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 .
Claim Objections
Claim(s) 1-19 is/are objected to because of the following informalities:
Claim 1, line 16, “additional information” should be corrected as “supplemental information”
Claim 1, line 20, “detected identifying” should be corrected as “detected, identifying”
Claim 1, line 24, “the memory location” should be corrected as “the measurement location”
Claim 1, line 27, “the smart glasses” should be corrected as “smart glasses”
Claim 3, line 2, “measurement” should be corrected as “measurement instrument”
Claim 8, lines 2-3, “additional information” should be corrected as “supplemental information”
Claims 1-19, “analog instrument” and “analog measurement instrument” seem to be the same term, thus need to be corrected uniformly and clearly. For instance, change all “analog instrument” to “analog measurement instrument” in claims 1-19.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 9, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mullins et al. (US 20160055674) in view of Amico et al. (US 20180211122).
Regarding claim 1, Mullins discloses A digital retrofit device comprising: a camera; a processor coupled to the camera and configured with computer-executable instructions that cause the processor to (“[0026] a device includes at least one camera, a display, and a hardware processor. [0035] In another example embodiment, a non-transitory machine-readable storage device may store a set of instructions that, when executed by at least one processor, causes the at least one processor to perform the method operations discussed within the present disclosure”):
activate the camera to capture an image of an analog measurement instruments (“[0040] The user 102 may point a camera of the head mounted device 101 to capture an image of analog sensing device A 116 and digital sensing device B 118. [0049] The sensors 202 may be used to generate internal tracking data of the head mounted device 101 to determine what the head mounted device 101 is capturing or looking at in the real physical world.”);
process the image so as to detect whether the analog measurement instrument contains an embedded identifier wherein the processing further includes: when an embedded identifier is detected, recalling a data record of the analog measurement instrument in a database corresponding to the identifier, wherein the data record includes a type of the analog measurement instrument, and a measurement location (“[0052] The object recognition module 210 may detect, generate, and identify identifiers such as feature points of the physical object being viewed or pointed at by the head mounted device 101 using an optical device of the head mounted device 101 to capture the image of the physical object. [0065] the AR content generator module 214 may retrieve 3D models of virtual objects associated with a captured real world object. For example, the captured image may include a visual reference (also referred to as a marker) that consists of an identifiable image, symbol, letter, number, machine-readable code. [0067] The storage device 209 may be configured to store a database of identifiers of analog and digital sensing devices 116 and 118, corresponding thresholds, physical objects, tracking data, and corresponding virtual user interfaces. [0074] At operation 802, the head mounted device 101 identifies and recognizes analog and digital sensing devices A 116 and B 118 in a scene and tracks data related to the analog and digital sensing devices A 116 and B 118 (e.g., reading, position, type of sensor) being captured by the head mounted device 101.”);
focus the camera on the measurement location of the measurement to obtain measurement data; convert the measurement data into converted digital information (“[0038] A user 102 may wear the head mounted device 101 to capture a view of a scene including several analog sensing devices (e.g., analog sensing device A 116, digital sensing device B 118) in a real world physical environment 114 viewed by the user 102. [0056] The level identifier module 406 identifies a measured level or a current reading from the sensing device. For example, the level identifier module 406 identifies a position of a needle or hand from an analog or digital sensing device. The level identifier module 406 then identifies its position relative to the minimum and maximum levels or other indicators in the gauge or dial. [0060] The digital gauge module 506 generates a virtual digital gauge based on the measured level from an analog sensing device. For example, virtual digital gauge may include a current level number, a maximum number, a minimum number and a threshold number for the analog sensing device.”);
obtain supplemental information from a database related to the analog instrument; and superimpose the additional information in a graphical representation over the captured image of the analog instrument in real time in the display together with the digital information (fig.8, “[0026] AR content is generated based on the extracted visual data, mapped and displayed in the display to form a layer on a view of the sensing device. [0074] At operation 818, the server 110 generates an alert notification to the head mounted device 101 based on the visual data being outside acceptable thresholds. At operation 820, the head mounted device 101 generates an alert based on the alert notification from the server 110. The alert may include a virtual indicator in the display 204 of the head mounted device 101. [0077] At operation 1108, the AR application generates an alert notification in the AR content based on predefined parameters of the corresponding sensing device.”); and
when an embedded identifier is not detected, performing further actions (“[0041] If the captured image is not recognized locally at the head mounted device 101, the head mounted device 101 can download additional information (e.g., 3D model or other augmented data) corresponding to the captured image, from a database of the server 110 over the network 108”); and
a display coupled to the processor upon which the digital information and supplemental information is displayed to a wearer of the smart glasses (“[0048] the head mounted device 101 may be a wearable computing device (e.g., glasses or helmet), a tablet computer, a navigational device, or a smart phone of a user. [0062] The AR range mapping module 602 maps a first location in the display to display the virtual range indicator as a visual layer on the sensing device. The AR level mapping module 604 maps a second location in the display to display the virtual measured level indicator as a visual layer on the sensing device. [0074] At operation 820, the head mounted device 101 generates an alert based on the alert notification from the server 110. The alert may include a virtual indicator in the display 204 of the head mounted device 101”).
On the other hand, Mullins fails to explicitly disclose but Amico discloses identifying the analog measurement instrument by at least one of: a) comparing a geographic location of the analog measurement instrument with known locations of analog measurement instruments in a database; b) a machine learning algorithm that is trained to identify a type of the analog measurement instrument using a set of images of analog measurement instruments and to identify the memory location by feature recognition (“[0036] The machine learning models may be generated by a machine learning algorithm as an initial set-up process before capturing the image of the vehicle dashboard. During the initial set-up, the machine learning algorithm may train the machine learning models to identify the make and model of the vehicle, the location of each component within the vehicle dashboard, and obtain readings associated with each component of the vehicle dashboard. [0060] For example, to train a vehicle type model (a machine learning model) to identify a make and a model of a vehicle from an image of the vehicle's dashboard, the machine learning algorithm may be provided with hundred labelled images of vehicle dashboards of different makes and models of vehicles.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Mullins and Amico, to include all limitations of claim 1. That is, adding the identifying the object type and reading of each component based on machine learning of Amico to the AR device of Mullins. The motivation/ suggestion would have been to provide automated extraction of data associated with a vehicle from an image of the vehicle's dashboard using artificial intelligence technology (Amico, [0001]).
Regarding claim 2, Mullins in view of Amico discloses The digital retrofit device of claim 1.
Mullins further discloses wherein the embedded identifier is one of a QR code, bar code and RFID (“[0065] For example, the visual reference may include a bar code, a quick response (QR) code, or an image that has been previously associated with a 3D virtual object (e.g., an image that has been previously determined to correspond to the three-dimensional virtual object). For example, a QR or other indicator may be visibly affixed next to an analog sensing device or other object.”).
Regarding claim 9, Mullins in view of Amico discloses The digital retrofit device of claim 1.
Mullins further discloses wherein the supplemental information includes nominal safe range data and instrument condition information of the analog instrument (“[0077] At operation 1108, the AR application generates an alert notification in the AR content based on predefined parameters of the corresponding sensing device. [0080] A range 1208 is also displayed with the low level corresponding to the minimum level. [0083] FIG. 12F is a diagram illustrating an example of a display of a head mounted device (e.g., head mounted device 101) displaying AR content identifying a range of an analog device. A virtual range 1214 is displayed around the dial of the analog device.”).
Regarding claim 10, Mullins in view of Amico discloses The digital retrofit device of claim 1.
On the other hand, Mullins fails to explicitly disclose but Amico discloses wherein the processor is further configured to identify a type and features of the analog measurement instrument using a supervised machine learning algorithm that is trained to classify types and features of analog instruments based on tagged training data (Amico, “[0059] To generate the one or more machine learning models, a plurality of training images stored in the training dataset database 240 of the dashboard analysis engine 220, e.g., images of vehicle dashboards and images of one or more components of vehicle dashboards for different makes and models of vehicles and different readings associated with the components, may be inputted to a machine learning algorithm of the training engine 222. Different training methods such as supervised, unsupervised, or semi-supervised training may be used to train the one or more machine learning models. [0102] In operation 606, the dashboard analysis engine 220 of the portable computing device 106 may input analog gauge training data comprising a plurality of labelled and/or unlabeled training images of analog gauges to a machine learning algorithm of the training engine 222. The plurality of training images of analog gauges may include images of analog gauges of a vehicle dashboard for different makes and models of vehicles and for different analog gauge readings. [0123] In FIG. 10, once the location of each component of the vehicle dashboard in the captured image is identified and the image of said components have been cropped out from the captured image of the vehicle dashboard, the cropped images of each component may be inputted in parallel to respective machine learning models such that the machine learning models may process the cropped images in parallel to obtain readings associated with the components of the vehicle dashboard 104. [0125] In FIG. 4, the make and the model of the vehicle 102 and/or the location of one or more components of the vehicle dashboard in the captured image are identified using machine learning models, e.g., the vehicle type model and/or the location model”).
Claim(s) 3, 12-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mullins et al. (US 20160055674) in view of Amico et al. (US 20180211122), and further in view of O'Connor (US 20130193200).
Regarding claim 3, Mullins in view of Amico discloses The digital retrofit device of claim 1, wherein the analog instrument has been disclosed.
On the other hand, Mullins in view of Amico fails to explicitly disclose but O'Connor discloses wherein the geographic location of the object is obtained using global position satellite (GPS) coordinates, and the database includes GPS coordinates of objects located in one or more facilities (O'Connor, “[0024] The location of a particular product determined by mobile communication device 102 (i.e., the location of mobile communication device determined via a GPS receiver when a QR code associated with a product is scanned) can be recorded in current location 416 and previous location 418. [0031] For example, when a user scans a QR code associated with a product, information indicating the location of the product can be transmitted to product server 106. This location information can be stored in product information database 400 and used by manufacturers or sellers to track the location of the product.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined O'Connor into the combination of Mullins and Amico. That is, applying the determining location of the product of O'Connor to determine the location of the analog measurement instrument of Amico and Mullins. The motivation/ suggestion would have been to provide a method and apparatus for tracking items using QR codes and providing information pertaining to the items (O'Connor, [0001]).
Regarding claim(s) 12, 18, they are covered by the claim mapping in claim 3, thus rejected for the same reasons set forth in claim(s) 3.
Regarding claim 13, Mullins in view of Amico and O'Connor discloses The method of claim 12.
Mullins further discloses displaying the supplemental information (“[0026] AR content is generated based on the extracted visual data, mapped and displayed in the display to form a layer on a view of the sensing device. [0074] At operation 818, the server 110 generates an alert notification to the head mounted device 101 based on the visual data being outside acceptable thresholds. At operation 820, the head mounted device 101 generates an alert based on the alert notification from the server 110. The alert may include a virtual indicator in the display 204 of the head mounted device 101.”);
determining whether the extracted measurement data is within an expected range of values; and assessing whether the analog instrument is functioning properly based on whether the extracted measurement data is within the expected range or within expected historical trends (“[0033] the AR application communicates the image of the sensing device to the server. The server identifies the sensing device from the image, extracts visual data from the image, accesses notification parameters associated with the identified sensing device, and generates an alert notification in the display in response to one of the visual data exceeding a threshold of the notification parameters. [0061] A general red hue may be displayed when a level reading of the sensing device exceeds a predetermined threshold. [0074] At operation 816, the server 110 verifies that the visual data is within the parameters corresponding to the sensing device. At operation 818, the server 110 generates an alert notification to the head mounted device 101 based on the visual data being outside acceptable thresholds”).
Regarding claim 14, Mullins in view of Amico and O'Connor discloses The method of claim 12.
Mullins further discloses capturing the visual analog information using a camera (“[0038] A user 102 may wear the head mounted device 101 to capture a view of a scene including several analog sensing devices (e.g., analog sensing device A 116, digital sensing device B 118) in a real world physical environment 114 viewed by the user 102.”).
Regarding claim 15, Mullins in view of Amico and O'Connor discloses The method of claim 12.
Mullins further discloses wherein the supplemental information includes nominal safe range data and instrument condition information of the analog instrument (“[0077] At operation 1108, the AR application generates an alert notification in the AR content based on predefined parameters of the corresponding sensing device. [0080] A range 1208 is also displayed with the low level corresponding to the minimum level. [0083] FIG. 12F is a diagram illustrating an example of a display of a head mounted device (e.g., head mounted device 101) displaying AR content identifying a range of an analog device. A virtual range 1214 is displayed around the dial of the analog device.”).
Regarding claim 16, Mullins in view of Amico and O'Connor discloses The method of claim 12.
Mullins further discloses wherein features of the analog instrument identified include a type of measurement made by the analog instrument, and a scale and range of parameters values appearing on the analog instrument (Mullins, “[0053] The data extraction module 212 determines a current reading from the analog or digital sensing device, a range of the analog or digital sensing device, a type of analog or digital sensing device based on an image of the analog or digital sensing device. In one example embodiment, the data extraction module 212 may include a units identifier module 402, a range identifier module 404, and a level identifier module 406 as illustrated in FIG. 4”).
Regarding claim 17, Mullins in view of Amico and O'Connor discloses The method of claim 12.
Mullins further discloses wherein the embedded identifier is one of a QR code, bar code and RFID (“[0065] For example, the visual reference may include a bar code, a quick response (QR) code, or an image that has been previously associated with a 3D virtual object (e.g., an image that has been previously determined to correspond to the three-dimensional virtual object). For example, a QR or other indicator may be visibly affixed next to an analog sensing device or other object.”).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mullins et al. (US 20160055674) in view of Amico et al. (US 20180211122), and further in view of AIN-UL-AISHA (US 20250157221).
Regarding claim 4, Mullins in view of Amico discloses The digital retrofit device of claim 1.
On the other hand, Mullins in view of Amico fails to explicitly disclose but AIN-UL-AISHA discloses updating the measurement location of the analog instrument in the database upon determining the measurement location using the machine learning algorithm (“[0084] Then, an ML module may be trained to identify the readings on the at least one meter and record the readings in a database. [0132] Processor(s) 1210 can be configured to execute instructions for a method of autonomous visual inspection, the method involving capturing meter images via the at least one image capturing device; identifying a reading of the meter based on the meter images; recording the reading of the meter in a database; periodically tracking readings associated with the meter over a period of time; and updating the database based on the tracking, as described in connection with FIGS. 1 to 3.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined AIN-UL-AISHA into the combination of Mullins and Amico. That is, applying the updating the database with the tracking readings of AIN-UL-AISHA to the AR system of Amico and Mullins. The motivation/ suggestion would have been to provide an autonomous method/system to inspect multiple objects regardless of their geo-location and an end-to-end methodology to detect failure modes with high accuracy (AIN-UL-AISHA, [0019]).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mullins et al. (US 20160055674) in view of Amico et al. (US 20180211122), and further in view of Kostrzewski et al. (US 20130083960).
Regarding claim 5, Mullins in view of Amico discloses The digital retrofit device of claim 1.
On the other hand, Mullins in view of Amico fails to explicitly disclose but Kostrzewski discloses wherein the digital retrofit device is fixed in position with respect to the analog measurement instrument and oriented so as to be able to capture the image of the analog measurement instrument (Kostrzewski, “[0107] video cameras are disposed on an aircraft to capture various articulated objects, such as instrument gauges, aerodynamic control surfaces, landing gear, or other objects that change in time. These articulated objects have orientations with respect to the video image that are known beforehand. For example, images of instrument gauges have a known orientation with respect to a fixed video cockpit camera”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Kostrzewski into the combination of Mullins and Amico. That is, applying the relative position between video cameras and articulated objects of Kostrzewski to the AR device and measuring instrument of Mullins and Amico. The motivation/ suggestion would have been to have a digital recording of analog data as seen by a pilot--for example, a recording of a view of gauges, dials, screens, and windows (Kostrzewski, [0107]).
Claim(s) 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mullins et al. (US 20160055674) in view of Amico et al. (US 20180211122), and further in view of Nishi (US 20180307045).
Regarding claim 6, Mullins in view of Amico discloses The digital retrofit device of claim 1, wherein the converted digital information has been disclosed.
On the other hand, Mullins in view of Amico fails to explicitly disclose but Nishi discloses a memory unit coupled to the processor to which the processor delivers the digital information for storage (Nishi, fig.1, “[0004] a character recognition unit to generate digital measurement data from the character image; and a recording unit to record the digital data. [0064] At step S104, the data storage unit 12 stores the information indicating the operational state extracted by the image analysis unit 11 in association with the information on the identity of the piece of factory equipment corresponding to the information indicating the operational state, in a database.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Nishi into the combination of Mullins and Amico. That is, adding the data storage unit of Nishi to the AR system to store the converted digital information of Mullins and Amico. The motivation/ suggestion would have been easily collecting information, with little burden on the operators, for the maintenance of factory equipment to support the operators in carrying out the maintenance (Nishi, [0009]).
Regarding claim 7, Mullins in view of Amico and Nishi discloses The digital retrofit device of claim 6, wherein the converted digital information has been disclosed.
On the other hand, Mullins in view of Amico fails to explicitly disclose but Nishi discloses a wireless communication unit coupled to the memory unit adapted to transmit the digital information to a database server (Nishi, fig.1, “[0004] a character recognition unit to generate digital measurement data from the character image; and a recording unit to record the digital data. [0035] The communication unit 23 serves to communicate with peripheral devices, which include the maintenance support device 1. The communication unit 23 of the head-mounted display 2 is connected with the communication unit 15 of the maintenance support device 1 by, for example, wireless communication. [0057] a plurality of maintenance support devices 1 may be incorporated in a network with which a plurality of manufacturing cells are connected. The information indicating the operational state and the information on the identity of a piece of factory equipment extracted by the image analysis unit 11 of each maintenance support device 1 connected with the network can be shared in a cloud server, by cell controllers superordinate to the manufacturing cells or by a production control apparatus superordinate to the cell controllers. [0064] At step S104, the data storage unit 12 stores the information indicating the operational state extracted by the image analysis unit 11 in association with the information on the identity of the piece of factory equipment corresponding to the information indicating the operational state, in a database.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Nishi into the combination of Mullins and Amico. That is, applying the wireless connection for data sharing in cloud server of Nishi to the AR device and servers of Mullins and Amico. The motivation/ suggestion would have been easily collecting information, with little burden on the operators, for the maintenance of factory equipment to support the operators in carrying out the maintenance (Nishi, [0009]).
Regarding claim 8, Mullins in view of Amico and Nishi discloses The digital retrofit device of claim 7.
Mullins further discloses wherein the processor is further configured with computer-executable instructions that cause the processor to request the supplemental information from the database server and to superimpose the additional information in a graphical representation in the display together with the digital information (fig.8, fig.13, “[0062] The AR range mapping module 602 maps a first location in the display to display the virtual range indicator as a visual layer on the sensing device. The AR level mapping module 604 maps a second location in the display to display the virtual measured level indicator as a visual layer on the sensing device. [0074] At operation 818, the server 110 generates an alert notification to the head mounted device 101 based on the visual data being outside acceptable thresholds. At operation 820, the head mounted device 101 generates an alert based on the alert notification from the server 110. The alert may include a virtual indicator in the display 204 of the head mounted device 101. [0077] At operation 1108, the AR application generates an alert notification in the AR content based on predefined parameters of the corresponding sensing device.”).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mullins et al. (US 20160055674) in view of Amico et al. (US 20180211122), and further in view of Hassman et al. (US 20170004552).
Regarding claim 11, Mullins in view of Amico discloses The digital retrofit device of claim 10.
On the other hand, Mullins in view of Amico fails to explicitly disclose but Hassman discloses wherein the processor is further configured to run a trained classifier trained using a supervised machine learning algorithm to perform at least one of edge detection, corner detection, and blob detection (“[0044] In embodiments, the visualizer 136 may be configured to utilize a statistical classifier such as, for example, one or more supervised and/or unsupervised machine-learning algorithms, any number of various digital image processing techniques (e.g., edge detection, foreground detection, region of interest detection, etc.), neural networks, and/or the like to determine the dimensions of the installation location”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Hassman into the combination of Mullins and Amico. That is, applying the algorithm and techniques of dimension determination of Hassman to the AR device of Amico and Mullins. The motivation/ suggestion would have been to provide an algorithm of determining the dimension based on captured images (Hassman, “[0006] the visualizer is further configured to: determine, based on the digital image of the scene, dimensions of the installation location”).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mullins et al. (US 20160055674) in view of Amico et al. (US 20180211122), and further in view of O'Connor (US 20130193200) and AIN-UL-AISHA (US 20250157221).
Regarding claim 19, Mullins in view of Amico and O'Connor discloses The method of claim 12.
On the other hand, Mullins in view of Amico and O'Connor fails to explicitly disclose but AIN-UL-AISHA discloses updating the measurement location of the analog instrument in the database upon determining the measurement location using the machine learning algorithm (“[0084] Then, an ML module may be trained to identify the readings on the at least one meter and record the readings in a database. [0132] Processor(s) 1210 can be configured to execute instructions for a method of autonomous visual inspection, the method involving capturing meter images via the at least one image capturing device; identifying a reading of the meter based on the meter images; recording the reading of the meter in a database; periodically tracking readings associated with the meter over a period of time; and updating the database based on the tracking, as described in connection with FIGS. 1 to 3.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined AIN-UL-AISHA into the combination of Mullins and Amico. That is, applying the updating the database with the tracking readings of AIN-UL-AISHA to the AR system of Amico and Mullins. The motivation/ suggestion would have been to provide an autonomous method/system to inspect multiple objects regardless of their geo-location and an end-to-end methodology to detect failure modes with high accuracy (AIN-UL-AISHA, [0019]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACE Q LI whose telephone number is (571)270-0497. The examiner can normally be reached Monday - Friday, 8:00 am-5:00 pm.
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.
/GRACE Q LI/Primary Examiner, Art Unit 2618 8/3/2026
Prosecution Insights