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 .
Priority
Acknowledgement is made of Applicant’s claim of this application being a National Stage Application of International Patent Application No. PCT/JP2022/037139, filed on October 4, 2022, and claim of priority and benefit from Japan Application No. JP2021-177665, filed on 10/29/2021.
Status of Claims
Claims 1-4, 6-7, 9-20 are currently pending. Claims 5 and 8 are canceled. Claims 11-20 has been added.
Response to Amendment
The amendments of Claims 1, 6 and 9-10 are accepted and entered.
Claims 5 and 8 are canceled.
New Claims 11-20 are accepted and entered.
Response to Argument
Applicant states: “As acknowledged by the Examiner, none of Ratsamee, Hasegawa, or Morales Teraoka teaches or suggests “the posterior distribution being expressed by using the motion model and relative positions of the moving bodies from the observing moving body at respective times” which was originally recited in claim 5 and is not rejected over Ratsamee, Hasegawa, and Morales Teraoka.”. Applicant further, incorrectly cites Blaiotta as the prior art being used in rejecting the claimed "generating bird's-eye view data by maximizing a posterior distribution of the on-ground positions..." rather than Hasegawa.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Hasegawa teaches generating a hybrid map of an autonomous robot from a bird’s eye view (Hasegawa; Fig. 5 (See figure below) and maximizes the vehicles posterior probability in relation to feature points in the environment (Hasegawa; [0117]). Blaiotta teaches “predicting a location of a pedestrian moving in an environment” (Blaiotta; Abstract and Fig. 6b (See figure below)) and “to maximize the expected joint probability of the hidden states and the measurements” (Blaiotta; [0201]) of the pedestrian detected by the autonomous vehicle. Blaiotta further teaches the limitation: “being expressed by using the motion model and relative positions of the moving bodies from the observing moving body at respective times.” (Blaiotta; [0203]; “A sample drawn from such posterior probability distribution, at a given time-step, may comprise a collection of hidden variable vectors, one per agent. For example, …a discrete motion model variable [0207]…a continuous 2-d position vector [0209]…” (emphasis added)). It would have been obvious to one of ordinary skill in the art to combine Hasegawa and Blaiotta to further maximize both the posterior distribution of on-ground positions of the observing moving body and the respective moving bodies given on-ground positions of the observing moving body. One of ordinary skill in the art would be motivated to combine Hasegawa with Blaiotta to “predict the future state of the environment so that the machine control may safely adapt to it. In particular, predicting the movement of pedestrians is important for controlling a physical system, like a computer-controlled machine, e.g., a robot or a vehicle, in order to ensure that, while operating, this machine interacts safely with a pedestrian that might be in its way, for instance, by not hitting them.” (Blaiotta, [0003]). Accordingly, THIS ACTION IS MADE FINAL.
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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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 is advised of the obligation under 37 CFR 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 1-4, 9-13, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ratsamee et. al. ( “People Tracking with Body Pose Estimation for Human Path Prediction” with the publication date of 08/05/2012 – 08/08/2012) in view of Hasegawa et. al. (US 2013/0216098), in further view of Morales Teraoka et. al. (US 2016/0355181), and still in further view of Blaiotta (US 2020/0283016).
Consider Claim 1, Ratsamee discloses “A bird's-eye view data generating device comprising: a memory; and at least one processor coupled to the memory, the at least one processor being configured to:” (Ratsamee; Section 3.A; “The implemented 3D human skeleton tracking runs at 25 Hz on a PC (E5420 2.50 GHz Xeon CPU, 4096M RAM, NVIDIA Quadro FX 1700 graphic card)” (emphasis added)) “acquire time-series data that is two-dimensional observation information expressing at least one moving body” (Ratsamee; Section 3.A, “For data collection, a sequence of images was taken using the Kinect camera, recording approximately 28 fps.”) “observed in a dynamic environment” (Ratsamee; Abstract; “The proposed method is verified in an indoor environment where humans pass by each other.”) “from a viewpoint of an observing moving body that is equipped with an observation device;” (Ratsamee; Figure 6 (See image below); Section 3.A, “In this experiments, our proposed 3D skeleton-based body pose estimation and tracking system will be evaluated. Figure 6 shows the experimental system, which consists of Kinect placed on an Enon Robot (a product of Fujitsu Frontech Ltd.) in an environment.”) “that are obtained in a case in which the observing moving body is observed from a bird's-eye view position, from the time-series data of the two-dimensional observation information” (Ratsamee; Figure 7 (See image below); Section 3.B, “Figure 7 shows the top view of the raw data consisting of left shoulder, right shoulder and human center position obtained with the Kinect sensor….Figure 7(a) shows the plot of the human motion tracking using the proposed model with the extended Kalman filter.”) “and by using a motion model, which is determined in advance and expresses motions of the moving bodies,” (Ratsamee; Section 1; “a human kinematic walking model with body pose tracking using the extended Kalman Filter is proposed. This simple but efficient model is suitable for real-time multi-body pose tracking and path prediction.”) “and relative positions of the moving bodies from the observing moving body in an actual space,” (Ratsamee; Figure 9 (See image below)) “
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Ratsamee does not explicitly disclose “and generate bird's-eye view data, which expresses an on-ground locus of movement of the observing moving body”. However, in an analogous field of endeavor, Hasegawa teaches, “the map generation method further includes a position information acquisition step of obtaining a position and a posture of the moving body, and a position of a feature point corresponding to the invariant feature quantity from information and an observation values.” (Hasegawa; Fig. 15 (See image below); [0020]). Furthermore, Hasegawa teaches “wherein the at least one processor generates the bird's-eye view data so as to maximize a posterior distribution of on-ground positions of the observing moving body and the respective moving bodies given on-ground positions of the observing moving body…” (Hasegawa; [0117]; “The map generation unit 16 constructs a relative position relation between the state of the robot and each feature point (local map) by using the maximum posterior probabilities x and m and their corresponding covariance obtained in the step S3 (step S5).”). Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Ratsamee with teachings of Hasegawa to further express the movement of the observing moving body in an environment. One of ordinary skill in the art would be motivated to combine Ratsamee and Hasegawa to express all moving bodies, including the observing moving body, to better analyze the interactions between all objects and people in an actual space and effectively perform collision avoidance. Accordingly, the combination of Ratsamee and Hasegawa discloses the above-discussed limitations of Claim 1.
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The combination of Ratsamee and Hasegawa does not explicitly disclose “which relative positions are determined by using prior information, which relates to sizes of the moving bodies in an actual space, and sizes and positions of the moving bodies in the two-dimensional observation information.” However, in an analogous field of endeavor, Morales Teraoka teaches “which relative positions are determined by using prior information,” (Morales Teraoka; [0015]; “the animal presence area prediction unit may include a behavior characteristics index value storage unit that stores in advance a group of data on the “behavior characteristics index values” of an animal of a type supposed to enter a traveling road of the vehicle…” (emphasis added)) “which relates to sizes of the moving bodies in an actual space, and sizes and positions of the moving bodies in the two-dimensional observation information,” (Morales Teraoka; [0087 - 0088]; “…in order to reduce the number of candidate patterns to be used for matching with the image of an animal in an image, the animal may be classified into one of the sizes, for example, into the large size, medium size, and small size, before performing pattern matching. This allows the pattern, which will be used for matching, to be selected from the patterns of animals having the size determined by the classification….Next, the detected animal is classified by size into one of the sizes as described above according to the animal size estimated from the size of the image of the object (step S32)….the size of the animal in the image can be estimated from the size of the image of the object in the image (angle of view) and its position in the image.”). Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Ratsamee and Hasegawa with the teachings of Morales Teraoka to further determine the variable sizes of the surrounding moving bodies. One of ordinary skill in the art would be motivated to combine Ratsamee, Hasegawa with Morales Teraoka to more effectively estimate the position of the moving bodies in an environment by accounting for perceptive size. Accordingly, the combination of Ratsamee, Hasegawa, and Morales Teraoka discloses the above-discussed limitations of Claim 1.
The combination of Ratsamee, Hasegawa, and Morales Teraoka does not explicitly disclose “and the respective moving bodies of one time before, the posterior distribution being expressed by using the motion model and relative positions of the moving bodies from the observing moving body at respective times.”. However, in an analogous field of endeavor, Blaiotta teaches “and the respective moving bodies of one time before, the posterior distribution” (Blaiotta; FIG. 6b(c) (See image below); [0201]; “to maximize the expected joint probability of the hidden states and the measurements”; [0246], “FIG. 6b(a) illustrates the past (solid line) and future (dotted line) trajectory of a pedestrian agent walking on the sidewalk while two cars are driving by…. FIG. 6b(c) illustrates the posterior probability of crossing intention along the entire track” (emphasis added) ) “being expressed by using the motion model and relative positions of the moving bodies from the observing moving body at respective times.” (Blaiotta; [0203]; “A sample drawn from such posterior probability distribution, at a given time-step, may comprise a collection of hidden variable vectors, one per agent. For example, …a discrete motion model variable [0207]…a continuous 2-d position vector [0209]…” (emphasis added)). Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Ratsamee, Hasegawa, and Morales Teraoka with the teachings of Blaiotta to further maximize both the posterior distribution of on-ground positions of the observing moving body and the respective moving bodies given on-ground positions of the observing moving body. One of ordinary skill in the art would be motivated to combine Hasegawa with Blaiotta to “predict the future state of the environment so that the machine control may safely adapt to it. In particular, predicting the movement of pedestrians is important for controlling a physical system, like a computer-controlled machine, e.g., a robot or a vehicle, in order to ensure that, while operating, this machine interacts safely with a pedestrian that might be in its way, for instance, by not hitting them.” (Blaiotta, [0003]). Accordingly, the combination of Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta discloses the invention of Claim 1.
Consider Claim 2, the combination of Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta teaches “The bird's-eye view data generating device of Claim 1, wherein: the prior information is information relating to a distribution of sizes of the moving bodies in an actual space,” (Morales; [0087 - 0088]; “…in order to reduce the number of candidate patterns to be used for matching with the image of an animal in an image, the animal may be classified into one of the sizes, for example, into the large size, medium size, and small size, before performing pattern matching….Next, the detected animal is classified by size into one of the sizes as described above according to the animal size estimated from the size of the image of the object (step S32)….the size of the animal in the image can be estimated from the size of the image of the object in the image (angle of view) and its position in the image.”) “and the at least one processor” (Ratsamee; Section 3.A; “The implemented 3D human skeleton tracking runs at 25 Hz on a PC (E5420 2.50 GHz Xeon CPU…” (emphasis added)) “generates the bird's-eye view data, which expresses the locus of movement expressing a distribution of on-ground positions of the observing moving body at respective times” (Hasegawa; [01212] “map construction can be performed while taking account of a local map that was constructed when visiting there in the past by substituting, as a prior distribution of the map” (emphasis added); Examiner notes Hasegawa teaches the prior distribution to be “prior distribution for position and posture of robot; p(m) is prior distribution for map” (emphasis added) (Hasegawa, [0090])) “and the loci of movement expressing a distribution of on-ground positions of the respective moving bodies at respective times, from the time-series data of the two-dimensional observation information” (Ratsamee; Figure 9 (See image above)) “and by using a distribution of relative positions of the moving bodies from the observing moving body and the motion model that expresses a distribution of motions of the moving bodies.” (Morales Teraoka; [008]; “the prediction result may be represented as a distribution of future presence probabilities of the animal in the planar area around the vehicle. The animal's future presence area is predicted using the current direction, position, and movement speed of the animal, obtained from the image, and the behavior characteristics index values of the determined animal type” (emphasis added); Examiner notes Morales teaches the index values derived from the animal movement model.) The proposed combination as well as the motivation for combining the Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta references presented in the rejection of Claim 1, apply to claim 2 and are incorporated herein by reference. Thus, the device recited in claim 2 is met by Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta.
Consider Claim 3, the combination of Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta teaches “The bird's-eye view data generating device of Claim 1, wherein the motion model is a model expressing uniform motions of the moving bodies, or is a model expressing motions corresponding to interactions between the moving bodies. However, in an analogous field of endeavor, Blaiotta teaches “It is assumed in this embodiment that the kinematic state of all agents, as represented by their position, orientation and velocity can be at least partially measured at a constant rate” (emphasis added) (Blaiotta; [0215]). The proposed combination as well as the motivation for combining the Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta references presented in the rejection of Claim 1, apply to claim 3 and are incorporated herein by reference. Thus, the device recited in claim 3 is met by Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta.
Consider Claim 4, the combination of Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta teaches “ The bird's-eye view data generating device of Claim 1, wherein the at least one processor” (Ratsamee; Section 3.A; “The implemented 3D human skeleton tracking runs at 25 Hz on a PC (E5420 2.50 GHz Xeon CPU…” (emphasis added)) “is further configured track the respective moving bodies from the time-series data of the two- dimensional observation information,” (Ratsamee; Figure 7 (See image above)) “and acquire positions and sizes of the respective moving bodies at respective times in the two-dimensional observation information,” (Morales Teraoka; [0088]; “At this time, because the angle of view of the whole camera image is known and the vehicle is supposed to travel essentially on a plane, the size of the animal in the image can be estimated from the size of the image of the object in the image (angle of view) and its position in the image.”) “and wherein the at least one processor generates the bird's-eye view data from positions and sizes of the respective moving bodies at respective times in the acquired two-dimensional observation information.” (Ratsamee; Figure 9 (See image above); Examiner notes the z-axis seen in Figure 9 of Ratsamee is interpreted as the size or height of the moving bodies). The proposed combination as well as the motivation for combining the Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta references presented in the rejection of claim 1, apply to claim 4 and are incorporated herein by reference. Thus, the device recited in claim 4 is met by Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta.
Consider Claim 9, Claim 9 recites a method with steps corresponding to the elements of the device recited in Claim 1. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding device claim. Additionally, the rationale and motivation to combine the Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta references, presented in rejection of Claim 1, apply to this claim.
Consider Claim 10, Claim 10 recites a robot with elements corresponding to the components recited in Claim 1. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding device claim. Additionally, the rationale and motivation to combine the Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta references discloses “e[[ing]] the robot to travel autonomously;” (Ratsamee; Section 3.A; “…the experimental system, which consists of Kinect placed on an Enon Robot (a product of Fujitsu Frontech Ltd.)”; As an evidentiary clarification, Examiner notes that the recited Enon Robot (first offered for sale in 2005) in Ratsamee reference is “a service robot developed by Fujitsu Frontech Limited and Fujitsu Laboratories Ltd…. the robot's ability to autonomously support tasks while connected to a network. Enon was designed to assist with multiple tasks in offices and commercial establishments, including providing guidance, escorting guests, transporting objects, and security patrolling.” (Source: https://en.wikipedia.org/wiki/Enon_(robot))) “(Ratsamee; Section 1; “With this prediction model, the robot can generate a path which is safe with regard to the actual human motion.”) “wherein the at least one processor generates the bird's-eye view data so as to maximize a posterior distribution of on-ground positions of the observing moving body and the respective moving bodies given on-ground positions of the observing moving body” (Hasegawa; [0117]; “The map generation unit 16 constructs a relative position relation between the state of the robot and each feature point (local map) by using the maximum posterior probabilities x and m and their corresponding covariance obtained in the step S3 (step S5).”). (Blaiotta; FIG. 6b(c) (See image below); [0201]; “to maximize the expected joint probability of the hidden states and the measurements”; [0246], “FIG. 6b(a) illustrates the past (solid line) and future (dotted line) trajectory of a pedestrian agent walking on the sidewalk while two cars are driving by…. FIG. 6b(c) illustrates the posterior probability of crossing intention along the entire track” (emphasis added) ) “(Blaiotta; [0203]; “A sample drawn from such posterior probability distribution, at a given time-step, may comprise a collection of hidden variable vectors, one per agent. For example, …a discrete motion model variable [0207]…a continuous 2-d position vector [0209]…” (emphasis added)).
Claim 11 recites a robot with components corresponding to the components of the device recited in Claim 2. Therefore, the recited components of this claim are mapped to the proposed combination in the same manner as the corresponding components in its corresponding device claim. Furthermore, Claim 16 recites a method with steps corresponding to the components of the device recited in Claim 2. Therefore, the recited components and steps of these claims are mapped to the proposed combination in the same manner as the corresponding components in its corresponding device claim. Additionally, the rationale and motivation to combine the Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta references, presented in rejection of Claim 2, apply to this claim.
Claim 12 recites a robot with components corresponding to the components of the device recited in Claim 3. Therefore, the recited components of this claim are mapped to the proposed combination in the same manner as the corresponding components in its corresponding device claim. Furthermore, Claim 17 recites a method with steps corresponding to the components of the device recited in Claim 3. Therefore, the recited components and steps of these claims are mapped to the proposed combination in the same manner as the corresponding components in its corresponding device claim. Additionally, the rationale and motivation to combine the Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta references, presented in rejection of Claim 3, apply to this claim.
Claim 13 recites a robot with components corresponding to the components of the device recited in Claim 4. Therefore, the recited components of this claim are mapped to the proposed combination in the same manner as the corresponding components in its corresponding device claim. Furthermore, Claim 18 recites a method with steps corresponding to the components of the device recited in Claim 4. Therefore, the recited components and steps of these claims are mapped to the proposed combination in the same manner as the corresponding components in its corresponding device claim. Additionally, the rationale and motivation to combine the Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta references, presented in rejection of Claim 4, apply to this claim.
Claims 7, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ratsamee et. al. ( “People Tracking with Body Pose Estimation for Human Path Prediction” with the publication date of 08/05/2012 – 08/08/2012) in view of Hasegawa et. al. (US 2013/0216098), in further view of Morales Teraoka et. al. (US 2016/0355181), in further view of Blaiotta (US 2020/0283016), and still in further view of Morales Morales et. al. (US 2021/0192748).
Consider Claim 7, the combination of Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta does not explicitly disclose “The bird's-eye view data generating device of Claim 1, wherein, under a condition that a static landmark is detected from the two-dimensional observation information, the at least one processor generates the bird's-eye view data by using the static landmark expressed by the two-dimensional observation information. However, in an analogous field of endeavor, Morales Morales teaches “a technique in which stationary objects such as trees are learned from image data in advance and they are used as landmarks for the SLAM” (emphasis added) (Morales Morales; [0014]). Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta with the teachings of Morales Morales to further generate bird's-eye view data using a detected static landmark. One of ordinary skill in the art would be motivated to combine Ratsamee, Hasegawa, Morales Teraoka, and Blaiotta with Morales Morales to generate a more accurate bird’s eye view of relative positions based on a static feature. Accordingly, the combination of Ratsamee, Hasegawa, Morales Teraoka, Blaiotta, and Morales Morales discloses the invention of Claim 7.
Claim 15 recites a robot with components corresponding to the components of the device recited in Claim 7. Therefore, the recited components of this claim are mapped to the proposed combination in the same manner as the corresponding components in its corresponding device claim. Furthermore, Claim 20 recites a method with steps corresponding to the components of the device recited in Claim 7. Therefore, the recited components and steps of these claims are mapped to the proposed combination in the same manner as the corresponding components in its corresponding device claim. Additionally, the rationale and motivation to combine the Ratsamee, Hasegawa, Morales Teraoka, Blaiotta, and Morales Morales references, presented in rejection of Claim 7, apply to this claim.
Allowable Subject Matter
Claims 6, 14 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter: none of the cited prior art references, alone or in combination, provides a motivation to teach the limitations: “…alternately repeating: fixing on-ground positions of the respective moving bodies, and estimating an on-ground position of the observing moving body and an observation direction of the observation device so as to optimize an energy cost function that expresses the posterior distribution, and fixing an on-ground position of the observing moving body and an observation direction of the observation device, and estimating on-ground positions of the respective moving bodies so as to optimize the energy cost function that expresses the posterior distribution.”, or the ordered combination of the limitations recited in Claims 6, 14 and 19 with the limitations of claims it depends from.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Annie Pham whose telephone number is (571)272-1673. The examiner can be normally be reached Mon-Fri 9:00a – 5:00p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on (571)272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANNIE H PHAM/Examiner, Art Unit 2662
/Siamak Harandi/Primary Examiner, Art Unit 2662
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