Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This communication is a Final Office Action in response to Applicant’s amendment for application number 18/137,736 received on 03/09/2026.
In accordance with Applicant’s amendment, claim 21 has been canceled, and claim 22 is new. Claims 1-2, 4-20, and 22 are amended, currently pending, and have been examined.
Response to Amendment
The amendment filed on 03/09/2026 has been entered.
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Response to Arguments
Response to §103 arguments – Applicant’s arguments with respect to the §103 rejections previously applied to the previously presented claims are primarily raised in support of the amendments, which are believed to be fully addressed in the updated §103 rejections below.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4, 6, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Ghanbari et al. (US 20230084964 A1, hereinafter “Ghanbari”), in view of Boehm et al. (US 20230040437 A1, hereinafter “Boehm”), in further view of Ricci (US 20140309806 A1), in further view of Molin et al. (US 20140226010 A1, hereinafter “Molin”).
Regarding Claim 1: Ghanbari teaches:
and a system for mitigating operation of a machine by an operator if operator fatigue of the operator is detected, ([0032] system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator, based on the (absolute or relative) difference between these two performance values.; [0014] Determination of attentiveness may include gathering information by monitoring the vehicle operators of one or more vehicles (by way of non-limiting example, direction of gaze, blinking, rate of blinking, change in rate of blinking, duration of closing eyes, change in average duration of closing eyes, tilting of head, angle of tilting of head, frequency of tilting of head, change in frequency of tilting of head, shaking of head, frequency of shaking of head, change in frequency of shaking of head, and/or other bodily movements that may be related to attentiveness, distractedness, fatigue, and/or drowsiness, as well as derivatives thereof), as well as monitoring vehicle operations.; [0038] Referring to FIG. 1, action component 120 may be configured to determine whether to take an action based on one or more determinations and/or comparisons. For example, action component 120 may determine whether to schedule a break (or take another action) for a particular vehicle operator based on a comparison by comparison component 118. Examiner notes that one of ordinary skill in the art would reasonably consider the action to schedule a break based on a performance comparison, as equivalent to mitigating the operation of a machine if operator fatigue is detected.);
the system including: a fatigue mitigation system ([0038] Referring to FIG. 1, action component 120 may be configured to determine whether to take an action based on one or more determinations and/or comparisons. For example, action component 120 may determine whether to schedule a break (or take another action) for a particular vehicle operator based on a comparison by comparison component 118.); including: a first computing system ([0013] system 100); having a first processor, ([0013] processor(s) 132); first storage medium, ([0013] electronic storage 126); and a first network interface, ([0020] Output signals generated by individual sensors (and/or information based thereon) may be stored and/or transferred in electronic files. In some implementations, output signals may be transferred as one or more streams of data.; [0042] server(s) 102, client computing platform(s) 104, and/or external resources 130 may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via one or more network(s) 13 such as the Internet and/or other networks.);
and a machine interface coupled to the first computing system ([0041] Client computing platforms 104 may be associated with user interfaces 134. User interfaces 134 may be presented to users 135, including but not limited to vehicle operators, vehicle owners, fleet managers, and/or other stakeholders. In some implementations, notifications (e.g., from notification component 122) may be provided through one or more user interfaces 134 in one or more vehicles.), the machine interface: formed by a camera ([0016] set of sensors 108 may include one or more cameras 108a), a positioning system ([0020] a geolocation sensor (e.g., a Global Positioning System or GPS device), an intervention system ([Abstract] a system may recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator.), and a display, ([0041] an individual user interface 134 may include one or more controllers, joysticks, track pad, a touch screen, a keypad, touch sensitive and/or physical buttons, switches, buttons, a keyboard, knobs, levers, a display, speakers, a microphone, an indicator light, a printer, and/or other interface devices.);
and an operator scheduling system ([0033] system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator.), including a second computing system having a second processor, a second storage medium, and a second network interface, ([0013] In some implementations, system 100 may include one or more of server(s) 102, electronic storage 126, processor(s) 132, set of sensors 108, user interface(s) 134, network(s) 13, client computing platform(s) 104, external resources 130, a remote computing server 125, and/or other components.; [0047] The electronic storage media of electronic storage 126 may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with server(s) 102 and/or removable storage that is removably connectable to server(s) 102; ([0020] Output signals generated by individual sensors (and/or information based thereon) may be stored and/or transferred in electronic files. In some implementations, output signals may be transferred as one or more streams of data.; [0042] server(s) 102, client computing platform(s) 104, and/or external resources 130 may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via one or more network(s) 13 such as the Internet and/or other networks.), to: track, by the positioning system, a location of the machine, ([0016] Set of sensors 108 may be configured to generate output signals conveying information related to (operation of) vehicle 12, a location of vehicle 12; [0020] a geolocation sensor (e.g., a Global Positioning System or GPS device).);
schedule one or more breaks at pre-set default intervals, ([0032] system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator);
receive data, corresponding to the operator and the machine, captured by the fatigue mitigation system, ([0014] Individual vehicles may include a set of resources for information gathering, data processing, and/or electronic storage, including but not limited to persistent storage. Individual vehicles may include sensors (e.g., set of sensors 108 configured to generate and/or otherwise gather data, such as output signals). In some implementations, individual vehicles may be configured to detect vehicle events, e.g., based on output signals generated by set of sensors 108. As used herein, the term “vehicle event” may include occurrences of events involving one or more vehicles. As such, detection of vehicle events may include gathering information by monitoring the operation of one or more vehicles, including but not limited to information related to current or past vehicle speeds, current or current location, and/or other information pertinent to detecting of vehicle events. In some implementations, individual vehicles may be configured to determine operator attentiveness, e.g., based on output signals generated by set of sensors 108 (e.g., by one or more cameras 108a). Determination of attentiveness may include gathering information by monitoring the vehicle operators of one or more vehicles (by way of non-limiting example, direction of gaze, blinking, rate of blinking, change in rate of blinking, duration of closing eyes, change in average duration of closing eyes, tilting of head, angle of tilting of head, frequency of tilting of head, change in frequency of tilting of head, shaking of head, frequency of shaking of head, change in frequency of shaking of head, and/or other bodily movements that may be related to attentiveness, distractedness, fatigue, and/or drowsiness, as well as derivatives thereof), as well as monitoring vehicle operations.);
based on the received data, detect that fatigue is being experienced by the operator ([0027] one or more of the driver performance metrics may be related to determinations of operator attentiveness (and/or conversely, distractedness, drowsiness, fatigue, etc.) during a trip or work shift. a numerical value of a particular driver performance metric may be expressed as a percentage between 0% and 100%, where 100% indicates flawless performance (e.g., having perfect attentiveness), and 0% indicates a terribly flawed performance.);
calculate an amount of fatigue currently experienced by the operator based on the received data, ([0027] For example, one or more of the driver performance metrics may be related to determinations of operator attentiveness (and/or conversely, distractedness, drowsiness, fatigue, etc.) during a trip or work shift. By way of non-limiting example, such determinations may be based on image information captured of a particular vehicle operator during a trip or work shift. For example, such determinations may include direction of gaze, blinking, rate of blinking, change in rate of blinking, duration of closing eyes, change in average duration of closing eyes, tilting of head, angle of tilting of head, frequency of tilting of head, change in frequency of tilting of head, shaking of head, frequency of shaking of head, change in frequency of shaking of head, and/or other operator actions or bodily movements that may be related to attentiveness, distractedness, fatigue, and/or drowsiness, as well as derivatives thereof. For this example, more occurrences of such operator actions or bodily movements would correlate to a lower performance of the particular vehicle operator. For example, a numerical value of a particular driver performance metric may be expressed as a percentage between 0% and 100%, where 100% indicates flawless performance (e.g., having perfect attentiveness), and 0% indicates a terribly flawed performance. In some implementations, values for this particular driver performance metric may be determined at intervals and/or intermittently through a particular trip or work shift (e.g., more than once). In some implementations, values for this particular driver performance metric may be determined continuously through a particular trip or work shift (e.g., every minute, every 5 minutes, every 10 minutes, every 15 minutes, every hours, etc.).);
determine that the amount of fatigue currently experienced by the operator is greater than a threshold, ([0029] teaches vehicle operator performance at a particular duration may range between a lower level and a higher level. These levels may correspond to a standard deviation from average, or to the range within which 80% (or some other percentage) of a particular fleet's drivers operate, or to another mathematical definition of variance. In particular, an individual vehicle operator's performance outside of this range (in particular, below lower level performance function 40b) may be considered noteworthy and/or potentially in need of a subsequent action by system 100.);
advance at least one of the one or more breaks in response to the determination that the amount of fatigue is greater than the threshold, ([0032] Comparison component 118 may be configured to compare different performance values, in particular a first performance value (e.g., determined by performance component 116) with a second performance value (e.g., determined by aggregation component 112). For example, comparison component 118 may compare a current performance (of a particular driver on a particular trip) with a particular fleet-specific vehicle operator performance (e.g., a particular performance function such as, by way of non-limiting example, lower level performance function 40b of FIG. 4A). Comparisons may take the actual (current) duration of a particular trip into consideration. Comparisons may take the scheduled trip duration of a particular trip into consideration. For example, the first performance value at the 2-hour mark may be 60%, whereas the second performance value at the same time may be 80%. Accordingly, in some cases, system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator, based on the (absolute or relative) difference between these two performance values. In other cases, no action may be recommended, for example in light of the scheduled trip duration being 2 hours and 10 minutes.; [0033] In some implementations, comparison component 118 may be configured to compare the changes in a first performance value (e.g., since the start of a trip) with the changes in a second performance value. For example, the first performance value may have dropped 20% in the past 3 hours, whereas the second performance value only dropped 10% in the same timeframe. Accordingly, in some cases, system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator. A recommendation may also be based on the scheduled trip duration for the particular trip.; [0035] In some implementations, comparison component 118 may be configured to compare a first performance value with a threshold performance level. In some implementations, the threshold performance level may be a particular performance function determined by performance component 116, such as, by way of non-limiting example, lower level performance function 40b of FIG. 4A. In some implementations, the threshold performance level may be based on a particular performance function determined by performance component 116, such as, by way of non-limiting example, 10% less than lower level performance function 40b of FIG. 4A. In some implementations, the threshold performance level may be a constant performance level, such as a first threshold level 40d of FIG. 4A, which has the same value, here 60%, throughout the duration of exemplary diagram 400. In some implementations, the threshold performance level may be dynamically change, such as a second threshold level 40e of FIG. 4A, which gradually decreases in value, here from 70% to 60%, throughout the duration of exemplary diagram 400.);
and control the machine based on the threshold. ([0015] operation of vehicle 12 may be actively and primarily controlled by a vehicle operator (i.e., a human operator). In such a case, a non-human vehicle operator may take over (or be requested to take over) control of the vehicle in certain circumstances.; [0034] In some implementations, comparison component 118 may be configured to compare the rate of change of a first performance value with the rate of change of a second performance value. For example, the first rate of change may be minus 20% per hour, whereas the second rate of change may be minus 5% per hour at a similar moment or duration of a trip. Accordingly, in some cases, system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator. In some cases, the particular recommended action may vary based on the remaining duration of the particular trip. For example, a 5-minute break may be sufficient for a remaining trip duration of 30 minutes, whereas a 1-hour break may be better suited for a remaining trip duration of 3 hours.; [0038] Actions taken or recommended by action component 120 may include generating notifications, providing notifications (e.g., notifying a vehicle operator, a stakeholder of a particular fleet, a dispatcher, remote computing server 125, and/or others), scheduling a rest or break, modifying the planned route, modifying the effective speed limit for a particular vehicle, modifying the type of vehicle events a particular vehicle is currently detecting, modifying the sensitivity with which a particular vehicle event is being detected, and/or other actions. Examiner notes that one of ordinary skill in the art would reasonably interpret the actions for modifying the effective speed limit for a particular vehicle, modifying the type of vehicle events a particular vehicle is currently detecting, modifying the sensitivity with which a particular vehicle event is being detected, as equivalent to controlling the machine.).
Ghanbari doesn’t explicitly teach:
a frame;
a drivetrain supported by the frame;
a cabin supported by the frame;
Boehm teaches:
a frame; ([0042] FIGS. 2, 3 and 4, each vehicle 14 generally includes a chassis or frame 42.);
a drivetrain supported by the frame; ([0042] a drivetrain 82 that is operatively connected to at least one of the rear and/or front wheels 46 and/or 50. Additionally, paragraph [0042] also teaches a pair of rear wheels 50 and a pair of front wheels 46 operationally connected to the chassis.
a cabin supported by the frame; ([0042] a passenger compartment 54. Fig. 2 shows the passenger compartment is supported by the chassis or frame.
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Ghanbari with Boehm’s feature(s) listed above. One would’ve been motivated to do so in order to show the structure of the machine using the fatigue detection system as shown in at least [Fig. 2]. By incorporating the teachings of Boehm, one would’ve been able to provide in the cabin, a system for mitigating operation of a machine if operator fatigue is detected.
Boehm doesn’t teach:
and automatically customized for the operator upon starting the operation of the machine by the operator;
and cause the intervention system to provide a haptic alert to the operator,
Ricci teaches:
and automatically customized for the operator upon starting the operation of the machine by the operator; ([0123] The personal settings of the vehicle occupant can include a plurality of a seat setting, climate control setting, lighting setting, configuration of an instrument cluster on a screen, rear view mirror setting, driving mode, media channel setting or preset, media delivery preference, music genre preference, scheduled program, playlist, synchronization with cloud-based data associated with the vehicle occupant, application-specific personalization and selections, and a display setting and configuration.; [0695] Device detection can be in response to or triggered by a sensed event other than receipt of a ping from the computational device. As noted, device detection can be receipt of information from one or more on board sensors that a new occupant has entered the vehicle. Exemplary information includes a door opening or closing, a successful authentication of an occupant or computational device, a sensed load in a seat, detection of movement within the vehicle, and detection of initiation of a vehicle task, function or operation, such as a key inserted in an ignition, engine start up, and the like.; [0709] While this logic is described with respect to the location of the computational device, it is to be understood that this determination is optional. Whether or not a communication device is enabled to connect to the vehicle network 356 or communication subsystem 1008 can be based solely on successful authentication. In this way, the vehicle on board computer can connect automatically to the owner's home virtual private network to upload and/or download information, settings, and other information (such as user input into the vehicle computer, vehicle driving history (e.g., miles traveled, travel traceroutes, speeds traveled, and locations visited), vehicle service information (such as gas and fluid levels, engine problems, alarms or warnings activated, and the like), input received by on board applications from the user (such as scheduled appointments, notes, documents, and the like), applications downloaded, and the like. Synchronization of the on board vehicle and home computer can occur automatically whether the vehicle is turned on or off. This can be highly beneficial when the vehicle is parked in the garage.; [0711] In step 2020, the device discovery daemon 1020 permits or enables connection of the computational device with the vehicle network 356 or communication subsystem 1008 and optionally stipulates or defines what set of tasks, functions, and/or operations the user of the computational device can perform using the computational device, such as based on the location of the computational device within the vehicle and/or based on the authentication credentials (e.g., the identity of the computational device user).; [0753] In step 2220, the media server 2112 configures the filtered media for the capabilities of the user device and/or on board vehicle display and in accordance with user preferences.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ghanbari with Ricci’s feature(s) listed above. One would’ve been motivated to do so in order to maintain a persona of a vehicle occupant and, based on the persona of the vehicle occupant and vehicle-related information, performing an action assisting the vehicle occupant (Ricci; [Abstract]). By incorporating the teachings of Ricci, one would’ve been able to automatically customize the interface upon starting operation of the vehicle.
Ricci doesn’t teach:
and cause the intervention system to provide a haptic alert to the operator,
Molin teaches:
and cause the intervention system to provide a haptic alert to the operator, ([0184] Haptic Warning--This may be in the form of a brake pulse applied to the vehicle to warn of an impending collision if the driver has not reacted to the Distance Alert nor the Forward Collision Warning).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ghanbari with Molin’s feature(s) listed above. One would’ve been motivated to do so in order to monitor, correct or reward driver behavior, and to implement driver education and training programs (Molin; [Abstract]). By incorporating the teachings of Molin, one would’ve been able to issue haptic alerts to drivers.
Regarding Claim 2: Ghanbari further teaches:
wherein the data corresponding to the operator and the machine includes at least one of: operator head pose, body attitude, facial features, and operating characteristics of the operator ([0014] Determination of attentiveness may include gathering information by monitoring the vehicle operators of one or more vehicles (by way of non-limiting example, direction of gaze, blinking, rate of blinking, change in rate of blinking, duration of closing eyes, change in average duration of closing eyes, tilting of head, angle of tilting of head, frequency of tilting of head, change in frequency of tilting of head, shaking of head, frequency of shaking of head, change in frequency of shaking of head, and/or other bodily movements that may be related to attentiveness, distractedness, fatigue, and/or drowsiness, as well as derivatives thereof), as well as monitoring vehicle operations.).
Regarding Claim 4: Ghanbari further teaches:
wherein the fatigue mitigation system further includes at least one of: a microphone, a wheel speed sensor, or a ground speed sensor ([0020] teaches the set of sensors 108 may include, for example, one or more of an image sensor, a camera, a video camera, a microphone, an accelerometer, a gyroscope, a geolocation sensor (e.g., a Global Positioning System or GPS device), a radar detector, a magnetometer, lidar (e.g., for measuring distance of a leading vehicle), an altimeter (e.g. a sonic altimeter, a radar altimeter, and/or other types of altimeters), a barometer, a magnetometer, a pressure sensor (e.g. a static pressure sensor, a dynamic pressure sensor, a pitot sensor, etc.), a thermometer, an inertial measurement sensor, a tilt sensor, a motion sensor, a vibration sensor, an ultrasonic sensor, an infrared sensor, a light sensor, a depth sensor, an air speed sensor, a ground speed sensor, an altitude sensor, medical sensors (including but not limited to blood pressure sensor, pulse oximeter, heart rate sensor, etc.), degree-of-freedom sensors (e.g. 6-DOF and/or 9-DOF sensors), a compass, and/or other sensors.).
Regarding Claim 6: Ghanbari further teaches:
wherein the operator scheduling system progressively advances the one or more breaks commensurate with either a time to fatigue or the amount of fatigue ([0032] Comparison component 118 may be configured to compare different performance values, in particular a first performance value (e.g., determined by performance component 116) with a second performance value (e.g., determined by aggregation component 112). For example, comparison component 118 may compare a current performance (of a particular driver on a particular trip) with a particular fleet-specific vehicle operator performance (e.g., a particular performance function such as, by way of non-limiting example, lower level performance function 40b of FIG. 4A). Comparisons may take the actual (current) duration of a particular trip into consideration. Comparisons may take the scheduled trip duration of a particular trip into consideration. For example, the first performance value at the 2-hour mark may be 60%, whereas the second performance value at the same time may be 80%. Accordingly, in some cases, system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator, based on the (absolute or relative) difference between these two performance values. In other cases, no action may be recommended, for example in light of the scheduled trip duration being 2 hours and 10 minutes.);
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ghanbari et al. (US 20230084964 A1, hereinafter “Ghanbari”), in view of Boehm et al. (US 20230040437 A1, hereinafter “Boehm”), in further view of Ricci (US 20140309806 A1), in further view of Molin et al. (US 20140226010 A1, hereinafter “Molin”) as applied to claim 1 above, in further view of Brooks (US 20230054373 A1).
Regarding Claim 5: Ghanbari doesn’t explicitly teach:
wherein a machine learning model is used to detect fatigue in the operator, calculate the amount of fatigue currently experienced by the operator, and set the threshold.
Brooks teaches:
wherein a machine learning model is used to detect fatigue in the operator, calculate the amount of fatigue currently experienced by the operator, and set the threshold. ([0084] the system can monitor the fatigue or alertness of the operators. For example, the assignment system can include one or more sensors that monitor characteristics of the operators to determine if these characteristics indicate that one or more of the operators is not alert or is becoming fatigued. These sensors can include a camera (and optionally one or more processors) that monitors the gaze of an operator to ensure that the operator's eyes are open, attentive, and focused on the input/output device used by the operator to remotely control and/or monitor the vehicle system(s); [0371] teaches the system may have a local data collection system deployed that may use machine learning to enable derivation-based learning outcomes. The controller may learn from and make decisions on a set of data (including data provided by the various sensors).).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Ghanbari, Boehm and Lerner with Brooks’ feature(s) listed above. One would’ve been motivated to do so in order to enable the controller may learn from and make decisions on a set of data (including data provided by the various sensors), by making data-driven predictions and adapting according to the set of data (Brooks; [0371]). By incorporating the teachings of Brooks, one would’ve been able to calculate the amount of fatigue using machine learning.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ghanbari et al. (US 20230084964 A1, hereinafter “Ghanbari”), in view of Boehm et al. (US 20230040437 A1, hereinafter “Boehm”), in further view of Ricci (US 20140309806 A1), in further view of Molin et al. (US 20140226010 A1, hereinafter “Molin”) as applied to claim 1 above, in further view of Patzold et al. (TW 202241742 A).
Regarding Claim 7: Ghanbari teaches:
the system either decreases a power rating of the machine or requires the operator to interact with the display provided in the cabin. ([0038] action component 120 may determine whether to schedule a break (or take another action). Actions taken or recommended by action component 120 may include generating notifications, providing notifications (e.g., notifying a vehicle operator, a stakeholder of a particular fleet, a dispatcher, remote computing server 125, and/or others), scheduling a rest or break, modifying the planned route, modifying the effective speed limit for a particular vehicle, modifying the type of vehicle events a particular vehicle is currently detecting, modifying the sensitivity with which a particular vehicle event is being detected, and/or other actions.; [0041] Client computing platforms 104 may be associated with user interfaces 134. User interfaces 134 may be presented to users 135, including but not limited to vehicle operators, vehicle owners, fleet managers, and/or other stakeholders. In some implementations, notifications (e.g., from notification component 122) may be provided through one or more user interfaces 134 in one or more vehicles. In some implementations, an individual user interface 134 may include one or more controllers, joysticks, track pad, a touch screen, a keypad, touch sensitive and/or physical buttons, switches, buttons, a keyboard, knobs, levers, a display, speakers, a microphone, an indicator light, a printer, and/or other interface devices. User interfaces 134 may be configured to facilitate interaction between users 135 and system 100, including but not limited to receiving input from users 135 and providing notifications and/or recommendations to users 135. In some implementations, received input may, e.g., be used to select how to determine the current speed threshold, or how to detect vehicle events.).
Ghanbari doesn’t explicitly teach:
wherein the system controls the machine by intervening with the machine if, after a break modification is made, the system detects that the amount of fatigue remains greater than the threshold,
Patzold teaches:
wherein the system controls the machine by intervening with the machine if, after a break modification is made, the system detects that the amount of fatigue remains greater than the threshold, ([Par. 54] The present invention is also directed to a vehicle comprising a controller, an input device and an output device for executing the method according to any one of claims 1 to 9. The vehicle may be designed to be able to perform any of the presently outlined method steps, ie the vehicle may comprise any hardware necessary to perform the presently described method steps.; [Par. 70] In a practical example, the method can detect a driver's fatigue and can recommend switching to the refresh mode, including a number of possible suggested actions associated with the refresh mode. The driver can accept the suggestion and the vehicle can take appropriate action. After a given amount of time, such as after 15 minutes, the driver's fatigue level may be assessed again, and still an elevated fatigue level may still be detected. In such cases, it may be found that the suggested action was not successful. As a result, the method can output a number of suggested actions associated with the rest mode. The driver may be prompted to confirm the suggested choice. In case the driver accepts the suggestion, the method can indicate the next possible rest area on a navigation device so that the driver can select a suitable rest area. The method may take into account the state of charge of a vehicle battery when suggesting a rest area. If the state of charge is below a certain given value (eg, 60%), the method may propose an area to charge the vehicle. Preferably, a rest area that can be reached within a given time (eg 15 minutes) can be suggested. Once the area has been reached, the method may advise the driver to set a timer or consider a wake-up timer to complete charging. During breaks, the method may provide noise cancellation and irrelevant message suppression in addition to setting the vehicle to conditions including pleasant ambient light, suitable temperature, etc. Once the timer has expired, the method slowly wakes the driver up by, for example, playing quiet music and/or providing a massage. Then, the method can recheck the driver's fatigue level and update the success rate of the recommended action accordingly. Over time, the method's algorithm learns from a driver's reaction to recommendations from a number of outputs, i.e. whether the driver accepts or rejects the recommendations, from the current context (i.e. weekday, time of day, weather, Driving time, etc., and from the success of the proposed mode, namely the reassessed fatigue level, to establish the driver's preferences and characteristics. Therefore, many future proposals can be improved, and the driver can experience to a highly personalized and efficient vehicle. Examiner notes that one of ordinary skill in the art would reasonably consider the actions of playing quiet music and/or providing a massage as equivalent to controlling the machine.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ghanbari with Patzold’s feature(s) listed above. One would’ve been motivated to do so in order to determine if the suggested action was not successful (Patzold; [70]). By incorporating the teachings of Patzold, one would’ve been able to detect a high level of fatigue after a break, and control the machine in response.
Claims 8-11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ghanbari et al. (US 20230084964 A1, hereinafter “Ghanbari”), in view of Ricci (US 20140309806 A1), in further view of Molin et al. (US 20140226010 A1, hereinafter “Molin”).
Regarding Claim 8: Ghanbari teaches a system for mitigating operation of a machine by an operator if operator fatigue of the operator is detected ([Abstract] Systems and methods for determining and using fleet-specific vehicle operator performance for a set of vehicle operators are disclosed.; [0027] For example, one or more of the driver performance metrics may be related to determinations of operator attentiveness (and/or conversely, distractedness, drowsiness, fatigue, etc.) during a trip or work shift.), the system comprising:
and a system for mitigating operation of a machine by an operator if operator fatigue of the operator is detected, ([0032] system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator, based on the (absolute or relative) difference between these two performance values.; [0014] Determination of attentiveness may include gathering information by monitoring the vehicle operators of one or more vehicles (by way of non-limiting example, direction of gaze, blinking, rate of blinking, change in rate of blinking, duration of closing eyes, change in average duration of closing eyes, tilting of head, angle of tilting of head, frequency of tilting of head, change in frequency of tilting of head, shaking of head, frequency of shaking of head, change in frequency of shaking of head, and/or other bodily movements that may be related to attentiveness, distractedness, fatigue, and/or drowsiness, as well as derivatives thereof), as well as monitoring vehicle operations.; [0038] Referring to FIG. 1, action component 120 may be configured to determine whether to take an action based on one or more determinations and/or comparisons. For example, action component 120 may determine whether to schedule a break (or take another action) for a particular vehicle operator based on a comparison by comparison component 118. Examiner notes that one of ordinary skill in the art would reasonably consider the action to schedule a break based on a performance comparison, as equivalent to mitigating the operation of a machine if operator fatigue is detected.);
the system including: a fatigue mitigation system ([0038] Referring to FIG. 1, action component 120 may be configured to determine whether to take an action based on one or more determinations and/or comparisons. For example, action component 120 may determine whether to schedule a break (or take another action) for a particular vehicle operator based on a comparison by comparison component 118.);
including: a first computing system having ([0013] system 100), a first processor, ([0013] processor(s) 132), first storage medium, ([0013] electronic storage 126), and a first network interface, ([0020] Output signals generated by individual sensors (and/or information based thereon) may be stored and/or transferred in electronic files. In some implementations, output signals may be transferred as one or more streams of data.; [0042] server(s) 102, client computing platform(s) 104, and/or external resources 130 may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via one or more network(s) 13 such as the Internet and/or other networks.);
and a machine interface coupled to the first computing system, the machine interface: formed by a camera, ([0021] Regarding one or more cameras 108a, as used herein, the terms “camera” and/or “image sensor” may include any device that captures image information), a positioning system, (Set of sensors 108 may include, for example, one or more of an image sensor, a camera, a video camera, a microphone, an accelerometer, a gyroscope, a geolocation sensor (e.g., a Global Positioning System or GPS device).), an intervention system, ([0038] Referring to FIG. 1, action component 120 may be configured to determine whether to take an action based on one or more determinations and/or comparisons. Actions taken or recommended by action component 120 may include generating notifications, providing notifications (e.g., notifying a vehicle operator, a stakeholder of a particular fleet, a dispatcher, remote computing server 125, and/or others), scheduling a rest or break, modifying the planned route, modifying the effective speed limit for a particular vehicle, modifying the type of vehicle events a particular vehicle is currently detecting, modifying the sensitivity with which a particular vehicle event is being detected, and/or other actions. (Examiner notes that one of ordinary skill in the art would reasonably consider the action component 120 from Ghanbari as equivalent to the intervention system in Applicant’s claim. This interpretation is further supported by Applicant’s own specification, where in par. [0020], Applicant discloses the intervention system “may be configured to provide instantaneous feedback to the operator”, which can be reasonably interpreted as notifications to the operator.), and a display ([0041] an individual user interface 134 may include one or more controllers, joysticks, track pad, a touch screen, a keypad, touch sensitive and/or physical buttons, switches, buttons, a keyboard, knobs, levers, a display, speakers, a microphone, an indicator light, a printer, and/or other interface devices. User interfaces 134 may be configured to facilitate interaction between users 135 and system 100, including but not limited to receiving input from users 135 and providing notifications and/or recommendations to users 135. In some implementations, received input may, e.g., be used to select how to determine the current speed threshold, or how to detect vehicle events.);
and an operator scheduling system, ([0033] system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator.); including a second computing system having a second processor. a second storage medium, and a second network interface; (([0013] In some implementations, system 100 may include one or more of server(s) 102, electronic storage 126, processor(s) 132, set of sensors 108, user interface(s) 134, network(s) 13, client computing platform(s) 104, external resources 130, a remote computing server 125, and/or other components.; [0047] The electronic storage media of electronic storage 126 may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with server(s) 102 and/or removable storage that is removably connectable to server(s) 102; ([0020] Output signals generated by individual sensors (and/or information based thereon) may be stored and/or transferred in electronic files. In some implementations, output signals may be transferred as one or more streams of data.; [0042] server(s) 102, client computing platform(s) 104, and/or external resources 130 may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via one or more network(s) 13 such as the Internet and/or other networks.);
to: track, by the positioning system, a location of the machine, ([0016] Set of sensors 108 may be configured to generate output signals conveying information related to (operation of) vehicle 12, a location of vehicle 12; [0020] a geolocation sensor (e.g., a Global Positioning System or GPS device).);
schedule one or more breaks at pre-set default intervals, ([0032] system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator);
receive data, corresponding to the operator and the machine, captured by the fatigue mitigation system, ([0014] Individual vehicles may include a set of resources for information gathering, data processing, and/or electronic storage, including but not limited to persistent storage. Individual vehicles may include sensors (e.g., set of sensors 108 configured to generate and/or otherwise gather data, such as output signals). In some implementations, individual vehicles may be configured to detect vehicle events, e.g., based on output signals generated by set of sensors 108. As used herein, the term “vehicle event” may include occurrences of events involving one or more vehicles. As such, detection of vehicle events may include gathering information by monitoring the operation of one or more vehicles, including but not limited to information related to current or past vehicle speeds, current or current location, and/or other information pertinent to detecting of vehicle events. In some implementations, individual vehicles may be configured to determine operator attentiveness, e.g., based on output signals generated by set of sensors 108 (e.g., by one or more cameras 108a). Determination of attentiveness may include gathering information by monitoring the vehicle operators of one or more vehicles (by way of non-limiting example, direction of gaze, blinking, rate of blinking, change in rate of blinking, duration of closing eyes, change in average duration of closing eyes, tilting of head, angle of tilting of head, frequency of tilting of head, change in frequency of tilting of head, shaking of head, frequency of shaking of head, change in frequency of shaking of head, and/or other bodily movements that may be related to attentiveness, distractedness, fatigue, and/or drowsiness, as well as derivatives thereof), as well as monitoring vehicle operations.);
based on the received data, detect if fatigue is being experienced by the operator ([0027] For example, one or more of the driver performance metrics may be related to determinations of operator attentiveness (and/or conversely, distractedness, drowsiness, fatigue, etc.) during a trip or work shift. By way of non-limiting example, such determinations may be based on image information captured of a particular vehicle operator during a trip or work shift. For example, such determinations may include direction of gaze, blinking, rate of blinking, change in rate of blinking, duration of closing eyes, change in average duration of closing eyes, tilting of head, angle of tilting of head, frequency of tilting of head, change in frequency of tilting of head, shaking of head, frequency of shaking of head, change in frequency of shaking of head, and/or other operator actions or bodily movements that may be related to attentiveness, distractedness, fatigue, and/or drowsiness, as well as derivatives thereof.);
calculate an amount of fatigue currently experienced by the operator based on the received data, ([0027] For example, one or more of the driver performance metrics may be related to determinations of operator attentiveness (and/or conversely, distractedness, drowsiness, fatigue, etc.) during a trip or work shift. By way of non-limiting example, such determinations may be based on image information captured of a particular vehicle operator during a trip or work shift. For example, such determinations may include direction of gaze, blinking, rate of blinking, change in rate of blinking, duration of closing eyes, change in average duration of closing eyes, tilting of head, angle of tilting of head, frequency of tilting of head, change in frequency of tilting of head, shaking of head, frequency of shaking of head, change in frequency of shaking of head, and/or other operator actions or bodily movements that may be related to attentiveness, distractedness, fatigue, and/or drowsiness, as well as derivatives thereof. For this example, more occurrences of such operator actions or bodily movements would correlate to a lower performance of the particular vehicle operator. For example, a numerical value of a particular driver performance metric may be expressed as a percentage between 0% and 100%, where 100% indicates flawless performance (e.g., having perfect attentiveness), and 0% indicates a terribly flawed performance. In some implementations, values for this particular driver performance metric may be determined at intervals and/or intermittently through a particular trip or work shift (e.g., more than once). In some implementations, values for this particular driver performance metric may be determined continuously through a particular trip or work shift (e.g., every minute, every 5 minutes, every 10 minutes, every 15 minutes, every hours, etc.).);
determine that the amount of fatigue currently experienced by the operator is greater than a threshold, ([0029] teaches vehicle operator performance at a particular duration may range between a lower level and a higher level. These levels may correspond to a standard deviation from average, or to the range within which 80% (or some other percentage) of a particular fleet's drivers operate, or to another mathematical definition of variance. In particular, an individual vehicle operator's performance outside of this range (in particular, below lower level performance function 40b) may be considered noteworthy and/or potentially in need of a subsequent action by system 100.);
advance at least one of the one or more breaks in response to the determination that the amount of fatigue is greater than the threshold, ([0032] Comparison component 118 may be configured to compare different performance values, in particular a first performance value (e.g., determined by performance component 116) with a second performance value (e.g., determined by aggregation component 112). For example, comparison component 118 may compare a current performance (of a particular driver on a particular trip) with a particular fleet-specific vehicle operator performance (e.g., a particular performance function such as, by way of non-limiting example, lower level performance function 40b of FIG. 4A). Comparisons may take the actual (current) duration of a particular trip into consideration. Comparisons may take the scheduled trip duration of a particular trip into consideration. For example, the first performance value at the 2-hour mark may be 60%, whereas the second performance value at the same time may be 80%. Accordingly, in some cases, system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator, based on the (absolute or relative) difference between these two performance values. In other cases, no action may be recommended, for example in light of the scheduled trip duration being 2 hours and 10 minutes.; [0033] In some implementations, comparison component 118 may be configured to compare the changes in a first performance value (e.g., since the start of a trip) with the changes in a second performance value. For example, the first performance value may have dropped 20% in the past 3 hours, whereas the second performance value only dropped 10% in the same timeframe. Accordingly, in some cases, system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator. A recommendation may also be based on the scheduled trip duration for the particular trip.; [0035] In some implementations, comparison component 118 may be configured to compare a first performance value with a threshold performance level. In some implementations, the threshold performance level may be a particular performance function determined by performance component 116, such as, by way of non-limiting example, lower level performance function 40b of FIG. 4A. In some implementations, the threshold performance level may be based on a particular performance function determined by performance component 116, such as, by way of non-limiting example, 10% less than lower level performance function 40b of FIG. 4A. In some implementations, the threshold performance level may be a constant performance level, such as a first threshold level 40d of FIG. 4A, which has the same value, here 60%, throughout the duration of exemplary diagram 400. In some implementations, the threshold performance level may be dynamically change, such as a second threshold level 40e of FIG. 4A, which gradually decreases in value, here from 70% to 60%, throughout the duration of exemplary diagram 400.);
and control the machine based on the threshold. ([0015] operation of vehicle 12 may be actively and primarily controlled by a vehicle operator (i.e., a human operator). In such a case, a non-human vehicle operator may take over (or be requested to take over) control of the vehicle in certain circumstances.; [0034] In some implementations, comparison component 118 may be configured to compare the rate of change of a first performance value with the rate of change of a second performance value. For example, the first rate of change may be minus 20% per hour, whereas the second rate of change may be minus 5% per hour at a similar moment or duration of a trip. Accordingly, in some cases, system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator. In some cases, the particular recommended action may vary based on the remaining duration of the particular trip. For example, a 5-minute break may be sufficient for a remaining trip duration of 30 minutes, whereas a 1-hour break may be better suited for a remaining trip duration of 3 hours.; [0038] Actions taken or recommended by action component 120 may include generating notifications, providing notifications (e.g., notifying a vehicle operator, a stakeholder of a particular fleet, a dispatcher, remote computing server 125, and/or others), scheduling a rest or break, modifying the planned route, modifying the effective speed limit for a particular vehicle, modifying the type of vehicle events a particular vehicle is currently detecting, modifying the sensitivity with which a particular vehicle event is being detected, and/or other actions. Examiner notes that one of ordinary skill in the art would reasonably interpret the actions for modifying the effective speed limit for a particular vehicle, modifying the type of vehicle events a particular vehicle is currently detecting, modifying the sensitivity with which a particular vehicle event is being detected, as equivalent to controlling the machine.).
Ghanbari doesn’t explicitly teach:
and automatically customized for the operator upon starting the operation of the machine by the operator
and cause the intervention system to provide a haptic alert to the operator
Ricci teaches:
and automatically customized for the operator upon starting the operation of the machine by the operator; ([0123] The personal settings of the vehicle occupant can include a plurality of a seat setting, climate control setting, lighting setting, configuration of an instrument cluster on a screen, rear view mirror setting, driving mode, media channel setting or preset, media delivery preference, music genre preference, scheduled program, playlist, synchronization with cloud-based data associated with the vehicle occupant, application-specific personalization and selections, and a display setting and configuration.; [0695] Device detection can be in response to or triggered by a sensed event other than receipt of a ping from the computational device. As noted, device detection can be receipt of information from one or more on board sensors that a new occupant has entered the vehicle. Exemplary information includes a door opening or closing, a successful authentication of an occupant or computational device, a sensed load in a seat, detection of movement within the vehicle, and detection of initiation of a vehicle task, function or operation, such as a key inserted in an ignition, engine start up, and the like.; [0709] While this logic is described with respect to the location of the computational device, it is to be understood that this determination is optional. Whether or not a communication device is enabled to connect to the vehicle network 356 or communication subsystem 1008 can be based solely on successful authentication. In this way, the vehicle on board computer can connect automatically to the owner's home virtual private network to upload and/or download information, settings, and other information (such as user input into the vehicle computer, vehicle driving history (e.g., miles traveled, travel traceroutes, speeds traveled, and locations visited), vehicle service information (such as gas and fluid levels, engine problems, alarms or warnings activated, and the like), input received by on board applications from the user (such as scheduled appointments, notes, documents, and the like), applications downloaded, and the like. Synchronization of the on board vehicle and home computer can occur automatically whether the vehicle is turned on or off. This can be highly beneficial when the vehicle is parked in the garage.; [0711] In step 2020, the device discovery daemon 1020 permits or enables connection of the computational device with the vehicle network 356 or communication subsystem 1008 and optionally stipulates or defines what set of tasks, functions, and/or operations the user of the computational device can perform using the computational device, such as based on the location of the computational device within the vehicle and/or based on the authentication credentials (e.g., the identity of the computational device user).; [0753] In step 2220, the media server 2112 configures the filtered media for the capabilities of the user device and/or on board vehicle display and in accordance with user preferences.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ghanbari with Ricci’s feature(s) listed above. One would’ve been motivated to do so in order to maintain a persona of a vehicle occupant and, based on the persona of the vehicle occupant and vehicle-related information, performing an action assisting the vehicle occupant (Ricci; [Abstract]). By incorporating the teachings of Ricci, one would’ve been able to automatically customize the interface upon starting operation of the vehicle.
Ricci doesn’t teach:
and cause the intervention system to provide a haptic alert to the operator
Molin teaches:
and cause the intervention system to provide a haptic alert to the operator, ([0184] Haptic Warning--This may be in the form of a brake pulse applied to the vehicle to warn of an impending collision if the driver has not reacted to the Distance Alert nor the Forward Collision Warning).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ghanbari with Molin’s feature(s) listed above. One would’ve been motivated to do so in order to monitor, correct or reward driver behavior, and to implement driver education and training programs (Molin; [Abstract]). By incorporating the teachings of Molin, one would’ve been able to issue haptic alerts to drivers.
Regarding Claim 9: Ghanbari further teaches:
wherein the data corresponding to the operator and the machine includes at least one of: operator head pose, body attitude, facial features, and operating characteristics of the operator ([0014] Determination of attentiveness may include gathering information by monitoring the vehicle operators of one or more vehicles (by way of non-limiting example, direction of gaze, blinking, rate of blinking, change in rate of blinking, duration of closing eyes, change in average duration of closing eyes, tilting of head, angle of tilting of head, frequency of tilting of head, change in frequency of tilting of head, shaking of head, frequency of shaking of head, change in frequency of shaking of head, and/or other bodily movements that may be related to attentiveness, distractedness, fatigue, and/or drowsiness, as well as derivatives thereof), as well as monitoring vehicle operations.).
Regarding Claim 10: Ghanbari further teaches:
further comprising a server in communication with the fatigue mitigation system, the fatigue mitigation configured to store data captured by and received from the sensor system ([Fig. 1] Server(s) 102, Set of sensors 108, and Electronic Storage 126.; [0013] system 100 may include one or more of server(s) 102, electronic storage 126, processor(s) 132, set of sensors 108.; [0046] teaches server(s) 102 may include electronic storage 126.; [0047] teaches electronic storage 126 may comprise non-transitory storage media that electronically stores information. The electronic storage media of electronic storage 126 may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with server(s) 102 and/or removable storage that is removably connectable to server(s) 102 via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.).; [0027] For example, one or more of the driver performance metrics may be related to determinations of operator attentiveness (and/or conversely, distractedness, drowsiness, fatigue, etc.) during a trip or work shift.).
Regarding Claim 11: Ghanbari further teaches:
wherein the fatigue mitigation system further includes at least one of a microphone, a wheel speed sensor, or a ground speed sensor ([0020] teaches the set of sensors 108 may include, for example, one or more of an image sensor, a camera, a video camera, a microphone, an accelerometer, a gyroscope, a geolocation sensor (e.g., a Global Positioning System or GPS device), a radar detector, a magnetometer, lidar (e.g., for measuring distance of a leading vehicle), an altimeter (e.g. a sonic altimeter, a radar altimeter, and/or other types of altimeters), a barometer, a magnetometer, a pressure sensor (e.g. a static pressure sensor, a dynamic pressure sensor, a pitot sensor, etc.), a thermometer, an inertial measurement sensor, a tilt sensor, a motion sensor, a vibration sensor, an ultrasonic sensor, an infrared sensor, a light sensor, a depth sensor, an air speed sensor, a ground speed sensor, an altitude sensor, medical sensors (including but not limited to blood pressure sensor, pulse oximeter, heart rate sensor, etc.), degree-of-freedom sensors (e.g. 6-DOF and/or 9-DOF sensors), a compass, and/or other sensors.
Regarding Claim 13: Ghanbari further teaches:
wherein the operator scheduling system progressively advances the one or more breaks commensurate with either a time to fatigue or the amount of fatigue ([0027] For example, one or more of the driver performance metrics may be related to determinations of operator attentiveness (and/or conversely, distractedness, drowsiness, fatigue, etc.) during a trip or work shift. By way of non-limiting example, such determinations may be based on image information captured of a particular vehicle operator during a trip or work shift. For example, such determinations may include direction of gaze, blinking, rate of blinking, change in rate of blinking, duration of closing eyes, change in average duration of closing eyes, tilting of head, angle of tilting of head, frequency of tilting of head, change in frequency of tilting of head, shaking of head, frequency of shaking of head, change in frequency of shaking of head, and/or other operator actions or bodily movements that may be related to attentiveness, distractedness, fatigue, and/or drowsiness, as well as derivatives thereof. For this example, more occurrences of such operator actions or bodily movements would correlate to a lower performance of the particular vehicle operator. For example, a numerical value of a particular driver performance metric may be expressed as a percentage between 0% and 100%, where 100% indicates flawless performance (e.g., having perfect attentiveness), and 0% indicates a terribly flawed performance. In some implementations, values for this particular driver performance metric may be determined at intervals and/or intermittently through a particular trip or work shift (e.g., more than once). In some implementations, values for this particular driver performance metric may be determined continuously through a particular trip or work shift (e.g., every minute, every 5 minutes, every 10 minutes, every 15 minutes, every hours, etc.).; [0032] Comparison component 118 may be configured to compare different performance values, in particular a first performance value (e.g., determined by performance component 116) with a second performance value (e.g., determined by aggregation component 112). For example, comparison component 118 may compare a current performance (of a particular driver on a particular trip) with a particular fleet-specific vehicle operator performance (e.g., a particular performance function such as, by way of non-limiting example, lower level performance function 40b of FIG. 4A). Comparisons may take the actual (current) duration of a particular trip into consideration. Comparisons may take the scheduled trip duration of a particular trip into consideration. For example, the first performance value at the 2-hour mark may be 60%, whereas the second performance value at the same time may be 80%. Accordingly, in some cases, system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator, based on the (absolute or relative) difference between these two performance values. In other cases, no action may be recommended, for example in light of the scheduled trip duration being 2 hours and 10 minutes.).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Ghanbari et al. (US 20230084964 A1, hereinafter “Ghanbari”), in view of Ricci (US 20140309806 A1), in further view of Molin et al. (US 20140226010 A1, hereinafter “Molin”), as applied to claim 8 above, in further view of Brooks (US 20230054373 A1).
Regarding Claim 12: Ghanbari doesn’t explicitly teach:
wherein a machine learning model is used to detect fatigue in the operator, calculate the amount of fatigue currently experienced by the operator, and set the threshold.
Brooks teaches:
wherein a machine learning model is used to detect fatigue in the operator, calculate the amount of fatigue currently experienced by the operator, and set the threshold ([0084] the system can monitor the fatigue or alertness of the operators. For example, the assignment system can include one or more sensors that monitor characteristics of the operators to determine if these characteristics indicate that one or more of the operators is not alert or is becoming fatigued. These sensors can include a camera (and optionally one or more processors) that monitors the gaze of an operator to ensure that the operator's eyes are open, attentive, and focused on the input/output device used by the operator to remotely control and/or monitor the vehicle system(s).; [0371] the system may have a local data collection system deployed that may use machine learning to enable derivation-based learning outcomes. The controller may learn from and make decisions on a set of data (including data provided by the various sensors).).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ghanbari with Brooks’ feature(s) listed above. One would’ve been motivated to do so in order to enable the controller may learn from and make decisions on a set of data (including data provided by the various sensors), by making data-driven predictions and adapting according to the set of data (Brooks; [0371]). By incorporating the teachings of Brooks, one would’ve been able to calculate the amount of fatigue using machine learning.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Ghanbari et al. (US 20230084964 A1, hereinafter “Ghanbari”), in view of Ricci (US 20140309806 A1), in further view of Molin et al. (US 20140226010 A1, hereinafter “Molin”), as applied to claim 8 above, in further view of Patzold et al. (TW 202241742 A, hereinafter “Patzold”).
Regarding Claim 14: Ghanbari further teaches:
the system either decreases a power rating of the machine or requires the operator to interact with the display provided in the cabin. ([0038] action component 120 may determine whether to schedule a break (or take another action). Actions taken or recommended by action component 120 may include generating notifications, providing notifications (e.g., notifying a vehicle operator, a stakeholder of a particular fleet, a dispatcher, remote computing server 125, and/or others), scheduling a rest or break, modifying the planned route, modifying the effective speed limit for a particular vehicle, modifying the type of vehicle events a particular vehicle is currently detecting, modifying the sensitivity with which a particular vehicle event is being detected, and/or other actions.; [0041] Client computing platforms 104 may be associated with user interfaces 134. User interfaces 134 may be presented to users 135, including but not limited to vehicle operators, vehicle owners, fleet managers, and/or other stakeholders. In some implementations, notifications (e.g., from notification component 122) may be provided through one or more user interfaces 134 in one or more vehicles. In some implementations, an individual user interface 134 may include one or more controllers, joysticks, track pad, a touch screen, a keypad, touch sensitive and/or physical buttons, switches, buttons, a keyboard, knobs, levers, a display, speakers, a microphone, an indicator light, a printer, and/or other interface devices. User interfaces 134 may be configured to facilitate interaction between users 135 and system 100, including but not limited to receiving input from users 135 and providing notifications and/or recommendations to users 135. In some implementations, received input may, e.g., be used to select how to determine the current speed threshold, or how to detect vehicle events.).
Ghanbari doesn’t explicitly teach:
wherein the system controls the machine by intervening with the machine if, after a break modification is made, the system detects that the amount of fatigue remains greater than the threshold,
Patzold teaches:
wherein the system controls the machine by intervening with the machine if, after a break modification is made, the system detects that the amount of fatigue remains greater than the threshold, ([Par. 54] The present invention is also directed to a vehicle comprising a controller, an input device and an output device for executing the method according to any one of claims 1 to 9. The vehicle may be designed to be able to perform any of the presently outlined method steps, ie the vehicle may comprise any hardware necessary to perform the presently described method steps.; [Par. 70] In a practical example, the method can detect a driver's fatigue and can recommend switching to the refresh mode, including a number of possible suggested actions associated with the refresh mode. The driver can accept the suggestion and the vehicle can take appropriate action. After a given amount of time, such as after 15 minutes, the driver's fatigue level may be assessed again, and still an elevated fatigue level may still be detected. In such cases, it may be found that the suggested action was not successful. As a result, the method can output a number of suggested actions associated with the rest mode. The driver may be prompted to confirm the suggested choice. In case the driver accepts the suggestion, the method can indicate the next possible rest area on a navigation device so that the driver can select a suitable rest area. The method may take into account the state of charge of a vehicle battery when suggesting a rest area. If the state of charge is below a certain given value (eg, 60%), the method may propose an area to charge the vehicle. Preferably, a rest area that can be reached within a given time (eg 15 minutes) can be suggested. Once the area has been reached, the method may advise the driver to set a timer or consider a wake-up timer to complete charging. During breaks, the method may provide noise cancellation and irrelevant message suppression in addition to setting the vehicle to conditions including pleasant ambient light, suitable temperature, etc. Once the timer has expired, the method slowly wakes the driver up by, for example, playing quiet music and/or providing a massage. Then, the method can recheck the driver's fatigue level and update the success rate of the recommended action accordingly. Over time, the method's algorithm learns from a driver's reaction to recommendations from a number of outputs, i.e. whether the driver accepts or rejects the recommendations, from the current context (i.e. weekday, time of day, weather, Driving time, etc., and from the success of the proposed mode, namely the reassessed fatigue level, to establish the driver's preferences and characteristics. Therefore, many future proposals can be improved, and the driver can experience to a highly personalized and efficient vehicle. Examiner notes that one of ordinary skill in the art would reasonably consider the actions of playing quiet music and/or providing a massage as equivalent to controlling the machine.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ghanbari with Patzold’s feature(s) listed above. One would’ve been motivated to do so in order to determine if the suggested action was not successful (Patzold; [70]). By incorporating the teachings of Patzold, one would’ve been able to detect a high level of fatigue after a break, and control the machine in response.
Claims 15-19, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Ghanbari et al. (US 20230084964 A1), in view of Brooks (US 20230054373 A1), in further view of Ricci (US 20140309806 A1), in further view of Molin et al. (US 20140226010 A1).
Regarding Claim 15: Ghanbari teaches:
monitoring with the fatigue mitigation system, a face of the operator for instances of distraction including reduced or no response to at least one of auditory cues or visual cues and generating operator data; ([0018] teaches the set of sensors 108 may generate output signals conveying information related to a vehicle operator of vehicle 12, such as visual information, motion-related information, position-related information, biometric information, medical information, and/or other information. The information related to the biological activity of a particular vehicle operator may include heart rate, respiration rate, blood pressure, blinking, head nodding, head movement, verbal expressions, responses to conditions in the physical environment in and/or around vehicle 12.; [0027] For example, one or more of the driver performance metrics may be related to determinations of operator attentiveness (and/or conversely, distractedness, drowsiness, fatigue, etc.) during a trip or work shift. By way of non-limiting example, such determinations may be based on image information captured of a particular vehicle operator during a trip or work shift. For example, such determinations may include direction of gaze, blinking, rate of blinking, change in rate of blinking, duration of closing eyes, change in average duration of closing eyes, tilting of head, angle of tilting of head, frequency of tilting of head, change in frequency of tilting of head, shaking of head, frequency of shaking of head, change in frequency of shaking of head, and/or other operator actions or bodily movements that may be related to attentiveness, distractedness, fatigue, and/or drowsiness, as well as derivatives thereof.; [0026] For example, one or more of the driver performance metrics may be related to occurrences of particular vehicle events during a trip or work shift. By way of non-limiting example, such vehicle events may include speeding, hard braking, hard braking where the vehicle in front is not showing its brake lights on, near collisions, swerving, swerving-to-stay-within-a-lane, failing to maintain proper/predetermined following distance, and/or other vehicle events.);
tracking, with the fatigue mitigation system, operating conditions of the machine and generating machine data; ([0019] teaches in some implementations, set of sensors 108 may generate output signals conveying information related to the context of vehicle 12. The information related to the context of vehicle 12 may include information related to movement of vehicle 12, an orientation of vehicle 12, a geographic position of vehicle 12, a spatial position of vehicle 12 relative to other objects, a tilt angle of vehicle 12, an inclination/declination angle of vehicle 12, and/or other information.);
transmitting the operator data and the machine data from the machine interface to an operator scheduling system; ([0014] Individual vehicles may include a set of resources for information gathering, data processing, and/or electronic storage, including but not limited to persistent storage. Individual vehicles may include sensors (e.g., set of sensors 108 configured to generate and/or otherwise gather data, such as output signals). In some implementations, individual vehicles may be configured to detect vehicle events, e.g., based on output signals generated by set of sensors 108… Determination of attentiveness may include gathering information by monitoring the vehicle operators of one or more vehicles (by way of non-limiting example, direction of gaze, blinking, rate of blinking, change in rate of blinking, duration of closing eyes, change in average duration of closing eyes, tilting of head, angle of tilting of head, frequency of tilting of head, change in frequency of tilting of head, shaking of head, frequency of shaking of head, change in frequency of shaking of head, and/or other bodily movements that may be related to attentiveness, distractedness, fatigue, and/or drowsiness, as well as derivatives thereof), as well as monitoring vehicle operations.; [0032] computing server 125 may be configured to receive, analyze, and/or otherwise process information from one of more vehicles, including but not limited to vehicle 12.;
based on the operator data and the machine data, detecting that fatigue is currently being experienced by the operator; ([0027] teaches one or more of the driver performance metrics may be related to determinations of operator attentiveness (and/or conversely, distractedness, drowsiness, fatigue, etc.) during a trip or work shift.); [0014] individual vehicles may be configured to determine operator attentiveness, e.g., based on output signals generated by set of sensors 108 (e.g., by one or more cameras 108a). Determination of attentiveness may include gathering information by monitoring the vehicle operators of one or more vehicles (by way of non-limiting example, direction of gaze, blinking, rate of blinking, change in rate of blinking, duration of closing eyes, change in average duration of closing eyes, tilting of head, angle of tilting of head, frequency of tilting of head, change in frequency of tilting of head, shaking of head, frequency of shaking of head, change in frequency of shaking of head, and/or other bodily movements that may be related to attentiveness, distractedness, fatigue, and/or drowsiness, as well as derivatives thereof), as well as monitoring vehicle operations.);
calculating an amount of fatigue currently experienced by the operator; ([0027] For example, one or more of the driver performance metrics may be related to determinations of operator attentiveness (and/or conversely, distractedness, drowsiness, fatigue, etc.) during a trip or work shift. By way of non-limiting example, such determinations may be based on image information captured of a particular vehicle operator during a trip or work shift. For example, such determinations may include direction of gaze, blinking, rate of blinking, change in rate of blinking, duration of closing eyes, change in average duration of closing eyes, tilting of head, angle of tilting of head, frequency of tilting of head, change in frequency of tilting of head, shaking of head, frequency of shaking of head, change in frequency of shaking of head, and/or other operator actions or bodily movements that may be related to attentiveness, distractedness, fatigue, and/or drowsiness, as well as derivatives thereof. For this example, more occurrences of such operator actions or bodily movements would correlate to a lower performance of the particular vehicle operator. For example, a numerical value of a particular driver performance metric may be expressed as a percentage between 0% and 100%, where 100% indicates flawless performance (e.g., having perfect attentiveness), and 0% indicates a terribly flawed performance. In some implementations, values for this particular driver performance metric may be determined at intervals and/or intermittently through a particular trip or work shift (e.g., more than once). In some implementations, values for this particular driver performance metric may be determined continuously through a particular trip or work shift (e.g., every minute, every 5 minutes, every 10 minutes, every 15 minutes, every hours, etc.).);
determining that the amount of fatigue currently experienced by the operator is greater than a threshold; ([0029] teaches vehicle operator performance at a particular duration may range between a lower level and a higher level. These levels may correspond to a standard deviation from average, or to the range within which 80% (or some other percentage) of a particular fleet's drivers operate, or to another mathematical definition of variance. In particular, an individual vehicle operator's performance outside of this range (in particular, below lower level performance function 40b) may be considered noteworthy and/or potentially in need of a subsequent action by system 100.);
advancing one or more breaks for the operator in response to a determination that the amount of fatigue is greater than the threshold, ([0032] Comparison component 118 may be configured to compare different performance values, in particular a first performance value (e.g., determined by performance component 116) with a second performance value (e.g., determined by aggregation component 112). For example, comparison component 118 may compare a current performance (of a particular driver on a particular trip) with a particular fleet-specific vehicle operator performance (e.g., a particular performance function such as, by way of non-limiting example, lower level performance function 40b of FIG. 4A). Comparisons may take the actual (current) duration of a particular trip into consideration. Comparisons may take the scheduled trip duration of a particular trip into consideration. For example, the first performance value at the 2-hour mark may be 60%, whereas the second performance value at the same time may be 80%. Accordingly, in some cases, system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator, based on the (absolute or relative) difference between these two performance values. In other cases, no action may be recommended, for example in light of the scheduled trip duration being 2 hours and 10 minutes.; [0033] In some implementations, comparison component 118 may be configured to compare the changes in a first performance value (e.g., since the start of a trip) with the changes in a second performance value. For example, the first performance value may have dropped 20% in the past 3 hours, whereas the second performance value only dropped 10% in the same timeframe. Accordingly, in some cases, system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator. A recommendation may also be based on the scheduled trip duration for the particular trip.; [0035] In some implementations, comparison component 118 may be configured to compare a first performance value with a threshold performance level. In some implementations, the threshold performance level may be a particular performance function determined by performance component 116, such as, by way of non-limiting example, lower level performance function 40b of FIG. 4A. In some implementations, the threshold performance level may be based on a particular performance function determined by performance component 116, such as, by way of non-limiting example, 10% less than lower level performance function 40b of FIG. 4A. In some implementations, the threshold performance level may be a constant performance level, such as a first threshold level 40d of FIG. 4A, which has the same value, here 60%, throughout the duration of exemplary diagram 400. In some implementations, the threshold performance level may be dynamically change, such as a second threshold level 40e of FIG. 4A, which gradually decreases in value, here from 70% to 60%, throughout the duration of exemplary diagram 400.);
the one or more breaks being previously scheduled at pre-set default intervals; ([0032] system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator);
and controlling machine based on the threshold. ([0015] operation of vehicle 12 may be actively and primarily controlled by a vehicle operator (i.e., a human operator). In such a case, a non-human vehicle operator may take over (or be requested to take over) control of the vehicle in certain circumstances.; [0034] In some implementations, comparison component 118 may be configured to compare the rate of change of a first performance value with the rate of change of a second performance value. For example, the first rate of change may be minus 20% per hour, whereas the second rate of change may be minus 5% per hour at a similar moment or duration of a trip. Accordingly, in some cases, system 100 may be configured to recommend taking a particular action, including but not limited to scheduling a break for the particular vehicle operator. In some cases, the particular recommended action may vary based on the remaining duration of the particular trip. For example, a 5-minute break may be sufficient for a remaining trip duration of 30 minutes, whereas a 1-hour break may be better suited for a remaining trip duration of 3 hours.; [0038] Actions taken or recommended by action component 120 may include generating notifications, providing notifications (e.g., notifying a vehicle operator, a stakeholder of a particular fleet, a dispatcher, remote computing server 125, and/or others), scheduling a rest or break, modifying the planned route, modifying the effective speed limit for a particular vehicle, modifying the type of vehicle events a particular vehicle is currently detecting, modifying the sensitivity with which a particular vehicle event is being detected, and/or other actions. Examiner notes that one of ordinary skill in the art would reasonably interpret the actions for modifying the effective speed limit for a particular vehicle, modifying the type of vehicle events a particular vehicle is currently detecting, modifying the sensitivity with which a particular vehicle event is being detected, as equivalent to controlling the machine.).
Ghanbari doesn’t teach:
automatically customizing a machine interface mitigation system for an operator of a machine upon starting an operation of the machine by the operator; (
compiling the operator data and the machine data through a machine interface;
and causing the fatigue mitigation system to provide a haptic alert to the operator;
Brooks teaches:
compiling the operator data and the machine data through a machine interface; ([0200] teaches data representing the states of these vehicle systems can be aggregated and presented to the remote operator by the CRM unit via the output device. These data include current locations, speeds, and statuses of the vehicle systems and the crew members on the vehicle systems (e.g., from the controller, alerter system, CRM unit, or other data source), the location of each vehicle system relative to each other and other waypoints, and physical aspects of the region of operation (e.g., network switch states, signals from dispatcher, maintenance areas, slippery areas, etc.). [0217] The sensor array can provide data representative of the onboard operator alertness and/or the operational health of components of the vehicle system.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Ghanbari with Brooks’ feature(s) listed above. One would’ve been motivated to do so in order to determine when to increase and/or decrease the number of off-board operators to assign to controlling operations of the same vehicle system. (Brooks; [0217]). By incorporating the teachings of Brooks, one would’ve been able to compile operator data and the machine data through a machine interface.
Brooks doesn’t teach:
automatically customizing a machine interface mitigation system for an operator of a machine upon starting an operation of the machine by the operator;
and causing the fatigue mitigation system to provide a haptic alert to the operator;
Ricci teaches:
automatically customizing a machine interface mitigation system for an operator of a machine upon starting an operation of the machine by the operator; (([0123] The personal settings of the vehicle occupant can include a plurality of a seat setting, climate control setting, lighting setting, configuration of an instrument cluster on a screen, rear view mirror setting, driving mode, media channel setting or preset, media delivery preference, music genre preference, scheduled program, playlist, synchronization with cloud-based data associated with the vehicle occupant, application-specific personalization and selections, and a display setting and configuration.; [0695] Device detection can be in response to or triggered by a sensed event other than receipt of a ping from the computational device. As noted, device detection can be receipt of information from one or more on board sensors that a new occupant has entered the vehicle. Exemplary information includes a door opening or closing, a successful authentication of an occupant or computational device, a sensed load in a seat, detection of movement within the vehicle, and detection of initiation of a vehicle task, function or operation, such as a key inserted in an ignition, engine start up, and the like.; [0709] While this logic is described with respect to the location of the computational device, it is to be understood that this determination is optional. Whether or not a communication device is enabled to connect to the vehicle network 356 or communication subsystem 1008 can be based solely on successful authentication. In this way, the vehicle on board computer can connect automatically to the owner's home virtual private network to upload and/or download information, settings, and other information (such as user input into the vehicle computer, vehicle driving history (e.g., miles traveled, travel traceroutes, speeds traveled, and locations visited), vehicle service information (such as gas and fluid levels, engine problems, alarms or warnings activated, and the like), input received by on board applications from the user (such as scheduled appointments, notes, documents, and the like), applications downloaded, and the like. Synchronization of the on board vehicle and home computer can occur automatically whether the vehicle is turned on or off. This can be highly beneficial when the vehicle is parked in the garage.; [0711] In step 2020, the device discovery daemon 1020 permits or enables connection of the computational device with the vehicle network 356 or communication subsystem 1008 and optionally stipulates or defines what set of tasks, functions, and/or operations the user of the computational device can perform using the computational device, such as based on the location of the computational device within the vehicle and/or based on the authentication credentials (e.g., the identity of the computational device user).; [0753] In step 2220, the media server 2112 configures the filtered media for the capabilities of the user device and/or on board vehicle display and in accordance with user preferences.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ghanbari with Ricci’s feature(s) listed above. One would’ve been motivated to do so in order to maintain a persona of a vehicle occupant and, based on the persona of the vehicle occupant and vehicle-related information, performing an action assisting the vehicle occupant (Ricci; [Abstract]). By incorporating the teachings of Ricci, one would’ve been able to automatically customize the interface upon starting operation of the vehicle.
Ricci doesn’t teach:
and cause the intervention system to provide a haptic alert to the operator
Molin teaches:
and cause the intervention system to provide a haptic alert to the operator, ([0184] Haptic Warning--This may be in the form of a brake pulse applied to the vehicle to warn of an impending collision if the driver has not reacted to the Distance Alert nor the Forward Collision Warning).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ghanbari with Molin’s feature(s) listed above. One would’ve been motivated to do so in order to monitor, correct or reward driver behavior, and to implement driver education and training programs (Molin; [Abstract]). By incorporating the teachings of Molin, one would’ve been able to issue haptic alerts to drivers.
Regarding Claim 16: Ghanbari further teaches:
storing the operator data and the machine data on a server of the operator scheduling system ([0047] teaches electronic storage media of electronic storage 126 may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with server(s) 102 and/or removable storage that is removably connectable to server(s) 102. Electronic storage 126 may store software algorithms, information determined by processor(s) 132, information received from server(s) 102, information received from client computing platform(s) 104, and/or other information that enables server(s) 102 to function as described herein. Examiner’s Note: The server(s) 102 disclosed in Ghanbari receive—via its various components, as shown in Fig. 1—data from sensors 108 related to the operator and the vehicle.);
Regarding Claim 17: Ghanbari further teaches:
wherein the monitoring is performed using a camera, and the operator data includes video data ([0020] teaches sensors 108 may include a video camera.; [0021] teaches individual sensors may be configured to capture information, including video information.);
Regarding Claim 18: Ghanbari further teaches:
wherein the monitoring is performed using a microphone, and the operator data includes audio data ([0020] teaches sensors 108 may include a microphone.; [0021] teaches individual sensors may be configured to capture information, including audio information.);
Regarding Claim 19: Ghanbari further teaches:
wherein the tracking is performed using a positioning system ([0020] teaches sensors 108 may include a geolocation sensor (e.g., a Global Positioning System or GPS device).).
Regarding Claim 22: Ghanbari doesn’t teach:
automatically resetting the machine interface in response to the machine being turned off and subsequently turned on.
Ricci teaches:
automatically resetting the machine interface in response to the machine being turned off and subsequently turned on. ([0123] The personal settings of the vehicle occupant can include a plurality of a seat setting, climate control setting, lighting setting, configuration of an instrument cluster on a screen, rear view mirror setting, driving mode, media channel setting or preset, media delivery preference, music genre preference, scheduled program, playlist, synchronization with cloud-based data associated with the vehicle occupant, application-specific personalization and selections, and a display setting and configuration.; [0695] Device detection can be in response to or triggered by a sensed event other than receipt of a ping from the computational device. As noted, device detection can be receipt of information from one or more on board sensors that a new occupant has entered the vehicle. Exemplary information includes a door opening or closing, a successful authentication of an occupant or computational device, a sensed load in a seat, detection of movement within the vehicle, and detection of initiation of a vehicle task, function or operation, such as a key inserted in an ignition, engine start up, and the like.; [0709] While this logic is described with respect to the location of the computational device, it is to be understood that this determination is optional. Whether or not a communication device is enabled to connect to the vehicle network 356 or communication subsystem 1008 can be based solely on successful authentication. In this way, the vehicle on board computer can connect automatically to the owner's home virtual private network to upload and/or download information, settings, and other information (such as user input into the vehicle computer, vehicle driving history (e.g., miles traveled, travel traceroutes, speeds traveled, and locations visited), vehicle service information (such as gas and fluid levels, engine problems, alarms or warnings activated, and the like), input received by on board applications from the user (such as scheduled appointments, notes, documents, and the like), applications downloaded, and the like. Synchronization of the on board vehicle and home computer can occur automatically whether the vehicle is turned on or off. This can be highly beneficial when the vehicle is parked in the garage.; [0711] In step 2020, the device discovery daemon 1020 permits or enables connection of the computational device with the vehicle network 356 or communication subsystem 1008 and optionally stipulates or defines what set of tasks, functions, and/or operations the user of the computational device can perform using the computational device, such as based on the location of the computational device within the vehicle and/or based on the authentication credentials (e.g., the identity of the computational device user).; [0753] In step 2220, the media server 2112 configures the filtered media for the capabilities of the user device and/or on board vehicle display and in accordance with user preferences.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Ghanbari with Ricci’s feature(s) listed above. One would’ve been motivated to do so in order to maintain a persona of a vehicle occupant and, based on the persona of the vehicle occupant and vehicle-related information, performing an action assisting the vehicle occupant (Ricci; [Abstract]). By incorporating the teachings of Ricci, one would’ve been able to automatically resetting the interface when starting the vehicle.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Ghanbari et al. (US 20230084964 A1), in view of Brooks (US 20230054373 A1), in further view of Ricci (US 20140309806 A1), in further view of Molin et al. (US 20140226010 A1), as applied to claim 15 above, in further view of Patzold et al. (TW 202241742 A).
Regarding Claim 20: Ghanbari further teaches:
the fatigue mitigation system either decreases a power rating of the machine or requires the operator to interact with the display provided in the cabin. ([0038] action component 120 may determine whether to schedule a break (or take another action). Actions taken or recommended by action component 120 may include generating notifications, providing notifications (e.g., notifying a vehicle operator, a stakeholder of a particular fleet, a dispatcher, remote computing server 125, and/or others), scheduling a rest or break, modifying the planned route, modifying the effective speed limit for a particular vehicle, modifying the type of vehicle events a particular vehicle is currently detecting, modifying the sensitivity with which a particular vehicle event is being detected, and/or other actions.; [0041] Client computing platforms 104 may be associated with user interfaces 134. User interfaces 134 may be presented to users 135, including but not limited to vehicle operators, vehicle owners, fleet managers, and/or other stakeholders. In some implementations, notifications (e.g., from notification component 122) may be provided through one or more user interfaces 134 in one or more vehicles. In some implementations, an individual user interface 134 may include one or more controllers, joysticks, track pad, a touch screen, a keypad, touch sensitive and/or physical buttons, switches, buttons, a keyboard, knobs, levers, a display, speakers, a microphone, an indicator light, a printer, and/or other interface devices. User interfaces 134 may be configured to facilitate interaction between users 135 and system 100, including but not limited to receiving input from users 135 and providing notifications and/or recommendations to users 135. In some implementations, received input may, e.g., be used to select how to determine the current speed threshold, or how to detect vehicle events.).
Ghanbari doesn’t explicitly teach:
wherein in the controlling step, if, after a break modification is made, the fatigue mitigation system detects that the amount of fatigue remains greater than the threshold,
Patzold teaches:
wherein in the controlling step, if, after a break modification is made, the fatigue mitigation system detects that the amount of fatigue remains greater than the threshold, ([70] detect a driver's fatigue and can recommend switching to the refresh mode, including a number of possible suggested actions associated with the refresh mode. The driver can accept the suggestion and the vehicle can take appropriate action. After a given amount of time, such as after 15 minutes, the driver's fatigue level may be assessed again, and still an elevated fatigue level may still be detected. In such cases, it may be found that the suggested action was not successful. As a result, the method can output a number of suggested actions associated with the rest mode.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Ghanbari and Brooks with Patzold’s feature(s) listed above. One would’ve been motivated to do so in order to determine if the suggested action was not successful (Patzold; [70]). By incorporating the teachings of Patzold, one would’ve been able to detect a high level of fatigue after a break.
Conclusion
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 GABRIEL J TORRES CHANZA whose telephone number is (571)272-3701. The examiner can normally be reached Monday thru Friday 8am - 5pm ET.
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/G.J.T./Examiner, Art Unit 3625
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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