DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicants
2. This communication is in response to the application filed on 12/12/2024.
3. Claims 1-20 are pending.
4. Limitations appearing inside {} are intended to indicate the limitations not taught by said prior art(s)/combinations.
Information Disclosure Statement
5. The information disclosure statements (IDS) submitted on 03/14/2025, 12/05/2025, 01/21/2026, 03/17/2026, and 07/14/2026 have been considered by the examiner.
Claim Rejections - 35 USC § 103
6. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
7. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2020/0174979 to St. Martin et al. (hereinafter St. Martin) and further in view of “Vetaverse: A Survey on the Intersection of Metaverse, Vehicles, and Transportation Systems” to Zhou et al. (hereinafter Zhou).
8. Regarding Claim 1, St. Martin discloses a method for generating a searchable arrangement of data associated with a mobile object ([col. 2, ln. 63 to col. 3, ln. 2] “…the system further comprises a search function enabling registered members who are not agents of the museum to search the database using a variety of search criteria. In one embodiment the search criteria comprise at least SIO codes, names of objects, categories of objects, objects by dates of entry, and identity of contributors of individual media files...”, [col 3, ln. 12-19] “…a method for providing social identity to an object is provided, comprising creating a data repository comprising virtual objects as names and descriptions of physical objects in a collection of physical objects organized and displayed under control of a specific enterprise, the names and descriptions entered by agents of the enterprise, assigning to each virtual object on entry a unique SIO code…”, [col. 6, ln. 9-20] “FIG. 1 is a simple architectural diagram illustrating one embodiment of the invention. An object 103 is illustrated in FIG. 1, and represents one of a very large number of objects that may be uniquely associated with (instantiated by) an SIO code in embodiments of the present invention, in the broad sense of objects as described above. A mobile communication device 101 having an image-capturing capability may be used in various embodiments, and is shown in this representation as imaging object 102 in image field 102. Mobile device 101 may be, for example, a cellular telephone, a pad device, or any other mobile, computerized device that incorporates image capture…”), the method comprising:
receiving sensor data from a plurality of sensors of the mobile object over time ([col. 6, ln. 58 to col. 7, ln. 11] “At step 204 the user identifies the object to be instantiated, and uploads information about the object to the server. This information may be of many sorts, and may in some instances be quite extensive. The user may photograph the object, create video of the object, and may enter any amount of textual information or recorded audio regarding the object. In one embodiment this object information, which may include extensive contextual information, such as where the object is presently, where the object has been, where how and when the object has been associated with specific persons, and so forth, is stored temporarily while the system attempts to determine whether the object in question may already be instantiated with an SIO code and related information in databases coupled to the server. It is important to understand that this uploading of information is not a one-time process. Once an object is instantiated with a social identity the instantiating user, and indeed other users, may upload more information over time at different points in time.”, [col. 16, ln. 40-52] “System 401 illustrated in FIG. 4 is a system comprising hardware, one or more memory elements, and one or processors executing coded instructions. The hardware includes a plurality of input feeds 402 labeled 1-n, and a data connection to the virtual world. Input feeds 1-n may be drawing information from a variety of sources, such as a Global Positioning System (GPS) Device in an automobile that may have been instantiated into the virtual world. At the time of instantiating the automobile the member doing so may have access to selections enabling tracking of the automobile. That is, the member may be presented by the system with a configuration interface wherein the member may identify a GPS device (the one in the car).”);
receiving positioning data associated with {a pose} of the mobile object over time ([col. 16, ln. 40-52], [col. 16, ln. 53-59] “With GPS enabled for the car that is instantiated, system 401 will look for signals from the GPS through one of feeds 402, and finding same will update information associated in the virtual world with the virtual object mirroring the car, so that real-time position (space and time) for the virtual car may match real time position of the actual car. The virtual world car now has time and position of the real world car.”);
generating a plurality of data points associated with the mobile object, wherein at least a first subset of the plurality of data points track changes within the sensor data over time ([col. 6, ln. 58 to col. 7, ln. 11], [col. 7, ln. 57-67] “It will be apparent to the skilled person that some contextual information for some objects may be fluid in nature. For example, a user may register a personal aircraft, or a locomotive, for example. Location on the planet may be known at the time of registration and assigning a unique SIO code, but as time passes the location may change drastically. In some embodiments the system may have functionality for periodically or continually updating contextual information regarding many objects, such as by accessing data from other Internet-connected sites like server 108 of FIG. 1, which might be a server hosted by an airline, for example.”, [col. 16, ln. 40-52], [col. 16, ln. 53-59], [col. 18, ln. 1-15] “FIG. 10 is a diagram of a complex object having an SIO code and how one SIO code can transition into many SIO codes. The example illustrates a ship that was built in the 1950′s. The diagram shows how the ship, the people in the ship, the parts of the ship can all have SIO codes and histories. In this example after the ship is put out of commission, each of the parts can be issued a separate SIP code and these can be tracked. If the ship was dismantled the parts can be repurposed and used to make different items. Each type of item if it were to go to different manufactures could be tracked as it is made into parts, sheet metal, iron beams, and blocks of steel. If it was then used to make cell phones, went to a collector's museum, used to make a skyscraper, or melted to make a gun—the entire history of all the SOI could be tracked at different time in their history.”), wherein at least a second subset of the plurality of data points track changes to {the pose} of the mobile object over time ([col. 6, ln. 58 to col. 7, ln. 11], [col. 7, ln. 57-67], [col. 16, ln. 40-52], [col. 16, ln. 53-59]);
updating the plurality of data points based on receipt of additional sensor data from the plurality of sensors ([col. 6, ln. 58 to col. 7, ln. 11], [col. 7, ln. 57-67], [col. 16, ln. 40-52], [col. 16, ln. 53-59], [col. 18, ln. 1-15]); and
outputting an arrangement of at least a third subset of the plurality of data points based on a request for information about the mobile object ([col. 13, ln. 12-26] “SW 302 in FIG. 3 is shown to include functions 303 for a user to input information, always related to an object, and to instantiate objects in the system. These input and output functions operate together in many ways. For example, a user may connect to the system to find an object in the system. The user may search for an object through entry of an SIOO code, or by keywords related to any of the categories in the system. As a single example, a user may elect to look for stories about race horses. The system might return a list of such stories much as might be returned by Google™, but from the system's data storage facilities rather than the Internet. The specificity in this list will depend on the detail of the search criteria entered by the user. There may be rich interactive guidance provided by the system to provide specificity.”, [col. 18, ln. 22-36] “…a very sophisticated statistical ability is provided whereby the nature of objects, associations of persons with objects and objects with objects, including thoughts and emotions entered by persons about objects, and personal characteristics and profiles of persons with objects are tracked relative to time, and periodic snapshots of segments of the database may be made, and analyzed in a wide variety of ways, with statistical representations of such snapshots made available, either publically or in a restricted manner. Segment definition may be made in real time in a search facility, and any one or more of a wide variety of algorithms may be called to provide statistical output relative to one or more segments. The algorithms available for statistical analysis may be created in real time (on-the-fly) as well.”).
One of ordinary skill in the art would recognize St. Marin discloses wherein the global position (e.g., GPS data) is tracked over time, which one of ordinary skill in the art would recognize as related to the pose of an object, but that pose is positioning of an object in space on a more local level (e.g., 3D modeling, or object within surrounding environment). Therefore, St. Martin does not specifically disclose wherein the pose of an object is tracked over time.
However, Zhou specifically teaches wherein a pose of an object is tracked over time ([pg. 8, Fig. 5], [pg. 8, col. 2, I. Image-based 3D Reconstruction, par. 1, ln. 1 to pg. 9, col. 1, par. 1, ln. 16] “In the Vetaverse, drivers and passengers and their digital representatives (i.e., avatars) are connected and interact with each other at the junction of the physical driving environment and the virtual world. Therefore, building such a junction across the physical driving scene and the virtual environment inspires us to deeply understand human behaviors, which empower the action of the avatar and the surroundings of the vehicle. In the physical driving environment, the driver can estimate the overall size and geometry of an object with their eyes and construct a 3D world based on our mental model of what an object looks like. Similarly, the Vetaverse also needs to build up the 3D structure of the driving environment and localize the moving objects. Image-based 3D reconstruction is a hot topic in computer vision and graphics that infers the 3D geometry and structure of objects and scenes (e.g., vehicles) from visual cues such as RGB image [56], [232]. Among existing techniques, formulating the 2D-to-3D pro jection is an important problem. Depth is thus a crucial piece of information for inferring such a projection. Depth can be obtained from a depth sensor, which can be combined with the RGB camera to form an RGB-D sensor, such as Kinect 5. 3D reconstruction can be decomposed into two types: 1) 3D reconstruction from multiple views, and 2) 3D reconstruction from a single view. In recent years, extensive research has been focused on exploring the potential of deep learning to estimate depth in VR/AR, e.g., [105], [117], [151]. Depth estimation from the multi-view is a critical task in achieving the Vetaverse. The estimated distance directly determines the position of the contents in the immersive environment. The common way to estimate depth is to use a stereo camera [41]. In VR, stereo depth estimation is conducted in the virtual space. Therefore, depth estimation estimates the absolute distance between a virtual object and the virtual camera (first-person view) or the referred object (third-person view). The traditional methods first extract feature points and then use them to compute the cost volumes for disparity estimation [176].”, [pg. 9, Figure 6], [pg. 9, col. 2, par. 1, ln. 1 to pg. 10, col. 1, par. 1, ln. 2] “With the tracked key points (features), the second step for visual SLAM is how to map the 2D camera frames to get 3D coordinates or landmarks, which is closely related to camera pose estimation [166]. The SLAM algorithm first estimates the key points when the camera outputs a new frame. These points are then mapped to the previous frame to estimate the scene’s optical flow. As a result, camera motion estimation paves the way for the same key points to be found in the new frame. However, in some cases, the estimated camera pose is not precise enough. Some SLAM algorithms, e.g., ORB SLAM [142], [144] add extra data to refine the camera pose by finding more key point correspondences. Triangulation of the matching key points from the connected frames generates new map points. This procedure combines the 2D positions of key points in the frames, as well as the translations and rotations between them. The last key step of SLAM aims to recover the camera pose and obtain a geometrically consistent map, also called close loop detection [13]. As shown in Figure 6 for autonomous driving [139], local map generation or vehicle pose estimation is an essential step for the driving system’s scene understanding. ORB-SLAM [142] determines whether key points in a frame match previously detected key points from a different location. If the similarity exceeds a certain threshold, it indicates that the user has returned to a previously visited location. Some SLAM algorithms have recently combined the camera with other sensors, such as the IMU sensor, to improve loop detection precision [157], and some works, e.g., [179], have attempted to fuse semantic information to SLAM algorithms to ensure loop detection performance. Although current SOTA visual SLAM algorithms have established a solid foundation for spatial understanding, Veta verse requires understanding of more complex environments, particularly the integration of virtual objects and real environments. Hololens has already begun to improve its spatial understanding, and Apple has introduced ARKitv26 for 3D keypoint tracking. In Vetaverse, the perceived virtual universe is built in the shared 3D virtual space. As a result, acquiring the 3D structure of an unknown environment and sensing its motion is critical but difficult. This could aid in data collection for purposes such as digital twin construction, which can be linked with AI to achieve auto conversion from the physical world. Furthermore, it is critical in Vetaverse to ensure the accuracy of object registration and interaction with the physical world. With these stringent requirements, we anticipate that Vetaverse’s SLAM algorithms will become more precise and computationally efficient.”, [pg. 14, Figure 11] “An example of a digital twin of an urban transportation system, which can transcend some realistic constraints and provide full support in the utilization of computing resources, route planning, connection of users to services, statistical analysis of data, etc.” see repeated sensor transmissions from vehicle in bottom middle and 3. Real time Data transmission, [pg. 15. Figure 12] “After establishing the digital twin, stable and effective algorithms can be used to reversely control the controllable physical entity by driving the virtual twin in real time. Thus, user experience and driving safety can be improved based on large-scale data analysis and wide-scale information collection”). One of ordinary skill in the art, before the effective filling date of the claimed invention, would specifically recognize St. Martin and Zhou as within the same field of image processing for digitalization of vehicles and their environments, and as analogous to the claimed invention. Specifically, the motivation to combine is disclosed in Zhou, wherein such pose estimation determined in images allows for automatic digital construction of the object and its surrounding space and allows for creation of a more immersive environment by determining positioning of the content based on their actual position ([pg. 8, col. 2, I. Image-based 3D Reconstruction, par. 1, ln. 1 to pg. 9, col. 1, par. 1, ln. 16], [pg. 9, col. 2, par. 1, ln. 1 to pg. 10, col. 1, par. 1, ln. 2]). One of ordinary skill in the art, before the effective filling date of the claimed invention, would have combined the method of St. Martin with the pose over time of Zhou through known means, with no change to their respective function, and the combination would have yielded nothing more than predicable results. Specifically, one of ordinary skill in the art would have combined the method of St. Martin with the pose over time of Zhou such that the mobile object pose and local environment was digitalized and tracked using an analogous methodology to Zhou.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claim 1.
9. Regarding Claim 2, a combination of St. Martin and Zhou teaches the method of claim 1. St. Martin specifically discloses wherein the mobile object may be a vehicle ([col. 16, ln. 40-52], [col. 16, ln. 53-59]). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claim 2.
10. Regarding Claim 3, a combination of St. Martin and Zhou teaches the method of claim 1. St. Martin discloses wherein the plurality of sensors include a camera, and wherein the sensor data includes image data captured using the camera ([col. 6, ln. 9-20]). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claim 3.
11. Regarding Claim 4, a combination of St. Martin and Zhou teaches the method of claim 1. St. Martin specifically discloses tracking modifications to the mobile object based on the sensor data and inputted data associated with the mobile object ([col. 6, ln. 58 to col. 7, ln. 11], [col. 7, ln. 57-67], [col. 16, ln. 40-52], [col. 16, ln. 53-59], [col. 18, ln. 1-15]). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claim 4.
12. Regarding Claim 5, a combination of St. Martin and Zhou teaches the method of claim 4. St. Martin specifically discloses wherein at least one modification of the modifications to the mobile object is a modification to materials used in association with the mobile object ([col. 18, ln. 1-15]). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claim 5.
13. Regarding Claim 6, a combination of St. Martin and Zhou teaches the method of claim 4. St. Martin specifically discloses wherein the inputted data include materials used in association with the mobile object and historical information associated with the materials ([col. 18, ln. 1-15], [col. 18, ln. 19-21] “My cell phone carries the metal used in the 1950 Destroyer that survived all attacks that Robert Harris was on.”). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claim 6.
14. Regarding Claim 7, a combination of St. Martin and Zhou teaches the method of claim 1. St. Martin specifically discloses wherein {the pose} is associated with at least one of a location or {an orientation} of the mobile object ([col. 6, ln. 58 to col. 7, ln. 11], [col. 7, ln. 57-67], [col. 16, ln. 40-52], [col. 16, ln. 53-59]). Specifically, St. Martin discloses wherein the location of the mobile object is tracked but does not specifically disclose a pose and an orientation of the object on a local level.
However, Zhou specifically discloses determining a pose, and wherein the pose is associated with an orientation of an object ([pg. 8, Fig. 5], [pg. 8, col. 2, I. Image-based 3D Reconstruction, par. 1, ln. 1 to pg. 9, col. 1, par. 1, ln. 16], [pg. 9, Figure 6], [pg. 9, col. 2, par. 1, ln. 1 to pg. 10, col. 1, par. 1, ln. 2], [pg. 14, Figure 11], [pg. 15. Figure 12]). Specifically, the motivation to combine remains analogous to claim 1. ne of ordinary skill in the art, before the effective filling date of the claimed invention, would have combined the method of St. Martin with the pose and orientation over time of Zhou through known means, with no change to their respective function, and the combination would have yielded nothing more than predicable results. Specifically, one of ordinary skill in the art would have combined the method of St. Martin with the pose over time of Zhou such that the mobile object pose and orientation within a local environment was digitalized and tracked using an analogous methodology to Zhou.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claim 7.
15. Regarding Claim 8, a combination of St. Martin and Zhou teaches the method of claim 1. St. Martin specifically discloses generating a transferrable data asset associated with the plurality of data points ([col. 22, ln. 37-42] “In one instance the SIO Code is used to access the data stored within the SIO network. By accessing the network through the code, users can transfer ownership of or give access to the use of the SIO code to another user in the system or authorize the continued storage of the historical information related to the object.”). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claim 8.
16. Regarding Claim 9, a combination of St. Martin and Zhou teaches the method of claim 1. St. Martin specifically discloses wherein the plurality of data points is continually updated based on receipt of the additional sensor data ([col. 6, ln. 58 to col. 7, ln. 11], [col. 7, ln. 57-67], [col. 16, ln. 40-52], [col. 16, ln. 53-59], [col. 18, ln. 1-15]). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claim 9.
17. Regarding Claim 10, a combination of St. Martin and Zhou teaches the method of claim 1. St. Martin specifically discloses wherein the plurality of data points is periodically updated based on receipt of the additional sensor data ([col. 6, ln. 58 to col. 7, ln. 11], [col. 7, ln. 57-67], [col. 16, ln. 40-52], [col. 16, ln. 53-59], [col. 18, ln. 1-15]). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claim 10.
18. Regarding Claim 11, a combination of St. Martin and Zhou teaches the method of claim 1. St. Martin specifically discloses wherein the plurality of data points is updated in response to an event. ([col. 22, ln. 14-26] “In one example there is an ability to take a physical item's SIO code and its attributes into the virtual world. Physical objects using their attributes and those like them to interpret and react to events and human activities occurring in the physical world through gaming. Placing the Physical Object in the Virtual World giving the Virtual Worlds Object chosen attributes to be used in different Virtual Worlds giving them selected use of those attributes. An example would be a famous soccer players cleats used in the World Cup Soccer game that was able to score an amazing number of goals or a method to the goal and allowing those to be associated with a particular virtual player for a specific given amount of time.”). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claim 11.
19. Regarding Claim 12, a combination of St. Martin and Zhou teaches the method of claim 1. St. Martin specifically discloses wherein the request for information is a search or a database query ([col. 2, ln. 63 to col. 3, ln. 2], [col. 13, ln. 12-26], [col. 18, ln. 22-36]). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claim 12.
20. Regarding Claim 13, the claim language is analogous to claim 1 with the exception of “A system for generating a searchable arrangement of data associated with a mobile object, the system comprising: memory; and a processor that executes instructions in memory, wherein execution of the instructions by the processor causes the processor to”, wherein the reminder of the claim is analogous to claim 1. St. Martin further discloses a system for generating a searchable arrangement of data associated with a mobile object, the system comprising: memory; and a processor that executes instructions in memory, wherein execution of the instructions by the processor causes the processor to perform the method ([col. 3, ln. 20-26] “…registering members to the system through a network-connected server coupled to the data repository and having a processor executing coded instructions from a non-transitory medium, the coded instructions enabling registration of members operating network-connected computerized platforms, each registered member creating a profile and being assigned a unique SIO code;”, [col. 16, ln. 40-52]). Rejections analogous to claim 1 are further applicable to claim 13 in view of the analogous claim language. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the system of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claim 13.
21. Regarding Claims 14-19, a combination of St. Martin and Zhou teaches the system of claim 1. The claim language of claims 14-19 is analogous to claims 2-7 respectively. Rejections analogous to claims 2-7 are further applicable to claims 14-19 in view of the system of the combination of St. Martin and Zhou. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the system of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claims 14-19.
22. Regarding Claim 20, the claim language is analogous to claim 1 with the exception of “A non-transitory computer-readable storage medium, having embodied thereon a program executable by a processor to perform a method…”, wherein the remainder of the claim is analogous to claim 1. St. Martin further discloses a non-transitory computer-readable storage medium having instructions for preforming the method ([col. 3, ln. 20-26], [col. 16, ln. 40-52]). Rejections analogous to claim 1 are further applicable to the remainder of claim 20 in view of the analogous claim language. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the non-transitory computer-readable storage medium of St. Martin with the object pose tracking over time of Zhou to obtain the invention as specified in claim 13.
Conclusion
23. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULO ANDRES GARCIA whose telephone number is (703)756-5493. The examiner can normally be reached Mon-Fri, 8-4:30PM ET.
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, Chan Park can be reached on (571)272-7409. 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.
/PAULO ANDRES GARCIA/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
