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
The following is a final office action.
Claims [1 and 3-10] are currently pending and have been examined based on their merits.
Claim 2 is newly cancelled see REMARKS December 15, 2025.
Claims 1 and 3 are currently amended see REMARKS December 15, 2025.
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 and 3-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception that is an abstract idea without a practical application or significantly more.
Step 1: Claims 1 and 3-10 recite a method (i.e. a process such as an act or series of steps), and therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claim 1 recites: A method of monitoring a crowd comprising: receiving data; analyzing the data; determining crowd density based on the aligned three-dimensional point cloud representation; determining if the crowd density in a location is above a predefined risk threshold; generating, in response to determining that the predefined risk threshold has been exceeded, an alert indicating a crowd-density risk condition; taking mitigation measures in response to the alert to reduce the crowd density in that location when the crowd density is above the predefined risk threshold by adjusting flow paths, modifying access, or deploying personnel to the monitored region.
The claims recite a certain method of organizing human activity. The claims recite a certain method of organizing human activity as the disclosure recites managing personal behavior or relationships or interactions between people. The claims simply recite a series of steps to monitor the density of a crowd in an area and determine if the density is above a predefined threshold to generate a response. Therefore, the claims recite a method of managing the personal behavior of an individual by receiving and analyzing crowd information to determine crowd density and performing mitigation measures in response to the density being above a threshold.
Alternatively, the claims recite a mental process. The claims merely recite the process of receiving and analyzing crowd information to determine crowd density and if the crowd density is above a threshold. The claims are found to merely recite a series of steps that can be performed in the human mind or with the use of a simple tool such as pen and paper. The claimed invention of analyzing crowd information to determine crowd density is found to be similar to concepts the courts have defined as a mental process including observations, evaluations, judgements, and opinions. As a person would be capable of mentally receiving crowd analytical information, assessing the information to determine a metric, and determine if the metric is above a threshold in order to trigger a response.
Therefore, the claims are found to recite an abstract idea.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite additional elements, including;
Claim 1: LiDAR point-cloud data from a plurality of LiDAR sensors; analyzing the LiDAR point-cloud data using surface-normal estimation, background filtering, and iterative closest-point alignment to generate an aligned three- dimensional point-cloud representation of a monitored region;.
The additional elements of a plurality of sensors to receive information are directed to merely reciting instructions to apply a generic computer and technology to execute the method in the recited claim limitations.
The claim limitations recite mere instructions to implement the abstract idea of receiving and analyzing crowd information by using generic sensor components.
Therefore, the limitations merely amount to adding the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Furthermore, a method for transmitting, receiving, and processing information does not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. As the additional elements are not significant improvements to the functionality of a generic computer and are directed to merely “apply it” or applying the abstract idea on a computer.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?):
As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, and merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The additional elements of a sensor for receiving information to be utilized in the abstract idea are not directed to an improvement in a technology or technical field. Therefore, the additional elements do not amount to significantly more than the judicial exception.
The dependent claims 3-10 further narrow the abstract idea of analyzing crowd information to determine if a risk score is above a threshold and implementing mitigation measures as recited in the independent claim 1.
The dependent claims recite the following additional elements:
Claims 3-7: Machine learning.
Claim 8: dynamic background model.
However, the additional elements are directed to merely “apply it” or applying generic computer elements to perform the abstract idea.
Therefore, claims 1 and 3-10 are rejected under 35 U.S.C. 101.
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1 and 3-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lehmann (US 2014/0372348) in view of Alzahrani (US 11417106) further in view of Anderson (US 2017/0176202).
Claim 1: Lehmann discloses a method of monitoring a crowd comprising: receiving data from a plurality of sensors (Paragraph [0006-0007]; [0019]; [0026] certain embodiments include a method of detecting an anomaly in crowd behavior. The method includes receiving sensor data from one or more sensors, the sensor data representing a crowd in motion, and partitioning the sensor data into a set of local areas. Each sub-population can be characterized by a pattern of motion based at least in part on sensor data. In some embodiments, the real-time estimates of the motion can include real-time estimates of velocity and a density of each sub-population near the local area. Sensors may use raw sensor data such as analog data including raw video streams or
analyzing the data (Paragraph [0006-0007]; [0019]; [0026] certain embodiments include a method of detecting an anomaly in crowd behavior. The method includes receiving sensor data from one or more sensors, the sensor data representing a crowd in motion, and partitioning the sensor data into a set of local areas. Each sub-population can be characterized by a pattern of motion based at least in part on sensor data. In some embodiments, the real-time estimates of the motion can include real-time estimates of velocity and a density of each sub-population near the local area. Sensors may use raw sensor data such as analog data including raw video streams or radar data);
determining crowd density based on the aligned three-dimensional point-cloud representation (Paragraph [0006-0007]; [0019]; [0026] certain embodiments include a method of detecting an anomaly in crowd behavior. The method includes receiving sensor data from one or more sensors, the sensor data representing a crowd in motion, and partitioning the sensor data into a set of local areas. Each sub-population can be characterized by a pattern of motion based at least in part on sensor data. In some embodiments, the real-time estimates of the motion can include real-time estimates of velocity and a density of each sub-population near the local area. Sensors may use raw sensor data such as analog data including raw video streams or radar data);
determining if the crowd density in a location is above a predefined risk threshold (Paragraph [0021] the present system the identifies an occurrence of a potential anomaly associated with the local area, by comparing predictions form the set of auxiliary stochastic models with the set of parametric values of the crowd model. For example, the present system determines when the difference between predictions by the model and the parametric values of the crowd model exceed pre-determine threshold. If the difference between predictions exceeds the pre-determined threshold, the present system identifies a potential anomaly);
generating, in response to determining that the predefined risk threshold has been exceeded, an alert indicating a crowd-density risk condition (Paragraph [0021-0023] the present system the identifies an occurrence of a potential anomaly associated with the local area, by comparing predictions form the set of auxiliary stochastic models with the set of parametric values of the crowd model. For example, the present system determines when the difference between predictions by the model and the parametric values of the crowd model exceed pre-determine threshold. If the difference between predictions exceeds the pre-determined threshold, the present system identifies a potential anomaly. An example of a system for detecting anomalies in crowd behavior in accordance with embodiments of the present disclosure. System receives sensor data representing crowd behavior from sensors and outputs an alert of an anomaly via user interface and/or alert notifications through data analysis controller).
Lehmann discloses a system of using a plurality of sensors to monitor a crowd in an area and determine if the crowd characteristics exceed a threshold to identify a potential anomaly. However, Lehmann does not specifically disclose the following claim limitations: Receiving LiDAR point-cloud data from a plurality of LiDAR sensors; analyzing the LiDAR point-cloud data using surface-normal estimation, background filtering, and iterative closest-point alignment to generate an aligned three- dimensional point-cloud representation of a monitored region; taking mitigation measures in response to the alert to reduce the crowd density in that location when the crowd density is above the predefined risk threshold by adjusting flow paths, modifying access, or deploying personnel to the monitored region.
In the same field of endeavor of determining the crowd density of an area and determining an emergency response for a crowd Alzahrani teaches Receiving LiDAR point-cloud data from a plurality of LiDAR sensors ([Col. 1 ll. 65-Col. 2 ll. 19]; [Col. 4 ll. 53- Col. 5 ll. 3]; [Col. 11 ll. 6- Col. 11 ll. 23]; Fig. 5A, in an exemplary embodiment a method for real-time crowd management is disclosed. The method includes receiving LiDAR point cloud data collected by unmanned aerial vehicles (UAVs) flying over an area of interest forming a 3D static surface model of the area of interest for the LiDAR point cloud data, obtaining real-time CCTV camera images of the area of interest, adding the real-time CCTV camera images to the 3D static surface model to generate a real-time dynamic 3D model, identifying a plurality of dynamic object in the model, generating a density map of the area of interest, adding the density map to the real-time dynamic 3D model, monitoring the real-time dynamic 3D model in the area of interest for dangerous situations, simulating an evacuation of the area of interest by manipulating the characters onto pathways leading away from the area of interest, forming an evacuation strategy for the crowd, and transmitting a notice of the dangerous situation and the evacuation strategy to an authority. The computing device may process data obtained from sensors using a variety of point cloud processing or 3D modeling algorithms such as LiDAR 360, Meshlab, Massmotion, and blender. The edge cloud server may generate the density map and the real-time dynamic 3D model using artificial intelligence algorithms stored in the artificial intelligence algorithms. Further the edge cloud server may be configured to generate a data packet using the LiDAR point cloud data);
analyzing the LiDAR point-cloud data using surface-normal estimation, background filtering, and iterative closest-point alignment to generate an aligned three- dimensional point-cloud representation of a monitored region ([Col. 1 ll. 65-Col. 2 ll. 19]; [Col. 4 ll. 53- Col. 5 ll. 3]; [Col. 11 ll. 6-23]; Fig. 5A, in an exemplary embodiment a method for real-time crowd management is disclosed. The method includes receiving LiDAR point cloud data collected by unmanned aerial vehicles (UAVs) flying over an area of interest forming a 3D static surface model of the area of interest for the LiDAR point cloud data, obtaining real-time CCTV camera images of the area of interest, adding the real-time CCTV camera images to the 3D static surface model to generate a real-time dynamic 3D model, identifying a plurality of dynamic object in the model, generating a density map of the area of interest, adding the density map to the real-time dynamic 3D model, monitoring the real-time dynamic 3D model in the area of interest for dangerous situations, simulating an evacuation of the area of interest by manipulating the characters onto pathways leading away from the area of interest, forming an evacuation strategy for the crowd, and transmitting a notice of the dangerous situation and the evacuation strategy to an authority. The computing device may process data obtained from sensors using a variety of point cloud processing or 3D modeling algorithms such as LiDAR 360, Meshlab, Massmotion, and blender. The edge cloud server may generate the density map and the real-time dynamic 3D model using artificial intelligence algorithms stored in the artificial intelligence algorithms. Further the edge cloud server may be configured to generate a data packet using the LiDAR point cloud data).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of Receiving LiDAR point-cloud data from a plurality of LiDAR sensors; analyzing the LiDAR point-cloud data using surface-normal estimation, background filtering, and iterative closest-point alignment to generate an aligned three- dimensional point-cloud representation of a monitored region as taught by Alzahrani (Alzahrani [Col. 1 ll. 65-Col. 2 ll. 19]). With the motivation of helping to alleviate crowd density in an area and reduce a level of risk (Alzahrani [Col. 1 ll. 39-56]).
In the same field of endeavor of monitoring a crowd in an area Anderson teaches taking mitigation measures in response to the alert to reduce the crowd density in that location when the crowd density is above the predefined risk threshold by adjusting flow paths, modifying access, or deploying personnel to the monitored region (Paragraph [0013-0015]; [0050]; Fig. 1, the pedestrian movement direction system receives crowd data electronically form one or more sensors and/or other crowd data sources. The crowd data enables the system to monitor crowd density. Crowd density may be a concentration of individuals within a given geographic area. The pedestrian movement directions system supplies one or more AR commands to one or more pedestrian client devices to direct movement of one or more users. The pedestrian movement direction system may determine the AR commands based on crowd density. The system may detect and monitor the density levels of the zones and provides AR commands to pedestrians devices to alter or create new traffic patterns away from more dense zones and toward less dense zones).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of determining if the density of a crowd in an area is above a threshold as disclosed by Lehmann with the system of taking mitigation measures to reduce the crowd density in that location when the crowd density is above the predefined risk threshold as taught by Anderson (Anderson 0013]). With the motivation of helping to alleviate crowd density in an area and reduce a level of risk (Anderson [0002]).
Claim 3: Modified Lehmann discloses the method as per claim 1. Lehmann further discloses wherein the step of analyzing the data uses machine learning applied to the data (Paragraph [0006-0007]; [0019]; [0026]; [0049] certain embodiments include a method of detecting an anomaly in crowd behavior. The method includes receiving sensor data from one or more sensors, the sensor data representing a crowd in motion, and partitioning the sensor data into a set of local areas. Each sub-population can be characterized by a pattern of motion based at least in part on sensor data. In some embodiments, the real-time estimates of the motion can include real-time estimates of velocity and a density of each sub-population near the local area. The present system provides a dynamic crowd model for each local area, where each model represents dynamics of a continuous function describing expected motion near the local area. Crowd model parameter dynamics learning module learns the dynamic system of functions in the crowd model and evolution over time).
Claim 4: Modified Lehmann discloses the method as per claim 3. Lehmann further discloses where the predefined risk threshold is 5 persons per square meter (Paragraph [0019]; [0021]; [0042] in some embodiments, the real-time estimates of the motion can include real-time estimates of density of each subpopulation near the local area. The present system the identifies an occurrence of a potential anomaly associated with the local area, by comparing predictions form the set of auxiliary stochastic models with the set of parametric values of the crowd model. For example, the present system determines when the difference between predictions by the model and the parametric values of the crowd model exceed pre-determine threshold. If the difference between predictions exceeds the pre-determined threshold, the present system identifies a potential anomaly. (The examiner notes that the broadest reasonable interpretation of a crowd density threshold would be able to be set to any desired number of persons per any desired area)).
Claim 5: Modified Lehmann discloses the method as per claim 4. However, Lehmann does not disclose wherein the mitigation measure comprises directing personnel to the location to reduce the crowd density at the location.
In the same field of endeavor of monitoring a crowd in an area Anderson teaches wherein the mitigation measure comprises directing personnel to the location to reduce the crowd density at the location (Paragraph [0013-0015]; [0050]; Fig. 1, the pedestrian movement direction system receives crowd data electronically form one or more sensors and/or other crowd data sources. The crowd data enables the system to monitor crowd density. Crowd density may be a concentration of individuals within a given geographic area. The pedestrian movement directions system supplies one or more AR commands to one or more pedestrian client devices to direct movement of one or more users. The pedestrian movement direction system may determine the AR commands based on crowd density. The system may detect and monitor the density levels of the zones and provides AR commands to pedestrians devices to alter or create new traffic patterns away from more dense zones and toward less dense zones).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of determining if the density of a crowd in an area is above a threshold as disclosed by Lehmann with the system of wherein the mitigation measure comprises directing personnel to the location to reduce the crowd density at the location as taught by Anderson (Anderson 0013]). With the motivation of helping to alleviate crowd density in an area and reduce a level of risk (Anderson [0002]).
Claim 6: Modified Lehmann discloses the method as per claim 5. However, Lehmann does not disclose further including a step of determining if the mitigation measures reduced the crowd density below the pre-defined risk threshold.
In the same field of endeavor of monitoring a crowd in an area Anderson teaches further including a step of determining if the mitigation measures reduced the crowd density below the pre-defined risk threshold (Paragraph [0013-0015]; [0050]; Fig. 1, the pedestrian movement direction system receives crowd data electronically form one or more sensors and/or other crowd data sources. The crowd data enables the system to monitor crowd density. Crowd density may be a concentration of individuals within a given geographic area. The pedestrian movement directions system supplies one or more AR commands to one or more pedestrian client devices to direct movement of one or more users. The pedestrian movement direction system may determine the AR commands based on crowd density. The system may detect and monitor the density levels of the zones and provides AR commands to pedestrians devices to alter or create new traffic patterns away from more dense zones and toward less dense zones).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of determining if the density of a crowd in an area is above a threshold as disclosed by Lehmann with the system of further including a step of determining if the mitigation measures reduced the crowd density below the pre-defined risk threshold. as taught by Anderson (Anderson 0013]). With the motivation of helping to alleviate crowd density in an area and reduce a level of risk (Anderson [0002]).
Claim 7: Modified Lehmann discloses the method as per claim 6. However, Lehmann does not disclose wherein mitigation measures continue when the crowd density is not reduced below the pre-defined risk threshold.
In the same field of endeavor of monitoring a crowd in an area Anderson teaches wherein mitigation measures continue when the crowd density is not reduced below the pre-defined risk threshold (Paragraph [0013-0015]; [0050]; Fig. 1, the pedestrian movement direction system receives crowd data electronically form one or more sensors and/or other crowd data sources. The crowd data enables the system to monitor crowd density. Crowd density may be a concentration of individuals within a given geographic area. The pedestrian movement directions system supplies one or more AR commands to one or more pedestrian client devices to direct movement of one or more users. The pedestrian movement direction system may determine the AR commands based on crowd density. The system may detect and monitor the density levels of the zones and provides AR commands to pedestrians devices to alter or create new traffic patterns away from more dense zones and toward less dense zones).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of wherein mitigation measures continue when the crowd density is not reduced below the pre-defined risk threshold as taught by Anderson (Anderson 0013]). With the motivation of helping to alleviate crowd density in an area and reduce a level of risk (Anderson [0002]).
Claim 8: Modified Lehmann discloses the method as per claim 1. Lehmann further discloses wherein the step of analyzing the data uses a dynamic background model (Paragraph [0006-0007]; [0019]; [0026] certain embodiments include a method of detecting an anomaly in crowd behavior. The method includes receiving sensor data from one or more sensors, the sensor data representing a crowd in motion, and partitioning the sensor data into a set of local areas. Each sub-population can be characterized by a pattern of motion based at least in part on sensor data. In some embodiments, the real-time estimates of the motion can include real-time estimates of velocity and a density of each sub-population near the local area. The present system provides a dynamic crowd model for each local area, where each model represents dynamics of a continuous function describing expected motion near the local area).
Claims 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lehmann (US 2014/0372348) in view of Alzahrani (US 11417106) further in view of Anderson (US 2017/0176202) further in view of Eswara (US 2022/0366665).
Claim 9: Modified Lehmann discloses the method as per claim 8. However, Lehmann does not disclose wherein the step of analyzing the data uses cluster detection.
In the same field of endeavor of determining crowd density in an area Eswara teaches wherein the step of analyzing the data uses cluster detection (Paragraph [0004-0007]; Fig. 1, the present disclosure relates to surveillance systems for estimating crowd size in an area. A method using a computing devices having one or more processors to estimate a number of people that are currently in a region of interest. The identified one or more neighbor pairs are then clustered into one or more clusters. An estimated number of people in each of the one or more clusters is determined).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of using a plurality of analyzing information using a model to determine if the density of a crowd in an area is above a threshold as disclosed by Lehmann (Lehmann [0006]) with the system of wherein the step of analyzing the data uses cluster detection as taught by Eswara (Eswara [0007]). With the motivation of helping to determining the presence and density of a population in an area (Eswara [0003]).
Claim 10: Modified Lehmann discloses the method as per claim 9. However, Lehmann does not disclose wherein the step of analyzing the data uses object tracking.
In the same field of endeavor of determining crowd density in an area Eswara teaches wherein the step of analyzing the data uses object tracking (Paragraph [0004-0007]; [0039]; Fig. 1, the present disclosure relates to surveillance systems for estimating crowd size in an area. A method using a computing devices having one or more processors to estimate a number of people that are currently in a region of interest. The identified one or more neighbor pairs are then clustered into one or more clusters. An estimated number of people in each of the one or more clusters is determined. The method includes receiving a video stream form the video camera and analyzing the video stream to detect each of a plurality of objects within the region of interest).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of using a plurality of analyzing information using a model to determine if the density of a crowd in an area is above a threshold as disclosed by Lehmann (Lehmann [0006]) with the system of wherein the step of analyzing the data uses object tracking as taught by Eswara (Eswara [0007]). With the motivation of helping to determining the presence and density of a population in an area (Eswara [0003]).
Therefore, claim 1 and 3-10 are rejected under U.S.C. 103.
Response to Arguments
Applicant’s arguments, see REMARKS, filed December 15 2025, with respect to the rejections of claims 1 and 3-10 under U.S.C. 101 have been fully considered but are not persuasive.
The applicant argues that the claims are do not recite an abstract idea as the claims recite receiving LiDAR point cloud data generating by a plurality of LiDAR sensors and processing the LiDAR data, determining crowd density, and generating an alert indicating a crowd-density risk condition, which cannot be practically performed in the human mind. However, the examiner respectfully disagrees as the claims recite a method for monitoring a crowd by receiving data, analyzing the data to determine crowd density, determining if the crowd density is above a threshold, generating an alert, and taking mitigation measures to reduce the crowd density in that location by adjusting flow paths, modifying access, or deploying personnel to the region. The examiner finds that the steps of merely receiving and analyzing crowd data to calculate a crowd density and in response to the crowd density being above a threshold taking mitigation measures to reduce the density are all steps that can be performed in the mind of a person. A person is capable of mentally receiving and analyzing information to determine an output such as crowd density and initiating a response such as mitigation actions to reduce the crowd. Such as a safety officer monitoring a crowded area and determining that actions are needed to help reduce the density of the crowd. The claims are found to recite concepts the courts have identified as being mental processes such as “observation (receiving crowd data), evaluation (determining crowd density), judgement (determining if a crowd density is above a risk threshold), and opinion. Alternatively, the claims recite a certain method of organizing human activity as they recite a series of steps for managing personal behavior. The claims recite a series of steps for determining if a crowd density is above a pre-determined threshold and taking mitigation measures in response such as adjusting flow paths, modifying access, or deploying personnel. The claims merely recite a series of steps for receiving and processing crowd information and determining a response based on the analysis. Therefore, the claims are found to recite an abstract idea.
The applicant further argues that the claims are directed to a practical application as the claims recite a method for generating and analyzing three-dimensional point-cloud data using operations such as surface normal estimation, background filtering, and iterative closest-point alignment which are an improvement in using a LiDAR system for determining crowd density, which is an improvement in LiDAR systems. However, the examiner respectfully disagrees as the additional elements of receiving LiDAR point-cloud data from a plurality of LiDAR sensors is directed to merely “apply it” or applying generic computer elements to perform the abstract idea. The claims merely recite using a plurality of LiDAR sensors to gather information to be used in the abstract idea of determining crowd density and taking mitigation measures based on the crowd density. The claims do not recite any additional elements such as a computer system or similar structure for performing the steps of receiving data, analyzing the LiDAR point-cloud data, and generating an alert. Additionally, the claim limitation of “surface normal estimation, background filtering, and iterative closest point alignment” are found to be generic and common techniques used to determine 3D models. (See J. Zheng, S. Yang, X. Wang, Y. Xiao and T. Li, "Background Noise Filtering and Clustering With 3D LiDAR Deployed in Roadside of Urban Environments," in IEEE Sensors Journal, vol. 21, no. 18, pp. 20629-20639, 15 Sept.15, 2021, doi: 10.1109/JSEN.2021.3098458. and https://en.wikipedia.org/wiki/Iterative_closest_point). Therefore, merely using a plurality of sensors to gather information and performing standard techniques to analyze the information are not an improvement to a technology or technical field. Therefore, the claims do not recite a practical application and do not amount to significantly more.
Therefore, the examiner maintains the current 101 rejection.
Applicant argues that claims 3-10 are allowable as being dependent on claims 1 and therefore are rejected under the same rejection.
Applicant’s arguments, see REMARKS, filed December 15, 2025, with respect to the rejections of claims 1 and 3-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lehmann (US 2014/0372348) in view of Alzahrani (US 11417106) further in view of Anderson (US 2017/0176202) are moot as the claims were amended which required further search and consideration and new art was applied.
Claim 1: Applicant argues that the combination of prior art does not teach the newly amended claim limitations. However, upon further search and consideration the examiner finds that Alzahrani can be used in combination with the current prior art to teach the newly amended claim limitations. Lehmann discloses a system for monitoring crowd behavior and density in an area using a plurality of sensors and sending alerts to mitigate a risk in a crowd (Lehmann [0006]). Lehmann can be used in combination with Alzahrani which teaches a system of crowd management using a plurality of LiDAR point cloud sensors collecting data and forming 3D surface models of an area to determine crowd density and potential risk in an area (Alzahrani [Col. 1 ll. 65-Col. 2 ll. 19]). Alzahrani further teaches using a plurality of 3D modeling algorithms and processes to generate a 3D model using artificial intelligence algorithms to generate analyzing crowd density of an area and determining a response such as forming an evacuation strategy (Alzahrani [Col. 4 ll. 53- Col. 5 ll. 3]). Additionally, Anderson teaches a system for determining a response to a crowd density being above a desired amount such as creating new traffic patterns away from more dense zones to less dense zones (Anderson [0050]). Therefore, the examiner finds that the combination of prior art teaches the newly amended claim limitations.
Therefore, the examiner finds claim 1 newly rejected under U.S.C. 103.
Claims 3-10 were argued as being allowable only as being dependent on claim 1. Therefore, the claims are also found to be newly rejected under U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Erickson (US 2017/0341746) Unmanned aerial vehicle for crowd control amelioration.
Kilambi (US 2008/0118106) Crowd counting and monitoring.
Chembakassery Rajendran (US 2024/0412521) Method and system for crowd counting.
Felemban (US 2022/0254162) Deep learning framework for congestion detection and prediction in human crowds.
Tav (US 2024/0323640) Crowd density analysis with multiple regions.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY RUSS whose telephone number is (571)270-5902. The examiner can normally be reached on M-F 7:30-4:30.
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/COREY RUSS/Examiner, Art Unit 3629