DETAILED ACTION
This Office Action is in response to Applicant’s Amendments and Remarks filed on 07/30/2025.
Claim 2 has been cancelled.
Claim 8 has been added.
Claims 1, 3-8 are pending for examination.
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
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2022-076205, filed on 05/02/2022.
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e). Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Response to Amendment
With regards to the objection to the title of the specification. The amended title is clearly indicative of the invention to which the claims are directed. Therefore, the objection to the specification is withdrawn.
With regards to the claim interpretations made to claims 1, 3-5. The amendments made to these claims clarify the structure of the limitations that were being interpreted. Therefore, claims 1-5 do not invoke 112(f).
Response to Argument
Applicant’s arguments, see pages 1-2, filed 07/30/2025, with respect to the rejections of claims 1-7 under U.S.C. 101 have been filly considered. However, the Examiner respectfully disagrees that the amended claims overcome the 101 rejection made in the non-final. See pages #-# for details regarding the 101 rejection made to the amended claims.
Applicant’s arguments, see pages 3-5, filed 07/30/2025, with respect to the rejections of claims 1-7 under U.S.C. 101 have been filly considered and persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Fujii et al. (US 2020/0364800 A1; hereafter Fujii) as evidenced by Yoshiaki (JP 2017182317 A), Beck et al. (US 2019/0038204 A1; hereafter Beck), Suzuki et al. (JP 2020123214 A; hereafter Suzuki), and Scott et al. (US 20080255722 A1; hereafter Scott).
Claim Objections
Claim 1 objected to because of the following informality. “estimate a score for each of individual characteristics of a driver based on the acquired driving data”. This claim contains a grammatical error and should be corrected for clarity. Examiner suggests the claim to recite “estimate a score for each individual characteristics of a driver based on the acquired driving data”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, and 3-8 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contains subject matter which were not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventors, at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “the driving characteristics including a timing of judgment, correctness of operation, safe driving, emotional stability, danger sensitivity, and polite driving”. However, the collection of these characteristics is not thoroughly explained in the specification. It is unclear how these characteristics are determined from the data gathered from the device.
Claims 6 and 7 also recite similar limitations that are not thoroughly explained in the specification. Therefore, are also rejected for the same reasoning.
Claim 1 recites “adjust a method of controlling a vehicle of the driver based on the estimated score for each of the individual characteristics of the driver”. However, it is not explained in the specification how the method of controlling the vehicle is adjusted.
Claims 6 and 7 also recite similar limitations that are not thoroughly explained in the specification. Therefore, are also rejected for the same reasoning.
Claims 3-5, and 8 are dependent to claims 1 and 7 respectively and do not cure the deficiencies thereof. Therefore, they are rejected for the same reasons as claim 1 and 7.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, and 3-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “calculate a score or measurement of a number of times, for each of the driving evaluation items…” and “estimate a score for each of individual characteristics of a driver based on the acquired driving data…”. It is unclear what the difference is between “calculating a score” verses “estimating a score” as under the broadest reasonable interpretation both limitations could mean the same thing. For example a device gathers a plurality of driving data and using machine learning is able to calculate/estimate a score for each individual characteristic. The examiner suggests that “calculate a score or measurement” is amended to recite “calculate a measurement” to avoid the 112(b) rejection.
Claims 6 and 7 also recite similar limitations that are indefinite for failing to particularly point out the subject matter. Therefore, are also rejected for the same reasoning.
Claims 3-5, and 8 are dependent to claims 1 and 7 respectively and do not cure the deficiencies thereof. Therefore, they are rejected for the same reasons as claim 1 and 7.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a device configured to estimate the individual characteristics of a driver. Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
In this case, the independent claims 1, 6, and 7 are directed to an abstract idea without significantly more. Specifically, the claims, under their broadest reasonable interpretation cover certain mental processes. The language of independent claim 1 is used for illustration:
Claim 1:
An individual characteristics management device comprising:
a processor configured to:
acquire driving data relating to a plurality of driving evaluation items that are set in advance, with diagnostic categories of the plurality of driving evaluation items divided into safety, compliance with laws/regulations, and driver state, with the diagnostic category that is safety divided into acceleration operations, braking operations, steering operations, and dangerous operations;
calculate a score or measurement of a number of times, for each of the driving evaluation items, based on the acquired driving data, wherein the dangerous operations are evaluated based on a number of times of detection of U-turns, a number of times of detection of failure to use turn signals, and a number of times that an inter-vehicle distance to a preceding vehicle is less than or equal to a predetermined inter-vehicle distance;
estimate a score for each of individual characteristics of a driver based on the acquired driving data and the calculated score or measurement of the number of times, wherein the individual characteristics include driving characteristics and a cognitive characteristic, the driving characteristics including a timing of judgement, correctness of operation, safe driving, emotional stability, danger sensitivity, and polite driving, and
adjust a method of controlling a vehicle of the driver based on the estimated score for each of the individual characteristics of the driver.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind.
“… calculate a score or measurement of a number of times, for each of the driving evaluation items, based on the acquired driving data” in the context of this claim may be a person calculating a score mentally based on the data received from the device.
“… estimate a score for each of individual characteristics of a driver based on the acquired driving data and the calculated score or measurement of the number of times” in the context of this claim may be a person observing data collected from a vehicle and determining characteristics of the driver mentally.
As explained above, independent claim 1 recites at least one abstract idea. The other independent claims 6 and 7, which are of similar scope to claim 1. Likewise recite at least one abstract idea under Step 2A, Prong I.
101 Analysis - Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application."
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”)
Claim 1:
An individual characteristics management device comprising:
a processor configured to:
acquire driving data relating to a plurality of driving evaluation items that are set in advance, with diagnostic categories of the plurality of driving evaluation items divided into safety, compliance with laws/regulations, and driver state, with the diagnostic category that is safety divided into acceleration operations, braking operations, steering operations, and dangerous operations;
calculate a score or measurement of a number of times, for each of the driving evaluation items, based on the acquired driving data, wherein the dangerous operations are evaluated based on a number of times of detection of U-turns, a number of times of detection of failure to use turn signals, and a number of times that an inter-vehicle distance to a preceding vehicle is less than or equal to a predetermined inter-vehicle distance;
estimate a score for each of individual characteristics of a driver based on the acquired driving data and the calculated score or measurement of the number of times, wherein the individual characteristics include driving characteristics and a cognitive characteristic, the driving characteristics including a timing of judgement, correctness of operation, safe driving, emotional stability, danger sensitivity, and polite driving, and
adjust a method of controlling a vehicle of the driver based on the estimated score for each of the individual characteristics of the driver.
For the following reasons, the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
“a processor configured to”, the examiner submits that this limitation merely apply the mental process onto a generic computer.
“acquire driving data relating to a plurality of driving evaluation items that are set in advance”, the examiner submits that this limitation is merely data gathering. Therefore is an insignificant extra solution activity.
“with diagnostic categories of the plurality of driving evaluation items divided into safety, compliance with laws/regulations, and driver state, with the diagnostic category that is safety divided into acceleration operations, braking operations, steering operations, and dangerous operations”, “wherein the dangerous operations are evaluated based on a number of times of detection of U-turns, a number of times of detection of failure to use turn signals, and a number of times that an inter-vehicle distance to a preceding vehicle is less than or equal to a predetermined inter-vehicle distance;”, and “wherein the individual characteristics include driving characteristics and a cognitive characteristic, the driving characteristics including a timing of judgement, correctness of operation, safe driving, emotional stability, danger sensitivity, and polite driving” the examiner submits that this limitation merely narrows the data gathering limitation by categorizing the data into specific characteristics.
“adjust a method of controlling a vehicle of the driver based on the estimated score for each of the individual characteristics of the driver.” The examiner submits that this limitation is an insignificant application of the mental process. One of ordinary skill in the art would recognize that when a driver would receive a score of their driving they would typically adjust their driving in order to receive a better score during the next evaluation.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitations as an ordered combination or as a whole, the limitations add nothing that is not already present when looking at the elements taken individually. Accordingly, the additional limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, the claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application in Step 2A, Prong II, the additional element of limiting the use of the idea to one particular environment employs generic computer functions to execute the abstract idea and, therefore does not add significantly more. Limiting the use of the abstract idea to a particular environment or field of use cannot provide an inventive concept. Additionally, as discussed above, the limitations “acquire driving data relating to a plurality of driving evaluation items that are set in advance”, “adjust a method of controlling a vehicle of the driver based on the estimated score for each of the individual characteristics of the driver.” as well as the limitations that merely narrow the scope of the limitation as recited above, are considered insignificant extra solution activities.
A conclusion that an additional element is insignificant extra solution activity in Step 2A must be re-evaluated in Step 2B to determine if the element is more than what is well-understood, routine, and conventional in the field. In this case, the additional limitations of “acquire driving data relating to a plurality of driving evaluation items that are set in advance”, “adjust a method of controlling a vehicle of the driver based on the estimated score for each of the individual characteristics of the driver.” as well as the limitations that merely narrow the scope of the limitation are well-understood, routine, and conventional activities, because they have all been deemed insignificant extra solution activity by one or more Courts; see at least MPEP 2106.05(d) and MPEP 2106.05(g)
“acquire driving data relating to a plurality of driving evaluation items that are set in advance” is considered well-understood, routine, and conventional activity under Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);
“adjust a method of controlling a vehicle of the driver based on the estimated score for each of the individual characteristics of the driver.” is considered well-understood, routine, and conventional activity under Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016)
Claims 6 & 7 recite a method for the device detailed in claim 1, therefore it is rejected for the same reason.
Dependent claims 3-5, and 8 do not recite any further limitations that cause the claim to be patent eligible. Rather, the limitations of dependent claims are directed towards additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. These claims merely further narrow down the mental process, none of which integrate the judicial exception into a practical application.
Therefore, dependent claims 3-5, and 8 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Because the claims fail to recite anything sufficient to amount to significantly more than the judicial exception, independent claims 1, 6, and 7 are patent ineligible under 35 U.S.C. 101.
Examiner encourages Applicant to set an interview to discuss potential amendments for overcoming the above rejections under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-8 are rejected under 35 U.S.C. 103 as being obvious in view of Fujii as evidenced by Yoshiaki, Beck, Suzuki, and Scott.
Fujii and Beck was cited in the previous Office Action.
Regarding claim 1, Fujii discloses:
An individual characteristics management device, comprising:
a processor ([0036]; “A general purpose processor may include a microprocessor, as well as any conventional processor, controller, microcontroller, or state machine… Furthermore, the processor may be implemented as one or more processors, one or more controllers, and/or other structure configured to execute executable programming.”) configured to:
acquire driving data relating to a plurality of driving evaluation items that are set in advance ([0048]; “From the vehicle sensor data and/or video meta data gathered, the processor 110 computes the gathered data into scores using an artificial intelligence and/or machine learning module 124 based on insurance machine learning algorithms and an extensive data collection and analysis previously gathered to calculate a driving score that includes risk and safety for a particular trip.”
[0093] & Table 1 on pg. 22; “All the severity levels of the risky events detected over the course of the trip are used to calculate a trip severity that can be used to assess a trip score… Table 1 below identifies severity levels associated with risky events...”
Table 1 lists risky events and a score associated with each event. The higher the severity of the event, the higher the score. This severity score is considered when computing a trip score.), with diagnostic categories of the plurality of driving evaluation items divided into safety, with the diagnostic category that is safety divided into acceleration operations, braking operations, steering operations, and dangerous operations ([Table 1]; Shows a list of events and the associated severity level that is used to calculate a score of the user.);
calculate a score or measurement of a number of times, for each of the driving evaluation items, based on the acquired driving data ([0095]; “Based on all the data gathered from the trip, the system computes a trip-based driver scoring for a cumulative overall driver score 716.”), and a number of times that an inter-vehicle distance to a preceding vehicle is less than or equal to a predetermined inter-vehicle distance ([Table 1]; shows tailgating as a detected event.);
estimate a score for each of individual characteristics of a driver based on the acquired driving data and the calculated score or measurement of the number of times ([0095]; “Next, the system computes the collected vehicle sensor and/or video meta data using artificial intelligence and/or machine learning based on data collection and analysis to develop driving scores including scores for risk and safety 718.”), wherein the individual characteristics include driving characteristics, the driving characteristics including polite driving ([Table 1]; under broadest reasonable interpretation one of ordinary skill in the art would recognize that events such as tailgating, harsh braking, rolling stops would be considered impolite driving. Therefore the evaluation system disclosed by Fujii would be able to determine the polite driving of the user if these characteristics are not triggered during a trip.);
Although Fujii discloses a scoring system and detects a multitude of events to score the driver. Fujii does not explicitly state the following categories:
driving evaluation items divided into safety, compliance with laws/regulations, and driver state
dangerous operations are evaluated based on a number of times of detection of U-turns, a number of times of detection of failure to use turn signals
cognitive characteristic
timing of judgement, correctness of operation, safe driving, emotional stability, danger sensitivity
In addition, Although Fujii discloses a scoring system to evaluate drivers. Fujii does not explicitly recite adjust a method of controlling a vehicle of the driver based on the estimated score for each of the individual characteristics of the driver.
However Yoshiaki within the same field of endeavor does teach:
compliance with laws/regulations ([0106]; “The driving evaluation unit 13a can also evaluate the road traffic law, such as the legal speed limit, within the route to be evaluated.”)
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to have modified Fujii with Yoshiaki. This modification would have been obvious as both Fujii and Yoshiaki cover subject matter within the same field of endeavor (driver evaluation and scoring) and it would have been beneficial for the device to incorporate the categories disclosed in Yoshiaki to provide a score than encompasses a wider range of categories.
However Beck within the same field of endeavor does teach:
driver state ([0098] & [0102]; “Cameras can also be included to monitor the body movements and optical activity of the driver as described below… The system can utilize pupillometry and data from other sensors to detect/predict cognitive distraction. Data can be "fused" from multiple sensors/cameras to analyze levels of cognitive distraction experienced by the driver.”)
wherein the individual characteristics include driving characteristics and a cognitive characteristic ([0098] & [0102]; “Cameras can also be included to monitor the body movements and optical activity of the driver as described below… The system can utilize pupillometry and data from other sensors to detect/predict cognitive distraction. Data can be "fused" from multiple sensors/cameras to analyze levels of cognitive distraction experienced by the driver.”), the driving characteristics including emotional stability ([0135]; “Pupil Diameter can indicate cognitive and auditory distraction. The system can account for the driver's typical pupil responses as well as individual differences, lighting, emotions and other external factors by "fusing" data from multiple sensors.”)
danger sensitivity ([0098] & [0103]; “Cameras can also be included to monitor the body movements and optical activity of the driver as described below… Fatigue reduces situational awareness and also affects the central nervous system and consequently mental and motor coordination. The system can analyze, for example, eye-lid movement, blinking behavior and body posture. It can detect behavior such as yawning, nodding, slouching and raising eyebrows.”
Note: These issues that are detected by Beck directly impact the danger sensitivity of a driver.)
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to have modified the combination of Fujii and Yoshiaki with Beck. This modification would have been obvious as both Fujii, Yoshiaki, and Beck cover subject matter within the same field of endeavor (driver evaluation and scoring) and it would have been beneficial for the device to incorporate the categories disclosed in Yoshiaki to provide a score than encompasses a wider range of categories.
However Suzuki within the same field of endeavor does teach:
wherein the dangerous operations are evaluated based on a number of times of detection of U-turns,
([Abstract]; “A digital tachograph determines whether a driving operation was done to change a lane, turn right or left at an intersection, or make a U-turn on the basis of the detected driving information of a vehicle (Steps S21, S28)”
[0039]; “Specifically, the CPU 11 calculates the radius of curvature based on the vehicle's left-right acceleration (lateral G) detected by the acceleration sensor 28 and the vehicle speed pulse input from the vehicle speed sensor 51, and determines whether the driving operation is a right-left turn, etc.”)
the driving characteristics including a timing of judgment ([0050]; “The driving evaluation system described in [1] above, characterized in that the operation information includes a turn indicator signal indicating that the turn indicator of the vehicle has been operated, and the judgment unit, when the driving operation is identified as a lane change, judges whether the safety confirmation action was taken by going back to the time when the turn indicator signal was issued, and when the driving operation is identified as a right or left turn, judges whether the safety confirmation action was taken by going back to a predetermined time before the time when the turn indicator signal was issued.”)
safe driving ([0007]; “According to the driving evaluation system having the configuration described in (1) above, by determining whether or not a safety check action was taken by tracing back from the point in time when the driving operation was identified, such as a lane change or a right or left turn at an intersection, it is possible to determine whether or not an appropriate safety check action was taken during or before the start of the driving operation.”) and
adjust a method of controlling a vehicle of the driver based on the estimated score for each of the individual characteristics of the driver. ([0038]; “At this time, the CPU 11 may display a warning on the display unit 27. The warning issued by the digital tachograph 10 can directly alert the driver, thereby helping to ensure that proper safety checks are made while driving.”)
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to have modified the combination of Fujii, Yoshiaki and Beck with Suzuki. This modification would have been obvious as both Fujii, Yoshiaki, Beck, and Suzuki cover subject matter within the same field of endeavor (driver evaluation and scoring) and it would have been beneficial for the device to incorporate the categories disclosed in Yoshiaki to provide a score than encompasses a wider range of categories.
However Scott within the same field of endeavor does teach:
a number of times of detection of failure to use turn signals ([0097]; “Turn signal use can also be detected. By comparing the vehicle's route to a map in the GPS system, for example, the vehicle monitoring system may identify when the driver fails to use a turn signal.”)
the driving characteristics including a correctness of operation ([0093]; “During the evaluation period, the vehicle's operation is compared to preset criteria (902). A record is made each time a preset criteria is violated (903).”)
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to have modified the combination of Fujii, Yoshiaki, Beck and Suzuki with Scott. This modification would have been obvious as both Fujii, Yoshiaki, Beck, Suzuki, and Scott cover subject matter within the same field of endeavor (driver evaluation and scoring) and it would have been beneficial for the device to incorporate the categories disclosed in Yoshiaki to provide a score than encompasses a wider range of categories.
Regarding claim 3, Fujii in combination with Yoshiaki, Beck, Suzuki, and Scott discloses all of the limitations of claim 1. Additionally, Fujii discloses the processor is configured to acquire driving data, which includes at least acceleration and steering of a vehicle, as the driving evaluation items. ([0030] & Table 1 on pg. 22; “The term "sensor" may refer to any type of known sensor for sensing the dynamic conditions of a vehicle… The sensors can include, but are not limited to… accelerometers… steering angle sensors”
Table 1 list acceleration events and their associated severity score. For example, Acceleration at one G-force equates to a severity score of 3.)
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(Table 1: Disclosing a non-limiting example of events and their corresponding severity level, which is a component used to determine driving score)
Regarding claim 4, Fujii in combination with Yoshiaki, Beck, Suzuki, and Scott discloses all of the limitations of claim 3. Additionally, Fujii discloses the processor is configured to acquire, as the driving evaluation items, information captured by peripheral cameras that capture images of a periphery of the vehicle ([0059] & [0061]; “When the system detects a risky event, the GPU may save 10 seconds of video before and after the risky event on the on-board storage of the vehicle module. The GPU inputs can be received from vehicle cameras, such as dashboard cameras and driving assistance cameras.”)
Although Fujii does disclose a camera used to evaluate the characteristics of the driver. Fujii does not explicitly disclose a camera that captures images of the driver.
However, Beck within the same field of endeavor does teach:
information captured by a camera for a driver that captures images of the driver. ([0098] & [0102]; “Cameras can also be included to monitor the body movements and optical activity of the driver as described below… The system can utilize pupillometry and data from other sensors to detect/predict cognitive distraction. Data can be "fused" from multiple sensors/cameras to analyze levels of cognitive distraction experienced by the driver.”)
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to have modified the combination of Fujii, Yoshiaki, Suzuki, Scott with Beck. This modification would have been obvious as both Fujii, Yoshiaki, Beck, Suzuki, and Scott cover subject matter within the same field of endeavor (driver evaluation and scoring) and it would have been beneficial for the device to capture images of the driver to evaluate the characteristics such as driver state, cognitive characteristics, etc.
Regarding claim 5, Fujii in combination with Yoshiaki, Beck, Suzuki, and Scott discloses all of the limitations of claim 1. Additionally, Fujii discloses the processor is configured to estimate the score for each of the individual characteristics by inputting the acquired driving data into a learned model on which machine learning for estimating the score for each of individual characteristics has been carried out, and executing computing processing of the learned model. ([0048]; “From the vehicle sensor data and/or video meta data gathered, the processor 110 computes the gathered data into scores using an artificial intelligence and/or machine learning module 124 based on insurance machine learning algorithms and an extensive data collection and analysis previously gathered to calculate a driving score that includes risk and safety for a particular trip.”)
Claim 6 recites a method to perform the system of claim 1. Therefore, it is rejected for the same reasoning.
Regarding claim 7, Fujii discloses:
A method of generating a learned model, the method comprising:
acquiring teacher data in which information relating to a plurality of driving evaluation items that are set in advance, and correct answer values of a driving characteristic and a cognitive characteristic respectively, are set in correspondence with one another, ([0048]; “From the vehicle sensor data and/or video meta data gathered, the processor 110 computes the gathered data into scores using an artificial intelligence and/or machine learning module 124 based on insurance machine learning algorithms and an extensive data collection and analysis previously gathered to calculate a driving score that includes risk and safety for a particular trip.”
Note: “extensive data collection and analysis previously gathered” indicate that training the machine learning algorithm has already been completed by providing data and determining correct values prior to being gathered by the processor.)
diagnostic categories of the plurality of driving evaluation items being divided, with the diagnostic category that is safety divided into acceleration operations, braking operations, steering operations, and dangerous operations;
calculating a score or measurement of a number of times, for each of the driving evaluation items, based on the acquired driving data, wherein the dangerous operations are evaluated based on a number of times that an inter-vehicle distance to a preceding vehicle is less than or equal to a predetermined inter-vehicle distance; and
in response to a driving evaluation item being received, causing a computer to execute processing of generating a learned model that estimates a score for each of individual characteristics of a driver including a driving characteristic, based on acquired teacher data and the calculated score or measurement of the number of times, ([0048]; “From the vehicle sensor data and/or video meta data gathered, the processor 110 computes the gathered data into scores using an artificial intelligence and/or machine learning module 124 based on insurance machine learning algorithms and an extensive data collection and analysis previously gathered to calculate a driving score that includes risk and safety for a particular trip.”) wherein the driving characteristics include polite driving
Although Fujii discloses a scoring system and detects a multitude of events to score the driver. Fujii does not explicitly state the following categories:
driving evaluation items divided into safety, compliance with laws/regulations, and driver state
dangerous operations are evaluated based on a number of times of detection of U-turns, a number of times of detection of failure to use turn signals
cognitive characteristic
timing of judgement, correctness of operation, safe driving, emotional stability, danger sensitivity
In addition, Although Fujii discloses a scoring system to evaluate drivers. Fujii does not explicitly recite adjust a method of controlling a vehicle of the driver based on the estimated score for each of the individual characteristics of the driver.
However Yoshiaki within the same field of endeavor does teach:
compliance with laws/regulations ([0106]; “The driving evaluation unit 13a can also evaluate the road traffic law, such as the legal speed limit, within the route to be evaluated.”)
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to have modified Fujii with Yoshiaki. This modification would have been obvious as both Fujii and Yoshiaki cover subject matter within the same field of endeavor (driver evaluation and scoring) and it would have been beneficial for the device to incorporate the categories disclosed in Yoshiaki to provide a score than encompasses a wider range of categories.
However Beck within the same field of endeavor does teach:
driver state ([0098] & [0102]; “Cameras can also be included to monitor the body movements and optical activity of the driver as described below… The system can utilize pupillometry and data from other sensors to detect/predict cognitive distraction. Data can be "fused" from multiple sensors/cameras to analyze levels of cognitive distraction experienced by the driver.”)
individual characteristics include driving characteristics and a cognitive characteristic ([0098] & [0102]; “Cameras can also be included to monitor the body movements and optical activity of the driver as described below… The system can utilize pupillometry and data from other sensors to detect/predict cognitive distraction. Data can be "fused" from multiple sensors/cameras to analyze levels of cognitive distraction experienced by the driver.”), the driving characteristics including emotional stability ([0135]; “Pupil Diameter can indicate cognitive and auditory distraction. The system can account for the driver's typical pupil responses as well as individual differences, lighting, emotions and other external factors by "fusing" data from multiple sensors.”)
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to have modified the combination of Fujii and Yoshiaki with Beck. This modification would have been obvious as both Fujii, Yoshiaki, and Beck cover subject matter within the same field of endeavor (driver evaluation and scoring) and it would have been beneficial for the device to incorporate the categories disclosed in Yoshiaki to provide a score than encompasses a wider range of categories.
However Suzuki within the same field of endeavor does teach:
wherein the dangerous operations are evaluated based on a number of times of detection of U-turns,
([Abstract]; “A digital tachograph determines whether a driving operation was done to change a lane, turn right or left at an intersection, or make a U-turn on the basis of the detected driving information of a vehicle (Steps S21, S28)”
[0039]; “Specifically, the CPU 11 calculates the radius of curvature based on the vehicle's left-right acceleration (lateral G) detected by the acceleration sensor 28 and the vehicle speed pulse input from the vehicle speed sensor 51, and determines whether the driving operation is a right-left turn, etc.”)
the driving characteristics including a timing of judgment ([0050]; “The driving evaluation system described in [1] above, characterized in that the operation information includes a turn indicator signal indicating that the turn indicator of the vehicle has been operated, and the judgment unit, when the driving operation is identified as a lane change, judges whether the safety confirmation action was taken by going back to the time when the turn indicator signal was issued, and when the driving operation is identified as a right or left turn, judges whether the safety confirmation action was taken by going back to a predetermined time before the time when the turn indicator signal was issued.”)
safe driving ([0007]; “According to the driving evaluation system having the configuration described in (1) above, by determining whether or not a safety check action was taken by tracing back from the point in time when the driving operation was identified, such as a lane change or a right or left turn at an intersection, it is possible to determine whether or not an appropriate safety check action was taken during or before the start of the driving operation.”) and
adjust a method of controlling a vehicle of the driver based on the estimated score for each of the individual characteristics of the driver. ([0038]; “At this time, the CPU 11 may display a warning on the display unit 27. The warning issued by the digital tachograph 10 can directly alert the driver, thereby helping to ensure that proper safety checks are made while driving.”)
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to have modified the combination of Fujii, Yoshiaki and Beck with Suzuki. This modification would have been obvious as both Fujii, Yoshiaki, Beck, and Suzuki cover subject matter within the same field of endeavor (driver evaluation and scoring) and it would have been beneficial for the device to incorporate the categories disclosed in Yoshiaki to provide a score than encompasses a wider range of categories.
However Scott within the same field of endeavor does teach:
a number of times of detection of failure to use turn signals ([0097]; “Turn signal use can also be detected. By comparing the vehicle's route to a map in the GPS system, for example, the vehicle monitoring system may identify when the driver fails to use a turn signal.”)
the driving characteristics including a correctness of operation ([0093]; “During the evaluation period, the vehicle's operation is compared to preset criteria (902). A record is made each time a preset criteria is violated (903).”)
It would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to have modified the combination of Fujii, Yoshiaki, Beck and Suzuki with Scott. This modification would have been obvious as both Fujii, Yoshiaki, Beck, Suzuki, and Scott cover subject matter within the same field of endeavor (driver evaluation and scoring) and it would have been beneficial for the device to incorporate the categories disclosed in Yoshiaki to provide a score than encompasses a wider range of categories.
Regarding claim 8, Fujii in combination with Yoshiaki, Beck, Suzuki, and Scott discloses all of the limitations of claim 7. Additionally, Fujii discloses the processor is configured to estimate the score for each of the individual characteristics by inputting the acquired driving data into a learned model on which machine learning for estimating the score for each of individual characteristics has been carried out, and executing computing processing of the learned model. ([0051]; “The machine learning module 124 is comprised of at least one insurance machine learning algorithm to analyze the data from the sensors 114, diagnostics module 116, engine control unit 118 and self-driving module 121 to generate driver scores and trip information. In various embodiments, the machine learning module 124 uses a machine learning training pipeline to generate a machine learning model.”
Note: It would be obvious to one or ordinary skill in the art to generate scores for each type of characteristic as a means to organize all the data into multiple categories. This would help the user determine which category they would need to improve on in order to achieve a higher total score.)
Conclusion
THIS ACTION IS MADE FINAL. 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.S.L./Examiner, Art Unit 3668
/ABDHESH K JHA/Primary Examiner, Art Unit 3668