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 .
Status of Claims
This Office Action is in response to the Applicants’ filing on June 23, 2026. Claims 1-18 were previously pending, of which claims 1, 7, 8 and 14 have been amended, no claims have been cancelled, and no claims have been newly added. Accordingly, claims 1-18 are currently pending and are being examined below.
Response to Arguments
With respect to Applicant's remarks, see pages 8-13 filed June 23, 2026; Applicant’s “Amendment and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
With respect to the 35 U.S.C. §101 Rejection, the arguments and amendments have been reviewed by the examiner, and they are persuasive. The basic elements of the invention are merely data gathering or presenting information, however the addition of the amended claim limitations present enough evidence to reconsider the rejection. These include features of transmitting a video upload to a server via a network when the degree of risk of driving of the vehicle crosses a risk threshold value under the adopted scoring criterion, which may be different based on whether the movable object exists in the vicinity of the vehicle or not, for reducing amount of video uploads to the server. Therefore, the rejection is withdrawn.
Applicant's arguments regarding 35 U.S.C. §102(a)(1)/103 Rejections have been fully considered and they are persuasive. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., determining whether a movable object exists in vicinity of the vehicle and adopts a differing scoring criterion based on the determination of whether the movable object exists in the vicinity of the vehicle or not) are not clearly defined in the prior art and the rejection has been withdrawn in view of the amended claims. Although the claims are interpreted in light of the specification, the new limitations from the amended claims are not persuasive in view of further search and prior art. Therefore, the rejection under 35 U.S.C. § 103 is maintained, as presented in the Final Office Action below.
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.
Claims 1-11 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sathyanarayana et al., US 2018/0365533 A1 (Hereinafter, “Sathyanarayana”), in view of Shinoda et al., US 2019/0115049 A1 (Hereinafter, “Shinoda”), in further view of Julian et al., US 20230227058 A1 (Hereinafter, “Julian”).
Regarding Claims 1 and 14, Sathyanarayana discloses a driving assistance system installed in a vehicle, the driving assistance system comprising:
memory; and a processor connected to the memory, wherein the processor: detects a driving scene of a vehicle according to an output from one or more sensors provided in the vehicle based on a movement operation of the vehicle; See at least Fig.2 and [0013], “onboard vehicle system 200, which includes one or more sensors 210 (e.g., optical sensors, audio sensors, stereo cameras, stereo microphones, inertial sensors, accelerometers, magnetometers, gyroscopes, ambient light sensors, etc.) … can also include a power system 220, a processing system 230 including a memory 235, and a communication system 240 … The onboard vehicle system is preferably separate from the vehicle (e.g., installed into the vehicle.” Also [0042], “extracting exterior activity data … exterior activity data can include data related to driver actions (e.g., following distance, lane drift, lane changes, turning, braking, braking rate, slowing and/or stopping distance, reaction of the vehicle to control inputs by the driver, etc.)(i.e. based on movement of the vehicle) … intrinsic vehicle information perceptible from views of a scene external to the vehicle … activity data related to the vehicle exterior and/or collected by a sensor that can detect aspects of the environment external to the vehicle (e.g., at the onboard vehicle system).”
determines whether a movable object exists in vicinity of the vehicle and adopts a , See [0059] In an example of Block S110, determining the exterior event can determining that a distance between the vehicle and an object depicted in a first region of an image stream (e.g., corresponding to an exterior-facing camera) has fallen below a threshold distance (i.e. in the vicinity). The exterior event in this example is the crossing of the threshold distance by the object, wherein the distance is determined (e.g., as exterior activity data) in accordance with a variation of Block S106. In a related example, the object can be a secondary vehicle, and determining the exterior event includes determining that the relative position between the vehicle and the secondary vehicle has fallen below a threshold distance (e.g., such as in an instance of tailgating, the secondary vehicle cutting off the vehicle, the secondary vehicle suddenly braking, etc.). “ And [0067], “Block S130 can include determining that a driver gaze direction … corresponding to an object as it crosses a threshold relative distance determined in accordance with a variation of Block S110) … and determining a risk value, risk score, and any other suitable distraction metric value based on gaze direction overlap with an exterior feature of interest (e.g., the region of the external image stream sampled from an exterior-facing camera camera) and/or an object of interest (e.g., a blob in vehicle trajectory or path; a moving or stationary obstacle; road signage such as traffic light, stop sign, road-painted signs, etc.; and any other suitable object, etc.).”
detects a cognitive action of a driver who drives the vehicle, according to an output from one or more sensors provided in the vehicle; and See [0031], “method can enable semantic correlation of interior events with exterior events to generate combined event data. For example, an interior event can include a driver distraction event (e.g., a cognitive distraction event, a look-down event, a look-away event, a mobile device interaction, a vehicle console interaction, etc.) and the exterior event can include a lane drift event, and the combined event data can include an assignment of causality to the interior event (e.g., labeling the exterior event with a cause that includes the interior event).”
determines a degree of risk of driving of the vehicle by the driver, based on at least the driving scene detected and the cognitive action detected. See at least [0070-0072], “correlating the interior event and the exterior event can include determining a multifactorial risk score. Block S130 can include determining a multifactorial risk score based on a combination of the interior event (e.g., a driver distraction event) and the exterior event (e.g., an aggressive driving event such as tailgating, overly rapid lane change, etc.), wherein the multifactorial risk score is nonlinearly related to the risk scores associated with each of the interior and/or exterior events individually.“
calculates a driving score under the adopted scoring criterion, based on a deviation of a timing or a time period of the cognitive action detected from a predetermined timing or a prescribed time period; See [0067], “Block S130 can include determining that a driver gaze direction (e.g., determined in accordance with a variation of Block S120) overlaps with a region of an external image stream (e.g., corresponding to an object as it crosses a threshold relative distance determined in accordance with a variation of Block S110) at a time point within a time window (e.g., a time window in which the source data for determination of the driver's gaze direction is sampled in accordance with a variation of Block S104; concurrently, simultaneously, contemporaneously, etc.), and determining a risk value, risk score, and any other suitable distraction metric value based on gaze direction overlap with an exterior feature of interest (e.g., the region of the external image stream sampled from an exterior-facing camera camera) and/or an object of interest (e.g., a blob in vehicle trajectory or path; a moving or stationary obstacle; road signage such as traffic light, stop sign, road-painted signs, etc.; and any other suitable object, etc.).” And [0070], “Block S130 includes determining a distraction score based on correlating driver gaze parameters (e.g., a gaze direction determined in one or more variations of Block S120) with a position of an object (e.g., a risky object position determined in one or more variations of Block S110). The determined distraction score in this variation can define a static value, and can also define a dynamic value (e.g., a value that increases as a function of gaze distance from the risky object, a value based on a probability of the gaze overlapping the risky object within a time window, a value based on a gaze sweep velocity, etc.).”
Sathyanarayana discloses a driver assistance system, but does not explicitly disclose driver video recording or risk threshold triggers for uploading to a server. However Shinoda teaches driver video recording and uploading including the following limitation: transmits a video upload to a server via a network when the driving score crosses a risk threshold value under the adopted scoring criterion for reducing amount of video uploads to the server. See [0051], “In recording device 5, controller 52 accesses remote server device 6 at predetermined timing, reads the vehicle exterior image and the vehicle interior image to which first mark A is added and the vehicle interior image to which second mark B is added from recorder 53, and uploads the images through wireless communicator 55. Server device 6 records the received vehicle exterior image and the vehicle interior images in an incident database (DB) (see “(f) Upload” in FIG. 4B) … controller 52 can reproduce only the vehicle interior image to which the risk level previously set on the user side is added.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Sathyanarayana’s device with the video uploading limitations disclosed in Shinoda with reasonable expectation of success. The motivation for doing so would have been to allow for later investigation for insurance purposes and/or driver's education, see Shinoda [0003].
Sathyanarayana and Shinoda teach driver assistance system with driver video recording, but do not teach differing scoring criterion based on if an object is detected the vicinity of the vehicle. However, Julan teaches adjusting of alerting safety threshold (i.e. changing the criterion for the alert) based on complexity of outward scene, which includes determining if a moving object (vehicle) is in the vicinity of the vehicle; i.e. “determines whether a movable object exists in vicinity of the vehicle and adopts a differing scoring criterion based on the determination of whether the movable object exists in the vicinity of the vehicle or not, wherein the adoption of the differing scoring criterion includes: adopting a first scoring criterion when the movable object is determined not to exist in the vicinity of the vehicle, and adopting a second scoring criterion when the movable object is determined to exist in the vicinity of the vehicle “ [0070]-[0071] here teaches object detection in vicinity+ [0134] “…. In one embodiment, the threshold time may be a function of the outward scene with two threshold times. If the road ahead of the driver does not have any vehicles within a given distance of travel time, and the driver is maintaining his/her lane position, then the threshold time that the driver may look away from the road before an alert is sounded may be set to the long threshold time. If there are vehicles detected in the road ahead or the lane position varies by more than a set threshold, then the short threshold time may be used.” + [0136] “Another embodiment may use a series of threshold times or a threshold function that takes as inputs one or more of the distance to the nearest vehicle, number of vehicles on the road, lane position, vehicle speed, pedestrians present, road type, weather, time of day, and the like, to determine a threshold time.”
[0137] “Many other alert thresholds are contemplated for which the threshold may be varied for inward alerts based on the determination of the outward scene complexity, and vice versa. That is, the threshold of an outward alert may be based on a determination of the inward scene complexity. In addition, there are a number of additional unsafe driving events that may be captured and that may be a basis for issuing a warning alert to the driver. Several examples are described in detail below.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Sathyanarayana and Shinoda’s device with the uploading limitations disclosed in Julian with reasonable expectation of success. The motivation for doing so would have been to improve the quality of reporting by allowing the system to adjust the thresholds (criterions) for alerts based on the surrounding environment thereby improving operation of the device by avoiding false alarms and allowing for quicker alerts depending on the situation, see Julian [0128] “In a first is a series of embodiments, inward and outward determinations may be combined to improve in-cab alerts. Accordingly, unnecessary alerts may be reduced, and consequently more alerts may feel actionable to the driver leading the driver to respond to the alerts more attentively and to keep the alerts active. According to certain aspects of the present disclosure, an earlier warning may be provided if the driver is distracted or determined to not be observing what is happening.”
Regarding Claim 2, Sathyanarayana discloses the following limitation dependent on Claim 1: wherein in determination of the degree of risk, the processor determines the degree of risk by performing scoring of driving of the vehicle by the driver, and a driving score See at least [0069], “In relation to determining a weighting, the event data can include a weightable characteristic (e.g., a characteristic that can be weighted). A weightable characteristic can include an event score, an event severity (e.g., a quantitative severity spanning a number from 0-100 wherein 0 corresponds to a non-event and a 100 corresponds to a collision that totals the vehicle; a qualitative severity selected from among several predetermined severity categories such as near-miss, fender-bender, vehicle inoperative, vehicle totaled; etc.), and any other suitable characteristic of an event to which a weight can be assigned (e.g., wherein the weighting is determined as described above).”
Sathyanarayana discloses a driving assistance system with risk scoring, but does not explicitly disclose a driving score is smaller when the degree of risk is higher. Examiner notes that the applied reference has been interpreted and applied assuming basic knowledge of one of ordinary skill in the art. According to in re Jacoby, 135 USPQ 317 (CCPA 1962), the skilled artisan is presumed to know something more about the art than only what is disclosed in the applied references. Also, in In re Bode, 193 USPQ 12 (CCPA 1977), the court found that every reference relies to some extent on knowledge of persons skilled in the art to complement that, which is disclosed therein. As applied to Sathyanarayana, it is within the basic knowledge of a skilled artisan that a driving score scaling based on degree of risk can be directly or inversely proportional with predictable results depending on the desired outcome. It is purely design choice. In other words, Sathyanarayana’s driving score target would function with predicable results with either direction from 0 to x (i.e. 100) or x (i.e. 100) to 0. Therefore one would be motivated to have a driving score inversely proportional to risk level because the results would be predicable in the same manner.
Regarding Claim 3, Sathyanarayana discloses a driver assistance system, but does not explicitly disclose driver video recording or risk threshold triggers for uploading to a server. However Shinoda teaches driver video recording and uploading including the following limitation:
wherein in the determination of the degree of risk, the processor further determines whether or not the driving score is less than a risk threshold value, and the processor further uploads, to a server, driving video data obtained by imaging in the driving scene, when it is determined that the driving score is less than the risk threshold value. See [0051], “In recording device 5, controller 52 accesses remote server device 6 at predetermined timing, reads the vehicle exterior image and the vehicle interior image to which first mark A is added and the vehicle interior image to which second mark B is added from recorder 53, and uploads the images through wireless communicator 55. Server device 6 records the received vehicle exterior image and the vehicle interior images in an incident database (DB) (see “(f) Upload” in FIG. 4B) … controller 52 can reproduce only the vehicle interior image to which the risk level previously set on the user side is added.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Sathyanarayana’s device with the video uploading limitations disclosed in Shinoda with reasonable expectation of success. The motivation for doing so would have been to allow for later investigation for insurance purposes and/or driver's education, see Shinoda [0003].
Regarding Claim 4, Sathyanarayana discloses the following limitation dependent on Claim 3:
wherein the processor further generates, from one or more items of moving image data obtained by imaging with a camera provided in the vehicle, the driving video data by selecting and editing a portion corresponding to the driving scene detected. See [0051], “In recording device 5, controller 52 accesses remote server device 6 at predetermined timing, reads the vehicle exterior image and the vehicle interior image to which first mark A is added and the vehicle interior image to which second mark B is added from recorder 53, and uploads the images through wireless communicator 55. Server device 6 records the received vehicle exterior image and the vehicle interior images in an incident database (DB) (see “(f) Upload” in FIG. 4B). Similarly to the conventional case, the vehicle exterior image and vehicle interior images recorded in incident DB are used by an automobile insurer or a transport service provider. Similarly to the previous description, preferably terminal device 7 on the user (the automobile insurer or the motor carrier) side synchronously reproduces the vehicle exterior image and the vehicle interior image, and displays the images on the display device (see “(e) Data Reproduction and Display” in FIG. 4B). At this point, controller 52 can reproduce only the vehicle interior image to which the risk level previously set on the user side is added.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Sathyanarayana’s device with the video uploading limitations disclosed in Shinoda with reasonable expectation of success. The motivation for doing so would have been to allow for later investigation for insurance purposes and/or driver's education, see Shinoda [0003].
Regarding Claim 5, Sathyanarayana discloses the following limitation dependent on Claim 1:
wherein the processor further detects a movable object in a vicinity of the vehicle, according to an output from one or more sensors provided in the vehicle, and in determination of the degree of risk, the processor determines the degree of risk based on a detection result of the movable object. See [0042-0043], “Block S106, extracting data can include … external object activity (e.g., relative distances between the vehicle and external objects such as other vehicles, pedestrians, lane lines, traffic signage and signals, roadway features, etc.).“ And [0059], “In an example of Block S110, determining the exterior event can determining that a distance between the vehicle and an object depicted in a first region of an image stream (e.g., corresponding to an exterior-facing camera) has fallen below a threshold distance. The exterior event in this example is the crossing of the threshold distance by the object, wherein the distance is determined (e.g., as exterior activity data) in accordance with a variation of Block S106.“
Regarding Claim 6, Sathyanarayana discloses the following limitation dependent on Claim 1:
wherein the processor further detects visual observation by the driver of one or more objects in vicinity of the vehicle, according to an output from one or more sensors provided in the vehicle, and in determination of the degree of risk, the processor determines the degree of risk based on a detection result of the visual observation. See [0070], “Block S130 includes determining a distraction score based on correlating driver gaze parameters (e.g., a gaze direction determined in one or more variations of Block S120) with a position of an object (e.g., a risky object position determined in one or more variations of Block S110). The determined distraction score in this variation can define a static value, and can also define a dynamic value (e.g., a value that increases as a function of gaze distance from the risky object, a value based on a probability of the gaze overlapping the risky object within a time window, a value based on a gaze sweep velocity, etc.).” And [0071], “Block S130 includes determining a driver distraction score based on a reaction of the driver (e.g., determined in accordance with one or more variations of Block S100) to an object (e.g., a position or behavior of the object determined in accordance with one or more variations of Block S100).”
Regarding Claim 7, Sathyanarayana discloses the following limitation dependent on Claim 1:
wherein the processor further detects behavior of the vehicle, and in determination of the degree of risk, the processor determines the degree of risk based on a detection result of the behavior by the vehicle behavior detector. See [0044], “Block S100, determining event data (e.g., an exterior event, an interior event, etc.) can include determining vehicle operation data in relation to a driving event. Vehicle operation data is preferably data related to vehicle operation (e.g., characteristics of the vehicle while the vehicle is operated and/or driven), but can additionally or alternatively include any other suitable data … Block S110 can include collecting vehicle sensor data and extracting vehicle operational data from vehicle sensor data, which can be performed substantially simultaneously, sequentially, or with any other suitable relative temporal characteristics.” And [0072], “In relation to Block S130, correlating the interior event and the exterior event can include determining a multifactorial risk score. Block S130 can include determining a multifactorial risk score based on a combination of the interior event (e.g., a driver distraction event) and the exterior event (e.g., an aggressive driving event such as tailgating, overly rapid lane change, etc.).”
Regarding Claim 8, Sathyanarayana discloses the following limitation dependent on Claim 1:
wherein the processor further detects a predetermined device operation by the driver as a dangerous operation, according to an output from one or more sensors provided in the vehicle, and in determination of the degree of risk, the processor determines the degree of risk based on a detection result of the dangerous operation by the dangerous operation detector. See [0031], “variants of the method can enable semantic correlation of interior events with exterior events to generate combined event data. For example, an interior event can include a driver distraction event (e.g., a cognitive distraction event, a look-down event, a look-away event, a mobile device interaction, a vehicle console interaction, etc.) and the exterior event can include a lane drift event, and the combined event data can include an assignment of causality to the interior event (e.g., labeling the exterior event with a cause that includes the interior event).” And [0071], “Block S130 includes determining a driver distraction score based on a reaction of the driver (e.g., determined in accordance with one or more variations of Block S100) to an object (e.g., a position or behavior of the object determined in accordance with one or more variations of Block S100). For example, Block S130 can include determining a gaze region in an exterior image based on a gaze direction of the driver (e.g., based on a standard viewing area associated with a human gaze, based on a historical perception region from historical data, based on historical data related to perceived objects or objects reacted to when a particular driver gazes toward a particular region, etc.), determining objects within the gaze region (e.g., in accordance with one or more variations of Block S110), determining a driver reaction (e.g., in accordance with one or more variations of Block S120, based on a subsequent driver action, etc.) to the determined objects, and determining (e.g., increasing, decreasing, selecting, computing, etc.) a distraction score based on the driver reaction.”
Regarding Claim 9, Sathyanarayana discloses the following limitation dependent on Claim 3:
wherein in determination of the degree of risk, when the processor determines that the driving score is less than the risk threshold value, the processor further: selects, from a plurality of individual risk threshold values, an individual risk threshold value associated with the driver; and determines whether or not the driving score is less than the selected individual risk threshold value, and in uploading of the driving video data, the processor uploads the driving video data to the server when the driving score is less than the risk threshold value and is less than the individual risk threshold value. See [0048], “Various behaviors can be defined as the second behavior based on intension of a design developer or a user of recording device 5. A plurality of risk levels different from each other can be previously determined in the second behavior. Vehicle interior image selector 63 can assign the previously-determined risk level to the vehicle interior image to which second mark B is added as an example of the metadata.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Sathyanarayana’s device with the video uploading limitations disclosed in Shinoda with reasonable expectation of success. The motivation for doing so would have been to allow for later investigation for insurance purposes and/or driver's education, see Shinoda [0003].
Regarding Claim 10, Sathyanarayana discloses the following limitation dependent on Claim 9:
wherein the processor further determines, based on driving histories of a plurality of persons, each of the plurality of individual risk threshold values corresponding to the plurality of persons. See [0068], “In relation to Block S130 … Determining the weighting can include determining a driver score (e.g., from driver behavior determined in Block S105, historic driving behavior retrieved from a database, from a driver distraction factor, from a driver profile stored onboard the vehicle at the onboard vehicle system, etc.) … and/or otherwise suitably determining a weighting (e.g., weight factor).” And [0069], “determining a weighting, the event data can include a weightable characteristic (e.g., a characteristic that can be weighted). A weightable characteristic can include an event score, an event severity (e.g., a quantitative severity spanning a number from 0-100 wherein 0 corresponds to a non-event and a 100 corresponds to a collision that totals the vehicle; a qualitative severity selected from among several predetermined severity categories … any other suitable characteristic of an event to which a weight can be assigned (e.g., wherein the weighting is determined as described above).”
Regarding Claim 11, Sathyanarayana discloses the following limitation dependent on Claim 10:
wherein in determination of the plurality of individual risk threshold values, the processor determines, for each of a plurality of combinations, the individual risk threshold value corresponding to the combination, each of the plurality of combinations is a combination including one of the plurality of persons including the driver, and one of a plurality of driving scenes, And in selection of the individual risk threshold value, the processor selects, from the plurality of individual risk threshold values, the individual risk threshold value associated with a combination of the driver and the driving scene detected. See [0069], “determining a weighting, the event data can include a weightable characteristic (e.g., a characteristic that can be weighted). A weightable characteristic can include an event score, an event severity (e.g., a quantitative severity spanning a number from 0-100 wherein 0 corresponds to a non-event and a 100 corresponds to a collision that totals the vehicle; a qualitative severity selected from among several predetermined severity categories … any other suitable characteristic of an event to which a weight can be assigned (e.g., wherein the weighting is determined as described above).” And [0072], “In relation to Block S130, correlating the interior event and the exterior event can include determining a multifactorial risk score. Block S130 can include determining a multifactorial risk score based on a combination of the interior event (e.g., a driver distraction event) and the exterior event (e.g., an aggressive driving event such as tailgating, overly rapid lane change, etc.), wherein the multifactorial risk score is nonlinearly related to the risk scores associated with each of the interior and/or exterior events individually. For example, a multifactorial risk score for a combined event (e.g., a driving event including a correlated interior event and exterior event) can be four times the risk score associated with the interior event or the exterior event in isolation; however, the magnitude of the multifactorial risk score can additionally or alternatively be related to the magnitudes of the risk scores of the interior and/or exterior events in any other suitable manner.”
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Sathyanarayana in view of Shinoda, in further view of Arar et al., US 20220121867 A1 (Hereinafter, “Arar”).
Regarding Claim 12, Sathyanarayana discloses the following limitation dependent on Claim 10:
wherein in determination of the plurality of individual risk threshold values, the processor determines, for each of a plurality of combinations, the individual risk threshold value corresponding to the combination, each of the plurality of combinations is a combination including one of the plurality of persons including the driver, and one of a See Claim 12.
Sathyanarayana discloses a driver assistance system, but does not explicitly disclose risk threshold combinations at multiple locations. However, Arar teaches a driver assistance system with location specific risk thresholds in [0029], “The attentiveness determiner 108 may then use some or all of the eye tracking information to determine the attentiveness of the occupant(s). For example, where the heat map indicates that the occupant has been scanning the road frequently and over a wide range and the current gaze direction is toward the driving surface or immediately adjacent thereto, the current attentiveness value or score may be determined to be high. As another example, where the heat map indicates that the occupant has been focusing more on only off-road locations—e.g., toward sidewalks, buildings, landscapes, etc.—and the current gaze is also toward a side of the vehicle 500 (e.g., where the actual driving surface is only in the periphery of the field of view of the occupant), the attentiveness score may be low at least for the eye tracking based information—e.g., the occupant may be determined to be daydreaming or in a blank stare state.” And [0034], “The profile of an occupant may also include increased granularity, such that the behavior of the occupant may be tracked over certain streets, highways, routes, weather conditions, etc., and this information may be used to determine the attentiveness and/or cognitive load of the occupant during future instances of traveling the same streets, highways, in the same weather, etc.”
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Sathyanarayana’s device with the location limitations disclosed in Arar with reasonable expectation of success. The motivation for doing so would have been to capture a complete state of the user as determined based on cognitive load, attentiveness, and/or a comparison between external perception of the vehicle and estimated perception of the user as projected externally of the vehicle, see Arar [0005].
Regarding Claim 13, Sathyanarayana discloses a driver assistance system, but does not explicitly disclose risk threshold for multiple drivers. However, Arar teaches discloses the following limitation dependent on Claim 1: wherein the processor further determines, based on driving histories of a plurality of persons, a prescribed time period corresponding to each of the plurality of persons, and in determination of the degree of risk, the processor: selects a prescribed time period associated with the driver from a plurality of prescribed time periods determined, the plurality of prescribed time periods each being the prescribed time period; and determines the degree of risk by comparing a time period during which the cognitive action detected has been performed with the prescribed time period. See [0034] “In some embodiments, the cognitive load and/or attentiveness of the occupant(s) may be based on a profile corresponding to the occupant(s). For example, over a current drive and/or one or more prior drives by the occupant(s), the eye tracking information, body tracking information, and/or other information of the occupant(s) may be monitored and used to determine a customized profile for the occupant(s) that indicates when the particular occupant(s) is attentive, inattentive, has a higher cognitive load, has a lower cognitive load, etc. For example, a first occupant may scan the road less, but may process objects and make the correct decisions more quickly or with higher frequency, while a second occupant may scan the road less, but may process objects and make correct decisions less quickly or with less frequency. As such, by customizing profiles for the first occupant and the second occupant, the first occupant may not be warned excessively while the second occupant may receive more frequent warnings to ensure safe driving when they are both performing similar road scanning behaviors.” + [0043]-[0044] which teaches that object detection/recognition (cognitive action/state) are based on period of time the user recognizes/looks at an object thus in the context of [0034] teaches user profiles includes shorter threshold/detection time periods for quick/accurate users compared to longer detection time periods/threshold for slower/less accurate users.
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Sathyanarayana’s device with the location limitations disclosed in Arar with reasonable expectation of success. The motivation for doing so would have been to capture a complete state of the user as determined based on cognitive load, attentiveness, and/or a comparison between external perception of the vehicle and estimated perception of the user as projected externally of the vehicle, see Arar [0005].
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.
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/B.K.P./Examiner, Art Unit 3669
/KENNETH M DUNNE/Primary Examiner, Art Unit 3669
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