DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/30/2026 has been entered.
Status of Claims
This is a Non-Final Action for Request for Continued Examination (RCE) application
in response to the communication received. Claim(s) 1, 3-7, 9, 11-13, and 15-28 have been examined and fully considered. Claim(s) 1, 3-5, 9, 11-13, 15, 16, 18, 19, 21, 22, 24, 25, and 27 have been amended. Claim(s) 8 is canceled and claim(s) 28 are newly added. Claim(s) 1, 3-7, 9, 11-13, and 15-28 are pending in Instant Application.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 03/04/2026 and 05/26/2026
is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered if signed and initialed by the Examiner.
Response to Arguments/Rejections
Applicant’s arguments with respect to the rejection of claim(s) 1, 3-5, 9, 11-13, 15, 16, 18, 19, 21, 22, 24, 25, and 27 under 35 USC 103 rejection have been fully considered and are persuasive. However, upon further consideration, a new ground(s) of rejection is made in view of Joshua et al. (Pub. No.: US 2014/0279707).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 1, 3-5, 9, 11-13, 15, 18-19, 21-22, 24 and 27-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chalfant et al. (Pub. No.: US 2009/0210257; previously recorded), hereinafter, referred to as “Chalfant” in view of Joshua et al. (Pub. No.: US 2014/0279707), hereinafter, referred to as “Joshua”.
Regarding [claim 1]. Chalfant discloses a method, comprising:
generating, by a processor, a data model based at least in part on:
(see, Chalfant Fig. 5, 502-504 (generate dynamic driver profile); Fig. 6 – 602-606 (generate dynamic driver profile); and Paragraph [0076]: “Steps 602, 604, 606, 608, 610, and 612 are similar to steps 502, 504, 506, 508, 510, and 512 of method 5 as described in FIG. 5. )
historical operating data indicating driving behaviors of a plurality of drivers (see, Paragraph [0050]-[0059]: “Selection of an out-of-vehicle feedback mode may also be based in part on demographic and individual driver characteristics. After determining the mode of feedback (step 306) the system 100 monitors driving (step 308) of the driver via the sensors 112 of the vehicle on-board system 104”), and driving conditions corresponding to the driving behaviors of the plurality of drivers (see, Paragraph [0040]: “The driver evaluation processor 132 includes a driver evaluation module 134 and an optional dynamic feedback module 136 (the dynamic feedback module may instead be included as part of the insurance system 110). The driver evaluation module 134 module receives driving data from the front-end server 126 and evaluates the safety performance of the driver using the driving data based on the rating and non-rating characteristics in a dynamic driver profile associated with the driver. The resulting evaluation indicates the safety of a driver's actual driving behavior. The driver evaluation module 134 also updates the dynamic driver profile based on the evaluation.” [0027]: “The plurality of sensors 112 track vehicle event and behavioral data related to the driver's driving behavior. ... The sensors may track other data such as lane changing and distance of trip, as well as other conditions including light level, time of day, climate conditions, road type (e.g., highway, city street, rural route), and road conditions. The plurality of sensors 112 relay the event and behavioral data to the vehicle processor 114, where it is processed to convert the raw data into behavioral and safety information (such as instances of a driver's exceeding the speed limit, tailgating, or accelerating too hard). In alternative implementations, such processing is carried out by the driver evaluation system 106.”; and [0059]: “The sensors 112 track vehicle event and behavioral data related to driver's performance. Examples of data tracked by sensors include location, the vehicle's position relative to other vehicles or obstacles, acceleration, speed, braking, cornering, status of the seatbelts (i.e., engaged or not engaged) and airbags (i.e., deployed or not deployed), lane changing, distance of trip, light level, time of day, climate conditions, road type (e.g., highway, city street, rural route), road conditions, and obedience to speed limits.”; [0041]: “The dynamic driver profile includes one or more rating characteristics (such as, for example, age, gender, driving record, compliance with feedback, annual mileage driven, and vehicle use and one or more non-rating characteristics of the driver. Examples of non-rating characteristics may include, for example, analyses of eyesight, cognitive ability (such as reaction time and perception skills), physical fitness, other indicators of health and fitness, driver preferences and driver supervisor preferences. Two drivers who display similar driving behaviors but whose associated dynamic driving profiles differ may receive different safety performance evaluations.”; and Fig. 2);
obtaining, by the processor, operating data, wherein:
the operating data is indicative of a driving behavior exhibited by a driver operating a vehicle during a current driving condition, and at least a portion of the operating data is obtained from one or more sensors carried by the vehicle and while the vehicle is operating under the current driving condition; (see, Fig. 5 – 508 (monitor driving); Fig. 6 – 608 (monitor driving); Paragraphs [0076]: “Steps 602, 604, 606, 608, 610, and 612 are similar to steps 502, 504, 506, 508, 510, and 512 of method 5 as described in FIG. 5. After outputting feedback (step 612), the system 200 monitors the driving of the driver via the sensors 112 of the vehicle on-board system 104 (step 614). The sensors 112 track vehicle event and behavioral data as described above. The monitoring may take place over the course of one or more driving episodes”; [0027]: “The plurality of sensors 112 track vehicle event and behavioral data related to the driver's driving behavior. Examples of sensors include global positioning system (GPS) sensors to track location and speed, range finders to detect position relative to other vehicles or obstacles (e.g., to detect tailgating or potential collisions), and accelerometers to detect acceleration, braking, and cornering. Other examples of sensors include seatbelt and airbag sensors that can be accessed via a standard interface, such as OBD-II or OBD-III interfaces, cameras, which may be coupled to other vehicle systems, including, e.g., drowsiness detection systems and driver identification systems. The sensors may track other data such as lane changing and distance of trip, as well as other conditions including light level, time of day, climate conditions, road type (e.g., highway, city street, rural route), and road conditions. The plurality of sensors 112 relay the event and behavioral data to the vehicle processor 114, where it is processed to convert the raw data into behavioral and safety information (such as instances of a driver's exceeding the speed limit, tailgating, or accelerating too hard). In alternative implementations, such processing is carried out by the driver evaluation system 106.”
[0059]: “After determining the mode of feedback (step 306) the system 100 monitors driving (step 308) of the driver via the sensors 112 of the vehicle on-board system 104. The sensors 112 track vehicle event and behavioral data related to driver's performance. Examples of data tracked by sensors include location, the vehicle's position relative to other vehicles or obstacles, acceleration, speed, braking, cornering, status of the seatbelts (i.e., engaged or not engaged) and airbags (i.e., deployed or not deployed), lane changing, distance of trip, light level, time of day, climate conditions, road type (e.g., highway, city street, rural route), road conditions, and obedience to speed limits.”;
determining, by the processor, based on inputting the operating data into the safe-driver model, a performance indicator indicative of a response of the driver to a change in the current driving condition (see, Paragraph [0041]: “The dynamic driver profile includes one or more rating characteristics (such as, for example, age, gender, driving record, compliance with feedback, annual mileage driven, and vehicle use and one or more non-rating characteristics of the driver. Examples of non-rating characteristics may include, for example, analyses of eyesight, cognitive ability (such as reaction time and perception skills), physical fitness, other indicators of health and fitness, driver preferences and driver supervisor preferences. Two drivers who display similar driving behaviors but whose associated dynamic driving profiles differ may receive different safety performance evaluations.; [0074]: “After determining the mode of feedback (step 506), the system 200 monitors the driving of the driver via the sensors 112 of the vehicle on-board system 104 (step 508). The sensors 112 track vehicle event and behavioral data as described above. The driver evaluation and insurance processor 212 of the driver evaluation and insurance system 202 then determines feedback to present to the driver as described above (step 510). The front-end server 204 of the driver evaluation and insurance system 202 then outputs the feedback (step 512) to the driver along with an indication of the selected feedback mode, including timing and presentation instructions.”; [0049]: “As described above, the feedback may include an analysis of the driver's driving behavior, an instruction for the driver to alter his or her driving behavior, and/or an incentive to adjust or maintain driving behaviors.”; [0060]: “Next, the driver evaluation processor 132 of the driver evaluation system 106 of system 100 determines the feedback (step 310) to be sent to the driver. The specific feedback given is derived in part on the dynamic driver profile of the driver, including rating and/or non-rating characteristics of the driver, as well as preferences expressed by the driver, the driver's guardian, and/or the driver's supervisor. Driving behavior by individuals with different combinations of rating and non-rating characteristics pose different risks.”; [0061]: “Night vision may be problematic for certain individuals with, for example, severe myopia and/or cataracts or other similar conditions. As a result, night driving is relatively riskier for these individuals. Thus, thresholds applied for providing warnings to these drivers are lowered at night, whereas such thresholds remain constant or are lowered to a lesser degree for other drivers.”; [0062]: “Tailgating, among other behaviors, is riskier for individuals with slower reaction times than those with faster reaction times. Thus, feedback to increase a separation distance with respect to another vehicle will be triggered beginning at a greater distance and/or a lower speed for those with decreased reaction times than for drivers with normal reaction times.”; [0063]: “Driving at increased speed is riskier for drivers with poor vision and/or poor reaction times in comparison to those with perfect vision and normal reaction times. Thus, drivers with poor eyesight are alerted to slow down at lower speeds (e.g., at or near the posted speed limit) than those with perfect eyesight (e.g., at five or ten miles per hour above the posted speed limit).”; [0064]: “In one embodiment, feedback and feedback thresholds may also be adjusted based on other real-time data, including road and/or weather conditions.” and [0076]: “Next, the driver evaluation module 214 calculates a level of driver compliance (step 616), i.e., the degree with which the driver's behavior changes to comply with feedback instructions, based on the monitoring of the driving after the feedback was provided to the driver (step 614).”…
As Chalfant discloses in paragraph [0035]: “Suitable in-vehicle feedback can include an analysis of the driver's driving behavior, alerts to the driver to alter his or her driving behavior, notifications of potential insurance impacts of driving behavior, and/or incentives to alter or maintain certain driving behaviors. For example, in-vehicle alerts include warnings to slow down, reduce acceleration, increase the distance from behind the vehicle in front, reduce the number of lane changes, or increase braking distance. Other suitable in-vehicle feedback include warnings that premium levels or deductibles may increase if un-safe driving behavior is not modified”; [0037]: “In one implementation, the vehicle on-board system 104, in coordination with the driver evaluation system 106 may take steps to initiate automatic control over certain vehicle operations. For example, in a vehicle that can operate in both two-wheel and four-wheel drive, the vehicle on-board system 104, in response to detecting a slick road surface, may automatically engage the four-wheel drive, if not already engaged.…While preferably, such automatic control is limited to activation of safety features, in certain embodiments, upon receiving prior approval from the driver or the driver's guardian or supervisor, the on-board system may optionally take more direct control of driving operations upon detection of an unsafe driving environment. For example, the on-board system may automatically engage the brakes if a driver is determined to be speeding. In one particular implementation, such automatic control is not activated until a driver fails to react to feedback instructions a pre-determined number of times.”, and [0061]-[0066]; and [0065]: “In the final step, the front-end server 126 of the driver evaluation system 106 outputs the feedback (step 312) to the driver, e.g., by forwarding in-vehicle feedback to the vehicle on-board system 104 or out-of-vehicle feedback to the out-of-vehicle feedback system 108, along with the selected timing and presentation instructions.”; [0076]: “Next, the driver evaluation module 214 calculates a level of driver compliance (step 616), i.e., the degree with which the driver's behavior changes to comply with feedback instructions, based on the monitoring of the driving after the feedback was provided to the driver (step 614).”) as it relates to driving conditions corresponding to the driving behaviors (see, Paragraph [0027]), and as shown above strongly suggest analysis of a plurality of drivers.
…
However, additionally, Joshua teaches
at least a portion of the operating data is obtained from irst sensors carried by the vehicle and while the vehicle is operating under the current driving condition (see, Paragraph [0056]: “Referring now to FIG. 1, there is shown a block diagram of a system 10 for collecting and analyzing vehicle data according to Some embodiments. System 10 may be operable to collect real-time data from a variety of sources, such as onboard devices 14 located in vehicles 12, store the collected vehicle data in user profiles and/or vehicle profiles at a central server 16, analyze and correlate the collected vehicle data to compute metrics and detect vehicle events, transmit near real-time alerts or notifications to devices 14 for the detected vehicle events, collect additional data in relation to the real-time notifications, compute metrics relating to compliance with the notifications, and store the computed metrics and notifications in user profiles and/or vehicle pro files at a central server 16.”; and [0059]: “The system 10 may include a central server 16 connected to onboard devices 14a, 14b, 14c located in vehicles 12a, 12b, 12c via a network 20. The onboard device 14 may function as an output device to communicate recommendations to user 18 within vehicle 12. The onboard device 14 may function as a vehicle sensor device and may couple with sensors within vehicle 12. Central server 16 may also connect to additional data source servers 22 managing vehicle and driving related data, Such as environment, weather, traffic, speed limits, road maps, and news data sources, for example.”);
obtaining, by the processor and from second sensors associated with the vehicle, vehicle condition data indicative of a usability of a physical component of the vehicle (see, Paragraph [0061]: “Onboard devices 14 may be specialized built-in devices within the vehicle 12 or may be plug-in devices to a vehicle 12. For example, an onboard device 14b may be a specialized device operable to integrate and interface with on-board diagnostics systems, and other devices and sensors located within a vehicle 14b. An onboard device 14a may also be a smartphone located in the vehicle 12a, or the onboard device 14c may be a combination of a specialized device connected to a smartphone located in a vehicle 14c. Other onboard devices 14 that can collect data relating to a vehicle, directly or indirectly, may also be used. Generally, onboard devices 14 can be any suitable device (or combination of devices) for collecting and transmitting data relating to a vehicle 12… Onboard device 14 may also be connected to and receive data from other devices that collect data relating to the vehicle. For example, onboard device 14 may include or be connected to a navigation system, electronic mapping tool, satellite device, a diagnostic tool, tracking devices, radio device, receiver/transmitter/modem and other vehicle telematics devices. For example, a vehicle telematics device is a way of monitoring the location, movements, status, components and behavior of a vehicle. Further details of onboard device 14 will be described in relation to FIG. 3.”; and [0065]: “As another example, system 10 may be used for vehicle diagnostics. For example, system 10 may collect vehicle diagnostic data from onboard devices 14. Such as vehicle diagnostic trouble codes (DTC) using the telematics. System 10 may provide users 18 and onboard devices 14 with proactive and reactive notifications about potential problems with vehicles 12 that may be delivered through web portal to computing device 24 or real-time notifications to onboard devices 14, which may be used to lower or save on repair costs. The notifications may include recommended vehicle actions, and telemetry data used to determine compliance data indicating compliance with the recommended vehicle actions may be collected by system 10.”);
determining, by the processor, based on inputting the operating data into the data model (see, Paragraph [0092]: “Data aggregator module 36 is operable to correlate the collected vehicle data sets based on a variety of factors, in order to group data sets relating to a particular location or area, a particular vehicle 12, a particular user 18, a particular time, and so on. Data aggregator module 36 is operable to filter the collected vehicle data sets to remove unwanted or irrelevant data. Data aggregator module 36 is operable to perform error correction on collected telemetry data, and data collected from other sources. For example, a time and date may be clearly erroneous (e.g. 20 years ago) and that data may be ignored for calculation purposes.”), …;
determining, by the processor, and based on the performance indicator and the vehicle condition data (see, Paragraph [0056]: “System 10 may be operable to collect real-time data from a variety of sources, such as onboard devices 14 located in vehicles 12, store the collected vehicle data in user profiles and/or vehicle profiles at a central server 16, analyze and correlate the collected vehicle data to compute metrics and detect vehicle events, transmit near real-time alerts or notifications to devices 14 for the detected vehicle events, collect additional data in relation to the real-time notifications, compute metrics relating to compliance with the notifications, and store the computed metrics and notifications in user profiles and/or vehicle profiles at a central server 16. The alerts or notifications may include a recommended vehicle action (e.g. slow down a particular speed, service engine within a particular time) and system 10 can be further operable to collect telemetry data used to determine compliance databased on compliance with the alert or notifications and to use the compliance data as additional metrics for user profiles or vehicle profiles.”), a recommended action (see, “recommended vehicle actions”), wherein the recommended action comprises at least one of:
reducing speed of the vehicle,
steering the vehicle in a particular direction, or
bringing the vehicle to a stop (see, Paragraphs [0101]: “The conditions may be associated with one or
more recommended vehicle actions that may be included in
the notification. For example, if the vehicle data sets indicate
that there is a traffic accident ahead detected from other
vehicles 12 already at the accident location and the vehicle 12
is heading towards the accident then this may trigger a notification to that onboard device 14 in the vehicle 12 and the
recommended vehicle action may be take an alternative route.
Accordingly, conditions may relate to measurements of traffic flow and location of other vehicles. Traffic flow measurements may include traffic conditions, average speed of vehicles, total volume of vehicles, weather, problems with vehicle, and other measurements.”; [0102]: “The conditions stored in condition module 40 may be modified or deleted by the appropriate personnel (e.g.
service provider or system administrator). A list of exemplary
conditions and corresponding notifications or recommended
actions may include, but is not limited to:
[0103]: “In the event of detecting sudden airbag deployment, send a notification to inquire if the driver and occupants need emergency assistance”; [0104]: “In the event of detecting repeated sudden brakes or ABS application, send a notification to remind the
user to drive safely”);; and
providing, by the processor and to an additional processor disposed at the vehicle (see, Paragraph [0063]: “System 10 may further include one or more computing devices 24 operable by a user 18 (or other user 26 such as an administrative user or service provider) to interface with central server 16, onboard devices 14, and other components of system 10. For example, notifications and recommendations may be sent from central server 16 to computing device 24. Computing devices 24 may function as an output device to communicate recommendations to user 18. Computing device 24 may be any networked (wired or wireless) computing device including a processor”) , a control signal, wherein providing the control signal causes the additional processor to perform the recommended action (see, Paragraphs [0114] : “In the event of detecting alcohol via onboard breathalyzer or air content analyzer, send a notification requesting the user to slow down in a safe area and cease driving”; [0115]: “In the event of detecting a lane change or a turn of the vehicle with turn signals off, send notification alerting user to proper use of turn signals”; [0116]: “In the event of low fuel level, send a notification alerting user to add fuel as soon as possible; and [0118]: “Recommendations may be personalized to users 18, and different users 18 may get different recommendations and may be associated with different vehicle conditions which trigger the notification and recommendation.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to analyzing vehicle related data, and in particular to, collecting and analyzing real-time vehicle related data as taught by Joshua. One would be motivated to make this modification in order to improve process efficiencies within an organization and provide a real-time information exchange environment (see, Paragraph [0057]).
As to [claim 3], Chalfant in view of Joshua teaches the method of claim 1. Chalfant discloses wherein the vehicle condition data indicative of one or more of:
a current wear of [[a]]the physical component of the vehicle, an oil level, (see, Paragraph [0061]: “Onboard device 14 may also have additional embedded components such as a global positioning system (GPS), a clock, a calendar, sensors, transceivers, input/output, and so on. Onboard device 14 may also be connected to and receive data from other devices that collect data relating to the vehicle. For example, onboard device 14 may include or be connected to a navigation system, electronic mapping tool, satellite device, a diagnostic tool, tracking devices, radio device, receiver/transmitter/modem and other vehicle telematics devices. For example, a vehicle telematics device is a way of monitoring the location, movements, status, components and behavior of a vehicle. Further details of onboard device 14 will be described in relation to FIG. 3.”; and ).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to analyzing vehicle related data, and in particular to, collecting and analyzing real-time vehicle related data as taught by Joshua. One would be motivated to make this modification in order to improve process efficiencies within an organization and provide a real-time information exchange environment (see, Paragraph [0057]).
As to [claim 4]. Chalfant in view of Joshua teaches the method of claim 1. Chalfant mentions further comprising: obtaining, by the processor and from third sensors within the vehicle, cabin condition data indicative of:
in-cabin temperature, in-cabin noise level, a number of passengers, orwherein the recommended action is further determined based on the cabin condition data (see, Paragraph [0059]: “After determining the mode of feedback (step 306) the system 100 monitors driving (step 308) of the driver via the sensors 112 of the vehicle on-board system 104. The sensors 112 track vehicle event and behavioral data related to driver's performance. Examples of data tracked by sensors include location, the vehicle's position relative to other vehicles or obstacles, acceleration, speed, braking, cornering, status of the seatbelts (i.e., engaged or not engaged)”).
And Joshua teaches mentions further comprising: obtaining, by the processor and from third sensors within the vehicle, cabin condition data indicative of:
in-cabin temperature, in-cabin noise level, a number of passengers, orwherein the recommended action is further determined based on the cabin condition data (see, Paragraph [0058]: “System 10 may provide an integrated approach to address safety, security, and service costs tailored to individual users. Specific features of system 10 can include: a real-time connection to vehicles 12 via onboard devices 14, vehicle 12 interaction via onboard devices 14, personalized web portal for users 18 to review reports and configure preferences, mobile applications for onboard devices 14, data collection from vehicles 12 via onboard devices 14, remote vehicle 14 diagnostics, safety via notifications with recommended vehicle actions, monitoring compliance with recommended actions, security via vehicle 12 monitoring, data management and analysis to detect vehicle 12 conditions for notification, road side assistance through provision of vehicle 12 related services, travel medical insurance, and usage based insurance based on data collection and analysis.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to analyzing vehicle related data, and in particular to, collecting and analyzing real-time vehicle related data as taught by Joshua. One would be motivated to make this modification in order to improve process efficiencies within an organization and provide a real-time information exchange environment (see, Paragraph [0057]).
As to [claim 5], Chalfant in view of Joshua teaches the method of claim 4. Chalfant discloses wherein third sensors comprise one or more of: seat sensors, seat belt sensors, in-cabin camera, or a temperature senso (see, Paragraph [0059]: “Selection of an out-of-vehicle feedback mode may also be based in part on demographic and individual driver characteristics. After determining the mode of feedback (step 306) the system 100 monitors driving (step 308) of the driver via the sensors 112 of the vehicle on-board system 104. The sensors 112 track vehicle event and behavioral data related to driver's performance. Examples of data tracked by sensors include location, the vehicle's position relative to other vehicles or obstacles, acceleration, speed, braking, cornering, status of the seatbelts (i.e., engaged or not engaged)”).
As to [claim 9], Chalfant in view of Joshua teaches the method of claim 1. Chalfant discloses wherein… an alert, and the method (see, Paragraph [0035]: “Suitable in-vehicle feedback can include an analysis of the driver's driving behavior, alerts to the driver to alter his or her driving behavior, notifications of potential insurance impacts of driving behavior, and/or incentives to alter or maintain certain driving behaviors. For example, in-vehicle alerts include warnings to slow down, reduce acceleration, increase the distance from behind the vehicle in front, reduce the number of lane changes, or increase braking distance. Other suitable in-vehicle feedback include warnings that premium levels or deductibles may increase if un-safe driving behavior is not modified. Alternatively, or in addition, in vehicle feedback may include congratulatory feedback announcing favorable insurance policy feature alterations, Such as premium or deductible reductions, or discounts on goods or services (e.g., offered by a monitoring service or fleet supervisor).”) further comprises: causing, by the additional processor, an audio message, a video message, or a pictorial message associated with the alert to be presented via a user interface accessible to the driver (see, Paragraph [0029]: “The vehicle feedback module 124 controls the output of feedback messages by applying feedback instructions (identifying the feedback mode) and translating in-vehicle safety feedback messages into vehicle feedback output-de Vice-specific output formats. For example, various in-vehicle feedback timing modes include real-time feedback, pre-ride feedback, and post-ride feedback. Presentation modes include audio feedback modes, visual feedback modes, and audiovisual feedback modes.”).
Additionally Joshua teaches
… wherein providing the recommended action includes an alert (see, Paragraph [0056]: “The alerts or notifications may include a recommended vehicle action (e.g. slow down a particular speed, service engine within a particular time) and system 10 can be further operable to collect telemetry data used to determine compliance databased on compliance with the alert or notifications and to use the compliance data as additional metrics for user profiles or vehicle profiles.”; and [0147]: “the onboard device 14 also communicates the recommendation within the vehicle 12 in near-real time in a preventive manner. This recommendation enables vehicle 12 to avoid a potential incident by transmitting the recommendation as an alert. The recommendation may alert the vehicle 12 to upcoming unsafe conditions and Suggest an alternative route or speed. The recommendation may alert to upcoming traffic conditions. The recommendation may alert to a problem within the vehicle 12.”)…
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to analyzing vehicle related data, and in particular to, collecting and analyzing real-time vehicle related data as taught by Joshua. One would be motivated to make this modification in order to improve process efficiencies within an organization and provide a real-time information exchange environment (see, Paragraph [0057]).
As to [claim 11], Chalfant in view of Joshua teaches the method of claim [[1]]9. Chalfant discloses wherein the response is a first response, the method further comprising: determining, by the processor, a second response of the driver to the alert; and determining, by the processor and based on the second response, a cost associated with an a digital contract corresponding to the driver (see, Paragraph [0035]: “Suitable in-vehicle feedback can include an analysis of the driver's driving behavior, alerts to the driver to alter his or her driving behavior, notifications of potential insurance impacts of driving behavior, and/or incentives to alter or maintain certain driving behaviors. For example, in-vehicle alerts include warnings to slow down, reduce acceleration, increase the distance from behind the vehicle in front, reduce the number of lane changes, or increase braking distance. Other suitable in-vehicle feedback include warnings that premium levels or deductibles may increase if un-safe driving behavior is not modified. Alternatively, or in addition, in vehicle feedback may include congratulatory feedback announcing favorable insurance policy feature alterations, Such as premium or deductible reductions, or discounts on goods or services (e.g., offered by a monitoring service or fleet supervisor)”).
As to [claim 12], Chalfant in view of Joshua teaches the method of claim 1. Chalfant discloses wherein, …alerts associated with the historical operating data are pre-determined by an insurance provider of an insurance policy associated with the vehicle (see, Paragraph [0070]: “In response to the monitoring the driving of the driver (step 406), the business rules module 218 of the driver evaluation and insurance processor 212 applies insurance rules (step 408) to safety evaluations output by the driver evaluation module 214. The insurance rules may result in the driver's being provided incentives to maintain or alter his or her driving behavior (step 410) and/or a change in the insurance policy and/or services (step 412) Such as those described above. As the feedback relayed to the driver depends upon the rating characteristics of the driver (as discussed in the examples above), said rating characteristics similarly affect insurance adjustment determinations.”).
Additionally, Joshua teaches
… wherein: the recommended action includes providing an alert to the driver (see, Paragraph [0056]: “The alerts or notifications may include a recommended vehicle action (e.g. slow down a particular speed, service engine within a particular time) and system 10 can be further operable to collect telemetry data used to determine compliance databased on compliance with the alert or notifications and to use the compliance data as additional metrics for user profiles or vehicle profiles.”; and [0147]: “the onboard device 14 also communicates the recommendation within the vehicle 12 in near-real time in a preventive manner. This recommendation enables vehicle 12 to avoid a potential incident by transmitting the recommendation as an alert. The recommendation may alert the vehicle 12 to upcoming unsafe
conditions and Suggest an alternative route or speed. The recommendation may alert to upcoming traffic conditions. The recommendation may alert to a problem within the
vehicle 12.”)…
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to analyzing vehicle related data, and in particular to, collecting and analyzing real-time vehicle related data as taught by Joshua. One would be motivated to make this modification in order to improve process efficiencies within an organization and provide a real-time information exchange environment (see, Paragraph [0057]).
Regarding [claim 13], recites analogous limitations that are present in claim 1, therefore claim 13 would be rejected for the same/similar premise above. Chalfant discloses a system, comprising: a processor; and a non-transitory memory storing computer-executable instructions that, when executed, cause the processor (see, Paragraphs [0085]: “The program may be stored, for example, in a compressed, an uncompiled and/or an encrypted format, and may include computer program code. The instructions of the program may be read into a main memory of the processor from a computer-readable medium other than the data storage device 706, such as from a ROM 703 or from a RAM 705. While execution of sequences of instructions in the program causes the processor 702 to perform the process steps described herein, hard-wired circuitry may be used in place of or in combination with, Software instructions for implementation of the processes of the present invention. Thus, embodiments of the present invention are not limited to any specific combination of hardware and software” and [0088]) to…
As to [claim 15], the Chalfant in view of Joshua teaches the system of claim 13. As Joshua teaches wherein the recommended action includes an alert to be provided to the driver (see, Paragraph [0056]: “The alerts or notifications may include a recommended vehicle action (e.g. slow down a particular speed, service engine within a particular time) and system 10 can be further operable to collect telemetry data used to determine compliance databased on compliance with the alert or notifications and to use the compliance data as additional metrics for user profiles or vehicle profiles.”; and [0147]: “the onboard device 14 also communicates the recommendation within the vehicle 12 in near-real time in a preventive manner. This recommendation enables vehicle 12 to avoid a potential incident by transmitting the recommendation as an alert. The recommendation may alert the vehicle 12 to upcoming unsafe conditions and Suggest an alternative route or speed. The recommendation may alert to upcoming traffic conditions. The recommendation may alert to a problem within the vehicle 12.”). Mollicone further teaches wherein the instructions further cause the processor to: provide, for presentation to a user interface associated with an additional vehicle, the alert, wherein the additional vehicle is operating under a driving condition matching the current driving condition (see, Paragraphs [0074]: “Method 410 then proceeds to step 425 in which driving task characteristics from the measurement signal are compared to driving-task characteristics from the reference signal. Measurement-signal driving task characteristics are received from foregoing step 421, but reference-signal driving-task characteristics may be received from either step 413 or step 417, depending upon results of the step-412 query. Step 425 accomplishes the signal comparison by determining a mathematical distance between the two sets of driving taak characteristics” and [0087]: “any driver performance level is determined in steps 410, 430, or 450, the present invention may also invoke one or more alerting operations or “alert events.’’ according to step 471 of FIG. 4A. An alert event comprises any action, mechanism, function, or activity that notifies one or more drivers, administrative users, operators, operational managers, first responders, law enforcement, witnesses, the general public, or any other individuals impacted directly or indirectly by the operation of the vehicle when it is determined that the drivers performance level obtains one more values or states”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to implement a vehicle driver's performance based on contextual changes and the driver's response as taught by Mollicone. One would be motivated to make this modification in order to multiple sensors or several measurements from the same sensor over a period of time) can be combined to improve the accuracy, precision, and reliability of measurements of the vehicle's physical state and any signals derived therefrom.
Regarding [claim 18], recites analogous limitations that are present in claim 1, therefore claim 18 would be rejected for the same/similar premise above.
As to [claim 19], recites analogous limitations that are present in claim 9, therefore claim 19 would be rejected for the same/similar premise above.
Regarding [claim 21], recites analogous limitations that are present in claim (s) 1 and 13, therefore claim 13 would be rejected for the same/similar premise above. Chalfant discloses A system, …
As to [claim 22], Chalfant in view of Joshua teaches the method of claim 21. Chalfant discloses wherein the instructions further cause the processor to: generate, based on the performance indicator, a warning; and provide, to the additional processor, the warning (see, Paragraph [0035]: “Suitable in-vehicle feedback can include an analysis of the driver's driving behavior, alerts to the driver to alter his or her driving behavior, notifications of potential insurance impacts of driving behavior, and/or incentives to alter or maintain certain driving behaviors. For example, in-vehicle alerts include warnings to slow down, reduce acceleration, increase the distance from behind the vehicle in front, reduce the number of lane changes, or increase braking distance. Other suitable in-vehicle feedback include warnings that premium levels or deductibles may increase if un-safe driving behavior is not modified. Alternatively, or in addition, in vehicle feedback may include congratulatory feedback announcing favorable insurance policy feature alterations, Such as premium or deductible reductions, or discounts on goods or services (e.g., offered by a monitoring service or fleet supervisor).”) wherein providing the warning causes the additional processor to present an audio message, a video message, or a pictorial message indicative of the warning to be presented via a user interface accessible to the driver (see, Paragraph [0029]: “The vehicle feedback module 124 controls the output of feedback messages by applying feedback instructions (identifying the feedback mode) and translating in-vehicle safety feedback messages into vehicle feedback output-de Vice-specific output formats. For example, various in-vehicle feedback timing modes include real-time feedback, pre-ride feedback, and post-ride feedback. Presentation modes include audio feedback modes, visual feedback modes, and audiovisual feedback modes.”).
Regarding [claim 24], recites analogous limitations that are present in claim 3, therefore claim 24 would be rejected for the same/similar premise above.
As to [claim 27], Chalfant in view of Joshua teaches the method of claim 1. Chalfant teaches wherein the recommended action is determined in real-time after obtaining the operating data and the vehicle condition data (see, Paragraph [0032]: “If the vehicle receives the same feedback message with instructions to present the message using a real-time video feedback mode, the vehicle feedback module 124 likewise translates the message into an appropriate format for outputting to the driver based on the specific visual feedback output devices 118 incorporated into the vehicle. For example, in one embodiment, the vehicle feedback module 124 searches a look-up table using the text to identify a light to illuminate on the vehicle's dashboard. Alternatively, the vehicle feedback module 124 outputs the text in ASCII or other text format to a display screen”).
Regarding [claim 28], recites analogous limitations that are present in claim 4, therefore claim 28 would be rejected for the same/similar premise above.
Claim(s) 6 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chalfant in view of Joshua, and in view of Hodges et al. (Pub. No.: US 2016/011792; previously recorded), hereinafter, referred to as “Hodges”).
As to [claim 6], Chalfant in view of Joshua teaches the method of claim 1. Neither Chalfant nor Joshua explicitly disclose wherein generating the data model further comprises: determining a set of weights corresponding to one or more parameters associated with the respective historical operating data, wherein determining the performance indicator comprises applying the set of weights to one or more values associated with the operating data.
However, Hodges teaches
…determining a set of weights corresponding to one or more parameters associated with the historical operating data, wherein determining the performance indicator comprises applying the set of weights to one or more values associated with the operating data (see, Paragraphs [0063]: “Additionally or alternatively, the determination of real-time feedback may be based on combinations of parameters, such as speed, acceleration, traction control, posted speed limits, or some combination thereof. Historical real time feedback may further weight or influence the severity of current real-time feedback”; [0072]: “In one example, the onboard driver alerting module 170B can use the user input to adjust (for instance, rewrite or revise an associated algorithm or conditional statement) or process differently (for instance, change an associated weighting or threshold) the multiple rules or the multiple feedbacks stored in the memory 215. As a result, the onboard driver alerting module 170B may, in effect, be (i) better able to determine erroneous rule violations or feedbacks, (ii) more or less forgiving or understanding towards the rule violation or feedback, or (iii) more or less inclined to discard, ignore, or report the rule violation or feedback to the driver.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to implement generating the safe-driver model as by taught by Chalfant. By combing determining a set of weights corresponding to one or more parameters associated with the respective historical operating data would achieve the successful results. One would be motivated to make this modification in order to providing driver information related to driver safety, how they are well driving, heavy braking or acceleration events, speeding occurrences, fuel efficiency, suggestions for improving driving, and idle times, for instance.
Regarding [claim 20], recites analogous limitations that are present in claim 6, therefore claim 20 would be rejected for the same/similar premise above.
Claim(s) 7, 16-17, 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chalfant in view of Joshua, and in view of Mollicone et al. (Pub. No.: US 2016/0362118A1; previously recorded).
As to [claim 7], Chalfant in view of Joshua teaches the method of claim 1. Chalfant does not explicitly disclose
wherein the performance indicator is determined based at least in part on a length of time elapsed for the response of the driver to a change in the current driving condition.
However, Mollicone teaches
wherein the performance indicator is determined based at least in part on a length of time elapsed for the response of the driver to a change in the current driving condition (See, Table 3).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to implement a vehicle driver's performance based on contextual changes and the driver's response as taught by Mollicone. One would be motivated to make this modification in order to multiple sensors or several measurements from the same sensor over a period of time) can be combined to improve the accuracy, precision, and reliability of measurements of the vehicle's physical state and any signals derived therefrom.
As to [claim 16], Chalfant in view of Joshua teaches the system of claim 13. As Joshua teaches, cause the processor to: determine, based on the performance indicator and the vehicle condition data, an alert to be provided to the driver (see, Paragraph [0056]: “The alerts or notifications may include a recommended vehicle action (e.g. slow down a particular speed, service engine within a particular time) and system 10 can be further operable to collect telemetry data used to determine compliance databased on compliance with the alert or notifications and to use the compliance data as additional metrics for user profiles or vehicle profiles.”; and [0147]: “the onboard device 14 also communicates the recommendation within the vehicle 12 in near-real time in a preventive manner. This recommendation enables vehicle 12 to avoid a potential incident by transmitting the recommendation as an alert. The recommendation may alert the vehicle 12 to upcoming unsafe conditions and Suggest an alternative route or speed. The recommendation may alert to upcoming traffic conditions. The recommendation may alert to a problem within the vehicle 12.”);
…
Mollicone teaches further wherein the operating data includes information indicating fully autonomous or semi-autonomous operations of the vehicle, and the instructions further cause, by the additional processor, an audio message, a video message, or a pictorial message associated with the alert to be presented via a user interface accessible to the driver; and based on the information indicating fully autonomous or semi-autonomous operations of the vehicle, control the vehicle in accordance with [[a]]an additional recommended action associated with the alert. (see, [0097]: “implementations of the invention may comprise transmission of information across networks, and distributed computational elements which perform one or more methods of the inventions. Such a system may enable a distributed team of operational planners and monitored individuals to utilize the information provided by the invention. A networked system may also allow individuals to utilize a graphical interface, printer, or other display device to receive personal alertness predictions and/or recommended future inputs through a remote computational device. Such a system would advantageously minimize the need for local computational devices”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to implement a vehicle driver's performance based on contextual changes and the driver's response as taught by Mollicone. One would be motivated to make this modification in order to multiple sensors or several measurements from the same sensor over a period of time) can be combined to improve the accuracy, precision, and reliability of measurements of the vehicle's physical state and any signals derived therefrom.
As to [claim 17], Chalfant in view of Joshua teaches the system of claim 13. Mollicone teaches wherein the current driving condition comprises at least one of: a state of an environment in which the vehicle is operating, or a state of an environment within a cabin of the vehicle (see, Paragraph [0088]: “According to particular embodiments, performance-related alert events may include vehicle-specific operations, such as an audible or visible signal within the vehicle itself for example (without limitation) a buzzer, a light or LED on the dashboard, haptic feedback in the steering wheel or the driver's seat, and/or the like. Other vehicle-specific fatigue alert operations may be designed to increase the drivers alertness level (i.e., decrease his or her fatigue””).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to implement a vehicle driver's performance based on contextual changes and the driver's response as taught by Mollicone. One would be motivated to make this modification in order to multiple sensors or several measurements from the same sensor over a period of time) can be combined to improve the accuracy, precision, and reliability of measurements of the vehicle's physical state and any signals derived therefrom.
As to [claim 23], the combination of Chalfant and Barber teaches the system of claim 22. Chalfant discloses the provision of multiple warnings (see rejection of claim 1) but does not explicitly provide, for presentation to a user interface associated with an additional vehicle, the warning, wherein the additional vehicle is operating under a driving condition matching the current driving condition.
However, Mollicone teaches
provide, for presentation to a user interface associated with an additional vehicle, the warning, wherein the additional vehicle is operating under a driving condition matching the current driving condition (see, Paragraph [0087]: turning on the radio, increasing the radio’s Volume, opening one or more windows in the vehicle, and/or the like. Other vehicle specific fatigue alert operations may include operations that impact operational control of the vehicle, for example (without limitation), limiting the vehicle's speed, invoking an autonomous driving mode or an autopilot mode, reducing the vehicle's speed, in”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to implement a vehicle driver's performance based on contextual changes and the driver's response as taught by Mollicone. One would be motivated to make this modification in order to multiple sensors or several measurements from the same sensor over a period of time) can be combined to improve the accuracy, precision, and reliability of measurements of the vehicle's physical state and any signals derived therefrom.
Claim(s) 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chalfant, Joshua and Mollicone, and in view of Barber (Pub. No.: US 2014/0278574; previously recorded)
As to [claim 25], the combination of Chalfant, Joshua and Mollicone teaches method of claim 7. Barber teaches comprising:
a set of time-series data collected by the first sensors, the set of time-series data represents (i) the driving behavior and (ii) the current driving condition at each time instance, and the response of the driver is determined based at least in part on the set of time-series data (see, Paragraph [0063]: “Hours of Day—a tabulation of the times a driver is documented using a toll highway from time stamp data during a 24 hour time frame of a day. This metric may be tabulated by creating a simple usage table based a 24 hour clock. The time stamp of each trip may be transferred to the table and a summary for the rating period prepared to show the percentage of trips in each hour of the 24 hour clock. This metric can be used in subsequent metric calculations to identify drivers who avoid high volume traffic or conversely, who routinely drive during congested rush hours or in other metrics in which a comparison to a time of day metric would be beneficial”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify the driving behaviors of the plurality of drivers as taught Barber and combining the operating data is indicative of a driving behavior exhibited by a driver operating a vehicle by Chalfant. One would be motivated to make this modification in order to improve driving behaviors in specific conditions encountered by the driver using the experience of how a baseline group performed in those conditions, and may be further provided with a recommendation for improving the driver's driving behavior (see, Paragraph [0045]).
Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chalfant in view of Joshua, and in view of Konrardy et al (Patent No.: US 9,715,711; previously recorded), hereinafter, referred to as “Konrardy”.
As to [claim 26], Chalfant in view of Joshua teaches the method of claim 1.
As Chalfant teaches wherein:
the operating data includes information, Chalfant nor Joshua teaches autonomous operation features of the vehicle, and the recommended action is performed by an autonomous operation feature of the autonomous operation features.
Additionally, Konrardy teaches
autonomous operation features of the vehicle, and the recommended action is performed by an autonomous operation feature of the autonomous operation features (see, Figure 3; and col. 18, “The autonomous vehicle operation application 232 or other applications 230 or routines 240 may cause the con troller 204 to process the received sensor data at block 306 in accordance with the autonomous operation features. The controller 204 may process the sensor data to determine whether an autonomous control action is required or to determine adjustments to the controls of the vehicle 108. For example, the controller 204 may receive sensor data indicating a decreasing distance to a nearby object in the vehicle's path and process the received sensor data to determine whether to begin braking…”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to modify the operating data includes information as taught by Chalfant in view of Barber. One would be motivated to make this modification in order to convey an autonomous communication routine 250 for receiving and transmitting information between the vehicle 108 and external sources to improve the effectiveness of the autonomous operation features (see, col. 16).
Conclusion
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/B.U./Examiner, Art Unit 3663
/JAMES M MCPHERSON/Examiner, Art Unit 3663
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