprising the relevant object 418 overlaid on the selected placement space 416, where the shape, position and lighting of the object are adapted to the selected placement space 416 such that the object 418 appears as if it was a physical depiction of an umbrella displayed on the wall,” (page 9, para [0150]-[0151]; Fig 3; Fig 4). “In the context of the present technology, the image processing models 270 are used to detect placement spaces in images where objects may be overlaid. Additionally, the image processing models 270 may be configured to scale and modify the objects such that the objects appear as if they were physically present on the placement spaces ,” (page 8, para [0111]). After combination Maeder’s placement space becomes Benedetto’s 3D map data, exterior data and / or external 3D geometry. Modifying includes scaling and / or adapting the shape, position, and lighting of an object. ), wherein the modified virtual elements are instantiated within the at least part of the virtual environment ( “The object placement procedure 326 may use different techniques for positioning and displaying objects on placement spaces. Once the placement spaces of the physical environment are modeled, the dimensions of the object are adapted to suit the environment dimensions, and the object is projected on a given placement space . The object placement procedure 326 may match the light projection of the displayed object with the lighting and shading of the placement space onto which the object is projected. Additionally, the boundaries of the object may be adapted to match the shape of the placement space onto which the object is projected to ensure a natural blend of the object and the placement space,” (page 11, para [0173]). Instantiating the modified virtual elements includes projecting the adapted / modified object on a given placement space. Additionally, from Fig 4 it is clear that the umbrella object is instantiated. ). Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Maeder to Benedetto in view of Goyal. The motivation would have been “to ensure a natural blend of the object and the placement space,” (page 11, para [0173]). Additional motivation would have been to improve the realism of the rendered environment and / or visually please the user . 07-21-aia AIA Claim 28 is rejected under 35 U.S.C. 103 as being unpatentable over Benedetto in view of Goyal in further view of Mannor et al. (US 20250053284 A1; hereinafter Mannor) . Regarding claim 28, Benedetto in view of Goyal is not relied upon teaching but Mannor teaches the method of claim 27, wherein the selecting model is trained using one or more of the performed changes ( “…A feedback analyzer may extract feedback information from input data generated by client device 104 using input device(s) 120. For example, feedback information may be identified and extracted from input data that corresponds to user inputs made to client device 104 . …implicit feedback may be based on measurements of user interaction with client device 104. In at least one embodiment, implicit feedback is based on recording information associated with a user. In some examples, information associated with a user may be determined based on the user's interaction with input device(s) 120. For example, implicit feedback may be based on … interactions, and/or objects that a user may interact with during one or more intervals of time associated with application 118 (e.g., during one or more game sessions of a game, within a 3D scene, etc .),” (para [0075]). “In some examples, feedback data may be applied to machine learning model(s) 140 and/or machine learning model(s) 142 to be trained using reinforcement learning or inverse reinforcement learning . For example, to learn to detect or identify modifications to objects in one or more 3D scenes, inverse reinforcement learning may be used to observe objects and content items through many video streams associated with the 3D scene and learn to detect interactions with the objects (e.g., to calculate feedback associated with the objects.)…,” (pages 4-5, para [0077] – [0078]). Feedback data is used to train a machine learning model. Feedback data can be based on user interactions with objects. After combination, Mannor’s user interactions with objects becomes Benedetto’s changes to objects, portions of the virtual environment, and / or items based on user actions/ interactions. ). Before the effective filling date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Mannor to Benedetto in view of Goyal. The motivation would have been to “to determine a modification of one or more objects in a 3D environment,” (Mannor; page 5, para [0078]). Additional motivation would have been to improve machine learning decisions and / or improve user experience. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERICA G THERKORN whose telephone number is (571)272-2939. The examiner can normally be reached Monday - Friday 9:00am - 5:00pm. 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, Devona Faulk can be reached at 571-272-7515. 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. /ERICA G THERKORN/ Examiner, Art Unit 2618 /DEVONA E FAULK/ Supervisory Patent Examiner, Art Unit 2618 Application/Control Number: 18/948,151 Page 2 Art Unit: 2618 Application/Control Number: 18/948,151 Page 3 Art Unit: 2618 Application/Control Number: 18/948,151 Page 4 Art Unit: 2618 Application/Control Number: 18/948,151 Page 5 Art Unit: 2618 Application/Control Number: 18/948,151 Page 6 Art Unit: 2618 Application/Control Number: 18/948,151 Page 7 Art Unit: 2618 Application/Control Number: 18/948,151 Page 8 Art Unit: 2618 Application/Control Number: 18/948,151 Page 9 Art Unit: 2618 Application/Control Number: 18/948,151 Page 10 Art Unit: 2618 Application/Control Number: 18/948,151 Page 11 Art Unit: 2618 Application/Control Number: 18/948,151 Page 12 Art Unit: 2618 Application/Control Number: 18/948,151 Page 13 Art Unit: 2618 Application/Control Number: 18/948,151 Page 14 Art Unit: 2618 Application/Control Number: 18/948,151 Page 15 Art Unit: 2618 Application/Control Number: 18/948,151 Page 16 Art Unit: 2618 Application/Control Number: 18/948,151 Page 17 Art Unit: 2618 Application/Control Number: 18/948,151 Page 18 Art Unit: 2618 Application/Control Number: 18/948,151 Page 19 Art Unit: 2618 Application/Control Number: 18/948,151 Page 20 Art Unit: 2618 Application/Control Number: 18/948,151 Page 21 Art Unit: 2618 Application/Control Number: 18/948,151 Page 22 Art Unit: 2618
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