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 .
Remarks
The claims being considered in this application are those submitted on 05/04/2026. No Claims have been amended, added, or canceled. Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments regarding prior art reference Nilsson et. al. (US 20220297706 A1) were found to be persuasive, and the examiner is issuing a subsequent non-final office action to address the claims.
Priority
The applicant’s claim to priority of PRO63/590,899 on 10/17/2023 is acknowledged.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim 20 recites:
means for obtaining image information from at least one camera module disposed on a vehicle
means for obtaining target information from at least one radar module disposed on the vehicle;
means for generating a first detection representation with a first signal path based on the image information and the target information;
means for generating a second detection representation with a second signal path based on the image information and the target information, wherein the second signal path is different than the first signal path; and
means for outputting the first detection representation and the second detection representation.
Structure and support for these limitations in Claim 20 that invoke 112(f) was found in at least specification ¶0062-¶0066 via processor(s) and sensor(s).
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4-5, 8-9, 11-16, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nehmadi et. al. (US 20220398851 A1, IDS dated 01/14/2025).
Regarding Claim 1, Nehmadi discloses:
An apparatus, comprising: at least one memory; (See at least Figure 1A which depicts the top-level system design, and also ¶0012 via "…storing in a machine-readable storage of the data processing entity…")
at least one camera module; at least one radar module; (See at least Figure 1A which depicts at least one camera and at least one radar, as well as Figure 2 via the camera perception module and Figure 6 via the radar perception module)
at least one processor communicatively coupled to the at least one memory, the at least one camera module, and the at least one radar module, and configured to: (See at least ¶0007 via "The process also includes providing a software-based perception module, including an input to receive the sensor data from each sensor module, an input to receive the primary detection results from each sensor module and processing the sensor data" and ¶0012 via " storing in a machine-readable storage of the data processing entity")
obtain image information from the at least one camera module disposed on a vehicle; (See at least ¶0012 via "providing an image capturing device to generate image data of the scene" and ¶0056 via " The camera perception module 16 has a camera pre-processing block 18 with input 20 receiving the high-resolution image native to the camera sensor.")
obtain target information from the at least one radar module disposed on the vehicle; (See at least ¶0067 via "The radar perception module 44 has an input 46 to receive radar signals. The input 46 can support both imaging radar and 2D radar" and ¶0128 via "radar object detection")
generate a first detection representation with a first signal path based on the image information and the target information; (See at least annotated Figure 13 via the first detection representation which corresponds to a first output of the raw data fusion 102 via "enhanced objects 3D". Furthermore, see Figure 16A and ¶0088 which describes that the sensor data input to the raw data fusion module 102 includes both camera and radar via "The 3D reconstruction functional block receives at input 114 the camera image output from the camera pre-process functional block 18…and input 120 receiving the unified radar data output by the imaging pre-processing functional block 48 and by the 2D radar raw pre-processing and merge functional block 50.". Additionally, this processing is illustrated through a first signal path as the enhanced objects 3D continues to be processed through the detection merger 182 and so on.)
generate a second detection representation with a second signal path based on the image information and the target information, wherein the second signal path is different than the first signal path; and (See at least annotated Figure 13 via the second detection representation which corresponds to a separate processing of the raw data fusion 102 via "geometric occupancy grid". Furthermore, see Figure 16A which also illustrates a separate pipeline that produces the geometric occupancy grid. Additionally, this processing is illustrated through a second, separate signal path as the geometric occupancy grid is processed separately and not through the detection merger 182 such as the first signal path.)
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output the first detection representation and the second detection representation (See at least Figure 13 which illustrates the first and second detection representations being output through separate processing pipelines).
Regarding Claim 15, Nehmadi discloses:
A method for generating object representations with multiple signal paths, comprising: (See at least ¶0012 via "… the invention provides an automated method system…" and Figures 16B, etc.).
(Regarding the method steps, see Claim 1 rejection as the steps are the same as Claim 1’s processor’s function/steps)
Regarding Claim 20, Nehmadi discloses:
An apparatus for generating object representations with multiple signal paths, comprising: (See at least Figure 1A which depicts the top-level system design).
Means for….
The functions/steps performed by Claim 20’s “means for” limitations are the same as Claim 15’s processor’s function/method steps. See Claim 1 rejection for those.
Regarding the "means" to perform the function/steps, applicants specification describes the means as processor(s) and sensor(s), thus, see at least ¶0012 via "…storing in a machine-readable storage of the data processing entity…" which corresponds to the processor(s),
and see at least ¶0055 via "the sensors are different modalities sensors, for instance a camera sensor, a lidar sensor and a radar sensor, among other possible sensors" which corresponds to sensor(s)).
Regarding Claim 2 and Claim 16 respectively, Nehmadi discloses the apparatus of Claim 1 and the method of Claim 15.
Furthermore, Nehmadi discloses: wherein the first detection representation includes a parametric representation for a target object,(See at least Figure 13 and Figure 16A which illustrates the enhanced objects 3D which are interpreted as a parametric representation of target object(s). Also see ¶0115 via "The 3D enhancement functional block 106 will merge the object detections from the camera image with 3D information, namely distance information to provide more accurate detection in cases where the camera image is unclear or for any other reason does not allow a sufficient level of confidence in the detection. Accordingly, for detected objects in the camera image where the confidence is lower than what would normally be required, the distance information from the radar data and/or lidar point cloud allows confirming that the detection is positive and thus increase the confidence level or determine that the detection is a false detection")
and the second detection representation includes a non-parametric representation for the target object (See at least Figure 13 and Figure 16A which illustrates the geometric occupancy grid which is interpreted as non-parametric representation for the target object(s). Also see ¶0118 via "The raw data fusion functional block 102 also includes a geometric occupancy grid functional block 112 which receives at input 172 the RGB-D-V data of the 3D map of the environment and the free space and lanes detection at input 174 from the camera pre-processing module 16. The RGB-D-V model allows to model the geometric occupancy grid. Using the camera's detection of free space and lanes and the additional information received from the lidar/radar which enables the RGB-D-V model—the “Geometric Occupancy grid” module creates a bird eye view grid of the world surrounding the host vehicle which is more accurate than using the camera alone.").
Regarding Claims 4 and Claim 18 respectively, Nehmadi discloses the apparatus of Claim 2 and the method of Claim 16.
Furthermore, Nehmadi discloses: wherein the non-parametric representation for the target object is an occupancy map (See at least Figure 13 and Figure 16A which illustrates the geometric occupancy grid which is interpreted as non-parametric representation for the target object(s). Also see ¶0118 via "The raw data fusion functional block 102 also includes a geometric occupancy grid functional block 112 which receives at input 172 the RGB-D-V data of the 3D map of the environment and the free space and lanes detection at input 174 from the camera pre-processing module 16. The RGB-D-V model allows to model the geometric occupancy grid. Using the camera's detection of free space and lanes and the additional information received from the lidar/radar which enables the RGB-D-V model—the “Geometric Occupancy grid” module creates a bird eye view grid of the world surrounding the host vehicle which is more accurate than using the camera alone.").
Regarding Claim 5 and Claim 19 respectively, Nehmadi discloses the apparatus of Claim 2 and the method of Claim 16.
Furthermore, Nehmadi discloses: wherein the first signal path includes at least a first machine learning model and the at least one processor is further configured to generate the parametric representation based at least in part on the image information and the target information, and (See at least Figures 2, 13, and 16A, as well as ¶0057 via "The camera detector block 22 can be implemented by a convolutional neural network, which is trained to detect objects of interest in the image." **Wherein this convolutional neural network is included in the Camera Detector 22 of the Camera Perception Module, which is included in at least the first signal path that yields a parametric representation)
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the second signal path includes at least a second machine learning model and the at least one processor is further configured to generate the non-parametric representation based at least in part on the image information and the target information (See at least Figures 6, 13, and 16A, as well as ¶0069 via "The 2D detector functional block 54 also uses a Deep Neural Network concept, implemented by a convolutional neural network to perform object detection and geometrical features detection in the scene" **Wherein this convolutional neural network is included in the 2D Detector 54 of the Radar Perception Module, which is included in at least the second signal path that yields a non-parametric representation)
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Regarding Claim 8, Nehmadi discloses the apparatus of Claim 1.
Furthermore, Nehmadi discloses: wherein the at least one processor is further configured to: receive the first detection representation via the first signal path and the second detection representation via the second signal path; (See at least Figure 13 via the first detection representation which corresponds to a first output of the raw data fusion 102 via "enhanced objects 3D", as well as the illustration of a first signal path as the enhanced objects 3D continues to be processed through the detection merger 182 and so on. Furthermore, see at least Figure 13 via the second detection representation which corresponds to a separate processing of the raw data fusion 102 via "geometric occupancy grid". Furthermore, see Figure 16A which also illustrates a separate pipeline that produces the geometric occupancy grid. Additionally, this processing is illustrated through a second, separate signal path as the geometric occupancy grid is processed separately and not through the detection merger 182 such as the first signal path.)
generate one or more object lists based at least in part on the first detection representation and the second detection representation; and output the one or more object lists (See at least Claim 1 via "performing object level fusion on the primary and the secondary detection results." and ¶0117 via "The RGB-D-V outputs at 168 a description of the detected objects including a level of confidence." **Wherein the detected objects being output is interpretted as corresponding to an object list).
Regarding Claim 9, Nehmadi discloses the apparatus of Claim 8.
Furthermore, Nehmadi discloses: wherein the one or more object lists includes an object track list indicating a location and velocity of an object (See at least ¶0030 via " FIG. 15 is a reconstruction map of the environment using an RGB-D-V model showing the velocity computed for objects of interest" as well as ¶0126 via "The tracker 184 implements an algorithm to follow the motion path of the detected objects in 3D space by tracking the angular velocity through successive image frames, and the radial velocity through depth-mage frames" and Figure 5 as well as ¶0066 via "Each bounding box shows the location of the object of interest into the point cloud and also shows velocity and trajectory. The arrows associated with each bounding box depict the velocity and the trajectory.").
Regarding Claim 11, Nehmadi discloses the apparatus of Claim 9.
Furthermore, Nehmadi discloses: wherein the one or more object lists includes static object information (See at least ¶0091 via "The camera frame is processed to distinguish between static and dynamic objects in the image")
Regarding Claim 12, Nehmadi discloses the apparatus of Claim 8.
Furthermore, Nehmadi discloses: wherein the at least one processor is further configured to output the one or more object lists to an environment model (See at least ¶0125 via "The sensor fusion module 100 also includes a detection merger which receives the primary objects detection information from the camera perception module 16, the lidar perception module 24 and the radar perception module 44. The detection merger also receives the detection output form the raw data fusion functional block 102. The detection merger unifies these detection inputs, which each reflect the environment of the host vehicle in one unified detection output and adds semantic information to them which makes the scene interpretation easier by the host vehicle path planning controller." **Wherein the unified detection output + semantic information being used to make scene interpretation easier is interpreted under BRI as functioning as an environmental model used for controlling a vehicle based on an environmental scene).
Regarding Claim 13, Nehmadi discloses the apparatus of Claim 8.
Furthermore, Nehmadi discloses: further comprising at least one lidar module disposed on the vehicle, wherein the at least one processor is further configured to: receive further target information from the at least one lidar module via a secondary path that is separate from the first signal path and the second signal path; (See at least ¶0062 via "…could be one lidar sensor (sensor 1) mounted at the front of the vehicle and scanning the scene ahead of the vehicle…" as well as Figure 4A via LiDAR perception module which is interpreted as a secondary path that processes through the detection merger 182 rather than through the raw data fusion 102, as further illustrated in Figure 13. Additionally see annotated Figures 4A and 13 below.)
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generate object detection information based on the further target information; and output the object detection information (See at least Figure 13 as well as ¶0125 via "The sensor fusion module 100 also includes a detection merger which receives the primary objects detection information from the camera perception module 16, the lidar perception module 24 and the radar perception module 44. The detection merger also receives the detection output form the raw data fusion functional block 102. The detection merger unifies these detection inputs, which each reflect the environment of the host vehicle in one unified detection output and adds semantic information to them which makes the scene interpretation easier by the host vehicle path planning controller")
Regarding Claim 14, Nehmadi discloses the apparatus of Claim 13.
Furthermore, Nehmadi discloses: wherein the at least one processor is further configured to: generate the object detection information based on the target information and the image information; and output the object detection information (See at least Figure 2 via the Camera perception module, Figure 4A via the LiDAR perception module, and Figure 13 which illustrates the processing of both the outputs of the camera and LiDAR perception modules being merged into unified object detections).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Nehmadi et. al. (US 20220398851 A1, IDS dated 01/14/2025) in view of Das et. al. (US 20210181758 A1).
Regarding Claim 3 and Claim 17 respectively, Nehmadi discloses the apparatus of Claim 2 and the method of Claim 16.
Furthermore, Nehmadi discloses: wherein the parametric representation for the target object includes coordinate information for the target object and (See at least ¶0115 via the coordinate information (distance information): "The 3D enhancement functional block 106 will merge the object detections from the camera image with 3D information, namely distance information to provide more accurate detection in cases where the camera image is unclear or for any other reason does not allow a sufficient level of confidence in the detection. Accordingly, for detected objects in the camera image where the confidence is lower than what would normally be required, the distance information from the radar data and/or lidar point cloud allows confirming that the detection is positive and thus increase the confidence level or determine that the detection is a false detection")
However, although Nehmadi discloses bounding boxes (¶0058 via " The objects of interest, namely vehicles are identified by bounding boxes in blue") which can be 3D bounding boxes (See Figure 18 via the 3D detection and enhancement); Nehmadi does not explicitly disclose dimension information.
Nevertheless, Das--who is directed towards object detection and tracking--discloses: dimension information for the target object (See at least ¶0018 via " the object detections associated with an object may vary in dimension, location, or even existence between the different pipelines" and ¶0013 via "For example, a first object detection may indicate an ROI associated with an object that has different dimensions that an ROI indicated by a second object detection associated with a same object" and also ¶0097 via "…The ML model may be trained to output an ROI (e.g., a center and/or extents) associated with an object, an object classification associated with the object, an estimated pose (e.g., position and/or orientation) of the object, an estimated velocity of the object, and/or an estimated height of the object…" and ¶0034 via "The extents may be defined based at least in part on an anchor box associated with an object classification that was determined by the ML model in association with the estimated object detection").
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the given invention to modify Nehmadi in view of including the object shape as part of the output object detection(s) such as in Das in order to improve the accuracy of object detecting and tracking by including more information for detected objects generated from multiple sources of perception by including more of the objects' representations, such as dimensions (height, extent, etc.): "…increase the accuracy of object detection (e.g., object location, segmentation) and/or tracking. Tracks generated using the techniques discussed herein jitter less since the underlying object detections are more stable." [¶0022 Das].
Regarding Claim 10, Nehmadi discloses the apparatus of Claim 9.
However, although Nehmadi discloses bounding boxes and outputting object detections (which under BRI is interpreted as an object list), Nehmadi does not explicitly teach the shape of the object.
Nevertheless, Das discloses: wherein the object track list indicates a shape of the object (See at least ¶0097 via "as output from the ML model, an estimated object detection 426, according to any of the techniques discussed herein. In some examples, the ML model may be trained to output a final environment representation 428 and/or an estimated object detection 426…The ML model may be trained to output an ROI (e.g., a center and/or extents) associated with an object, an object classification associated with the object, an estimated pose (e.g., position and/or orientation) of the object, an estimated velocity of the object, and/or an estimated height of the object. …In some examples, the ROI may be generated based at least in part on an anchor box or any other canonic object shape associated with the object classification upon which the ML model has been trained.").
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the given invention to modify Nehmadi in view of including the object shape as part of the output object detection(s) such as in Das in order to improve the accuracy of object detecting and tracking by including more information for detected objects generated from multiple sources of perception by including more of the objects' representations, such as shape information: "…increase the accuracy of object detection (e.g., object location, segmentation) and/or tracking. Tracks generated using the techniques discussed herein jitter less since the underlying object detections are more stable." [¶0022 Das].
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Nehmadi et. al. (US 20220398851 A1, IDS dated 01/14/2025) in view of Urtasun et. al. (US 20210012116 A1).
Regarding Claim 6, Nehmadi discloses he apparatus of Claim 5.
However, although Nehmadi discloses the first and second machine learning models, Nehmadi does not detail whether they utilize a common backbone architecture.
Nevertheless, Urtasun--who is directed towards systems and methods for identifying unknown instances in a vehicle environment--discloses: wherein the first machine learning model and the second machine learning model utilize a common backbone (See at least ¶0026 via "The instance detection system can feed the sensor point cloud input data into machine-learned model(s) to identify one or more known and unknown instances within an environment. As described in further detail below, the machine-learned model(s) can include a backbone feature network (e.g., a machine-learned feature embedding model) with two branches. A first branch can include a machine-learned instance scoring model (e.g., a scoring head) configured to detect known instances (e.g., instances associated with known semantic labels) within an environment. A second branch can include a machine-learned category-agnostic instance model (e.g., an embedding head) configured to provide point embeddings for each point in the sensor point cloud input data. For example, the machine-learned category-agnostic instance model can branch into three outputs. A first output can include a class embedding (e.g., a BEV “thing” embedding) used as a prototypical instance embedding for known classes; a second output can include an instance embedding (e.g., an instance-aware point embedding); and a third output can include a background embedding (e.g., a “stuff” embedding) for known background classes." as well as ¶0087 via "…The three components can include a shared backbone feature extractor such as a machine-learned feature embedding model 205; a detection head such as a machine-learned instance scoring model 215 configured to detect anchors representing instances of known things; and/or an embedding head such as a machine-learned category-agnostic instance model 225 configured to predict instance-aware features for each point as well as prototypes for each object anchor and/or background class…").
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the given invention to specify Nehmadi's two machine learning models that produce complimentary outputs as multiple machine learning models that utilize a common backbone for shared model outputs of machine learning models such as in Urtasun as a predictable implementation supporting the generation of multiple outputs for later processing for a vehicle system.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Nehmadi et. al. (US 20220398851 A1, IDS dated 01/14/2025) in view of Philiion et. al. (US 20210398338 A1).
Regarding Claim 7, Nehmadi discloses the apparatus of Claim 5.
However, although Nehmadi discloses the first and second machine learning models, Nehmadi does not detail the first and second backbone architecture.
Nevertheless, Philion--who is directed towards image generation using one or more neural networks in a vehicle system--discloses: wherein the first machine learning model utilizes at least a first backbone, and the second machine learning model utilize a least a second backbone (See at least ¶0053 via "In at least one embodiment, these backbones can comprise feature extractor networks, such as convolutional neural networks (CNNs)…In at least one embodiment, a first backbone can include a feature extractor network 310 that can operate on each image individually in order to featurize a point cloud generated from each input image 302. In at least one embodiment, a second backbone can operate on this point cloud once this cloud is projected into pillars in a reference frame").
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the given invention to specify Nehmadi's two machine learning models that produce complimentary outputs as multiple machine learning models that utilize separate backbones for downstream outputs of machine learning models such as in Philion as a predictable implementation supporting the generation of multiple specialized outputs for later processing for a vehicle system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAYLA RENEE DOROS whose telephone number is (703)756-1415. The examiner can normally be reached Generally: M-F (8-5) EST.
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/K.R.D./Examiner, Art Unit 3657
/ABBY LIN/Supervisory Patent Examiner, Art Unit 3657
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