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 .
Response to Amendment
This is a Non-Final Office action in response to communications filed on June 12, 2026. Applicant amended claims 1-20. Claims 1-20 remain pending in this application.
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.
Step 1: Does the claimed invention fall inside one of the four statutory categories (process, machine, manufacture, or composition of matter)? Yes for claims 1-20.
Claims 1-14 are drawn to a method for determining psychomotor vigilance scores and whether the scores indicate that an individual experienced a fatigue event (i.e., process). Claims 15-20 are drawn to a system for determining psychomotor vigilance scores and whether the scores indicate that an individual experienced a fatigue event (i.e., a manufacture).
Step 2A - Prong One: Do the claims recite a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon)? Yes, for claims 1-20.
Claim 1 recites:
A method, comprising: accessing, by one or more server computing devices, results of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, the results of the plurality of PVTs including respective response times for the particular test driver while monitoring operation of that vehicle in the autonomous driving mode;
determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs;
accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times;
determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events;
and training, by the one or more server computing devices, a neural network to execute a model to output, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, an indication of a likelihood that the particular test driver
will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, wherein training the neural network is based on (i) the respective scores for each of the plurality of PVTs and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, and training the neural network individualizes the model to the particular test driver to predict a future fatigue event of the particular test driver
while avoiding initiation of an intervention response based on a false positive of the future fatigue event.
These steps amount to a form of mental process and organizing human activity (i.e., an abstract idea) because a human can collect psychomotor vigilance data, determine psychomotor vigilance scores, and then based on psychomotor vigilance scores determine whether an individual experienced a fatigue event. Applicant of claimed invention discloses “the self-reported data may be used to determine whether a set of PVTs corresponds to a fatigue event” [0024]. Independent claim 15 describes nearly identical steps as claim 1 (and therefore recite limitations that fall within this subject matter of grouping abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis. Dependent claims 2-14 and 16-20 are directed towards mini-tasks (accessing response times, self-reported data, and scores, determining individual pass or fail rates, and identifying individual circadian rhythm states, etc.) for a method to determine psychomotor vigilance scores and whether the scores indicate that an individual experienced a fatigue event. Each claim amounts to a form of collecting, generating, and analyzing human data to determine whether or not a human experienced a fatigue event, and therefore falls within the scope of a method for organizing human activity, (i.e., an abstract idea). The enumerated groupings of abstract ideas include organizing human activity (see MPEP §2106.04(a)(2)(II)) and mental processes (see MPEP §2106.04(a)(2)(III)). As such, the Examiner concludes that claims 2-14 and 16-20 recite an abstract idea.
Step 2A – Prong Two: Do the claims recite additional elements that integrate the exception into a practical application of the exception? No
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. The requirement to execute the claimed steps/functions using computing devices (independent claims 1 and 15 and dependent claims 2-14 and 16-20) is equivalent to adding the words “apply it” on a computer and/or mere instructions to implement the abstract idea on a computer. Similarly, the limitations of computing devices (independent claims 1 and 15 and dependent claims 2-14 and 16-20) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using computer components. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Use of a computer, processor, memory or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) (See MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serve merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, they serve to limit the application of the abstract idea to a computerized environment (e.g., identifying and displaying, etc.) performed by a computing device, processor, and memory, etc. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
Dependent claims 2-14 and 16-20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims are further part of the abstract idea as identified by the Examiner for each respective independent claim (i.e., they are part of the abstract idea recited in each respective claim). The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? i.e., Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea? No
In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an “inventive concept.” An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amount to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed above in “Step 2A – Prong Two”, the identified additional elements in independent claims 1 and 15 and dependent claims 2-14 and 16-20 are equivalent to adding the words “apply it” on a computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a computer and/or mere instructions to implement the abstract idea on a computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Dependent claims 2-14 and 16-20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that no additional element, or combination of additional claims elements are sufficient to ensure the claims amount to significantly more than the abstract idea identified above. Therefore, claims 1-20 are not eligible subject matter 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable under US 20090066521 A1 (“Atlas”) in view of US 20200062265 A1 (“Wunderlich”) and US 20160270718 A1 (“Heneghan”).
In regards to claim 1, Atlas discloses the following limitations with the exception of the underlined limitations.
A method, comprising: accessing, by one or more server computing devices, results ([0095], “The system includes a controller …, which … is a processor or microprocessor…, which receives and processes … readings”) of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver ([0031], “there is provided a method for conducting a Psychomotor Vigilance Test of an operator”) at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency ([0070], “sudden and abrupt total loss of gripping pressure is detected and signaled to other personnel as an emergency condition”), the results of the plurality of PVTs including respective response times for the particular test driver ([0072], “a series of … PVT's … is administered to the operator according to a predetermined schedule … (e.g., a … PVT every 10 minutes)”), while monitoring operation of that vehicle in the autonomous driving mode;
determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs ([0071], “PVT … involves giving the subject a stimulus … and then gauging the quality of the response in terms of parameters such as reaction time” Examiner notes that reaction time may be used to determine PVT scores.);
accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times ([0027], “It is … an objective of the present invention to provide a means for detecting the onset of fatigue in a passive manner” Examiner notes that passive detection of fatigue can be performed using a remote system.);
determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events ([0030], “there is provided a method for detecting the physiological onset of fatigue in an operator”);
and training, by the one or more server computing devices, a neural network to execute a model to output, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency ([0070], “sudden and abrupt total loss of gripping pressure is detected and signaled to other personnel as an emergency condition”), an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency ([0070], “sudden and abrupt total loss of gripping pressure is detected and signaled to other personnel as an emergency condition”), wherein training the neural network is based on (i) the respective scores for each of the plurality of PVTs ([0031], “there is provided a method for conducting a Psychomotor Vigilance Test of an operator” Examiner notes that testing typically includes scores.) and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, and wherein training the neural network individualizes the model to the particular test driver to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event ([0030], “there is provided a method for detecting … the physiological progression of operator fatigue being associated with a plurality of … stages having a specified sequence in time”).
Wunderlich discloses
while monitoring operation of that vehicle in the autonomous driving mode ([0025], “a person ... is able to ... monitor the ... autonomous drive”);
Atlas and Wunderlich combined are considered analogous to the claimed invention because they are in the field of systems for fatigue detectors and methods for operating autonomous motor vehicles. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method, comprising: accessing, by one or more server computing devices, results of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, the results of the plurality of PVTs including respective response times for the particular test driver; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs; accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times;
determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, (i) the respective scores for each of the plurality of PVTs and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event, as disclosed by Atlas, while monitoring operation of that vehicle in the autonomous driving mode, as disclosed by Wunderlich, to provide a person to monitor the autonomous drive for a method of operating a motor vehicle in an activated autonomous driving mode. One skilled in the art would understand and recognize the value of the addition of a person who monitors autonomous driving to improve a method of operating a motor vehicle in an activated autonomous driving mode.
Heneghan discloses
and training, by the one or more server computing devices ([0055], “fatigue monitoring module may be implemented at a … server”), a neural network to execute a model to output ([0148], “the fatigue monitoring module … could use … neural networks”),
wherein training the neural network is based on ([0148], “the fatigue monitoring module … could use … neural networks”)
and wherein training the neural network ([0148], “the fatigue monitoring module … could use … neural networks”) individualizes the model to the particular test driver ([0070], “fatigue monitoring and management system ... can be implemented ... serving a single individual”)
Atlas and Heneghan combined are considered analogous to the claimed invention because they are in the field of systems for fatigue detectors and technology for monitoring and managing fatigue. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method, comprising: accessing, by one or more server computing devices, results of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, the results of the plurality of PVTs including respective response times for the particular test driver; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs; accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times; determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, (i) the respective scores for each of the plurality of PVTs and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event, as disclosed by Atlas, and training, by the one or more server computing devices, a neural network to execute a model to output, wherein training the neural network is based on and wherein training the neural network individualizes the model to the particular test driver, as disclosed by Heneghan, to provide a fatigue monitoring module and a neural network for technology that relates to monitoring and managing fatigue. One skilled in the art would understand and recognize the value of the addition of a fatigue monitoring module and a neural network to improve technology that relates to monitoring and managing fatigue.
In regards to claim 2, Atlas discloses
wherein accessing the results of the plurality of PVTs includes retrieving, by the one or more server computing devices, the results of the plurality of PVTs from a remote monitoring system ([0095], “The system includes … a controller …, which receives and processes … readings” Examiner notes that a controller can be a remote system.).
In regards to claim 3, Atlas does not disclose wherein the results of the plurality of PVTs include self-reported data for the particular test driver while monitoring operation of the vehicle in the autonomous driving mode.
Heneghan discloses
wherein the results of the plurality of PVTs include self-reported data for the particular test driver while monitoring operation of the vehicle in the autonomous driving mode ([0050], “a device configured to capture subjective user data related to the user's self-perceived fatigue state”).
Atlas and Heneghan combined are considered analogous to the claimed invention because they are in the field of systems for fatigue detectors and technology for monitoring and managing fatigue. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method, comprising: accessing, by one or more server computing devices, results of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, the results of the plurality of PVTs including respective response times for the particular test driver; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs; accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times; determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, (i) the respective scores for each of the plurality of PVTs and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event, as disclosed by Atlas, wherein the results of the plurality of PVTs include self-reported data for the particular test driver while monitoring operation of the vehicle in the autonomous driving mode, as disclosed by Heneghan, to provide a fatigue monitoring module, a neural network, and a user’s self-perceived fatigue state for technology that relates to monitoring and managing fatigue. One skilled in the art would understand and recognize the value of the addition of a fatigue monitoring module, a neural network, and a user’s self-perceived fatigue state to improve technology that relates to monitoring and managing fatigue.
In regards to claim 4, Atlas discloses
wherein the results of the plurality of PVTs include a plurality of scores ([0030], “there is provided a method for detecting the physiological onset of fatigue in an operator”).
In regards to claim 5, Atlas discloses
wherein the plurality of scores represents a passing or failing rate for the particular test driver ([0071], “PVT … involves giving the subject a stimulus … and then gauging the quality of the response in terms of parameters such as reaction time” Examiner notes that reaction time may be used to determine PVT scores and that scores can be reported in terms of pass or fail rates.).
In regards to claim 6, Atlas discloses
wherein the results of the plurality of PVTS include date and time information associated with the plurality of PVTs ([0030], “the physiological progression of operator fatigue being associated with … stages having a specified sequence in time”).
In regards to claim 7, Atlas discloses the following limitation with the exception of the underlined limitation.
wherein training the neural network to execute the model includes providing, by the one or more server computing devices, one or more parameter values for the model to predict the second fatigue event for the particular test driver ([0025], “the present invention correlates measurable parameters of operator performance with … stages indicative of fatigue”).
Heneghan discloses
wherein training the neural network to execute the model ([0148], “the fatigue monitoring module ... could use ... neural networks”) includes providing, by the one or more server computing devices ([0055], “fatigue monitoring module may be implemented at a … server”)
Atlas and Heneghan combined are considered analogous to the claimed invention because they are in the field of systems for fatigue detectors and technology for monitoring and managing fatigue. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method, comprising: accessing, by one or more server computing devices, results of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, the results of the plurality of PVTs including respective response times for the particular test driver; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs; accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times; determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, (i) the respective scores for each of the plurality of PVTs and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event, as disclosed by Atlas, wherein training the neural network to execute the model includes providing, by the one or more server computing devices, as disclosed by Heneghan, to provide a fatigue monitoring module, a neural network, and a server for technology that relates to monitoring and managing fatigue. One skilled in the art would understand and recognize the value of the addition of a fatigue monitoring module, a neural network, and a server to improve technology that relates to monitoring and managing fatigue.
In regards to claim 8, Atlas does not disclose wherein information includes data identifying where the particular test driver is with respect to his or her circadian rhythm.
Heneghan discloses
wherein information includes data identifying where the particular test driver is with respect to his or her circadian rhythm ([0134], “Time of day data … can capture the underlying diurnal or circadian variation of fatigue”).
Atlas and Heneghan combined are considered analogous to the claimed invention because they are in the field of systems for fatigue detectors and technology for monitoring and managing fatigue. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method, comprising: accessing, by one or more server computing devices, results of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, the results of the plurality of PVTs including respective response times for the particular test driver; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs; accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times; determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, (i) the respective scores for each of the plurality of PVTs and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event, as disclosed by Atlas, and training, by the one or more server computing devices, a neural network to execute a model to output, wherein training the neural network is based on and wherein training the neural network individualizes the model to the particular test driver, wherein information includes data identifying where the particular test driver is with respect to his or her circadian rhythm, as disclosed by Heneghan, to provide a fatigue monitoring module, a neural network, and time of day data that captures circadian fatigue for technology that relates to monitoring and managing fatigue. One skilled in the art would understand and recognize the value of the addition of a fatigue monitoring module, a neural network, and time of day data that captures circadian fatigue to improve technology that relates to monitoring and managing fatigue.
In regards to claim 9, Atlas does not disclose wherein data identifying a relative point in time for a shift for the particular test driver for monitoring operation of the vehicle in the autonomous driving mode.
Wunderlich discloses
wherein data identifying a relative point in time for a shift for the particular test driver for monitoring operation of the vehicle in the autonomous driving mode ([0025], “a person ... is able to ... monitor the ... autonomous drive” Examiner notes that shift monitoring data is inherently associated with the autonomous drive.).
Atlas and Wunderlich combined are considered analogous to the claimed invention because they are in the field of systems for fatigue detectors and methods for operating autonomous motor vehicles. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method, comprising: accessing, by one or more server computing devices, results of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, the results of the plurality of PVTs including respective response times for the particular test driver; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs; accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times;
determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, (i) the respective scores for each of the plurality of PVTs and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event, as disclosed by Atlas, while monitoring operation of that vehicle in the autonomous driving mode, wherein data identifying a relative point in time for a shift for the particular test driver for monitoring operation of the vehicle in the autonomous driving mode, as disclosed by Wunderlich, to provide a person to monitor the autonomous drive for a method of operating a motor vehicle in an activated autonomous driving mode. One skilled in the art would understand and recognize the value of the addition of a person who monitors autonomous driving to improve a method of operating a motor vehicle in an activated autonomous driving mode.
In regards to claim 10, Atlas does not disclose wherein information includes accessing data identifying an amount of time since a last break of the particular test driver.
Wunderlich discloses
wherein information includes accessing data identifying an amount of time since a last break of the particular test driver ([0025], “a person ... is able to ... monitor the ... autonomous drive” Examiner notes that data identifying amount of time since last break may be collected for the autonomous drive.).
Atlas and Wunderlich combined are considered analogous to the claimed invention because they are in the field of systems for fatigue detectors and methods for operating autonomous motor vehicles. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method, comprising: accessing, by one or more server computing devices, results of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, the results of the plurality of PVTs including respective response times for the particular test driver; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs; accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times; determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, (i) the respective scores for each of the plurality of PVTs and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event, as disclosed by Atlas, while monitoring operation of that vehicle in the autonomous driving mode, wherein information includes accessing data identifying an amount of time since a last break of the particular test driver, as disclosed by Wunderlich, to provide a person to monitor the autonomous drive for a method of operating a motor vehicle in an activated autonomous driving mode. One skilled in the art would understand and recognize the value of the addition of a person who monitors autonomous driving to improve a method of operating a motor vehicle in an activated autonomous driving mode.
In regards to claim 11, Atlas discloses the following limitation with the exception of the underlined limitation.
wherein the information includes data corresponding to an amount of uninterrupted time that the particular test driver has spent in a particular vehicle and monitoring operation of that particular vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency ([0070], “sudden and abrupt total loss of gripping pressure is detected and signaled to other personnel as an emergency condition”).
Wunderlich discloses
wherein the information includes data corresponding to an amount of uninterrupted time that the particular test driver has spent in a particular vehicle and monitoring operation of that particular vehicle in the autonomous driving mode ([0025], “a person ... is able to ... monitor the ... autonomous drive” Examiner notes that data corresponding to time spent monitoring uninterrupted may be collected for the autonomous drive.).
Atlas and Wunderlich combined are considered analogous to the claimed invention because they are in the field of systems for fatigue detectors and methods for operating autonomous motor vehicles. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method, comprising: accessing, by one or more server computing devices, results of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, the results of the plurality of PVTs including respective response times for the particular test driver; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs; accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times;
determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, (i) the respective scores for each of the plurality of PVTs and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event, as disclosed by Atlas, while monitoring operation of that vehicle in the autonomous driving mode, wherein the information includes data corresponding to an amount of uninterrupted time that the particular test driver has spent in a particular vehicle and monitoring operation of that particular vehicle in the autonomous driving mode, as disclosed by Wunderlich, to provide a person to monitor the autonomous drive for a method of operating a motor vehicle in an activated autonomous driving mode. One skilled in the art would understand and recognize the value of the addition of a person who monitors autonomous driving to improve a method of operating a motor vehicle in an activated autonomous driving mode.
In regards to claim 12, Atlas does not disclose wherein the indication of the likelihood that the particular test driver will experience the second fatigue event includes a value on a scale of 0 to 1.
Heneghan discloses
wherein the indication of the likelihood that the particular test driver will experience the second fatigue event includes a value on a scale of 0 to 1 ([0141], “In one example, a fatigue index value of 1 indicates a high level of fatigue, and a value of 0 indicates a low level of fatigue.”).
Atlas and Heneghan combined are considered analogous to the claimed invention because they are in the field of systems for fatigue detectors and technology for monitoring and managing fatigue. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method, comprising: accessing, by one or more server computing devices, results of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, the results of the plurality of PVTs including respective response times for the particular test driver; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs; accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times; determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, (i) the respective scores for each of the plurality of PVTs and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event, as disclosed by Atlas, and training, by the one or more server computing devices, a neural network to execute a model to output, wherein training the neural network is based on and wherein training the neural network individualizes the model to the particular test driver, wherein the indication of the likelihood that the particular test driver will experience the second fatigue event includes a value on a scale of 0 to 1, as disclosed by Heneghan, to provide a fatigue monitoring module, a neural network, and a fatigue index for technology that relates to monitoring and managing fatigue. One skilled in the art would understand and recognize the value of the addition of a fatigue monitoring module, a neural network, and a fatigue index to improve technology that relates to monitoring and managing fatigue.
In regards to claim 13, Atlas discloses
further comprising, subsequent to training the neural network to execute the model, associating, by the one or more server computing devices, the model with an identifying code corresponding to the particular test driver ([0025], “the present invention correlates measurable parameters of operator performance with … stages indicative of fatigue” Examiner notes that parameters associated with a particular test driver may include an identifying code.).
In regards to claim 14, Atlas discloses
further comprising, storing, by the one or more server computing devices, the model having an identifying code associated therewith in memory accessible by the one or more server computing devices ([0098], “a data unit … can be supplemented by a data recording and logging unit” Examiner notes that a data recording and logging unit can be used to store the data and the corresponding model in memory.).
In regards to claim 15, Atlas discloses the following limitations with the exception of the underlined limitations.
A system, comprising one or more processors configured to: access results ([0095], “The system includes a controller …, which … is a processor or microprocessor…, which receives and processes … readings”) of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver ([0031], “there is provided a method for conducting a Psychomotor Vigilance Test of an operator”) at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency ([0070], “sudden and abrupt total loss of gripping pressure is detected and signaled to other personnel as an emergency condition”), the results of the plurality of PVTs including respective response times for the particular test driver ([0072], “a series of … PVT's … is administered to the operator according to a predetermined schedule … (e.g., a … PVT every 10 minutes)”), while monitoring operation of that vehicle in the autonomous driving mode;
determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs ([0071], “PVT … involves giving the subject a stimulus … and then gauging the quality of the response in terms of parameters such as reaction time” Examiner notes that reaction time may be used to determine PVT scores.);
accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times ([0027], “It is … an objective of the present invention to provide a means for detecting the onset of fatigue in a passive manner” Examiner notes that passive detection of fatigue can be performed using a remote system.);
determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events ([0030], “there is provided a method for detecting the physiological onset of fatigue in an operator”);
and training, by the one or more server computing devices, a neural network to execute a model to output, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency ([0070], “sudden and abrupt total loss of gripping pressure is detected and signaled to other personnel as an emergency condition”), an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency ([0070], “sudden and abrupt total loss of gripping pressure is detected and signaled to other personnel as an emergency condition”), wherein training the neural network is based on (i) the respective scores for each of the plurality of PVTs ([0031], “there is provided a method for conducting a Psychomotor Vigilance Test of an operator” Examiner notes that testing typically includes scores.) and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, and wherein training the neural network individualizes the model to the particular test driver to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event ([0030], “there is provided a method for detecting … the physiological progression of operator fatigue being associated with a plurality of … stages having a specified sequence in time”).
Wunderlich discloses
while monitoring operation of that vehicle in the autonomous driving mode ([0025], “a person ... is able to ... monitor the ... autonomous drive”);
Atlas and Wunderlich combined are considered analogous to the claimed invention because they are in the field of systems for fatigue detectors and methods for operating autonomous motor vehicles. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a system, comprising one or more processors configured to: access results of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, the results of the plurality of PVTs including respective response times for the particular test driver; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs; accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times;
determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, (i) the respective scores for each of the plurality of PVTs and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event, as disclosed by Atlas, while monitoring operation of that vehicle in the autonomous driving mode, as disclosed by Wunderlich, to provide a person to monitor the autonomous drive for a method of operating a motor vehicle in an activated autonomous driving mode. One skilled in the art would understand and recognize the value of the addition of a person who monitors autonomous driving to improve a method of operating a motor vehicle in an activated autonomous driving mode.
Heneghan discloses
and training, by the one or more server computing devices ([0055], “fatigue monitoring module may be implemented at a … server”), a neural network to execute a model to output ([0148], “the fatigue monitoring module … could use … neural networks”),
wherein training the neural network is based on ([0148], “the fatigue monitoring module … could use … neural networks”)
and wherein training the neural network ([0148], “the fatigue monitoring module … could use … neural networks”) individualizes the model to the particular test driver ([0070], “fatigue monitoring and management system ... can be implemented ... serving a single individual”)
Atlas and Heneghan combined are considered analogous to the claimed invention because they are in the field of systems for fatigue detectors and technology for monitoring and managing fatigue. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a system, comprising one or more processors configured to: access results of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, the results of the plurality of PVTs including respective response times for the particular test driver; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs; accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times; determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, (i) the respective scores for each of the plurality of PVTs and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event, as disclosed by Atlas, and training, by the one or more server computing devices, a neural network to execute a model to output, wherein training the neural network is based on and wherein training the neural network individualizes the model to the particular test driver, as disclosed by Heneghan, to provide a fatigue monitoring module and a neural network for technology that relates to monitoring and managing fatigue. One skilled in the art would understand and recognize the value of the addition of a fatigue monitoring module and a neural network to improve technology that relates to monitoring and managing fatigue.
In regards to claim 16, Atlas discloses
wherein the one or more processors are further configured to retrieve the results of the plurality of PVTs from a remote monitoring system ([0095], “The system includes … a controller …, which receives and processes … readings” Examiner notes that a controller can be a remote system.).
In regards to claim 17, Atlas does not disclose wherein the results of the plurality of PVTs include self-reported data for the particular test driver while monitoring operation of the vehicle in the autonomous driving mode.
Heneghan discloses
wherein the results of the plurality of PVTs include self-reported data for the particular test driver while monitoring operation of the vehicle in the autonomous driving mode ([0050], “a device configured to capture subjective user data related to the user's self-perceived fatigue state”).
Atlas and Heneghan combined are considered analogous to the claimed invention because they are in the field of systems for fatigue detectors and technology for monitoring and managing fatigue. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a system, comprising: one or more processors configured to: access results of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, the results of the plurality of PVTs including respective response times for the particular test driver; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs; accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times; determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, (i) the respective scores for each of the plurality of PVTs and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event, as disclosed by Atlas, wherein the results of the plurality of PVTs include self-reported data for the particular test driver while monitoring operation of the vehicle in the autonomous driving mode, as disclosed by Heneghan, to provide a fatigue monitoring module, a neural network, and a user’s self-perceived fatigue state for technology that relates to monitoring and managing fatigue. One skilled in the art would understand and recognize the value of the addition of a fatigue monitoring module, a neural network, and a user’s self-perceived fatigue state to improve technology that relates to monitoring and managing fatigue.
In regards to claim 18, Atlas discloses the following limitation with the exception of the underlined limitation.
wherein the one or more processors are further configured to, in association with ([0095], “The system includes a controller …, which … is a processor or microprocessor) training of the neural network to execute the model, provide one or more parameter values for the model, to predict the second fatigue event for the particular test driver ([0025], “the present invention correlates measurable parameters of operator performance with … stages indicative of fatigue”).
Heneghan discloses
training of the neural network to execute the model ([0148], “the fatigue monitoring module ... could use ... neural networks”)
Atlas and Heneghan combined are considered analogous to the claimed invention because they are in the field of systems for fatigue detectors and technology for monitoring and managing fatigue. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a system, comprising one or more processors configured to: access results of a plurality of first sets of psychomotor vigilance tests (PVTs) administered to a particular person at different points in time, the results including respective response times for the particular person; determine, based on the results, respective scores for each of the plurality of first sets of PVTs; access, from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular person at different times; determine whether the information indicates that the particular person experienced one or more first fatigue events; a model to output a value indicative of a likelihood of the particular person experiencing a second fatigue event based on a second set of PVTs administered to the particular person, wherein the model is trained based on (i) the scores and (ii) determining whether the information indicates that the particular person experienced the one or more first fatigue events, and wherein the trained model is individualized to the particular person, provide one or more parameter values for the model, to predict the second fatigue event for the particular test driver, as disclosed by Atlas, and training a neural network, by the one or more server computing devices based on the scores and determining whether the information indicates that the particular person experienced the one or more first fatigue events, and train a neural network, training of the neural network to execute the model, as disclosed by Heneghan, to provide a fatigue monitoring module and a neural network for technology that relates to monitoring and managing fatigue. One skilled in the art would understand and recognize the value of the addition of a fatigue monitoring module and a neural network to improve technology that relates to monitoring and managing fatigue.
In regards to claim 19, Atlas discloses the following limitations with the exception of the underlined limitation.
wherein the one or more processors are further configured to, in association with ([0095], “The system includes a controller …, which … is a processor or microprocessor”) training of the neural network to execute the model, associate the model with an identifying code corresponding to the particular test driver ([0025], “the present invention correlates measurable parameters of operator performance with … stages indicative of fatigue” Examiner notes that parameters associated with a particular test driver may include an identifying code.).
Heneghan discloses
training of the neural network to execute the model ([0148], “the fatigue monitoring module ... could use ... neural networks”)
Atlas and Heneghan combined are considered analogous to the claimed invention because they are in the field of systems for fatigue detectors and technology for monitoring and managing fatigue. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a system, comprising one or more processors configured to: access results of a plurality of first sets of psychomotor vigilance tests (PVTs) administered to a particular person at different points in time, the results including respective response times for the particular person; determine, based on the results, respective scores for each of the plurality of first sets of PVTs; access, from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular person at different times; determine whether the information indicates that the particular person experienced one or more first fatigue events; a model to output a value indicative of a likelihood of the particular person experiencing a second fatigue event based on a second set of PVTs administered to the particular person, wherein the model is trained based on (i) the scores and (ii) determining whether the information indicates that the particular person experienced the one or more first fatigue events, and wherein the trained model is individualized to the particular person, provide one or more parameter values for the model, to predict the second fatigue event for the particular test driver, associate the model with an identifying code corresponding to the particular test driver, as disclosed by Atlas, and training a neural network, by the one or more server computing devices based on the scores and determining whether the information indicates that the particular person experienced the one or more first fatigue events, and train a neural network, training of the neural network to execute the model, as disclosed by Heneghan, to provide a fatigue monitoring module and a neural network for technology that relates to monitoring and managing fatigue. One skilled in the art would understand and recognize the value of the addition of a fatigue monitoring module and a neural network to improve technology that relates to monitoring and managing fatigue.
In regards to claim 20, Atlas discloses
wherein the one or more processors are further configured to ([0095], “The system includes a controller …, which … is a processor or microprocessor”), store the model having the identifying code associated therewith ([0025], “the present invention correlates measurable parameters of operator performance with … stages indicative of fatigue” Examiner notes that parameters associated with a particular test driver may include an identifying code.) in memory accessible by the one or more processors ([0098], “a data unit … can be supplemented by a data recording and logging unit” Examiner notes that a data recording and logging unit can be used to store the data and the corresponding model in memory.).
Response to Remarks
Applicant's arguments filed June 12, 2026 have been fully considered but they are not persuasive. Claims 1-20 remain pending in this application. With respect to rejections under 35 U.S.C. § 101, Applicant submits that “claim 1 does not recite an abstract idea” (See AMENDMENT, REMARKS, Claim Rejections Under 35 USC § 101, page 8, paragraph 2), “the human mind is not equipped to perform the features of claim 1, as amended, and the rejection does not explain how a person could practically perform the claimed features” (See AMENDMENT, REMARKS, Claim Rejections Under 35 USC § 101, page 10, paragraph 1), “such abstract idea is integrated into a practical application” (See AMENDMENT, REMARKS, Claim Rejections Under 35 USC § 101, page 12, paragraph 3), and “the features of claim 1, as described in Applicant’s specification, ‘constitutes an improvement to how the machine learning model itself operates’” (See AMENDMENT, REMARKS, Claim Rejections Under 35 USC § 101, page 15, paragraph 1). Examiner acknowledges Applicant’s remarks. Claim 1 recites a method, comprising: accessing, by one or more server computing devices, results of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, the results of the plurality of PVTs including respective response times for the particular test driver while monitoring operation of that vehicle in the autonomous driving mode; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs; accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times; determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events; and training, by the one or more server computing devices, a neural network to execute a model to output, based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency, wherein training the neural network is based on (i) the respective scores for each of the plurality of PVTs and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, and training the neural network individualizes the model to the particular test driver to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event.
These steps amount to a form of mental process and organizing human activity (i.e., an abstract idea) because a human can collect psychomotor vigilance data, determine psychomotor vigilance scores, and then based on psychomotor vigilance scores determine whether an individual experienced a fatigue event. Applicant of claimed invention discloses “the self-reported data may be used to determine whether a set of PVTs corresponds to a fatigue event” [0024]. Independent claim 15 describes nearly identical steps as claim 1 (and therefore recite limitations that fall within this subject matter of grouping abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis. Dependent claims 2-14 and 16-20 are directed towards mini-tasks (accessing response times, self-reported data, and scores, determining individual pass or fail rates, and identifying individual circadian rhythm states, etc.) for a method to determine psychomotor vigilance scores and whether the scores indicate that an individual experienced a fatigue event. Each claim amounts to a form of collecting, generating, and analyzing human data to determine whether or not a human experienced a fatigue event, and therefore falls within the scope of a method for organizing human activity, (i.e., an abstract idea). The enumerated groupings of abstract ideas include organizing human activity (see MPEP §2106.04(a)(2)(II)) and mental processes (see MPEP §2106.04(a)(2)(III)). As such, the Examiner concludes that claims 2-14 and 16-20 recite an abstract idea.
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. The requirement to execute the claimed steps/functions using computing devices (independent claims 1 and 15 and dependent claims 2-14 and 16-20) is equivalent to adding the words “apply it” on a computer and/or mere instructions to implement the abstract idea on a computer. Similarly, the limitations of computing devices (independent claims 1 and 15 and dependent claims 2-14 and 16-20) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using computer components. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Use of a computer, processor, memory or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) (See MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serve merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, they serve to limit the application of the abstract idea to a computerized environment (e.g., identifying and displaying, etc.) performed by a computing device, processor, and memory, etc. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(h)). Dependent claims 2-14 and 16-20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims are further part of the abstract idea as identified by the Examiner for each respective independent claim (i.e., they are part of the abstract idea recited in each respective claim). The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an “inventive concept.” An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amount to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). As discussed above in “Step 2A – Prong Two”, the identified additional elements in independent claims 1 and 15 and dependent claims 2-14 and 16-20 are equivalent to adding the words “apply it” on a computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a computer and/or mere instructions to implement the abstract idea on a computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. Dependent claims 2-14 and 16-20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim). The Examiner has therefore determined that no additional element, or combination of additional claims elements are sufficient to ensure the claims amount to significantly more than the abstract idea identified above.
To reject a claim as an abstract idea based on mental processes, the Examiner should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas. Managing personal behavior or relationships or interactions between people fall within one grouping of abstract ideas. An example of a claim reciting managing personal behavior is a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982).
MPEP § 2111 discusses proper claim interpretation, including giving claims their
broadest reasonable interpretation (“BRI”) in light of the specification during examination. Under BRI, the words of a claim must be given their plain meaning unless such meaning is inconsistent with the specification, and it is improper to import claim limitations from the specification into the claim. Applicant’s argument is not persuasive because the BRI is broader than what is argued. Therefore, the rejections of claims 1-20, under 35 USC 101, are maintained.
With respect to claim rejections under 35 USC § 103, Applicant submits that “the Office Action fails to establish a prima facie case of obvious for independent claims 1 and 15, as amended” (See AMENDMENT, REMARKS, Claim Rejections Under 35 USC § 103, page 16, paragraph 4), “the rejection of claim 1 is improper because the rejection unreasonably break down features of claim 1 such that Applicant’s actual claim features are not what is being rejected” (See AMENDMENT, REMARKS, Claim Rejections Under 35 USC § 103, page 18, paragraph 2), “the rejection of claim 15 is improper because the rejection unreasonably break down features of claim 15 such that Applicant’s actual claim features are not what is being rejected” (See AMENDMENT, REMARKS, Claim Rejections Under 35 USC § 103, page 20, paragraph 2), “There is no prima facie case of obviousness for claims 1 and 15, as amended” (See AMENDMENT, REMARKS, Claim Rejections Under 35 USC § 103, page 22, line 1), “Wunderlich does not teach or even suggest, ‘the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency’ as claimed” (See AMENDMENT, REMARKS, Claim Rejections Under 35 USC § 103, page 23, lines 13-15), “the Final Office Action has not established that Atlas and Heneghan, taken alone or in combination, remedy the aforementioned deficiencies of Wunderlich” (See AMENDMENT, REMARKS, Claim Rejections Under 35 USC § 103, page 23, paragraph 1). Examiner acknowledges Applicant’s remarks. Regarding amended claim 1, Atlas discloses a method, comprising: accessing, by one or more server computing devices, results ([0095], “The system includes a controller …, which … is a processor or microprocessor…, which receives and processes … readings”) of a plurality of psychomotor vigilance tests (PVTs) administered to a particular test driver ([0031], “there is provided a method for conducting a Psychomotor Vigilance Test of an operator”) at different points in time while the particular test driver is in a vehicle and monitoring operation of that vehicle in an autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency ([0070], “sudden and abrupt total loss of gripping pressure is detected and signaled to other personnel as an emergency condition”), the results of the plurality of PVTs including respective response times for the particular test driver ([0072], “a series of … PVT's … is administered to the operator according to a predetermined schedule … (e.g., a … PVT every 10 minutes)”), determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of PVTs ([0071], “PVT … involves giving the subject a stimulus … and then gauging the quality of the response in terms of parameters such as reaction time” Examiner notes that reaction time may be used to determine PVT scores.); accessing, by the one or more server computing devices from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular test driver at different times ([0027], “It is … an objective of the present invention to provide a means for detecting the onset of fatigue in a passive manner” Examiner notes that passive detection of fatigue can be performed using a remote system.); determining, by the one or more server computing devices, whether the information indicates that the particular test driver previously experienced one or more first fatigue events ([0030], “there is provided a method for detecting the physiological onset of fatigue in an operator”); based on an additional PVT administered to the particular test driver while the particular test driver is monitoring operation of a given vehicle in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency ([0070], “sudden and abrupt total loss of gripping pressure is detected and signaled to other personnel as an emergency condition”), an indication of a likelihood that the particular test driver will experience a second fatigue event and thereby avoid the second fatigue event while the particular test driver is monitoring operation of the given vehicle operating in the autonomous driving mode in order for the particular test driver to take over control of that vehicle in an emergency ([0070], “sudden and abrupt total loss of gripping pressure is detected and signaled to other personnel as an emergency condition”), (i) the respective scores for each of the plurality of PVTs ([0031], “there is provided a method for conducting a Psychomotor Vigilance Test of an operator” Examiner notes that testing typically includes scores.) and (ii) the determination of whether the information indicates that the particular test driver previously experienced the one or more first fatigue events, to predict a future fatigue event of the particular test driver while avoiding initiation of an intervention response based on a false positive of the future fatigue event ([0030], “there is provided a method for detecting … the physiological progression of operator fatigue being associated with a plurality of … stages having a specified sequence in time”), Wunderlich discloses while monitoring operation of that vehicle in the autonomous driving mode ([0025], “a person ... is able to ... monitor the ... autonomous drive”), and Heneghan discloses and training, by the one or more server computing devices ([0055], “fatigue monitoring module may be implemented at a … server”), a neural network to execute a model to output ([0148], “the fatigue monitoring module … could use … neural networks”), wherein training the neural network is based on ([0148], “the fatigue monitoring module … could use … neural networks”) and wherein training the neural network ([0148], “the fatigue monitoring module … could use … neural networks”) individualizes the model to the particular test driver ([0070], “fatigue monitoring and management system ... can be implemented ... serving a single individual”).
MPEP § 2111 discusses proper claim interpretation, including giving claims their
broadest reasonable interpretation (“BRI”) in light of the specification during examination. Under BRI, the words of a claim must be given their plain meaning unless such meaning is inconsistent with the specification, and it is improper to import claim limitations from the specification into the claim. Applicant’s argument is not persuasive because the BRI is broader than what is argued. Therefore, the rejections of independent claim 1 and dependent claims 2-14, as obvious by Atlas in view of Wunderlich and Heneghan, are maintained. Independent claim 15 is almost identical to independent claim 1. Therefore, the rejections of independent claim 15 and dependent claims 16-20, as obvious by Atlas in view of Wunderlich and Heneghan, are maintained.
As to establishing a prima facie case of obviousness, upon review, the
examiner’s rejection satisfied the requirements for MPEP 2143(I)(G). Applicant’s argument is not persuasive because the argument does not meet the requirements of 37 C.F.R. 1.111(b), and, upon review, the rejections do make a prima facie case using 2143(I)(G). Therefore, the rejections of independent claims 1 and 15 and dependent claims 2-14 and 16-20, as obvious by Atlas in view of Wunderlich and Heneghan, are maintained.
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/LISA H ANTOINE/
Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
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