DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Status
This action is in response to applicant’s filing on 3/25/2025 and 7/1/2025. Claims 1-20 are pending and considered below.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent Number 12,258,048. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Comparing claims 1-11 of the instant application with claims 1-10 of U.S. Patent Number 12,258,048:
“A method for predicting vehicle actions, comprising: at a first vehicle including one or more processors and memory:” of the instant application is the same as “A method for predicting vehicle actions, comprising: at a first vehicle including one or more processors and memory:” of U.S. Patent Number 12,258,048;
“obtaining one or more images of a road” of the instant application is the same as “obtaining one or more images of a road” of U.S. Patent Number 12,258,048;
“applying a machine learning model to predict a sequence of vehicle actions of a second vehicle according to a hierarchy of interconnected vehicle actions by processing the one or more images” of the instant application would have been obvious over “predicting a sequence of vehicle actions of a second vehicle through a hierarchy of interconnected vehicle actions using the one or more images” of U.S. Patent Number 12,258,048;
“the hierarchy of interconnected vehicle actions including a plurality of action levels” of the instant application would have been obvious over “wherein the hierarchy of interconnected vehicle actions includes a plurality of action levels” of U.S. Patent Number 12,258,048; and
“controlling the first vehicle to at least partially autonomously drive based on the predicted sequence of vehicle actions” of the instant application is the same as “controlling the first vehicle to at least partially autonomously drive based on the predicted sequence of vehicle actions” of U.S. Patent Number 12,258,048.
Comparing claims 12-17 of the instant application with claims 11-15 of U.S. Patent Number 12,258,048:
“A first vehicle, comprising: a plurality of sensors; a vehicle control system; one or more processors; and memory storing one or more programs configured for execution by the one or more processors, the one or more programs including instructions for:” of the instant application would have been obvious over “A first vehicle, comprising: a plurality of sensors; a vehicle control system; one or more processors; and memory storing one or more programs configured for execution by the one or more processors, the one or more programs comprising instructions for:” of U.S. Patent Number 12,258,048;
“obtaining one or more images of a road” of the instant application is the same as “obtaining one or more images of a road” of U.S. Patent Number 12,258,048;
“applying a machine learning model to predict a sequence of vehicle actions of a second vehicle according to a hierarchy of interconnected vehicle actions by processing the one or more images” of the instant application would have been obvious over “predicting a sequence of vehicle actions of a second vehicle through a hierarchy of interconnected vehicle actions using the one or more images” of U.S. Patent Number 12,258,048;
“the hierarchy of interconnected vehicle actions including a plurality of action levels” of the instant application would have been obvious over “wherein the hierarchy of interconnected vehicle actions includes a plurality of action levels” of U.S. Patent Number 12,258,048; and
“controlling the first vehicle to at least partially autonomously drive based on the predicted sequence of vehicle actions” of the instant application is the same as “controlling the first vehicle to at least partially autonomously drive based on the predicted sequence of vehicle actions” of U.S. Patent Number 12,258,048.
Comparing claims 18-20 of the instant application with claims 16-20 of U.S. Patent Number 12,258,048:
“A non-transitory computer-readable storage medium storing one or more programs configured for execution by one or more processors of a first vehicle, the first vehicle further including a plurality of sensors and a vehicle control system, the one or more programs comprising instructions for:” of the instant application is the same as “A non-transitory computer-readable storage medium storing one or more programs configured for execution by one or more processors of a first vehicle, the first vehicle further including a plurality of sensors and a vehicle control system, the one or more programs comprising instructions for:” of U.S. Patent Number 12,258,048;
“obtaining one or more images of a road” of the instant application is the same as “obtaining one or more images of a road” of U.S. Patent Number 12,258,048;
“applying a machine learning model to predict a sequence of vehicle actions of a second vehicle according to a hierarchy of interconnected vehicle actions by processing the one or more images” of the instant application would have been obvious over “predicting a sequence of vehicle actions of a second vehicle through a hierarchy of interconnected vehicle actions using the one or more images” of U.S. Patent Number 12,258,048;
“the hierarchy of interconnected vehicle actions including a plurality of action levels” of the instant application would have been obvious over “wherein the hierarchy of interconnected vehicle actions includes a plurality of action levels” of U.S. Patent Number 12,258,048; and
“controlling the first vehicle to at least partially autonomously drive based on the predicted sequence of vehicle actions” of the instant application is the same as “controlling the first vehicle to at least partially autonomously drive based on the predicted sequence of vehicle actions” of U.S. Patent Number 12,258,048.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4, 9-15, 17-18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Unnikrishnan (US-2021/0406262-A1, hereinafter Unnikrishnan).
Regarding claim 1, Unnikrishnan discloses:
A method for predicting vehicle actions, comprising: at a first vehicle including one or more processors and memory (paragraphs [0047-0049] and [0114-0123]; FIG. 2, scenario search module-202, and data store-220; and FIG. 12, computer system-1200, processor-1202, memory-1204, and storage-1206);
obtaining one or more images of a road (paragraph [0056]; and FIG. 3, encoded image-300, and vehicles-302,304,306,308);
applying a machine learning model to predict a sequence of vehicle actions of a second vehicle according to a hierarchy of interconnected vehicle actions by processing the one or more images, the hierarchy of interconnected vehicle actions including a plurality of action levels (paragraphs [0046] and [0065-0067]; FIG. 1B, example scenario-150, and other scenarios-170,180,190; FIG. 7A, low-level parameters-720; and FIG. 7B, high-level primitives-770,772,774); and
controlling the first vehicle to at least partially autonomously drive based on the predicted sequence of vehicle actions (paragraph [0112]; FIG. 7B, first vehicle-758a, second vehicle-760a, cyclist-762a, and high-level primitives-770,772,774; and FIG. 11, vehicle-1140, array of sensors-1144, and navigation system-1146).
Regarding claims 4 and 20, Unnikrishnan further discloses:
wherein: the sequence of vehicle actions includes two or more vehicle actions, each of the vehicle actions corresponding to a distinct action level of the plurality of the action levels (paragraphs [0065-0067]; FIG. 7A, low-level parameters-720; and FIG. 7B, high-level primitives-770,772,774);
the machine learning model comprises a single end-to-end machine learning model (paragraphs [0052] and [0102]; and FIG. 2, scenario search module-202, and training module-206); and
configured to generate a vector identifying each vehicle action (paragraphs [0065-0067]).
Regarding claim 9, Unnikrishnan further discloses:
wherein: the machine learning model is configured to output a feature vector including a plurality of elements divided into a plurality of subsets of elements (paragraphs [0065-0067]; FIG. 7A, low-level parameters-720; and FIG. 7B, high-level primitives-770,772,774); and
each vehicle action of the predicted sequence of vehicle actions corresponds to a distinct action level, and is represented by a distinct subset of elements of the feature vector (paragraphs [0065-0067]).
Regarding claim 10, Unnikrishnan further discloses:
wherein: the machine learning model is configured to output an embedding vector (paragraphs [0053] and [0057]; FIG. 2, embedding module-208; and FIG. 4, train embedding model-404, trained model-406, generate embeddings-410, and embeddings-412);
the method further comprises: projecting the embedding vector to a feature vector including a plurality of elements that are divided into a plurality of subsets of elements (paragraphs [0060] and [0065-0067]; and FIG. 4, similarity search-416, and similar scenarios-418); and
each vehicle action of the predicted sequence of vehicle actions corresponds to a distinct action level, and is represented by a distinct subset of elements of the feature vector (paragraphs [0065-0067]).
Regarding claim 11, Unnikrishnan further discloses:
wherein the machine learning model is applied to predict the sequence of vehicle actions of the second vehicle in accordance with a determination that the second vehicle is within a predefined distance of the first vehicle (paragraphs [0046], [0065-0067] and [0110]; and FIG. 1B, example scenario-150, and other scenarios-170,180,190).
Regarding claim 12, Unnikrishnan further discloses:
A first vehicle, comprising: a plurality of sensors (paragraphs [0044] and [0110-0112]; and FIG. 11, vehicle-1140, array of sensors-1144, and navigation system-1146);
a vehicle control system (paragraph [0112]);
one or more processors (paragraph [0117]);
memory storing one or more programs configured for execution by the one or more processors, the one or more programs including instructions for: (paragraphs [0047-0049] and [0114-0123]; FIG. 2, scenario search module-202, and data store-220; and FIG. 12, computer system-1200, processor-1202, memory-1204, and storage-1206);
obtaining one or more images of a road (paragraph [0056]; and FIG. 3, encoded image-300, and vehicles-302,304,306,308);
applying a machine learning model to predict a sequence of vehicle actions of a second vehicle according to a hierarchy of interconnected vehicle actions by processing the one or more images, the hierarchy of interconnected vehicle actions including a plurality of action levels (paragraphs [0046] and [0065-0067]; FIG. 1B, example scenario-150, and other scenarios-170,180,190; FIG. 7A, low-level parameters-720; and FIG. 7B, high-level primitives-770,772,774); and
controlling the first vehicle to at least partially autonomously drive based on the predicted sequence of vehicle actions (paragraph [0112]; FIG. 7B, first vehicle-758a, second vehicle-760a, cyclist-762a, and high-level primitives-770,772,774; and FIG. 11, vehicle-1140, array of sensors-1144, and navigation system-1146).
Regarding claim 13, Unnikrishnan further discloses:
the one or more programs including instructions for: (paragraphs [0047-0049] and [0114-0123]);
obtaining sensor data from at least one of: a light detection and ranging (LiDAR) scanner and an inertial navigation system (INS), the INS including accelerometers and gyroscopes (paragraphs [0044] and [0110-0112]; and FIG. 11, vehicle-1140, array of sensors-1144, and navigation system-1146);
wherein the machine learning model is applied (paragraph [0046]);
to process the one or more images and sensor data jointly (paragraph [0056] and FIG. 3, encoded image-300, and vehicles-302,304,306,308); and
to predict the sequence of vehicle actions of the second vehicle (paragraphs [0046] and [0065-0067]; and FIG. 1B, example scenario-150, and other scenarios-170,180,190).
Regarding claim 14, Unnikrishnan further discloses:
wherein the instructions for controlling the first vehicle further include instructions for: (paragraph [0112]); and
adjusting a vehicle control plan in accordance with a determination that the predicted sequence of vehicle actions of the second vehicle satisfies a high frequency action sequence criterion (paragraphs [0051] and [0084]).
Regarding claim 15, Unnikrishnan further discloses:
wherein: the instructions for obtaining the one or more images of the road include instructions for capturing the one or more images of the road by a camera of the first vehicle (paragraphs [0110-0112]); and
the second vehicle is an obstacle vehicle that appears in a field of view of the first vehicle (paragraph [0066], the low-level parameters can comprise occlusion of the ego or various agents by one or more other agents or obstacles).
Regarding claim 17, Unnikrishnan further discloses:
the one or more programs further including instructions for: (paragraphs [0047-0049] and [0114-0123]);
obtaining the hierarchy of interconnected vehicle actions, the hierarchy including a plurality of predefined vehicle actions that are organized to define a plurality of vehicle action sequences (paragraphs [0065-0067]; FIG. 7A, low-level parameters-720; and FIG. 7B, high-level primitives-770,772,774); and
wherein each of the plurality of vehicle action sequences includes a respective subset of vehicle actions that are ordered according to the plurality of action levels, and each vehicle action in the respective subset of vehicle actions corresponds to a distinct one of the plurality of action levels (paragraphs [0065-0067]).
Regarding claim 18, Unnikrishnan further discloses:
A non-transitory computer-readable storage medium storing one or more programs configured for execution by one or more processors of a first vehicle (paragraphs [0047-0049] and [0114-0123]; FIG. 2, scenario search module-202, and data store-220; and FIG. 12, computer system-1200, processor-1202, memory-1204, and storage-1206);
the first vehicle further including a plurality of sensors and a vehicle control system (paragraphs [0044] and [0110-0112]; and FIG. 11, vehicle-1140, array of sensors-1144, and navigation system-1146);
the one or more programs comprising instructions for: (paragraphs [0047-0049] and [0114-0123]);
obtaining one or more images of a road (paragraph [0056]; and FIG. 3, encoded image-300, and vehicles-302,304,306,308);
applying a machine learning model to predict a sequence of vehicle actions of a second vehicle according to a hierarchy of interconnected vehicle actions by processing the one or more images, the hierarchy of interconnected vehicle actions including a plurality of action levels (paragraphs [0046] and [0065-0067]; FIG. 1B, example scenario-150, and other scenarios-170,180,190; FIG. 7A, low-level parameters-720; and FIG. 7B, high-level primitives-770,772,774); and
controlling the first vehicle to at least partially autonomously drive based on the predicted sequence of vehicle actions (paragraph [0112]; FIG. 7B, first vehicle-758a, second vehicle-760a, cyclist-762a, and high-level primitives-770,772,774; and FIG. 11, vehicle-1140, array of sensors-1144, and navigation system-1146).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2, 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Unnikrishnan, as applied to claims 1 and 18 above, and further in view of Rasouli et al. (US-2022/0156576-A1, hereinafter Rasouli).
Regarding claim 2, Unnikrishnan does not disclose a plurality of neural network models. However, Rasouli discloses a method for predicting the behavior of a dynamic object of interest in an environment of a vehicle, including the following features:
wherein: the machine learning model includes a plurality of neural network models, including a first neural network and a second neural network coupled to the first neural network (paragraphs [0089] and [0099]; and FIG. 3, categorical interaction subsystem-400, spatiotemporal encoder-410, categorical encoder-412a-e, and interaction attention subsystem-420);
applying the machine learning model to predict the sequence of vehicle actions of the second vehicle includes: (paragraphs [0099-0105]; and FIG. 5, encode categorized sets of data into respective categorical representations representing temporal changes of features in respective category-506, and input categorical interaction representation to action predictor to generate predicted data representing a predicted future behavior of a dynamic object of interest-518);
applying the first neural network (paragraphs [0089] and [0099]);
to process the one or more images (paragraph [0073], the categorical interaction subsystem receives feature data as input);
predict a first vehicle action (paragraphs [0099-0105]);
of the sequence of vehicle actions on a first action level of the plurality of action levels (paragraph [0073], a predicted action may be a higher level prediction);
applying a second neural network (paragraphs [0089] and [0099]);
to predict a second vehicle action (paragraphs [0099-0105]); and
of the sequence of vehicle actions on a second action level, of the plurality of action levels, the second action level following the first action level (paragraph [0073], a predicted trajectory may be a lower level prediction).
Rasouli teaches that the behavior of a dynamic object in the environment of a vehicle should be predicted using multiple trained neural networks (paragraph [0089]) which provide a higher level predicted action and a lower level predicted trajectory (paragraph [0073]). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the use of multiple neural networks of Rasouli into the machine learning model for encoding and searching scenario information using high-level primitives and low-level parameters of an agent of Unnikrishnan. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of training the machine learning model. A person of ordinary skill would be familiar with the use of multiple neural networks to process complex data.
Regarding claim 5, Unnikrishnan does not disclose a plurality of neural network models. However, Rasouli further discloses:
wherein: the machine learning model includes a plurality of neural network models that are coupled to each other in a series (paragraphs [0091-0095]; and FIG. 4, object behavior prediction subsystem-320, behavior predictor-322, and categorical interaction subsystem-400); and
each neural network model in the plurality of neural network models provides a respective output defining a respective vehicle action in a respective action level for the sequence of vehicle actions of the second vehicle (paragraphs [0073] and [0099-0105]).
Rasouli teaches that input data should be transmitted from a categorical interaction subsystem (first neural network) into a behavior predictor (second neural network) (paragraphs [0091-0095]). Rasouli further teaches that the behavior of a dynamic object in the environment of a vehicle should be predicted using multiple trained neural networks (paragraph [0089]) which provide a higher level predicted action and a lower level predicted trajectory (paragraph [0073]). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the use of multiple neural networks of Rasouli into the machine learning model for encoding and searching scenario information using high-level primitives and low-level parameters of an agent of Unnikrishnan. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of training the machine learning model. A person of ordinary skill would be familiar with the use of multiple neural networks to process complex data.
Regarding claim 19, Unnikrishnan does not disclose a plurality of neural network models. However, Rasouli further discloses:
wherein: the machine learning model includes a plurality of neural network models, including a first neural network and a second neural network coupled to the first neural network (paragraphs [0089] and [0099]; and FIG. 3, categorical interaction subsystem-400, spatiotemporal encoder-410, categorical encoder-412a-e, and interaction attention subsystem-420);
the instructions for applying the machine learning model to predict the sequence of vehicle actions of the second vehicle include instructions for: (paragraphs [0099-0105]; and FIG. 5, encode categorized sets of data into respective categorical representations representing temporal changes of features in respective category-506, and input categorical interaction representation to action predictor to generate predicted data representing a predicted future behavior of a dynamic object of interest-518);
applying the first neural network (paragraphs [0089] and [0099]);
to process the one or more images (paragraph [0073], the categorical interaction subsystem receives feature data as input);
predict a first vehicle action (paragraphs [0099-0105]);
of the sequence of vehicle actions on a first action level of the plurality of action levels (paragraph [0073], a predicted action may be a higher level prediction);
applying a second neural network (paragraphs [0089] and [0099]);
to predict a second vehicle action (paragraphs [0099-0105]); and
of the sequence of vehicle actions on a second action level, of the plurality of action levels, the second action level following the first action level (paragraph [0073], a predicted trajectory may be a lower level prediction).
Rasouli teaches that the behavior of a dynamic object in the environment of a vehicle should be predicted using multiple trained neural networks (paragraph [0089]) which provide a higher level predicted action and a lower level predicted trajectory (paragraph [0073]). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the use of multiple neural networks of Rasouli into the machine learning model for encoding and searching scenario information using high-level primitives and low-level parameters of an agent of Unnikrishnan. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of training the machine learning model. A person of ordinary skill would be familiar with the use of multiple neural networks to process complex data.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Unnikrishnan, as applied to claim 12 above, and further in view of Hashimoto et al. (US-2020/0051435-A1, hereinafter Hashimoto).
Regarding claim 16, Unnikrishnan does not disclose visualizing on a graphical user interface of the first vehicle a map including a vehicle trajectory of the second vehicle. However, Hashimoto discloses a vehicle moving-body detection unit and image processing unit, including the following features:
the one or more programs further including instructions for: displaying, via a graphical user interface, a map (paragraphs [0055] and [0098]; and FIG. 1, on-vehicle system-10, information processing unit-13, display unit-14, and HMI controller unit-34); and
visualizing the predicted sequence of vehicle actions of the second vehicle as a vehicle trajectory of the second vehicle on the map (paragraphs [0110-0123]; and FIG. 8, vehicle-221, bicycle-222, pedestrian-223, and motion prediction bars-M1,M2).
Hashimoto teaches that an on-vehicle system should display a three-dimensional space map which includes a motion prediction bar indicating the predicted trajectories of other vehicles, bicycles, and pedestrians (paragraphs [0055], [0098] and [0110-0123]). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the three-dimensional space map with motion prediction bars of Hashimoto into the machine learning model for encoding and searching scenario information using high-level primitives and low-level parameters of an agent of Unnikrishnan. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of providing a driver or passenger with information about the movement of objects in the environment of a vehicle. A person of ordinary skill would understand that a display of the trajectory of surrounding moving objects would enhance the safety of a driver or passenger of a vehicle.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Feit et al., Subgoal Planning Algorithm for Autonomous Vehicle Guidance, Cornell University Library, April 27, 2020, discloses a subgoal algorithm which uses a graph search to determine a subgoal sequence that links a series of unconstrained motion guidance elements into a constrained solution trajectory.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAMARA L WEBER whose telephone number is (303)297-4249. The examiner can normally be reached 8:30-5:00 MTN.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Faris Almatrahi can be reached at 3134464821. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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TAMARA L. WEBER
Examiner
Art Unit 3667
/TAMARA L WEBER/ Examiner, Art Unit 3667
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