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 .
Status of Claims
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 09/17/2024 and 02/17/2026 have been considered by the examiner and initialed copies of the IDS are hereby attached.
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 because the claimed invention is directed to a judicial exception without significantly more. The claim(s) recite(s) judicial exceptions as explained in the Step 2A, Prong 1 analysis below. The judicial exceptions are not integrated into a practical application as explained in the Step 2A, Prong 2 analysis below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as explained in the Step 2B analysis below.
Claim 1:
A device comprising control circuitry that includes a set of processors coupled to memory, the control circuitry constructed and arranged to perform a method of identifying features of objects in a physical environment of a set of radar sensors, the method including:
accessing a plurality of bin identifiers assigned to respective radar detections made by the set of radar sensors, the bin identifiers specifying respective bins, a bin having at least (i) a first dimension representing a range of distance values and (ii) a second dimension representing a range of Doppler values;
selecting radar detections based on the bin identifiers, the selecting producing a plurality of samples; and
processing the plurality of samples to determine one or more features of the objects.
Step
Analysis
1: Statutory Category?
Yes. The claim recites a system and therefore, is an apparatus and eligible for further analysis.
2A - Prong 1: Judicial Exception Recited (i.e., mathematical concepts, certain methods of organizing human activities such as a fundamental economic practice, or mental processes)?
Yes. The claim recites the limitations of:
“accessing a plurality of bin identifiers assigned to respective radar detections made by the set of radar sensors”
“selecting radar detections based on the bin identifiers, the selecting producing a plurality of samples;”
These limitations, as drafted, are processes that, under their broadest reasonable interpretation, can be performed in the human mind. Thus, the claim recites a mental process.
2A - Prong 2: Integrated into a Practical Application?
No.
The claim does not recite any additional elements that would integrate the judicial exception into a practical application.
The recitation of the limitations of, “ the bin identifiers specifying respective bins, a bin having at least (i) a first dimension representing a range of distance values and (ii) a second dimension representing a range of Doppler values” is simply defining the data set format” and “processing the plurality of samples to determine one or more features of the objects” is simply mathematical manipulation and processing of data.
2B: Claim provides an Inventive Concept?
No.
Step 2 considers whether the claim provides limitations which amount to “significantly more” than the recited judicial exception. The claim as a whole does not provide any meaningful limitations which amount to significantly more than the mental process of claim 1. Therefore, the claim is ineligible.
Independent claim(s) 9 and 16 are also rejected under 35 U.S.C. 101 due to same analysis and rationale as independent claim 1 above where claim 9 is a method claim and claim 16 is a system claim.
Dependent claim(s) 2-8,10-15 and 17-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Specifically, the claims only recite limitations further defining the mental process and recite further data gathering and the mathematical manipulation of the gathered data. These limitations are considered mental process steps and additional steps that amount to necessary data gathering or data output. These additional elements fail to integrate the abstract idea into a practical application because they do not impose meaningful limits on the claimed invention. As such, the additional elements individually and in combination do not amount to significantly more than the abstract idea.
Therefore, when considering the combination of elements and the claimed invention as a whole, claims 1-20 are not patent eligible.
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.
Claim(s) 1,4,6-10,12-17 and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jin et al. (US 12560702 B2), hereinafter Jin.
Regarding claim 1, Jin discloses
A device comprising control circuitry that includes a set of processors coupled to memory (see Fig. 1B, further see Col. 5, lines 40-62, Now referring to FIG. 1A, FIG. 1A is a data flow diagram illustrating an example of a radar sampling system 106 performing a process 100 for sampling a patch of frequency bins, in accordance with some embodiments of the present disclosure…For instance, various functions may be carried out by a processor executing instructions stored in memory. In some embodiments, the systems, methods, and processes described herein may be executed using similar components, features, and/or functionality to those of example autonomous vehicle 800 of FIGS. 8A-8D, example computing device 900 of FIG. 9, and/or example data center 1000 of FIG. 10.), the control circuitry constructed and arranged to perform a method of identifying features of objects in a physical environment of a set of radar sensors (see Fig. 1B, radar sensors 104 and machine learning models 126, further see Col. 6, lines 37-47, “For example, the processing unit 116 (e.g., a post processing unit) may comprise a host processing unit located at a perception stack of the vehicle 800. The perception stack may use the MLMs 126 to implement machine learning and deep learning methods, such as to detect and classify the objects which may be indicated in the radar data.”), the method including:
accessing a plurality of bin identifiers assigned to respective radar detections made by the set of radar sensors, the bin identifiers specifying respective bins (see Fig. 2, further see col. 7, lines 21-32, “Referring back to FIG. 1A, the one or more radar sensors 104 may include the radar data analyzer 108. In at least one embodiment, the radar data analyzer 108 may analyze the radar data 102 to determine one or more frequency bins representing one or more points or frequency peaks in the radar data 102. Referring back to FIG. 2, in embodiments, the radar data analyzer 108 of the radar sampling system 106, may perform a fast Fourier transform (FFT). The FFT may be applied on the range domain (fast time) and on the Doppler domain (slow time) of each chirp, Chirp 0-Chirp N−1, of the radar data 102, such as the time-domain radar signal 202, to form a range-Doppler spectrum for a target.”, where the dimensions of the data cube in Fig. 2 are “bin identifiers”), a bin having at least (i) a first dimension representing a range of distance values and (ii) a second dimension representing a range of Doppler values (see Fig. 2 data cube forms a range-Doppler spectrum for a target, col. 7, lines 21-32 “The FFT may be applied on the range domain (fast time) and on the Doppler domain (slow time) of each chirp, Chirp 0-Chirp N−1, of the radar data 102, such as the time-domain radar signal 202, to form a range-Doppler spectrum for a target.”);
selecting radar detections based on the bin identifiers, the selecting producing a plurality of samples (see Fig. 3, selecting radar detections in bin 132 associated with a plurality of patch samples); and
processing the plurality of samples to determine one or more features of the objects (Col. 10, lines 48-60, “In embodiments, and referring back to FIG. 1B, the one or more radar sensors 104 or another device may transmit the sample data 114, e.g., data indicative of the patch, to the one or more processing units 116 or other downstream components. In further embodiments, the one or more processing units 116 may include the one or more machine learning models 126. In still further embodiments, the one or more machine learning models 126 may be separate from the one or more processing units 116. The sample data 114 may ultimately be used to train the one or more machine learning models 126 and/or to perform perception operations, such as object classification, object identification, and/or object tracking.”).
Regarding claim 4, Jin discloses
The device of claim 1, wherein selecting the radar detections further includes selecting at least one radar detection from each of the bins that contains at least one radar detection (see Fig. 3 where radar detections from bin 132 are selected, further see col. 8, lines 46-59, “When a communication link between the one or more radar sensors 104 and the processing unit 116 has a limited bandwidth or reduced bandwidth is otherwise desired, however, the patch of frequency bins selected and/or sampled for transmission to the processing unit 116 may not include the entire information for an object, such as the entire range (distance) and Doppler (velocity) information detected for an object. For example, in embodiments, the selected and/or sampled patch may only include certain frequency bins emanating from a peak(s) corresponding to the object. For example, the sampled patch may only include those frequency bins emanating from the peak that form a cross or other shape, such as frequency bins 310 of the frequency bin patch sample 306 illustrated in FIG. 3.”).
Regarding claim 6, Jin further discloses
The device of claim 1, wherein processing the plurality of samples includes:
forming a group of radar detections for a particular sample of the plurality of samples based at least in part on a bin identifier of the particular sample and bin identifiers of the radar detections at least one radar detection from each of the bins that contains at least one radar detection (see Fig. 3 where radar detections from bin 132 are selected, further see col. 8, lines 46-59, “When a communication link between the one or more radar sensors 104 and the processing unit 116 has a limited bandwidth or reduced bandwidth is otherwise desired, however, the patch of frequency bins selected and/or sampled for transmission to the processing unit 116 may not include the entire information for an object, such as the entire range (distance) and Doppler (velocity) information detected for an object. For example, in embodiments, the selected and/or sampled patch may only include certain frequency bins emanating from a peak(s) corresponding to the object. For example, the sampled patch may only include those frequency bins emanating from the peak that form a cross or other shape, such as frequency bins 310 of the frequency bin patch sample 306 illustrated in FIG. 3.”); and
providing the group as input to a neural network (Col. 10, lines 48-60, “In embodiments, and referring back to FIG. 1B, the one or more radar sensors 104 or another device may transmit the sample data 114, e.g., data indicative of the patch, to the one or more processing units 116 or other downstream components. In further embodiments, the one or more processing units 116 may include the one or more machine learning models 126. In still further embodiments, the one or more machine learning models 126 may be separate from the one or more processing units 116. The sample data 114 may ultimately be used to train the one or more machine learning models 126 and/or to perform perception operations, such as object classification, object identification, and/or object tracking.”, where the machine learning model is a neural network, see Col. 11, lines 10-12, “As an example, such as where the machine learning model 126 includes a convolution neural network (CNN), the CNN may include any number of layers.”).
Regarding claim 7, Jin further discloses
The device of claim 1, wherein the method further includes receiving the plurality of bin identifiers from the set of radar sensors (see Col. 9, lines 12-19, “Referring now to FIG. 4, FIG. 4 depicts an example of a process for sampling or filtering (e.g. decimating) a patch of frequency bins, in accordance with some embodiments of the present disclosure. A range-Doppler map 422 is shown, which may correspond to a radar signal generated by the radar data analyzer 108 and/or the radar sensor 104, such as an IF signal. As a non-limiting example, five detections are shown, which may include the detection 132 of FIG. 1A.”).
Regarding claim 8, Jin further discloses
The device of claim 1, wherein the method further includes:
receiving distance attributes and Doppler attributes of the respective radar detections from the set of radar sensors (see Col. 9, lines 12-19, “Referring now to FIG. 4, FIG. 4 depicts an example of a process for sampling or filtering (e.g. decimating) a patch of frequency bins, in accordance with some embodiments of the present disclosure. A range-Doppler map 422 is shown, which may correspond to a radar signal generated by the radar data analyzer 108 and/or the radar sensor 104, such as an IF signal. As a non-limiting example, five detections are shown, which may include the detection 132 of FIG. 1A. For example, the range-Doppler map 422 of the radar signal may depict the detection 132 at 150 meters, having an expected speed of −15 m/s; two detections both at 100 meters, having expected speeds of −10 m/s and 20 m/s, respectively; one detection at 75 meters, having an expected speed of 5 m/s; and one detection at 200 meters, having an expected speed of 10 m/s.”); and
assigning the plurality of bin identifiers to the respective radar detections based at least in part on the distance attributes and Doppler attributes of the respective radar detections (see Col. 9, lines 12-19, “Referring now to FIG. 4, FIG. 4 depicts an example of a process for sampling or filtering (e.g. decimating) a patch of frequency bins, in accordance with some embodiments of the present disclosure. A range-Doppler map 422 is shown, which may correspond to a radar signal generated by the radar data analyzer 108 and/or the radar sensor 104, such as an IF signal. As a non-limiting example, five detections are shown, which may include the detection 132 of FIG. 1A. For example, the range-Doppler map 422 of the radar signal may depict the detection 132 at 150 meters, having an expected speed of −15 m/s; two detections both at 100 meters, having expected speeds of −10 m/s and 20 m/s, respectively; one detection at 75 meters, having an expected speed of 5 m/s; and one detection at 200 meters, having an expected speed of 10 m/s.”).
Regarding claim 9, the same cited section and rationale as claim 1 is applied.
Regarding claim 10, the same cited section and rationale as claim 4 is applied.
Regarding claim 12, Jin further discloses
The method of claim 9, further comprising determining a size of the bins based at least in part on a density of radar detections (see Col. 8, line 60-Col. 9, line 3, “Moreover, to further preserve bandwidth and storage, one or more downsampling, clipping, and/or compression techniques may be used by the sample generator 112 to generate the samples for one or more dimensions of the selected patch. These techniques may be used to reduce the bitrate required to transmit the patch to downstream components. For example, once selected, a patch may be downsampled. In one or more embodiments, dynamic radar cross section (RCS) or received signal strength (RSS) sampling may be performed relative to the radar peak(s) and/or detection points.”, where dynamic radar cross section sampling is indeed adjusting the size of the bin (i.e. bitrate) based on a change in density of the radar detection (where RCS indicate a power density of the signal)).
Regarding claim 13, Jin further discloses
The method of claim 12, further comprising dynamically adjusting the size of the bins based at least in part on a change in the density of radar detections (see Col. 8, line 60-Col. 9, line 3, “Moreover, to further preserve bandwidth and storage, one or more downsampling, clipping, and/or compression techniques may be used by the sample generator 112 to generate the samples for one or more dimensions of the selected patch. These techniques may be used to reduce the bitrate required to transmit the patch to downstream components. For example, once selected, a patch may be downsampled. In one or more embodiments, dynamic radar cross section (RCS) or received signal strength (RSS) sampling may be performed relative to the radar peak(s) and/or detection points.”, where dynamic radar cross section sampling is indeed adjusting the size of the bin (i.e. bitrate) based on a change in density of the radar detection (where RCS indicate a power density of the signal)).
Regarding claim 14, Jin further discloses
The method of claim 9, wherein the bins have at least one additional dimension representing any of (i) elevation angle, (ii) azimuth angle, and (iii) time, and wherein assigning bin identifiers to the radar detections is further based on at least one of (i) elevation angle, (ii) azimuth angle, and (iii) time (see Col. 6, lines 20-25, “As non-limiting examples, the one or more radar sensors 104 may be two-dimensional, three-dimensional, or four-dimensional radar sensors. For example, the radar sensor 104 may detect one or more of an object's range (distance), Doppler (velocity), azimuth, and/or elevation.”).
Regarding claim 15, Jin further discloses
The method of claim 9, wherein sampling the radar detections includes performing sampling operations on multiple bins in parallel (see Fig. 2, where the radar sampling system 106 performs parallel processing using the radar data analyzer to sample across multiple bins in parallel).
Regarding claim 16, the same cited section and rationale as claim 1 is applied.
Regarding claim 17, the same cited section and rationale as claim 4 is applied.
Regarding claim 19, the same cited section and rationale as claim 14 is applied.
Regarding claim 20, the same cited section and rationale as claim 15 is applied.
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.
Claim(s) 2,3,11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (US 12560702 B2) in view of Gautron et al. (US 20230169721 A1), hereinafter Gautron.
Regarding claim 2, Jin discloses [Note: what Jin fails to disclose is strike-through]
The device of claim 1,
Gautron discloses,
wherein a particular bin of the bins includes multiple radar detections, and wherein selecting the radar detections includes selecting a radar detection from the particular bin using a pseudo-random process (see paragraph 0033, “In at least one embodiment, an approach to point randomization can be taken that is similar to spatial hashing. A hash value can be computed for each point of interest. This can be represented by H.sub.i(p.sub.i) as illustrated in FIG. 3A. There can be a pseudorandom index associated with each point, and a value can be chosen arbitrarily or according to a selection criterion, such as to select a point that has a largest hash value. As illustrated in the example situation 450 of FIG. 4B, this can correspond to different point locations 452 in each cell being selected as having the maximum hash value. The rays for each respective cell can then be traced from those locations.”).
It would have been obvious to someone with ordinary skill in the art prior to the
effective filing date of the claimed invention to incorporate the features as disclosed by Gautron into the invention of Jin. Both references are considered analogous arts to the claimed invention as they both disclose radar data sampling processes. The combination would be obvious with a reasonable expectation of success in order to reduce data size and lead to more efficient system.
Regarding claim 3, Jin discloses [Note: what Jin fails to disclose is strike-through]
The device of claim 2,
Gautron discloses,
wherein selecting the radar detections further includes selecting at least a second radar detection from the particular bin using the pseudo-random process (see paragraph 0033, “In at least one embodiment, an approach to point randomization can be taken that is similar to spatial hashing. A hash value can be computed for each point of interest. This can be represented by H.sub.i(p.sub.i) as illustrated in FIG. 3A. There can be a pseudorandom index associated with each point, and a value can be chosen arbitrarily or according to a selection criterion, such as to select a point that has a largest hash value. As illustrated in the example situation 450 of FIG. 4B, this can correspond to different point locations 452 in each cell being selected as having the maximum hash value. The rays for each respective cell can then be traced from those locations.”).
It would have been obvious to someone with ordinary skill in the art prior to the
effective filing date of the claimed invention to incorporate the features as disclosed by Gautron into the invention of Jin. Both references are considered analogous arts to the claimed invention as they both disclose radar data sampling processes. The combination would be obvious with a reasonable expectation of success in order to reduce data size and lead to more efficient system.
Regarding claim 11, Jin discloses [Note: what Jin fails to disclose is strike-through]
The method of claim 9,
Gautron discloses,
determining a size of the bins based at least in part on a desired number of samples in the plurality of samples (see paragraph 0034, “In at least one embodiment, a hash cell can have up to 128 pixels. Any more pixels can cause the approach to revert to also using the pixel index to manage colliding values. The pixel index can then be used to determine which pixel or point to select, as a selection process can select the largest pixel index. The size of the hash element itself, as mentioned, can be configurable, although sizes above 10×10 pixels may be too large in some implementations and may end up leaving out too many fine details.”).
It would have been obvious to someone with ordinary skill in the art prior to the
effective filing date of the claimed invention to incorporate the features as disclosed by Gautron into the invention of Jin. Both references are considered analogous arts to the claimed invention as they both disclose radar data sampling processes. The combination would be obvious with a reasonable expectation of success in order to reduce data size and lead to more efficient system.
Regarding claim 18, the same cited section and rationale as claim 11 is applied.
Allowable Subject Matter
Claim 5 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
In reference to independent claim 5, the prior arts made of record individually or in any combination, failed to teach, render obvious, or fairly suggest to one of ordinary skill in the art at the time of filing the combination of the claimed features of claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kurz et al. (US 12546888 B2) is considered close pertinent art to the claimed invention as it discloses a radar system which divides radar detections into a plurality of bins where samples are taken from a bin (see Fig. 4).
Schubert et al. (US 11592523 B2) discloses
A device comprising control circuitry that includes a set of processors coupled to memory (see Fig. 1), the control circuitry constructed and arranged to perform a method of identifying features of objects in a physical environment of a set of radar sensors (see Fig. 1, further see Col. 5, lines 19-31, “FIG. 1 shows a schematic representation of a block diagram of a radar system having an apparatus 1 for processing a range-Doppler matrix according to one specific embodiment. For example, the radar system may include a radar sensor 3 which emits radar signals S with the aid of a transmitting antenna 31. Emitted radar signals S may be reflected or scattered partially by an object 100. A portion of the reflected radar signals may be received as receive signal E by a receiving antenna 32 of radar sensor 3. The conditioned receive signals may be processed by a signal-processing device 2 of the radar system. In particular, signal-processing device 2 is able to generate a range-Doppler matrix M.”), the method including:
accessing a plurality of bin identifiers assigned to respective radar detections made by the set of radar sensors, the bin identifiers specifying respective bins, a bin having at least (i) a first dimension representing a range of distance values and (ii) a second dimension representing a range of Doppler values (see Col. 5, lines 35-39, “Depending on the resolution, range-Doppler matrix M may have a few hundred thousand, possibly several million or possibly even more cells. In this context, each cell of range-Doppler matrix M corresponds to one specific distance/relative-speed combination.”);
selecting radar detections based on the bin identifiers, the selecting producing a plurality of samples (see Fig. 3, further see Col. 7, lines 25-44, “FIG. 3 shows a schematic representation for ascertaining a detection threshold for a range-Doppler matrix M according to one specific embodiment. To that end, in the upper section of FIG. 3, a range-Doppler matrix M is represented schematically. First of all, a partial quantity of cells may be selected from this range-Doppler matrix M. As already described previously, in so doing, in each case single cells may be selected individually. Alternatively, it is also possible to first of all select a plurality of positions 50 in range-Doppler matrix M. A group 51 of adjacent cells may then be selected for each of positions 50. In this case, in each instance one common noise threshold may first be determined for group 51 of cells. For example, an average value, a median or any other suitable value may be formed for this purpose. During the further course, each of the values of the noise thresholds of groups 51 may thereupon be evaluated in order to determine one noise threshold. On the other hand, if the single cells of the partial quantity are considered individually, then the values of the cells may also be utilized directly for determining the noise threshold.”); and
processing the plurality of samples to determine one or more features of the objects (Col. 8, lines 7-17, “In step S1, first of all, a partial quantity of cells of range-Doppler matrix M is selected. In step S2, a detection threshold D may then be ascertained. Specifically, detection threshold D is ascertained utilizing the values of the selected cells of range-Doppler matrix M. In step S3, a scattering center may thereupon be detected in range-Doppler matrix M, the scattering center being detected utilizing ascertained detection threshold D. Namely, a scattering center may be detected in those cells of range-Doppler matrix M which have a value that lies above ascertained detection threshold D.”).
Roger et al. (US 20170131394 A1) discloses a radar systems which performs compressive sampling on the radar data using pseudo-random sequences (see paragraph 0026).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAZRA N. WAHEED whose telephone number is (571)272-6713. The examiner can normally be reached M-F (8 AM - 4:30 PM).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vladimir Magloire can be reached at (571)270-5144. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAZRA NUR WAHEED/Examiner, Art Unit 3648
Prosecution Insights