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 The following is a non-final, first office action in response to the communication filed 0 6 / 16 /202 3 . Claims 1- 18 are currently pending and have been examined. Claim Rejections - 35 USC § 102 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. Claims 1, 5, 7-10, 14 and 16-18 are rejected under 35 U.S.C. 102 FILLIN "Insert either \“(a)(1)\” or \“(a)(2)\” or both. If paragraph (a)(2) of 35 U.S.C. 102 is applicable, use form paragraph 7.15.01.aia, 7.15.02.aia or 7.15.03.aia where applicable." \d "[ 2 ]" (a) (1) as being FILLIN "Insert either—clearly anticipated—or—anticipated—with an explanation at the end of the paragraph." \d "[ 3 ]" anticipated by Zhao et al. ( Junxuan Zhao et al. , Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors , Transportation Research Part C: Emerging Technologies, Volume 100, 2019, Pages 68-87, ISSN 0968-090X, https://doi.org/10.1016/j.trc.2019.01.007; hereinafter Zhao ). Regarding claim 1 , Zhao discloses : a method for object tracking using raw roadside LiDAR sensor data (p.1: “detection and tracking of pedestrians and vehicles using roadside LiDAR sensors”) , comprising: receiving, by a computer processor of a computer from a roadside LiDAR sensor, raw LiDAR sensor data (“ demonstrates a selected frame of raw 3D LiDAR point clouds ,” p. 3 ) , wherein LiDAR systems receive raw point cloud data from roadside sensors (“ demonstrates a selected frame of raw 3D LiDAR point clouds ,” p. 3) ; generating, by the computer processor, object data by filtering out background data from the raw LiDAR sensor data (“ the LiDAR data collected from the test site were processed in the order of the following steps: background filtering, clustering, object classification, and tracking of movements ,” p.4) , wherein background filtering removes non-object points ( exclude as many as possible the points from the background objects , p. 4 ) ; clustering, by the computer processor, the object data into a plurality of clusters defining a plurality of objects (“ (DBSCAN) and K-means clustering methods and their variations are among the most popular ones for object clustering of LiDAR data ” , p. 2 ) , wherein clusters correspond to detected objects, wherein each object comprises discrete features data (“three features were extracted: number of points, distance, and direction,” p. 10 ) , which are discrete features (p.10) ; classifying, by the computer processor, each object of the plurality of objects (“pedestrian and vehicle classification,” p.1) , wherein objects are categorized based on extracted features (“ to categorize various entities into clusters based on their similarities such that the entities in the same cluster present more similar properties than those in other clusters ” p .5 ) ; and tracking, by the computer processor, each classified object of the plurality of objects based on the discrete features data (“ an object association process is conducted within a searching area to find the associated objects from two consecutive frames ,” p .12 ) , and on vehicle trajectory data collected over a predefined time period over a length of roadway (“ the trajectory of each road user successfully identified, their position, velocity, and direction can be obtained ” , p. 13 ), wherein tracking across successive frames collects positional data over time to form trajectory data over a predefined time period, and wherein LiDAR sensors are deployed at roadways and intersections (p. 13 ) , such that the trajectory data corresponds to movement over a length of roadway (“the trajectory of each road user successfully identified, their position, velocity, and direction can be obtained”, p.13 ) . Regarding claim 5, Zhao discloses : The method of claim 1, wherein the discrete features data comprises at least one of points, collections of edges, and lines, (“LiDAR data are point clouds,” p.2) , wherein point clouds correspond to points (p.2) ; wherein linear regression provides linear approximations corresponding to edges and lines (“LiDAR data are point clouds,” p.2; “ l inear regression method, a linear function can be generated to describe the main distribution direction of each cluster ,” p. 10 ) . Regarding claim 7 , Zhao discloses: The method of claim 1, wherein generating the object data comprises (p. 4 ) : identifying, by the computer processor based on spherical map input data, a plurality of core points that are within a window size (“core points… within a predefined searching radius,” p.5) , wherein the searching radius corresponds to the claimed window size (“core points… within a predefined searching radius,” p.5) ; labeling, by the computer processor, the core points on a spherical map (p.5) , wherein core points are explicitly labeled ( “ divides a dataset into three categories – core points, border points, and noise points ” p.5) ; joining, by the computer processor, the core points as different clusters according to connectivity of the core points (“ those data points will be clustered to form an object ,” p.6) , wherein density connectivity defines clusters (p . 6) ; determining, by the computer processor, that a non-core point is within a window of a core point and that the absolute value of the non-core point minus a core point is less than the window size (p. 4- 5) , wherein border points fall within the radius (“core points… within a predefined searching radius,” p.5) ; specifying a cluster label for the non-core point (p.5) , wherein non-core points are assigned cluster labels (p.5) ; and generating, by the computer processor, a labeled spherical map of object data ( Evaluation of object classification , Table 4 ) , wherein clustered LiDAR data is represented spatially ( Evaluation of object classification , Table 4 ) . Regarding claim 8, Zhao discloses : The method of claim 7, wherein after joining the core points as different clusters (“ divides a dataset into three categories – core points, border points, and noise points”p.5 ) : determining, by the computer processor, at least one of that a non-core point is not within a window of a core point or that the absolute value of the non-core point minus a core point is greater than the window size (p.5 -6 ) , wherein points outside the radius are identified (Background filtering, fig 3) ; and labeling, by the computer processor, the non-core point as noise (“noise points,” p.5) , wherein such points are categorized as noise . Regarding claim 9, Zhao discloses : The method of claim 1, wherein classifying each object of the plurality of objects (“pedestrian and vehicle classification,” p.1) comprises: determining, by the computer processor, a reference point for each cluster (“A reference point on the XY plane needs to be found to locate the position of each cluster.”, p.8) ; and classifying, by the computer processor based on the reference point for each cluster, different road users by utilizing at least one feature-based classification process combined with prior trajectory information. (“a major approach is feature-based machine learning classification”, p.2) Regarding claim 10 , Zhao discloses : A traffic data processing computer for tracking vehicles using raw roadside LiDAR sensor data ( “LiDAR-based system for traffic monitoring” p.1) comprising: a traffic data processor ( “ LiDAR to monitoring traffic at an intersection using a network of horizontal LiDAR sensors ”, pp.2) , wherein LiDAR data processing requires a processor (p.2) ; a communication device operably connected to the traffic data processor (p.1) , wherein LiDAR sensor data is received by the system ( “ LiDAR to monitoring traffic at an intersection using a network of horizontal LiDAR sensors ”, pp.2) ; and a storage device operably connected to the traffic data processor ( “T hree-dimensional (3D) scan (with line scan pattern) and the collected point clouds are stored in the packet capture ( pcap ) format ”, p . 3) , wherein tracking across frames requires storage of prior data (“multiple LiDAR data frames and divides the 3D space into continuous tiny cubes, subsequently a corresponding 3D matrix is built to store the number of points in each cube in the space.”, p.4 ) ; wherein the storage device stores processor executable instructions which when executed cause the traffic data processor to (p.4) : receive raw LiDAR sensor data from a roadside LiDAR sensor (“ the LiDAR data collected from the test site ,” p. 4 ) ; generate object data by filtering background data from the raw LiDAR sensor data (“ processed in the order of the following steps: background filtering ,” p.4) ; cluster the object data into a plurality of clusters defining a plurality of objects, wherein each object comprises discrete features data (“to categorize various entities into clusters based on their similarities such that the entities in the same cluster present more similar properties than those in other clusters” p.5) ; classify each object of the plurality of objects (p. 5 ) ; and track each classified object of the plurality of objects based on the discrete features data and on vehicle trajectory data collected over a predefined time period over a length of roadway (“ the trajectory of each road user successfully identified, their position, velocity, and direction can be obtained ” , p. 13 ) , wherein tracking over successive frames forms trajectory data over time along roadways (p.13) . Regarding claim 14, Zhao discloses : The traffic data processing computer of claim 10, wherein the discrete features data comprises at least one of points, collections of edges, and lines, (“ the LiDAR data are point clouds ,” p.2) , wherein linear approximations correspond to edges and lines (“linear regression method, a linear function can be generated to describe the main distribution direction of each cluster,” p.10) . Regarding claim 16 , Zhao discloses : The traffic data processing computer of claim 10, wherein the processor executable instructions for generating the object data include instructions which when executed cause the traffic data processor ( ‘ ‘ (DBSCAN)… It divides a dataset into three categories – core points, border points, and noise points within a predefined searching radius, with core points and border points being clustered points which describe the shape of the objects ’ ’, p.5 ) to : identify, based on spherical map input data, a plurality of core points that are within a window size (“core points… within a predefined searching radius,” p.5) ; label the core points on a spherical map (p.5) ; join the core points as different clusters according to connectivity of the core points (pp.5–6) ; determine that a non-core point is within a window of a core point and that the absolute value of the non-core point minus a core point is less than the window size (p. 4- 5) ; specify a cluster label for the non-core point (p. 4- 5) ; and generate a labeled spherical map of object data (pp.4 - 5) . Regarding claim 17 , Zhao discloses : The traffic data processing computer of claim 16, wherein the processor executable instructions for joining the core points as different clusters include instructions which when executed cause the traffic data processor (‘‘(DBSCAN)… It divides a dataset into three categories – core points, border points, and noise points within a predefined searching radius, with core points and border points being clustered points which describe the shape of the objects’’, p.5) to: determine at least one of that a non-core point is not within a window of a core point or that the absolute value of the non-core point minus a core point is greater than the window size (‘‘ An outlier filtering method that considers the distribution of the density of clustered points in 3D space is applied to reduce the error caused by outliers’’ , p.10 ) ; and label the non-core point as noise (“noise points,” p.5) . Regarding claim 18 , Zhao discloses : The traffic data processing computer of claim 10, wherein the processor executable instructions for classifying each object of the plurality of objects include instructions which when executed cause the traffic data processor (‘‘A reference point on the XY plane needs to be found to locate the position of each cluster’’, p.8) to: determine a reference point for each cluster (p.8) ; and classify, based on the reference point for each cluster, different road users by utilizing at least one feature-based classification process combined with prior trajectory information ( “ With clustering and referencing process described, attention can now be directed to pedestrian and vehicle classification …”, p. 10 ) , wherein trajectory data contributes to classification (p.10) . 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. 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 . Claims 3 , 4 , 1 2 , and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao in view of Jagjeet Singh ( US - 11,131,993 -B2 ; hereinafter, Singh) . Regarding claim 3, Zhao discloses : The method of claim 2, further comprising generating, by the computer processor, a frequency-grid-map that captures movement of objects (“ the associated objects from two consecutive frames, the size of the searching area is determined by the estimated position of the object ” Zhao, p. 12 ) from one cell to another cell within the grid environment (“divides the 3D space into continuous tiny cubes, subsequently a corresponding 3D matrix” Zhao, p.4) ; however, Zhao does not explicitly disclose generating a frequency-grid-map that captures movement between cells , and Singh discloses ( “ determine a plurality of object trajectory sequences corresponding to the plurality of objects ” Singh, abstract ) ; “trajectory sequences are generated from observed movement data over time” ( Singh, Fig. 3) ; and it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Zhao with Singh in order to aggregate object movement transitions between grid cells to generate a frequency-grid-map, thereby improving tracking accuracy and enabling analysis of movement patterns. Regarding claim 4 , Zhao discloses : The method of claim 3, wherein the computer processor receives verified historical trajectory data that captures the frequencies of objects moving from one cell to another cell within the grid environment ( “the outputs of the proposed systematic approach are real-time trajectory data of all road users” (Zhao, p. 3 ); ( “historical trajectory and real-time location/speed/direction” Zhao, p. 3 ) ; however, Zhao does not explicitly disclose that the historical trajectory data captures frequencies of objects moving from one cell to another cell within a grid environment , and Singh discloses ( “ plurality of object trajectory sequences corresponding to each of the plurality of objects ” , Singh col. 15 ) and ( “reference paths may be generated based on historical observations of vehicles or other objects over a period of time” col. 4 ) ; and it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Zhao with Singh to utilize historical trajectory data to determine movement patterns of objects, including aggregating movements between spatial regions, in order to capture frequencies of movement within a grid environment and improve the accuracy and robustness of object tracking and traffic analysis . Regarding claim 12, Zhao discloses : The traffic data processing computer of claim 11, wherein the storage device stores further processor executable instructions which when executed cause the traffic data processor to generate a frequency-grid-map (“the distance and time information of two consecutive frames” Zhao, p.3) ; that captures movement of objects from one cell to another cell within the grid environment; ( “ multiple LiDAR data frames and divides the 3D space into continuous tiny cubes, subsequently a corresponding 3D matrix is built to store the number of points in each cube in the space.”, Zhao p.4) ; however, Zhao does not explicitly disclose generating a frequency-grid-map that captures movement of objects from one cell to another cell within the grid environment , and Singh discloses (“plurality of object trajectory sequences corresponding to each of the plurality of objects”, Singh col. 15) , and it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Zhao with Singh to determine movement patterns of objects, including movement between spatial regions . Regarding claim 13, Zhao discloses : The traffic data processing computer of claim 12, wherein the storage device stores further processor executable instructions which when executed cause the traffic data processor to receive verified historical trajectory data “historical trajectory and real-time location/speed/direction” (Zhao, p.3) ; that captures the frequencies of objects moving from one cell to another cell within the grid environment; however, Zhao does not explicitly disclose that the historical trajectory data captures frequencies of objects moving from one cell to another cell within a grid environment , and Singh discloses ( “ plurality of object trajectory sequences corresponding to each of the plurality of objects”, Singh col. 15) , and it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Zhao with Singh to determine movement patterns of objects, including frequencies of movement between spatial regions . Claims 2 , 6 , 1 1 , and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao . Regarding claim 2, Zhao discloses : The method of claim 1, further comprising, prior to receiving the raw LiDAR sensor data: (“multiple LiDAR data frames and divides the 3D space into continuous tiny cubes, subsequently a corresponding 3D matrix is built to store the number of points in each cube in the space.”, Zhao p.4 ) ; generating, by the computer processor, a grid of cells defining a grid environment in which objects travel; (“multiple LiDAR data frames and divides the 3D space into continuous tiny cubes, subsequently a corresponding 3D matrix is built to store the number of points in each cube in the space.”, Zhao p.4) ; generating, by the computer processor, a look-up map by assigning a unique index to each cell of the grid of cells; and generating, by the computer processor, a reverse-look-up map by assigning a unique inverse index to each cell of the grid of cells. however, Zhao does not explicitly disclose assigning a unique index to each cell of the grid of cells or generating a reverse-look-up map by assigning a unique inverse index to each cell of the grid of cells . I t would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to assign unique indices to grid cells and implement corresponding reverse index mappings in order to facilitate efficient storage, retrieval, and bidirectional mapping of spatial data within the grid environment, as such indexing and reverse mapping techniques are well-known in computer-based data processing systems and would improve the efficiency and functionality of Zhao’s grid-based environment. Regarding claim 6 , Zhao discloses: The method of claim 1, wherein the predefined time period is twenty-four (24) hours over the length of roadway (“detect traffic changes and risks around the corners by receiving real-time movement status of each road user in extended distances” Zhao, p.3) ; however, Zhao does not explicitly disclose a twenty-four (24) hour time period , and it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to select a twenty-four (24) hour duration, since selecting an appropriate observation period is a known parameter optimization . Regarding claim 11, Zhao discloses : The traffic data processing computer of claim 10, wherein the storage device stores further processor executable instructions which when executed cause the traffic data processor to generate a grid of cells defining a grid environment in which objects travel; (“multiple LiDAR data frames and divides the 3D space into continuous tiny cubes, subsequently a corresponding 3D matrix is built to store the number of points in each cube in the space.”, Zhao p.4) ; generate a look-up map by assigning a unique index to each cell of the grid of cells; and generate a reverse-look-up map by assigning a unique inverse index to each cell of the grid of cells. however, Zhao does not explicitly disclose assigning a unique index to each cell of the grid of cells or generating a reverse-look-up map by assigning a unique inverse index to each cell of the grid of cells . I t would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to assign unique indices to grid cells and implement corresponding reverse index mappings in order to facilitate efficient storage, retrieval, and bidirectional mapping of spatial data within the grid environment . Regarding claim 15, Zhao discloses : The traffic data processing computer of claim 10, wherein the predefined time period is twenty-four (24) hours over the length of roadway (“detect traffic changes and risks around the corners by receiving real-time movement status of each road user in extended distances” Zhao, p.3) ; however, Zhao does not explicitly disclose a twenty-four (24) hour time period , and it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to select a twenty-four (24) hour duration to capture daily traffic behavior . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT Dominick J. Cabrera whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)317-1401 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday - Friday, 8 AM - 4 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, FILLIN "SPE Name?" \* MERGEFORMAT Vladimir Magloire can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 270-5144 . 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. / DOMINICK JACOB CABRERA / Examiner, Art Unit 3648 /VLADIMIR MAGLOIRE/ Supervisory Patent Examiner, Art Unit 3648