CTNF 18/624,809 CTNF 96939 DETAILED ACTIONS Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 06-52 The information disclosure statements (IDS) submitted on 04/02/2024 and 02/09/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Claim Rejections- 35 USC §101 07-04 AIA 07-04-01 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 1-5 are rejected under 35 U.S.C.§101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Regarding Claim 1, A prediction accuracy evaluation method executed by a computer, the prediction accuracy evaluation method comprising: acquiring detection information indicating a detected position, a detected velocity, an error range of the detected position, and an error range of the detected velocity of an obstacle detected by using a sensor mounted on a moving body; a predicted distribution generation process that generates a predicted distribution of a position of the obstacle at a second time later than a first time, based on first detection information that is the detection information at the first time ; and a prediction abnormality determination process that determines whether or not the predicted distribution is abnormal based on the predicted distribution and a second detected position that is the detected position of the obstacle at the second time . The claim limitations underlined above is abstract idea, and the remaining limitations are “additional elements”. Step 1 (Statutory Category): Yes. 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 (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., a mathematical manipulation . Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes. In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. 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 and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation). For example, steps of “, a predicted distribution generation process that generates a predicted distribution of a position of the obstacle at a second time later than a first time, based on first detection information that is the detection information at the first time” represents the mathematical concepts manipulated using algorithm/ mathematical formula/prediction Model by a computer processor to detect position of the obstacle and calculate position error range using standard deviation depending on the object detection algorithm. See (Specification [0026]- [0027], [0032]). and the steps of “ a prediction abnormality determination process that determines whether or not the predicted distribution is abnormal based on the predicted distribution and a second detected position that is the detected position of the obstacle at the second time” . represents the manipulation of historical data and current position data using mathematical formula/algorithm to predict the current position or velocity of an obstacle by a computer processor. See (Specification [0042], [0050] [). The prediction accuracy calculation or abnormality determination is done using known method in the art and formula (determining "Mahalanobis' distance [0051], comparing and evaluating the accuracy base on a score, using Machine learning see [0052]-[0064]).These steps encompass under its broadest reasonable interpretation a mathematical concept/ mathematical manipulation by a processor to predict/estimate/ making evaluation/judgement based on the detected and historical data. Furthermore, nothing in the claim reasonably indicates that anything other than a generic computer (i.e., "input interface" and "one or more processors") needs to be used to carry out the abstract idea. Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No. Claim 1 recites additional elements “A prediction accuracy evaluation method executed by a computer, acquiring detection information indicating a detected position, a detected velocity, an error range of the detected position, and an error range of the detected velocity of an obstacle detected by using a sensor mounted on a moving body” are data gathering steps for the particular technological environment or field of use and describing type of data. Obtaining sensor data as a time series data at a particular point or position of time. These steps represent mere routine data gathering steps and only add an insignificant extra-solution activity to the judicial exception. 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 neither integrate the judicial exception into a practical application. Furthermore, nothing in the claim reasonably indicates that the predicted value is displayed to user or implemented the abstract idea in practical use as is disclosed in the specifications [0066]-[0067]. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B. Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring data from external factors such as obstacle, objects vehicle, pedestrian position obtained by the sensors. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible. claims 2-4 are rejected under 35 U.S.C. 101 because claims depend on claim 1, therefore, has the abstract idea of claim 1 and also has the routine and conventional structure above of claim 1. In addition, claims 2-4 further recite the elements which are simply more standard computational, mathematical-calculation to data gathering /generate data and/ or a model, and. Furthermore, claims 2-4 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Regarding Claim 5, A prediction accuracy evaluation system comprising processing circuitry, wherein the processing circuitry is configured to execute: acquiring detection information indicating a detected position, a detected velocity, an error range of the detected position, and an error range of the detected velocity of an obstacle detected by using a sensor mounted on a moving body; a predicted distribution generation process that generates a predicted distribution of a position of the obstacle at a second time later than a first time, based on first detection information that is the detection information at the first time; and a prediction abnormality determination process that determines whether or not the predicted distribution is abnormal based on the predicted distribution and a second detected position that is the detected position of the obstacle at the second time. . The claim limitations underlined above is abstract idea, and the remaining limitations are “additional elements”. Step 1 (Statutory Category): Yes. 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 (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., a mathematical manipulation . Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes. In the above claim 5, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. 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 and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation). For example, steps of “ a predicted distribution generation process that generates a predicted distribution of a position of the obstacle at a second time later than a first time, based on first detection information that is the detection information at the first time;” represents the mathematical concepts manipulated using algorithm/ mathematical formula/prediction Model by a computer processor to detect position of the obstacle and calculate position error range using standard deviation depending on the object detection algorithm. See (Specification [0026]- [0027], [0032]). and the steps of “ a prediction abnormality determination process that determines whether or not the predicted distribution is abnormal based on the predicted distribution and a second detected position that is the detected position of the obstacle at the second time.” represents the manipulation of historical data and current position data using mathematical formula/algorithm to predict the current position or velocity of an obstacle by a computer processor. See (Specification [0042], [0050] [). The prediction accuracy calculation or abnormality determination is done using known method in the art and formula (determining "Mahalanobis' distance [0051], comparing and evaluating the accuracy base on a score, using Machine learning see [0052]-[0064]).These steps encompass under its broadest reasonable interpretation a mathematical concept/ mathematical manipulation by a processor to predict/estimate/ making evaluation/judgement based on the detected and historical data. Furthermore, nothing in the claim reasonably indicates that anything other than a generic computer (i.e., "input interface" and "one or more processors") needs to be used to carry out the abstract idea. Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No. Claim 5 recites additional elements “A prediction accuracy evaluation system comprising processing circuitry, wherein the processing circuitry is configured to execute: acquiring detection information indicating a detected position, a detected velocity, an error range of the detected position, and an error range of the detected velocity of an obstacle detected by using a sensor mounted on a moving body;” are data gathering steps for the particular technological environment or field of use and describing type of data. Obtaining sensor data as a time series data at a particular point or position of time. These steps represent mere routine data gathering steps and only add an insignificant extra-solution activity to the judicial exception. 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 neither integrate the judicial exception into a practical application. Furthermore, nothing in the claim reasonably indicates that the predicted value is displayed to user or implemented the abstract idea in practical use as is disclosed in the specifications [0066]-[0067]. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B. Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring data from external factors such as obstacle, objects vehicle, pedestrian position obtained by the sensors. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15 AIA Claim s 1-2, and 5 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Horiguchi et al. ( US 2020/0070847 A1 , hereinafter Horiguch i, IDS ref.) Regarding Claim 1, Horiguchi teaches A prediction accuracy evaluation method executed by a computer, the prediction accuracy evaluation ( Horiguchi, Figure 1, [0009] an action prediction unit comprising: acquiring detection information (Horiguchi, Figure 1, Figure 3 , external information detection unit acquires information about objects surrounding the vehicle , [0024]. The external information detection unit 20 includes, for example, a millimeter wave radar 21 and a camera 22. In addition to these sensors 21 and 22, an ultrasonic sensor, an infrared sensor or the like may be used. In the following description, the millimeter wave radar 21 and the camera 22 may be referred to as sensors 21 and 22.)” indicating a detected position , a detected velocity (Horiguchi, Figure 4, [0033] FIG. 4 illustrates an example plotting objects, using the sensor fusion processing S1, on a map of object around the own vehicle M by integrating information of positions, size, and moving speed of the objects around the own vehicle detected with the millimeter wave radar 21 and the stereo camera 22”), an error range of the detected position, and an error range of the detected velocity of an obstacle detected by using a sensor mounted on a moving body (Horiguchi, Figure 4, and Figure 6, [0049] Here, an example of the comparison method in step S43 is described. For example, there is a method of comparing the deviation width of the position of each surrounding object with a predetermined threshold. There is a position error of less than one grid on the drawing between the predicted position 103(2) of the bicycle at time T2 calculated at time Tl in FIG. 4(2) and the position 103(3) of the bicycle at time T2 in FIG. 4(3)”) a predicted distribution generation process that generates a predicted distribution of a position of the obstacle at a second time later than a first time, (Horiguchi, FIG. 1, 6 [0048] It is assumed that the current time is T2.”The microcomputer 11 acquires the surrounding object prediction map M2(T1) based on first detection information that is the detection information at the first time ([0048] “predicted at time T1 from the surrounding object action prediction processing S2 (S41)”) ,; and a prediction abnormality determination process that determines whether or not the predicted distribution is abnormal based on the predicted distribution and a second detected position that is the detected position of the obstacle at the second time .(Horiguchi, Figures 1, 4, and 6, [0048] By comparing the prediction map around the own vehicle M2(T1) predicted at time T1 to the sensor data (sensor data after time synchronization processing) acquired from the sensors 21 and 22 at time T2 (S45), it is possible to determine whether the sensors 21 and 22 have an abnormality.[0053] Similar to the above-described abnormality determination (S43) of the sensor fusion processing S1, the presence or absence of abnormality of the sensor data can be determined using the threshold ”). Regarding Claim 2 , Horiguchi teaches the prediction accuracy evaluation method according to claim 1, Horiguchi further teaches further comprising a prediction accuracy calculation process that calculates accuracy of the predicted distribution by comparing the second detected position with the predicted distribution, wherein the prediction abnormality determination process includes determining that the predicted distribution is abnormal when the accuracy of the predicted distribution is lower than a predetermined level. (Horiguchi, [0049] For example, there is a method of comparing the deviation width of the position of each surrounding object with a predetermined threshold. There is a position error of less than one grid on the drawing between the predicted position 103(2) of the bicycle at time T2 calculated at time Tl in FIG. 4(2) and the position 103(3) of the bicycle at time T2 in FIG. 4(3). [0050 it can be determined that an abnormality has occurred in the bicycle position 103(3) at time T2. This makes it possible to detect the presence or absence of abnormality that has occurred before the sensor fusion processing S1 is done in the main function processing flow of the automatic driving ECU 10. The determination result of step S43 is sent to the comprehensive determination step S46”). Regarding Claim 5, Horiguchi teaches, A prediction accuracy evaluation system comprising processing circuitry ( Horiguchi, Figure 1, [0009] an action prediction unit comprising: acquiring detection information (Horiguchi, Figure 1, Figure 3 , external information detection unit acquires information about objects surrounding the vehicle , [0024]. The external information detection unit 20 includes, for example, a millimeter wave radar 21 and a camera 22. In addition to these sensors 21 and 22, an ultrasonic sensor, an infrared sensor or the like may be used. In the following description, the millimeter wave radar 21 and the camera 22 may be referred to as sensors 21 and 22.)” indicating a detected position , a detected velocity (Horiguchi, Figure 4, [0033] FIG. 4 illustrates an example plotting objects, using the sensor fusion processing S1, on a map of object around the own vehicle M by integrating information of positions, size, and moving speed of the objects around the own vehicle detected with the millimeter wave radar 21 and the stereo camera 22”), an error range of the detected position, and an error range of the detected velocity of an obstacle detected by using a sensor mounted on a moving body (Horiguchi, Figure 4, and Figure 6, [0049] Here, an example of the comparison method in step S43 is described. For example, there is a method of comparing the deviation width of the position of each surrounding object with a predetermined threshold. There is a position error of less than one grid on the drawing between the predicted position 103(2) of the bicycle at time T2 calculated at time Tl in FIG. 4(2) and the position 103(3) of the bicycle at time T2 in FIG. 4(3)”) a predicted distribution generation process that generates a predicted distribution of a position of the obstacle at a second time later than a first time, (Horiguchi, FIG. 1, 6 [0048] It is assumed that the current time is T2.”The microcomputer 11 acquires the surrounding object prediction map M2(T1) based on first detection information that is the detection information at the first time ([0048] “predicted at time T1 from the surrounding object action prediction processing S2 (S41)”) ,; and a prediction abnormality determination process that determines whether or not the predicted distribution is abnormal based on the predicted distribution and a second detected position that is the detected position of the obstacle at the second time .(Horiguchi, Figures 1, 4, and 6, [0048] By comparing the prediction map around the own vehicle M2(T1) predicted at time T1 to the sensor data (sensor data after time synchronization processing) acquired from the sensors 21 and 22 at time T2 (S45), it is possible to determine whether the sensors 21 and 22 have an abnormality.[0053] Similar to the above-described abnormality determination (S43) of the sensor fusion processing S1, the presence or absence of abnormality of the sensor data can be determined using the threshold ”) . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Horiguchi et al. ( US 2020/0070847 A1 , hereinafter Horiguch i, IDS ref.) and in view of Hiroi et al. ( US 2021/0333387 A1 , hereinafter Hiroi, IDS ref.) . Regarding Claim 3 , Horiguchi teaches t he prediction accuracy evaluation method according to claim 2, Horiguchi is silent on wherein the prediction accuracy calculation process includes: calculating a Mahalanobis' distance between a center position of the predicted distribution and the second detected position; and acquiring an evaluation value that increases as the Mahalanobis' distance increases, as an index indicating the accuracy of the predicted distribution, and the prediction abnormality determination process is performed based on the evaluation value. However, Hiroi teaches wherein the prediction accuracy calculation process includes: calculating a Mahalanobis' distance between a center position of the predicted distribution and the second detected position; and acquiring an evaluation value that increases as the Mahalanobis' distance increases, as an index indicating the accuracy of the predicted distribution, and the prediction abnormality determination process is performed based on the evaluation value . (Hiroi, [0011] a reliability calculation unit to take, as a subject sensor, each of the plurality of sensors, and to calculate a reliability of the detection value that is calculated on the basis of the observation value obtained with the subject sensor, by using a Kalman gain in addition to a Mahalanobis distance between the observation value and a prediction value, the observation value being obtained with the subject sensor, the prediction value being a value of the detection item of the object at the subject time which is predicted at a time before the subject time, the prediction value being used in calculation of calculating the detection value by the tracking unit on the basis of the observation value, the Kalman gain being obtained in the calculation”). It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Horiguchi’s method for calculating accurate object position and velocity to incorporate Hiroi’s calculation method of measuring Mahalanobis distance between the observation value and a prediction value as taught by Hiroi and obtain an accurate abnormality values (Hiroi, [0011]-[0013], [0050]-[0055]). It would have been obvious to a person of ordinary skill to include the well-known Mahalanobis distance along with the other analysis, in order to yield the predicted results of generating accurate detection value in consideration of both a high reliability of most recent information and a high reliability of time-series information, yet with higher accuracy (KSR). Regarding Claim 4, combination of Horiguchi and Hiroi teaches the prediction accuracy evaluation method according to claim 3, Horiguchi teaches generating a histogram of the evaluation value for each elapsed time from the first time to the second time; acquiring, as a degree of abnormality, a number or a percentage of samples whose evaluation value exceeds a threshold value in the histogram; and determining that the predicted distribution is abnormal when the degree of abnormality exceeds an abnormality degree threshold (Horiguchi, Figure 4, and Figure 6, [0049] Here, an example of the comparison method in step S43 is described. For example, there is a method of comparing the deviation width of the position of each surrounding object with a predetermined threshold. There is a position error of less than one grid on the drawing between the predicted position 103(2) of the bicycle at time T2 calculated at time Tl in FIG. 4(2) and the position 103(3) of the bicycle at time T2 in FIG. 4(3) [0059] Refer to FIG. 6 again. The microcomputer 11 determines whether the comprehensive determination result is "abnormal" (S47) and, if the abnormality is determined (S47: YES), notifies the driver in the vehicle 1 of the abnormality being detected (S48). For example, the microcomputer 11 provides voice output or displays a message such as "abnormality is detected in the automatic driving system".” NOTE: the evaluation result can be presented as a histogram data. Displaying evaluation data as a histogram is a design choice, not an inventive step ) . Conclusion Citation of Pertinent Prior Art 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. HARA et al. (JP 2016-53846 A) recites “ An automatic driving support system capable of appropriately controlling the behavior of a vehicle while reducing the possibility of collision between a moving object around the vehicle and the vehicle. A roadside machine (2) of an automatic driving support system detects a moving object moving in a monitoring target area, and a plurality of sections for each of a plurality of sections dividing a route on which a vehicle (3) is scheduled to travel. For each time, prediction information indicating the probability that a moving object exists in the section is generated, and the prediction information is transmitted to the automatic driving device (4) mounted on the vehicle (3). On the other hand, the automatic driving device (4) determines the target acceleration of the vehicle (3) at each predetermined time interval so that the vehicle (3) does not collide with a moving object when traveling along the route based on the prediction information. Control information for controlling the vehicle (3) is generated so that the acceleration according to the target acceleration at each time is set, and the control information is output to the control unit that controls the vehicle (3” (abstract) Djuricet al. (US 2021/0269059 A1) The invention provides “Systems, methods, tangible non-transitory computer-readable media, and devices associated with trajectory prediction are provided. For example, trajectory data and goal path data can be accessed. The trajectory data can be associated with an object's predicted trajectory. The predicted trajectory can include waypoints associated with waypoint position uncertainty distributions that can be based on an expectation maximization technique. The goal path data can be associated with a goal path and include locations the object is predicted to travel. Solution waypoints for the object can be determined based on application of optimization techniques to the waypoints and waypoint position uncertainty distributions. The optimization techniques can include operations to maximize the probability of each of the solution waypoints. Stitched trajectory data can be generated based on the solution waypoints. The stitched trajectory data can be associated with portions of the solution waypoints and the goal path(abstract). Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILARA SULTANA whose telephone number is (571)272-3861. The examiner can normally be reached Mon-Fri, 9 AM-5:30 PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, EMAN ALKAFAWI can be reached on (571) 272-4448. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DILARA SULTANA/Examiner, Art Unit 2858 06/12/2026 /EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 6/15/2026 Application/Control Number: 18/624,809 Page 2 Art Unit: 2858 Application/Control Number: 18/624,809 Page 3 Art Unit: 2858 Application/Control Number: 18/624,809 Page 4 Art Unit: 2858 Application/Control Number: 18/624,809 Page 5 Art Unit: 2858 Application/Control Number: 18/624,809 Page 6 Art Unit: 2858 Application/Control Number: 18/624,809 Page 7 Art Unit: 2858 Application/Control Number: 18/624,809 Page 8 Art Unit: 2858 Application/Control Number: 18/624,809 Page 9 Art Unit: 2858 Application/Control Number: 18/624,809 Page 10 Art Unit: 2858 Application/Control Number: 18/624,809 Page 11 Art Unit: 2858 Application/Control Number: 18/624,809 Page 12 Art Unit: 2858 Application/Control Number: 18/624,809 Page 13 Art Unit: 2858 Application/Control Number: 18/624,809 Page 14 Art Unit: 2858 Application/Control Number: 18/624,809 Page 16 Art Unit: 2858 Application/Control Number: 18/624,809 Page 17 Art Unit: 2858 Application/Control Number: 18/624,809 Page 18 Art Unit: 2858 Application/Control Number: 18/624,809 Page 19 Art Unit: 2858
Prosecution Insights