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 .
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 11/28/2023 and 10/13/2025 and. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-14 are rejected under 35 U.S.C. 101. The claimed invention is directed to an abstract concept significantly more.
Independent Claim 1 recites abstract concepts emphasized below in BOLD:
“A lane detection method comprising:
generating a time-frequency spectrogram pattern based on a magnetic signal from a magnetic paint lane to which an alternating magnetic pattern is applied;
inputting the spectrogram pattern of the magnetic signal detected from the magnetic paint lane in real time to an artificial intelligence (AI) model trained using training data corresponding to the spectrogram pattern; and
detecting the magnetic paint lane based on an output value from the AI model.”
STEP 1: Determination of whether Claim(s) are in eligible statutory category.
Independent Claims 1 and 8 limitations recite an invention that falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. (MPEP § 2106.03).
STEP 2A – PRONG 1: Determination of whether claim(s) recite a judicial exception.
Consideration of independent Claim 1 limitations reveals a judicial exception. (MPEP2106.04) Limitations as above, emphasized in bold, constitute an abstract idea based on broadest reasonable interpretation (BRI) limitations recite ideas that fall within definition of Abstract Idea in the Mathematical Concept grouping (MPEP 2106.04(a)(2), subsection I) or Mental Process grouping. (MPEP 2106.04(a)(2), subsection III) Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, Claim 1 limitations fall into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps.
Specifically, the limitation “inputting the spectrogram pattern of the magnetic signal… into an artificial intelligence (AI) model trained using training data…” is specifically directed to a mathematical process as performed by a computer, as indicated by use of “an artificial intelligence (AI) model”. Further, plain meaning of limitation “detecting the magnetic paint lane based on an output value from the AI model” encompasses mental observations or evaluations, which could be performed by a computer programmer or user with mental identification of an anomaly in a data set produces by an AI model output, as indicated by “based on an output value from the AI model.”
STEP 2A – Prong 2: Determination of whether limitations integrate identified judicial exception into a practical application.
Claim 1 does recite additional elements, but these additional elements do not integrate the recited judicial exception into a practical application. Using BRI, additional element found in limitation “generating a time-frequency spectrogram pattern based on a magnetic signal from a magnetic paint lane” is considered to be mere data gathering required to perform the mathematical or mental processes as recited. The limitations also include an additional element of “detected from the magnetic paint lane in real time”, which merely indicates a field of use or technological environment in which the judicial exception is performed.
The judicial exception(s) identified above is/are not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly there are no other meaningful limitations linking the use to a particular technological environment.
As noted above, limitations recite the element(s) of using generic AI/ML technology, i.e., ”inputting the spectrogram pattern…to an artificial intelligence (AI) model trained using training data”, to perform data evaluations or calculations, as identified under Prong 1 above. The claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer, as defined in MPEP 2106.05(f). See MPEP 2106.05(h) and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
STEP 2B – Determination of whether additional elements are sufficient to amount to significantly more than the judicial exception.
Additional elements identified is Claim 1 do not amount to significantly more than the judicial exception. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. receiving data), as claimed with “based on a magnetic signal from a magnetic paint lane to which an alternating magnetic pattern is applied” is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
In consideration of independent Claim 8, computer elements including “processor configured to generate” and “memory configured to store the AI model” are not considered significantly more than the abstract idea. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94. Likewise, additional elements in Claim 8 of “input the spectrogram pattern detected from a magnetic paint lane” , and “detect the magnetic paint lane” represent insignificant field of use limitations that is not meaningful to indicate a practical application.
Thus, independent Claims 1 and Claim 8 are held to be patent ineligible..
Dependent claims 2-7 and 9-14 further limit the abstract idea without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea. Specifically, Claims 2 -4, and 9-11 recite limitations related to data gathering including “real-time magnetic signal”, considered to be data gathering, and “short-time Fourier transform is performed”, further limiting the mathematical concepts recited in Claims 1 and 8. Claims 5-7 and 12-14 recite further limitations related to performing mathematical processes as recited by Claims 1 and 8, with limitations directed to AI model and training, further limiting the mathematical concept without integrating the abstract concept into a practical application.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 7-8 and 14 are rejected under 35 U.S.C. § 103(a) as being unpatentable over LEE (KR 20200120969 A)* in view of KOVARIK (US 20180157878 A1) and further in view of YANG (US 20200210726 A1).
With respect to Claim 1, LEE teaches:
A lane detection method comprising: (LEE is in same technical field, Abstract: “invention discloses a device for determining a driving state of a vehicle”, and Page2, paragraph 5: “camera detects a lane to detect whether a driving lane is deviated (i.e., “lane detection”)”)
generating a time-frequency spectrogram pattern based on a signal (FIG. 1 with Page 3, Paragraph 1: “FIG. 1, the apparatus 100…inertial measurement unit (IMU) sensor 101 that detects raw data (i.e., “signal”)”; and Page 3, Paragraph 8: “dividing unit 112 divides the raw data…transform unit 114 is a short-term Fourier transform (STFT: short time Fourier transform) method is used to output a spectrogram matrix (i.e., “spectrogram pattern based on signal”)”; Examiner asserts one of ordinary skill would know a spectrogram is defined as the squared magnitude of a matrix produced by short time Fourier Transform)
inputting the spectrogram pattern detected from the lane in real time to a model (Abstract: “deep learning unit determining a driving state of the vehicle for the newly inputted row data while receiving and learning the second feature value”; and Page3, Paragraph 8: “feature value extracting unit 120 including a first feature value extracting unit 122 and a second feature value extracting unit 124 is provided to extract a feature value from the preprocessed spectrogram matrix”; Page 4, Paragraph 6: “newly input raw data is directly provided to the deep learning unit 140 through the preprocessor 110”; and Page 4 Paragraph 7: “in the case of real-time abnormal driving determination of deep learning, in this embodiment, newly input raw data is directly provided to the deep learning unit 140 (i.e., “inputting to a model”)” )
LEE does not teach:
based on a magnetic signal from a magnetic paint lane to which an alternating magnetic pattern is applied;
inputting the pattern of the magnetic signal detected from the magnetic paint lane in real time to an artificial intelligence (AI) model trained using training data corresponding to the pattern;
detecting the magnetic paint lane based on an output value from the AI model.
KOVARIK teaches:
based on a magnetic signal from a magnetic paint lane to which an alternating magnetic pattern is applied; (KOVARIK is in same technical field, [0023]: “embodiments are directed to pavement markings that permit unprecedented sensor feedback… pavement markings that incorporate, for example, magnetic aspects that can be detected by sensors located in a vehicle…paving material employ magnetic particles that are oriented during the placement of the material on a roadway…particles are dispersed in a wet paint form of pavement marking”; and [0042]: “In various embodiments, the magnet plates are arranged alternately with faces of equal polarity opposing one another…such that a series of alternating North-South-North-etc. magnetic fields” (i.e., “alternating magnetic pattern”)
inputting pattern of the magnetic signal detected from the magnetic paint lane in real time ([0013]: “real time adjustability”; [0057]: “a lane-identification system…the signals detected from the pavement marking material…employs at least three sensory inputs (GPS, visual sensors and lane-marking magnetic sensors” and
detecting magnetic paint lane ([0057]: “controller may establish a current lane where the vehicle is located”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify LEE to include lane determination based on a magnetic signal from a magnetic paint lane to which an alternating magnetic pattern is applied, inputting pattern of the magnetic signal detected from the magnetic paint lane in real time, and detecting magnetic paint lane, such as that of KOVARIK because combining the magnetic sensor and detection methods as described by KOVARIK provides the advantage of a camera-free approach for precise determination of a vehicle position on a path marked with magnetic materials. LEE teaches the proven and reliable method for using periodic sensor data and generation of a spectrogram based on the data to determine vehicle position. One of ordinary skill would see the obvious advantage of using magnetic sensors as taught by KOVARIK to acquire a periodic signal from an alternating pattern of magnetic field lines that would be permanently affixed to a path, with the added advantage of utilizing known and reliable magnetic sensor devices mounted in vehicle(s).
LEE, as modified by KOVARIK as taught above does not teach:
inputting the signal detected in real time to an artificial intelligence (AI) model trained using training data corresponding to the pattern
detecting the lane based on an output value from the AI model.
YANG teaches:
inputting the signal detected in real time to an artificial intelligence (AI) model trained using training data corresponding to the pattern (YANG is in related technical field, [0007]: “disclosure relate to distance to obstacle computation in autonomous machine applications. Systems and methods are disclosed that accurately and robustly predict distances to objects or obstacles”; and [0009]: “may use sensor data from depth sensor(s) to—automatically, without manual annotation, in embodiments—encode ground truth data corresponding to training image data in order to train the DNN to make accurate predictions from image data alone” (i.e., “corresponding to the pattern”).FIG. 1, element “102 sensor data” , element “104 Machine learning models”, with [0012]: “process of training a machine learning model(s) to predict distances”; and [0122]: “block B1102, includes receiving an image (or other sensor data 702) at the machine learning model(s) 104 ; [0133]: “third controller 1436 for artificial intelligence functionality”)
detecting based on an output value from the AI model.([0067]: “single machine learning model(s) 104 to predict distances (i.e., “output value”) reliably from images”; and FIG.11 with [0122]: “machine learning model(s) 104 (and/or the object detector 708) may output a bounding shape…block B1102, the machine learning model(s) 104 may output a distance(s) 106, as a predicted distance corresponding to the object instance represented by the bounding shape”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify LEE as modified by KOVARIK and taught above, to include the more advanced computational method of using artificial intelligence trained using real-time data and basing position detection on the output of the AI model as taught by YANG because this would add a robust level of determination to the method of LEE as modified by KOVARIK, improving the ability to better determine and detect a lane. While YANG does not explicitly teach use of periodic magnetic sensor data for training AI models to accurately determining positional information for a vehicle, YANG does imply and leave open the option of using any sensor data, including magnetic measurements (see YANG [0039], for example) for the machine-learning/decision method taught therein. One of ordinary skill would be motivated by the obvious combination of the more advanced computational techniques of YANG as a way to expand the deep learning method taught by LEE, when combined with the magnetic measurement techniques of KOVARIK to quickly and efficiently arrive at an invention able to perform complex data analysis in real-time to improve vehicle lane management, particularly for an autonomous vehicle.
With respect to Claim 8, LEE teaches:
A detection apparatus, comprising: a processor (See above with parallel limitations discussed in Claim 1, Abstract, and FIG. 1 with Page 3, Paragraph 1: “apparatus 100…inertial measurement unit (IMU) sensor 101 that detects raw data”, and Page 3, Paragraph 1: “pre-processing unit 110 (i.e., “processor”) for pre-processing raw data is provided”)
configured to generate a time-frequency spectrogram pattern based on a signal (As above, parallel limitation, Claim 1, FIG. 1 with Page 3, Paragraph 1 and Page 3, Paragraph 8; Examiner notes interpretation as discussed above)
inputting the spectrogram pattern detected from the lane in real time to a model (As above, see Abstract, Page3, Paragraph 8, Page 4, Paragraph 6, and Page 4 Paragraph 7)
As above, LEE does not teach:
based on a magnetic signal corresponding to an alternating magnetic pattern,
inputting the pattern of the magnetic signal detected from the magnetic paint lane in real time to an artificial intelligence (AI) model trained using training data corresponding to the pattern;
detecting the magnetic paint lane based on an output value from the AI model
a memory configured to store the AI model.
KOVARIK teaches:
based on a magnetic signal corresponding to an alternating magnetic pattern, (As above, [0023] and [0042])
inputting pattern of the magnetic signal detected from the magnetic paint lane in real time (As above, [0013], [0057])
detecting the magnetic paint lane (As above, [0057])
As above, it would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify LEE to include lane determination based on a magnetic signal from a magnetic paint lane to which an alternating magnetic pattern is applied, inputting pattern of the magnetic signal detected from the magnetic paint lane in real time, and detecting magnetic paint lane, such as that of KOVARIK using the same reasoning and rationale as presented above in discussion of Claim 1.
LEE, as modified by KOVARIK as taught above does not teach:
inputting the signal detected in real time to an artificial intelligence (AI) model trained using training data corresponding to the pattern
detecting based on an output value from the AI model.
a memory configured to store the AI model.
YANG teaches as above,
inputting the signal detected in real time to an artificial intelligence (AI) model trained using training data corresponding to the pattern (As above, parallel limitation discussed in Claim 1, [0012], [0122], and [0133])
detecting based on an output value from the AI model (As above, ([0067], and FIG.11 with [0122])
a memory configured to store the AI model. (FIG. 6, with [0089]: “block of method 600…computing process that may be performed using any combination of hardware, firmware, and/or software… functions may be carried out by a processor executing instructions stored in memory”; and see [0154-60] for detailed description of memory structure integration with GPUs, and [0194]: “GPU(s) 1420 provide additional artificial intelligence functionality, such as by executing redundant and/or different neural networks”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify LEE as modified by KOVARIK and taught above, to include the more advanced computational method of using artificial intelligence trained using real-time data and basing position detection on the output of the AI model as taught by YANG for the same reasons and rationale discussed above for Claim1. Further, one of ordinary skill would understand the obvious combination of including memory for storing artificial intelligence instructional code to facilitate the execution of the method to improve lane detection.
With respect to Claims 7 and 14, , LEE, as modified by KOVARIK and YANG as taught above, teaches limitations of Claims 1 and 8.
YANG teaches, as above,
AI model corresponds to an artificial neural network.(YANG teaches use of AI model method, and [0194]: “executing redundant and/or different neural networks”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify LEE, s modified by KOVARIK and YANG as taught above, to include the use of an artificial neural network in an artificial intelligence-based modeling and prediction process, as further taught by YANG because use of neural network allows the AI modeling process to include and handle more complex and unstructured datasets, including features with non-linear relationships without needing to provide detailed explicitly functional assumptions about those relationships. It would be an obvious way to improve the method/apparatus of LEE as modified by KOVARIK to include magnetic data, and by YANG, as above, to include AI-based modeling and analysis of real-time data to provide adaptability, and the ability to better handle faults, including missing or noisy data that one would expect when measuring real-world data from magnetic markers on a road surface.
Claims 2 and 9 are rejected under 35 U.S.C. § 103(a) as being unpatentable over LEE in view of KOVARIK and YANG, as applied to Claims 1 and 8 above, and further in view of MARKKULA (US 20110320163 A1).
With respect to Claims 2 and 9, LEE, as modified by KOVARIK and YANG as taught above, teaches limitations of Claims 1 and 8.
LEE teaches as above:
real-time signal is detected Page 4 Paragraph 7: “in the case of real-time abnormal driving determination” (i.e. “real-time signal is detected”))
wherein a short-time Fourier transform is performed to generate the spectrogram pattern in real time (Page 3, Paragraph 8: “dividing unit 112 divides the raw data…transform unit 114 is a short-term Fourier transform (STFT: short time Fourier transform) method is used to output a spectrogram matrix (i.e., “spectrogram pattern based on signal”)”)
KOVARIK teaches, as above:
the real-time magnetic signal is detected ([0013]: “present invention is directed to… real time adjustability”; and [0034]: “position recognition system includes at least one magnetic marker for forming a magnetic field at a predetermined position on the road surface and at least one magnetic sensor for detecting the intensity of the magnetic field (i.e. “magnetic signal is detected”) formed by the magnetic marker”; .)
magnetic signal detected in real time (As above, [0013]: “real time adjustability”; [0057]: “employs at least three sensory inputs (GPS, visual sensors and lane-marking magnetic sensors”)
LEE, as modified by KOVARIK and YANG as taught above, does not teach:
signal is detected for each preset interval
MARKKULA teaches:
signal is detected for each preset interval (MARKKULA is in same technical field, [0001]: “relates to a method and a system for determining road data”, and [0006]: “lane width is obtained from the difference between the positions of the left and right lane markings recognized by a lane tracking sensor”, and [0019]: “determination of the virtual road is performed by model based signal processing methods”; [0040]: “time interval TS between the determination of two subsequent measurement vectors, or the length of the time series of vehicle states can be adjustable or can have a constant pre-set value (i.e., “preset interval”)”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify LEE, s modified by KOVARIK and YANG as taught above, to include signal detection using pre-set intervals, as taught by MARKKULA because this technique is based on real-time data input, and when used with vehicle information, including average speed/velocity would allow for better training for a lane detection model by providing advantageous spatiotemporal context for accurate positional analysis. Using customized, pre-set detection intervals allows for accurate understanding of a time-changing lane geometry. Further, as MARKKULA notes, adjustable time intervals are advantageous, providing adaptability that can be customized based on a wide range of situations.
Claims 4 and 11 are rejected under 35 U.S.C. § 103(a) as being unpatentable over LEE, in view of KOVARIK, YANG, and MARKKULA as applied to Claims 2 and 9 above, and further in view of YAMAMOTO (US 20180283904 A1).
With respect to Claims 4 and 11, LEE, as modified by KOVARIK and YANG as taught above, teaches limitations of Claims 2 and 9
LEE teaches as above:
spectrogram pattern is generated (As above, Claim 1)
LEE, as modified by KOVARIK and YANG as taught above, does not teach:
using a magnetic signal filtered based on a high pass filter.
YAMAMOTO further teaches:
using a magnetic signal filtered based on a high pass filter (YAMAMOTO is in same technical field, [0001]: “relates to a method of detecting a magnetic marker laid on a road”; Abstract: “filter processing process of generating a filter output value by performing filter processing by a high-pass filter as to a change of the first magnetic gradient (i.e., “magnetic signal”)”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify LEE, s modified by KOVARIK and YANG as taught above, to include using a high pass filter process for a magnetic signal, as taught by YAMAMATO, because this technique is a known and effective way to remove low-frequency noise that may detract from more relevant information for lane detection based on sensor input. Use a high pass filter would allow for discernment of drift, and allow analysis focus on higher frequency, transient magnetic events that would be important for more clear determination of a position relative to a lane marker.
Allowable Subject Matter
Dependent Claims 3, 5-6, 10, and 12-13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
With respect to Claims 3 and 10, with dependence to Claims 2 and 9 respectively, Examiner best identified art, as cited above, does not teach or suggest in an obvious combination: “preset interval is set in consideration of a braking distance according to a vehicle speed”. While the inventive concept of “preset interval” is taught, as above, by MARKKULA, using braking distance according to vehicle speed was not found. In search for other prior art available on or before the claimed priority date, Examiner notes BREED (US 20140012431 A1) teaches use of a relative speed for detection, but does not relate this idea to determination of a preset detection interval. Further search did not result in prior art, made available on or before the claimed priority date of the invention, which individually or in obvious combination teaches each and every element of Claims 3 and 10.
With respect to dependent Claims 5 and 12, with dependence to Claims 1 and 8, respectively, Examiner finds the best identified art, as cited above, does not teach or suggest in an obvious combination the limitation of “training data is generated by labeling the spectrogram pattern for each average vehicle speed”. While prior art as cited above does teach limitation of “training data generated by spectrogram” (YANG [0009]), the inventive concept of “labeling the spectrogram pattern for each average vehicle speed” was not taught individually or with obvious combination by the best art identified as above. Further search revealed, as noted above, prior art by BREED (US 20140012431 A1) teaching use of a relative speed for detection and diagnostic data but fails to teach or suggest average speed used for labeling.
With respect to Claims 6 and 13, with dependency to Claims 5 and 12, respectively, Examiner finds that YANG, as cited above, does teach “AI model is trained based on a transfer learning algorithm using the training data”, but does not remedy the deficiencies as noted above in Claims 5 and 12. Further search did not reveal prior art made available on or before the claimed priority date to overcome the identified deficiency.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
BREED (US 20140012431 A1) – teaches use of set time intervals for detection and positional determination of a vehicle using computational methods directed to collision prevention; teaches sensor data processed using artificial intelligence, machine learning for lane detection with similar data input structure and methodology for data analysis.
LEE (KR 20180048407 A) – teaches similar method for lane detection using artificial intelligence technology, including deep learning; lane detection analysis using image patterns using neural network with supervised learning based on multiple sensor inputs.
MAYR (DE-102019215658-B3) – teaches system and method for autonomous vehicle control and management using sensor data.
NOH (KR 20190115503 A) – teaches lane recognition for an autonomous vehicle using road partitions made from magnetic materials and front and rear sensor modules installed on vehicle, specifically, a magnetic sensor detecting a magnetic material of the road recognition lane.
BYUN, et al. (“Localization Based on Magnetic Markers for an All-Wheel Steering Vehicle”, Sensors 2016, 16, 2015) – teaches general machine learning methods for analysis of magnetic marker data for autonomous vehicle management.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TONI D SAUNCY whose telephone number is (703)756-4589. The examiner can normally be reached Monday - Friday 8:30 a.m. - 5:30 p.m. ET.
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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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/TONI D SAUNCY/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
Prosecution Insights