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 .
DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/13/2024 & 9/18/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2) and 2106.05(a) thru (d) for explanations.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to a method of evaluating automobile driving safety events (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories. Similarly, Claims 9 & 10 are directed to equipment and a non-transitory machine readable medium for evaluating automobile driving safety events (i.e., machines) and are also within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method for evaluation of automobile safety driving events, comprising:
obtaining a static environmental characteristic, a dynamic environmental characteristic, a vehicle kinetic energy characteristic and an event degree characteristic of an event; and
inputting the static environmental characteristic, the dynamic environmental characteristic, the vehicle kinetic energy characteristic and the event degree characteristic into a fully-connected neural network that has been trained in advance, and [mental process/step]
determining an evaluation result of the event by means of the fully-connected neural network. [mental process/step]
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “inputting…” and “determining…” in the context of this claim encompass a person applying data to a mathematical model, and determining an output of the model, which are mathematical operations of evaluating data under their broadest reasonable interpretation. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). 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” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A method for evaluation of automobile safety driving events, comprising: [generic linking to technical field, 2106.05(h), Apply it, 2106.05(f)]
obtaining a static environmental characteristic, a dynamic environmental characteristic, a vehicle kinetic energy characteristic and an event degree characteristic of an event; and [pre-solution activity (data gathering), 2106.05(g)]
inputting the static environmental characteristic, the dynamic environmental characteristic, the vehicle kinetic energy characteristic and the event degree characteristic into a fully-connected neural network that has been trained in advance, and
determining an evaluation result of the event by means of the fully-connected neural network.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation of “obtaining…,” the examiner submits that this limitation is insignificant extra-solution activity, the obtaining step being recited at a high level of generality (i.e. as a general means of gathering vehicle and environment data), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/does 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 Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform a mathematical evaluation amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitation of “obtaining…,” the examiner submits that this limitation is insignificant extra-solution activity.
Dependent claim(s) 2 – 8 & 11 – 20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. Specifically:
Claim 2 recites wherein the event degree characteristic comprises one of a plurality of specific characteristics, which merely narrows the data input into the model, which is a form of insignificant extra-solution activity.
Claim 3 recites wherein the event degree characteristic comprises an event duration, duration of driving when the event occurs, frequency of event occurrence, and event attribute value, which is insignificant extra-solution activity in the context of the claim.
Claim 4 recites wherein the degree characteristic comprises one of a plurality of specific values, which is insignificant extra-solution activity in the context of the claim.
Claim 5 recites the vehicle kinetic energy characteristic comprises states at the beginning, end, and during the event, which is insignificant extra-solution activity in the context of the claim.
Claim 6 recites wherein the vehicle state comprises one of a plurality of specific characteristics, which merely narrows the data input into the model, which is a form of insignificant extra-solution activity.
Claim 7 recites wherein the static and dynamic environmental characteristics comprise one of a plurality of specific characteristics each, which merely narrows the data input into the model, which is a form of insignificant extra-solution activity.
Claim 8 recites wherein the traffic flow or front-to-vehicle distance comprises states at the beginning, end, and during the event, which is insignificant extra-solution activity in the context of the claim.
Claims 11 – 20 recite substantially similar limitations as those found in Claims 2 – 8 as set forth above, and are rejected under similar rationale.
Therefore, dependent claims 2 – 8 & 11 – 20 are not patent eligible under the same rationale as provided for in the rejection of Claim 1.
Therefore, claim(s) 1 – 20 is/are ineligible under 35 USC §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.
Claim(s) 1, 2, 7, 9 - 11, 16, & 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wong (US 2021/0370955 A1) in view of Zhang (US 2021/0125076 A1).
Regarding Claim 1:
Wong discloses: A method for evaluation of automobile safety driving events, comprising: (Wong discloses in at least Paragraphs 0005 & 0006 a method for evaluating driving risk based on driving situations using machine learning models [i.e. a method for evaluation of automobile safety driving events])
obtaining a static environmental characteristic, a dynamic environmental characteristic, a vehicle kinetic energy characteristic and an event degree characteristic of an event; and (Wong discloses in at least Paragraph 0020 – 0022 wherein the system for evaluating vehicle driving risk may include a plurality of devices for collecting data, including a GPS, accelerometers, camera, and the like, which collect data to pass to a situation classification model, said data including speed and acceleration data [i.e. vehicle kinetic energy characteristics], road condition and weather data [i.e. static environmental data], as well as traffic condition and other vehicle data [i.e. dynamic environmental characteristics] as disclosed in at least Paragraph 0019. At least Paragraph 0032 of Wong discloses wherein weightages for each of a plurality of risk categories may be assigned, such as by user definition [i.e. obtaining an event degree characteristic of an event])
inputting the static environmental characteristic, the dynamic environmental characteristic, the vehicle kinetic energy characteristic and the event degree characteristic into a [model] (Wong discloses in at least Paragraphs 0027, 0028, & 0029 wherein a situation and maneuver classification model may be supplied with data received from the vehicle, and utilized to determine risks based on said data [i.e. inputting the static environmental characteristic, the dynamic environmental characteristic, and the vehicle kinetic energy characteristic into a model]. At least Paragraph 0028 of Wong discloses a specific example of identifying risk, including classifying a hard braking [i.e. kinetic energy event] in the context of surrounding vehicles [i.e. dynamic environmental characteristics] and at least Paragraph 0029 of Wong discloses an example of a vehicle’s acceleration being safe or unsafe based on an approach to a traffic light junction [i.e. static environmental characteristics]. At least Paragraphs 0033 – 0035 of Wong further disclose wherein the weightages for each of a plurality of events according to their degree may be provided to generate a safety score based on the assessed risks and their relative weight [i.e. inputting the event degree characteristic into the model])
determining an evaluation result of the event by means of the [model] (Wong discloses in at least Paragraphs 0035 – 0037 wherein a score may be generated based on the assessed risks and the weights of each of the respective risks [i.e. an evaluation result of the event may be determined by means of a model])
Wong however appears to be silent regarding:
Wherein the machine learning model comprises a fully-connected neural network that has been trained in advance
However Zhang teaches wherein a prediction model may include one or more fully connected neural networks configured to predict a risk level for aggressive driving behavior of a subject vehicle.
Wherein the machine learning model comprises a fully-connected neural network that has been trained in advance (However Zhang teaches in at least Paragraphs 0138, 0139, & 0142 wherein a prediction model may include one or more trained fully connected neural networks, configured to compute weighted parameters for different vehicle operating features, in order to predict a risk level for aggressive driving behavior of a subject vehicle as taught in at least Paragraphs 0081 & 0156 [i.e. wherein the machine learning model used to evaluate automobile safety driving events comprises a fully-connected neural network that has been trained in advance])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wong by incorporating the use of a neural network to evaluate automobile safety driving events as taught by Zhang.
The motivation to do so is that, as acknowledged by Zhang in at least Paragraphs 0142 & 0143, the model may be trained and adjusted based on input data, improving he determination of importance of features in the vehicle data set.
Regarding Claim 2:
The method according to claim 1, wherein the event degree characteristic comprises at least one of the following: a degree characteristic of sharp acceleration, a degree characteristic of sharp deceleration, a degree characteristic of sharp turning, a degree characteristic of acceleration, a degree characteristic of deceleration, a degree characteristic of turning, a degree characteristic of vibration, a degree characteristic of speeding, a degree characteristic of low speed, a degree characteristic of relative collision, a degree characteristic of pedestrian collision, a degree characteristic of lane keeping, a degree characteristic of eye closing, a degree characteristic of yawning, a degree characteristic of head lowering, a degree characteristic of left-and-right observation, a degree characteristic of down glancing, a degree characteristic of talking on or playing with a mobile phone, a degree characteristic of smoking, a degree characteristic of eating and drinking, a degree characteristic of unfastened seat belt, a degree characteristic of one-hand off handlebar, a degree characteristic of both-hands off handlebar and a degree characteristic of facial expression, or any combination thereof.
Wong discloses in at least Paragraphs 0032 & 0033 wherein weightages [i.e. event degree characteristics] may be defined for a plurality of driving risk event types, including speeding and accelerating when a traffic light is amber, with weightages being further defined based on a degree or frequency of engaging in the behavior, such as exceeding speed limits by different degrees, or increasing the weight if the behavior is observed multiple times in a defined time period as disclosed in at least Paragraphs 0033 & 0034 [i.e. the event degree characteristic comprises at least one of a degree characteristic of speeding and a degree characteristic of acceleration].
Regarding Claim 7:
The method according to claim 1, wherein the static environmental characteristic comprises at least one of the following: a characteristic of road location element, a characteristic of road type, a characteristic of temperature and a characteristic of weather, and the dynamic environmental characteristic comprises one or two of a quantity characteristic of a traffic flow and a front-to-vehicle distance characteristic of a current lane, or any combination thereof.
Wong discloses in at least Paragraph 0019 wherein the data received from the vehicle may include road condition and weather condition information [i.e. the static environmental characteristic comprises a characteristic of road type and a characteristic of weather]. Wong further discloses in at least Paragraph 0019 wherein traffic condition information may be acquired and input to determine driving situations [i.e. the dynamic environmental characteristic comprises a quantity characteristic of a traffic flow].
Regarding Claim 9:
Wong discloses: Equipment for evaluation of automobile safety driving events, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor, when executing the computer program, is configured to perform operations comprising: (Wong discloses in at least Paragraphs 0006, 0016, & 0020 a system for evaluating a driving risk of a vehicle, the system including a memory and processor configured to execute program instructions [i.e. equipment for evaluation of automobile safety driving events, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor])
obtaining a static environmental characteristic, a dynamic environmental characteristic, a vehicle kinetic energy characteristic and an event degree characteristic of an event; and (Wong discloses in at least Paragraph 0020 – 0022 wherein the system for evaluating vehicle driving risk may include a plurality of devices for collecting data, including a GPS, accelerometers, camera, and the like, which collect data to pass to a situation classification model, said data including speed and acceleration data [i.e. vehicle kinetic energy characteristics], road condition and weather data [i.e. static environmental data], as well as traffic condition and other vehicle data [i.e. dynamic environmental characteristics] as disclosed in at least Paragraph 0019. At least Paragraph 0032 of Wong discloses wherein weightages for each of a plurality of risk categories may be assigned, such as by user definition [i.e. obtaining an event degree characteristic of an event])
inputting the static environmental characteristic, the dynamic environmental characteristic, the vehicle kinetic energy characteristic, and the event degree characteristic into a [model] (Wong discloses in at least Paragraphs 0027, 0028, & 0029 wherein a situation and maneuver classification model may be supplied with data received from the vehicle, and utilized to determine risks based on said data [i.e. inputting the static environmental characteristic, the dynamic environmental characteristic, and the vehicle kinetic energy characteristic into a model]. At least Paragraph 0028 of Wong discloses a specific example of identifying risk, including classifying a hard braking [i.e. kinetic energy event] in the context of surrounding vehicles [i.e. dynamic environmental characteristics] and at least Paragraph 0029 of Wong discloses an example of a vehicle’s acceleration being safe or unsafe based on an approach to a traffic light junction [i.e. static environmental characteristics]. At least Paragraphs 0033 – 0035 of Wong further disclose wherein the weightages for each of a plurality of events according to their degree may be provided to generate a safety score based on the assessed risks and their relative weight [i.e. inputting the event degree characteristic into the model])
determining an evaluation result of the event by means of the [model] (Wong discloses in at least Paragraphs 0035 – 0037 wherein a score may be generated based on the assessed risks and the weights of each of the respective risks [i.e. an evaluation result of the event may be determined by means of a model])
Wong however appears to be silent regarding:
Wherein the machine learning model comprises a fully-connected neural network that has been trained in advance
However Zhang teaches wherein a prediction model may include one or more fully connected neural networks configured to predict a risk level for aggressive driving behavior of a subject vehicle.
Wherein the machine learning model comprises a fully-connected neural network that has been trained in advance (However Zhang teaches in at least Paragraphs 0138 & 0139 wherein a prediction model may include one or more fully connected neural networks, configured to compute weighted parameters for different vehicle operating features, in order to predict a risk level for aggressive driving behavior of a subject vehicle as taught in at least Paragraphs 0081 & 0156 [i.e. Wherein the machine learning model used to evaluate automobile safety driving events comprises a neural network)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wong by incorporating the use of a neural network to evaluate automobile safety driving events as taught by Zhang.
The motivation to do so is that, as acknowledged by Zhang in at least Paragraphs 0142 & 0143, the model may be trained and adjusted based on input data, improving he determination of importance of features in the vehicle data set.
Regarding Claim 10:
Wong discloses: A non-transitory computer-readable storage medium in which a computer program is stored, wherein the computer program, when executed by a processor, causes the processor to perform operations comprising: (Wong discloses in at least Paragraphs 0006, 0016, & 0020 a system for evaluating a driving risk of a vehicle, the system including a memory and processor configured to execute program instructions [i.e. a non-transitory computer-readable storage medium in which a computer program is stored, wherein the computer program, when executed by a processor, causes the processor to perform operations])
obtaining a static environmental characteristic, a dynamic environmental characteristic, a vehicle kinetic energy characteristic, and an event degree characteristic of an event; and (Wong discloses in at least Paragraph 0020 – 0022 wherein the system for evaluating vehicle driving risk may include a plurality of devices for collecting data, including a GPS, accelerometers, camera, and the like, which collect data to pass to a situation classification model, said data including speed and acceleration data [i.e. vehicle kinetic energy characteristics], road condition and weather data [i.e. static environmental data], as well as traffic condition and other vehicle data [i.e. dynamic environmental characteristics] as disclosed in at least Paragraph 0019. At least Paragraph 0032 of Wong discloses wherein weightages for each of a plurality of risk categories may be assigned, such as by user definition [i.e. obtaining an event degree characteristic of an event])
inputting the static environmental characteristic, the dynamic environmental characteristic. the vehicle kinetic energy characteristic, and the event degree characteristic into a [model], and (Wong discloses in at least Paragraphs 0027, 0028, & 0029 wherein a situation and maneuver classification model may be supplied with data received from the vehicle, and utilized to determine risks based on said data [i.e. inputting the static environmental characteristic, the dynamic environmental characteristic, and the vehicle kinetic energy characteristic into a model]. At least Paragraph 0028 of Wong discloses a specific example of identifying risk, including classifying a hard braking [i.e. kinetic energy event] in the context of surrounding vehicles [i.e. dynamic environmental characteristics] and at least Paragraph 0029 of Wong discloses an example of a vehicle’s acceleration being safe or unsafe based on an approach to a traffic light junction [i.e. static environmental characteristics]. At least Paragraphs 0033 – 0035 of Wong further disclose wherein the weightages for each of a plurality of events according to their degree may be provided to generate a safety score based on the assessed risks and their relative weight [i.e. inputting the event degree characteristic into the model])
determining an evaluation result of the event by means of the [model] (Wong discloses in at least Paragraphs 0035 – 0037 wherein a score may be generated based on the assessed risks and the weights of each of the respective risks [i.e. an evaluation result of the event may be determined by means of a model])
Wong however appears to be silent regarding:
Wherein the machine learning model comprises a fully-connected neural network that has been trained in advance
However Zhang teaches wherein a prediction model may include one or more fully connected neural networks configured to predict a risk level for aggressive driving behavior of a subject vehicle.
Wherein the machine learning model comprises a fully-connected neural network that has been trained in advance (However Zhang teaches in at least Paragraphs 0138 & 0139 wherein a prediction model may include one or more fully connected neural networks, configured to compute weighted parameters for different vehicle operating features, in order to predict a risk level for aggressive driving behavior of a subject vehicle as taught in at least Paragraphs 0081 & 0156 [i.e. Wherein the machine learning model used to evaluate automobile safety driving events comprises a neural network)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wong by incorporating the use of a neural network to evaluate automobile safety driving events as taught by Zhang.
The motivation to do so is that, as acknowledged by Zhang in at least Paragraphs 0142 & 0143, the model may be trained and adjusted based on input data, improving he determination of importance of features in the vehicle data set.
Regarding Claim 11:
The equipment according to claim 9, wherein the event degree characteristic comprises at least one of the following: a degree characteristic of sharp acceleration, a degree characteristic of sharp deceleration, a degree characteristic of sharp turning, a degree characteristic of acceleration, a degree characteristic of deceleration, a degree characteristic of turning, a degree characteristic of vibration, a degree characteristic of speeding, a degree characteristic of low speed, a degree characteristic of relative collision, a degree characteristic of pedestrian collision, a degree characteristic of lane keeping, a degree characteristic of eye closing, a degree characteristic of yawning, a degree characteristic of head lowering, a degree characteristic of left-and-right observation, a degree characteristic of down glancing, a degree characteristic of talking on or playing with a mobile phone, a degree characteristic of smoking, a degree characteristic of eating and drinking, a degree characteristic of unfastened seatbelt, a degree characteristic of one-hand off handlebar, a degree characteristic of both-hands off handlebar, a degree characteristic of facial expression, or any combination thereof.
Wong discloses in at least Paragraphs 0032 & 0033 wherein weightages [i.e. event degree characteristics] may be defined for a plurality of driving risk event types, including speeding and accelerating when a traffic light is amber, with weightages being further defined based on a degree or frequency of engaging in the behavior, such as exceeding speed limits by different degrees, or increasing the weight if the behavior is observed multiple times in a defined time period as disclosed in at least Paragraphs 0033 & 0034 [i.e. the event degree characteristic comprises at least one of a degree characteristic of speeding and a degree characteristic of acceleration].
Regarding Claim 16:
The equipment according to claim 9, wherein the static environmental characteristic comprises at least one of the following: a characteristic of road location element, a characteristic of road type, a characteristic of temperature and a characteristic of weather, and the dynamic environmental characteristic comprises one or two of a quantity characteristic of a traffic flow, a front-to-vehicle distance characteristic of a current lane, or any combination thereof.
Wong discloses in at least Paragraph 0019 wherein the data received from the vehicle may include road condition and weather condition information [i.e. the static environmental characteristic comprises a characteristic of road type and a characteristic of weather]. Wong further discloses in at least Paragraph 0019 wherein traffic condition information may be acquired and input to determine driving situations [i.e. the dynamic environmental characteristic comprises a quantity characteristic of a traffic flow].
Regarding Claim 18:
The equipment according to claim 10, wherein the event degree characteristic comprises at least one of the following: a degree characteristic of sharp acceleration, a degree characteristic of sharp deceleration, a degree characteristic of sharp turning, a degree characteristic of acceleration, a degree characteristic of deceleration, a degree characteristic of turning, a degree characteristic of vibration, a degree characteristic of speeding, a degree characteristic of low speed, a degree characteristic of relative collision, a degree characteristic of pedestrian collision, a degree characteristic of lane keeping, a degree characteristic of eye closing, a degree characteristic of yawning, a degree characteristic of head lowering, a degree characteristic of left-and-right observation, a degree characteristic of down glancing, a degree characteristic of talking on or playing with a mobile phone, a degree characteristic of smoking, a degree characteristic of eating and drinking, a degree characteristic of unfastened seat belt, a degree characteristic of one-hand off handlebar, a degree characteristic of both-hands off handlebar, a degree characteristic of facial expression, or any combination thereof.
Wong discloses in at least Paragraphs 0032 & 0033 wherein weightages [i.e. event degree characteristics] may be defined for a plurality of driving risk event types, including speeding and accelerating when a traffic light is amber, with weightages being further defined based on a degree or frequency of engaging in the behavior, such as exceeding speed limits by different degrees, or increasing the weight if the behavior is observed multiple times in a defined time period as disclosed in at least Paragraphs 0033 & 0034 [i.e. the event degree characteristic comprises at least one of a degree characteristic of speeding and a degree characteristic of acceleration].
Claim(s) 3 - 6, 8, 12 - 15, 17, & 19 - 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wong (US 2021/0370955 A1) in view of Zhang (US 2021/0125076 A1) as applied to claims 1, 2, 7, 9, 11, 16, & 18 above, and further in view of Rosenbaum (US 2022/0092893 A1).
Regarding Claim 3:
The method according to claim 2, wherein the event degree characteristic comprises a duration of the event, a duration of driving when the event occurs, a frequency of occurrence of the event and an attribute value of the event.
Wong discloses in at least Paragraphs 0033 & 0034 wherein the weightage [i.e. event degree characteristic] may be assigned based on a type or degree of behavior, such as a speeding amount [i.e. an attribute value of an event], as well as the frequency of the occurrence of the behavior in a specified period of time [i.e. a frequency of occurrence of the event]. Wong however appears to be silent regarding wherein the event degree characteristic comprises a duration of the event and a duration of driving when the event occurs.
However Rosenbaum teaches in at least Paragraphs 0189 & 0475 wherein a driving characteristic value may be derived as a function of past reactions of a driver based on a driving time over a predetermined driving time [i.e. a duration of driving when the event occurs], as well as a duration of the event as taught in at least Paragraphs 0049 & 0111 [i.e. the event degree characteristic comprises a duration of the event].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wong by incorporating the determination of event degree characteristic based on driving time and event duration as taught by Rosenbaum.
The motivation to do so is that, as acknowledged by Rosenbaum in at least Paragraph 0094, the estimation of driving risk of a driver based on behavior during a driving event and the parameters of the driving event may be improved.
Regarding Claim 4:
The method according to claim 3, wherein the degree characteristic of sharp turning further comprises a lateral direction angle during a sharp turning; the degree characteristic of vibration further comprises an average interval duration of a vibration event; the degree characteristic of speeding further comprises a speed limit value; the degree characteristic of relative collision further comprises a time distribution characteristic of relative collision occurrence times; the degree characteristic of pedestrian collision further comprises a time distribution characteristic of pedestrian collisions occurrence times; the degree characteristic of lane keeping further comprises a type of lane line; the degree characteristic of yawning further comprises a distribution characteristic of mouth length-to-width ratios; the degree characteristic of head lowering further comprises an angle distribution characteristic of head lowering; and the degree characteristic of left-and-right observation further comprises a distribution characteristic of head deflection angles.
Wong discloses in at least Paragraph 0033 wherein the weightage assigned to speeding risk may include different weights depending on the degree to which the regulated speed is exceeded [i.e. the degree characteristic of speeding further comprises a speed limit value].
Regarding Claim 5:
The method according to claim 1, wherein the vehicle kinetic energy characteristic comprises a vehicle state at a beginning of the event, a vehicle state at an end of the event and a distribution characteristic of vehicle states in a duration of the event.
Wong does not appear to specifically disclose wherein the vehicle kinetic energy characteristic comprises a vehicle state at a beginning of the event, a vehicle state at an end of the event and a distribution characteristic of vehicle states in a duration of the event.
However Rosenbaum teaches in at least Paragraphs 0037 – 0046 wherein driving characteristics may be acquired for correlation with an acceleration event, including vehicle speed at the beginning and end of the acceleration event, as well as intermediate speeds during the event, such as maximum speeds [i.e. the vehicle kinetic energy characteristic comprises a vehicle state at a beginning of the event, a vehicle state at an end of the event and a distribution characteristic of vehicle states in a duration of the event] which may be used to assess the driving style of a driver as taught in at least Paragraph 0094.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wong by incorporating the assessment of vehicle speeds and accelerations at different points during an acceleration event as taught by Rosenbaum.
The motivation to do so is that, as acknowledged by Rosenbaum in at least Paragraph 0094, the estimation of driving risk of a driver based on behavior during a driving event may be improved.
Regarding Claim 6:
The method according to claim 5, wherein the vehicle state comprises at least one of the following: a speed of a vehicle, a longitudinal acceleration of the vehicle, a lateral acceleration of the vehicle, an acceleration in a vertical direction of the vehicle and a relative position of the vehicle to a lane line during the event, or any combination thereof.
Wong discloses in at least Paragraphs 0020 & 0022 wherein a system for assessing vehicle driving risk may collect data from the vehicle during operation, including vehicle speed and GPS position data [i.e. the vehicle state comprises at least speed of the vehicle].
Regarding Claim 8:
The method according to claim 7, wherein: the quantity characteristic of the traffic flow comprises a quantity of the traffic flow at a beginning of the event, a quantity of the traffic flow at an end of the event and a distribution characteristic of quantities of the traffic flow in a duration of the event; and the front-to-vehicle distance characteristic of the current lane comprises a front-to-vehicle distance of the current lane at the beginning of the event, a front-to-vehicle distance of the current lane at the end of the event and a distribution characteristic of front-to-vehicle distance of the current lane in the duration of the event.
Wong does not appear to specifically disclose the above recited claim limitations.
However Rosenbaum teaches in at least Paragraph 0472 wherein traffic conditions may quantified for a current road of the vehicle, including traffic speed and traffic density, as well as fluctuations thereof for surrounding vehicles, which may be correlated to the time period of an acceleration event as taught in at least Paragraph 0027 of Rosenbaum [i.e. the quantity characteristic of the traffic flow comprises a quantity of the traffic flow at a beginning of the event, a quantity of the traffic flow at an end of the event and a distribution characteristic of quantities of the traffic flow in a duration of the event].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wong by incorporating the correlation of acceleration events to traffic conditions as taught by Rosenbaum.
The motivation to do so is that, as acknowledged by Rosenbaum in at least Paragraph 0472, the influence of traffic conditions on driving events may be quantified, improving the assessment of driving behaviors under different conditions.
Regarding Claim 12:
The equipment according to claim 11, wherein the event degree characteristic comprises a duration of the event, a duration of driving when the event occurs, a frequency of occurrence of the event, and an attribute value of the event.
Wong discloses in at least Paragraphs 0033 & 0034 wherein the weightage [i.e. event degree characteristic] may be assigned based on a type or degree of behavior, such as a speeding amount [i.e. an attribute value of an event], as well as the frequency of the occurrence of the behavior in a specified period of time [i.e. a frequency of occurrence of the event]. Wong however appears to be silent regarding wherein the event degree characteristic comprises a duration of the event and a duration of driving when the event occurs.
However Rosenbaum teaches in at least Paragraphs 0189 & 0475 wherein a driving characteristic value may be derived as a function of past reactions of a driver based on a driving time over a predetermined driving time [i.e. a duration of driving when the event occurs], as well as a duration of the event as taught in at least Paragraphs 0049 & 0111 [i.e. the event degree characteristic comprises a duration of the event].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wong by incorporating the determination of event degree characteristic based on driving time and event duration as taught by Rosenbaum.
The motivation to do so is that, as acknowledged by Rosenbaum in at least Paragraph 0094, the estimation of driving risk of a driver based on behavior during a driving event and the parameters of the driving event may be improved.
Regarding Claim 13:
The equipment according to claim 12, wherein: the degree characteristic of sharp turning further comprises a lateral direction angle during a sharp turning; the degree characteristic of vibration further comprises an average interval duration of a vibration event; the degree characteristic of speeding further comprises a speed limit value; the degree characteristic of relative collision further comprises a time distribution characteristic of relative collision occurrence times; the degree characteristic of pedestrian collision further comprises a time distribution characteristic of pedestrian collisions occurrence times; the degree characteristic of lane keeping further comprises a type of lane line; the degree characteristic of yawning further comprises a distribution characteristic of mouth length-to-width ratios; the degree characteristic of head lowering further comprises an angle distribution characteristic of head lowering; and the degree characteristic of left-and-right observation further comprises a distribution characteristic of head deflection angles.
Wong discloses in at least Paragraph 0033 wherein the weightage assigned to speeding risk may include different weights depending on the degree to which the regulated speed is exceeded [i.e. the degree characteristic of speeding further comprises a speed limit value].
Regarding Claim 14:
The equipment according to claim 9, wherein the vehicle kinetic energy characteristic comprises a vehicle state at a beginning of the event, a vehicle state at an end of the event and a distribution characteristic of vehicle states in a duration of the event.
Wong does not appear to specifically disclose wherein the vehicle kinetic energy characteristic comprises a vehicle state at a beginning of the event, a vehicle state at an end of the event and a distribution characteristic of vehicle states in a duration of the event.
However Rosenbaum teaches in at least Paragraphs 0037 – 0046 wherein driving characteristics may be acquired for correlation with an acceleration event, including vehicle speed at the beginning and end of the acceleration event, as well as intermediate speeds during the event, such as maximum speeds [i.e. the vehicle kinetic energy characteristic comprises a vehicle state at a beginning of the event, a vehicle state at an end of the event and a distribution characteristic of vehicle states in a duration of the event] which may be used to assess the driving style of a driver as taught in at least Paragraph 0094.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wong by incorporating the assessment of vehicle speeds and accelerations at different points during an acceleration event as taught by Rosenbaum.
The motivation to do so is that, as acknowledged by Rosenbaum in at least Paragraph 0094, the estimation of driving risk of a driver based on behavior during a driving event may be improved.
Regarding Claim 15:
The equipment according to claim 14, wherein the vehicle state comprises at least one of the following: a speed of a vehicle, a longitudinal acceleration of the vehicle, a lateral acceleration of the vehicle, an acceleration in a vertical direction of the vehicle, a relative position of the vehicle to a lane line during the event, or any combination thereof.
Wong discloses in at least Paragraphs 0020 & 0022 wherein a system for assessing vehicle driving risk may collect data from the vehicle during operation, including vehicle speed and GPS position data [i.e. the vehicle state comprises at least speed of the vehicle].
Regarding Claim 17:
The equipment according to claim 16, wherein: the quantity characteristic of the traffic flow comprises a quantity of the traffic flow at a beginning of the event, a quantity of the traffic flow at an end of the event, and a distribution characteristic of quantities of the traffic flow in a duration of the event; and the front-to-vehicle distance characteristic of the current lane comprises a front-to-vehicle distance of the current lane at the beginning of the event, a front-to-vehicle distance of the current lane at the end of the event, and a distribution characteristic of front-to-vehicle distance of the current lane in the duration of the event.
Wong does not appear to specifically disclose the above recited claim limitations.
However Rosenbaum teaches in at least Paragraph 0472 wherein traffic conditions may quantified for a current road of the vehicle, including traffic speed and traffic density, as well as fluctuations thereof for surrounding vehicles, which may be correlated to the time period of an acceleration event as taught in at least Paragraph 0027 of Rosenbaum [i.e. the quantity characteristic of the traffic flow comprises a quantity of the traffic flow at a beginning of the event, a quantity of the traffic flow at an end of the event and a distribution characteristic of quantities of the traffic flow in a duration of the event].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wong by incorporating the correlation of acceleration events to traffic conditions as taught by Rosenbaum.
The motivation to do so is that, as acknowledged by Rosenbaum in at least Paragraph 0472, the influence of traffic conditions on driving events may be quantified, improving the assessment of driving behaviors under different conditions.
Regarding Claim 19:
The equipment according to claim 18, wherein the event degree characteristic comprises a duration of the event, a duration of driving when the event occurs, a frequency of occurrence of the event, and an attribute value of the event.
Wong discloses in at least Paragraphs 0033 & 0034 wherein the weightage [i.e. event degree characteristic] may be assigned based on a type or degree of behavior, such as a speeding amount [i.e. an attribute value of an event], as well as the frequency of the occurrence of the behavior in a specified period of time [i.e. a frequency of occurrence of the event]. Wong however appears to be silent regarding wherein the event degree characteristic comprises a duration of the event and a duration of driving when the event occurs.
However Rosenbaum teaches in at least Paragraphs 0189 & 0475 wherein a driving characteristic value may be derived as a function of past reactions of a driver based on a driving time over a predetermined driving time [i.e. a duration of driving when the event occurs], as well as a duration of the event as taught in at least Paragraphs 0049 & 0111 [i.e. the event degree characteristic comprises a duration of the event].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wong by incorporating the determination of event degree characteristic based on driving time and event duration as taught by Rosenbaum.
The motivation to do so is that, as acknowledged by Rosenbaum in at least Paragraph 0094, the estimation of driving risk of a driver based on behavior during a driving event and the parameters of the driving event may be improved.
Regarding Claim 20:
The equipment according to claim 19, wherein: the degree characteristic of sharp turning further comprises a lateral direction angle during a sharp turning; the degree characteristic of vibration further comprises an average interval duration of a vibration event; the degree characteristic of speeding further comprises a speed limit value; the degree characteristic of relative collision further comprises a time distribution characteristic of relative collision occurrence times; the degree characteristic of pedestrian collision further comprises a time distribution characteristic of pedestrian collisions occurrence times; the degree characteristic of lane keeping further comprises a type of lane line; the degree characteristic of yawning further comprises a distribution characteristic of mouth length-to-width ratios; the degree characteristic of head lowering further comprises an angle distribution characteristic of head lowering; and the degree characteristic of left-and-right observation further comprises a distribution characteristic of head deflection angles.
Wong discloses in at least Paragraph 0033 wherein the weightage assigned to speeding risk may include different weights depending on the degree to which the regulated speed is exceeded [i.e. the degree characteristic of speeding further comprises a speed limit value].
Conclusion
The following prior art made of record but not relied upon is considered pertinent to the Applicant’s disclosure:
Smith (US 11,718,288 B2): Smith recites a transport event severity determination system, including receiving data by a server, and determining an event severity based on atypical data received. The vehicle may then take an action based on the determined severity.
Pifko (US 2019/0066535 A1): Pifko recites a system for contextual driver evaluation, including segmenting trip motion data into a plurality of segments, and identifying context and behavior for driving in each that assess driver abilities. The driver’s ability may be scored to compare to the abilities of other drivers.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER RYAN CARDIMINO whose telephone number is (571)272-2759. The examiner can normally be reached M-Th 8:30-5:00.
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/CHRISTOPHER R CARDIMINO/Examiner, Art Unit 3661
/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
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