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 .
Response to Arguments
Claims 1-20 are pending, independent claims 1, 14, and 18 and dependent claims 13, 15-17, 19 and 20 are amended.
Applicant’s arguments on pages 8-9, filed 6/10/2026, with respect to U.S.C. 101 rejections of claims 1-20 have been fully considered and are not persuasive.
Applicant argues that the claimed language improves accuracy in tracking an object for a vehicle and prevents deterioration in the reliability of the results. Such a technical solution to a technical problem should be found to be patent eligible.
Examiner respectfully disagrees. Although the recited abstract limitations may improve the result output by a computer, (i.e., accuracy in tracking an object, prevention of reliability deterioration of results), there is no improvement in the functioning of the computer (i.e., LiDAR, processors). Similar to the reasoning applied in the Federal Circuit Court decision in the Electric Power Troup LLC v. Alstom S.A. case of August 1, 2016, page 8, “In Enfish, we applied the distinction to reject the § 101 challenge at stage one because the claims at issue focused not on asserted advances in uses to which existing computer capabilities could be put, but on a specific improvement—a particular database technique—in how computers could carry out one of their basic functions of storage and retrieval of data. Enfish, 822 F.3d at 1335–36; see Bascom, 2016 WL 3514158, at *5; cf. Alice, 134 S. Ct. at 2360 (noting basic storage function of generic computer). The present case is different: the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools.” No specific computer improvement, such as to how computers could carry out an improved version one of their basic functions of storage and retrieval of data, is present in the claims of the instant application; therefore, the claims in the instant application are an example of an abstract idea that uses computers as tools. For at least these reasons, Applicant' s arguments are not persuasive.
Applicant argues that claims recite significantly more than the abstract idea itself by reciting a specific and complex approach to object tracking mechanism by generating a whole-layer representation from the multi-layer point data and determining shape flags based on both the individual layers and the whole layer which is not taught in the prior art and thus are significantly more than the abstract idea.
Regarding Applicant's argument that the controller is implementing a specific solution in response to data, i.e., "obtaining LiDAR points of a single layer (referred to as 'whole layer') by processing data received from the LiDAR sensor to project whole LiDAR points associated with the object onto the whole layer or projecting LiDAR points of the first to Mth layers onto the whole layer", Examiner respectfully disagrees. Examiner notes that a specific abstract idea is still an abstract idea. The solution to the problem, as claimed by the applicant, must recite additional elements that integrate the judicial exception into a practical application. Examiner has examined the claims and has not found any elements that fulfill this requirement, the obtaining of data and projection of said data into a specific number of layers is not significantly more than an extra solution activity. As recited in MPEP section 2106.05(g), adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information so that the information can be analyzed by an abstract mental process, is found not enough to be “significantly more” when recited in a claim with a judicial exception in light of CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011). For at least these reasons, Applicant's arguments are not persuasive.
Applicant’s arguments on pages 9-11, filed 6/10/2026, with respect to U.S.C. 102 and 103 rejections of claims 1-20 have been fully considered and are persuasive. The U.S.C. 102 and 103 rejections of claims 1-20 have been withdrawn.
Applicant argues that Kaithakapuzha also fails to disclose the feature "obtaining LiDAR points of a single layer (referred to as 'whole layer') by processing data received from the LiDAR sensor to project whole LiDAR points associated with the object onto the whole layer or projecting LiDAR points of the first to Mth layers onto the whole layer" as set forth in the claims.
Kaithakapuzha does not disclose projecting three-dimensional LiDAR data into a two-dimensional layer. Rather, the 2D data in Kaithakapuzha is image data acquired by a camera. Furthermore, applicant argues Kaithakapuzha, these disclosures merely state generating 3D clusters based on LiDAR data and generating a 3D bounding box for a target object using 2D bounding boxes derived from 2D image data together with camera calibration parameters.
Examiner respectfully agrees. Kaithakapuzha does not teach “first to Mth (M is an integer of 2 or greater) layers of LiDAR points” or "obtaining LiDAR points of a single layer (referred to as 'whole layer') by processing data received from the LiDAR sensor to project whole LiDAR points associated with the object onto the whole layer or projecting LiDAR points of the first to Mth layers onto the whole layer" and for these reasons the U.S.C. 102 and U.S.C. 103 rejection is withdrawn
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 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing mental steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
Claim 1. A method for tracking an object for a vehicle, the method comprising:
obtaining, by a Light Detection and Ranging (LiDAR) sensor mounted on the vehicle, first to Mth (M is an integer of 2 or greater) layers of LiDAR points spaced apart in a vertical direction with respect to an object around the vehicle while the vehicle travels;
obtaining, by one or more processors mounted on the vehicle, LiDAR points of a single layer (referred to as ‘whole layer’) by processing data received from the LiDAR sensor to project whole LiDAR points for the object obtained by the LiDAR sensor onto the whole layer or projecting LiDAR points of the first to Mth layers onto the whole layer; and
determining, by one or more processors, shape flags of the first to Mth layers and the whole layer based on a plurality of predetermined shape types stored in a non-transitory memory and determining a shape flag of the object based on the respective shape flags of the first to Mth layers and the whole layer.
14. A device for tracking an object by use of a LiDAR sensor, the device comprising:
the Light Detection and Ranging LiDAR sensor mounted on the vehicle configured to obtain a point cloud including LiDAR points for a target object around the vehicle while the vehicle travels, the point cloud including first to Mth (M is an integer of 2 or greater) layers of LiDAR points spaced apart in a vertical direction with respect to the target object;
a clustering unit configured process data received from the LiDAR sensor to group the LiDAR points of the point cloud to determine a grouped LiDAR points of the target object; and
a shape analysis unit configured to process data of the grouped LiDAR points of the point cloud to analyze a shape of the target object,
wherein the shape analysis unit comprises:
a layer shape determination unit configured to process data received from the LiDAR sensor to obtain LiDAR points of a single laver (referred to as 'whole laver') by projecting whole LiDAR points associated with the target object onto the whole layer or projecting the LiDAR points of the first to Mth layers onto the whole layer and determine respective shape flags of the first to Mth layers and the whole layer, based on a plurality of predetermined shape types, stored in a non-transitory memory, and
a target shape determination unit configured to determine a shape flag of the target object based on the respective shape flags of the first to Mth layers and the whole layer.
18. A vehicle comprising: a device for tracking an object by use of a Light Detection and Ranging (LiDAR) sensor, the device comprising:
the LiDAR sensor configured to obtain a point cloud including LiDAR points for a target object around the vehicle while the vehicle travels, the point cloud including first to Mth (M is an integer of 2 or greater) layers of LiDAR points spaced apart in a vertical direction with respect to the target object;
a clustering unit configured process data received from the LiDAR sensor to group the LiDAR points of the point cloud to determine a grouped LiDAR points of the target object; and
a shape analysis unit configured to process data of the grouped LiDAR points of the point cloud to analyze a shape of the target object,
wherein the shape analysis unit comprises:
a layer shape determination unit configured to process data received from the LiDAR sensor to obtain LiDAR points of a single laver (referred to as 'whole laver') by projecting whole LiDAR points associated with the target object onto the whole layer or projecting the LiDAR points of the first to Mth layers onto the whole layer and determine respective shape flags of the first to Mth layers and the whole layer, based on a plurality of predetermined shape types, stored in a non-transitory memory, and
a target shape determination unit configured to determine a shape flag of the target object based on the respective shape flags of the first to Mth layers and the whole layer.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation the fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly, there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a LiDAR sensor, by one or more processors, non-transitory memory, a device, a clustering unit, a shape analysis unit, a target shape determination unit, are generic computer elements and not considered significantly more than the abstract idea. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
The additional element of obtaining first to Mth (M is an integer of 2 or greater) layers of LiDAR points spaced apart in a vertical direction with respect to an object around the vehicle while the vehicle travels; obtaining LiDAR points of a single layer (referred to as ‘whole layer’) by processing data received from the LiDAR sensor
obtain a point cloud including LiDAR points for a target object; is considered necessary data gathering and is not sufficient to integrate the abstract idea into a practical application. As recited in MPEP section 2106.05(g), necessary data gathering (i.e., receiving data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
Claims 2-13, 15-17, and 19-20 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea.
Examiner’s Note
The following is the examiner’s statement of reasons for possible allowance, pending overcoming U.S.C. 101 rejections of 1-20.
Claims 1-20 are allowable.
The most pertinent art is Kaithakapuzha et al. (WO 2021/207106 A1) hereinafter Kaithakapuzha in view of Wittman et al. (Improving Lidar Data Evaluation for Object Detection and Tracking Using a Priori Knowledge and Sensorfusion, 2014, Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics, 794-801) hereinafter Wittman.
Regarding Claim 1, Kaithakapuzha teaches obtaining of LiDAR points spaced apart in a with respect to an object by the LiDAR sensor ([0085] “As illustrated in FIG. 8, various ones of the bounding boxes 424 from a particular corresponding FOV may be combined together into a single view. The neighbor relations for a given bounding box 424 may be defined by connecting a straight-line 810 from a center of the bounding box 424 with every other box center (i.e., layering the bounding boxes on top of one another.” Where bounding boxes are the 2D layers [0006] “generate respective 2D bounding boxes for the target object based on the 2D image data.”; and [0005] “a Light Detection and Ranging (lidar) system includes a control circuit configured to receive three-dimensional (3D) point data and two-dimensional (2D) image data representing a field of view including a target object and an object volume prediction circuit configured to detem1ine a predicted volume occupied by the target object within the 3D point data based on the 3D point data and the 2D image data.”); and determining, by the one or more processors ([00103] “execute via the processor of the computer or other programmable data processing apparatus”) shape flags for layer based on a plurality of predetermined shape types stored in the non-transitory memory ([00101] “ a non-transitory computer-usable or computer-readable storage medium having computer-usable or computer- readable program code embodied in the medium for use by or in connection with an instruction execution system.”), and determining a shape flag of the object based on the respective shape flags of the points ([0091] “The 2D-3D integration module circuit according to some embodiments of the present disclosure may include at least two functions. The first function may include co-locating objects 1020 (e.g., in the point cloud) based on 2D bounding boxes 620 and/or 3D cluster centroids 932. The second function is creating 1022 projected 3D bounding boxes 1030 using information from 2D bounding boxes (M layers), 3D clusters, and/or camera calibration parameters. The shape of the projected 3D bounding boxes 1030 can be a frustum, cylinder, etc. The projected 3D bounding boxes 1030 may identify a 3D area projected to enclose an object detected within the point cloud 320.” And [0094] “Meshing may include the generation of the 3D representation made of a series of interconnected shapes (e.g., a "mesh") that outline a surface of the 3D object. The mesh can polygonal or triangular, though the present disclosure is not limited thereto. Furthermore, the template matching for each object class may be used to predict correct bounding box predictions. Voxel templates may be created for each class depending on their dimensions and shape features.”; [0095] “After the volume estimation first phase, the volume shape may be compared to a set of predefined shape templates”; where [0068] “The neural networks 415 may be configured to detect objects within a 2D image 310 based on prior training of the neural network 415 with particular datasets of images. Respective ones of the neural networks 415 may execute on one or more processors of a computer system.”).
Furthermore, regarding Claim 1, Wittman teaches obtaining data by light detection and ranging (LIDAR) sensor mounted on the vehicle (pg. 794 col 1 pp 1 “Among radar sensors, ultrasonic sensors, cameras and others, lidar-sensors are typically used for perceiving the automobile's environment (i.e., systems are attached to or part of the vehicle).”), around while the vehicle travels (pg. 796 col 2 pp 3 “In addition to the motion of the own vehicle (vehicle is traveling while in motion), it is necessary to consider the motion of the objects.”), processor mounted to the vehicle (pg. 800 col 1 pp 2 “Here we used the radar sensor installed with series ACC to insert additional knowledge of the detected scene. The extended functional overview is illustrated in figure 6. The object list provided by the radar sensor with its integrated processing and tracking is filtered for relevant dynamical objects to add them to the knowledge base.”)
However, Kaithakapuzha and Wittman do not teach first to Mth (M is an integer of 2 or greater) layers of LiDAR points, obtaining LiDAR points of a single layer (referred to as 'whole layer') by processing data received from the LiDAR sensor to project whole LiDAR points associated with the object onto the whole layer or projecting LiDAR points of the first to Mth layers onto the whole layer". There is no evidence to support that one of ordinary skill in the art would have reason to combine the prior arts in such a way to arrive at the amended claim invention. For these reasons, the claims invention in claim 1 distinguishes itself from the prior arts but is rejected under U.S.C. 101 rejections of claims 1-20.
Regarding Claim 14, Kaithakapuzha teaches a Light Detection and Ranging (LiDAR) sensor configured to obtain a point cloud including LiDAR points for a target object ([0005] “a Light Detection and Ranging (lidar) system includes a control circuit configured to receive three-dimensional (3D) point data and two-dimensional (2D) image data representing a field of view including a target object and an object volume prediction circuit configured to detem1ine a predicted volume occupied by the target object within the 3D point data based on the 3D point data and the 2D image data.”; [0057]” Light emission output from one or more of the emitters 11 Se impinges on and is reflected by one or more targets 150, and the reflected light is detected as an optical signal (also referred to herein as a return signal, echo signal, or echo) by one or more of the detectors 110d (e.g., via receiver optics 112), converted into an electrical signal representation (referred to herein as a detection signal), and processed (e.g., based on time of flight) to define a 3-D point cloud representation 170 of the field of view 190.”); a clustering unit configured to process data received from the LiDAR sensor to group the LiDAR points of the point cloud to determine a grouped LiDAR points of the target object ([0088] “The point cloud clustering module/circuit 9 IO may process (i.e., processing data from LiDAR) large amounts of 3D points and extract clusters (e.g., groupings of detecting points) related to the objects in the scene (i.e., grouped to the target object).”); and a shape analysis unit configured to process data of the grouped LiDAR points of the point cloud to analyze a shape of the target object ([0091] “The 2D-3D integration module circuit (i.e., shape analysis unit) according to some embodiments of the present disclosure may include at least two functions. The first function may include co-locating objects 1020 (e.g., in the point cloud) based on 2D bounding boxes 620 and/or 3D cluster centroids 932. The second function is creating 1022 projected 3D bounding boxes 1030 using information from 2D bounding boxes, 3D clusters, and/or camera calibration parameters. The shape of the projected 3D bounding boxes 1030 can be a frustum, cylinder, etc.”), and determine respective shape flags data, based on a plurality of predetermined shape types stored in a non-transitory memory ([00101] “ a non-transitory computer-usable or computer-readable storage medium having computer-usable or computer- readable program code embodied in the medium for use by or in connection with an instruction execution system.”; [0094] “Meshing may include the generation of the 3D representation made of a series of interconnected shapes (e.g., a "mesh") that outline a surface of the 3D object. The mesh can polygonal or triangular, though the present disclosure is not limited thereto. Furthermore, the template matching for each object class may be used to predict correct bounding box predictions. Voxel templates may be created for each class depending on their dimensions and shape features.”; [0095] “After the volume estimation first phase, the volume shape may be compared to a set of predefined shape templates”), and a target shape determination unit configured to determine a shape flag of the target based on the respective shape flags of the data ([0016] “a computer program product for operating an electronic device comprising a non-transitory computer readable storage medium having computer readable program code embodied in the medium that when executed by a processor causes the processor to perform the operations comprising: receiving three dimensional (3D) point data and two-dimensional (2D) image data representing a field of view including a target object; and determining a predicted volume occupied by the target object within the 3D point data based on the 3D point data and the 2D image data.”).
Furthermore, regarding Claim 14, Wittman teaches obtaining data by light detection and ranging (LIDAR) sensor mounted on the vehicle (pg. 794 col 1 pp 1 “Among radar sensors, ultrasonic sensors, cameras and others, lidar-sensors are typically used for perceiving the automobile's environment (i.e., systems are attached to or part of the vehicle).”), around while the vehicle travels (pg. 796 col 2 pp 3 “In addition to the motion of the own vehicle (vehicle is traveling while in motion), it is necessary to consider the motion of the objects.”).
However, Kaithakapuzha and Wittman do not teach first to Mth (M is an integer of 2 or greater) layers of LiDAR points, obtaining LiDAR points of a single layer (referred to as 'whole layer') by processing data received from the LiDAR sensor to project whole LiDAR points associated with the object onto the whole layer or projecting LiDAR points of the first to Mth layers onto the whole layer". There is no evidence to support that one of ordinary skill in the art would have reason to combine the prior arts in such a way to arrive at the amended claim invention. For these reasons, the claims invention in claim 14 distinguishes itself from the prior arts but is rejected under U.S.C. 101 rejections of claims 1-20.
Regarding Claim 18, Kaithakapuzha teaches the LiDAR sensor configured to obtain a point cloud including LiDAR points for a target object ([0005] “a Light Detection and Ranging (lidar) system includes a control circuit configured to receive three-dimensional (3D) point data and two-dimensional (2D) image data representing a field of view including a target object and an object volume prediction circuit configured to detem1ine a predicted volume occupied by the target object within the 3D point data based on the 3D point data and the 2D image data.”; [0057]” Light emission output from one or more of the emitters 11 Se impinges on and is reflected by one or more targets 150, and the reflected light is detected as an optical signal (also referred to herein as a return signal, echo signal, or echo) by one or more of the detectors 110d (e.g., via receiver optics 112), converted into an electrical signal representation (referred to herein as a detection signal), and processed (e.g., based on time of flight) to define a 3-D point cloud representation 170 of the field of view 190.”); a clustering unit configured to process data received from the LiDAR sensor to group the LiDAR points of the point cloud to determine a grouped LiDAR points of the target object ([0088] “The point cloud clustering module/circuit 9 IO may process (i.e., processing data from LiDAR) large amounts of 3D points and extract clusters (e.g., groupings of detecting points) related to the objects in the scene (i.e., grouped to the target object).”); and a shape analysis unit configured to process data of the grouped LiDAR points of the point cloud to analyze a shape of the target object([0091] “The 2D-3D integration module circuit (i.e., shape analysis unit) according to some embodiments of the present disclosure may include at least two functions. The first function may include co-locating objects 1020 (e.g., in the point cloud) based on 2D bounding boxes 620 and/or 3D cluster centroids 932. The second function is creating 1022 projected 3D bounding boxes 1030 using information from 2D bounding boxes, 3D clusters, and/or camera calibration parameters. The shape of the projected 3D bounding boxes 1030 can be a frustum, cylinder, etc.”), and determine respective shape flags of the data, based on a plurality of predetermined shape types stored in a non-transitory memory ([00101] “ a non-transitory computer-usable or computer-readable storage medium having computer-usable or computer- readable program code embodied in the medium for use by or in connection with an instruction execution system.”; [0094] “Meshing may include the generation of the 3D representation made of a series of interconnected shapes (e.g., a "mesh") that outline a surface of the 3D object. The mesh can polygonal or triangular, though the present disclosure is not limited thereto. Furthermore, the template matching for each object class may be used to predict correct bounding box predictions. Voxel templates may be created for each class depending on their dimensions and shape features.”; [0095] “After the volume estimation first phase, the volume shape may be compared to a set of predefined shape templates”), and a target shape determination unit configured to determine a shape flag of the target based on the respective shape flags of data ([0016] “a computer program product for operating an electronic device comprising a non-transitory computer readable storage medium having computer readable program code embodied in the medium that when executed by a processor causes the processor to perform the operations comprising: receiving three dimensional (3D) point data and two-dimensional (2D) image data representing a field of view including a target object; and determining a predicted volume occupied by the target object within the 3D point data based on the 3D point data and the 2D image data.”).
Furthermore, regarding Claim 18, Wittman teaches obtaining data by light detection and ranging (LIDAR) sensor mounted on the vehicle (pg. 794 col 1 pp 1 “Among radar sensors, ultrasonic sensors, cameras and others, lidar-sensors are typically used for perceiving the automobile's environment (i.e., systems are attached to or part of the vehicle).”), around while the vehicle travels (pg. 796 col 2 pp 3 “In addition to the motion of the own vehicle (vehicle is traveling while in motion), it is necessary to consider the motion of the objects.”).
However, Kaithakapuzha and Wittman do not teach first to Mth (M is an integer of 2 or greater) layers of LiDAR points, obtaining LiDAR points of a single layer (referred to as 'whole layer') by processing data received from the LiDAR sensor to project whole LiDAR points associated with the object onto the whole layer or projecting LiDAR points of the first to Mth layers onto the whole layer". There is no evidence to support that one of ordinary skill in the art would have reason to combine the prior arts in such a way to arrive at the amended claim invention. For these reasons, the claimed invention in claim 18 distinguishes itself from the prior arts but is rejected under U.S.C. 101 rejections of claims 1-20.
Independent Claims 1, 14, and 18 and dependent claims 2-13, 15-17, 19 and 20 are rejected under U.S.C. 101 rejections of claims 1-20.
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 Emma L. Alexander whose telephone number is (571)270-0323. The examiner can normally be reached Monday- Friday 8am-5pm EST.
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/EMMA ALEXANDER/Patent Examiner, Art Unit 2857
/LAL CE MANG/Primary Examiner, Art Unit 2857
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