DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 20) and system (claims 1-19) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of the mental process grouping.
The limitations reciting the abstract idea(s) (Mental process), as set forth in exemplary claim 1, are: receive…the data pertaining to operations of the refuse vehicle and the performance of the operator; analyze the data based on baseline data, the baseline data pertaining to a baseline value of the operations of the refuse vehicle and the performance of the operator; generate, based on an analysis of the data, an operator score; and provide…a user interface that includes the operator score. Independent claims 8 and 14 recite the CRM and system for performing the method of independent claim 1 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to “a device configured to collect data pertaining to operations of the refuse vehicle and a performance of an operator; and one or more processing circuits in communication with the device, the one or more processing circuits configured to…via a display…; wherein the at least one sensor includes at least one of a visible light camera, a LIDAR camera, or a radar sensor; wherein the one or more processing circuits includes at least one of (i) a first processing circuit located on the refuse vehicle or (ii) a second processing circuit located remote from the refuse vehicle” (as recited in claims 1, 14, 16 ). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: “a device configured to collect data pertaining to operations of the refuse vehicle and a performance of an operator; and one or more processing circuits in communication with the device, the one or more processing circuits configured to…via a display…; wherein the at least one sensor includes at least one of a visible light camera, a LIDAR camera, or a radar sensor; wherein the one or more processing circuits includes at least one of (i) a first processing circuit located on the refuse vehicle or (ii) a second processing circuit located remote from the refuse vehicle” (as recited in claims 1, 14, 16) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g). The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h).
In addition, Applicant’s Specification (paragraph [0030]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (2-16 and 18-19) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 2-13 and 15 “wherein the data pertaining to operations of the refuse vehicle and the performance of the operator is associated with a performance parameter including at least one of an alertness parameter, a component operation parameter, a driving condition parameter, a time parameter, a payload parameter, a route parameter, or a refuse vehicle performance parameter; wherein the performance parameter includes the alertness parameter, and wherein the one or more processing circuits are configured to analyze data associated with body movement and positioning of the operator to determine a degree of alertness of the operator and generate the operator score based on the degree of alertness; wherein the performance parameter includes the component operation parameter, and wherein the one or more processing circuits are configured to analyze data associated with operation of at least one of a lift assembly or a grabber assembly of the refuse vehicle to generate the operator score; wherein the performance parameter includes the driving condition parameter, and wherein the one or more processing circuits are configured to analyze data associated with at least one of an acceleration or an collision of the refuse vehicle to generate the operator score; wherein the performance parameter includes the time parameter, and wherein the one or more processing circuits are configured to analyze data associated with a time it takes the refuse vehicle to complete a task to generate the operator score; wherein the performance parameter includes the payload parameter, and wherein the one or more processing circuits are configured to analyze data associated with at least one of a weight or a volume of refuse received in a refuse compartment of the refuse vehicle to generate the operator score; wherein the performance parameter includes the route parameter, and wherein the one or more processing circuits are configured to analyze data associated with a route procedure performed by the operator to generate the operator score; wherein the performance parameter includes the refuse vehicle performance parameter, and wherein the one or more processing circuits are configured to analyze data associated with operation of at least one of a prime mover or an energy storage device of the refuse vehicle to generate the operator score; wherein the data pertaining to operations of the refuse vehicle and the performance of the operator associated with a first performance parameter of a first type is different than a second type of a second performance parameter; aggregate the data collected by the device; normalize the data associated the first performance parameter and the data associated with the second performance parameter to have a common type; and generate the operator score based on the normalized data; generate the operator score based on a weight associated with the performance parameter; wherein the device configured to collect data pertaining to operations of the refuse vehicle and the performance of the operator includes at least one sensor coupled to the refuse vehicle; wherein the one or more processing circuits are configured to provide, responsive to an input from the operator, via the display, the user interface that includes information relating to an operation of the refuse vehicle”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (18-19) recite the system for performing the system of claims 2-16. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-10, 12-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent 9129460 (hereinafter “McClellan”) et al., in view of U.S. PGPub 20210225095 to (hereinafter “Koga”) et al.
As per claim 1, McClellan teaches a system for monitoring an operation of… the system comprising:
a device configured to collect data pertaining to operations …and a performance of an operator; and one or more processing circuits in communication with the device, the one or more processing circuits configured to: receive, from the device, the data pertaining to operations of the refuse vehicle and the performance of the operator; McClellan 09: “ the invention is directed to a system and method for providing feedback to drivers. The system monitors selected vehicle parameters while a vehicle is being driven, and detects one or more vehicle operation violations by comparing the selected vehicle parameters to predetermined thresholds. A mentoring message is provided to the driver if the threshold is exceeded. If a vehicle operation violation has not been corrected within a preselected time period, then a violation report may be sent to a third party or a central server. If a vehicle operation violation has not been corrected within a preselected time period, then a different mentoring message may be provided to the driver. Vehicle parameter data may be monitored from an on-board vehicle diagnostic system. The mentoring message may be an audible warning, such as a spoken message, or a visual warning, such as a text message. The selected vehicle parameters may be a vehicle speed, a vehicle speed for the specific road conditions, a vehicle acceleration, an errant lane departure, following too close to a subsequent vehicle, a vehicle seatbelt use, the use of a mobile phone, the detection of unlawful ethanol concentrations, the detection of fatigue (blink rate) and/or other traceable, detectable activities, elements, and/or behaviors…013: Monitoring device 201 is illustrated as being coupled to OBD 102, from which it may receive inputs associated with vehicle operating parameters. Monitoring devices 202 and 203 may be similarly coupled to OBD 102 (connections not shown). Moreover, the vehicle monitoring system may be coupled to other sensors, such as a sensor for detecting the operation and use of a cellular or wireless device in the vehicle.”
analyze the data based on baseline data, the baseline data pertaining to a baseline value of the operations of the refuse vehicle and the performance of the operator; McClellan 024: “The vehicle monitoring system may be self-orienting, which allows it to be mounted in any position, angle or orientation in the vehicle or on the dashboard. In embodiments of the invention, the vehicle monitoring system determines a direction of gravity and a direction of vehicle movement and determines its orientation within the vehicle using this information. In order to provide more accurate measurements of driver behavior, in one embodiment, the present invention filters gravitational effects out of the longitudinal, lateral and vertical acceleration measurements when the vehicle is on an incline or changes its horizontal surface orientation. Driver performance is monitored and mentored using the accelerometer module, which preferably will be a tri-axial accelerometer. Acceleration is measured in at least one of lateral, longitudinal and/or vertical directions over a predetermined time period, which may be a period of seconds or minutes. An acceleration input signal is generated when a measured acceleration exceeds a predetermined threshold.”
generate, based on an analysis of the data, an operator score; and provide, via a display, a user interface that includes the operator score;McClellan 024-033: “The vehicle monitoring system may be self-orienting, which allows it to be mounted in any position, angle or orientation in the vehicle or on the dashboard. In embodiments of the invention, the vehicle monitoring system determines a direction of gravity and a direction of vehicle movement and determines its orientation within the vehicle using this information. In order to provide more accurate measurements of driver behavior, in one embodiment, the present invention filters gravitational effects out of the longitudinal, lateral and vertical acceleration measurements when the vehicle is on an incline or changes its horizontal surface orientation. Driver performance is monitored and mentored using the accelerometer module, which preferably will be a tri-axial accelerometer. Acceleration is measured in at least one of lateral, longitudinal and/or vertical directions over a predetermined time period, which may be a period of seconds or minutes. An acceleration input signal is generated when a measured acceleration exceeds a predetermined threshold… The alerts may contain, for example, driver performance reports such as a speeding index, "harsh" driving (e.g. acceleration, braking, turning, vertical indices) conditions, a seatbelt index and the like. The vehicle monitoring system may be configured to provide an immediate alert, or a grace period may be configured to allow the driver to correct the violation. If the violation is corrected by the driver, then no alert is sent to report the violation, thereby allowing the vehicle monitoring system to mentor the driver without human intervention… For thresholds assigned in step 301, a grace period is assigned in step 303. The grace period may be a number of seconds or minutes. The grace period corresponds to a period of time in which the driver is allowed to correct a violation without triggering a report to a third party. The grace period may also be zero (i.e. no time for correction of the violation). In step 304, alerts are selected for the thresholds assigned in step 301. The alerts are messages or reports to be sent to third parties, such as a fleet manager, parent or supervisor, if the grace period expires and the violation has not been corrected.”
McClellan may not explicitly teach the following. However, Koga teaches:
a refuse vehicle…;
Koga, Abstract: “A system for digital twinning a refuse vehicle includes a refuse vehicle, and a controller. The controller is configured to receive multiple datasets from the refuse vehicle, and generate a virtual refuse vehicle based on the multiple datasets. The virtual refuse vehicle includes a visual representation of the refuse vehicle and the multiple datasets. The controller is further configured to operate a display of a user device to provide the visual representation of the refuse vehicle and one or more of the multiple datasets to a user.”
McClellan and Koga are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified McClellan with the aforementioned teachings from Koga with a reasonable expectation of success, by adding steps that allow the software to utilize refuse vehicle data with the motivation to more efficiently and accurately organize and analyze data [Koga 0022].
As per claim 2, McClellan and Koga teach all the limitations of claim 1.
In addition, McClellan teaches:
wherein the data pertaining to operations of the refuse vehicle and the performance of the operator is associated with a performance parameter including at least one of an alertness parameter, a component operation parameter, a driving condition parameter, a time parameter, a payload parameter, a route parameter, or a refuse vehicle performance parameter;
McClellan 009: “The system monitors selected vehicle parameters while a vehicle is being driven, and detects one or more vehicle operation violations by comparing the selected vehicle parameters to predetermined thresholds. A mentoring message is provided to the driver if the threshold is exceeded. If a vehicle operation violation has not been corrected within a preselected time period, then a violation report may be sent to a third party or a central server. If a vehicle operation violation has not been corrected within a preselected time period, then a different mentoring message may be provided to the driver. Vehicle parameter data may be monitored from an on-board vehicle diagnostic system. The mentoring message may be an audible warning, such as a spoken message, or a visual warning, such as a text message. The selected vehicle parameters may be a vehicle speed, a vehicle speed for the specific road conditions, a vehicle acceleration, an errant lane departure, following too close to a subsequent vehicle, a vehicle seatbelt use, the use of a mobile phone, the detection of unlawful ethanol concentrations, the detection of fatigue (blink rate) and/or other traceable, detectable activities, elements, and/or behaviors.”
As per claim 3, McClellan and Koga teach all the limitations of claim 2.
In addition, Koga teaches:
wherein the performance parameter includes the alertness parameter, and wherein the one or more processing circuits are configured to analyze data associated with body movement and positioning of the operator to determine a degree of alertness of the operator and generate the operator score based on the degree of alertness;
Koga 0056: “Safety system sub-manager 608 may be configured to validate, sort, summarize, aggregate, report, filter, classify, group, etc., or determine a time series value with a corresponding date, time, and unique vehicle identification number/value relevant to a vehicle safety system (e.g., a driver alertness detection system configured to monitor at least one of driving patterns, steering patterns, a driver's eyes, etc., to prevent the driver from falling asleep while operating refuse vehicle 10) disposed on the refuse vehicle 10, by using an equation, a set of equations, a set of rules, a lookup table, a graph, a set of conditions, a decision tree, an algorithm, etc. Weighting system sub-manager 610 may be configured to validate, sort, summarize, aggregate, report, filter, classify, group, etc., or determine a time series value with a corresponding date, time, and unique vehicle identification number/value relevant to a weighting system module (e.g., a system that monitors the weight of the refuse vehicle and may use strain gauges) disposed on the refuse vehicle 10, by using an equation, a set of equations, a set of rules, a lookup table, a graph, a set of conditions, a decision tree, an algorithm, etc.”
McClellan and Koga are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified McClellan with the aforementioned teachings from Koga with a reasonable expectation of success, by adding steps that allow the software to utilize refuse vehicle data with the motivation to more efficiently and accurately organize and analyze data [Koga 0022].
As per claim 4, McClellan and Koga teach all the limitations of claim 2.
In addition, Koga teaches:
wherein the performance parameter includes the component operation parameter, and wherein the one or more processing circuits are configured to analyze data associated with operation of at least one of a lift assembly or a grabber assembly of the refuse vehicle to generate the operator score;
Koga 0025-0029: “Lift arms 42 may be removably coupled to a container, shown as refuse container 200 in FIG. 1. In some embodiments, lift arms 42 include a pair of grabber fingers rotatably coupled to lift arms 42 and each configured to rotate about an axis extending through a pivot point of each of the grabber fingers. The grabber fingers may each rotate about the axis extending through the pivot point of each of the grabber fingers such that the grabber fingers grasp a refuse container. The lift arms 42 may then rotate about the axis extending through the pivot to empty contents of the refuse container in the refuse compartment 30… refuse vehicle 10 is shown to include lift assembly sensors, shown as lift arm sensors 46, according to an exemplary embodiment. Lift arm sensors 46 are positioned at any position where the lift assembly 40 is configured to pivot. Lift arm sensors 46 are configured to determine an angle of a pivot point of lift assembly 40. In some embodiments, lift arm sensors 46 measure a quantity of hydraulic fluid which has entered or exited a chamber of lift arm actuators 44.”
McClellan and Koga are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified McClellan with the aforementioned teachings from Koga with a reasonable expectation of success, by adding steps that allow the software to utilize refuse vehicle data with the motivation to more efficiently and accurately organize and analyze data [Koga 0022].
As per claim 5, McClellan and Koga teach all the limitations of claim 2.
In addition, McClellan teaches:
wherein the performance parameter includes the driving condition parameter, and wherein the one or more processing circuits are configured to analyze data associated with at least one of an acceleration or an collision of the refuse vehicle to generate the operator score;
McClellan 006-016: “ Vehicle parameter data may be monitored from an on-board vehicle diagnostic system. The mentoring message may be an audible warning, such as a spoken message, or a visual warning, such as a text message. The selected vehicle parameters may be a vehicle speed, a vehicle speed for the specific road conditions, a vehicle acceleration, an errant lane departure, following too close to a subsequent vehicle, a vehicle seatbelt use, the use of a mobile phone, the detection of unlawful ethanol concentrations, the detection of fatigue (blink rate) and/or other traceable, detectable activities, elements, and/or behaviors… the vehicle monitoring system includes an accelerometer module (XLM) that includes at least one accelerometer for measuring at least one of lateral (sideways), longitudinal (forward and aft) and vertical acceleration in order to determine whether the driver is operating the vehicle in an unsafe or aggressive manner… The CDR is adapted to continuously monitor vehicle motion and begin recording upon supra-threshold impacts whereupon it records the magnitude and direction of accelerations or G-forces experienced by the vehicle as well as recording an acceleration time-history of the impact event and velocity change between pre- and post-impact for a configurable duration following the impact. The CDR may be separate from the accelerometer module (XLM) or may be the same device.”
As per claim 6, McClellan and Koga teach all the limitations of claim 2.
In addition, McClellan teaches:
wherein the performance parameter includes the time parameter, and wherein the one or more processing circuits are configured to analyze data associated with a time it takes the refuse vehicle to complete a task to generate the operator score;
McClellan 0040-044: “For thresholds assigned in step 301, a grace period is assigned in step 303. The grace period may be a number of seconds or minutes. The grace period corresponds to a period of time in which the driver is allowed to correct a violation without triggering a report to a third party. The grace period may also be zero (i.e. no time for correction of the violation). In step 304, alerts are selected for the thresholds assigned in step 301. The alerts are messages or reports to be sent to third parties, such as a fleet manager, parent or supervisor, if the grace period expires and the violation has not been corrected…044: When the training mode is completed, and the vehicle monitoring system has created a profile of acceptable driving parameters, those parameters are stored in memory in the monitoring device. The acceptable driving parameters may be reviewed by the experienced driver, such as via a display on the vehicle monitoring system. The acceptable driving parameters may also be sent to central server 109 or other computer for review by the experienced driver or others, such as via terminal 111. The parameters and thresholds observed during the training mode may be reviewed at any time during the training period or after the training is completed. The parent, supervisor or experienced driver accept all of the parameters and thresholds established during the training period for use during the monitoring mode, or those parameters and thresholds may be used as initial values that can be further adjusted for use in the monitoring mode. For example, a parent reviewing his driving parameters captured during training period may determine that certain features of his driving should not be emulated, such as excessive speeding violations or failure to use seatbelts. The parent may adjust the thresholds for these elements to require more strict compliance by the teen driver in the monitoring mode.”
As per claim 7, McClellan and Koga teach all the limitations of claim 2.
In addition, Koga teaches:
wherein the performance parameter includes the payload parameter, and wherein the one or more processing circuits are configured to analyze data associated with at least one of a weight or a volume of refuse received in a refuse compartment of the refuse vehicle to generate the operator score;
Koga 0056-0064: “ Weighting system sub-manager 610 may be configured to validate, sort, summarize, aggregate, report, filter, classify, group, etc., or determine a time series value with a corresponding date, time, and unique vehicle identification number/value relevant to a weighting system module (e.g., a system that monitors the weight of the refuse vehicle and may use strain gauges) disposed on the refuse vehicle 10, by using an equation, a set of equations, a set of rules, a lookup table, a graph, a set of conditions, a decision tree, an algorithm, etc… controller 300 is shown receiving information from a wheel sensor, shown as wheel speed sensor 26, a lift arm sensor, shown as lift arm sensor 46, a fuel level sensor, shown as fuel level sensor 24, a pressure sensor, shown as tire pressure sensor 801, a weight sensor, shown as weight sensor 803, and a GPS transceiver, shown as GPS transceiver 805, and sending the information to remote server 320 according to the illustrative embodiment.”
McClellan and Koga are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified McClellan with the aforementioned teachings from Koga with a reasonable expectation of success, by adding steps that allow the software to utilize refuse vehicle data with the motivation to more efficiently and accurately organize and analyze data [Koga 0022].
As per claim 8, McClellan and Koga teach all the limitations of claim 2.
In addition, McClellan teaches:
wherein the performance parameter includes the route parameter, and wherein the one or more processing circuits are configured to analyze data associated with a route procedure performed by the operator to generate the operator score;
McClellan 029: “Other violations, such as operating the vehicle outside or inside a specific area or off of a specific route can trigger a visual or audible mentoring message to the driver. Areas and routes may be defined, for example, using a geofence that is compared to the vehicle's current location as determined by the GPS system. Seat belt use violation, such as failing to use a seatbelt while driving, may be detected, for example, via the OBD bus and may trigger a mentoring message to use the seatbelt. Sensors that detect exposure to ethanol (EtOH) vapor and/or detect blood EtOH levels, may be used to determine if a driver has been drinking and may have a blood alcohol level that is illegal. Based upon inputs from an EtOH sensor, the vehicle monitoring device may provide mentoring feedback to the driver or may disable the vehicle. Other sensors may detect the use of wireless devices, such as cell phones, in the vehicle. Upon detecting cell phone use, the vehicle monitoring system may issue a warning to the driver to stop using the cell phone.”
As per claim 9, McClellan and Koga teach all the limitations of claim 2.
In addition, McClellan teaches:
wherein the performance parameter includes the refuse vehicle performance parameter, and wherein the one or more processing circuits are configured to analyze data associated with operation of at least one of a prime mover or an energy storage device of the refuse vehicle to generate the operator score; McClellan 023: “The monitoring system may be self-powered, such as by a battery, or powered by the vehicle's battery and/or power generating circuitry. Access to the vehicle's battery power may be by accessing the power available on the vehicle's OBD and/or CAN bus.”
As per claim 9, McClellan and Koga teach all the limitations of claim 2.
In addition, McClellan teaches:
wherein the data pertaining to operations of the refuse vehicle and the performance of the operator associated with a first performance parameter of a first type is different than a second type of a second performance parameter
McClellan 024-033: “The vehicle monitoring system may be self-orienting, which allows it to be mounted in any position, angle or orientation in the vehicle or on the dashboard. In embodiments of the invention, the vehicle monitoring system determines a direction of gravity and a direction of vehicle movement and determines its orientation within the vehicle using this information. In order to provide more accurate measurements of driver behavior, in one embodiment, the present invention filters gravitational effects out of the longitudinal, lateral and vertical acceleration measurements when the vehicle is on an incline or changes its horizontal surface orientation. Driver performance is monitored and mentored using the accelerometer module, which preferably will be a tri-axial accelerometer. Acceleration is measured in at least one of lateral, longitudinal and/or vertical directions over a predetermined time period, which may be a period of seconds or minutes. An acceleration input signal is generated when a measured acceleration exceeds a predetermined threshold… The alerts may contain, for example, driver performance reports such as a speeding index, "harsh" driving (e.g. acceleration, braking, turning, vertical indices) conditions, a seatbelt index and the like. The vehicle monitoring system may be configured to provide an immediate alert, or a grace period may be configured to allow the driver to correct the violation. If the violation is corrected by the driver, then no alert is sent to report the violation, thereby allowing the vehicle monitoring system to mentor the driver without human intervention… For thresholds assigned in step 301, a grace period is assigned in step 303. The grace period may be a number of seconds or minutes. The grace period corresponds to a period of time in which the driver is allowed to correct a violation without triggering a report to a third party. The grace period may also be zero (i.e. no time for correction of the violation). In step 304, alerts are selected for the thresholds assigned in step 301. The alerts are messages or reports to be sent to third parties, such as a fleet manager, parent or supervisor, if the grace period expires and the violation has not been corrected.”
As per claim 12, McClellan and Koga teach all the limitations of claim 2.
In addition, Koga teaches:
wherein the one or more processing circuits are configured to generate the operator score based on a weight associated with the performance parameter;
Koga 0056-0064: “ Weighting system sub-manager 610 may be configured to validate, sort, summarize, aggregate, report, filter, classify, group, etc., or determine a time series value with a corresponding date, time, and unique vehicle identification number/value relevant to a weighting system module (e.g., a system that monitors the weight of the refuse vehicle and may use strain gauges) disposed on the refuse vehicle 10, by using an equation, a set of equations, a set of rules, a lookup table, a graph, a set of conditions, a decision tree, an algorithm, etc… controller 300 is shown receiving information from a wheel sensor, shown as wheel speed sensor 26, a lift arm sensor, shown as lift arm sensor 46, a fuel level sensor, shown as fuel level sensor 24, a pressure sensor, shown as tire pressure sensor 801, a weight sensor, shown as weight sensor 803, and a GPS transceiver, shown as GPS transceiver 805, and sending the information to remote server 320 according to the illustrative embodiment.”
McClellan and Koga are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified McClellan with the aforementioned teachings from Koga with a reasonable expectation of success, by adding steps that allow the software to utilize refuse vehicle data with the motivation to more efficiently and accurately organize and analyze data [Koga 0022].
As per claim 13, McClellan and Koga teach all the limitations of claim 1.
In addition, McClellan teaches:
wherein the device configured to collect data pertaining to operations of the refuse vehicle and the performance of the operator includes at least one sensor coupled to the refuse vehicle;
McClellan 020: “ The vehicle monitoring system receives inputs from a number of internal and external sources. The OBD II/CAN bus, which provides data from the vehicle's on-board diagnostic system, including engine performance data and system status information. A GPS receiver provides location information. The CDR, XLM, or accelerometers provide information regarding the vehicle's movement and driving conditions. Any number of other sensors, such as but not limited to, a seat belt sensor, proximity sensor, driver monitoring sensors, or cellular phone use sensors, also provide inputs to the vehicle monitoring system.”
As per claim 14, McClellan and Koga teach all the limitations of claim 13.
In addition, McClellan teaches:
wherein the at least one sensor includes at least one of a visible light camera, a LIDAR camera, or a radar sensor;
McClellan 004: “FIG. 2 is a diagram of possible locations of cameras and/or other technologies used in embodiments of the invention.”
As per claim 15, McClellan and Koga teach all the limitations of claim 1.
In addition, McClellan teaches:
wherein the one or more processing circuits are configured to provide, responsive to an input from the operator, via the display, the user interface that includes information relating to an operation of the refuse vehicle;
McClellan 013: “Referring to FIG. 2, exemplary mounted locations for the vehicle monitoring system are illustrated, such as on a dashboard 201, windshield 202, or headliner 203. It will be understood that all or parts of the vehicle monitoring system may be mounted in any other location that allows for audio and/or visual feedback to the driver of the vehicle while the vehicle is in operation. Monitoring device 201 is illustrated as being coupled to OBD 102, from which it may receive inputs associated with vehicle operating parameters. Monitoring devices 202 and 203 may be similarly coupled to OBD 102 (connections not shown). Moreover, the vehicle monitoring system may be coupled to other sensors, such as a sensor for detecting the operation and use of a cellular or wireless device in the vehicle…024: Acceleration is measured in at least one of lateral, longitudinal and/or vertical directions over a predetermined time period, which may be a period of seconds or minutes. An acceleration input signal is generated when a measured acceleration exceeds a predetermined threshold.”
As per claim 16, McClellan and Koga teach all the limitations of claim 1.
In addition, McClellan teaches:
wherein the one or more processing circuits includes at least one of (i) a first processing circuit located on the refuse vehicle or (ii) a second processing circuit located remote from the refuse vehicle;
McClellan 017: “In one aspect, the vehicle monitoring system may be in data communication with an on board diagnostic (OBD) TI system of the vehicle such as via a port. In some vehicle models, the vehicle monitoring system is in data communication with a controller area network (CAN) system (bus) to allow acquisition of certain vehicle operating parameters including, but not limited to, vehicle speed such as via the speedometer, engine speed or throttle position such as via the tachometer, mileage such as via the odometer reading, seat belt status, condition of various vehicle systems including anti-lock-braking (ABS), turn signal, headlight, cruise control activation and a multitude of various other diagnostic parameters such as engine temperature, brake wear, and the like. The OBD or CAN allows for acquisition of the above-mentioned vehicle parameters for processing thereby and/or for subsequent transmission to the central server 109.”
Claims 17-18 and 20 are directed to the system and method for performing the system of claims 1-16 above. Since McClellan and Koga teach the system and method, the same art and rationale apply.
Claims 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent 9129460 (hereinafter “McClellan”) et al., in view of U.S. PGPub 20210225095 to (hereinafter “Koga”) et al., in further view of U.S. PGPub 20150254955 (hereinafter “Fields”) et al.
As per claim 11, McClellan and Koga teach all the limitations of claim 10.
McClellan and Koga may not explicitly teach the following. However, Fields teaches:
aggregate the data collected by the device; normalize the data associated the first performance parameter and the data associated with the second performance parameter to have a common type; and generate the operator score based on the normalized data;
Fields 0076: “ FIG. 15 is a flow diagram depicting an exemplary embodiment of a comprehensive impairment score determination method 1500 implemented by the vehicle operator emotion management system 100 while determining a comprehensive impairment score for the vehicle operator 106 at block 1404. The method 1500 may receive sensor data from the mobile device 110 or onboard computer 114 (block 1502), individual impairment indicators from the mobile device 110 or onboard computer 114 (block 1504), or total impairment scores calculated by the mobile device 110 or onboard computer 114 (block 1506). The method 1500 may also receive additional sensor data or telematic information regarding the operation of the vehicle 108. If the method 1500 receives sensor data, the server 140 may generate each impairment indicator score and total impairment score in a manner similar to how the mobile device 110 or onboard computer 114 calculates the scores as discussed above with respect to FIGS. 4 and 5. For example, the server 140 may determine a GSR score using GSR sensor data transmitted via network 130. Because the memory and computing power of the server 140 may be greater than the mobile device or onboard computer 114, it may be advantageous to calculate the various scores using a longer period of time (e.g., an average hard braking score over one week rather than over a number of minutes). Sensor data may also be directly used by the comprehensive impairment score determination method, in which case it may be normalized to an appropriate scale (e.g., 0-100 points, etc.) by any of the various known adjustment methods. The server 140 may also receive individual impairment indicator scores or total impairment scores from the mobile device 110 or onboard computer 114. In a manner similar to FIGS. 4 and 5, the method 1500 may determine a comprehensive impairment score by multiplying each score by a weighting factor 1508a, b, and c. Each score may be weighted equally, or it may be advantageous to weight the scores differently. The method 1500 may then sum the weighted scores to determine a comprehensive impairment score (block 1510). The comprehensive impairment score may be logged with a timestamp and stored in the system database 246..”
McClellan, Koga, and Fields are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified McClellan and Koga with the aforementioned teachings from Fields with a reasonable expectation of success, by adding steps that allow the software to utilize refuse vehicle data with the motivation to more efficiently and accurately organize and analyze data [Koga 0022].
Claim 19 is directed to similar subject matter already taught in claim 11. Since McClellan, Koga, and Fields teaches the limitations, the same art and rationale apply.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kumar; Avishek. MATERIALS HANDLING VEHICLE TECHNOLOGY MONITOR, .U.S. PGPub 20210375080 Materials handling vehicles are commonly used for picking stock in warehouses and distribution centers. Such vehicles typically include a power unit and a load handling assembly, which may include load carrying forks. The vehicle also has control structures for controlling operation and movement of the vehicle. Moreover, wireless strategies are deployed by various enterprises to improve the efficiency and accuracy of operations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”)./Arif Ullah/Primary Examiner, Art Unit 3625