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 .
DETAILED ACTION
This action is in response to papers filed on 4/21/2026.
Claims 1, 10, and 15 have been amended.
Claims 11 and 20-23 have been cancelled.
No claims have been added.
Claims 1-10, 12-19, and 24 are pending.
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 1-10, 12-19, and 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claims are directed to a process (method as introduced in Claim 1) and/or system (Claim 15), and/or computer readable storage medium with executable instructions (Claim 10). Thus, Claims 1-10, 12-19, and 24 fall within one of the four statutory categories. See MPEP 2106.03.
Step 2A, Prong 1:
The claimed invention recites an abstract idea according to MPEP §2106.04. The independent claims which recite the following claim limitations as an abstract idea, are underlined below.
Claims 1, 10, and 15 recite:
accessing an exercise plan stored in a data store, the exercise plan defining the incident training exercise, the exercise plan specifying a type of an incident and a location and time of the incident within a theater of operation and specifying characteristics of objects within the theater of operation including a hazardous emission rate or hazardous shielding properties;
receiving a current location of a detector located within the theater of operation at a current time during the incident exercise, wherein the detector is designed to detect a hazardous material, event or condition, and wherein the detector is a physical detector that includes detection hardware;
enabling the exercise plan to be dynamically modified during the incident exercise;
applying a machine learning algorithm in real-time to images of a scene of the theater of operation to detect a real object within the theater of operation and to identify at least one of a type of the real object or a material of the real object;
dynamically modifying the exercise plan during the incident exercise in response to detection of the real object by the machine learning algorithm, by:
updating the exercise plan to account for the real object based on the identified at least one of the type or the material, and
adding simulated detector data representative of a characteristic effect associated with the identified material or type corresponding to the real object to the exercise plan during the incident exercise based on the current location of the detector within the theater of operation;
calculating incident effects of the incident at the current location and at the current time based on the dynamically modified exercise plan;
calculating characteristic effects of the characteristics of the theater of operation at the current location and at the current time;
generating detector signals by an incident exercise system for the detector that represent signals the detector would generate during a real incident, based on a combination of the incident effects and the characteristic effects, wherein generating the detector signals comprises generating detector signals representing combined effects of the incident effects and the characteristic effects, including background radiation as background noise; and
performing a training of the detector by sending, to a processing portion of the detector the generated detector signals to simulate detection of the real incident by the detection hardware of the detector, wherein the sending comprises placing the detector into a training mode in which the detector signals are received from the incident exercise system rather than from the detection hardware of the detector, and wherein, while in the training mode, the detector filters out the background noise from the generated detector signals to generate filtered signals representative of the incident effects;
distinguishing the incident signatures from the location-dependent background based on the training.
The underlined claim limitations as emphasized above, as drafted, recite a process that, under its broadest reasonable interpretation, the performance of managing personal behavior or relationships or interactions between people in the form of providing real-world simulations and exercises (including simulated event related data) for incidence response training. Other than reciting a computer implementation, nothing in the claim elements precludes the step from encompassing the managing personal behavior or relationships or interactions between people which represents the abstract idea of certain methods of organizing human activity. But for the recitation of generic implementation of computer system components, the claimed invention merely recites a process for determining performance of managing personal behavior or relationships or interactions between people which represents the abstract idea of certain methods of organizing human activity.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, the claims recite additional elements such as:
accessing an exercise plan stored in a data store
wherein the detector is designed to detect a hazardous material, event or condition, and wherein the detector is a physical detector that includes detection hardware;
applying a machine learning algorithm in real-time to images of a scene of the theater of operation to [detect objects and materials]
generating detector signals by an incident exercise system for the detector that represent signals the detector would generate during a real incident, based on a combination of the incident effects and the characteristic effects, wherein generating the detector signals comprises generating detector signals representing combined effects of the incident effects and the characteristic effects, including background radiation as background noise; and
performing a training of the detector by sending, to a processing portion of the detector, the generated detector signals to simulate detection of the real incident by the detection hardware of the detector, wherein the sending comprises placing the detector into a training mode in which the detector signals are received from the incident exercise system rather than from the detection hardware of the detector, and wherein, while in the training mode, the detector filters out the background noise from the generated detector signals to generate filtered signals representative of the incident effects;
In particular, the additional elements cited above beyond the abstract idea are recited at a high-level of generality and simply equivalent to a generic recitation and basic functionality that amount to no more than mere instructions to apply the judicial exception using generic computer technology components.
Accordingly, since the specification describes the additional elements in general terms, without describing the particulars, the additional elements may be broadly but reasonably construed as generic computing components being used to perform the judicial exception (see specification at [0027], “…may include desktop computers, laptops, tablets, e-readers, personal digital assistants, smartphones, gaming devices, servers, and computer systems…”; [0002], “…may include spectrometers, radiation detectors, seismometers, chemical agent detectors, and so on.”; and [0014], “…detectors may be specified by brand and model number, type (e.g., chemical detectors and radiation detectors)…”). These claimed additional elements merely recite the words “apply it" (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e)). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
Step 2B:
The claims do not include additional elements, individually or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept at Step 2B. Thus, the claim is not patent eligible.
Dependent Claims:
Claims 2-9, 12-14, 16-19, and 24 recite further elements related to the data collection and processing steps of the parent claims. These activities fail to differentiate the claims from the related activities in the parent claims and fail to provide any material to render the claimed invention to be significantly more than the identified abstract ideas, as outlined below.
Claim 2 recites “wherein the incident is selected from a group consisting of a release of radiation, a release of a chemical, a seismic event, an atmospheric event, and an underwater event”, which further specifies types of incidents to be used for training exercises, but does not lead toward eligibility because the type of event does not significantly affect how the claim steps are performed.
Claims 3 and 16 recites “wherein the incident is based on a release of radiation by a radioactive material and a characteristic of the theater of operation is background radiation”, which further specifies types of incidents to be used for training exercises, but does not lead toward eligibility because the type of event does not significantly affect how the claim steps are performed.
Claims 4 and 17 recites “wherein the background radiation is based on an object within the theater of operation that emits radiation”, which further specifies a source of an incident to be used for training exercises, but does not lead toward eligibility because the type of event does not significantly affect how the claim steps are performed.
Claim 5 recites “wherein the object is a building”; Claim 6 recites “wherein the object is a walking surface”; Claim 7 recites “wherein the object is a container containing a non-hazardous content”; Claim 8 recites “wherein the object is a person with a medical implant”; Claim 9 recites “wherein the object is a subsurface naturally occurring radioactive source”, which further specifies types of objects to be used for training exercises, but does not lead toward eligibility because the type of objects being identified does not significantly affect how the objects are identified and/or how the information processed.
Claim 12 recites “wherein the dynamically modifying includes adding an object to the theater of operation”; Claim 13 recites “wherein the dynamically modifying includes resetting a current exercise time to an earlier time”; Claim 14 recites “wherein the dynamically modifying includes resetting a current exercise time to an earlier time”, which further specifies types of modifications to be added to the training exercises, but does not lead toward eligibility because these would merely represent additional steps in the abstract idea (certain methods of organizing human activity).
Claim 18 recites “wherein the computer-executable instructions identify objects based on analysis of images collected during the incident exercise”, which recites material similar to its parent claim, therefore subject to the same analysis and rejection as provided for Claim 15 (images “collected during the incident exercise” would be equivalent to “images of a scene of the theater of operation to detect and recognize a real object that has entered or moved within the theater of operation after a start of the incident exercise). Merely using the computer-executable instructions for the identification does not integrate the abstract idea into a practical application or provide an inventive concept (for the same reasons that merely applying a machine learning algorithm for identification does not integrate the abstract idea into a practical application or provide an inventive concept).
Claim 19 repeats material recited in its parent claim and is therefore subject to the same analysis and rejection as provided for Claim 15.
Claim 24 recites “wherein applying the machine learning algorithm to the images of the theater of operation comprises applying a machine learning algorithm to identify an object representing unexpected activity”, which would merely represent additional step in the abstract idea (certain methods of organizing human activity). This unexpected activity characteristic of the object is not used in any manner as part of the claimed invention and merely applying a machine learning algorithm for identification does not integrate the abstract idea into a practical application or provide an inventive concept.
The claims do not provide any new additional limitations or meaningful limits beyond abstract idea that are not addressed above in the independent claims therefore, they do not integrate the abstract idea into a practical application nor do they provide significantly more to the abstract idea. Thus, after considering all claim elements, both individually and as a whole, it has been determined that the claims do not integrate the judicial exception into a practical application or provide an inventive concept. Therefore, Claims 2-9, 12-14, 16-19, and 24 are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claim(s) 1-10, 12, 14-19, and 24 is/are rejected under 35 U.S.C. 103 as being obvious over White et al. (Pub. No. US 2016/0203727 A1) in view of Lee et al. (KR 20230043561 A) in further view of Labov et al. (Pub. No. US 2019/0353822 A1).
Examiner notes on claim interpretation: Applicant’s specification does not include the term “hazardous emission rate” or similar terminology (“emission”, etc.). The specification does refer to radiation/gas that is emitted and amounts emitted, but does not discuss emission rates in these sections. However, review of the specification did identify material in [0015] that discusses decay rates of the material in addition to shielding properties (“…the incident exercise system may calculate incident effects based on a model of radiation concentration at various locations considering factors such as amount and decay rate of the radioactive material…”). Examiner’s further review of concepts such as decay rate and Bateman equations (as recited in the specification) indicate that these are terms of art that one of ordinary skill would recognize as being related to emission rates. For example, one of ordinary skill in the art would recognize the correlation between hazardous material decay rate and the amount of hazardous material emitted. This interpretation is being used to interpret the instant claims and Examiner considers the claims to be supported by the disclosure. Applicant is advised that, should Applicant disagree with this interpretation and/or provide evidence that it is incorrect, this could result in additional 35 USC § 112(a) and/or 35 USC § 112(b) rejections.
In regards to Claims 1, 10, and 15, White discloses:
A computer-implemented method/system that improves training and verification of a detector for distinguishing incident signatures from location-dependent background (Abstract and Figs. 1-4; [0018], “…provides simulated signals to the detectors so that the detectors can present the actual user experience of a real incident.”, simulated data would represent “manufactured detector data”), the method comprising:
one or more computer-readable storage mediums…computer-executable instructions for controlling the one or more computing systems…one or more processors for executing the computer-executable instructions stored in the one or more computer-readable storage mediums ([0028]; Claim 10)
Examiner’s note: It is noted that the systems/methods performed in White can be performed in “real environment“ mode, “virtual environment” mode, or a combination of the two environments (such as an augmented reality). See [0017]; [0018]; and throughout the reference.
accessing an exercise plan stored in a data store, the exercise plan defining the incident exercise, the exercise plan specifying a type of an incident and a location and time of the incident within a theater of operation and specifying characteristics of objects within the theater of operation including a hazardous emission rate or hazardous shielding properties; ([0015], “…simulation system administers an incident exercise based on a simulation plan that defines the incident exercise. The simulation plan may specify a theater of operation, the incidents within the theater of operation, detectors for detecting effects of the incidents, and the entities (e.g., people) participating in the incident exercise, For example, a simulation plan specifies the theater of operation, such as the National Mall area in Washington D.C., an international airport such as the Los Angeles International Airport, a cruise ship, and so on. The simulation plan also specifies incidents, such as explosion of a dirty bomb and release of toxic gases (e.g., sarin gas). The simulation plan also specifies detectors…”; [0016], “…The incident simulation system implements a simulation plan by generating incident data (e.g., hazard data) indicating effects of the incident at target locations within the theater of operation and at target times.”; [0023], “The object storage contains information describing characteristics of the objects within the theater of operation. For example, an object may be a building and the characteristics may include the type of building material (e.g., wood or concrete), the thickness of walls, and so on.”; [0023], “…also includes a simulation storage…simulation storage stores the simulation plan…”; [0021]; [0026]; [0033], the simulation/exercise data includes characteristics of the hazardous material incident including hazardous emission rate (as represented by decay rates of the hazardous material, described in further detail in the assigning and adding steps, provided below))
receiving a current location of a detector located within the theater of operation at a current time during the incident exercise, wherein the detector is designed to detect a hazardous material, event or condition, and wherein the detector is a physical detector that includes detection hardware; ([0017], “…the incident simulation system may also generate a simulated user experience for a detector at a target location and at a target time based on the incident data…”, “If a real detector is used, the incident simulation system generates detector signals based on the generated incident data that represent the signals the real detector would encounter in an actual incident.”; [0015], “…detectors, such as certain brands of spectrometers and radiation detectors, that are used in the incident exercise.”)
enabling the exercise plan to be dynamically modified during the incident exercise; ([0033], “…actions taken to reduce the effects of the hazardous materials, and other factors that may affect the effects of the hazardous material.”, actions taken by participants to reduce effects would be actions made based on their observations of the hazards, conditions, etc. used to make decisions, changes in effects of the hazard would affect the simulation; see also [0021], “…incident commander can monitor and control an incident exercise…monitor and control the response to a real incident…incident control user interface may be presented to a person who is in overall control of the incident exercise…”; [0019]; “…event-driven simulation, the incident simulation system detects events (e.g., movement of participants and change in effects of a hazardous material) and updates the simulation displays based on the events…”)
dynamically modifying the exercise plan during the incident exercise in response to detection of the real object by the machine learning algorithm, by:
updating the exercise plan to account for the real object based on the identified at least one of the type or the material, and
adding simulated detector data representative of a characteristic effect associated with the identified material or type corresponding to the real object to the exercise plan during the incident exercise based on the current location of the detector within the theater of operation; ([0018], “In the real environment mode, the incident exercise is conducted in the actual theater of operation with real detectors. The participants in the incident exercise actually move through the theater of operation and hold real detectors…generate the hazard data based on the current locations of the participants and detectors in the virtual environment”; [0017], detector location is the target location; [0021]; [0026], the hazardous material can be associated with (or assigned to) a moving real object (such as the moving vehicle) and the simulated detector data incorporates characteristics of the hazardous material release related to the moving real object; [0033], the hazard/detector data is affected by the moving vehicle (type of moving object is identified as a vehicle) and included in the simulation/training plan, hazardous material characteristics can include factors that indicate the effects of the hazardous material including the rate of decay (the effects caused by the rate of decay would provide data relating to the emission rate of the hazardous material and/or object with which it is associated, one of ordinary skill in the art would understand that as the rate of decay changes, so does the rate of emissions), hazard data is generated for a target location (location of a detector, see [0017]) by analyzing the effects of identified objects on the hazard data and updating the hazard data (such as “…rate of decay…and other factors that may affect the effects of the hazardous material…”), one of ordinary skill would recognize that, in the “real environment mode” (as discussed above), objects would represent real objects in the real-world theater of operation through which the user moves, these effects are used to update the hazard data that is sent to the detector (the new data with the effects factored in represent data added to the simulated detector data), during the incident exercise, the exercise plan can be dynamically modified as conditions change over locations and times (such as temperature, wind speed, etc.), see [0015]; [0016]; [0019], shows incident data being measured over a time interval (indicating multiple times measured), including for a single location; FIG. 2; [0020]; [0021], graph 203 shows data for a given location, the description provided for the graph would be understood by one of ordinary skill in the art to likely represent data over time points for that location, “…the incident simulation system may generate the incident data based on a model of the dispersal of the toxic gas at various locations and times given the current environmental conditions (e.g., temperature and wind speed).”; [0020], the display of the theater or operation is dynamically modifying/updated as participants move through the path; [0033], identifies and tracks moving objects and their effects on the simulation along the path, additional objects are selected/identified and their effects added to the exercise plan/scenario (updated effects on the hazard), the above cited paragraphs make multiple mentions of images (displays) provided to a user as they move though the simulation and the updating of displays/images as events occur, indicating “real-time” imagery being used and “real-time” updates based on “real-time” events and data; [0019], specifies that movement of participants can affect the simulation (“With an event-driven simulation, the incident simulation system detects events (e.g., movement of participants and change in effects of a hazardous material) and updates the simulation displays based on the events.”), participants would be real-world objects when they are participating in the real-world environment (see [0018]) that effect the simulation based on their movements and decisions, see also, [0021], free moving vehicles controlled by users, the exercise may be controlled by a user or by the simulation plan, the example given is the route of a vehicle (the route of a vehicle would represent a dynamically modified condition as the exercise proceeds), the route of the vehicle can be determined by the simulation plan or by a user controlling it, the exercise may be controlled by users (control users, input from participants interacting with the software/computer-executable instructions), for example, a user operating a vehicle in the simulation may control the route of the vehicle, changes in the route of the vehicle may affect the simulation and effects of the hazard in different ways (different or changes in routes would modify the exercise/simulation plan)], although the claim does not require both, in addition to the “hazardous emission rate”, the above cited material also discusses “hazardous shielding properties” such as incorporating the effects of object so the dispersal of the hazardous gas/radioactive material, including moving objects (see at least [0023]; [0033])
calculating incident effects of the incident at the current location and at the current time based on the dynamically modified exercise plan; ([0017]; [0018]; [0031], etc., “The simulate detector signals component 700 is invoked to determine the effects of each hazardous material specified in an incident in the simulation plan on a detector at its current location.”; during the incident exercise, the exercise plan can be dynamically modified as conditions change over locations and times (such as temperature, wind speed, etc.), see [0015]; [0016]; [0019], shows incident data being measured over a time interval (indicating multiple times measured), including for a single location; FIG. 2; [0020], graph 203 shows data for a given location, the description provided for the graph would be understood by one of ordinary skill in the art to likely represent data over time points for that location, “…the incident simulation system may generate the incident data based on a model of the dispersal of the toxic gas at various locations and times given the current environmental conditions (e.g., temperature and wind speed).”; [0020], the display of the theater or operation is dynamically modifying/updated as participants move through the path; [0033], along the path, additional objects are selected/identified and their effects added to the exercise plan/scenario (updated effects on the hazard))
calculating characteristic effects of the characteristics of the theater of operation at the current location and at the current time; ([0023]; [0033], characteristics of the theater of operation (for example, buildings and characteristics of their construction) are included in the effects determination for incidents at locations and times)
generating detector signals bv an incident exercise system for the detector that represent signals the detector would generate during a real incident, based on a combination of the incident effects and the characteristic effects, wherein generating the detector signals comprises generating detector signals representing combined effects of the incident effects and the characteristic effects; (Claim 1; Claim 7, generated incident data includes incident effects and object effects; see also [0015]-[0018]; [0023]; [0031]; [0033], as described above; [0017], “If a real detector is used, the incident simulation system generates detector signals based on the generated incident data that represent the signals the real detector would encounter in an actual incident.”; [0033], the generate hazard data include combination of effective data (incident effects, characteristic effects, objects, movement, etc.))
performing a training of the detector by sending, to a processing portion of the detector, the generated detector signals to simulate detection of a real incident by detection hardware of the detector, wherein the sending comprises placing the detector into a training mode in which the detector signals are received from the incident exercise system rather than from the detection hardware of the detector, and wherein, (Claim 7, “…wherein the hazard data generating module generates hazard data factoring in the topography and presence of objects that may affect the effects of a hazardous material incident.”; at least [0021]; [0023]; [0033], data includes characteristics of the event, locations, objects, etc.; Claim 6, “…wherein a user experience detector module generates displays representing the user experience a detector would provide based on the generated signals.”; [0017], “If a real detector is used, then the simulated visual experience for the theater of operation may be provided via augmented reality using special glasses that display the visual experience of the theater of operation but allow the participant to hold and see the real detector.”; [0018], “In the real environment mode, the incident exercise is conducted in the actual theater of operation with real detectors. The participants in the incident exercise actually move through the theater of operation and hold real detectors. The incident simulation system tracks the locations of the participant and provides simulated signals to the detectors so that the detectors can present the actual user experience of a real incident.”, shows detectors receiving simulated detection of real incidents; [0017], “…provides the detector signals to the detector for processing as if the detector had actually detected the effects of the incident”, “Each real detector may have a training mode in which the signals are received from the incident simulation system rather than the detection hardware of the detector. The incident simulation system then provides the detector signals to the detector for processing as if the detector had actually detected the effects of the incident.”)
White discloses dynamically modifying the exercise plan (simulations) in real time based on changes in conditions (such as moving vehicles, moving people, wind, temperature, etc.) and the identification of real objects moving within a theater of operation after a start of the incident exercise, as described above.
White does not explicitly disclose the use of a machine learning algorithm applied to real-time images for this task, but Lee teaches:
apply a machine learning algorithm in real-time to images of a scene of the theater of operation to detect a real object within the theater of operation and to identify at least one of a type of the real object or a material of the real object (page 7, lines 21-38, images are taken within a theater of operation, (CCTV, 2d image data), the sensors (for image capture) are dispersed throughout a city (see also page 3, line 47-page 4, line 3), the city or equivalent area) represents a specified are in which activity is monitored, thus representing the same concept as a “theater of operation”, “real objects” such as pedestrians and vehicles are identified and analyzed using deep-learning based object tracking (apply a machine learning algorithm) from the image, one of ordinary skill in the art would recognize that the collecting of image data regarding moving objects and their changing locations indicates “real-time” data [as additional evidence, the reference mentions in the “BACKGRUND-ART” section that the system/method are addressing the need for real-time data on moving objects (page 2, lines 30-36, “…precise statistical data, dynamic data (data that changes in real time) on movable objects such as people as the subject of the city and automobiles…”) furthering indicating the intent of the reference] (see also page 11, lines 7-9; page 16, lines 2-4); page 3, lines 20-26; page 7, lines 21-29; and throughout reference, types of moving objects can be identified (vehicle, persons, bicycles, etc.))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have further modified the system of White so as to have included applying a machine learning algorithm in real-time to images of a scene of the theater of operation to detect and recognize a real object that has entered or moved within the theater of operation, as taught by Lee.
White discloses a “base” method/system which collects data regarding a theater of operation and analyses the effects of objects, as shown above. Lee teaches a comparable method/system which also collects data regarding objects in a theater of operation, as shown above. Lee also teaches an embodiment in which a machine learning algorithm is applied in real-time to images of a scene of the theater of operation to detect and recognize a real object that has entered or moved within the theater of operation, as shown above. One of ordinary skill in the art would have recognized the adaptation of applying a machine learning algorithm in real-time to images of a scene of the theater of operation to detect and recognize a real object that has entered or moved within the theater of operation to White could be performed with the technical expertise demonstrated in the applied references. (See KSR [127 S Ct. at 1739] "The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results."). One of ordinary skill in the art would understand how to apply the machine learning used to analyze the objects/environments in Lee to the comparable objects/environments of White.
White/Lee discloses the above method/system for using detectors (including radiation detectors), training detectors by sending data that is representative of real data, and identifying/including background data such as objects, buildings, and materials. White/Lee does not explicitly disclose the filtering of background noise/effects or that the background noise/effects is background radiation, but Labov teaches:
[generating detector signals] including background radiation as background noise; ([0025], detector signals include radiation signatures and background data, identifies radiation signatures and filters out data (including other background radiations signals and effects on radiation signals by objects or materials, such as lead or buildings))
the detector filters out the background noise from the generated detector signals to generate filtered signals representative of the incident effects; ([0025], detector signals include radiation signatures and background data, identifies radiation signatures and filters out data (including other background radiations signals and effects on radiation signals by objects or materials, such as lead or buildings) to determine the radiation effects (radiation incident))
distinguishing the incident signatures from the location-dependent background based on the training ([0025], detector signals include radiation signatures and background data, identifies radiation signatures and filters out data (including other background radiations signals and effects on radiation signals by objects or materials, such as lead or buildings) to determine the radiation effects (radiation incident); [0016]; [0023]; [0094]; [0095] etc., the detector is trained to identify background and signatures and to filter them (metric trainer, that is part of the detector, trains filtering data)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of White/Lee so as to have included [generating detector signals] including background radiation as background noise; the detector filters out the background noise from the generated detector signals to generate filtered signals representative of the incident effects; and distinguishing the incident signatures from the location-dependent background based on the training, as taught by Labov in order to ensure that the correct materials data, effects data, and other related data are being provided by the detector (Labov, [0005]; [0016]; [0025]) for use in the training exercises of White/Lee.
It is also noted that, while Labov does not explicitly disclose the training performed during a “training mode” of the detector, Applicant (as well as White/Lee) identify the training mode as the detector performing its functions in regards to the supplied data the same as it would in “real mode” with real data. Therefore, one of ordinary skill in the art would recognize that the filtering processes of Labov would be performed in the same manner when combined with White/Lee regardless of what mode it is in.
In regards to Claim 2, White further discloses:
wherein the incident is selected from a group consisting of a release of radiation, a release of a chemical, a seismic event, an atmospheric event, and an underwater event ([0001], detectors are known to include “spectrometers, radiation detectors, seismometers, and so on”; [0016]; [0021], and multiple places throughout the reference, incidents include those related to releases of hazardous material, toxic gas)
In regards to Claim 3 and 16, White further discloses wherein the incident is based on a release of a harmful substance in the atmosphere of the theater of operation (“background”) ([0015]; [0016];[ [0021], “…specifies incidents, such as explosion of a dirty bomb and release of toxic gases (e.g., sarin gas)…”, it is noted that [0015] also references the potential use of radiation detectors in the incident monitoring/simulation)
While White discloses a method for simulating incident related to the release of hazardous materials into a location, White/Lee does not disclose that the hazardous material is specifically radiation and/or background radiation.
However, the Examiner asserts that the data identifying the type of incident, hazard, material, etc. is simply a label for the data and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the specific type incident, hazard, material, etc.) which does not explicitly alter or impact the steps of the method does not patentably distinguish the claimed invention from the prior art in terms of patentability.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have simulated a background radiation incident because the type of incident being simulated does not functionally alter or relate to the steps of the method and merely labeling the information differently from that in the prior art does not patentably distinguish the claimed invention. The actual functioning and processing of the claimed invention would not differ significantly if performed on a background radiation incident rather than a hazardous gas release incident.
In regards to Claims 4 and 17, White further discloses wherein the incident is based on a release of a harmful substance in the atmosphere of the theater of operation (“background”) emitted by an object ([0015]; [0016];[ [0021], “…specifies incidents, such as explosion of a dirty bomb and release of toxic gases (e.g., sarin gas)…”, it is noted that [0015] also references the potential use of radiation detectors in the incident monitoring/simulation)
While White discloses a method for simulating incident related to the release of hazardous materials into a location by an object, White/Lee does not disclose that the hazardous material is specifically radiation and/or background radiation emitted by the object.
However, the Examiner asserts that the data identifying the type of incident, hazard, material, etc. is simply a label for the data and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the specific type incident, hazard, material, etc.) which does not explicitly alter or impact the steps of the method does not patentably distinguish the claimed invention from the prior art in terms of patentability.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have simulated a background radiation incident because the type of incident being simulated does not functionally alter or relate to the steps of the method and merely labeling the information differently from that in the prior art does not patentably distinguish the claimed invention. The actual functioning and processing of the claimed invention would not differ significantly if performed on a background radiation incident rather than a hazardous gas release incident.
In regards to Claim 5, White further discloses: wherein the object is a building ([0023]; [0033]).
In regards to Claim 6, White further discloses: wherein the object is a walking surface ([0015], “…the National Mall area in Washington D.C….”).
In regards to Claims 7-9, although White/Lee discloses a method for simulating incident related to the release of hazardous materials into a location by an object, wherein the objects can be a building or walking surface (as recited in claims 5-6; see White; [0023], [0033] and [0015]), White/Lee does not further specify the specific type of object verbatim, as a container containing a non-hazardous content; a person with a medical implant; or a subsurface naturally occurring radioactive source.
However, the Examiner asserts that the data identifying type of object is simply a label for the object and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the specific type of object) which does not explicitly alter or impact the steps of the method does not patentably distinguish the claimed invention from the prior art in terms of patentability. The type of object does not significantly affect the process of the claimed invention implicitly or explicitly. For example, the type of object and its particular effects, do not alter of change the manner in which the invention analyses data and determines outcomes, they merely provide examples of types of objects that could be identified. It is also noted that no material was found in the disclosure that could be used to alter the claims/interpretation to indicate how/why any particular object type would cause the claimed invention to process differently.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have included the above listed (or any other) types of objects because the type of object being analyzed does not functionally alter or relate to the steps of the method and merely labeling the type of object information differently from that in the prior art object does not patentably distinguish the claimed invention.
In regards to Claim 12, White further discloses:
wherein the dynamically modifying includes adding an object to the theater of operation (FIG. 2; [0015]; [0016]; [0019]; [0020]; [0033], “dynamically modifying” as applied to the parent claim(s), above; [0020], the display of the theater or operation is updated as participants move through the path; [0033], along the path, additional objects are selected/identified and their effects added to the exercise plan/scenario (updated effects on the hazard)).
In regards to Claim 14, White further discloses wherein the incident is based on a release of a harmful substance in the atmosphere of the theater of operation (“background”) ([0015]; [0016];[ [0021], “…specifies incidents, such as explosion of a dirty bomb and release of toxic gases (e.g., sarin gas)…”, it is noted that [0015] also references the potential use of radiation detectors in the incident monitoring/simulation) and dynamically modifying the simulation and results based on the hazard/material/etc. (FIG. 2; [0015]; [0016]; [0019]; [0020]; [0033], “dynamically modifying” as applied to the parent claim(s), above; [0032], “…updates the cumulative exposure of the participant to the selected hazardous material based on the hazard data…”; [0019]; [0020], “…the incident simulation system detects events (e.g., movement of participants and change in effects of a hazardous material) and updates the simulation displays based on the events.”)
While White discloses a method for simulating incident related to the release of hazardous materials into a location and updating data based on effects, White/Lee does not disclose that the hazardous material is specifically radiation and/or background radiation.
However, the Examiner asserts that the data identifying the type of incident, hazard, material, etc. is simply a label for the data and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the specific type incident, hazard, material, etc.) which does not explicitly alter or impact the steps of the method does not patentably distinguish the claimed invention from the prior art in terms of patentability.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have simulated a background radiation incident because the type of incident being simulated does not functionally alter or relate to the steps of the method and merely labeling the information differently from that in the prior art does not patentably distinguish the claimed invention. The actual functioning and processing of the claimed invention would not differ significantly if performed on a background radiation incident rather than a hazardous gas release incident.
In regards to Claim 18, White discloses a system/method for implementing a virtual environment simulation including identifying objects affecting the simulation during the incident exercise (real-time), as discussed above. White does not explicitly disclose, but Lee teaches:
wherein the computer- executable instructions identify objects based on analysis of images collected [in real time] (page 13, line 34-page 14, line16, explains that the processes performed by the system/method (such as those in the flowchart that includes the objectification process performed by the deep learning technology) are performed using computer-executable instructions, this indicating that the deep learning (machine learning model) is performed using computer-executable instructions (see also page 11, lines 7-9; page 16, lines 2-4))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have further modified the system of White so as to have included wherein the computer-executable instructions identify objects based on analysis of images collected [in real time], as taught by Lee.
White discloses a “base” method/system which collects data regarding a theater of operation and analyses the effects of objects, as shown above. Lee teaches a comparable method/system which also collects data regarding objects in a theater of operation, as shown above. Lee also teaches an embodiment in which computer-executable instructions identify objects based on analysis of images collected [in real time], as shown above. One of ordinary skill in the art would have recognized the adaptation of wherein the computer-executable instructions identify objects based on analysis of images collected [in real time] to White could be performed with the technical expertise demonstrated in the applied references (See KSR [127 S Ct. at 1739] "The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results."). One of ordinary skill in the art would understand how to apply the machine learning used to analyze the objects/environments in Lee to the comparable objects/environments of White.
In regards to Claim 19, White further discloses a system/method for implementing a virtual environment simulation including identifying objects affecting the simulation. White does not explicitly disclose, but Lee teaches:
wherein the objects are identified using a machine learning model (page 7, lines 21-38, “real objects” such as pedestrians and vehicles are identified and analyzed using deep-learning based object tracking (apply a machine learning algorithm) from the image (as further discussed in the parent claim rejection, provided above))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have further modified the system of White so as to have included wherein the objects are identified using a machine learning model, as taught by Lee.
White discloses a “base” method/system which collects data regarding a theater of operation and analyses the effects of objects, as shown above. Lee teaches a comparable method/system which also collects data regarding objects in a theater of operation, as shown above. Lee also teaches an embodiment in which objects are identified using a machine learning model, as shown above. One of ordinary skill in the art would have recognized the adaptation of wherein the objects are identified using a machine learning model to White could be performed with the technical expertise demonstrated in the applied references (See KSR [127 S Ct. at 1739] "The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results."). One of ordinary skill in the art would understand how to apply the machine learning used to analyze the objects/environments in Lee to the comparable objects/environments of White.
In regards to Claim 24, White/Lee disclose all of the above limitations. Additionally, White discloses the ability to include an object that represents unexpected activity ([0021], the control of the moving vehicle’s route would provide a dynamic path that can affect the conditions, since the path can change based on the controller’s choices)). White does not explicitly disclose, but Lee teaches:
wherein applying the machine learning algorithm to the images of the theater of operation comprises applying a machine learning algorithm to identify an object (page 7, lines 21-38, images are taken within a theater of operation, (CCTV, 2d image data), the sensors (for image capture) are dispersed throughout a city (see also page 3, line 47-page 4, line3), the city or equivalent area) represents a specified are in which activity is monitored, thus representing the same concept as a “theater of operation”, “real objects” such as pedestrians and vehicles are identified and analyzed using deep-learning based object tracking (apply a machine learning algorithm) from the image, one of ordinary skill in the art would recognize that the collecting of image data regarding moving objects and their changing locations indicates “real-time” data [as additional evidence, the reference mentions in the “BACKGRUND-ART” section that the system/method are addressing the need for real-time data on moving objects (page 2, lines 30-36, “…precise statistical data, dynamic data (data that changes in real time) on movable objects such as people as the subject of the city and automobiles…”) furthering indicating the intent of the reference])
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have further modified the system of White so as to have included wherein applying the machine learning algorithm to the images of the theater of operation comprises applying a machine learning algorithm to identify an object, as taught by Lee.
White discloses a “base” method/system which collects data regarding a theater of operation and analyses the effects of objects, as shown above. Lee teaches a comparable method/system which also collects data regarding objects in a theater of operation, as shown above. Lee also teaches an embodiment wherein applying the machine learning algorithm to the images of the theater of operation comprises applying a machine learning algorithm to identify an object, as shown above. One of ordinary skill in the art would have recognized the adaptation of wherein applying the machine learning algorithm to the images of the theater of operation comprises applying a machine learning algorithm to identify an object to White could be performed with the technical expertise demonstrated in the applied references. (See KSR [127 S Ct. at 1739] "The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results."). One of ordinary skill in the art would understand how to apply the machine learning used to analyze the objects/environments in Lee to the comparable objects/environments of White.
Claims 13 is/are rejected under 35 U.S.C. 103 as being obvious over White in view of Lee in further view of Labov in further view of Genovese (WO 0164014 A2).
In regards to Claim 13, White/Lee/Labov discloses a system/method for implementing a virtual simulation plan for responding to hazardous incidents including dynamically modifying the exercise plan, as discussed above. White/Lee/Labov does not explicitly disclose, but Genovese teaches:
wherein the dynamically modifying includes resetting a current exercise time to an earlier time (page 48, lines 1-11, exercises can be rest to an earlier time in the simulation to allow the user to make another attempt at a best response)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of White/Lee/Labov so as to have included wherein the updating includes resetting a current exercise time to an earlier time, as taught by Genovese in order to “…[provide] the option of replaying a portion of the scenario to allow the user to make another attempt at how best to respond to the facts presented…” (Genovese, page 48, lines 1-11).
Response to Arguments
Applicant’s arguments filed 4/21/2026 have been fully considered but they are not persuasive.
I. Rejection of Claims under 35 U.S.C. §101:
Applicant asserts that the rejection is insufficient because it does not point out the limitations that represent the abstract ideas or explanation of why those limitations fails. The above rejection does clearly identify the elements (underlined) and the reasons why (these steps merely provide real-world simulations and exercises (including simulated event related data) for incidence response training). These steps would not necessarily require computer and Applicant has not provided evidence that these activities could not be performed without a computer. Applicant’s remarks mainly refer to the detector and how its functions are performed/used. The physical detector (and any functions performed in the detector) are treated under subsequent steps as additional elements. Applicant appears to be arguing the use of the device as part of the abstract idea under Step 2A, Prong 1.
As is identified in the specification, claims, and previous remarks, Applicant has repeatedly stated that the detector performs the same with the simulated/training data as it would on “real” data. As previously argued by Examiner, this does not clearly demonstrate how/why the detector would be used in a different manner. The detector performs the same functions, only it is performed on a different data set (simulated data instead of real data). This different data set does not alter the functioning of the device or how it processes or outputs data. This does not provide a transformation, provide an improvement, and/or anchor the method/system to a specific device. Simply providing different data set (especially when that data is the same type of data as the “real” data) or using the device for a different (but similar) purpose does inherently not alter the functioning of the device. Additionally, the data provided to the detector is merely simulated data and no other detail regarding this data is provided. The simulated data is merely fake or “made up” data that represents and is used in a similar manner to real data. Simply providing some simulated data to the detector does not provide a practical application because it does not change the operational behavior of the detector.
In regards to Desjardins, Applicant has not demonstrated how/why it is relevant. Applicant recites claim features (already discussed above) and asserts that they are similar. However, Applicant provides no remarks or evidence to demonstrate how/why the findings of Desjardins would be comparable to the features of Applicant’s claims. Additionally, as stated above, Applicant has not demonstrated a practical application or technological improvement.
Applicant’s descriptions of concrete detector-signal generation pipeline, constrained signal-generation-and-injection mechanism, etc. to describe the use of the detector does not clearly indicate a practical application or improvement, because the alleged “pipeline” consists of merely sending data from a source to a destination (detector). Even if data returned by the detector is included, this “pipeline” and “signal-generation-and-injection mechanism” merely consist of two devices transmitting data between them. There is no indication that the manner of transmitting data is anything more than generic data transmission techniques.
See MPEP 2106.05(a), Improvements to the Functioning of a Computer or To Any Other Technology or Technical Field (“If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.”).
Applicant asserts that the office action does not provide factual evidence, in addition to the evidence provided in the above rejection and remarks, many of these issues (including the training mode of the detector not being a change to the functioning of the detector) have been addressed in multiple prior office actions (remarks sections) and interview summaries. Additionally, the above rejections do not rely on a well-understood, routine, or conventual analysis at this time.
Related remarks from the previous office action are provided here for reference:
Applicant’s previous arguments are directed to the detectors having a training mode that can be used. Merely using a mode that is already part of the detector is not improving the detector. This is not circular logic as the detector is using programming that it already includes. For example, the training mode is not created out of nothing by performing the claimed invention. Additionally, Applicant’s specification describes the training mode merely as “Each detector may have a training mode in which the detector signals are received from the incident exercise system rather than the detection hardware of the detector.” [0016], indicating that detectors with training modes have them included prior to performing the steps in the claims. The disclosure does not provide detail regarding how these training modes would constitute an improvement to the device.
In regards to “existence of a training mode was somehow inherent in all detectors”, this is a different argument. Examiner has not stated that all detectors inherently include a training mode. Applicant’s specification states that detectors may have a training mode (see above) and does not demonstrate that prior detectors could not or did not have a training mode. This does not provide sufficient evidence that the inclusion of a training mode in a detector is an improvement over prior systems and/or the device itself.
Applicant does not explain how/why the particular data pipeline that training mode displays represents a practical application.
Applicant does not provide sufficient evidence to demonstrate that the claimed invention provides a practical application, improvement to the technology, and/or improvement to the field of art.
Related remarks from the previous office action are provided here for reference:
Applicant argues that the operation of the detector changed based on interventions including the claim features. However, the detector is described as reporting something it did not detect because it is provided spoofed/faked/manufactured data that it did not detect itself. It is not clear how this would change the operation of the detector. The detector is programmed to receive and display the spoofed/faked/manufactured data (training mode), so it is being used in the manner for which it was designed. The processing of the claims does not change the operation of the detector from detecting to receiving spoofed/faked/manufactured data. The operation performed by the detector in the context of the claims is the operation for which it was set-up and intended (no alteration is made to its operation during its use in the claimed invention).
Additionally, the sections of the specification cited prior to this argument. Applicant describes physical sensors, but this does not indicate that the sensors are not generic or general-use technology. Even specifying a type of sensor (Geiger counter, chemical detectors, and/or radiation detectors) or specifying by brand, model number, and so on does not demonstrate any special-purpose or change/alteration since any of these are still recited at a high level of generality and could still be general-purpose devices performing their expected function.
Specifying that the detectors/sensors have a “training mode” for receiving spoofed/faked/manufactured data further demonstrates that these features are pre-programmed in the sensors/devices and not the result of a change or alteration during use.
Applicant’s explanation regarding the objects being introduced into the theater and optical observations using the machines learning model includes additional descriptions not in the claims and fails to explain how these processes (machine learning, optical recognition, etc.) are performed in a manner that would not use generic, general-purpose components. The additional language and descriptions do demonstrate evidence to this affect.
The cited specification material (including [0011]-[0024]) merely provide descriptions of the claimed invention and what activities it performs; however, it fails to provide sufficient evidence to demonstrate how/why these activities or additional elements would perform in a manner that is significantly more than the abstract ideas.
See MPEP 2106.05(a), Improvements to the Functioning of a Computer or To Any Other Technology or Technical Field (“If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.”).
As explained above, the operation of the detector is not modified, therefore it is not clear what the practical application would be.
II. Rejection of Claims under 35 U.S.C. §103:
Applicant’s remarks regain labels is mischaracterized. Th labels refer to specifying a type of object, not merely identifying an object. For example, identifying a vehicle or abuilding would be identifying a type of object. However, this is different than identifying a specific type of object. The claims identify an object and incorporate it into the simulation, however the claims do not provide any material indicating how any specific types of objects or materials would affect the claimed invention. The identifying of an object that effects the incident would not be a label, however specifying a type of object (such as claim 7) would not affect the claimed invention. Whether the object is building, walking surface a container containing a non-hazardous content, a person with a medical implant, a subsurface naturally occurring radioactive source; or any other type of object does not affect how that data is used or how the system functions, as claimed. These represent labels for the identified objects.
In response to Applicant’s remarks about an articulated rationale and allegation that combining Lee to White would take more than a substitution, Lee is merely used to identify objects from images using machine learning. White has the ability to take images and to identify objects, but does not explicitly use Machine learning to do so. Applicant has not made clear how/why this would not be combinable. It’s merely the addition of a machine learning to identify the objects in the collected images. This would not significantly later or destroy White, nor would it take nay skill not provided in the references. Applicant has not provided evidence to support the allegations that ne of skill in the art would not be motivated to (or able to) combine the references. There is no evidence that the engineering would be non-trivial and/or why the inference of objects form an image would not be suitable for a hazard-signal modelling, signal generating, filtering, etc. (or how some of these are relevant), since White includes images and object identification. As stated above, Lee merely applies machine leaning to the step of identifying objects from images.
Applicant’s remaining remarks are drawn to the newly provided claim material and are therefore moot in view of the newly provided prior art rejections, citations, and/or explanations, provided above.
Additional Relevant Prior Art not Relied Upon
Afrouzi et al. (Patent No. US 11,274,929 B1). Discloses the use of machine learning for real-time condition analysis and updating paths and environmental factors for traversing an environment (see at least column 35, lines 12-44; column 92, line 43-67; column 95, line56-column 96, line 24; column 11, line 30-column 115, line 37; ).
Ammirato et al. (WO 2021086796 A1). Discloses the use of machine learning to identify changes in condition and update map and route information (see at least [0259]-[0256]).
Brown et al. (Pub. No. US 2020/0143481 A1). Disclose a method system for identifying objects in a virtual environment and conditions of those objects, including with image analysis and machine learning (see at least [0226]).
Delamont (Pub. No. US 2020/0368616 A1). Discloses “Here matrix operations may be performed where as opposed to the output being used to transform a 3D rendered holographic virtual image, here the matrix operations are performed to track changes in the position, shape and movement in real-time of real-world objects in the relative three-dimensional space of the game as a means to formulate collision detections in which a change in the real-world object model coordinates or world coordinates could invoke a separate transformation on a virtual game object or could invoke the games application 36 to change the game scene to introduce for example new virtual game objects.” (see at least [0210]; [0290]; [0766]; [1006]; [1009]; [1063]; [2223]-[2246]).
Dunlop et al. (Pub. No. US 2014/0167953 A1). Disclose hazardous release characteristics including emission rate (dispersal rate) and shielding effects (see at least [0048]; [0050]; [0069]; [0107]).
Geisner et al. (Pub. No. US 2013/0083011 A1). Disclose a method system for identifying objects in a virtual environment and updating the environment for changes in objects, including with image analysis (see at least [0020]; [0037]; [0047]; [0069]; [0083]); [0097]-[0099]).
Ishii et al. (Pub. No. US 2019/0220088 A1). Disclose analysis of image data to update simulation displays in real-time (see at least [0130]).
Clark (Patent No. US 11,631,339 B1). Disclose a host trainer able to enter data based on observations and alter a simulation and trainees can make decisions based on observations and affect the simulations based on actions (including use of vehicles) (see at least Abstract; column 2, paragraphs 3 & 5; column 3, paragraph 3; column 7, paragraph 1; column 16, paragraph 2). See also, related publication Clark (Pub. No. US 2021/0174695 A1).
Uppal et al. (Pub. No. US 2021/0134072 A1). Disclose generating of simulated versions of identified real-world objects (see at least [0079]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAUN D SENSENIG whose telephone number is (571)270-5393. The examiner can normally be reached M-F: 10:00am-4:00pm.
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/S.D.S/June 13, 2026
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629
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