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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 8 November 2024 is being considered by the examiner.
Claim Interpretation
Examiner notes that the reference Zender is primarily dedicated to determining the drowsiness level of the driver and suggesting an additional stop based on information from the vehicle and the driver. The reference Hori teaches the mood, physical condition, and the drowsiness of the target user affects the driver fatigue level. The applicant’s invention includes “classifying the fatigue status into a drowsiness-inducing fatigue, a negative emotion fatigue, or a lack-of-exercise fatigue, wherein the negative emotion fatigue includes a relaxed fatigue and a tense fatigue” based on the vehicle information and the user information. The examiner notes the difference between the applicant’s naming of the fatigue status and the drowsiness level or the driver fatigue level. However, the difference in classifying the fatigue status and the level of drowsiness or fatigue are non-significant. The examiner is interpreting the applicant's classifying convention of what the data represents or conveys to the human mind, i.e. what the metric represent. The terms “drowsiness-inducing fatigue”, “negative emotion fatigue”, “lack-of-exercise fatigue”, “negative emotion fatigue”, “relaxed fatigue”, and “tense fatigue” are interpreted as nonfunctional descriptive material, therefore no patentable weight will be given to the naming convention (MPEP 2111.05).
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claims 1-2 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Zender et al. (US Publication 2020/0182635 A1) in view of Hori et al. (US Publication 2020/0234224 A1).
Regarding claim 1, Zender teaches a stopover recommendation method performed by a computing device, the method comprising: acquiring vehicle information indicating a state of a vehicle and user information about a user (Zender: Para. 24; determine an amount of fuel remaining in the vehicle; determine information regarding a drowsiness level of the driver); detecting a fatigue status of the user using the vehicle information and the user information (Zender: Para. 24; determine information regarding a drowsiness level of the driver); …………. ; recommending a stopover based on the fatigue status of the user and a past destination setting history of the user (Zender: Para. 47; driver preferences for these factors can be modeled by keeping a tally of how often a driver chooses a specific option); displaying detailed information on the recommended stopover (Zender: Para. 35; presenting at least one candidate waypoint to the driver based on the scores for the candidate waypoints); and determining a route including the recommended stopover as a final route in response to an approval of the user on the recommended stopover (Zender: Para. 54; system accepts as input from the driver one of the candidate waypoints and begins navigation to the waypoint).
Zender doesn’t explicitly teach classifying the fatigue status into a drowsiness-inducing fatigue, a negative emotion fatigue, or a lack-of-exercise fatigue, wherein the negative emotion fatigue includes a relaxed fatigue and a tense fatigue.
However Hori, in the same field of endeavor, teaches classifying the fatigue status into a drowsiness-inducing fatigue, a negative emotion fatigue, or a lack-of-exercise fatigue, wherein the negative emotion fatigue includes a relaxed fatigue and a tense fatigue (Hori: Para. 85, 91; estimate the mood, physical condition, and drowsiness of the target user while driving the vehicle; estimate the driving fatigue level of the target user).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) with a reasonable expectation of success because the mood, physical condition, and drowsiness of the target user while driving the vehicle based on the driver’s past and future schedule, the frequency the driver visits the destination, and current driving conditions affect the driver’s fatigue level (Hori: Para. 85, 91, 95).
Regarding claim 2, Zender teaches the stopover recommendation method of claim 1, wherein the vehicle information includes a location (GPS) of a vehicle, a driving speed, a total driving time duration, a window opening time, a frequency of window opening and closing, a brake pedal stepping strength, a signal indicating a departure of a preceding vehicle, information on media being played, a frequency of air conditioner control, whether ADAS (Advanced Driver Assistance Systems function) is used, or whether a seat massage function is used (Zender: Para. 36; whether the expected duration of the trip is greater than the personalized recommended consecutive driving time).
Zender doesn’t explicitly teach wherein the user information is extracted from usage history of the computing device and a user device of the user, and wherein the user information includes a heart rate of the user, a volume of a voice of the user during a call, the past destination setting history of the user, whether or not the user has visited a specific location, or a posture of the user.
However Hori, in the same field of endeavor, teaches wherein the user information is extracted from usage history of the computing device and a user device of the user (Hori: Para. 95; when the target user has not visited the destination many times, the driving fatigue level of the target user is thought to become relatively higher since the user is not familiar with the moving route and the parking lot; information on the frequency of target user's visits (the number of visits) to the destination of the vehicle 10 may be obtained based on the schedule for the past), and wherein the user information includes a heart rate of the user, a volume of a voice of the user during a call, the past destination setting history of the user, whether or not the user has visited a specific location, or a posture of the user (Hori: Para. 91, 95; information on the frequency of target user's visits (the number of visits) to the destination of the vehicle may be obtained based on the schedule for the past).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) with a reasonable expectation of success because the mood, physical condition, and drowsiness of the target user while driving the vehicle based on the driver’s past and future schedule, the frequency the driver visits the destination, and current driving conditions affect the driver’s fatigue level (Hori: Para. 85, 91, 95).
Regarding claim 11, Zender teaches a computing device comprising: a processor; and a memory connected to the process and configured to store therein instructions, wherein when the instructions are executed by the processor, the instructions cause the processor to perform a method comprising: (Zender: Para. 30; on-board computer of the vehicle using, for example, an application programming an interface) acquiring vehicle information indicating a state of a vehicle and user information about a user (Zender: Para. 24; determine an amount of fuel remaining in the vehicle; determine information regarding a drowsiness level of the driver); detecting a fatigue status of the user using the vehicle information and the user information (Zender: Para. 24; determine information regarding a drowsiness level of the driver); ………. ; recommending a stopover based on the fatigue status of the user and a past destination setting history of the user (Zender: Para. 47; driver preferences for these factors can be modeled by keeping a tally of how often a driver chooses a specific option); displaying detailed information on the recommended stopover (Zender: Para. 35; presenting at least one candidate waypoint to the driver based on the scores for the candidate waypoints); and determining a route including the recommended stopover as a final route in response to an approval of the user on the recommended stopover (Zender: Para. 54; system accepts as input from the driver one of the candidate waypoints and begins navigation to the waypoint).
Zender doesn’t explicitly teach classifying the fatigue status into a drowsiness-inducing fatigue, a negative emotion fatigue, or a lack-of-exercise fatigue, wherein the negative emotion fatigue includes a relaxed fatigue and a tense fatigue.
However Hori, in the same field of endeavor, teaches classifying the fatigue status into a drowsiness-inducing fatigue, a negative emotion fatigue, or a lack-of-exercise fatigue, wherein the negative emotion fatigue includes a relaxed fatigue and a tense fatigue (Hori: Para. 85, 91; estimate the mood, physical condition, and drowsiness of the target user while driving the vehicle; estimate the driving fatigue level of the target user).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) with a reasonable expectation of success because the mood, physical condition, and drowsiness of the target user while driving the vehicle based on the driver’s past and future schedule, the frequency the driver visits the destination, and current driving conditions affect the driver’s fatigue level (Hori: Para. 85, 91, 95).
Regarding claim 12, Zender teaches the computing device of claim 11, wherein the vehicle information includes a location (GPS) of a vehicle, a driving speed, a total driving time duration, a window opening time, a frequency of window opening and closing, a brake pedal stepping strength, a signal indicating a departure of a preceding vehicle, information on media being played, a frequency of air conditioner control, whether ADAS (Advanced Driver Assistance Systems function) is used, or whether a seat massage function is used (Zender: Para. 36; whether the expected duration of the trip is greater than the personalized recommended consecutive driving time).
Zender doesn’t explicitly teach wherein the user information is extracted from usage history of the computing device and a user device of the user, and wherein the user information includes a heart rate of the user, a volume of a voice of the user during a call, the past destination setting history of the user, whether or not the user has visited a specific location, or a posture of the user.
However Hori, in the same field of endeavor, teaches wherein the user information is extracted from usage history of the computing device and a user device of the user (Hori: Para. 95; when the target user has not visited the destination many times, the driving fatigue level of the target user is thought to become relatively higher since the user is not familiar with the moving route and the parking lot; information on the frequency of target user's visits (the number of visits) to the destination of the vehicle 10 may be obtained based on the schedule for the past), and wherein the user information includes a heart rate of the user, a volume of a voice of the user during a call, the past destination setting history of the user, whether or not the user has visited a specific location, or a posture of the user (Hori: Para. 91, 95; information on the frequency of target user's visits (the number of visits) to the destination of the vehicle may be obtained based on the schedule for the past).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) with a reasonable expectation of success because the mood, physical condition, and drowsiness of the target user while driving the vehicle based on the driver’s past and future schedule, the frequency the driver visits the destination, and current driving conditions affect the driver’s fatigue level (Hori: Para. 85, 91, 95).
Claims 3, 9, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zender et al. (US Publication 2020/0182635 A1) in view of Hori et al. (US Publication 2020/0234224 A1) and in further view of Wipperfurth (US Publication 2019/0324600 A1).
Regarding claim 3, Zender teaches the stopover recommendation method of claim 2, wherein the classifying of the fatigue status includes: when a driving timing is evening, night, or dawn, when the total driving time duration is equal to or greater than a first threshold time duration, or when a window opening time duration is equal to or greater than a second threshold time duration, classifying the fatigue status as the drowsiness-inducing fatigue (Zender: Para. 36; whether the expected duration of the trip is greater than the personalized recommended consecutive driving time), wherein each of the first threshold time duration (Zender: Para. 36; whether the expected duration of the trip is greater than the personalized recommended consecutive driving time).
Zender and Hori don’t explicitly teach the second threshold time duration is determined based on a driving pattern of the user acquired from the vehicle.
However Wipperfurth, in the same field of endeavor, teaches the second threshold time duration is determined based on a driving pattern of the user acquired from the vehicle (Wipperfurth: Para. 16; high music tempo with the trip parameter of slow traffic, or an association of low music tempo with the trip parameter of a driver detected speeding or driving faster than usual).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and the driver’s detected versus usual speed (Wipperfurth: Para. 16) with a reasonable expectation of success because a modified trip parameter portrays a sensed change in vehicle speed, weather, and change in user’s sensed mental state or mood that can be aided by a databased stored association of a vehicle action based on sensed user’s mental state or mood (Wipperfurth: Para. 16, 18, 66).
Regarding claim 9, Zender and Hori don’t explicitly teach wherein the recommending of the stopover includes: determining whether the fatigue status of the user is classified as the lack-of-exercise fatigue; when the fatigue status of the user is classified as the lack-of-exercise fatigue, determining whether there is an exercise place that the user visited, based on the past destination setting history of the user; and when there is the exercise place that the user visited, recommending the exercise place that the user visited as the recommended stopover; and when there is no exercise place that the user visited, recommending a place at which the vehicle can stop and park as the recommended stopover.
However Wipperfurth, in the same field of endeavor, teaches wherein the recommending of the stopover includes: determining whether the fatigue status of the user is classified as the lack-of-exercise fatigue; when the fatigue status of the user is classified as the lack-of-exercise fatigue, determining whether there is an exercise place that the user visited, based on the past destination setting history of the user (Wipperfurth: Para. 103-105; provides information on nearby parks, playgrounds; Kids selector if the vehicle microphone picks up a child's voice and the trip is longer than a half hour); and when there is the exercise place that the user visited, recommending the exercise place that the user visited as the recommended stopover (Wipperfurth: Para. 103, 168; utilizing personal information and drive histories to learn preferences and interests and adjusting behavior accordingly; provides information on nearby parks, playgrounds); and when there is no exercise place that the user visited, recommending a place at which the vehicle can stop and park as the recommended stopover.
Wipperfurth teaches a kids selector which provides information on nearby parks, playgrounds, kid-friendly restaurants and so forth (Wipperfurth: Para. 103). It would be obvious to one of ordinary skill to realize that parks and playgrounds are movement based stops that are replacement for there being no exercise place that the user has visited.
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and the driver’s detected versus usual speed (Wipperfurth: Para. 16) with a reasonable expectation of success because a modified trip parameter portrays a sensed change in vehicle speed, weather, and change in user’s sensed mental state or mood that can be aided by a databased stored association of a vehicle action based on sensed user’s mental state or mood (Wipperfurth: Para. 16, 18, 66).
Regarding claim 13, Zender teaches the computing device of claim 12, wherein the classifying of the fatigue status includes: when a driving timing is evening, night, or dawn, when the total driving time duration is equal to or greater than a first threshold time duration, or when a window opening time duration is equal to or greater than a second threshold time duration, classifying the fatigue status as the drowsiness-inducing fatigue (Zender: Para. 36; whether the expected duration of the trip is greater than the personalized recommended consecutive driving time), wherein each of the first threshold time duration (Zender: Para. 36; whether the expected duration of the trip is greater than the personalized recommended consecutive driving time).
Zender and Hori don’t explicitly teach the second threshold time duration is determined based on a driving pattern of the user acquired from the vehicle.
However Wipperfurth, in the same field of endeavor, teaches the second threshold time duration is determined based on a driving pattern of the user acquired from the vehicle (Wipperfurth: Para. 16; high music tempo with the trip parameter of slow traffic, or an association of low music tempo with the trip parameter of a driver detected speeding or driving faster than usual).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and the driver’s detected versus usual speed (Wipperfurth: Para. 16) with a reasonable expectation of success because a modified trip parameter portrays a sensed change in vehicle speed, weather, and change in user’s sensed mental state or mood that can be aided by a databased stored association of a vehicle action based on sensed user’s mental state or mood (Wipperfurth: Para. 16, 18, 66).
Regarding claim 19, Zender and Hori don’t explicitly teach wherein the recommending of the stopover includes: determining whether the fatigue status of the user is classified as the lack-of-exercise fatigue; when the fatigue status of the user is classified as the lack-of-exercise fatigue, determining whether there is an exercise place that the user visited, based on the past destination setting history of the user; and when there is the exercise place that the user visited, recommending the exercise place that the user visited as the recommended stopover; and when there is no exercise place that the user visited, recommending a place at which the vehicle can stop and park as the recommended stopover.
However Wipperfurth, in the same field of endeavor, teaches wherein the recommending of the stopover includes: determining whether the fatigue status of the user is classified as the lack-of-exercise fatigue; when the fatigue status of the user is classified as the lack-of-exercise fatigue, determining whether there is an exercise place that the user visited, based on the past destination setting history of the user (Wipperfurth: Para. 103-105; provides information on nearby parks, playgrounds; Kids selector if the vehicle microphone picks up a child's voice and the trip is longer than a half hour); and when there is the exercise place that the user visited, recommending the exercise place that the user visited as the recommended stopover (Wipperfurth: Para. 103, 168; utilizing personal information and drive histories to learn preferences and interests and adjusting behavior accordingly; provides information on nearby parks, playgrounds); and when there is no exercise place that the user visited, recommending a place at which the vehicle can stop and park as the recommended stopover.
Wipperfurth teaches a kids selector which provides information on nearby parks, playgrounds, kid-friendly restaurants and so forth (Wipperfurth: Para. 103). It would be obvious to one of ordinary skill to realize that parks and playgrounds are movement based stops that are replacement for there being no exercise place that the user has visited.
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and the driver’s detected versus usual speed (Wipperfurth: Para. 16) with a reasonable expectation of success because a modified trip parameter portrays a sensed change in vehicle speed, weather, and change in user’s sensed mental state or mood that can be aided by a databased stored association of a vehicle action based on sensed user’s mental state or mood (Wipperfurth: Para. 16, 18, 66).
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Zender et al. (US Publication 2020/0182635 A1) in view of Hori et al. (US Publication 2020/0234224 A1), Kim (US Patent 12,427,984 B2), and in further view of Wipperfurth (US Publication 2019/0324600 A1).
Regarding claim 4, Zender doesn’t explicitly teach wherein the classifying of the fatigue status includes: when the heart rate is smaller than a reference value, when a brake pedal stepping strength is smaller than an average brake pedal stepping strength of the user, when a difference between a speed limit on a road on which the vehicle is currently driving and a current driving speed of the vehicle is larger than a predetermined threshold value, or when the vehicle does not start despite a preceding vehicle movement-start notification signal, classifying the fatigue status as the relaxed fatigue of the negative emotion fatigue.
However Hori, in the same field of endeavor, teaches wherein the classifying of the fatigue status includes: when the heart rate is smaller than a reference value, when a brake pedal stepping strength is smaller than an average brake pedal stepping strength of the user, when a difference between a speed limit on a road on which the vehicle is currently driving and a current driving speed of the vehicle is larger than a predetermined threshold value, or when the vehicle does not start despite a preceding vehicle movement-start notification signal, classifying the fatigue status as the relaxed fatigue of the negative emotion fatigue (Hori: Para. 95; the fatigue level estimation unit may estimate the driving fatigue level of the target user based on whether the road to the destination is congested, based on whether the facilities at the destination have a parking lot and whether the parking lot is congested; driving fatigue level of the target user is thought to become relatively higher as the road to the destination is more congested).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) with a reasonable expectation of success because the mood, physical condition, and drowsiness of the target user while driving the vehicle based on the driver’s past and future schedule, the frequency the driver visits the destination, and current driving conditions affect the driver’s fatigue level (Hori: Para. 85, 91, 95).
Zender and Hori don’t explicitly teach wherein the brake pedal stepping strength is stored in the vehicle every time the user drives the vehicle, wherein the average brake pedal stepping strength is calculated from values of the brake pedal stepping strength stored in the vehicle.
However Kim, in the same field of endeavor, teaches wherein the brake pedal stepping strength is stored in the vehicle every time the user drives the vehicle (Kim: Col. 10 Lines 35-44; cumulatively store the brake pedal pressures by the driver), wherein the average brake pedal stepping strength is calculated from values of the brake pedal stepping strength stored in the vehicle (Kim: Claim 1; driver tendency data further includes a brake pedal pressure by the driver, and the controller is configured to derive an average value of the brake pedal pressures and reflect the average value of the brake pedal pressures).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and storing and analyzing the brake pedal pressures by the driver (Kim: Col. 10 Lines 35-44) with a reasonable expectation of success because the driver tendency can be determined by stored brake pedal pressures by the driver and derived average values reflecting the driver’s tendency for deceleration control (Kim: Col. 10 Lines 35-44, Claim 1).
Zender, Hori, and Kim don’t explicitly teach wherein the predetermined threshold value is determined as an average of difference in values between a driving speed stored in a navigation system of the vehicle and the speed limit on the road on which the vehicle is currently driving.
However Wipperfurth, in the same field of endeavor, teaches wherein the predetermined threshold value is determined as an average of difference in values between a driving speed stored in a navigation system of the vehicle and the speed limit on the road on which the vehicle is currently driving.
Wipperfurth teaches modifying a trip parameter based on the change in vehicle speed, and the change in the user's sensed mental state or mood such as gripping of steering wheel, tone of voice, speed of vehicle (Wipperfurth: Para. 18, 66). Wipperfurth teaches a stored database association of high music sentiment positivity when the driver and vehicle are in slow traffic versus a low music tempo based on detecting the driver is speeding or driving faster than usual (Wipperfurth: Para. 16). One of ordinary skill understands that speeding is a driving speed above the speed limit of the current road. Wipperfurth teaches a change in vehicle state based on detected speeding which one of ordinary skill could explain as a predetermined threshold value of the current speed limit versus the detected speeding of the vehicle.
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91), storing and analyzing the brake pedal pressures by the driver (Kim: Col. 10 Lines 35-44), and the driver’s detected versus usual speed (Wipperfurth: Para. 16) with a reasonable expectation of success because a modified trip parameter portrays a sensed change in vehicle speed, weather, and change in user’s sensed mental state or mood that can be aided by a databased stored association of a vehicle action based on sensed user’s mental state or mood (Wipperfurth: Para. 16, 18, 66).
Regarding claim 14, Zender doesn’t explicitly teach wherein the classifying of the fatigue status includes: when the heart rate is smaller than a reference value, when a brake pedal stepping strength is smaller than an average brake pedal stepping strength of the user, when a difference between a speed limit on a road on which the vehicle is currently driving and a current driving speed of the vehicle is larger than a predetermined threshold value, or when the vehicle does not start despite a preceding vehicle movement-start notification signal, classifying the fatigue status as the relaxed fatigue of the negative emotion fatigue.
However Hori, in the same field of endeavor, teaches wherein the classifying of the fatigue status includes: when the heart rate is smaller than a reference value, when a brake pedal stepping strength is smaller than an average brake pedal stepping strength of the user, when a difference between a speed limit on a road on which the vehicle is currently driving and a current driving speed of the vehicle is larger than a predetermined threshold value, or when the vehicle does not start despite a preceding vehicle movement-start notification signal, classifying the fatigue status as the relaxed fatigue of the negative emotion fatigue (Hori: Para. 95; the fatigue level estimation unit may estimate the driving fatigue level of the target user based on whether the road to the destination is congested, based on whether the facilities at the destination have a parking lot and whether the parking lot is congested; driving fatigue level of the target user is thought to become relatively higher as the road to the destination is more congested).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) with a reasonable expectation of success because the mood, physical condition, and drowsiness of the target user while driving the vehicle based on the driver’s past and future schedule, the frequency the driver visits the destination, and current driving conditions affect the driver’s fatigue level (Hori: Para. 85, 91, 95).
Zender and Hori don’t explicitly teach wherein the brake pedal stepping strength is stored in the vehicle every time the user drives the vehicle, wherein the average brake pedal stepping strength is calculated from values of the brake pedal stepping strength stored in the vehicle.
However Kim, in the same field of endeavor, teaches wherein the brake pedal stepping strength is stored in the vehicle every time the user drives the vehicle (Kim: Col. 10 Lines 35-44; cumulatively store the brake pedal pressures by the driver), wherein the average brake pedal stepping strength is calculated from values of the brake pedal stepping strength stored in the vehicle (Kim: Claim 1; driver tendency data further includes a brake pedal pressure by the driver, and the controller is configured to derive an average value of the brake pedal pressures and reflect the average value of the brake pedal pressures).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and storing and analyzing the brake pedal pressures by the driver (Kim: Col. 10 Lines 35-44) with a reasonable expectation of success because the driver tendency can be determined by stored brake pedal pressures by the driver and derived average values reflecting the driver’s tendency for deceleration control (Kim: Col. 10 Lines 35-44, Claim 1).
Zender, Hori, and Kim don’t explicitly teach wherein the predetermined threshold value is determined as an average of difference in values between a driving speed stored in a navigation system of the vehicle and the speed limit on the road on which the vehicle is currently driving.
However Wipperfurth, in the same field of endeavor, teaches wherein the predetermined threshold value is determined as an average of difference in values between a driving speed stored in a navigation system of the vehicle and the speed limit on the road on which the vehicle is currently driving.
Wipperfurth teaches modifying a trip parameter based on the change in vehicle speed, and the change in the user's sensed mental state or mood such as gripping of steering wheel, tone of voice, speed of vehicle (Wipperfurth: Para. 18, 66). Wipperfurth teaches a stored database association of high music sentiment positivity when the driver and vehicle are in slow traffic versus a low music tempo based on detecting the driver is speeding or driving faster than usual (Wipperfurth: Para. 16). One of ordinary skill understands that speeding is a driving speed above the speed limit of the current road. Wipperfurth teaches a change in vehicle state based on detected speeding which one of ordinary skill could explain as a predetermined threshold value of the current speed limit versus the detected speeding of the vehicle.
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91), storing and analyzing the brake pedal pressures by the driver (Kim: Col. 10 Lines 35-44), and the driver’s detected versus usual speed (Wipperfurth: Para. 16) with a reasonable expectation of success because a modified trip parameter portrays a sensed change in vehicle speed, weather, and change in user’s sensed mental state or mood that can be aided by a databased stored association of a vehicle action based on sensed user’s mental state or mood (Wipperfurth: Para. 16, 18, 66).
Claims 5-7, 10, 15-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zender et al. (US Publication 2020/0182635 A1) in view of Hori et al. (US Publication 2020/0234224 A1) and in further view of Penilla et al. (US Publication 2020/0152197 A1).
Regarding claim 5, Zender and Hori don’t explicitly teach wherein the classifying of the fatigue status includes: when the heart rate is higher than a reference value, or when the volume of the voice of the user during the call is greater than an average volume of the voice of the user during the call, classifying the fatigue status as the tense fatigue of the negative emotion fatigue, wherein the average volume of the voice of the user during the call is calculated from a call record of the user stored in the user device.
However Penilla, in the same field of endeavor, teaches wherein the classifying of the fatigue status includes: when the heart rate is higher than a reference value, or when the volume of the voice of the user during the call is greater than an average volume of the voice of the user during the call, classifying the fatigue status as the tense fatigue of the negative emotion fatigue (Penilla: Para. 137, 300- 301; recognizing emotional information; speech recognition, speech waveforms, natural language processing, or facial expression detection, and produce either labels (i.e. “sad,” “mad,” “happy,” “hurried,” “stressed,” etc.); voice sample can include identification of frequency and magnitude markers), wherein the average volume of the voice of the user during the call is calculated from a call record of the user stored in the user device (Penilla: Para. 137, 301; recognizing emotional information; speech recognition, speech waveforms, natural language processing, or facial expression detection, and produce either labels (i.e. “sad,” “mad,” “happy,” “hurried,” “stressed,” etc.); voice sample can include identification of frequency and magnitude markers).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and a vehicle adjust response catering to the user’s tone (Penilla: Para. 137) with a reasonable expectation of success because a system that detects the user’s mood can change the queries or suggestions from the vehicle, change screen and vehicle settings aid the user’s mood (Penilla: Para. 150-151).
Regarding claim 6, Zender and Hori don’t explicitly teach wherein the classifying of the fatigue status includes: when the posture of the user is different from a correct posture, or when the seat massage function is activated, classifying the fatigue status as the lack-of-exercise fatigue.
However Penilla, in the same field of endeavor, teaches wherein the classifying of the fatigue status includes: when the posture of the user is different from a correct posture, or when the seat massage function is activated, classifying the fatigue status as the lack-of-exercise fatigue (Penilla: Para. 136; detecting emotional information can also use passive sensors which capture data about the user's physical state; body posture and gestures).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and a vehicle adjust response catering to the user’s tone (Penilla: Para. 137) with a reasonable expectation of success because a system that detects the user’s mood can change the queries or suggestions from the vehicle, change screen and vehicle settings aid the user’s mood (Penilla: Para. 150-151).
Regarding claim 7, Zender and Hori don’t explicitly teach determining whether the fatigue status of the user is classified as the drowsiness-inducing fatigue, when the fatigue status of the user is classified as the drowsiness-inducing fatigue, determining whether a current time is night or dawn and a total driving time duration has exceeded a first threshold time duration, when the current time is night or dawn and the total driving time duration has exceeded the first threshold time duration, recommending a nearest accommodation place among searched accommodation places based on a current location as the recommended stopover, and when the current time is not night or dawn, or when the total driving time duration has not exceeded the first threshold time duration, recommending a nearest drowsiness shelter or a nearest rest area searched based on the current location as the recommended stopover, wherein the first threshold time duration is determined based on a driving pattern of the user acquired from the vehicle.
However Penilla, in the same field of endeavor, teaches determining whether the fatigue status of the user is classified as the drowsiness-inducing fatigue, when the fatigue status of the user is classified as the drowsiness-inducing fatigue, determining whether a current time is night or dawn and a total driving time duration has exceeded a first threshold time duration, when the current time is night or dawn and the total driving time duration has exceeded the first threshold time duration (Penilla: Para. 281; time of day is late at night, and if the user is far from home, and/or the vehicle is traveling to a mapped destination, and/or the vehicle has been traveling for an extended period of time, e.g., 6-12 hours, the vehicle system can deduct that the user is getting tired, and may need to rest or find a hotel), ……….. , and when the current time is not night or dawn, or when the total driving time duration has not exceeded the first threshold time duration, recommending a nearest drowsiness shelter or a nearest rest area searched based on the current location as the recommended stopover (Penilla: Para. 281; the vehicle has been traveling for an extended period of time, e.g., 6-12 hours, the vehicle system can deduct that the user is getting tired, and may need to rest or find a hotel. The vehicle can say, for example: “Are you ok?” “Are you tired?” “Would you like me find you a hotel or rest stop?”), wherein the first threshold time duration is determined based on a driving pattern of the user acquired from the vehicle (Penilla: Para. 150-151; rushed mood is sensed: vehicle queries are clear, the vehicle queries are brief, the GUI on all screens change to limit distractions, routes are changed to quickest based on traffic; agitated mood may also be sensed: the vehicle may react by scaling back the number of questions posed to the user while in the vehicle, GUI becomes more standard and easy to read).
Penilla doesn’t explicitly teach recommending a nearest accommodation place among searched accommodation places based on a current location as the recommended stopover.
Penilla teaches detecting that it is night, the user is far from home, and has been traveling an extended period of time. From this detection it is deducted that the user is getting tired and needs to rest or find a hotel (Penilla: Para 281). Penilla teaches recognizing the conversation keywords of hurry and hospital to display a map to the hospital immediately (Penilla: Para. 346). It would be obvious to one of ordinary skill to recommend the nearest rest accommodation based on a very tired user by determining that the user is tired while taking into account the severity of the drowsiness.
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and a vehicle adjust response catering to the user’s tone (Penilla: Para. 137) with a reasonable expectation of success because a system that detects the user’s mood can change the queries or suggestions from the vehicle, change screen and vehicle settings aid the user’s mood (Penilla: Para. 150-151).
Regarding claim 10, Zender and Hori don’t explicitly teach wherein the detailed information on the recommended stopover includes the fatigue status of the user, a type of rest that may be taken at the recommended stopover, or an expected driving time to drive along a route including the recommended stopover thereto.
However Penilla, in the same field of endeavor, teaches wherein the detailed information on the recommended stopover includes the fatigue status of the user, a type of rest that may be taken at the recommended stopover, or an expected driving time to drive along a route including the recommended stopover thereto (Penilla: Para. 346; hungry: vehicle ask the user if they wish to stop for burgers; conversation the keywords are hurry, hospital, then the conversation sample will provide and display a map to the hospital immediately; conversation keywords are yawn, tired, bed: recommendations can be to provide information regarding hotels, coffee shops, rest areas).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and a vehicle adjust response catering to the user’s tone (Penilla: Para. 137) with a reasonable expectation of success because a system that detects the user’s mood can change the queries or suggestions from the vehicle, change screen and vehicle settings aid the user’s mood (Penilla: Para. 150-151).
Regarding claim 15, Zender and Hori don’t explicitly teach wherein the classifying of the fatigue status includes: when the heart rate is higher than a reference value, or when the volume of the voice of the user during the call is greater than an average volume of the voice of the user during the call, classifying the fatigue status as the tense fatigue of the negative emotion fatigue, wherein the average volume of the voice of the use during the call is calculated from a call record of the user stored in the user device.
However Penilla, in the same field of endeavor, teaches wherein the classifying of the fatigue status includes: when the heart rate is higher than a reference value, or when the volume of the voice of the user during the call is greater than an average volume of the voice of the user during the call, classifying the fatigue status as the tense fatigue of the negative emotion fatigue (Penilla: Para. 137, 300- 301; recognizing emotional information; speech recognition, speech waveforms, natural language processing, or facial expression detection, and produce either labels (i.e. “sad,” “mad,” “happy,” “hurried,” “stressed,” etc.); voice sample can include identification of frequency and magnitude markers), wherein the average volume of the voice of the use during the call is calculated from a call record of the user stored in the user device (Penilla: Para. 137, 301; recognizing emotional information; speech recognition, speech waveforms, natural language processing, or facial expression detection, and produce either labels (i.e. “sad,” “mad,” “happy,” “hurried,” “stressed,” etc.); voice sample can include identification of frequency and magnitude markers).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and a vehicle adjust response catering to the user’s tone (Penilla: Para. 137) with a reasonable expectation of success because a system that detects the user’s mood can change the queries or suggestions from the vehicle, change screen and vehicle settings aid the user’s mood (Penilla: Para. 150-151).
Regarding claim 16, Zender and Hori don’t explicitly teach wherein the classifying of the fatigue status includes: when the posture of the user is different from a correct posture, or when the seat massage function is activated, classifying the fatigue status as the lack-of-exercise fatigue.
However Penilla, in the same field of endeavor, teaches wherein the classifying of the fatigue status includes: when the posture of the user is different from a correct posture, or when the seat massage function is activated, classifying the fatigue status as the lack-of-exercise fatigue (Penilla: Para. 136; detecting emotional information can also use passive sensors which capture data about the user's physical state; body posture and gestures).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and a vehicle adjust response catering to the user’s tone (Penilla: Para. 137) with a reasonable expectation of success because a system that detects the user’s mood can change the queries or suggestions from the vehicle, change screen and vehicle settings aid the user’s mood (Penilla: Para. 150-151).
Regarding claim 17, Zender and Hori don’t explicitly teach wherein the recommending of the stopover includes: determining whether the fatigue status of the user is classified as the drowsiness-inducing fatigue, when the fatigue status of the user is classified as the drowsiness-inducing fatigue, determining whether a current time is night or dawn and a total driving time duration has exceeded a first threshold time duration, when the current time is night or dawn and the total driving time duration has exceeded the first threshold time duration, recommending a nearest accommodation place among searched accommodation places based on a current location as the recommended stopover, and when the current time is not night or dawn, or when the total driving time duration has not exceeded the first threshold time duration, recommending a nearest drowsiness shelter or a nearest rest area searched based on the current location as the recommended stopover, wherein the first threshold time duration is determined based on a driving pattern of the user acquired from the vehicle.
However Penilla, in the same field of endeavor, teaches wherein the recommending of the stopover includes: determining whether the fatigue status of the user is classified as the drowsiness-inducing fatigue, when the fatigue status of the user is classified as the drowsiness-inducing fatigue, determining whether a current time is night or dawn and a total driving time duration has exceeded a first threshold time duration, when the current time is night or dawn and the total driving time duration has exceeded the first threshold time duration (Penilla: Para. 281; time of day is late at night, and if the user is far from home, and/or the vehicle is traveling to a mapped destination, and/or the vehicle has been traveling for an extended period of time, e.g., 6-12 hours, the vehicle system can deduct that the user is getting tired, and may need to rest or find a hotel), …………. , and when the current time is not night or dawn, or when the total driving time duration has not exceeded the first threshold time duration, recommending a nearest drowsiness shelter or a nearest rest area searched based on the current location as the recommended stopover (Penilla: Para. 281; the vehicle has been traveling for an extended period of time, e.g., 6-12 hours, the vehicle system can deduct that the user is getting tired, and may need to rest or find a hotel. The vehicle can say, for example: “Are you ok?” “Are you tired?” “Would you like me find you a hotel or rest stop?”), wherein the first threshold time duration is determined based on a driving pattern of the user acquired from the vehicle (Penilla: Para. 150-151; rushed mood is sensed: vehicle queries are clear, the vehicle queries are brief, the GUI on all screens change to limit distractions, routes are changed to quickest based on traffic; agitated mood may also be sensed: the vehicle may react by scaling back the number of questions posed to the user while in the vehicle, GUI becomes more standard and easy to read).
Penilla doesn’t explicitly teach recommending a nearest accommodation place among searched accommodation places based on a current location as the recommended stopover.
Penilla teaches detecting that it is night, the user is far from home, and has been traveling an extended period of time. From this detection it is deducted that the user is getting tired and needs to rest or find a hotel (Penilla: Para 281). Penilla teaches recognizing the conversation keywords of hurry and hospital to display a map to the hospital immediately (Penilla: Para. 346). It would be obvious to one of ordinary skill to recommend the nearest rest accommodation based on a very tired user by determining that the user is tired while taking into account the severity of the drowsiness.
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and a vehicle adjust response catering to the user’s tone (Penilla: Para. 137) with a reasonable expectation of success because a system that detects the user’s mood can change the queries or suggestions from the vehicle, change screen and vehicle settings aid the user’s mood (Penilla: Para. 150-151).
Regarding claim 20, Zender and Hori don’t explicitly teach wherein the detailed information on the recommended stopover includes the fatigue status of the user, a type of rest that may be taken at the stopover, or an expected driving time to drive along a route including the recommended stopover thereto.
However Penilla, in the same field of endeavor, teaches wherein the detailed information on the recommended stopover includes the fatigue status of the user, a type of rest that may be taken at the stopover, or an expected driving time to drive along a route including the recommended stopover thereto (Penilla: Para. 346; hungry: vehicle ask the user if they wish to stop for burgers; conversation the keywords are hurry, hospital, then the conversation sample will provide and display a map to the hospital immediately; conversation keywords are yawn, tired, bed: recommendations can be to provide information regarding hotels, coffee shops, rest areas).
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and a vehicle adjust response catering to the user’s tone (Penilla: Para. 137) with a reasonable expectation of success because a system that detects the user’s mood can change the queries or suggestions from the vehicle, change screen and vehicle settings aid the user’s mood (Penilla: Para. 150-151).
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zender et al. (US Publication 2020/0182635 A1) in view of Hori et al. (US Publication 2020/0234224 A1) and in further view of Penilla et al. (US Publication 2025/0157472 A1, here within as “Penilla 2”).
Regarding claim 8, Zender teaches the stopover recommendation method of claim 1, ……. ; and recommending a park or a cafe among the destinations as the recommended stopover (Zender: Para. 37; a list of candidate POIs of a requested type: gas station, rest stop, restaurant, car park).
Zender and Hori don’t explicitly teach wherein the recommending of the stopover includes: determining whether the fatigue status of the user is classified as the negative emotion fatigue, when the fatigue status of the user is classified as the negative emotion fatigue, searching for destinations that the user visited on a holiday based on the past destination setting history of the user.
However Penilla 2, in the same field of endeavor, teaches wherein the recommending of the stopover includes: determining whether the fatigue status of the user is classified as the negative emotion fatigue, when the fatigue status of the user is classified as the negative emotion fatigue, searching for destinations that the user visited on a holiday based on the past destination setting history of the user.
Penilla 2 teaches detecting voice profiles of a user being happy, sad, angry, frustrated, hurried, fear, surprise, or disgust (Penilla 2: Para. 294). The system also uses historical data to define and infer a user's preferences in order to make decisions as to the vehicle response to be made based on an identified tone (Penilla 2: Para. 294). Penilla 2 teaches learning the user plays rock 'n roll rock music on the weekends and classical music during the weekdays in order to determine the goods or services of interest at other times similar to the current time (Penilla 2: Para. 40, 173). The vehicle can determine from a historical pattern that when a user is sad, a negative emotion fatigue, the user historically has stopped at a Starbucks for a coffee drink and a cake pop. Therefore, when the system detects a sad user the vehicle would search for the closest Starbucks on the route to help the user's emotional state. It would be obvious to one of ordinary skill in the art to search for destinations that the user visited on a holiday based on the past destination setting history to aid the user when felling negative emotion fatigue because destinations visited on holiday most likely are locations that would make the user happier.
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and vehicle reaction based on the user’s tone (Penilla: Para. 294) with a reasonable expectation of success because destinations that the user visited on a holiday based on the past destination setting history to aid the user when felling negative emotion fatigue because destinations visited on holiday most likely are locations that would make the user happier (Penilla: Para. 40, 173, 294).
Regarding claim 18, Zender teaches the computing device of claim 11, ……. ; and recommending a park or a cafe among the destinations as the stopover (Zender: Para. 37; a list of candidate POIs of a requested type: gas station, rest stop, restaurant, car park).
Zender and Hori don’t explicitly teach wherein the recommending of the stopover includes: determining whether the fatigue status of the user is classified as the negative emotion fatigue, when the fatigue status of the user is classified as the negative emotion fatigue, searching for destinations that the user visited on a holiday based on the past destination setting history of the user.
However Penilla 2, in the same field of endeavor, teaches wherein the recommending of the stopover includes: determining whether the fatigue status of the user is classified as the negative emotion fatigue, when the fatigue status of the user is classified as the negative emotion fatigue, searching for destinations that the user visited on a holiday based on the past destination setting history of the user.
Penilla 2 teaches detecting voice profiles of a user being happy, sad, angry, frustrated, hurried, fear, surprise, or disgust (Penilla 2: Para. 294). The system also uses historical data to define and infer a user's preferences in order to make decisions as to the vehicle response to be made based on an identified tone (Penilla 2: Para. 294). Penilla 2 teaches learning the user plays rock 'n roll rock music on the weekends and classical music during the weekdays in order to determine the goods or services of interest at other times similar to the current time (Penilla 2: Para. 40, 173). The vehicle can determine from a historical pattern that when a user is sad, a negative emotion fatigue, the user historically has stopped at a Starbucks for a coffee drink and a cake pop. Therefore, when the system detects a sad user the vehicle would search for the closest Starbucks on the route to help the user's emotional state. It would be obvious to one of ordinary skill in the art to search for destinations that the user visited on a holiday based on the past destination setting history to aid the user when felling negative emotion fatigue because destinations visited on holiday most likely are locations that would make the user happier.
It would be obvious to one of ordinary skill in the art to modify the waypoint suggestion of a detour to the driver (Zender: Para. 24) with the different sources of driver fatigue (Hori: Para. 85, 91) and vehicle reaction based on the user’s tone (Penilla: Para. 294) with a reasonable expectation of success because destinations that the user visited on a holiday based on the past destination setting history to aid the user when felling negative emotion fatigue because destinations visited on holiday most likely are locations that would make the user happier (Penilla: Para. 40, 173, 294).
Conclusion
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/L.E.L./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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