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 .
Response to Arguments
Applicant's arguments filed 02/09/2026 have been fully considered, but they are not persuasive.
In regards to claim 1, Applicant argues the cited references, even if combinable, do not suggest or disclose the newly amended features. Applicant argues Spasojevic (US 20180009442) instead teaches estimating a cognitive load of a driver based on environmental parameters, vehicle parameters, and driving task parameters and Fischer (US 20140171097) merely teaches accessing network observations with a network listen receiver and improving coverage within a building. Applicant argues nothing in the cited references teaches the newly recited uploading.
However, DeLuca teaches vehicles may be grouped as part of a distributed cloud server with a control system that as part of the distributed cloud system receive and upload information between each other and the control system. Information for the programs operating through each vehicle and the data gathered by and gathered for each program is stored within memory of the vehicles. Fischer then teaches that nodes of a network periodically transmit and upload information, which is uploading information at a predetermined period. When combined, this provides a telematics system that uploads information received by each vehicle of the distributed cloud network at periodic intervals, which is stored in memory for further programming. This is what is required by the claim.
As such, this argument is unpersuasive.
Applicant argues claim 11 is not taught by the references for the same reasons.
However, this argument is unpersuasive for the same reasons as given above.
Applicant argues Colby (US 10527441) and any remaining references does not remedy the deficiencies of the independent claims.
However, none of the remaining references are required to remedy and challenged limitation, and therefore this argument is unpersuasive.
Applicant argues the dependent claims dependent claims are allowable by virtue of their dependency.
This argument is unpersuasive as each independent and dependent claim has been fully rejected and for the reasons as given above.
Claim Rejections - 35 USC § 103
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, 4-8, and 11-16 are rejected under 35 U.S.C. 103 as being unpatentable over DeLuca et al. (US 9384661) in view of Spasojevic et al. (US 20180009442) and Fischer et al. (US 20140171097).
In regards to claim 1, DeLuca teaches a vehicle comprising: (Fig 4, Col 10 lines 49-61, operations monitor vehicle and driver.)
a user input interface configured to allow a user to input burden values corresponding to burden conditions of the driver; (Col 10 lines 66-67, Col 11 lines 1-31, current and forecasted cognitive state of driver is received, which may be at least in part from inputs from the driver, particularly including for example sleep history, which is a burden condition of the user. Preferences of the driver may be input through interface of GPS, including cognitive load preferences as multidimensional vector, Col 8 lines 51-57, input output interface allows input of data by user. This allows for both qualitative and numeric inputs of sleep history, for example good or poor sleep or 5 or 8 hours of sleep, a numerical input of occupants within the vehicle, and a multidimensional vector of cognitive state, which is an aggregated numeric interpretation of input burden. The user is therefore allowed to enter a numeric input of their burden through an interface.)
at least one sensor that detects a vehicle condition of at least one of: an interior of the vehicle and a vicinity of the vehicle; (Col 10 lines 49-61, Col 11 lines 11-31, video camera captures expressions of driver and passengers to determine cognitive load of driver and passengers which may be done using sensors monitoring the inside of the vehicle, for example assessment of occupants and noise level. This is a sensor that detects an interior of the vehicle and determines conditions of the vehicle such as containing disruptive passengers. Col 8 lines 27-40, GPS device and display serve as satellite navigation device in communication with global position system. GPS devices acquire real-time information of position. Col 12 lines 25-32, route planning is determined in consideration of current road, weather, and traffic conditions. This factors real-time conditions around the vehicle into route planning using a GPS system. This is a sensor that determines a vehicle condition in the vicinity of the vehicle.)
an on-board satellite navigation device in communication with a global positioning system unit to acquire real-time information regarding conditions within the vicinity of the vehicle; (Col 8 lines 27-40, GPS device and display serve as satellite navigation device in communication with global position system. GPS devices acquire real-time information of position. Col 12 lines 25-32, route planning is determined in consideration of current road, weather, and traffic conditions. This factors real-time conditions around the vehicle into route planning using a GPS system.)
a telematics control unit in wireless communication with a cloud services or a vehicle network to upload and receive crowdsourced information regarding conditions near the vicinity of the vehicle, the telematics control unit being configured to upload the crowdsourced information; (Col 12 lines 33-42, a number of vehicles may be grouped and a recommended route for each of them determined, Col 6 lines 59-67, Col 7 lines 1-11, system communicates over network with computing devices, which when operating as a group, this communicates with each individual member of the group. Col 6 lines 2-5, computing system may particularly be a distributed cloud system, which acts as telematics control unit, such that each unit of the cloud, the vehicle processors, both upload and receive data as required for each individual operation. Col 10 lines 49-61, vehicle collects data of at least cognitive state of driver. As each vehicle collects data, which is then used as a group of multiple vehicles, this data is crowdsourced with a cloud computing system when determining the recommended route.)
a memory configured to store the crowdsourced information uploaded by the telematics control unit and the real-time information acquired by the on-board satellite navigation device; (Col 8 lines 61-67, Col 9 lines 1-34, memory stores program information and all information necessary to operate the programs, which necessarily includes the retrieved data for operations, including both real-time information and uploaded information.)
an electronic display device positioned in an interior compartment of the vehicle; (Col 10 lines 1-3, display shows information to occupants of vehicle. Display may be equally located anywhere such that it may communicate information to the occupants of the vehicle, particularly including within the interior of the vehicle.) and
a processor programmed to: (Col 2 lines 26-27, processor performs operations.)
control the electronic display device to display one or more route selections based on: the vehicle condition detected by the at least one sensor, the real-time information stored in the memory, the crowdsourced information stored in the memory, and. (Col 11 lines 66-67, Col 12 lines 1-9, a route is recommended to the driver from among the candidate routes based on cognitive preferences, current cognitive state of the driver, and cognitive load of the route, Col 10 lines 1-3, display shows information to occupants of vehicle, which here includes the recommended route. This controls the display based on each individual cognitive load and the load as a whole. Col 10 lines 49-61, Col 11 lines 11-31, video camera captures expressions of driver and passengers to determine cognitive load of driver and passengers which may be done using sensors monitoring the inside of the vehicle, for example assessment of occupants and noise level, which is an interior condition of a vehicle and Col 8 lines 27-40, Col 12 lines 25-32, GPS device and display serve as satellite navigation device in communication with global position system to acquire real-time information of position, and route planning is determined in consideration of current road, weather, and traffic conditions, which factors real-time conditions around the vehicle into route planning using a GPS system, determining a vehicle condition in the vicinity of the vehicle and real-time information. Col 6 lines 2-5, Col 12 lines 33-42, a number of vehicles may be grouped and a recommended route for each of them determined by cloud computing system, Col 6 lines 59-67, Col 7 lines 1-11, system communicates over network with computing devices, which when operating as a group, this communicates with each individual member of the group, and Col 10 lines 49-61, each vehicle collects data of at least cognitive state of driver, which is crowdsourced data used when determining the recommended route.)
DeLuca does not teach:
the telematics control unit being configured to upload the crowdsourced information at predetermined intervals;
continuously download the real-time information and the crowdsourced information stored in the memory;
control the electronic display device to display one or more route selections based on: a sum of the burden values inputted by the user.
However, Spasojevic teaches continually determining a total cognitive load of a driver as the total load from each task, which is a summation of each individual cognitive load ([0028], [0057]) and adjusting routing based on a comparison of the current overall load of the driver with a baseline load ([0053]).
Further, Fischer teaches a network listening module that receives data continuously, where the data received is crowdsourced information and received global navigation satellite system information ([0015], [0059], [0083]). Fischer also teaches reporting measured data to a crowdsourcing server periodically ([0081]) which is periodic uploading at predetermined intervals.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle of DeLuca, by incorporating the teachings of Spasojevic and Fischer, such that the total load of the driver is determined by combining the individual loads of the driver, which is a summation composed of the inputs from the driver and the conditions of the vehicle, and used to further adjust routing of the vehicle as required including displaying route selections, and such that satellite positioning and crowdsourced information is received and downloaded continuously and the crowdsourced information is uploaded periodically at predetermined intervals.
The motivation to determined cognitive load is that, as acknowledged by Spasojevic, this allows for better determining the driver’s overall level of distraction which allows the driver to adjust and perform more safely ([0003], [0006], [0010]). The motivation to continuously receive satellite positioning a crowdsourced data is that, as acknowledged by Fischer, this allows for improved providing of location and navigation information to mobile devices while accounting for security and uncertainty ([0002], [0007], [0008]).
In regards to claim 4, DeLuca, as modified by Spasojevic and Fischer, teaches the vehicle according to claim 1, wherein
the burden conditions include any one of a user stress condition, user energy condition, and an urgency condition. (Col 11 lines 11-65, Col 12 lines 43-58, cognitive load may be assessed based upon how stressful a route or a condition of the route may be, requirement of fast decisions which is an urgency condition, and sleep history of a driver which is an energy condition, including user preferences input by the user specifying such.)
In regards to claim 5, DeLuca, as modified by Spasojevic and Fischer, teaches the vehicle according to claim 4, wherein
the burden conditions further include a user experience level. (Col 11 lines 11-65, cognitive load may be assessed using traveler experience.)
In regards to claim 6, DeLuca, as modified by Spasojevic and Fischer, teaches the vehicle according to claim 1, wherein
the at least one sensor is part of an on-board sensor network, and the at least one sensor includes an internal sensor that monitors conditions regarding a passenger compartment of the vehicle. (Col 10 lines 49-61, video camera captures expressions of driver and passengers to determine cognitive load of driver and passengers. This forms a sensor network.)
In regards to claim 7, DeLuca, as modified by Spasojevic and Fischer, teaches the vehicle according to claim 6, wherein
the one or more route selections are based on information received by the on-board sensor network. (Col 10 lines 49-61, video camera captures expressions of driver and passengers to determine cognitive load of driver and passengers. Col 11 lines 66-67, Col 12 lines 1-9, a route is recommended to the driver from among the candidate routes based on cognitive preferences, current cognitive state of the driver, and cognitive load of the route, Col 10 lines 1-3, display shows information to occupants of vehicle, which here includes the recommended route.)
In regards to claim 8, DeLuca, as modified by Spasojevic and Fischer, teaches the vehicle according to claim 7, wherein
the on-board sensor network includes at least one internal camera positioned to detect behavior of one or more passengers in the passenger compartment. (Col 10 lines 49-61, video camera captures expressions of driver and passengers to determine cognitive load of driver and passengers.)
In regards to claim 11, DeLuca teaches a method for displaying vehicle route selections, the method comprising: (Fig 5, Col 11 lines 66-67, Col 12 lines 1-9, a route is recommended to the driver of vehicle from among the candidate routes based on cognitive preferences, current cognitive state of the driver, and cognitive load of the route, Col 10 lines 1-3, display shows information to occupants of vehicle, which here includes the recommended route.)
acquiring burden values corresponding to burden conditions of the driver, the burden values being inputted to a user input interface by a user; (Col 10 lines 66-67, Col 11 lines 1-31, current and forecasted cognitive state of driver is received, which may be at least in part from inputs from the driver, particularly including for example sleep history, which is a burden condition of the user. Preferences of the driver may be input through interface of GPS, including cognitive load preferences, Col 8 lines 51-57, input output interface allows input of data by user. This occurs in steps 555 and 560. This allows for both qualitative and numeric inputs of sleep history, for example good or poor sleep or 5 or 8 hours of sleep, a numerical input of occupants within the vehicle, and a multidimensional vector of cognitive state, which is an aggregated numeric interpretation of input burden. The user is therefore allowed to enter a numeric input of their burden through an interface.)
acquiring real-time information using an on-board satellite navigation device in communication with a global positioning system unit; (Col 8 lines 27-40, GPS device and display serve as satellite navigation device in communication with global position system. GPS devices acquire real-time information of position. Col 12 lines 25-32, route planning is determined in consideration of current road, weather, and traffic conditions. This factors real-time conditions around the vehicle into route planning using a GPS system.)
uploading information to a telematics control unit in wireless communications with at least one of a cloud services and a vehicle network; (Col 12 lines 33-42, a number of vehicles may be grouped and a recommended route for each of them determined, Col 6 lines 59-67, Col 7 lines 1-11, system communicates over network with computing devices, which when operating as a group, this communicates with each individual member of the group. Col 6 lines 2-5, computing system may particularly be a distributed cloud system, which acts as telematics control unit, such that each unit of the cloud, the vehicle processors, both upload and receive data as required for each individual operation. Col 10 lines 49-61, vehicle collects data of at least cognitive state of driver. As each vehicle collects data, which is then used as a group of multiple vehicles, this data is crowdsourced with a cloud computing system when determining the recommended route.)
storing the real-time information acquired by the on-board satellite navigation device and the crowdsourced information uploaded to the telematics control unit in an on-board memory, (Col 8 lines 61-67, Col 9 lines 1-34, memory stores program information and all information necessary to operate the programs, which necessarily includes the retrieved data for operations, including both real-time information and uploaded information.)
acquiring a vehicle condition from at least one sensor, the vehicle condition being a condition of at least one of: an interior of the vehicle and a vicinity of the vehicle; (Col 10 lines 49-61, Col 11 lines 11-31, video camera captures expressions of driver and passengers to determine cognitive load of driver and passengers which may be done using sensors monitoring the inside of the vehicle, for example assessment of occupants and noise level. This is a sensor that detects an interior of the vehicle and determines conditions of the vehicle such as containing disruptive passengers. Col 8 lines 27-40, GPS device and display serve as satellite navigation device in communication with global position system. GPS devices acquire real-time information of position. Col 12 lines 25-32, route planning is determined in consideration of current road, weather, and traffic conditions. This factors real-time conditions around the vehicle into route planning using a GPS system. This is a sensor that determines a vehicle condition in the vicinity of the vehicle.)
controlling an electronic display device to display the one or more route selections based on: the vehicle condition acquired by the at least one sensor, the real-time information stored in memory, the crowdsourced information stored in the memory, and. (Col 11 lines 66-67, Col 12 lines 1-9, a route is recommended to the driver from among the candidate routes based on cognitive preferences, current cognitive state of the driver, and cognitive load of the route, Col 10 lines 1-3, display shows information to occupants of vehicle, which here includes the recommended route. This controls the display based on each individual cognitive load and the load as a whole. Col 10 lines 49-61, Col 11 lines 11-31, video camera captures expressions of driver and passengers to determine cognitive load of driver and passengers which may be done using sensors monitoring the inside of the vehicle, for example assessment of occupants and noise level, which is an interior condition of a vehicle and Col 8 lines 27-40, Col 12 lines 25-32, GPS device and display serve as satellite navigation device in communication with global position system to acquire real-time information of position, and route planning is determined in consideration of current road, weather, and traffic conditions, which factors real-time conditions around the vehicle into route planning using a GPS system, determining a vehicle condition in the vicinity of the vehicle and real-time information. Col 6 lines 2-5, Col 12 lines 33-42, a number of vehicles may be grouped and a recommended route for each of them determined by cloud computing system, Col 6 lines 59-67, Col 7 lines 1-11, system communicates over network with computing devices, which when operating as a group, this communicates with each individual member of the group, and Col 10 lines 49-61, each vehicle collects data of at least cognitive state of driver, which is crowdsourced data used when determining the recommended route.)
DeLuca does not teach:
uploading at predetermined intervals information to a telematics control unit in wireless communications with at least one of a cloud services and a vehicle network;
continuously downloading the real-time information and the crowdsourced information stored in the memory,
controlling an electronic display device to display the one or more route selections based on: a sum of the burden values inputted by the user.
However, Spasojevic teaches determining a total cognitive load of a driver as the total load from each task, which is a summation of each individual cognitive load ([0057]) and adjusting routing based on a comparison of the current overall load of the driver with a baseline load ([0053]).
Further, Fischer teaches a network listening module that receives data continuously, where the data received is crowdsourced information and received global navigation satellite system information ([0015], [0059], [0083]). Fischer also teaches reporting measured data to a crowdsourcing server periodically ([0081]) which is periodic uploading at predetermined intervals.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the method of DeLuca, by incorporating the teachings of Spasojevic and Fischer, such that the total load of the driver is determined by combining the individual loads of the driver, which is a summation composed of the inputs from the driver and the conditions of the vehicle, and used to further adjust routing and routing display of the vehicle as required including displaying route selections, and such that satellite positioning and crowdsourced information is received and downloaded continuously and the crowdsourced information is uploaded periodically at predetermined intervals.
The motivation to determined cognitive load is that, as acknowledged by Spasojevic, this allows for better determining the driver’s overall level of distraction which allows the driver to adjust and perform more safely ([0003], [0006], [0010]). The motivation to continuously receive satellite positioning a crowdsourced data is that, as acknowledged by Fischer, this allows for improved providing of location and navigation information to mobile devices while accounting for security and uncertainty ([0002], [0007], [0008]).
In regards to claim 12, DeLuca, as modified by Spasojevic and Fischer, teaches the method according to claim 11.
Claim 12 recites a method having substantially the same features of claim 4 above, therefore claim 12 is rejected for the same reasons as claim 4.
In regards to claim 13, DeLuca, as modified by Spasojevic and Fischer, teaches the method according to claim 12.
Claim 13 recites a method having substantially the same features of claim 5 above, therefore claim 13 is rejected for the same reasons as claim 5.
In regards to claim 14, DeLuca, as modified by Spasojevic and Fischer, teaches the method according to claim 13.
Claim 14 recites a method having substantially the same features of claim 6 above, therefore claim 14 is rejected for the same reasons as claim 6.
In regards to claim 15, DeLuca, as modified by Spasojevic and Fischer, teaches the method according to claim 14.
Claim 15 recites a method having substantially the same features of claim 7 above, therefore claim 15 is rejected for the same reasons as claim 7.
In regards to claim 16, DeLuca, as modified by Spasojevic and Fischer, teaches the method according to claim 15.
Claim 16 recites a method having substantially the same features of claim 8 above, therefore claim 16 is rejected for the same reasons as claim 8.
In regards to claim 19, DeLuca, as modified by Spasojevic and Fischer, teaches the vehicle according to claim 1, wherein
the memory includes at least one non-transitory computer readable medium. (Col 8 lines 61-67, Col 9 lines 1-34, memory stores program information and all information necessary to operate the programs, which necessarily includes the retrieved data for operations, including both real-time information and uploaded information.)
In regards to claim 20, DeLuca, as modified by Spasojevic and Fischer, teaches the method according to claim 11.
Claim 20 recites a method having substantially the same features of claim 19 above, therefore claim 20 is rejected for the same reasons as claim 19.
Claims 9, 10, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over DeLuca in view of Spasojevic and Fischer, in further view of Colby (US 10527441).
In regards to claim 9, DeLuca, as modified by Spasojevic and Fischer, teaches the vehicle according to claim 7,
DeLuca, as modified by Spasojevic and Fischer, does not teach: wherein
the on-board sensor network includes at least one internal microphone positioned to detect behavior of one or more passengers in the passenger compartment.
However, Colby teaches using a microphone as an input device of a route selection and determination system which determines route based on the load on the driver associated with the route, where the microphone at least detects audio from the occupants of the vehicle (Col 1 lines 49-63, Col 2 lines 46-52).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle of DeLuca, as already modified by Spasojevic and Fischer, by incorporating the teachings of Colby, such that the sensor of the vehicle of DeLuca include a microphone which detects at least voice behavior of occupants of the vehicle, which is then further used in selecting a route based on driver load.
The motivation to do so is that, as acknowledged by Colby, this allows for improved routing by better accounting for the psychological load of the driver, thereby improving comfort (Col 1 lines 21-27).
In regards to claim 10, DeLuca, as modified by Spasojevic and Fischer, teaches the vehicle according to claim 2.
DeLuca, as modified by Spasojevic and Fischer, does not teach: wherein
the electronic display device is programmed to display a navigation map, the one or more route selections are superimposed on the navigation map.
However, Colby teaches an output device as a display which shows multiple routes determined from mapping module, where the routes flow from a start location to an end location and the driver may select between these routes (Fig 1, Col 2 lines 41-62, Col 3 lines 1-23). This maps routes through the environment, displaying both the route and the map.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle of DeLuca, as already modified by Spasojevic and Fischer, by incorporating the teachings of Colby, such that the routes are displayed as an output on the display along with map information.
The motivation to do so is the same as acknowledged by Colby in regards to claim 9.
In regards to claim 17, DeLuca, as modified by Spasojevic and Fischer, teaches the method according to claim 16.
Claim 17 recites a method having substantially the same features of claim 9 above, therefore claim 17 is rejected for the same reasons as claim 9.
In regards to claim 18, DeLuca, as modified by Spasojevic and Fischer, teaches the vehicle according to claim 17.
Claim 18 recites a method having substantially the same features of claim 10 above, therefore claim 18 is rejected for the same reasons as claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Han et al. (US 10627248) teaches routing a vehicle based on cognitive load.
DeLuca et al. (US 9534914) teaches routing a vehicle based on current and forecasted cognitive load.
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.
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/MATTHIAS S WEISFELD/Examiner, Art Unit 3661