CTFR 18/984,277 CTFR 95535 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Status of the Claims This Office Action is in response to the amendments and/or arguments filed on May 19, 2024. Claims 1-20 are presently pending and are presented for examination. Response to Arguments Applicant's arguments, see Pages 6-11, filed May 19, 2026, in regards to 101 rejections have been fully considered but they are not persuasive. In regards to the arguments that there is no abstract idea taught within the claim, the examiner respectfully disagrees. For the limitation “determine a spatial position of the vehicle based on a signal of a GPS unit of the vehicle”, it is noted that a human can mentally make a judgement or evaluation of the position of the vehicle based data from a GPS unit of the vehicle. For the limitation “determine a road condition corresponding to the spatial position of the vehicle” it is noted that a human can mentally make a judgement or evaluation of a road condition corresponding to the position of the vehicle, such as the position of the vehicle compared to other objects. In regards to the limitation “compare the spatial position of the vehicle against the road condition to determine a risk probability corresponding to the spatial position of the vehicle, wherein the risk probability is determined based on a calculated likelihood of collision of the vehicle”, it is noted that a human can mentally make a judgement or evaluation or using pencil and paper to compare the position of the vehicle with a road condition, such as the location of another object, in order to calculate a likelihood of a collision. Therefore each of these limitations recite a mental process, therefore there is an abstract idea within the claim. In regards to the arguments concerning Step 2A Prong 2, the examiner respectfully disagrees as the claim does not integrate the abstract idea into a practical application. It is noted that “one or more vehicle input devices operable to capture environmental data and vehicle steering data”, “adjust a data collection parameter of at least one of the one or more vehicle input devices according to the risk probability”, and “automatically collect data with the one or more vehicle input devices according to the adjusted data collection parameter” merely involve the a generic computing component that is capable of collecting sensor data, and of adjusting the collection of the sensor data according to the abstract idea. Therefore these limitations do not amount to an inventive concept since it is insignificant extra-solution activity as it is merely a form of data collection and outputting (MPEP § 2106.05(g)). It is noted that the generic computing components of “one or more vehicle input devices…” and “one or more processors” are merely generic computing components used to apply the abstract idea. The examiner submits that these limitations are mere data collection and outputting components to apply the above-noted abstract idea within an indicated field of use (MPEP §2106.05). In regards to the arguments concerning Step 2B, the examiner respectfully disagrees as the claim does not recite additional components that 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 elements of “one or more vehicle input devices operable to capture environmental data and vehicle steering data”, “adjust a data collection parameter of at least one of the one or more vehicle input devices according to the risk probability”, and “automatically collect data with the one or more vehicle input devices according to the adjusted data collection parameter” amounts to extra-solution data gathering and outputting. Additionally, the specification demonstrates the well-understood, routine, conventional nature of additional elements as it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. §112(a). Additionally, “one or more vehicle input devices…” and “one or more processors” are each generic computing components that merely apply the judicial exception (See 2106.05(f)). Therefore the claim is rejected under 101, and a detailed rejection follows below. 07-38-02 AIA Applicant’s arguments, see Pages 11-12 , filed May 19, 2026 , with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 102 and/or 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Wendt et al. (US 20240185717; hereinafter Wendt; already of record from IDS) . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. 101 Analysis - Step 1 Claims 1-11 recite a system/apparatus, therefore claims 1-11 are within at least one of the four statutory categories. Claims 12-20 recite a method/process, therefore claims 12-20 are within at least one of the four statutory categories. 101 Analysis - Step 2A, Prong 1 Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. Independent claim 1 includes limitations that recites mathematical concepts and/or mental processes (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites: A system for collecting vehicle event information comprising: one or more vehicle input devices operable to capture environmental data and vehicle steering data; one or more processors operable to: determine a spatial position of the vehicle based on a signal of a GPS unit of the vehicle; determine a road condition corresponding to the spatial position of the vehicle; compare the spatial position of the vehicle against the road condition to determine a risk probability corresponding to the spatial position of the vehicle wherein the risk probability is determined based on a calculated likelihood of collision of the vehicle; adjust a data collection parameter of at least one of the one or more vehicle input devices according to the risk probability determined based on the calculated likelihood of collision of the vehicle; and automatically collect data with the one or more vehicle input devices according to the adjusted data collection parameter. These limitations, as drafted, is a system that, under its broadest reasonable interpretation, covers performance of the limitation as a mental process. That is, nothing in the claim elements preclude the steps from practically being performed as mental process. For example, " determine a spatial position of the vehicle …", " determine a road condition...", “compare the spatial position of the vehicle against the road condition…”, encompass mental processes as a human can perform these limitations using observations, evaluations, judgments, and/or opinions. " determine a spatial position of the vehicle …" and " determine a road condition..."involves a human observing and/or evaluating where a vehicle is located and a road condition based on sensor data and “compare the spatial position of the vehicle against the road condition…”, involves a human making a judgment or using paper and pencil to compare the position with a road condition to determine a risk probability. Thus, the claim recites at least a mental process. 101 Analysis - Step 2A, Prong 2 Regarding Prong 2 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application." In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the "additional limitations" while the bolded portions continue to represent the "abstract idea"): A system for collecting vehicle event information comprising: one or more vehicle input devices operable to capture environmental data and vehicle steering data; one or more processors operable to: determine a spatial position of the vehicle based on a signal of a GPS unit of the vehicle; determine a road condition corresponding to the spatial position of the vehicle; compare the spatial position of the vehicle against the road condition to determine a risk probability corresponding to the spatial position of the vehicle wherein the risk probability is determined based on a calculated likelihood of collision of the vehicle; adjust a data collection parameter of at least one of the one or more vehicle input devices according to the risk probability determined based on the calculated likelihood of collision of the vehicle; and automatically collect data with the one or more vehicle input devices according to the adjusted data collection parameter . For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application. Regarding the additional limitation of " A system for collecting vehicle event information” the examiner submits that this limitation characterizes the system as being associated with collecting vehicle event information, which merely amounts to indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate the judicial exception into a practical application or amount to significantly more than the exception itself (see MPEP 2106.05(h)). It is noted that the generic computing components of “one or more vehicle input devices…” and “one or more processors” are merely generic computing components used to apply the abstract idea. Additionally, the claim limitation “one or more vehicle input devices operable to …”, “adjust a data collection parameter…”, and “automatically collect data…” does not amount to an inventive concept since it is insignificant extra-solution activity as it is merely a form of data collection and outputting (MPEP § 2106.05(g)). The examiner submits that these limitations are mere data collection and outputting components to apply the above-noted abstract idea within an indicated field of use (MPEP §2106.05). Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular process for safety performance evaluation, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use 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 not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. 101 Analysis - Step 2B Regarding Step 2B in the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “one or more vehicle input devices operable to …”, “adjust a data collection parameter…”, and “automatically collect data…” amounts to extra-solution data gathering and outputting. Additionally, the specification demonstrates the well-understood, routine, conventional nature of additional elements as it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. §112(a). Additionally, “one or more vehicle input devices…” and “one or more processors” are each generic computing components that merely apply the judicial exception (See 2106.05(f)). Additionally, " A system for collecting vehicle event information” is merely a technological environment or field of use as the limitations merely link the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). Claims 12 recites analogous limitations to that of claim 1, and is therefore rejected by the same premise. Claims 5-6 , recites a camera and a LIDAR sensor that is mounted to a vehicle, however this is merely amounts to indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate the judicial exception into a practical application or amount to significantly more than the exception itself (see MPEP 2106.05(h)). Dependent claims 2-4, 7-11, and 13-20 specify limitations that elaborate on the abstract idea of claims 1 and 12, and thus are directed to an abstract idea nor do the claims recite additional limitations that integrate the claims into a practical application or amount to "significantly more" for similar reasons. Claim Rejections - 35 USC § 103 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 07-21-aia AIA Claim (s) 1-4, 7-9, and 11-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Marsolek et al. (US 20250052038; hereinafter Marsolek) in view of Wendt et al. (US 20240185717; hereinafter Wendt; already of record from IDS) . In regards to claim 1, Marsolek discloses of a system for collecting vehicle event information (“ A controller may monitor sensor data collected by one or more sensors of a machine while the machine is operating at a worksite. The controller may determine whether the sensor data is indicative of the machine operating in an unsafe condition at a location of the machine at the worksite . The controller may cause, based on a determination that the sensor data is indicative of the machine operating in the unsafe condition, at least one of: storing of the sensor data relating to the machine operating in the unsafe condition at least until the sensor data relating to the machine operating in the unsafe condition is offboarded from the machine, or transmission of the sensor data relating to the machine operating in the unsafe condition offboard the machine.” (Abstract)) comprising: one or more vehicle input devices operable to capture environmental data and vehicle steering data (“A sensor 122 may include a tilt sensor, an accelerometer, a gyroscope, an inertial measurement unit (IMU), a ground-penetrating radar (GPR) sensor, a sonar sensor, an ultrasound sensor, a lidar sensor, a camera, a moisture sensor, a temperature sensor, a torque sensor, and/or a battery level sensor (e.g., a voltage sensor), among other examples. A perception sensor 124 may include a camera, a lidar sensor, a radar sensor, and/or an ultrasound sensor, among other examples. The sensor(s) 122 and the perception sensor(s) 124 may be attached directly or indirectly to the frame. The sensor(s) 122 and the perception sensor(s) 124 may be communicatively coupled with the controller 114 to exchange data with the controller 114.” (Para 0021), see also 0022) ; one or more processors (“ The controller 114 may include one or more memories 116 and one or more processors 118 communicatively coupled to the one or more memories 116. A processor 118 may include a central processing unit, a graphics processing unit, a microprocessor, a controller, a microcontroller, a digital signal processor, a field-programmable gate array, an application-specific integrated circuit, and/or another type of processing component. The processor 118 may be implemented in hardware, firmware, or a combination of hardware and software. The processor 118 may be capable of being programmed to perform one or more operations or processes described elsewhere herein. A memory 116 may include volatile and/or nonvolatile memory. For example, the memory 116 may include random access memory (RAM), read only memory (ROM), a hard disk drive, and/or another type of memory (e.g., a flash memory, a magnetic memory, and/or an optical memory). The memory 116 may be a non-transitory computer-readable medium. The memory 116 may store information, one or more instructions, and/or software (e.g., one or more software applications) related to the operation of the controller 114. The controller 114 may also include a communication component 120 that enables the controller 114 to communicate with other devices (e.g., a device of the additional machine 100a and/or the supervisory system 210) via a wired connection and/or a wireless connection. For example, the communication component 120 may include a receiver, a transmitter, a transceiver, a modem, a network interface card, and/or an antenna. The controller 114 may be configured to perform one or more operations described herein .” (Para 0020)) operable to: determine a spatial position of the vehicle based on a signal of a GPS unit of the vehicle (“ The controller 114 may also obtain location data associated with a location of the machine 100 at the worksite. For example, the controller 114 may obtain the location data from a global navigation satellite system (GNSS) receiver (e.g., a global positioning system (GPS) receiver) of the machine 100. By obtaining the location data, the controller 114 can identify associations between locations of the worksite and the sensor data .” (Para 0023)) ; determine a road condition corresponding to the spatial position of the vehicle (“ The controller 114 may determine, based on monitoring the sensor data, whether the sensor data is indicative of the machine 100 operating in an unsafe condition at a location (e.g., a current location) of the machine 100. The unsafe condition may relate to a ground surface at the location . For example, the unsafe condition may relate to an interaction between the machine 100 and the ground surface (e.g., indicating how the machine 100 is responding to the ground surface). The unsafe condition may be due to uneven terrain, soft underfooting, excessive grade (e.g., the grade is too steep), muddy conditions, sandy conditions, or the like. For example, in the presence of unsafe conditions, the sensor data may indicate an excessive tilt of the machine 100, a wheel or track slippage of the machine 100, an overheating of the machine 100, and/or an excessive battery drain rate of the machine 100. Additionally, or alternatively, the sensor data may indicate a high moisture level of the ground surface, a low density of the ground surface, and/or a porousness of the ground surface. In some examples, the controller 114 may receive a manual input from an operator of the machine 100 indicating the unsafe condition at the location of the machine 100 (e.g., the controller 114 may determine that the machine 100 is operating in the unsafe condition at the location based on the manual input).” (Para 0024), “In some examples, the controller 114 may store information indicating (e.g., by geographic coordinates, or the like) one or more unsafe locations of the worksite (e.g., avoidance zones, as described below). The controller 114 may monitor the location of the machine 100 to detect when the machine 100 has entered one of the unsafe locations . Based on detecting that the machine 100 has entered one of the unsafe locations, the controller 114 may store and/or transmit the sensor data relating to the machine 100 operating in the unsafe location, as described herein.” (Para 0030)) ; compare the spatial position of the vehicle against the road condition to determine a risk probability corresponding to the spatial position of the vehicle (“ The controller 114 may determine that the sensor data is indicative of the machine 100 operating in the unsafe condition based on the sensor data satisfying one or more criteria. For example, for one or more measurements of the sensor data, the controller 114 may determine whether a measurement satisfies a threshold (e.g., is greater than, equal to, or less than the threshold, depending on a type of the measurement ). As an example, the controller 114 may determine whether a tilt angle of the machine 100, indicated in the sensor data, is greater than a threshold (e.g., thereby indicating that the machine 100 is on an excessively steep slope, is in excessively soft underfooting, or the like). A value of a threshold may be different for different types or models of machines. Moreover, a value of a threshold may be based on a current environmental condition at the worksite. For example, a first threshold for a measurement may be used in rainy conditions, or a second threshold for the measurement may be used in non-rainy conditions. The controller 114 may determine the current environmental condition at the worksite using one or more environmental sensors (e.g., a moisture sensor, a temperature sensor, a humidity sensor, or the like).” (Para 0025) and “As further shown in FIG. 3, process 300 may include determining whether the sensor data is indicative of the machine operating in an unsafe condition at a location of the machine at the worksite (block 320). For example, the controller may determine whether the sensor data is indicative of the machine operating in an unsafe condition at a location of the machine at the worksite, as described above. Determining whether the sensor data is indicative of the machine operating in the unsafe condition may include determining that the sensor data is indicative of the machine operating in the unsafe condition based on a measurement of the sensor data satisfying a threshold .” (Para 0049), see also Para 0030 and 0024) … and adjust a data collection parameter of at least one of the one or more vehicle input devices according to the risk probability (“In some examples, the controller 114 may store information indicating (e.g., by geographic coordinates, or the like) one or more unsafe locations of the worksite (e.g., avoidance zones, as described below). The controller 114 may monitor the location of the machine 100 to detect when the machine 100 has entered one of the unsafe locations. Based on detecting that the machine 100 has entered one of the unsafe locations, the controller 114 may store and/or transmit the sensor data relating to the machine 100 operating in the unsafe location, as described herein .” (Para 0030), “ The sensor data may be monitored at a first resolution or sampling rate, and process 300 may include obtaining, based on the determination that the sensor data is indicative of the machine operating in the unsafe condition, the sensor data at a second resolution or sampling rate. Additionally, or alternatively, the sensor data that is monitored may be collected by at least one non-perception sensor of the one or more sensors, and process 300 may include obtaining, based on the sensor data being indicative of the machine operating in the unsafe condition, perception sensor data collected by one or more perception sensors of the machine . Process 300 may include receiving, from a remote supervisory system, information indicating operation instructions for controlling the machine at the location. Process 300 may include receiving, from an additional machine, perception sensor data collected by the additional machine and relating to the machine, and causing presentation of information relating to the perception sensor data on a display of the machine. (Para 0051)) … automatically collect data with the one or more vehicle input devices according to the adjusted data collection parameter (“In some examples, the controller 114 may store information indicating (e.g., by geographic coordinates, or the like) one or more unsafe locations of the worksite (e.g., avoidance zones, as described below). The controller 114 may monitor the location of the machine 100 to detect when the machine 100 has entered one of the unsafe locations. Based on detecting that the machine 100 has entered one of the unsafe locations, the controller 114 may store and/or transmit the sensor data relating to the machine 100 operating in the unsafe location, as described herein .” (Para 0030), “ The sensor data may be monitored at a first resolution or sampling rate, and process 300 may include obtaining, based on the determination that the sensor data is indicative of the machine operating in the unsafe condition, the sensor data at a second resolution or sampling rate. Additionally, or alternatively, the sensor data that is monitored may be collected by at least one non-perception sensor of the one or more sensors, and process 300 may include obtaining, based on the sensor data being indicative of the machine operating in the unsafe condition, perception sensor data collected by one or more perception sensors of the machine . Process 300 may include receiving, from a remote supervisory system, information indicating operation instructions for controlling the machine at the location. Process 300 may include receiving, from an additional machine, perception sensor data collected by the additional machine and relating to the machine, and causing presentation of information relating to the perception sensor data on a display of the machine. (Para 0051)) . However, Marsolek does not specifically disclose of compare the spatial position of the vehicle against the road condition to determine a risk probability corresponding to the spatial position of the vehicle, wherein the risk probability is determined based on a calculated likelihood of collision of the vehicle ; adjust a data collection parameter of at least one of the one or more vehicle input devices according to the risk probability determined based on the calculated likelihood of collision of the vehicle . Wendt, in the same field of endeavor, teaches of compare the spatial position of the vehicle against the road condition to determine a risk probability corresponding to the spatial position of the vehicle, wherein the risk probability is determined based on a calculated likelihood of collision of the vehicle (“Disclosed safety systems, devices, and methods are data-driven. In several embodiments, disclosed safety systems, devices, and methods collect, receive, cleanse, aggregate, interpret, predict, and otherwise manipulate safety-related data from numerous data sources. Safety-related data may include data that relates to safety risks and/or real-time circumstances, conditions, and/or situations, including those that may pose a threat to a user's safety. The safety-related data may include, for example, data related to the type, location, motion, and/or route of other users, traffic, collision risk, road/surface conditions and obstacles, weather, crime, and the like. The safety-related data may be leveraged to create a safe zone around a user, enabling a user to have a safe and seamless travel experience (e.g., a safe bike ride or walk).” (Para 0069), “As shown in FIG. 6A, the application may receive user destination input 164, and, based on safety-related data received (e.g., from the one or more servers 108), provide a suggested initial route 168 to the destination. The one or more servers 108, or remote processing unit, may determine a safe route based on the location of the user device 160a, the destination input 164, and safety-related data (e.g., collision-related data, traffic-related data, entity data, and the like), and transmit the safe route to the safety application on the user device 160a , as discussed in more detail below with respect to method 250 of FIG. 8.” (Para 0167), “ The safety device may receive, determine, analyze, store, and/or transmit safety-related data, including, for example, object data (e.g., data related to the identity and relative position or movement of one or more objects, such as, for example, entities, animals, traffic lights, traffic signs, etc.) and collision data (e.g., collision probabilities or likelihood). Object data may include entity data, e.g., data related to an entity's location or position, motion, orientation, and the like, including, for example, data related to geographic coordinates, speed, heading, direction, proximity to others, acceleration, deceleration, and the like. Entity data may also include data related to entity type or identity (e.g., micromobility vehicle, other light mobility vehicle, car, truck, bus, pedestrian, etc.). As used herein, an entity may refer to a micromobility vehicle, a light mobility vehicle (e.g., motorcycle), an automotive vehicle, or user device (e.g., carried by a pedestrian). As used herein, automotive vehicles refer to vehicles other than micromobility vehicles and light mobility vehicles. The safety-related data may be used and/or stored by safety systems or methods described herein.” (Para 0073), “In several embodiments, the system includes a safety device coupled to a micromobility vehicle or other light mobility vehicle, the safety device including a local processing element configured to determine a proximity of, distance of, path of/trajectory, and/or collision probability with one or more other entities (e.g., an automotive vehicle, other light mobility vehicle, and/or other user device) within a short-distance range. In these embodiments, the system includes a server or remote processing element in communication, via a network, with the micromobility vehicle or other light mobility vehicle (e.g., via the safety device) and the one or more other entities (e.g., via an automotive vehicle connectivity device and/or other safety device), and configured to determine a proximity, distance, or path/trajectory of the one or more other entities relative to the micromobility vehicle or other light mobility vehicle and/or collision probability between the entities within a long-distance range .” (Para 0085)) ; adjust a data collection parameter of at least one of the one or more vehicle input devices according to the risk probability determined based on the calculated likelihood of collision of the vehicle (“As an example, safety-related data may include real-time collision data. The real-time collision data may be indicative of an actual or near collision and its associated location. As another example, safety-related data may include high collision risk areas determined based on real-time collision data received over time. The real-time collision data may include data on a high probability collision and its associated location. The server 108 may collect real-time collision data from various entities, aggregate the real-time collision data to determine high risk collision areas (e.g., based on numerous high probability collisions in the same or proximate location), and store the real-time collision data collected and high-risk collision areas determined in the one or more databases 112 as collision-related data .” (Para 0207), (“Disclosed safety systems, devices, and methods are data-driven. In several embodiments, disclosed safety systems, devices, and methods collect, receive, cleanse, aggregate, interpret, predict, and otherwise manipulate safety-related data from numerous data sources. Safety-related data may include data that relates to safety risks and/or real-time circumstances, conditions, and/or situations, including those that may pose a threat to a user's safety. The safety-related data may include, for example, data related to the type, location, motion, and/or route of other users, traffic, collision risk, road/surface conditions and obstacles, weather, crime, and the like. The safety-related data may be leveraged to create a safe zone around a user, enabling a user to have a safe and seamless travel experience (e.g., a safe bike ride or walk).” (Para 0069), “As shown in FIG. 6A, the application may receive user destination input 164, and, based on safety-related data received (e.g., from the one or more servers 108), provide a suggested initial route 168 to the destination. The one or more servers 108, or remote processing unit, may determine a safe route based on the location of the user device 160a, the destination input 164, and safety-related data (e.g., collision-related data, traffic-related data, entity data, and the like), and transmit the safe route to the safety application on the user device 160a , as discussed in more detail below with respect to method 250 of FIG. 8.” (Para 0167); wherein this can be combined with the disclosure of Marsolek, where Wendt teaches of the unsafe condition being a high probability of a collision). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the risk probability, as taught by Marsolek, to include being a calculated likelihood of a collision of the vehicle, as taught by Wendt, with a reasonable expectation of success in order to avoid safety risks that may pose a threat to a user’s safety and prevent collisions (Wendt Para 0067 and 0069). In regards to claim 2, Marsolek in view of Wendt teaches of the system for collecting vehicle event information of claim 1, further comprising one or more communication devices operable to transmit and receive data corresponding to the road condition (“ The controller 114 may also include a communication component 120 that enables the controller 114 to communicate with other devices (e.g., a device of the additional machine 100a and/or the supervisory system 210) via a wired connection and/or a wireless connection. For example, the communication component 120 may include a receiver, a transmitter, a transceiver, a modem, a network interface card, and/or an antenna . The controller 114 may be configured to perform one or more operations described herein.” (Marsolek Para 0020), “In some examples, the controller 114 may transmit the sensor data relating to the machine 100 operating in the unsafe condition to the supervisory system 210 and/or to the additional machine 100a accompanied by a support request. The support request may be configured to initiate a communication session between the machine 100 and at least one of the supervisory system 210 or the additional machine 100a (e.g., based on an acceptance of the support request by the supervisory system 210 and/or the additional machine 100a). The communication session may include a voice call and/or a video call. In this way, an operator of the machine 100 may receive guidance for handling the unsafe condition from a supervisor associated with the supervisory system 210 and/or an operator associated with the additional machine 100a (e.g., guidance based on the sensor data relating to the machine 100 operating in the unsafe condition).” (Marsolek Para 0036), “In some implementations, the controller 114 may output a notification alerting an operator of the machine 100 of the unsafe condition. For example, the controller 114 may cause presentation of the notification on a display of the machine 100. As another example, the controller 114 may transmit the notification to a user device associated with the operator. Moreover, the controller 114 may transmit, to the additional machine 100a, a notification alerting an operator of the additional machine 100a of the unsafe condition. The controller 114 may output or transmit one or more of the notifications responsive to a determination that the sensor data is indicative of the machine 100 operating in the unsafe condition, and/or responsive to receiving a notification indicating that the unsafe condition is present at the location of the machine 100 (e.g., from the supervisory system 210 ).” (Marsolek Para 0037)) . In regards to claim 3, Marsolek in view of Wendt teaches of the system for collecting vehicle event information of claim 2, wherein the one or more communication devices communicate with an external server to receive data corresponding to the road condition (“In some implementations, the controller 114 may output a notification alerting an operator of the machine 100 of the unsafe condition. For example, the controller 114 may cause presentation of the notification on a display of the machine 100. As another example, the controller 114 may transmit the notification to a user device associated with the operator. Moreover, the controller 114 may transmit, to the additional machine 100a, a notification alerting an operator of the additional machine 100a of the unsafe condition. The controller 114 may output or transmit one or more of the notifications responsive to a determination that the sensor data is indicative of the machine 100 operating in the unsafe condition, and/or responsive to receiving a notification indicating that the unsafe condition is present at the location of the machine 100 (e.g., from the supervisory system 210 ).” (Marsolek Para 0037), “FIG. 3 is a flowchart of an example process 300 associated with worksite condition assessment using sensors of a work machine. One or more process blocks of FIG. 3 may be performed by a controller (e.g., controller 114). Additionally, or alternatively, one or more process blocks of FIG. 3 may be performed by another device or a group of devices separate from or including the controller, such as another device or component that is internal or external to the machine 100, such as the supervisory system 210 .” (Marsolek Para 0047)) . In regards to claim 4, Marsolek in view of Wendt teaches of the system for collecting vehicle event information of claim 1, wherein the road condition is directly measured by one or more of the vehicle input devices (“ The controller 114 may determine, based on monitoring the sensor data, whether the sensor data is indicative of the machine 100 operating in an unsafe condition at a location (e.g., a current location) of the machine 100. The unsafe condition may relate to a ground surface at the location . For example, the unsafe condition may relate to an interaction between the machine 100 and the ground surface (e.g., indicating how the machine 100 is responding to the ground surface). The unsafe condition may be due to uneven terrain, soft underfooting, excessive grade (e.g., the grade is too steep), muddy conditions, sandy conditions, or the like. For example, in the presence of unsafe conditions, the sensor data may indicate an excessive tilt of the machine 100, a wheel or track slippage of the machine 100, an overheating of the machine 100, and/or an excessive battery drain rate of the machine 100. Additionally, or alternatively, the sensor data may indicate a high moisture level of the ground surface, a low density of the ground surface, and/or a porousness of the ground surface. In some examples, the controller 114 may receive a manual input from an operator of the machine 100 indicating the unsafe condition at the location of the machine 100 (e.g., the controller 114 may determine that the machine 100 is operating in the unsafe condition at the location based on the manual input).” (Marsolek Para 0024), see also Marsolek Para 0021-0022). In regards to claim 7, Marsolek in view of Wendt teaches of the system for collecting vehicle event information of claim 1, wherein adjusting the data collection parameter further comprises selectively engaging a portion of the vehicle input devices when the risk probability is below a first threshold value and selectively engaging all of the vehicle input devices when the risk probability is above a second threshold value (“ The sensor data may be monitored at a first resolution or sampling rate, and process 300 may include obtaining, based on the determination that the sensor data is indicative of the machine operating in the unsafe condition, the sensor data at a second resolution or sampling rate. Additionally, or alternatively, the sensor data that is monitored may be collected by at least one non-perception sensor of the one or more sensors, and process 300 may include obtaining, based on the sensor data being indicative of the machine operating in the unsafe condition, perception sensor data collected by one or more perception sensors of the machine . Process 300 may include receiving, from a remote supervisory system, information indicating operation instructions for controlling the machine at the location. Process 300 may include receiving, from an additional machine, perception sensor data collected by the additional machine and relating to the machine, and causing presentation of information relating to the perception sensor data on a display of the machine. (Marsolek Para 0051), “ The controller 114 may determine that the sensor data is indicative of the machine 100 operating in the unsafe condition based on the sensor data satisfying one or more criteria. For example, for one or more measurements of the sensor data, the controller 114 may determine whether a measurement satisfies a threshold (e.g., is greater than, equal to, or less than the threshold, depending on a type of the measurement ). As an example, the controller 114 may determine whether a tilt angle of the machine 100, indicated in the sensor data, is greater than a threshold (e.g., thereby indicating that the machine 100 is on an excessively steep slope, is in excessively soft underfooting, or the like). A value of a threshold may be different for different types or models of machines. Moreover, a value of a threshold may be based on a current environmental condition at the worksite. For example, a first threshold for a measurement may be used in rainy conditions, or a second threshold for the measurement may be used in non-rainy conditions. The controller 114 may determine the current environmental condition at the worksite using one or more environmental sensors (e.g., a moisture sensor, a temperature sensor, a humidity sensor, or the like).” (Marsolek Para 0025) and “As further shown in FIG. 3, process 300 may include determining whether the sensor data is indicative of the machine operating in an unsafe condition at a location of the machine at the worksite (block 320). For example, the controller may determine whether the sensor data is indicative of the machine operating in an unsafe condition at a location of the machine at the worksite, as described above. Determining whether the sensor data is indicative of the machine operating in the unsafe condition may include determining that the sensor data is indicative of the machine operating in the unsafe condition based on a measurement of the sensor data satisfying a threshold .” (Marsolek Para 0049)) . In regards to claim 8, Marsolek in view of Wendt teaches of the system for collecting vehicle event information of claim 1, wherein adjusting the data collection parameter further comprises selectively increasing a frequency at which at least one of the one or more vehicle input devices captures the environmental data and the vehicle steering data when the risk probability is above a threshold value (“ The sensor data may be monitored at a first resolution or sampling rate, and process 300 may include obtaining, based on the determination that the sensor data is indicative of the machine operating in the unsafe condition, the sensor data at a second resolution or sampling rate. Additionally, or alternatively, the sensor data that is monitored may be collected by at least one non-perception sensor of the one or more sensors, and process 300 may include obtaining, based on the sensor data being indicative of the machine operating in the unsafe condition, perception sensor data collected by one or more perception sensors of the machine . Process 300 may include receiving, from a remote supervisory system, information indicating operation instructions for controlling the machine at the location. Process 300 may include receiving, from an additional machine, perception sensor data collected by the additional machine and relating to the machine, and causing presentation of information relating to the perception sensor data on a display of the machine. (Marsolek Para 0051), “ The controller 114 may determine that the sensor data is indicative of the machine 100 operating in the unsafe condition based on the sensor data satisfying one or more criteria. For example, for one or more measurements of the sensor data, the controller 114 may determine whether a measurement satisfies a threshold (e.g., is greater than, equal to, or less than the threshold, depending on a type of the measurement ). As an example, the controller 114 may determine whether a tilt angle of the machine 100, indicated in the sensor data, is greater than a threshold (e.g., thereby indicating that the machine 100 is on an excessively steep slope, is in excessively soft underfooting, or the like). A value of a threshold may be different for different types or models of machines. Moreover, a value of a threshold may be based on a current environmental condition at the worksite. For example, a first threshold for a measurement may be used in rainy conditions, or a second threshold for the measurement may be used in non-rainy conditions. The controller 114 may determine the current environmental condition at the worksite using one or more environmental sensors (e.g., a moisture sensor, a temperature sensor, a humidity sensor, or the like).” (Marsolek Para 0025) and “As further shown in FIG. 3, process 300 may include determining whether the sensor data is indicative of the machine operating in an unsafe condition at a location of the machine at the worksite (block 320). For example, the controller may determine whether the sensor data is indicative of the machine operating in an unsafe condition at a location of the machine at the worksite, as described above. Determining whether the sensor data is indicative of the machine operating in the unsafe condition may include determining that the sensor data is indicative of the machine operating in the unsafe condition based on a measurement of the sensor data satisfying a threshold .” (Marsolek Para 0049)) . In regards to claim 9, Marsolek in view of Wendt teaches of the system for collecting vehicle event information of claim 1, wherein adjusting the data collection parameter further comprises selectively increasing a resolution at which at least one of the one or more vehicle input devices captures the environmental data and the vehicle steering data when the risk probability is above a threshold value (“ The sensor data may be monitored at a first resolution or sampling rate, and process 300 may include obtaining, based on the determination that the sensor data is indicative of the machine operating in the unsafe condition, the sensor data at a second resolution or sampling rate. Additionally, or alternatively, the sensor data that is monitored may be collected by at least one non-perception sensor of the one or more sensors, and process 300 may include obtaining, based on the sensor data being indicative of the machine operating in the unsafe condition, perception sensor data collected by one or more perception sensors of the machine . Process 300 may include receiving, from a remote supervisory system, information indicating operation instructions for controlling the machine at the location. Process 300 may include receiving, from an additional machine, perception sensor data collected by the additional machine and relating to the machine, and causing presentation of information relating to the perception sensor data on a display of the machine. (Marsolek Para 0051), “ The controller 114 may determine that the sensor data is indicative of the machine 100 operating in the unsafe condition based on the sensor data satisfying one or more criteria. For example, for one or more measurements of the sensor data, the controller 114 may determine whether a measurement satisfies a threshold (e.g., is greater than, equal to, or less than the threshold, depending on a type of the measurement ). As an example, the controller 114 may determine whether a tilt angle of the machine 100, indicated in the sensor data, is greater than a threshold (e.g., thereby indicating that the machine 100 is on an excessively steep slope, is in excessively soft underfooting, or the like). A value of a threshold may be different for different types or models of machines. Moreover, a value of a threshold may be based on a current environmental condition at the worksite. For example, a first threshold for a measurement may be used in rainy conditions, or a second threshold for the measurement may be used in non-rainy conditions. The controller 114 may determine the current environmental condition at the worksite using one or more environmental sensors (e.g., a moisture sensor, a temperature sensor, a humidity sensor, or the like).” (Marsolek Para 0025) and “As further shown in FIG. 3, process 300 may include determining whether the sensor data is indicative of the machine operating in an unsafe condition at a location of the machine at the worksite (block 320). For example, the controller may determine whether the sensor data is indicative of the machine operating in an unsafe condition at a location of the machine at the worksite, as described above. Determining whether the sensor data is indicative of the machine operating in the unsafe condition may include determining that the sensor data is indicative of the machine operating in the unsafe condition based on a measurement of the sensor data satisfying a threshold .” (Marsolek Para 0049)) . In regards to claim 11, Marsolek in view of Wendt teaches of the system for collecting vehicle event information of claim 1, further comprising a memory module to store the environmental data and the vehicle steering data (“ The controller 114 may include one or more memories 116 and one or more processors 118 communicatively coupled to the one or more memories 116 . A processor 118 may include a central processing unit, a graphics processing unit, a microprocessor, a controller, a microcontroller, a digital signal processor, a field-programmable gate array, an application-specific integrated circuit, and/or another type of processing component. The processor 118 may be implemented in hardware, firmware, or a combination of hardware and software. The processor 118 may be capable of being programmed to perform one or more operations or processes described elsewhere herein. A memory 116 may include volatile and/or nonvolatile memory. For example, the memory 116 may include random access memory (RAM), read only memory (ROM), a hard disk drive, and/or another type of memory (e.g., a flash memory, a magnetic memory, and/or an optical memory). The memory 116 may be a non-transitory computer-readable medium. The memory 116 may store information, one or more instructions, and/or software (e.g., one or more software applications) related to the operation of the controller 114. The controller 114 may also include a communication component 120 that enables the controller 114 to communicate with other devices (e.g., a device of the additional machine 100a and/or the supervisory system 210) via a wired connection and/or a wireless connection. For example, the communication component 120 may include a receiver, a transmitter, a transceiver, a modem, a network interface card, and/or an antenna. The controller 114 may be configured to perform one or more operations described herein .” (Marsolek Para 0020), “ Based on a determination that the sensor data is indicative of the machine 100 operating in the unsafe condition at the location (e.g., a determination that the sensor data satisfies the one or more criteria), the controller 114 may cause storing of the sensor data relating to the machine 100 operating in the unsafe condition and/or the controller 114 may cause transmission of the sensor data relating to the machine 100 operating in the unsafe condition. The controller 114 may cause storing (e.g., in nonvolatile memory) of the sensor data relating to the machine 100 operating in the unsafe condition at least until the sensor data relating to the machine 100 operating in the unsafe condition is offboarded from the machine 100 (e.g., transmitted or uploaded to a device external from the machine 100, such as the supervisory system 210). The controller 114 may cause transmission of the sensor data relating to the machine 100 operating in the unsafe condition offboard the machine (e.g., to the supervisory system 210 and/or to the additional machine 100a). The sensor data relating to the machine 100 operating in the unsafe condition may include the sensor data that is collected while the sensor data (e.g., one or more measurements thereof) is indicative of the machine 100 operating in the unsafe condition (or a time duration, such as 1 second or 2 seconds, before and after the sensor data being indicative of the machine 100 operating in the unsafe condition).” (Marsolek Para 0027)). In regards to claim 12, the claim recites analogous limitations to claim 1 and is therefore rejected on the same premise. In regards to claim 13, Marsolek in view of Wendt teaches of the method of claim 12, further comprising adjusting a data retention parameter according to the determined risk probability to selectively store the captured environmental data and the vehicle steering data to a memory module (“ The controller 114 may include one or more memories 116 and one or more processors 118 communicatively coupled to the one or more memories 116 . A processor 118 may include a central processing unit, a graphics processing unit, a microprocessor, a controller, a microcontroller, a digital signal processor, a field-programmable gate array, an application-specific integrated circuit, and/or another type of processing component. The processor 118 may be implemented in hardware, firmware, or a combination of hardware and software. The processor 118 may be capable of being programmed to perform one or more operations or processes described elsewhere herein. A memory 116 may include volatile and/or nonvolatile memory. For example, the memory 116 may include random access memory (RAM), read only memory (ROM), a hard disk drive, and/or another type of memory (e.g., a flash memory, a magnetic memory, and/or an optical memory). The memory 116 may be a non-transitory computer-readable medium. The memory 116 may store information, one or more instructions, and/or software (e.g., one or more software applications) related to the operation of the controller 114. The controller 114 may also include a communication component 120 that enables the controller 114 to communicate with other devices (e.g., a device of the additional machine 100a and/or the supervisory system 210) via a wired connection and/or a wireless connection. For example, the communication component 120 may include a receiver, a transmitter, a transceiver, a modem, a network interface card, and/or an antenna. The controller 114 may be configured to perform one or more operations described herein .” (Marsolek Para 0020), “ Based on a determination that the sensor data is indicative of the machine 100 operating in the unsafe condition at the location (e.g., a determination that the sensor data satisfies the one or more criteria), the controller 114 may cause storing of the sensor data relating to the machine 100 operating in the unsafe condition and/or the controller 114 may cause transmission of the sensor data relating to the machine 100 operating in the unsafe condition. The controller 114 may cause storing (e.g., in nonvolatile memory) of the sensor data relating to the machine 100 operating in the unsafe condition at least until the sensor data relating to the machine 100 operating in the unsafe condition is offboarded from the machine 100 (e.g., transmitted or uploaded to a device external from the machine 100, such as the supervisory system 210). The controller 114 may cause transmission of the sensor data relating to the machine 100 operating in the unsafe condition offboard the machine (e.g., to the supervisory system 210 and/or to the additional machine 100a). The sensor data relating to the machine 100 operating in the unsafe condition may include the sensor data that is collected while the sensor data (e.g., one or more measurements thereof) is indicative of the machine 100 operating in the unsafe condition (or a time duration, such as 1 second or 2 seconds, before and after the sensor data being indicative of the machine 100 operating in the unsafe condition).” (Marsolek Para 0027), see also Marsolek Para 0050). In regards to claim 14, Marsolek discloses of the method of claim 12. However, Marsolek does not specifically disclose of wherein the road condition is a level of traffic or a vehicle accident record corresponding to at least one vehicle event location. Wendt, in the same field of endeavor, teaches of wherein the road condition is a level of traffic or a vehicle accident record corresponding to at least one vehicle event location (“Disclosed safety systems, devices, and methods are data-driven. In several embodiments, disclosed safety systems, devices, and methods collect, receive, cleanse, aggregate, interpret, predict, and otherwise manipulate safety-related data from numerous data sources. Safety-related data may include data that relates to safety risks and/or real-time circumstances, conditions, and/or situations, including those that may pose a threat to a user's safety. The safety-related data may include, for example, data related to the type, location, motion, and/or route of other users, traffic, collision risk, road/surface conditions and obstacles, weather, crime, and the like. The safety-related data may be leveraged to create a safe zone around a user, enabling a user to have a safe and seamless travel experience (e.g., a safe bike ride or walk).” (Para 0069), “As shown in FIG. 6A, the application may receive user destination input 164, and, based on safety-related data received (e.g., from the one or more servers 108), provide a suggested initial route 168 to the destination. The one or more servers 108, or remote processing unit, may determine a safe route based on the location of the user device 160a, the destination input 164, and safety-related data (e.g., collision-related data, traffic-related data, entity data, and the like), and transmit the safe route to the safety application on the user device 160a , as discussed in more detail below with respect to method 250 of FIG. 8.” (Para 0167), “In some embodiments, safety application features may be turned on or off based on user preferences. As shown in FIG. 6L, a settings interface 475 may display one or more selections 477 to select different features to display on the safety application interface 471. In the depicted example, a user can select certain features by touching a selection that, when selected, displays a check mark. In this example, safety application features that can be turned on or off include connected lights (e.g., to alert other users of your presence, as discussed with respect to the safety device), other application users' data and/or location, average speed, lap speed, route suggestions (e.g., suggestions for alternate routes based on hazards, traffic, collisions, etc.), and traffic conditions (e.g., areas of congestion or high likelihood of congestion based on time of day ).” (Para 0183)) . It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the road condition, as taught by Marsolek, to include being a level of traffic or a vehicle accident record corresponding to at least one vehicle event location, as taught by Wendt, with a reasonable expectation of success in order to avoid safety risks that may pose a threat to a user’s safety (Wendt Para 0069). In regards to claims 15-18, the claims recite analogous limitations to claims 4, 7, 8, and 9, respectively, and are therefore rejected on the same premise. In regards to claim 19, Marsolek in view of Wendt teaches of the method of claim 13, wherein adjusting the data retention parameter further comprises selectively storing the data input at a higher resolution when the risk probability is above a threshold risk probability value (“As further shown in FIG. 3, process 300 may include causing, based on a determination that the sensor data is indicative of the machine operating in the unsafe condition, at least one of: storing of the sensor data relating to the machine operating in the unsafe condition at least until the sensor data relating to the machine operating in the unsafe condition is offboarded from the machine, or transmission of the sensor data relating to the machine operating in the unsafe condition offboard the machine (block 330). For example, the controller may cause, based on a determination that the sensor data is indicative of the machine operating in the unsafe condition, at least one of storing or transmission of the sensor data relating to the machine operating in the unsafe condition , as described above.” (Marsolek Para 0050), (“ The sensor data may be monitored at a first resolution or sampling rate, and process 300 may include obtaining, based on the determination that the sensor data is indicative of the machine operating in the unsafe condition, the sensor data at a second resolution or sampling rate. Additionally, or alternatively, the sensor data that is monitored may be collected by at least one non-perception sensor of the one or more sensors, and process 300 may include obtaining, based on the sensor data being indicative of the machine operating in the unsafe condition, perception sensor data collected by one or more perception sensors of the machine . Process 300 may include receiving, from a remote supervisory system, information indicating operation instructions for controlling the machine at the location. Process 300 may include receiving, from an additional machine, perception sensor data collected by the additional machine and relating to the machine, and causing presentation of information relating to the perception sensor data on a display of the machine. (Marsolek Para 0051), “ Based on a determination that the sensor data is indicative of the machine 100 operating in the unsafe condition at the location (e.g., a determination that the sensor data satisfies the one or more criteria), the controller 114 may cause storing of the sensor data relating to the machine 100 operating in the unsafe condition and/or the controller 114 may cause transmission of the sensor data relating to the machine 100 operating in the unsafe condition. The controller 114 may cause storing (e.g., in nonvolatile memory) of the sensor data relating to the machine 100 operating in the unsafe condition at least until the sensor data relating to the machine 100 operating in the unsafe condition is offboarded from the machine 100 (e.g., transmitted or uploaded to a device external from the machine 100, such as the supervisory system 210). The controller 114 may cause transmission of the sensor data relating to the machine 100 operating in the unsafe condition offboard the machine (e.g., to the supervisory system 210 and/or to the additional machine 100a). The sensor data relating to the machine 100 operating in the unsafe condition may include the sensor data that is collected while the sensor data (e.g., one or more measurements thereof) is indicative of the machine 100 operating in the unsafe condition (or a time duration, such as 1 second or 2 seconds, before and after the sensor data being indicative of the machine 100 operating in the unsafe condition).” (Marsolek Para 0027)) . In regards to claim 20, Marsolek in view of Wendt teaches of the method of claim 13, wherein adjusting the data retention parameter further comprises selectively increasing a buffer period to increase an amount of time the data input is stored before being deleted when the risk probability is above a threshold risk probability value (“As further shown in FIG. 3, process 300 may include causing, based on a determination that the sensor data is indicative of the machine operating in the unsafe condition, at least one of: storing of the sensor data relating to the machine operating in the unsafe condition at least until the sensor data relating to the machine operating in the unsafe condition is offboarded from the machine, or transmission of the sensor data relating to the machine operating in the unsafe condition offboard the machine (block 330). For example, the controller may cause, based on a determination that the sensor data is indicative of the machine operating in the unsafe condition, at least one of storing or transmission of the sensor data relating to the machine operating in the unsafe condition , as described above.” (Marsolek Para 0050), “ Based on a determination that the sensor data is indicative of the machine 100 operating in the unsafe condition at the location (e.g., a determination that the sensor data satisfies the one or more criteria), the controller 114 may cause storing of the sensor data relating to the machine 100 operating in the unsafe condition and/or the controller 114 may cause transmission of the sensor data relating to the machine 100 operating in the unsafe condition. The controller 114 may cause storing (e.g., in nonvolatile memory) of the sensor data relating to the machine 100 operating in the unsafe condition at least until the sensor data relating to the machine 100 operating in the unsafe condition is offboarded from the machine 100 (e.g., transmitted or uploaded to a device external from the machine 100, such as the supervisory system 210). The controller 114 may cause transmission of the sensor data relating to the machine 100 operating in the unsafe condition offboard the machine (e.g., to the supervisory system 210 and/or to the additional machine 100a). The sensor data relating to the machine 100 operating in the unsafe condition may include the sensor data that is collected while the sensor data (e.g., one or more measurements thereof) is indicative of the machine 100 operating in the unsafe condition (or a time duration, such as 1 second or 2 seconds, before and after the sensor data being indicative of the machine 100 operating in the unsafe condition).” (Marsolek Para 0027)) . 07-22-aia AIA Claim (s) 5-6 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Marsolek in view of Wendt , as applied to claim 1 above, and further in view of Yu et al. (US 20230360406; hereinafter Yu; already of record) . In regards to claim 5, Marsolek in view of Wendt teaches of the system for collecting vehicle event information of claim 1, wherein at least one of the one or more vehicle input devices comprises a camera positioned on an exterior of the vehicle (“A sensor 122 may include a tilt sensor, an accelerometer, a gyroscope, an inertial measurement unit (IMU), a ground-penetrating radar (GPR) sensor, a sonar sensor, an ultrasound sensor, a lidar sensor, a camera, a moisture sensor, a temperature sensor, a torque sensor, and/or a battery level sensor (e.g., a voltage sensor), among other examples. A perception sensor 124 may include a camera , a lidar sensor, a radar sensor, and/or an ultrasound sensor, among other examples. The sensor(s) 122 and the perception sensor(s) 124 may be attached directly or indirectly to the frame. The sensor(s) 122 and the perception sensor(s) 124 may be communicatively coupled with the controller 114 to exchange data with the controller 114.” (Marsolek Para 0021)) . However, Marsolek in view of Wendt does not specifically teach of a camera positioned on an exterior of the vehicle. Yu, in the same field of endeavor, teaches of a camera positioned on an exterior of the vehicle (“Referring to FIG. 2 and again to FIG. 1, the vehicle 10 is depicted in proximity to a fixed-position infrastructure camera 50. The infrastructure camera is affixed by a camera mount 52 to a camera support structure 54 located at a fixed location (latitude, longitude, and altitude) relative to the earth. The vehicle-mounted optical camera 40c is located to allow capture of image information in an area exterior to the vehicle 10 . It will be appreciated that for some locations and orientations of the vehicle 10, the field of view of the vehicle-mounted optical camera 40c will overlap the field of view of the infrastructure camera 50, i.e., objects in the overlapping region can be captured by both the vehicle-mounted optical camera 40c and by the infrastructure camera 50. FIG. 2 also depicts a data storage pool 64 in two-way communication with the vehicle 10 by means of a vehicle communication link 60. The data storage pool 64 is also in two-way communication with the infrastructure camera 50 by means of a camera communications link 62. Although not shown in FIG. 2, the vehicle 10 may also be in direct communication with the infrastructure camera 50. Communication along the communication links 60, 62 may be achieved by any of several available protocols, including but not limited to cellular (4G/5G), dedicated short-range communications (DSRC), and cellular-vehicle-to-everything (C-V2X).” (Para 0040), see also Fig 2) . It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the camera, as taught by Marsolek in view of Wendt, to include being positioned on the exterior of the vehicle, as taught by Yu, with a reasonable expectation of success in order to allow capture of image information in an area exterior to the vehicle (Yu Para 0040). In regards to claim 6, Marsolek in view of Wendt in view of Yu teaches of the system for collecting vehicle event information of claim 1, wherein at least one of the one or more vehicle input devices comprises a LIDAR sensor positioned on an exterior of the vehicle (“A sensor 122 may include a tilt sensor, an accelerometer, a gyroscope, an inertial measurement unit (IMU), a ground-penetrating radar (GPR) sensor, a sonar sensor, an ultrasound sensor, a lidar sensor, a camera, a moisture sensor, a temperature sensor, a torque sensor, and/or a battery level sensor (e.g., a voltage sensor), among other examples. A perception sensor 124 may include a camera, a lidar sensor , a radar sensor, and/or an ultrasound sensor, among other examples. The sensor(s) 122 and the perception sensor(s) 124 may be attached directly or indirectly to the frame. The sensor(s) 122 and the perception sensor(s) 124 may be communicatively coupled with the controller 114 to exchange data with the controller 114.” (Marsolek Para 0021), (“Referring to FIG. 2 and again to FIG. 1, the vehicle 10 is depicted in proximity to a fixed-position infrastructure camera 50. The infrastructure camera is affixed by a camera mount 52 to a camera support structure 54 located at a fixed location (latitude, longitude, and altitude) relative to the earth. The vehicle-mounted optical camera 40c is located to allow capture of image information in an area exterior to the vehicle 10 . It will be appreciated that for some locations and orientations of the vehicle 10, the field of view of the vehicle-mounted optical camera 40c will overlap the field of view of the infrastructure camera 50, i.e., objects in the overlapping region can be captured by both the vehicle-mounted optical camera 40c and by the infrastructure camera 50. FIG. 2 also depicts a data storage pool 64 in two-way communication with the vehicle 10 by means of a vehicle communication link 60. The data storage pool 64 is also in two-way communication with the infrastructure camera 50 by means of a camera communications link 62. Although not shown in FIG. 2, the vehicle 10 may also be in direct communication with the infrastructure camera 50. Communication along the communication links 60, 62 may be achieved by any of several available protocols, including but not limited to cellular (4G/5G), dedicated short-range communications (DSRC), and cellular-vehicle-to-everything (C-V2X).” (Yu Para 0040), “In another aspect of the present disclosure, the sensor mounted at the fixed location external to the vehicle is a lidar sensor.” (Yu Para 0011), “ While the foregoing description refers to images captured by a vehicle-mounted camera 40c and by an infrastructure camera 50, it is not intended that the teachings of the present disclosure be limited to images captured by an optical camera. Alternative image sensor technologies including but not limited to lidar and/or radar may be used to capture images to be processed in accordance with the present disclosure without departing from the spirit and scope of the disclosure.” (Yu Para 0054)). The motivation of combing Marsolek, Wendt, and Yu is the same as that recited for claim 5 above. In regards to claim 10, Marsolek in view of Wendt in view of Yu teaches of the system for collecting vehicle event information of claim 6, wherein optimizing the data collection parameter further comprises selectively increasing a data collection range of the LIDAR sensor when the risk probability is above a threshold value (“A sensor 122 may include a tilt sensor, an accelerometer, a gyroscope, an inertial measurement unit (IMU), a ground-penetrating radar (GPR) sensor, a sonar sensor, an ultrasound sensor, a lidar sensor, a camera, a moisture sensor, a temperature sensor, a torque sensor, and/or a battery level sensor (e.g., a voltage sensor), among other examples. A perception sensor 124 may include a camera, a lidar sensor , a radar sensor, and/or an ultrasound sensor, among other examples. The sensor(s) 122 and the perception sensor(s) 124 may be attached directly or indirectly to the frame. The sensor(s) 122 and the perception sensor(s) 124 may be communicatively coupled with the controller 114 to exchange data with the controller 114.” (Marsolek Para 0021), “The control system described herein is useful for identifying unsafe conditions at a worksite, such as uneven terrain, soft underfooting, excessive grade, and/or muddy areas. In particular, the control system may monitor sensor data collected by sensors of the work machine to detect when measurements of the sensor data are indicative of the work machine operating in an unsafe condition. Based on detecting that the work machine is operating in the unsafe condition, the control system may store the sensor data relating to the machine operating in the unsafe condition, at least until the sensor data can be offboarded from the work machine, and/or may transmit the sensor data relating to the machine operating in the unsafe condition offboard the machine. Moreover, the sensor data relating to the machine operating in the unsafe condition may include enhanced data, which may include perception sensor data (e.g., image data, lidar data, or the like) and/or may include the sensor data at an increased resolution and/or sampling rate . The sensor data relating to the work machine operating in the unsafe condition facilitates analysis (e.g., by the control system or another system in communication with the control system) of the unsafe condition and the circumstances of the machine 100 with respect to the unsafe condition. In this way, the control system enables real-time monitoring of unsafe conditions at the worksite to provide adaptability to changing terrain as work progresses at the worksite.” (Marsolek Para 0056), see also Marsolek Para 0051) . Conclusion 07-40 AIA 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 Kyle J Kingsland whose telephone number is (571)272-3268. The examiner can normally be reached Monday-Friday from 8:00-4:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Flynn can be reached at (571) 272-9855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /KYLE J KINGSLAND/ Primary Examiner, Art Unit 3663 Application/Control Number: 18/984,277 Page 2 Art Unit: 3663 Application/Control Number: 18/984,277 Page 3 Art Unit: 3663 Application/Control Number: 18/984,277 Page 4 Art Unit: 3663 Application/Control Number: 18/984,277 Page 5 Art Unit: 3663 Application/Control Number: 18/984,277 Page 6 Art Unit: 3663 Application/Control Number: 18/984,277 Page 7 Art Unit: 3663 Application/Control Number: 18/984,277 Page 8 Art Unit: 3663 Application/Control Number: 18/984,277 Page 9 Art Unit: 3663 Application/Control Number: 18/984,277 Page 10 Art Unit: 3663 Application/Control Number: 18/984,277 Page 11 Art Unit: 3663 Application/Control Number: 18/984,277 Page 12 Art Unit: 3663 Application/Control Number: 18/984,277 Page 13 Art Unit: 3663 Application/Control Number: 18/984,277 Page 14 Art Unit: 3663 Application/Control Number: 18/984,277 Page 15 Art Unit: 3663 Application/Control Number: 18/984,277 Page 16 Art Unit: 3663 Application/Control Number: 18/984,277 Page 17 Art Unit: 3663 Application/Control Number: 18/984,277 Page 18 Art Unit: 3663 Application/Control Number: 18/984,277 Page 19 Art Unit: 3663 Application/Control Number: 18/984,277 Page 20 Art Unit: 3663 Application/Control Number: 18/984,277 Page 21 Art Unit: 3663 Application/Control Number: 18/984,277 Page 22 Art Unit: 3663 Application/Control Number: 18/984,277 Page 23 Art Unit: 3663 Application/Control Number: 18/984,277 Page 24 Art Unit: 3663 Application/Control Number: 18/984,277 Page 25 Art Unit: 3663 Application/Control Number: 18/984,277 Page 26 Art Unit: 3663 Application/Control Number: 18/984,277 Page 27 Art Unit: 3663 Application/Control Number: 18/984,277 Page 28 Art Unit: 3663 Application/Control Number: 18/984,277 Page 29 Art Unit: 3663 Application/Control Number: 18/984,277 Page 30 Art Unit: 3663 Application/Control Number: 18/984,277 Page 31 Art Unit: 3663 Application/Control Number: 18/984,277 Page 32 Art Unit: 3663
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