DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to the Application filed on 04/19/2024. Claims 1-20 are pending in the case. Claims 1, 8, and 14 are independent claims.
Claim Rejections - 35 U.S.C. § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 3, 4, 13, and 20 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Dependent claims 3, 4, 13, and 20 recite “and/or.” It is unclear which of the list of elements needs to be present to read on the claim. For the purpose of prior art analysis, Examiner assumes an “or”.
Claim Rejections - 35 U.S.C. § 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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 8 and 13 are rejected under 35 U.S.C. § 103 as being unpatentable over Wang et al. (U.S. Pat. App. Pub. No. 2023/0102233, hereinafter Wang) in view of Sobhany et al. (U.S. Pat. App. Pub. No. 2020/0239007, hereinafter Sobhany).
As to independent claim 8, Wang teaches:
An apparatus comprising (Figure 2):
a memory (Figure 2, memory modules 206, 226, and 246); and
a processor coupled to the memory, wherein the processor is configured to (Figure 2, processors 202, 222, and 242):
transmit an initial… model to each of a plurality of vehicles (Paragraph 26, "the server 106 sends an initialized model to each of the edge nodes 101, 103, 105, 107, 109. The initialized model may be any model that may be utilized for operating a vehicle, for example, an image processing model, an object detection model, or any other model for advanced driver assistance systems");
receive a respective trained… model from each of the plurality of vehicles (Paragraph 26, "Each of the edge nodes 101, 103, 105, 107, 109 trains the received initialized model using local data to obtain an updated local model and sends the updated local model or parameters of the updated local model back to the server 106." Paragraph 33, "The ML model training module 207 obtains parameters of a trained model, which may be transmitted to the server as an updated local model." Paragraph 43, "trained local model and transmits the compressed parameters to the server 160");
aggregate the respective trained… models to create a global driver attention model (Paragraph 26, "The server 106 collects the updated local models, computes a global model based on the updated local models, and sends the global model to each of the edge nodes 101, 103, 105, 107, 109." Paragraph 43); and
transmit the global… model to each of the plurality of vehicles (Paragraph 26, "The server 106 collects the updated local models, computes a global model based on the updated local models, and sends the global model to each of the edge nodes 101, 103, 105, 107, 109". Paragraph 43. Figure 1).
Wang does not appear to expressly teach driver attention model.
Sobhany teaches driver attention model (Figure 2, driver attention model 205).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the verifying and monitoring physical attention of a driver of a vehicle techniques of Sobhany to reduce safety hazards ranging from trivial to catastrophic and potentially fatal (see Sobhany at paragraph 3).
As to dependent claim 13, Wang further teaches the apparatus is a central server configured to store the global… model,… data, and/or sensor data (Paragraphs 42 and 43. Figures 2 and 3).
Wang does not appear to expressly teach driver attention model.
Sobhany teaches driver attention model and driver attention data (Figure 2, driver attention model 205. Figure 3, boxes 302 and 304).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the verifying and monitoring physical attention of a driver of a vehicle techniques of Sobhany to reduce safety hazards ranging from trivial to catastrophic and potentially fatal (see Sobhany at paragraph 3).
Claims 1-3, 5-7, and 14-17 are rejected under 35 U.S.C. § 103 as being unpatentable over Wang in view of Sobhany and Gurevich et al. (U.S. Pat. App. Pub. No. 2012/0224060, hereinafter Gurevich).
As to independent claim 1, Wang teaches:
An apparatus comprising (Figures 1-3. Paragraph 24, "The system includes a plurality of edge nodes 101, 103, 105, 107, 109, and a server 106." Paragraph 25, "each of the edge nodes 101, 103, 105, 107, and 109 may be a vehicle, and the server 106 may be a centralized server or an edge server."):
a sensor (Paragraph 34, "edge node system 200 comprises one or more sensors 208." Paragraphs 35 and 36.);…
a memory (Figure 2, memory modules 206, 226, and 246); and
a processor coupled to the memory, the sensor…, wherein the processor is configured to (Figure 2, processors 202, 222, and 242):
receive an initial… model (Paragraph 26, "the server 106 sends an initialized model to each of the edge nodes 101, 103, 105, 107, 109. The initialized model may be any model that may be utilized for operating a vehicle, for example, an image processing model, an object detection model, or any other model for advanced driver assistance systems");
train the initial driver attention model to create a trained… model (Paragraph 26, "Each of the edge nodes 101, 103, 105, 107, 109 trains the received initialized model using local data to obtain an updated local model and sends the updated local model or parameters of the updated local model back to the server 106." Paragraph 33);
transmit the trained… model (Paragraph 26, "Each of the edge nodes 101, 103, 105, 107, 109 trains the received initialized model using local data to obtain an updated local model and sends the updated local model or parameters of the updated local model back to the server 106." Paragraph 33, "The ML model training module 207 obtains parameters of a trained model, which may be transmitted to the server as an updated local model." Paragraph 43, "trained local model and transmits the compressed parameters to the server 160.");
receive a global… model based in part on the trained… model (Paragraph 26, "The server 106 collects the updated local models, computes a global model based on the updated local models, and sends the global model to each of the edge nodes 101, 103, 105, 107, 109." Paragraph 43);
receive sensor data based on operation of the apparatus (Paragraphs 34-37);
run the global… model on the sensor data to generate output data (Paragraph 44, "The global model update module 247 of the server 160 averages the compressed parameters received from the first edge node 310 and the second edge node 320 to obtain average parameters for an updated global model at step 334. The server 160 transmits the average parameters to each of the first edge node 310 and the second edge node 320." Paragraph 44, "The first edge node 310 may infer objects in a captured image using its updated local model at step 318. Similarly, the second edge node 320 may infer objects in a captured image using it updated local model at step 328"); and
cause the output data to be displayed… (Paragraph 36, "displayed on an infotainment system display, a head-up display, or another display device in the vehicle").
Wang does not appear to expressly teach driver attention model; and trained driver attention model.
Sobhany teaches driver attention model (Figure 2, driver attention model 205); and trained driver attention model (Figure 2, driver attention model 205. Paragraph 25, "the training data can be labeled with a binary determination of attentiveness: either “attentive” or “inattentive.” In other cases, the training data can be labeled with additional levels of attentiveness, such as “attentive,” “moderately inattentive,” and “severely inattentive.”").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the verifying and monitoring physical attention of a driver of a vehicle techniques of Sobhany to reduce safety hazards ranging from trivial to catastrophic and potentially fatal (see Sobhany at paragraph 3).
Wang does not appear to expressly teach an augmented reality (AR) windshield; a processor coupled to… the AR windshield; and on the AR windshield.
Gurevich teaches an augmented reality (AR) windshield (Figure 1 HUD Projection 104. Paragraph 42, "The vehicle also has a heads-up display projector 103 displaying an image 104 on the windshield."); a processor coupled to… the AR windshield (Figure 9, processing unit 905, HUD Projector 906, and HUD Screen 907); and on the AR windshield (Figure 9, HUD Projector 906, and HUD Screen 907.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the HUD windshield projection techniques of Gurevich to reduce the distraction of a driver of a motor vehicle (see Gurevich at paragraph 5).
As to dependent claim 2, Wang further teaches the apparatus is a vehicle (Paragraph 25, "each of the edge nodes 101, 103, 105, 107, and 109 may be a vehicle"); and wherein the sensor is configured to record and transmit the sensor data to the processor (Paragraph 34, "the one or more sensors 208 may provide image data to the one or more processors 202").
As to dependent claim 3, Gurevich further teaches the AR windshield is configured to display the output data including pedestrian movement, driver behavior, traffic light timers, signs, safety information, and/or navigation information (Paragraphs 7, 48, 54, and 55. Figures 7, 10, 12, and 13).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the HUD windshield projection techniques of Gurevich to reduce the distraction of a driver of a motor vehicle (see Gurevich at paragraph 5).
As to dependent claim 5, Sobhany further teaches a camera, wherein the camera is configured to record a driver to generate driver attention data and transmit the driver attention data to the processor, wherein the processor is configured to run the trained driver attention model on the driver attention data to generate the output data (Paragraph 13. Figure 1. Paragraphs 25-30 and 34-36. Figure 3, boxes 302 and 304).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the verifying and monitoring physical attention of a driver of a vehicle techniques of Sobhany to reduce safety hazards ranging from trivial to catastrophic and potentially fatal (see Sobhany at paragraph 3).
As to dependent claim 6, Sobhany further teaches the driver attention data includes eye-tracking data of the driver (Paragraph 13, "direction of the driver's gaze").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the verifying and monitoring physical attention of a driver of a vehicle techniques of Sobhany to reduce safety hazards ranging from trivial to catastrophic and potentially fatal (see Sobhany at paragraph 3).
As to dependent claim 7, Sobhany further teaches the processor is configured to run the trained driver attention model on driver preferences, the driver attention data, and the sensor data to generate the output data (Paragraphs 27-29 and 35. Figure 2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the verifying and monitoring physical attention of a driver of a vehicle techniques of Sobhany to reduce safety hazards ranging from trivial to catastrophic and potentially fatal (see Sobhany at paragraph 3).
As to independent claim 14, Wang teaches:
A method comprising:
receiving an initial… model (Paragraph 26, "the server 106 sends an initialized model to each of the edge nodes 101, 103, 105, 107, 109. The initialized model may be any model that may be utilized for operating a vehicle, for example, an image processing model, an object detection model, or any other model for advanced driver assistance systems");
training the initial… model to create a trained… model (Paragraph 26, "Each of the edge nodes 101, 103, 105, 107, 109 trains the received initialized model using local data to obtain an updated local model and sends the updated local model or parameters of the updated local model back to the server 106." Paragraph 33);
transmitting the trained… model to a central server (Paragraph 26, "Each of the edge nodes 101, 103, 105, 107, 109 trains the received initialized model using local data to obtain an updated local model and sends the updated local model or parameters of the updated local model back to the server 106." Paragraph 33, "The ML model training module 207 obtains parameters of a trained model, which may be transmitted to the server as an updated local model." Paragraph 43, "trained local model and transmits the compressed parameters to the server 160.");
receiving a global… model based in part on the trained… model at a vehicle from the central server (Paragraph 26, "The server 106 collects the updated local models, computes a global model based on the updated local models, and sends the global model to each of the edge nodes 101, 103, 105, 107, 109." Paragraph 43. Figure 1);
receiving… data and sensor data based on operation of the vehicle in an area (Paragraphs 34-37);
running the global… model on the… data and the sensor data to generate output data (Paragraph 44, "The global model update module 247 of the server 160 averages the compressed parameters received from the first edge node 310 and the second edge node 320 to obtain average parameters for an updated global model at step 334. The server 160 transmits the average parameters to each of the first edge node 310 and the second edge node 320." Paragraph 44, "The first edge node 310 may infer objects in a captured image using its updated local model at step 318. Similarly, the second edge node 320 may infer objects in a captured image using it updated local model at step 328"); and
displaying the output data… (Paragraph 36, "displayed on an infotainment system display, a head-up display, or another display device in the vehicle").
Wang does not appear to expressly teach driver attention model; trained driver attention model; and driver attention data.
Sobhany teaches driver attention model (Figure 2, driver attention model 205); trained driver attention model (Figure 2, driver attention model 205. Paragraph 25, "the training data can be labeled with a binary determination of attentiveness: either “attentive” or “inattentive.” In other cases, the training data can be labeled with additional levels of attentiveness, such as “attentive,” “moderately inattentive,” and “severely inattentive.”"); and driver attention data (Figure 3, boxes 302 and 304).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the verifying and monitoring physical attention of a driver of a vehicle techniques of Sobhany to reduce safety hazards ranging from trivial to catastrophic and potentially fatal (see Sobhany at paragraph 3).
Wang does not appear to expressly teach on an augmented reality (AR) windshield.
Gurevich teaches on an augmented reality (AR) windshield (Figure 9, HUD Projector 906, and HUD Screen 907).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the HUD windshield projection techniques of Gurevich to reduce the distraction of a driver of a motor vehicle (see Gurevich at paragraph 5).
As to dependent claim 15, Wang further teaches creating the global… model at the central server (Paragraph 26, "The server 106 collects the updated local models, computes a global model based on the updated local models").
Wang does not appear to expressly teach driver attention model.
Sobhany teaches driver attention model (Figure 2, driver attention model 205).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the verifying and monitoring physical attention of a driver of a vehicle techniques of Sobhany to reduce safety hazards ranging from trivial to catastrophic and potentially fatal (see Sobhany at paragraph 3).
As to dependent claim 16, Wang further teaches creating the global… model at the central server using… data and sensor data recorded at a different vehicle (Figure 1, edge nodes 101, 103, 105, 107, and 109).
Wang does not appear to expressly teach attention model; and driver attention data.
Sobhany teaches attention model and driver attention data (Figure 2, driver attention model 205. Figure 3, boxes 302 and 304).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the verifying and monitoring physical attention of a driver of a vehicle techniques of Sobhany to reduce safety hazards ranging from trivial to catastrophic and potentially fatal (see Sobhany at paragraph 3).
As to dependent claim 17, Wang further teaches creating the global… model by aggregating the trained… model with different trained… models (Paragraph, "Each of the edge nodes 101, 103, 105, 107, 109 trains the received initialized model using local data to obtain an updated local model and sends the updated local model or parameters of the updated local model back to the server 106. The server 106 collects the updated local models, computes a global model based on the updated local models").
Wang does not appear to expressly teach driver attention model.
Sobhany teaches driver attention model (Figure 2, driver attention model 205).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the verifying and monitoring physical attention of a driver of a vehicle techniques of Sobhany to reduce safety hazards ranging from trivial to catastrophic and potentially fatal (see Sobhany at paragraph 3).
Claim 4 is rejected under 35 U.S.C. § 103 as being unpatentable over Wang in view of Sobhany, Gurevich, and Rider et al. (U.S. Pat. App. Pub. No. 2017/0228788, hereinafter Rider).
As to dependent claim 4, the rejection of claim 1 is incorporated.
Wang does not appear to expressly teach the sensor data includes a location, direction of travel, and/or speed of the apparatus.
Rider teaches the sensor data includes a location, direction of travel, and/or speed of the apparatus (Paragraph 21, "the location, direction of travel, and velocity." Figure 1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the digital signage techniques of Rider to provide an easy way to interact with digital or electronic billboards and signage (see Rider at paragraph 12).
Claim 18 is rejected under 35 U.S.C. § 103 as being unpatentable over Wang in view of Sobhany, Gurevich, and Yu et al. (U.S. Pat. App. Pub. No. 2019/0236386, hereinafter Yu).
As to dependent claim 18, the rejection of claim 14 is incorporated.
Wang does not appear to expressly teach determining what a driver of the vehicle is focusing on in the area using the global driver attention model.
Yu teaches determining what a driver of the vehicle is focusing on in the area using the global driver attention model (Paragraph 42, "The vehicle may also include a driver-facing camera that captures images of the driver. Using the captured images of the driver in conjunction with the captured images of the environment, a gaze detection system determines a focus point of the driver").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the driver gaze techniques of Yu to reduce accidents and loss of life (see Yu at paragraph 2).
Claim 19 is rejected under 35 U.S.C. § 103 as being unpatentable over Wang in view of Sobhany, Gurevich, and Avalos et al. (U.S. Pat. App. Pub. No. 2016/0379261, hereinafter Avalos).
As to dependent claim 19, the rejection of claim 14 is incorporated.
Wang does not appear to expressly teach displaying the output data on the AR windshield….
Gurevich teaches displaying the output data on the AR windshield… (Paragraph 54, "The output of each of these functions is data that will be formed into an image or images and displayed on the windshield").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the HUD windshield projection techniques of Gurevich to reduce the distraction of a driver of a motor vehicle (see Gurevich at paragraph 5).
Wang does not appear to expressly teach including an advertisement.
Avalos teaches including an advertisement (Paragraph 8, "placing targeted media content such as advertisements in a digital sign").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the digital sign techniques of Avalos to more effectively targeted to the specific demographics of the people viewing advertisements (see Avalos at paragraph 2).
Claim 20 is rejected under 35 U.S.C. § 103 as being unpatentable over Wang in view of Sobhany, Gurevich, Avalos, and Gardner et al. (U.S. Pat. App. Pub. No. 2008/0319852, hereinafter Gardner).
As to dependent claim 20, the rejection of claim 19 is incorporated.
Wang does not appear to expressly teach occluding, emphasizing, and/or displaying the advertisement.
Gardner teaches occluding, emphasizing, and/or displaying the advertisement (Paragraphs 23, 24, and 27. Figures 1 and 2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the advertisement overlay techniques of Gardner to provide more effective advertisements (see Gardner at paragraphs 1-4).
Claim 9 is rejected under 35 U.S.C. § 103 as being unpatentable over Wang in view of Sobhany and Avalos.
As to dependent claim 9, the rejection of claim 8 is incorporated.
Wang further teaches run the global driver attention model to generate output data (Paragraph 26, "The server 106 collects the updated local models, computes a global model based on the updated local models").
Wang does not appear to expressly teach transmit the output data to a digital sign configured to display the output data.
Avalos teaches transmit the output data to a digital sign configured to display the output data (Paragraphs 15-18).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the digital sign techniques of Avalos to more effectively targeted to the specific demographics of the people viewing advertisements (see Avalos at paragraph 2).
Claims 10 and 11 are rejected under 35 U.S.C. § 103 as being unpatentable over Wang in view of Sobhany, Avalos, and Gardner.
As to dependent claim 10, the rejection of claim 9 is incorporated.
Wang does not appear to expressly teach a portion of the digital sign is covered in response to the digital sign displaying the output data.
Gardner teaches a portion of the digital sign is covered in response to the digital sign displaying the output data (Paragraphs 23, 24, and 27. Figures 1 and 2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the advertisement overlay techniques of Gardner to provide more effective advertisements (see Gardner at paragraphs 1-4).
As to dependent claim 11, Wang does not appear to expressly teach the output data is an advertisement.
Avalos teaches the output data is an advertisement (Paragraph 8, "placing targeted media content such as advertisements in a digital sign").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the digital sign techniques of Avalos to more effectively targeted to the specific demographics of the people viewing advertisements (see Avalos at paragraph 2).
Claim 12 is rejected under 35 U.S.C. § 103 as being unpatentable over Wang in view of Sobhany, Avalos, and Hughes Network Systems (German Pub. No. DE-202022001728-U1, hereinafter Hughes).
As to dependent claim 12, the rejection of claim 8 is incorporated.
Wang does not appear to expressly teach transmit the global driver attention model to a digital sign.
Hughes teaches transmit the global driver attention model to a digital sign (Figures 5 and 6).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the vehicle based federated learning of Wang to include the machine learning signage techniques of Hughes Network Systems to provide more effective advertisements (see Hughes Network Systems at paragraphs 1-4).
Conclusion
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Casey R. Garner whose telephone number is 571-272-2467. The examiner can normally be reached Monday to Friday, 8am to 5pm, Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on 571-270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR to authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/Casey R. Garner/Primary Examiner, Art Unit 2123
Prosecution Insights