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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/02/2026 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of the Claims
Claims 1-9 have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis - Step 1
Claims 1-9 is/are recite a method/process, therefore claims 1-9 are within at least one of the four statutory categories.
101 Analysis - Step 2A, Prong 1
Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recites mental processes and/or mathematical concepts (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method for matching environmental sensor scans,
wherein the environmental sensors are configured to scan an environment and provide scans of the environment, the method comprising:
ascertaining at least one matching transformation between a scan of an environmental sensor and a reference scan of an environmental sensor serving as a reference sensor;
wherein, in addition to the scan and the reference scan, at least one further scan of the environmental sensor and/or of a further environmental sensor is taken into account when ascertaining the transformation.
These limitations, as drafted, is a system that, under its broadest reasonable interpretation, covers performance of the limitation as a mental process and/or mathematical concept. That is, nothing in the claim elements preclude the steps from practically being performed as mathematical concepts. For example, " ascertaining at least one matching transformation between a scan …" and " sensors are configured to scan an environment and provide scans of the environment...", encompass subject matter that a human can reasonably perform in the human mind with or without paper and pencil, involves a mathematical equation and is a mathematical concept, although this step could also be considered a mental process as well, perform a mathematical analysis of the data, comparing and matching information between different scans (mental activity). Thus, the claim recites at least a mathematical concept and a mental process.
101 Analysis - Step 2A, Prong 2
Regarding Prong 2 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application."
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the "additional limitations" while the bolded portions continue to represent the "abstract idea"):
A method for matching environmental sensor scans,
wherein the environmental sensors are configured to scan an environment and provide scans of the environment, the method comprising:
ascertaining at least one matching transformation between a scan of an environmental sensor and a reference scan of an environmental sensor serving as a reference sensor;
wherein, in addition to the scan and the reference scan, at least one further scan of the environmental sensor and/or of a further environmental sensor is taken into account when ascertaining the transformation.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of "environmental sensor…” the components are merely generic components to perform a function using computer code/gathering data. The generic components are recited at a high level of generality (i.e. a generic processor and memory) such that it amounts to no more than mere instructions to apply the exception using generic computer components. The examiner submits that these limitations are merely applying the above-noted abstract idea by merely using a general controller to perform the process (MPEP §2106.05).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular process for safety performance evaluation, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B
Regarding Step 2B in the 2019 PEG, representative independent claim 12 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of " environmental sensor…” amounts to nothing more than applying the exception using a generic computer component. Mere instructions cannot provide an inventive concept. Hence, the claim is not patent eligible.
Dependent claims 2-9 specify limitations that elaborate on the abstract idea of claims 1 and thus are directed to an abstract idea nor do the claims recite additional limitations that integrate the claims into a practical application or amount to “significantly more” for similar reasons.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1-9 is/are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Zeng(US20130242285A1).
Claim.1 Zeng discloses a method for matching environmental sensor scans (see at least abstract, a system and method for registering range images from objects detected by multiple LiDAR sensors on a vehicle), wherein the environmental sensors are configured to scan an environment and provide scans of the environment (see at fig.1, abstract, registering range images from objects detected by multiple LiDAR sensors on a vehicle, p7, LiDAR sensors are desirable because they are able to provide the heading of a tracked object, which other types of sensors, such as vision systems and radar sensors, are generally unable to do. For one type of LiDAR sensor, reflections from an object are returned as a scan point as part of a point cluster range map, p37, the host vehicle 12 includes four LiDAR sensors, namely, a forward looking sensor 16 having a field-of-view 18, a rearward looking sensor 20 having a field-of-view 22, a left looking sensor 24 having a field-of-view 26 and a right looking sensor 28 having a field-of-view 30. The sensors 16, 24 and 28 are mounted at the front of vehicle 12 and have over-lapping fields-of-view, as shown. If an object, such as the target vehicle 14, is in the field-of-view of a particular one of the sensors 16, 20, 24 and 30, the sensor returns a number of scan points that identifies the object), the method comprising: ascertaining at least one matching transformation between a scan of an environmental sensor and a reference scan of an environmental sensor serving as a reference sensor (see at least fig.1-3, abstract, a transformation value for at least one of the LiDAR sensors that identifies an orientation angle and position of the sensor and provides target scan points from the objects detected by the sensors where the target scan points for each sensor provide a separate target point map,p44, registering range images from objects detected by multiple LiDAR sensors on a vehicle, an enlarged area in the circle 122 for a few of the scan point returns. FIG. 3(B) shows how the left looking LiDAR sensor scan point returns 124 are mapped to the right looking LiDAR sensor scan point returns 126 by arrows 128. By using the currently available transformation T for the projection map arrows 128, the left looking LiDAR sensor scan point returns 124 are moved relative to the right looking LiDAR sensor scan point returns 126 in an attempt to make them overlap, p45-51, EM algorithm for determining the transformation T may be only locally optimal and sensitive to the initial transformation value. The algorithm can be enhanced using particle swarm optimization (PSO) to find the initial transformation T.sub.0., p37, The host vehicle 12 includes four LiDAR sensors, namely, a forward looking sensor 16 having a field-of-view 18, a rearward looking sensor 20 having a field-of-view 22, a left looking sensor 24 having a field-of-view 26 and a right looking sensor 28 having a field-of-view 30. The sensors 16, 24 and 28 are mounted at the front of the vehicle 12 and have over-lapping fields-of-view, as shown. If an object, such as the target vehicle 14, is in the field-of-view of a particular one of the sensors 16, 20, 24 and 30, the sensor returns a number of scan points that identifies the object. Points 32 on the target vehicle 14 represent the scan points that are returned from the target vehicle 14 from each of the sensors 16, 24 and 28. The points 32 are transferred into the vehicle coordinate system (x, y) on the host vehicle 12 using a coordinate transfer technique, and then the object detection is performed in the vehicle coordinate system using the points 32); wherein, in addition to the scan and the reference scan, at least one further scan of the environment sensor and/or of a further environmental sensor is taken into account when ascertaining the transformation (see at least fig.12-19,p40, a fusion system 36 for fusing scan point cloud returns from multiple LiDAR sensors, such as the sensors 16, 20, 24 and 28. Box 38 represents the scan point cloud return from the left LiDAR sensor 24, box 40 represents the scan point cloud return from the right looking LiDAR sensor 28, box 42 represents the scan point cloud return from the forward looking LiDAR sensor 16, and box 44 represents the scan point cloud return from the rearward looking LiDAR sensor 20. The range maps from the LiDAR sensors 16, 20, 24 and 28 are registered and a 360.degree. range map (point cloud) is constructed at box 46, p104, a flow diagram 80 showing the proposed fusion algorithm performed at each time step t. Box 78 represents the object files that are generated at each time step, and provide the position, velocity and heading of the objects that are detected and tracked and the object model M for each object that is tracked. When a new frame of range data from the sensors 16, 20, 24 and 28 arrives at the host vehicle 12 at box 82, the algorithm first constructs the 360.degree. point cloud at box 84, p56, first presents a proposed scan point registration algorithm that estimates the motion of the target vehicle 14 if the object model M and the current scan map S corresponding to the target vehicle 14 are given. The discussion above concerning the EM algorithm for determining the transformation T that aligns the frames between the LiDAR sensors provides spatial matching, particularly, matching between two frames from different LiDAR sensors at the same time. This discussion concerning scan point registration also uses a point set registration algorithm to find the transformation T that matches two frames temporally between the current scan map S and the object model M derived from the past scan maps).
Claim.2 Zeng discloses wherein a plurality of further scans of the environmental sensor and/or further environmental sensor are taken into account when ascertaining the transformation between the scan and the reference scan (see at least fig.1-3, abstract, a transformation value for at least one of the LiDAR sensors that identifies an orientation angle and position of the sensor and provides target scan points from the objects detected by the sensors where the target scan points for each sensor provide a separate target point map,p44, registering range images from objects detected by multiple LiDAR sensors on a vehicle, an enlarged area in the circle 122 for a few of the scan point returns. FIG. 3(B) shows how the left looking LiDAR sensor scan point returns 124 are mapped to the right looking LiDAR sensor scan point returns 126 by arrows 128. By using the currently available transformation T for the projection map arrows 128, the left looking LiDAR sensor scan point returns 124 are moved relative to the right looking LiDAR sensor scan point returns 126 in an attempt to make them overlap, p45-51, EM algorithm for determining the transformation T may be only locally optimal and sensitive to the initial transformation value. The algorithm can be enhanced using particle swarm optimization (PSO) to find the initial transformation T.sub.0., p37, The host vehicle 12 includes four LiDAR sensors, namely, a forward looking sensor 16 having a field-of-view 18, a rearward looking sensor 20 having a field-of-view 22, a left looking sensor 24 having a field-of-view 26 and a right looking sensor 28 having a field-of-view 30. The sensors 16, 24 and 28 are mounted at the front of the vehicle 12 and have over-lapping fields-of-view, as shown. If an object, such as the target vehicle 14, is in the field-of-view of a particular one of the sensors 16, 20, 24 and 30, the sensor returns a number of scan points that identifies the object. Points 32 on the target vehicle 14 represent the scan points that are returned from the target vehicle 14 from each of the sensors 16, 24 and 28. The points 32 are transferred into the vehicle coordinate system (x, y) on the host vehicle 12 using a coordinate transfer technique, and then the object detection is performed in the vehicle coordinate system using the points 32).
Claim.3 Zeng discloses wherein, for the at least one further scan, a further transformation between the further scan and the reference scan is ascertained, wherein, when ascertaining the further transformation between the further scan and the reference scan, at least the scan or an additional scan of an additional environmental sensor is taken into account (see at least fig.12-19,p40, a fusion system 36 for fusing scan point cloud returns from multiple LiDAR sensors, such as the sensors 16, 20, 24 and 28. Box 38 represents the scan point cloud return from the left LiDAR sensor 24, box 40 represents the scan point cloud return from the right looking LiDAR sensor 28, box 42 represents the scan point cloud return from the forward looking LiDAR sensor 16, and box 44 represents the scan point cloud return from the rearward looking LiDAR sensor 20. The range maps from the LiDAR sensors 16, 20, 24 and 28 are registered and a 360.degree. range map (point cloud) is constructed at box 46, p104, a flow diagram 80 showing the proposed fusion algorithm performed at each time step t. Box 78 represents the object files that are generated at each time step, and provide the position, velocity and heading of the objects that are detected and tracked and the object model M for each object that is tracked. When a new frame of range data from the sensors 16, 20, 24 and 28 arrives at the host vehicle 12 at box 82, the algorithm first constructs the 360.degree. point cloud at box 84, p56, first presents a proposed scan point registration algorithm that estimates the motion of the target vehicle 14 if the object model M and the current scan map S corresponding to the target vehicle 14 are given. The discussion above concerning the EM algorithm for determining the transformation T that aligns the frames between the LiDAR sensors provides spatial matching, particularly, matching between two frames from different LiDAR sensors at the same time. This discussion concerning scan point registration also uses a point set registration algorithm to find the transformation T that matches two frames temporally between the current scan map S and the object model M derived from the past scan maps).
Claim.4 Zeng discloses wherein the scan and the reference scan include information about different regions of an environment that do not overlap (see at least fig.8-11, p123, new objects are created and dying objects are deleted at box 94. Particularly, two special cases need to be handled to create new objects and to remove existing objects in the object file, where there exists no edge incident to scan cluster S.sub.5. A track initialization step will be triggered, and a new object will be added in the object file for scan cluster S.sub.5, and predicted object model {tilde over (M)}.sub.5 is dying since there is no edge incident from it and will be removed from the object file).
Claim.5 Zeng discloses wherein the ascertaining of the at least one transformation includes the following steps: generating a descriptor set for each of the scan, the reference scan, and the at least one further scan, estimating the at least one transformation based on the descriptor sets of the scan and reference scan, wherein the descriptor set of the at least one further scan is taken into account when estimating the transformation between the scan and the reference scan (see at least fig.12-19,p40, a fusion system 36 for fusing scan point cloud returns from multiple LiDAR sensors, such as the sensors 16, 20, 24 and 28. Box 38 represents the scan point cloud return from the left LiDAR sensor 24, box 40 represents the scan point cloud return from the right looking LiDAR sensor 28, box 42 represents the scan point cloud return from the forward looking LiDAR sensor 16, and box 44 represents the scan point cloud return from the rearward looking LiDAR sensor 20. The range maps from the LiDAR sensors 16, 20, 24 and 28 are registered and a 360.degree. range map (point cloud) is constructed at box 46, p104, a flow diagram 80 showing the proposed fusion algorithm performed at each time step t. Box 78 represents the object files that are generated at each time step, and provide the position, velocity and heading of the objects that are detected and tracked and the object model M for each object that is tracked. When a new frame of range data from the sensors 16, 20, 24 and 28 arrives at the host vehicle 12 at box 82, the algorithm first constructs the 360.degree. point cloud at box 84, p56, first presents a proposed scan point registration algorithm that estimates the motion of the target vehicle 14 if the object model M and the current scan map S corresponding to the target vehicle 14 are given. The discussion above concerning the EM algorithm for determining the transformation T that aligns the frames between the LiDAR sensors provides spatial matching, particularly, matching between two frames from different LiDAR sensors at the same time. This discussion concerning scan point registration also uses a point set registration algorithm to find the transformation T that matches two frames temporally between the current scan map S and the object model M derived from the past scan maps, p104, the segmenting operation, let denote the scan map in the current frame (t+1) and let G=(, E) be an undirected graph with vertex set . An edge (p.sub.1, p.sub.2).epsilon. E links p.sub.1 and p.sub.2 if .parallel.p.sub.1-p.sub.2.parallel. is less than a predefined distance threshold. The connected component labeling is then used to segment the scan map into a list of clusters {S.sub.n.sup.(t+1)}. Segmenting the scan points into clusters includes separating the clusters of scan points in the return point clouds so that clusters identify a separate object that is being tracked).
Claim.6 Zeng discloses wherein the at least one transformation is estimated based on a correspondence matrix between the descriptor set of the scan and the descriptor set of the reference scan, wherein at least one further correspondence matrix between the descriptor set of the at least one further scan and the descriptor set of the reference scan is taken into account when estimating the at least one transformation (see at least fig.12-19,p40, a fusion system 36 for fusing scan point cloud returns from multiple LiDAR sensors, such as the sensors 16, 20, 24 and 28. Box 38 represents the scan point cloud return from the left LiDAR sensor 24, box 40 represents the scan point cloud return from the right looking LiDAR sensor 28, box 42 represents the scan point cloud return from the forward looking LiDAR sensor 16, and box 44 represents the scan point cloud return from the rearward looking LiDAR sensor 20. The range maps from the LiDAR sensors 16, 20, 24 and 28 are registered and a 360.degree. range map (point cloud) is constructed at box 46, p104, a flow diagram 80 showing the proposed fusion algorithm performed at each time step t. Box 78 represents the object files that are generated at each time step, and provide the position, velocity and heading of the objects that are detected and tracked and the object model M for each object that is tracked. When a new frame of range data from the sensors 16, 20, 24 and 28 arrives at the host vehicle 12 at box 82, the algorithm first constructs the 360.degree. point cloud at box 84, p56, first presents a proposed scan point registration algorithm that estimates the motion of the target vehicle 14 if the object model M and the current scan map S corresponding to the target vehicle 14 are given. The discussion above concerning the EM algorithm for determining the transformation T that aligns the frames between the LiDAR sensors provides spatial matching, particularly, matching between two frames from different LiDAR sensors at the same time. This discussion concerning scan point registration also uses a point set registration algorithm to find the transformation T that matches two frames temporally between the current scan map S and the object model M derived from the past scan maps, p85, equation 47-50, p85, a zero-mean Gaussian random variable with covariance matrix Q (i.e., p(w)=)w/0, Q)). If p(y.sub.t/S.sup.(0:t)) is assumed to be a Dirac Delta distribution centered at y.sub.t).
Claim.7 Zeng discloses wherein the at least one transformation is ascertained by a neural network (see at least fig.8,24, p82, a dynamic Bayesian network 70 representing two time steps of the proposed tracking algorithm. In the network 70, nodes 72 represent the transformation parameters y.sub.t and y.sub.t+1, i.e., the object position and attitude of the targets, nodes 74 represent the object models M.sub.t and M.sub.t+1 and nodes 76 represent the scan maps S.sub.t and S.sub.t+1, p40, a fusion system 36 for fusing scan point cloud returns from multiple LiDAR sensors, such as the sensors 16, 20, 24 and 28. Box 38 represents the scan point cloud return from the left LiDAR sensor 24, box 40 represents the scan point cloud return from the right looking LiDAR sensor 28, box 42 represents the scan point cloud return from the forward looking LiDAR sensor 16, and box 44 represents the scan point cloud return from the rearward looking LiDAR sensor 20).
Claim.8 Zeng discloses wherein the at least one transformation is ascertained by a neural network, and wherein the descriptor sets are generated by a first subnet of the neural network and the at least one transformation is estimated by a separate second subnet of the neural network (see at least fig.8-11,24, p82, a dynamic Bayesian network 70 representing two time steps of the proposed tracking algorithm. In the network 70, nodes 72 represent the transformation parameters y.sub.t and y.sub.t+1, i.e., the object position and attitude of the targets, nodes 74 represent the object models M.sub.t and M.sub.t+1 and nodes 76 represent the scan maps S.sub.t and S.sub.t+1, p40, a fusion system 36 for fusing scan point cloud returns from multiple LiDAR sensors, such as the sensors 16, 20, 24 and 28. Box 38 represents the scan point cloud return from the left LiDAR sensor 24, box 40 represents the scan point cloud return from the right looking LiDAR sensor 28, box 42 represents the scan point cloud return from the forward looking LiDAR sensor 16, and box 44 represents the scan point cloud return from the rearward looking LiDAR sensor 20, p108, Let the E(p', q') be the subset of the edge set in B, i.e., E(p', q').ident.{(p, q)|(p, q).epsilon. E.sub.B .andgate. p .epsilon. p' .andgate. q' .epsilon. q}. The weight (p', q') and cardinality of the edge (p', q') are defined, equations 1-16, p104, scan map in the current frame (t+1) and let G=(, E) be an undirected graph with vertex set).
Claim.9 Zeng discloses wherein for each, the reference scan, and the at least one further scan, the first subnet has a separate subnetwork for generating the descriptor set, where the subnetworks are configured to generate the descriptor sets in parallel with one another (see at least fig.12-19,p40, a fusion system 36 for fusing scan point cloud returns from multiple LiDAR sensors, such as the sensors 16, 20, 24 and 28. Box 38 represents the scan point cloud return from the left LiDAR sensor 24, box 40 represents the scan point cloud return from the right looking LiDAR sensor 28, box 42 represents the scan point cloud return from the forward looking LiDAR sensor 16, and box 44 represents the scan point cloud return from the rearward looking LiDAR sensor 20. The range maps from the LiDAR sensors 16, 20, 24 and 28 are registered and a 360.degree. range map (point cloud) is constructed at box 46, p104, a flow diagram 80 showing the proposed fusion algorithm performed at each time step t. Box 78 represents the object files that are generated at each time step, and provide the position, velocity and heading of the objects that are detected and tracked and the object model M for each object that is tracked. When a new frame of range data from the sensors 16, 20, 24 and 28 arrives at the host vehicle 12 at box 82, the algorithm first constructs the 360.degree. point cloud at box 84, p56, first presents a proposed scan point registration algorithm that estimates the motion of the target vehicle 14 if the object model M and the current scan map S corresponding to the target vehicle 14 are given. The discussion above concerning the EM algorithm for determining the transformation T that aligns the frames between the LiDAR sensors provides spatial matching, particularly, matching between two frames from different LiDAR sensors at the same time. This discussion concerning scan point registration also uses a point set registration algorithm to find the transformation T that matches two frames temporally between the current scan map S and the object model M derived from the past scan maps).
Conclusion
Related References
The relevant art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Cooley (US20190051069A1) discloses a biometric recognition module for analyzing scan data of an environment based on a gesture recognition algorithm by using a processor. The processor analyzes the scan data of the environment based on a biometric feature, where the biometric feature comprises a flagging down gesture. A controller stops an autonomous vehicle at a position relative to a user and configures the autonomous vehicle to offer use of the autonomous vehicle to the user if the gesture recognition algorithm recognizes the user showing the flagging down gesture according to the biometric feature in the scan data of the environment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARDUL D PATEL whose telephone number is (571)270-7758. The examiner can normally be reached Monday-Friday 8am-5pm (IFP).
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, KITO ROBINSON can be reached at (571)270-3921. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHARDUL D PATEL/Primary Examiner, Art Unit 3664
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