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 .
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, 3-8, 10-13, 15, 18, 20-24 and 26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
A computer-implemented method of filtering a data stream of events from an event-based sensor, which comprises a pixel array and is arranged to receive photons reflected or scattered by a region on an object when illuminated by a scanning light beam, wherein each event in the data stream originates from a pixel in the pixel array and comprises an identifier of the pixel and a time stamp associated with the event, said method comprising:
initiating a data structure with data elements corresponding to pixels of the pixel array;
receiving the data stream of events;
updating the data structure to store time values in the data elements-based on the data stream of events, so that a respective time value of a data element represents a most recent time stamp associated with the pixel corresponding to the data element; and
performing a spatio-temporal filtering of the data structure to determine a filtered data stream of events, wherein said spatio-temporal filtering comprises:
evaluating, for a selected data element in the data structure, data elements within a search area around the selected data element to identify one or more data elements that store a time value with a predefined time difference to a reference time value stored in the selected data element,
generating a score for the search area based on the one or more data elements, and
selectively outputting, based on the score, a filtered event representing the selected data element.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the 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 claim is considered to be in a statutory category (process).
Under the 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 limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “initiating a data structure with data elements corresponding to pixels of the pixel array (deciding where/how to record data);
updating the data structure to store time values in the data elements-based on the data stream of events, so that a respective time value of a data element represents a most recent time stamp associated with the pixel corresponding to the data element (updating recorded data as its received); and
performing a spatio-temporal filtering of the data structure to determine a filtered data stream of events (organizing recorded data), wherein said spatio-temporal filtering comprises:
evaluating, for a selected data element in the data structure, data elements within a search area around the selected data element to identify one or more data elements that store a time value with a predefined time difference to a reference time value stored in the selected data element (observation of recorded data),
generating a score for the search area based on the one or more data elements (evaluation based on chosen criteria), and
selectively outputting, based on the score, a filtered event representing the selected data element (sharing results)” are treated by the Examiner as belonging to mental process grouping.
Next, under the 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.
The above claims comprise the following additional elements:
Claim 1: A computer-implemented method of filtering a data stream of events from an event-based sensor, which comprises a pixel array and is arranged to receive photons reflected or scattered by a region on an object when illuminated by a scanning light beam, wherein each event in the data stream originates from a pixel in the pixel array and comprises an identifier of the pixel and a time stamp associated with the event, receiving the data stream of events.
The additional element in the preamble of “A computer-implemented method of filtering a data stream of events from an event-based sensor, which comprises a pixel array and is arranged to receive photons reflected or scattered by a region on an object when illuminated by a scanning light beam, wherein each event in the data stream originates from a pixel in the pixel array and comprises an identifier of the pixel and a time stamp associated with the event” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. Note that the event-based sensor and its specifics are not part of the claimed method, which only comprises storing and evaluating data. Receiving the data stream of events represents a mere data gathering step and only adds an insignificant extra-solution activity to the judicial exception. A computer (generic processor) is generally recited and are not qualified as particular machines.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 3-8, 10-13, 15, 18, 20-24 and 26 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. 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.
Claim(s) 1, 3-6, 8, 11, 12, 15, 18, 20, 26, and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Smits (US 20180180733 A1) in view of Jing et al. ("Two-stage Local Spatio-temporal Event Filter based on Adaptive Thresholds," 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China, 2021, pp. 1-8, doi: 10.1109/IJCNN52387.2021.9534114), hereinafter “Jing”..
Regarding Claim 1, Smits teaches a computer-implemented method of filtering a data stream of events from an event-based sensor, which comprises a pixel array and is arranged to receive photons reflected or scattered by a region voxel on an object when illuminated by a scanning light beam, wherein each event in the data stream originates from a pixel in the pixel array and comprises an identifier of the pixel and a time stamp associated with the event (Smits [0104] FIG. 4A illustrates a portion of the system 100 including the transmitter 104 and cameras 106a, 106b, 106c, 106d. (The terms “camera” and “receiver” are used interchangeably herein unless indicated otherwise.) The system 100 sequentially illuminates one voxel of the one or more targets 108 at a time. Also see [0126] FIG. 6A illustrates one embodiment of a portion of system 100. A tightly collimated laser probe beam (or other light source), generated by a scanning transmitter 104, intersects a position P on surface S. Photons delivered by the beam reflect (for example, in a Lambertian fashion) in all directions at t=0. Four cameras positioned at projection centers O.sub.1, O.sub.2, O.sub.3, and O.sub.4 each capture a small bundle of photons from each of these reflections a few nanoseconds later. Each bundle has a chief ray (CR) entering the center of the aperture of each of the four cameras. CR.sub.1 enters the first camera and lands on column number 700 & row number 500, at t.sub.1. The other three cameras similarly record events within a 10-nanosecond window.), said method comprising:
initiating a data structure with data elements corresponding to pixels of the pixel array (Smits [0098] Memory 304 may further include one or more data storage 334, […] For example, data storage 334 may also be employed to store information that describes various capabilities of network computer 300. In one or more of the various embodiments, data storage 334 may store position information 335. Also see [0104] As illustrated in FIG. 4A, the scanned voxel illuminations are captured or detected by multiple cameras 106a, 106b, 106c, 106d and cause several voxel-illumination-synchronized streams of pixel data to flow to a central processing system, such as system computer device 110 (FIG. 1).);
receiving the data stream of events (Smits [0104] As illustrated in FIG. 4A, the scanned voxel illuminations are captured or detected by multiple cameras 106a, 106b, 106c, 106d and cause several voxel-illumination-synchronized streams of pixel data to flow to a central processing system, such as system computer device 110 (FIG. 1). Also see [0106] The sets of detection positions are sent to a central processing system, such as system computer device 110, that combines these streams of detection positions from the multiple cameras and combines them computationally);
updating the data structure to store time values in the data elements based on the data stream of events, so that a respective time value of a data element represents a most recent time stamp associated with the pixel corresponding to the data element (Smits [0104]For example, at time t, four cameras 106a, 106b, 106c, 106d can produce a set of positions [x1,y1]t; [x2,y2]t; [x3,y3]t; [x4,y4]t with similar sets at t+1 and t−1 and other time periods, as illustrated in FIG. 4.); and
performing a spatio-temporal filtering of the data structure to determine a filtered data stream of events (Smits [0228] Because only a few pixels will be open at any one time, a spatio-temporal filter is created that can effectively screen and therefore mask out (by selective activation, or pixel specific shuttering) the light of extraneous signals, for example, shutter out the ambient light to a very large degree, and enable the system to strongly favor the detection of the tracking beam.).
Smits is not relied upon to explicitly teach wherein said spatio-temporal filtering comprises:
evaluating, for a selected data element in the data structure, data elements within a search area around the selected data element to identify one or more data elements that store a time value with a predefined time difference to a reference time value stored in the selected data element,
generating a score for the search area based on the one or more data elements, and
selectively outputting, based on the score, a filtered event representing the selected data element.
Jing teaches evaluating, for a selected data element in the data structure, data elements within a search area around the selected data element to identify one or more data elements that store a time value with a predefined time difference to a reference time value stored in the selected data element (Jing. p. 3, Clo. 1: Para. 2, As shown in Fig. 2 (d), two-stage filtering is performed on image-like event frames. The whole process can be divided into two stages: spatial-based event noise candidate selection and temporal-based event noise filtering. The first stage is to select noises candidates in image-like event frames. To select out the above two types of noise, we set up a local sliding window Hi on the image-like event frames Ni. The window with too few (sparse noise candidate) or too many (dense noise candidate) triggered event pixels are judged as noise candidates.),
generating a score for the search area based on the one or more data elements (Jing p. 3, Col. 2: Para. 1,
H
m
,
n
i
represents the number of pixels in which the event occurs for the specified sliding window.), and
selectively outputting, based on the score, a filtered event representing the selected data element (Jing. p. 3, Col. 2: para. 2, For Hi, if the value of
H
m
,
n
i
is less than sparse noise candidate threshold Thsparse, or greater than dense noise candidate threshold Thdense, then its upper-left coordinate (m,n) will be added to the set of noise candidates Mi. […] The second stage is the temporal-based event noise filter. This stage evaluates whether a selected candidate noise event is actual noise based on temporal information of local events. […] For a sparse noise candidate, the event that is isolated in the temporal means it isolated in both spatial and temporal dimensions, so it will be judged as sparse noise. On the other hand, for the dense noise candidate, the event isolated in temporal will be judged as dense noise, otherwise, it will be taken as an effective event. Noise for the event is determined, scored sparse, normal, or dense, and filtered based on that score).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Smits in view of Jing to explicitly teach wherein said spatio-temporal filtering comprises:
evaluating, for a selected data element in the data structure, data elements within a search area around the selected data element to identify one or more data elements that store a time value with a predefined time difference to a reference time value stored in the selected data element,
generating a score for the search area based on the one or more data elements, and
selectively outputting, based on the score, a filtered event representing the selected data element, to explicitly disclose how the spatio-temporal filter disclosed by Smits is performed.
Regarding Claim 3, Smits in view of Jing (as stated above) further teaches wherein said evaluating (Smits [0104] As illustrated in FIG. 4A, the scanned voxel illuminations are captured or detected by multiple cameras 106a, 106b, 106c, 106d and cause several voxel-illumination-synchronized streams of pixel data to flow to a central processing system, such as system computer device 110 (FIG. 1). And [0228] Because only a few pixels will be open at any one time, a spatio-temporal filter is created that can effectively screen and therefore mask out (by selective activation, or pixel specific shuttering) the light of extraneous signals, for example, shutter out the ambient light to a very large degree, and enable the system to strongly favor the detection of the tracking beam. Also see Jing p. 3, Fig. 2 (a) Read the input event stream. (b) Divide event stream into blocks with a fixed time ∆t. The data is evaluated as it arrives when necessary).
Regarding Claim 4, Smits in view of Jing (as stated above) further teaches wherein the selected data element is a data element that is updated based on the current event (Smits [0104] As illustrated in FIG. 4A, the scanned voxel illuminations are captured or detected by multiple cameras 106a, 106b, 106c, 106d and cause several voxel-illumination-synchronized streams of pixel data to flow to a central processing system, such as system computer device 110 (FIG. 1). And [0228] Because only a few pixels will be open at any one time, a spatio-temporal filter is created that can effectively screen and therefore mask out (by selective activation, or pixel specific shuttering) the light of extraneous signals, for example, shutter out the ambient light to a very large degree, and enable the system to strongly favor the detection of the tracking beam.).
Regarding Claim 5, Smits in view of Jing (as stated above) further teaches wherein said evaluating is repeated for each event in the data stream (Smits [0104] As illustrated in FIG. 4A, the scanned voxel illuminations are captured or detected by multiple cameras 106a, 106b, 106c, 106d and cause several voxel-illumination-synchronized streams of pixel data to flow to a central processing system, such as system computer device 110 (FIG. 1). And [0228] Because only a few pixels will be open at any one time, a spatio-temporal filter is created that can effectively screen and therefore mask out (by selective activation, or pixel specific shuttering) the light of extraneous signals, for example, shutter out the ambient light to a very large degree, and enable the system to strongly favor the detection of the tracking beam. Also see Jing p. 3, Fig. 2 (a) Read the input event stream. (b) Divide event stream into blocks with a fixed time ∆t. The data is evaluated as it arrives when necessary).
Regarding Claim 6, Smits in view of Jing (as stated above) further teaches wherein the score is generated as function of the number of identified data elements in the search area (Jing p. 3, Col. 2: Para. 1,
H
m
,
n
i
represents the number of pixels in which the event occurs for the specified sliding window. The score is based on the number of events in the sliding window).
Regarding Claim 8, Smits in view of Jing (as stated above) further teaches operating a temporal filter function on the time value of a respective data element other than the selected data element in the search area to generate a filter value of the respective data element, wherein the temporal filter function is configured to generate the filter value to selectively indicate, by the filter value, each data element that stores time values with the predefined time difference to the reference time value (Jing p. 3, Clo. 1: Para. 2, As shown in Fig. 2 (d), two-stage filtering is performed on image-like event frames. The whole process can be divided into two stages: spatial-based event noise candidate selection and temporal-based event noise filtering. Also see p. 3, Col. 2, para. 3, The second stage is the temporal-based event noise filter. This stage evaluates whether a selected candidate noise event is actual noise based on temporal information of local events.).
Regarding Claim 11, Smits in view of Jing (as stated above) further teaches wherein said generating the score comprises: operating a predefined kernel on the filter values for the data elements in the search area (Smits [0104] As illustrated in FIG. 4A, the scanned voxel illuminations are captured or detected by multiple cameras 106a, 106b, 106c, 106d and cause several voxel-illumination-synchronized streams of pixel data to flow to a central processing system, such as system computer device 110 (FIG. 1). The processing is performed by some processor running instructions for both Smits and Jing, which is directed toward computer vision).
Regarding Claim 12, Smits in view of Jing (as stated above) further teaches wherein the predefined kernel is defined based on a known scan direction of the scanning light beam in relation to the pixel array (Smits [0187] Calculating camera system extrinsics can be done efficiently, where one doesn't have to recalculate the entire set of parameters each time, or, if one does have to recalculate, the procedure can be made fast and efficiently, seeded with values from prior, recent observations. Motions “freeze,” that is, changes are minimal, at the time scale of microseconds. Predictions based on recent observations—for example, Kalman filter estimations—are likely to be extremely close. Also see [0188] The scanner laser source Tx is used to illuminate the view of a conventional rolling shutter camera and this camera's perspective is co-located with the scanning laser illuminator, so that the illumination moment T.sub.ij for each pixel P.sub.ij in the camera can be determined with great accuracy. And [0121] Alternatively, the illumination of the beam might be pulsed in a rapid and intense fashion. For example, non-continuous sweeps might be used, probing “pinpricks” or pulses of light randomly in space or in specifically chosen directions. The direction of the beam is used for analysis).
Regarding Claim 15, Smits in view of Jing (as stated above) further teaches wherein the predefined time difference corresponds to an expected residence time of the scanning light beam on a predefined number of pixels (Smits [0122] As an example, at 10 nanosecond intervals a short 100 ps burst of photons might be sent out by a scanning mechanism (Transmitter Tx), such as transmitter 104. The reflected pulses are confined to a known interval (for example, 100 picoseconds (ps)) and are matched uniquely to a single pixel in the array. Also see [0143] In at least some embodiments, as more cameras are added to the system, more perspectives are simultaneously captured, less occlusions occur, surfaces may be scanned faster and with greater motion fidelity, and details may be scanned at shorter and shorter intervals.).
Regarding Claim 18, Smits in view of Jing (as stated above) further teaches wherein the filtered event is output if the score is within a score range (Jing. p. 3, Col. 2: para. 2, For Hi, if the value of
H
m
,
n
i
is less than sparse noise candidate threshold Thsparse, or greater than dense noise candidate threshold Thdense, then its upper-left coordinate (m,n) will be added to the set of noise candidates Mi. […] The second stage is the temporal-based event noise filter. This stage evaluates whether a selected candidate noise event is actual noise based on temporal information of local events. […] For a sparse noise candidate, the event that is isolated in the temporal means it isolated in both spatial and temporal dimensions, so it will be judged as sparse noise. On the other hand, for the dense noise candidate, the event isolated in temporal will be judged as dense noise, otherwise, it will be taken as an effective event. Noise for the event is determined, scored sparse, normal, or dense, and filtered based on that score).
Regarding Claim 20, Smits in view of Jing (as stated above) further teaches wherein the search area is centered on the selected data element (Smits [0104] FIG. 4A illustrates a portion of the system 100 including the transmitter 104 and cameras 106a, 106b, 106c, 106d. (The terms “camera” and “receiver” are used interchangeably herein unless indicated otherwise.) The system 100 sequentially illuminates one voxel of the one or more targets 108 at a time. Also see [0126] FIG. 6A illustrates one embodiment of a portion of system 100. A tightly collimated laser probe beam (or other light source), generated by a scanning transmitter 104, intersects a position P on surface S. Photons delivered by the beam reflect (for example, in a Lambertian fashion) in all directions at t=0. Four cameras positioned at projection centers O.sub.1, O.sub.2, O.sub.3, and O.sub.4 each capture a small bundle of photons from each of these reflections a few nanoseconds later. Each bundle has a chief ray (CR) entering the center of the aperture of each of the four cameras. CR.sub.1 enters the first camera and lands on column number 700 & row number 500, at t.sub.1. The other three cameras similarly record events within a 10-nanosecond window. Each camera naturally captures its center due to how cameras operate.).
Regarding Claim 26, Smits in view of Jing (as stated above) further teaches a processing device, which comprises an interface for receiving a data stream of events from an event-based sensor and is configured to perform the method of claim 1 (Smits [0104] As illustrated in FIG. 4A, the scanned voxel illuminations are captured or detected by multiple cameras 106a, 106b, 106c, 106d and cause several voxel-illumination-synchronized streams of pixel data to flow to a central processing system, such as system computer device 110 (FIG. 1). Also see [0106] The sets of detection positions are sent to a central processing system, such as system computer device 110, that combines these streams of detection positions from the multiple cameras and combines them computationally).
Regarding Claim 28, Smits teaches a system for determining a position of an object , said system comprising:
at least one beam scanning device configured to generate a scanning light beam to illuminate the object ;
at least one event-based sensor that comprises a pixel array and is arranged to receive photons reflected or scattered by a region on the object when illuminated by the scanning light beam , wherein said at least one event-based sensor is configured to generate an event for a pixel in the pixel array when a number of photons received by the pixel exceeds a threshold, and wherein said at least one event-based sensor is configured to output events as a respective data stream, wherein each event in the respective data stream comprises an identifier of the pixel and a time stamp associated with the event Smits [0104] FIG. 4A illustrates a portion of the system 100 including the transmitter 104 and cameras 106a, 106b, 106c, 106d. (The terms “camera” and “receiver” are used interchangeably herein unless indicated otherwise.) The system 100 sequentially illuminates one voxel of the one or more targets 108 at a time. Also see [0126] FIG. 6A illustrates one embodiment of a portion of system 100. A tightly collimated laser probe beam (or other light source), generated by a scanning transmitter 104, intersects a position P on surface S. Photons delivered by the beam reflect (for example, in a Lambertian fashion) in all directions at t=0. Four cameras positioned at projection centers O.sub.1, O.sub.2, O.sub.3, and O.sub.4 each capture a small bundle of photons from each of these reflections a few nanoseconds later. Each bundle has a chief ray (CR) entering the center of the aperture of each of the four cameras. CR.sub.1 enters the first camera and lands on column number 700 & row number 500, at t.sub.1. The other three cameras similarly record events within a 10-nanosecond window.);
the system further comprising a processing arrangement, which comprises at least one processing device in accordance with claim 26 and is configured to receive the respective data stream from the at least one event-based sensor and output a respective filtered data stream (Smits [0104] As illustrated in FIG. 4A, the scanned voxel illuminations are captured or detected by multiple cameras 106a, 106b, 106c, 106d and cause several voxel-illumination-synchronized streams of pixel data to flow to a central processing system, such as system computer device 110 (FIG. 1). Also see [0106] The sets of detection positions are sent to a central processing system, such as system computer device 110, that combines these streams of detection positions from the multiple cameras and combines them computationally); and
a detection device configured to receive the respective filtered data stream and determine the position of the region based thereon (Smits [0106] The sets of detection positions are sent to a central processing system, such as system computer device 110, that combines these streams of detection positions from the multiple cameras and combines them computationally. Also see [0228] Because only a few pixels will be open at any one time, a spatio-temporal filter is created that can effectively screen and therefore mask out (by selective activation, or pixel specific shuttering) the light of extraneous signals, for example, shutter out the ambient light to a very large degree, and enable the system to strongly favor the detection of the tracking beam. The filtered data must be stored somewhere for use in determining position.).
The Examiner notes that there are currently no prior art rejections for Claims 7, 10, 13, and 21-24.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Perrone et al. (US 20220100658 A1) discloses a Method Of Processing A Series Of Events Received Asynchronously From An Array Of Pixels Of An Event-Based Light Sensor.
Ghosh et al. ("Spatiotemporal filtering for event-based action recognition." arXiv preprint arXiv:1903.07067, 17 March 2019) discloses gesture and action recognition problems, with event-based cameras.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTIAN T BRYANT whose telephone number is (571)272-4194. The examiner can normally be reached Monday-Thursday and Alternate Fridays 7:00-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, CATHERINE RASTOVSKI can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTIAN T BRYANT/Examiner, Art Unit 2857
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