DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1, 4-9, and 13 are objected to because of the following informalities:
In claim 1, “determine based the frequency of detection” should be “determine based on the frequency of detection”
In claim 4, “configured with instructions stored in non-transitory memory that when executed cause the processor to generate a histogram” should be “configured with instructions stored in the non-transitory memory that, when executed, cause the processor to generate a histogram”
In claim 5, “configured with instructions stored in non-transitory memory that when executed cause the processor to generate an integral value histogram indicating percent values greater than a left bound for each bin of the integral value histogram.” should be “configured with instructions stored in the non-transitory memory that, when executed, cause the processor to generate an integral value histogram indicating percent values greater than a left bound for each of a plurality of bins of the integral value histogram.”
In claim 6, “wherein the threshold value is a value” should be “wherein the threshold sensor value is a value”
In claim 7, “wherein, to adjust one or more vehicle operating states in response to detection of the sensor value greater than the threshold value, the computing device is configured with instructions in non-transitory memory that when executed cause the computing device to adjust” should be “wherein, to adjust the one or more vehicle operating states in response to detection of the sensor value greater than the threshold sensor value, the computing device is configured with instructions in the non-transitory memory that, when executed, cause the computing device to adjust”
In claim 8, “greater than the threshold value, is presented” should be “greater than the threshold sensor value, is presented”
In claim 9, “lower than the threshold value, the computing device is configured with instructions in non-transitory memory that when executed cause the processor” should be “lower than the threshold sensor value, the computing device is configured with instructions in the non-transitory memory that, when executed, cause the processor”
In claim 13, “the driver is experiencing both a first and second human state affecting factor.” should be “the driver is experiencing both [[a]] the first and a second human state affecting factor.”
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claimed invention is directed to the concept of detecting frequencies of different sensor values, creating a distribution function based on these frequencies, determining a threshold based on that distribution function, and determining that an additional value violates that threshold. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Regarding claim 1, applicant recites A vehicle system, comprising:
a vehicle including a sensor and an advanced driver assistance system (ADAS), the sensor being configured to detect one or more parameters of a driver, the sensor mounted in the vehicle and communicating with the ADAS system; and
a computing device comprising a processor and non-transitory memory storing instructions executable by the processor that, when executed, cause the processor to:
obtain sensor output values;
determine, based on the sensor output values, a frequency of detection of each of the sensor output values;
determine based the frequency of detection of each of the sensor values, an integral distribution function of the sensor output values;
determine, based on the integral distribution function, a threshold sensor value; and
in response to detecting a sensor value greater than the threshold sensor value, outputting a notification to a user and/or adjusting one or more vehicle operating states.
The claim recites a system which performs a series of steps and therefore is directed to an apparatus, which satisfies step 1 of the Section 101 analysis. Under the two-prong inquiry, the claim is eligible at revised step 2A unless: Prong One: the claim recites a judicial exception; and Prong Two: the exception is not integrated into a practical application of the exception.
The above claim steps are directed to the concept of detecting frequencies of different sensor values, creating a distribution function based on these frequencies, determining a threshold based on that distribution function, and determining that an additional value violates that threshold, which is an abstract idea that can be performed by a user mentally or manually and falls within the Mental Processes grouping. (Prong one: YES, recites an abstract idea).
Other than reciting the use of a vehicle, a sensor, and a processor coupled to a non-transitory memory, nothing in the claim elements precludes the steps from being performed entirely by a human. The use of one or more computing devices is insufficient to amount to significantly more than the judicial exception and does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Moreover, the step of outputting a notification is merely outputting the judicial exception, which is insignificant extra-solution activity and therefore does not integrate the judicial exception into a practical application (Prong Two: NO, does not recite additional elements that integrate the abstract idea into a practical application similar to that shown in MPEP 2106.05).
Under step 2B, the claimed invention does not recite additional elements that are indicative of an inventive concept. The additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The vehicle, sensor, and processor coupled to the non-transitory memory are described in paragraphs [0033], [0077], and [0052] of applicant’s specification as merely general purpose computer components. Therefore these additional limitations are no more than mere instructions to apply the exception using generic computer components. The recitation of generic processors/computers does not take the above limitations out of the mental processes grouping.
Moreover, the implementation of the abstract idea on generic computers and/or generic computer components does not add significantly more, similar to how the recitation of the computer in Alice amounted to mere instructions to apply the abstract idea on a generic computer. The claims merely invoke the additional elements as tools that are being used in their ordinary capacity. Further, the courts have found that simply limiting the use of the abstract idea to a particular environment does not add significantly more. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
Examiner’s note to help applicant overcome the 101 rejections: applicant can overcome the 101 rejection by making an argument analogizing applicant’s claims to claim 1 of Example 40 of the USPTO’s Subject Matter Eligibility Examples 37 to 42. Claim 1 of Example 40 discusses a network traffic data gathering method that gathers different network data, compares it to a threshold, and collects additional data when a piece of collected data exceeds the threshold. Claim 1 of Example 40 was held to be eligible because, at Step 2A, Prong 2, “Although each of the collecting steps analyzed individually may be viewed as mere pre- or post-solution activity, the claim as a whole is directed to a particular improvement in collecting traffic data. Specifically, the method limits collection of additional Netflow protocol data to when the initially collected data reflects an abnormal condition, which avoids excess traffic volume on the network and hindrance of network performance. The collected data can then be used to analyze the cause of the abnormal condition. This provides a specific improvement over prior systems, resulting in improved network monitoring. The claim as a whole integrates the mental process into a practical application. Thus, the claim is eligible because it is not directed to the recited judicial exception” (See at least Page 11 in the USPTO’s Subject Matter Eligibility Examples 37 to 42).
Likewise, claim 1 of applicant’s application concerns collecting sensor data, performing an analysis of frequencies of different sensor values to determine a threshold, collecting additional data, and issuing an alert when a piece of additional collected data exceeds the threshold. Applicant could argue that this is analogous to claim 1 of Example 40 because applicant’s claim also limits collection of additional data when previously collected data reflects an abnormal condition, which avoids collection of excessive data and can be used to analyze the cause of an abnormal condition, just like claim 1 of Example 40, resulting in a specific improvement over prior sensor systems and integrating the mental process into a practical application such that the claim is not directed to the recited judicial exception.
If applicant can make such an argument persuasively, then examiner will withdraw the 101 rejections. Applicant should be sure to explicitly cite Example 40 in order to clarify the basis for applicant’s assertions.
Regarding claim 2, applicant recites The vehicle system of claim 1, wherein the sensor value greater than the threshold sensor value is a critical sensor value indicative of a relevant human state.
However, a human can mentally or manually detect a human state.
Regarding claim 3, applicant recites The vehicle system of claim 1, wherein the frequency of detection is determined for sub-ranges of values.
However, a human can mentally or manually perform detection for such sub-ranges.
Regarding claim 4, applicant recites The vehicle system of claim 1, wherein, to determine the frequency of detection, the computing device is further configured with instructions stored in non-transitory memory that when executed cause the processor to generate a histogram including frequencies of the sensor output values.
However, a human can mentally or manually generate such a histogram.
Regarding claim 5, applicant recites The vehicle system of claim 4, wherein, to determine the integral distribution function, the computing device is further configured with instructions stored in non-transitory memory that when executed cause the processor to generate an integral value histogram indicating percent values greater than a left bound for each bin of the integral value histogram.
However, a human can mentally or manually generate such a histogram.
Regarding claim 6, applicant recites The vehicle system of claim 15, wherein the threshold value is a value of the left bound of a given bin of the integral value histogram for which the percentage value greater than the left bound is 5% or less.
However, a human can mentally or manually generate such a histogram.
Regarding claim 7, applicant recites The vehicle system of claim 1, wherein, to adjust one or more vehicle operating states in response to detection of the sensor value greater than the threshold value, the computing device is configured with instructions in non-transitory memory that when executed cause the computing device to adjust one or more of a vehicle speed and an advanced driver assistance system parameter.
However, the advanced driver assistance system parameter could be a mere notification or warning, which would merely be outputting the judicial exception. Outputting the judicial exception is insignificant extra-solution activity that does not integrate the judicial exception into a practical application.
Regarding claim 8, applicant recites The vehicle system of claim 1, wherein the notification outputted in response to detection of the sensor value greater than the threshold value, is presented to the driver one or more of visually and audibly.
However, this is merely outputting the judicial exception. Outputting the judicial exception is insignificant extra-solution activity that does not integrate the judicial exception into a practical application.
Regarding claim 9, applicant recites The vehicle system of claim 1, wherein, in response to detection of a second sensor value lower than the threshold value, the computing device is configured with instructions in non-transitory memory that when executed cause the processor to continue acquiring sensor values without outputting a notification or adjusting a vehicle operating state.
However, a user can mentally or manually make the above evaluations and gather or read the above data.
Regarding claim 10, applicant recites A vehicle system, comprising:
a vehicle including a sensor and an advanced driver assistance system (ADAS), wherein the sensor is configured to detect one or more parameters of a driver, the sensor mounted in the vehicle and communicating with the ADAS; and
a computing device comprising a processor and non-transitory memory storing instructions executable by the processor that, when executed, cause the processor to:
obtain sensor output values;
determine, based on the sensor output values, a frequency of detection of each of the sensor output values;
determine, based on the frequency of detection of each of the sensor values, an integral distribution function of the sensor output values;
determine, based on the integral distribution function, a threshold sensor value, wherein a sensor value greater than the threshold sensor value is a critical sensor value indicative of a relevant human state;
obtain sensor values with the sensor; and
in response to detecting a critical sensor value among the obtained sensor values, outputting a notification to the driver and/or adjusting one or more vehicle operating states, wherein normal and uncritical fluctuations of the sensor values are ignored such that only outliers of the sensor values are determined to be critical so that both driver and sensor behaviour are considered when interpreting sensor values.
The claim recites a system which performs a series of steps and therefore is directed to an apparatus, which satisfies step 1 of the Section 101 analysis. Under the two-prong inquiry, the claim is eligible at revised step 2A unless: Prong One: the claim recites a judicial exception; and Prong Two: the exception is not integrated into a practical application of the exception.
The above claim steps are directed to the concept of detecting frequencies of different sensor values, creating a distribution function based on these frequencies, determining a threshold based on that distribution function, and determining that an additional value violates that threshold, which is an abstract idea that can be performed by a user mentally or manually and falls within the Mental Processes grouping. (Prong one: YES, recites an abstract idea).
Other than reciting the use of a vehicle, a sensor, and a processor coupled to a non-transitory memory, nothing in the claim elements precludes the steps from being performed entirely by a human. The use of one or more computing devices is insufficient to amount to significantly more than the judicial exception and does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Moreover, the step of outputting a notification is merely outputting the judicial exception, which is insignificant extra-solution activity and therefore does not integrate the judicial exception into a practical application (Prong Two: NO, does not recite additional elements that integrate the abstract idea into a practical application similar to that shown in MPEP 2106.05).
Under step 2B, the claimed invention does not recite additional elements that are indicative of an inventive concept. The additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The vehicle, sensor, and processor coupled to the non-transitory memory are described in paragraphs [0033], [0077], and [0052] of applicant’s specification as merely general purpose computer components. Therefore these additional limitations are no more than mere instructions to apply the exception using generic computer components. The recitation of generic processors/computers does not take the above limitations out of the mental processes grouping.
Moreover, the implementation of the abstract idea on generic computers and/or generic computer components does not add significantly more, similar to how the recitation of the computer in Alice amounted to mere instructions to apply the abstract idea on a generic computer. The claims merely invoke the additional elements as tools that are being used in their ordinary capacity. Further, the courts have found that simply limiting the use of the abstract idea to a particular environment does not add significantly more. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
Examiner’s note to help applicant overcome the 101 rejections: applicant can overcome the 101 rejection by making an argument analogizing applicant’s claims to claim 1 of Example 40 of the USPTO’s Subject Matter Eligibility Examples 37 to 42. Claim 1 of Example 40 discusses a network traffic data gathering method that gathers different network data, compares it to a threshold, and collects additional data when a piece of collected data exceeds the threshold. Claim 1 of Example 40 was held to be eligible because, at Step 2A, Prong 2, “Although each of the collecting steps analyzed individually may be viewed as mere pre- or post-solution activity, the claim as a whole is directed to a particular improvement in collecting traffic data. Specifically, the method limits collection of additional Netflow protocol data to when the initially collected data reflects an abnormal condition, which avoids excess traffic volume on the network and hindrance of network performance. The collected data can then be used to analyze the cause of the abnormal condition. This provides a specific improvement over prior systems, resulting in improved network monitoring. The claim as a whole integrates the mental process into a practical application. Thus, the claim is eligible because it is not directed to the recited judicial exception” (See at least Page 11 in the USPTO’s Subject Matter Eligibility Examples 37 to 42).
Likewise, claim 1 of applicant’s application concerns collecting sensor data, performing an analysis of frequencies of different sensor values to determine a threshold, collecting additional data, and issuing an alert when a piece of additional collected data exceeds the threshold. Applicant could argue that this is analogous to claim 1 of Example 40 because applicant’s claim also limits collection of additional data when previously collected data reflects an abnormal condition, which avoids collection of excessive data and can be used to analyze the cause of an abnormal condition, just like claim 1 of Example 40, resulting in a specific improvement over prior sensor systems and integrating the mental process into a practical application such that the claim is not directed to the recited judicial exception.
If applicant can make such an argument persuasively, then examiner will withdraw the 101 rejections. Applicant should be sure to explicitly cite Example 40 in order to clarify the basis for applicant’s assertions.
Regarding claim 11, applicant recites The vehicle system of claim 10, wherein, when the threshold sensor value is a first threshold value, the critical sensor value is detected when the driver is experiencing a first human state affecting factor.
However, a human can mentally or manually detect a human state.
Regarding claim 12, applicant recites The vehicle system of claim 11, wherein, when the threshold sensor value is a second threshold value, the obtained sensor values do not comprise a value greater than the second threshold value when the driver is experiencing the first human state affecting factor.
However, a human could observe the above trend.
Regarding claim 13, applicant recites The vehicle system of claim 12, wherein when the threshold sensor value is the second threshold value, the critical sensor value is detected when the driver is experiencing both a first and second human state affecting factor.
However, a human could observe the above trend.
Regarding claim 14, applicant recites The vehicle system of claim 10, wherein determining the threshold sensor value based on the integral distribution function comprises determining a boundary value that 5% or less of the sensor output values are greater than.
However, a human could mentally or manually perform the above calculations.
Regarding claim 15, applicant recites The vehicle system of claim 14, wherein the frequency of detection of each of the sensor values is represented by a frequency histogram and the integral distribution function is represented by an integral value histogram, wherein the boundary value is a left bound of a bin of the integral value histogram.
However, a human can mentally or manually generate both of the above histograms.
Examiner’s note to help applicant overcome the 101 rejections: applicant can overcome the 101 rejection by making an argument analogizing applicant’s claims to claim 1 of Example 40 of the USPTO’s Subject Matter Eligibility Examples 37 to 42. Claim 1 of Example 40 discusses a network traffic data gathering method that gathers different network data, compares it to a threshold, and collects additional data when a piece of collected data exceeds the threshold. Claim 1 of Example 40 was held to be eligible because, at Step 2A, Prong 2, “Although each of the collecting steps analyzed individually may be viewed as mere pre- or post-solution activity, the claim as a whole is directed to a particular improvement in collecting traffic data. Specifically, the method limits collection of additional Netflow protocol data to when the initially collected data reflects an abnormal condition, which avoids excess traffic volume on the network and hindrance of network performance. The collected data can then be used to analyze the cause of the abnormal condition. This provides a specific improvement over prior systems, resulting in improved network monitoring. The claim as a whole integrates the mental process into a practical application. Thus, the claim is eligible because it is not directed to the recited judicial exception” (See at least Page 11 in the USPTO’s Subject Matter Eligibility Examples 37 to 42).
Likewise, claim 1 of applicant’s application concerns collecting sensor data, performing an analysis of frequencies of different sensor values to determine a threshold, collecting additional data, and issuing an alert when a piece of additional collected data exceeds the threshold. Applicant could argue that this is analogous to claim 1 of Example 40 because applicant’s claim also limits collection of additional data when previously collected data reflects an abnormal condition, which avoids collection of excessive data and can be used to analyze the cause of an abnormal condition, just like claim 1 of Example 40, resulting in a specific improvement over prior sensor systems and integrating the mental process into a practical application such that the claim is not directed to the recited judicial exception.
If applicant can make such an argument persuasively, then examiner will withdraw the 101 rejections. Applicant should be sure to explicitly cite Example 40 in order to clarify the basis for applicant’s assertions.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3 and 7-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ben David (US 20110043350 A1), hereinafter referred to as Ben.
Regarding claim 1, Ben discloses A vehicle system (See at least Fig. 12 in Ben: Ben discloses A system according to an embodiment of the present invention is illustrated in the block diagram of FIG. 12 [See at least Ben, 0128]), comprising:
a vehicle including a sensor (See at least Fig. 12 in Ben: Ben discloses A steering wheel 1201 provides a right signal 1203 and a left signal 1205 which are input into a signal interface 1209, which accepts operator gripping signals indicative of the physical gripping of the operator upon steering wheel 1201, and also accepts additional inputs 1207 including, but not limited to factors such as vehicle speed [See at least Ben, 0128]) and an advanced driver assistance system (ADAS) (The whole system of Fig. 12 of Ben may be regarded as an ADAS), the sensor being configured to detect one or more parameters of a driver, the sensor mounted in the vehicle and communicating with the ADAS system (See at least Fig. 12 in Ben: Ben discloses A steering wheel 1201 provides a right signal 1203 and a left signal 1205 which are input into a signal interface 1209, which accepts operator gripping signals indicative of the physical gripping of the operator upon steering wheel 1201, and also accepts additional inputs 1207 including, but not limited to factors such as vehicle speed [See at least Ben, 0128]); and
a computing device comprising a processor and non-transitory memory storing instructions executable by the processor (See at least Fig. 12 in Ben: Ben discloses that signal interface 1209 also performs some pre-processing on the signals [See at least Ben, 0128]. Ben further discloses A data processor 1211 receives the input signals from signal interface 1209 [See at least Ben, 0128]. Ben further discloses that data processor 1211 performs the rest of the functions of the system discussed throughout the reference [See at least Ben, 0130]) that, when executed, cause the processor to:
obtain sensor output values (See at least Fig. 4 in Ben: Ben discloses 1. In a collection step 401 consecutive signal values 403 are collected over a given collection window [See at least Ben, 0057]);
determine, based on the sensor output values, a frequency of detection of each of the sensor output values (See at least Fig. 4 in Ben: Ben discloses that In a scoring step 417 scores 419 are calculated from these clusters and compared at a decision point 421 against empirically-derived thresholds 423--to determine whether the signal during the latest interval is significant 427 or non-significant 425 [See at least Ben, 0065]. Ben further discloses that Non-limiting examples of scores include: (a) the normalized distance between the two centers along the "mean" coordinate; (b) the highest position of a cluster center along the "standard deviation" coordinate; (c) the distances from the two cluster centers to the points derived from the latest two intervals [See at least Ben, 0066-0069]. Ben further discloses that comparison of these scores against empirically-obtained threshold values allows detecting a drowsiness pattern and thus to determining the state of drowsiness verus alertness of the operator [See at least Ben, 0089]. Also see at least Fig. 8 in Ben: Ben discloses that that 1. In a calculation step 801 the distribution function of each component of the proxy grip signal over the history window is continuously calculated [See at least Ben, 0091]. In other words, the distribution of Fig. 8 of Ben is derived using the sensor values of Fig. 4 of Ben, which detect how often the threshold is crossed);
determine based the frequency of detection of each of the sensor values, an integral distribution function of the sensor output values (See at least Fig. 8 in Ben: Ben discloses that 1. In a calculation step 801 the distribution function of each component of the proxy grip signal over the history window is continuously calculated [See at least Ben, 0091]);
determine, based on the integral distribution function, a threshold sensor value (See at least Fig. 8 in Ben: Ben discloses that 2. For each such distribution function, a quantile limit, q.sub.0 807 is determined in a step 803 that corresponds to a predetermined probability p.sub.0 805 [See at least Ben, 0092]. Ben further discloses that Sequences of consecutive samples whose value is below q.sub.0 807 are detected; if the length of any sequence exceeds a given threshold value 827 a drowsiness pattern is indicated, detecting onset of drowsiness 831 [See at least Ben, 0094]. The threshold value 827 may be regarded as applicant’s “threshold”); and
in response to detecting a sensor value greater than the threshold sensor value (See at least Fig. 8 in Ben: Ben discloses that Sequences of consecutive samples whose value is below q.sub.0 807 are detected; if the length of any sequence exceeds a given threshold value 827 a drowsiness pattern is indicated, detecting onset of drowsiness 831 [See at least Ben, 0094]. Also see at least Fig. 11 in Ben: Ben discloses 5. Check for drowsiness pattern in a detection step 1117 [See at least Ben, 0107]), outputting a notification to a user (See at least Fig. 11 in Ben: Ben discloses that a drowsiness pattern is detected and an alert is generated in a step 1133 [See at least Ben, 0107]. There are multiple intermediate steps between 1117 and 1133, but it will be appreciated from the figure that 1133 is still based on 1117) and/or adjusting one or more vehicle operating states (Ben discloses that the alert signal may further include an alert to initiate an automatic wind-down sequence of vehicle operation [See at least Ben, 0124]).
Regarding claim 2, Ben discloses The vehicle system of claim 1, wherein the sensor value greater than the threshold sensor value is a critical sensor value indicative of a relevant human state (See at least Fig. 8 in Ben: Ben discloses that Sequences of consecutive samples whose value is below q.sub.0 807 are detected; if the length of any sequence exceeds a given threshold value 827 a drowsiness pattern is indicated, detecting onset of drowsiness 831 [See at least Ben, 0094]. Also see at least Fig. 11 in Ben: Ben discloses 5. Check for drowsiness pattern in a detection step 1117 [See at least Ben, 0107]).
Regarding claim 3, Ben discloses The vehicle system of claim 1, wherein the frequency of detection is determined for sub-ranges of values (See at least Fig. 8 in Ben: Ben discloses that 1. In a calculation step 801 the distribution function of each component of the proxy grip signal over the history window is continuously calculated [See at least Ben, 0091]. Also see at least Fig. 6 in Ben: Ben discloses that A component 601 is a sum of signal values from sensors located on the left side of a steering wheel and a component 603 is a sum of signal values from sensors located at the right side of a steering wheel [See at least Ben, 0080]. The values summed to obtain each component may be broadly regarded as “sub-ranges of values”).
Regarding claim 7, Ben discloses The vehicle system of claim 1, wherein, to adjust one or more vehicle operating states in response to detection of the sensor value greater than the threshold value (See at least Fig. 8 in Ben: Ben discloses that Sequences of consecutive samples whose value is below q.sub.0 807 are detected; if the length of any sequence exceeds a given threshold value 827 a drowsiness pattern is indicated, detecting onset of drowsiness 831 [See at least Ben, 0094]. Also see at least Fig. 11 in Ben: Ben discloses 5. Check for drowsiness pattern in a detection step 1117 [See at least Ben, 0107]), the computing device is configured with instructions in non-transitory memory that when executed cause the computing device to adjust one or more of a vehicle speed and an advanced driver assistance system parameter (See at least Fig. 11 in Ben: Ben discloses that a drowsiness pattern is detected and an alert is generated in a step 1133 [See at least Ben, 0107]. There are multiple intermediate steps between 1117 and 1133, but it will be appreciated from the figure that 1133 is still based on 1117. The decision to display the alert may broadly be interpreted as adjusting an “advance driver assistance system parameter” because displaying a helpful alert to a driver may be broadly regarded as a function of such a system. Ben further discloses that the alert signal may further include an alert to initiate an automatic wind-down sequence of vehicle operation [See at least Ben, 0124]. This is an autonomous driving control ADAS feature).
Regarding claim 8, Ben discloses The vehicle system of claim 1, wherein the notification outputted in response to detection of the sensor value greater than the threshold value, is presented to the driver one or more of visually and audibly (Ben discloses that the alert may include, but is not limited to: a beep; a flashing light; a vibrating seat; or a combination thereof [See at least Ben, 0117]).
Regarding claim 9, Ben discloses The vehicle system of claim 1, wherein, in response to detection of a second sensor value lower than the threshold value, the computing device is configured with instructions in non-transitory memory that when executed cause the processor to continue acquiring sensor values without outputting a notification or adjusting a vehicle operating state (See at least Fig. 8 in Ben: Ben discloses that 2. For each such distribution function, a quantile limit, q.sub.0 807 is determined in a step 803 that corresponds to a predetermined probability p.sub.0 805 [See at least Ben, 0092]. Ben further discloses that Sequences of consecutive samples whose value is below q.sub.0 807 are detected; if the length of any sequence exceeds a given threshold value 827 a drowsiness pattern is indicated, detecting onset of drowsiness 831 [See at least Ben, 0094]. The threshold value 827 may be regarded as applicant’s “threshold”, and it will be appreciated that sequence lengths below this “threshold” do not trigger the alert).
Regarding claim 10, Ben discloses A vehicle system (See at least Fig. 12 in Ben: Ben discloses A system according to an embodiment of the present invention is illustrated in the block diagram of FIG. 12 [See at least Ben, 0128]), comprising:
a vehicle including a sensor (See at least Fig. 12 in Ben: Ben discloses A steering wheel 1201 provides a right signal 1203 and a left signal 1205 which are input into a signal interface 1209, which accepts operator gripping signals indicative of the physical gripping of the operator upon steering wheel 1201, and also accepts additional inputs 1207 including, but not limited to factors such as vehicle speed [See at least Ben, 0128]) and an advanced driver assistance system (ADAS) (The whole system of Fig. 12 of Ben may be regarded as an ADAS), wherein the sensor is configured to detect one or more parameters of a driver, the sensor mounted in the vehicle and communicating with the ADAS (See at least Fig. 12 in Ben: Ben discloses A steering wheel 1201 provides a right signal 1203 and a left signal 1205 which are input into a signal interface 1209, which accepts operator gripping signals indicative of the physical gripping of the operator upon steering wheel 1201, and also accepts additional inputs 1207 including, but not limited to factors such as vehicle speed [See at least Ben, 0128]); and
a computing device comprising a processor and non-transitory memory storing instructions executable by the processor (See at least Fig. 12 in Ben: Ben discloses that signal interface 1209 also performs some pre-processing on the signals [See at least Ben, 0128]. Ben further discloses A data processor 1211 receives the input signals from signal interface 1209 [See at least Ben, 0128]. Ben further discloses that data processor 1211 performs the rest of the functions of the system discussed throughout the reference [See at least Ben, 0130]) that, when executed, cause the processor to:
obtain sensor output values (See at least Fig. 4 in Ben: Ben discloses 1. In a collection step 401 consecutive signal values 403 are collected over a given collection window [See at least Ben, 0057]);
determine, based on the sensor output values, a frequency of detection of each of the sensor output values (See at least Fig. 4 in Ben: Ben discloses that In a scoring step 417 scores 419 are calculated from these clusters and compared at a decision point 421 against empirically-derived thresholds 423--to determine whether the signal during the latest interval is significant 427 or non-significant 425 [See at least Ben, 0065]. Ben further discloses that Non-limiting examples of scores include: (a) the normalized distance between the two centers along the "mean" coordinate; (b) the highest position of a cluster center along the "standard deviation" coordinate; (c) the distances from the two cluster centers to the points derived from the latest two intervals [See at least Ben, 0066-0069]. Ben further discloses that comparison of these scores against empirically-obtained threshold values allows detecting a drowsiness pattern and thus to determining the state of drowsiness verus alertness of the operator [See at least Ben, 0089]. Also see at least Fig. 8 in Ben: Ben discloses that that 1. In a calculation step 801 the distribution function of each component of the proxy grip signal over the history window is continuously calculated [See at least Ben, 0091]. In other words, the distribution of Fig. 8 of Ben is derived using the sensor values of Fig. 4 of Ben, which detect how often the threshold is crossed);
determine, based on the frequency of detection of each of the sensor values, an integral distribution function of the sensor output values (See at least Fig. 8 in Ben: Ben discloses that 1. In a calculation step 801 the distribution function of each component of the proxy grip signal over the history window is continuously calculated [See at least Ben, 0091]);
determine, based on the integral distribution function, a threshold sensor value (See at least Fig. 8 in Ben: Ben discloses that 2. For each such distribution function, a quantile limit, q.sub.0 807 is determined in a step 803 that corresponds to a predetermined probability p.sub.0 805 [See at least Ben, 0092]. Ben further discloses that Sequences of consecutive samples whose value is below q.sub.0 807 are detected; if the length of any sequence exceeds a given threshold value 827 a drowsiness pattern is indicated, detecting onset of drowsiness 831 [See at least Ben, 0094]. The threshold value 827 may be regarded as applicant’s “threshold”), wherein a sensor value greater than the threshold sensor value is a critical sensor value indicative of a relevant human state (See at least Fig. 8 in Ben: Ben discloses that Sequences of consecutive samples whose value is below q.sub.0 807 are detected; if the length of any sequence exceeds a given threshold value 827 a drowsiness pattern is indicated, detecting onset of drowsiness 831 [See at least Ben, 0094]. The threshold value 827 may be regarded as applicant’s “threshold” and a length of sequence greater than it may be regarded as a “critical sensor value”);
obtain sensor values with the sensor (See at least Fig. 8 in Ben: Ben discloses that Sequences of consecutive samples whose value is below q.sub.0 807 are detected; if the length of any sequence exceeds a given threshold value 827 a drowsiness pattern is indicated, detecting onset of drowsiness 831 [See at least Ben, 0094]. The length, which is periodically updated, may be broadly regarded as applicant’s “sensor values”, since each time the length is increased, it is like a new sensor value); and
in response to detecting a critical sensor value among the obtained sensor values (See at least Fig. 8 in Ben: Ben discloses that Sequences of consecutive samples whose value is below q.sub.0 807 are detected; if the length of any sequence exceeds a given threshold value 827 a drowsiness pattern is indicated, detecting onset of drowsiness 831 [See at least Ben, 0094]. Also see at least Fig. 11 in Ben: Ben discloses 5. Check for drowsiness pattern in a detection step 1117 [See at least Ben, 0107]), outputting a notification to the driver (See at least Fig. 11 in Ben: Ben discloses that a drowsiness pattern is detected and an alert is generated in a step 1133 [See at least Ben, 0107]. There are multiple intermediate steps between 1117 and 1133, but it will be appreciated from the figure that 1133 is still based on 1117) and/or adjusting one or more vehicle operating states (Ben discloses that the alert signal may further include an alert to initiate an automatic wind-down sequence of vehicle operation [See at least Ben, 0124]), wherein normal and uncritical fluctuations of the sensor values are ignored such that only outliers of the sensor values are determined to be critical (See at least Fig. 11 in Ben: Ben discloses that The ratio of the number of low-value samples over all subintervals to the total samples in the examination interval is computed as a score 1127 for drowsiness pattern detection in a step 1125 [See at least Ben, 0107]. Ben further discloses that If the scores for both grips are below a predetermined score threshold 1131 in a decision point 1129, then a drowsiness pattern is detected and an alert is generated in a step 1133 [See at least Ben, 0107]. Those situations that fall below the 1129 score threshold may be regarded as “normal and uncritical fluctuations”, whereas those that do not fall below the 1129 threshold may be regarded as “critical” and corresponding to “outliers”) so that both driver and sensor behaviour are considered when interpreting sensor values (See at least Fig. 11 in Ben: Ben discloses that The ratio of the number of low-value samples over all subintervals to the total samples in the examination interval is computed as a score 1127 for drowsiness pattern detection in a step 1125 [See at least Ben, 0107]. Ben further discloses that If the scores for both grips are below a predetermined score threshold 1131 in a decision point 1129, then a drowsiness pattern is detected and an alert is generated in a step 1133 [See at least Ben, 0107]. Both the data gathered by the sensors, and whether this is actually indicative of a drowsiness pattern (“driver behavior”) are considered in reaching the decision to issue the alert).
Regarding claim 11, Ben discloses The vehicle system of claim 10, wherein, when the threshold sensor value is a first threshold value, the critical sensor value is detected when the driver is experiencing a first human state affecting factor (See at least Fig. 8 in Ben: Ben discloses that 2. For each such distribution function, a quantile limit, q.sub.0 807 is determined in a step 803 that corresponds to a predetermined probability p.sub.0 805 [See at least Ben, 0092]. Ben further discloses that Sequences of consecutive samples whose value is below q.sub.0 807 are detected; if the length of any sequence exceeds a given threshold value 827 a drowsiness pattern is indicated, detecting onset of drowsiness 831 [See at least Ben, 0094]. The threshold value 827 may be regarded as applicant’s “first threshold”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Ben David (US 20110043350 A1) in view of Sugawara (US 20210073522 A1), hereinafter referred to as Ben and Sugawara, respectively.
Regarding claim 4, Ben discloses The vehicle system of claim 1, wherein, to determine the frequency of detection, the computing device is further configured with instructions stored in non-transitory memory that when executed cause the processor to generate a data structure including frequencies of the sensor output values (See at least Fig. 4 in Ben: Ben discloses that In a scoring step 417 scores 419 are calculated from these clusters and compared at a decision point 421 against empirically-derived thresholds 423--to determine whether the signal during the latest interval is significant 427 or non-significant 425 [See at least Ben, 0065]. Ben further discloses that Non-limiting examples of scores include: (a) the normalized distance between the two centers along the "mean" coordinate; (b) the highest position of a cluster center along the "standard deviation" coordinate; (c) the distances from the two cluster centers to the points derived from the latest two intervals [See at least Ben, 0066-0069]. Ben further discloses that comparison of these scores against empirically-obtained threshold values allows detecting a drowsiness pattern and thus to determining the state of drowsiness verus alertness of the operator [See at least Ben, 0089]. Also see at least Fig. 8 in Ben: Ben discloses that that 1. In a calculation step 801 the distribution function of each component of the proxy grip signal over the history window is continuously calculated [See at least Ben, 0091]. In other words, the distribution of Fig. 8 of Ben is derived using the sensor values of Fig. 4 of Ben, which detect how often the threshold is crossed).
However, Ben does not explicitly teach the system wherein the data structure is a histogram.
However, Sugawara does teach a system wherein the data structure used to determine that the values detected are abnormal (i.e., indicative of drowsiness) is a histogram (Sugawara teaches that a reference histogram suitable for comparison with a histogram for comparison (i.e., a reference histogram making it possible to determine whether or not the driver is in the moveless state more correctly by means of the comparison) can be generated [See at least Sugawara, 0135]). Both Sugawara and Ben teach methods for detecting whether or not a driver is fully awake in a vehicle by gathering data and counting frequencies. However, only, Sugawara explicitly teaches where the data may be stored in a histogram.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the user state monitoring system of Ben to also store its frequency data in a histogram, as in Sugawara. Anyone of ordinary skill in the art will appreciate that a histogram is an obvious structure for storing frequency information.
Regarding claim 5, Ben in view of Sugawara teaches The vehicle system of claim 4, wherein, to determine the integral distribution function (See at least Fig. 8 in Ben: Ben discloses that 1. In a calculation step 801 the distribution function of each component of the proxy grip signal over the history window is continuously calculated [See at least Ben, 0091]), the computing device is further configured with instructions stored in non-transitory memory that when executed cause the processor to generate an integral value histogram indicating percent values greater than a left bound for each bin of the integral value histogram (See at least Fig. 4 in Ben: Ben discloses that 3. In a statistical step 407, for each of the N intervals, a pair of statistical parameter values 409 is calculated from each of the signal values therein--in a non-limiting example, the mean and the standard deviation constitute the pair of statistical parameter values [See at least Ben, 0060]).
Allowable Subject Matter
Claims 6 and 12-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The closest prior art of record is Ben David (US 20110043350 A1), hereinafter referred to as Ben. The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 12, Ben discloses The vehicle system of claim 11.
However, none of the prior art of record, taken either alone or in combination, teaches or suggests the system wherein, when the threshold sensor value is a second threshold value, the obtained sensor values do not comprise a value greater than the second threshold value when the driver is experiencing the first human state affecting factor.
In order for a reference to read on this limitation, the reference would have to teach where, when the driving is experiencing a particular human state affecting factor, not only is there a first threshold based on the distributions which the sensor values must exceed, but there must also be a second threshold which the sensor values must not exceed. This further limitation on the same sensor value which is already limited by the first distribution-derived threshold does not exist in the prior art.
Ben comes somewhat close to teaching this missing limitation. However, Ben only has the one lower threshold (See at least Fig. 8 in Ben: Ben discloses that 2. For each such distribution function, a quantile limit, q.sub.0 807 is determined in a step 803 that corresponds to a predetermined probability p.sub.0 805 [See at least Ben, 0092]. Ben further discloses that Sequences of consecutive samples whose value is below q.sub.0 807 are detected; if the length of any sequence exceeds a given threshold value 827 a drowsiness pattern is indicated, detecting onset of drowsiness 831 [See at least Ben, 0094]. The threshold value 827 may be regarded as applicant’s “first threshold”). Note that in at least [Ben, 0094], there is no second additional higher threshold for detecting the first human state affecting factor. Therefore, Ben does not teach the missing limitation.
None of the prior art of record cures this deficiency in Ben.
For at least the above stated reasons, claim 12 contains allowable subject matter.
Regarding claim 13, this claim also contains allowable subject matter at least by virtue of its dependence from claim 12.
Regarding claim 14, Ben discloses The vehicle system of claim 10.
However, none of the prior art of record, taken either alone or in combination, teaches or suggests the system wherein determining the threshold sensor value based on the integral distribution function comprises determining a boundary value that 5% or less of the sensor output values are greater than.
Ben comes somewhat close to teaching this above missing limitation, since Ben teaches an integral distribution wherein a quantile is defined for an integral distribution curve that shows less than 5% of the area of the curve being less than the quintile (See at least Fig. 10 in Ben: The Figure shows <5% being less than the quantile, not greater than it [See at least Ben, 0095]). However, the claim recites defining a threshold where 5% or less of sensor output values are greater than that threshold, not less than that threshold. So This portion of Ben does not read on the claim limitations.
None of the other prior art of record remedy this deficiency of Ben.
For at least the above stated reasons, claim 14 contains allowable subject matter.
Regarding claims 6 and 15, these claims also contain allowable subject matter at least by virtue of their dependence from claim 14.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAEEM T ALAM whose telephone number is (571)272-5901. The examiner can normally be reached M-F, 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FADEY JABR can be reached at (571) 272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NAEEM TASLIM ALAM/Examiner, Art Unit 3668