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 Final Office action in response to communications filed on February
24, 2026. Applicant amended claims 1, 7, 14-15, 18, and 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 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, the particular person being tasked with monitoring a vehicle operating in an autonomous driving mode;
determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of first sets 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 person at different times;
determining, by the one or more server computing devices, whether the information indicates that the particular person experienced one or more first fatigue events;
and training a neural network, by the one or more server computing devices, to execute 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 training the model is 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.
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 information, and therefore falls within the scope of a method for organizing human activity, (i.e., an abstract idea). 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 generic computer and/or mere instructions to implement the abstract idea on a generic 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 generic 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 general-purpose 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 generic 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 generic computer and/or mere instructions to implement the abstract idea on a generic 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 first sets of psychomotor vigilance tests (PVTs) administered to a particular person ([0031], “there is provided a method for conducting a Psychomotor Vigilance Test of an operator”) at different points in time, the results including respective response times for the particular person ([0072], “a series of … PVT's … is administered to the operator according to a predetermined schedule … (e.g., a … PVT every 10 minutes)”), the particular person being tasked with monitoring a vehicle operating in an autonomous driving mode;
determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of first sets 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 person 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 person 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 a neural network, by the one or more server computing devices, to execute 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 training the model is based on (i) the scores ([0031], “there is provided a method for conducting a Psychomotor Vigilance Test of an operator” Examiner notes that testing typically includes 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 ([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
the particular person being tasked with monitoring a vehicle operating in an 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 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; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of first sets 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 person at different times; determining, by the one or more server computing devices, whether the information indicates that the particular person experienced one or more first fatigue events; to execute 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 training the model is 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, as disclosed by Atlas, the particular person being tasked with monitoring a vehicle operating in an 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 a neural network ([0148], “the fatigue monitoring module ... could use ... neural networks”), 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 ([0066], “FIG. 7 contains a plot of a psychomotor vigilance test (PVT) average reaction time predictions on both training and test data against the … fatigue index”)
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 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; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of first sets 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 person at different times; determining, by the one or more server computing devices, whether the information indicates that the particular person experienced one or more first fatigue events; to execute 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 training the model is 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, 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, as disclosed by Heneghan, to provide average reaction time, training and test data, a fatigue index, 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 average reaction time, training and test data, a fatigue index, 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 includes retrieving, by the one or more server computing devices, the results 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 accessing the results includes accessing, by the one or more server computing devices, self-reported data for the particular person.
Heneghan discloses
wherein accessing the results includes accessing, by the one or more server computing devices, self-reported data for the particular person ([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 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; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of first sets 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 person at different times; determining, by the one or more server computing devices, whether the information indicates that the particular person experienced one or more first fatigue events; to execute 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 training the model is 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, as disclosed by Atlas, wherein accessing the results includes accessing, by the one or more server computing devices, self-reported data for the particular person, as disclosed by Heneghan, to provide a user’s self-perceived fatigue state average reaction time, training and test data, a fatigue index, 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 average reaction time, training and test data, a fatigue index, 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 accessing the results includes accessing, by the one or more server computing devices, a plurality of scores associated with the plurality of first sets of PVTs ([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 person ([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 accessing the results includes include accessing, by the one or more server computing devices, date and time information associated with the plurality of first sets 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 includes providing, by the one or more server computing devices, one or more parameter values for the model, the one or more parameter values being used by the model to predict one or more future fatigue events for the particular person ([0025], “the present invention correlates measurable parameters of operator performance with … stages indicative of fatigue”).
Heneghan discloses
wherein training the neural network ([0148], “the fatigue monitoring module ... could use ... neural networks”) includes providing, by the one or more server computing devices ([0066], “FIG. 7 contains a plot of a psychomotor vigilance test (PVT) average reaction time predictions on both training and test data against the … fatigue index”)
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 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; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of first sets 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 person at different times; determining, by the one or more server computing devices, whether the information indicates that the particular person experienced one or more first fatigue events; to execute 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 training the model is 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, one or more parameter values for the model, the one or more parameter values being used by the model to predict one or more future fatigue events for the particular person, as disclosed by Atlas, wherein training the neural network includes providing, by the one or more server computing devices, as disclosed by Heneghan, to provide reaction time, training and test data, a fatigue index, 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 average reaction time, training and test data, a fatigue index, and a neural network to improve technology that relates to monitoring and managing fatigue.
In regards to claim 8, Atlas does not disclose wherein accessing the information includes accessing, by the one or more server computing devices, data identifying where the particular person is with respect to his or her circadian rhythm.
Heneghan discloses
wherein accessing the information includes accessing, by the one or more server computing devices, data identifying where the particular person 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 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; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of first sets 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 person at different times; determining, by the one or more server computing devices, whether the information indicates that the particular person experienced one or more first fatigue events; to execute 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 training the model is 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, as disclosed by Atlas, wherein accessing the information includes accessing, by the one or more server computing devices, data identifying where the particular person is with respect to his or her circadian rhythm, as disclosed by Heneghan, to provide reaction time, training and test data, a fatigue index, 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 average reaction time, training and test data, a fatigue index, 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 accessing the information includes accessing, by the one or more server computing devices, data identifying a relative point in time for a shift for monitoring the vehicle of the particular person.
Wunderlich discloses
wherein accessing the information includes accessing, by the one or more server computing devices, data identifying a relative point in time for a shift for monitoring the vehicle of the particular person ([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 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; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of first sets 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 person at different times; determining, by the one or more server computing devices, whether the information indicates that the particular person experienced one or more first fatigue events; to execute 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 training the model is 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, as disclosed by Atlas, wherein accessing the information includes accessing, by the one or more server computing devices, data identifying a relative point in time for a shift for monitoring the vehicle of the particular person, 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 accessing the information includes accessing, by the one or more server computing devices, data identifying an amount of time since a last break of the particular person.
Wunderlich discloses
wherein accessing the information includes accessing, by the one or more server computing devices, data identifying an amount of time since a last break of the particular person ([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 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; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of first sets 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 person at different times; determining, by the one or more server computing devices, whether the information indicates that the particular person experienced one or more first fatigue events; to execute 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 training the model is 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, as disclosed by Atlas, wherein accessing the information includes accessing, by the one or more server computing devices, data identifying an amount of time since a last break of the particular person, 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 does not disclose wherein accessing the information includes accessing, by the one or more server computing devices, data corresponding to an amount of time that the particular person has spent monitoring the vehicle uninterrupted.
Wunderlich discloses
wherein accessing the information includes accessing, by the one or more server computing devices, data corresponding to an amount of time that the particular person has spent monitoring the vehicle uninterrupted ([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 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; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of first sets 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 person at different times; determining, by the one or more server computing devices, whether the information indicates that the particular person experienced one or more first fatigue events; to execute 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 training the model is 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, as disclosed by Atlas, wherein accessing the information includes accessing, by the one or more server computing devices, data corresponding to an amount of time that the particular person has spent monitoring the vehicle uninterrupted, 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 value is on a scale of 0 to 1 representing the likelihood of the particular person experiencing the second fatigue event.
Heneghan discloses
wherein the value is on a scale of 0 to 1 representing the likelihood of the particular person experiencing the second fatigue event ([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 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; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of first sets 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 person at different times; determining, by the one or more server computing devices, whether the information indicates that the particular person experienced one or more first fatigue events; to execute 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 training the model is 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, as disclosed by Atlas, wherein the value is on a scale of 0 to 1 representing the likelihood of the particular person experiencing the second fatigue event, as disclosed by Heneghan, to provide average reaction time, training and test data, a fatigue index, 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 average reaction time, training and test data, a fatigue index, and a neural network to improve technology that relates to monitoring and managing fatigue.
In regards to claim 13, Atlas discloses
further comprising associating, by the one or more server computing devices, the model with the particular person using an identifying code ([0025], “the present invention correlates measurable parameters of operator performance with … stages indicative of fatigue” Examiner notes that parameters associated with a particular person may include an identifying code.).
In regards to claim 14, Atlas discloses the following limitations with the exception of the underlined limitation.
further comprising, subsequent to training the neural network, by the one or more server computing devices, storing the model 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.).
Heneghan discloses
the neural network ([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 method, comprising: accessing, by one or more server computing devices, 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; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of first sets 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 person at different times; determining, by the one or more server computing devices, whether the information indicates that the particular person experienced one or more first fatigue events; to execute 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 training the model is 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, further comprising, subsequent to training, by the one or more server computing devices, storing the model in memory accessible by the one or more server computing devices, 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, the neural network, as disclosed by Heneghan, to provide average reaction time, training and test data, a fatigue index, 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 average reaction time, training and test data, a fatigue index, and a neural network to improve technology that relates to monitoring and managing fatigue.
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 first sets of psychomotor vigilance tests (PVTs) administered to a particular person ([0031], “there is provided a method for conducting a Psychomotor Vigilance Test of an operator”) at different points in time, the results including respective response times for the particular person ([0072], “a series of … PVT's … is administered to the operator according to a predetermined schedule … (e.g., a … PVT every 10 minutes)”), the particular person being tasked with monitoring a vehicle operating in an autonomous driving mode;
determine, based on the results, respective scores for each of the plurality of first sets 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.);
access, from a remote monitoring system, information identifying respective estimated amounts of fatigue of the particular person 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.);
determine whether the information indicates that the particular person experienced one or more first fatigue events ([0030], “there is provided a method for detecting the physiological onset of fatigue in an operator”);
and train a neural network to execute 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 ([0031], “there is provided a method for conducting a Psychomotor Vigilance Test of an operator” Examiner notes that testing typically includes 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 ([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
the particular person being tasked with monitoring a vehicle operating in an 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 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, as disclosed by Atlas, the particular person being tasked with monitoring a vehicle operating in an 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 train a neural network to execute ([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, 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 to execute, as disclosed by Heneghan, to provide average reaction time, training and test data, a fatigue index, 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 average reaction time, training and test data, a fatigue index, 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 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 one or more processors are further configured to access self-reported data for the particular person, the results including the self-reported data.
Heneghan discloses
wherein the one or more processors are further configured to access self-reported data for the particular person, the results including the self-reported data ([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 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 information from a remote monitoring system 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 individualized to the particular person such that the model outputs a value indicative of a likelihood of the particular person experiencing a second fatigue event in response to inputting data from a second set of PVTs into the model, as disclosed by Atlas, wherein the one or more processors are further configured to access self-reported data for the particular person, the results including the self-reported data, as disclosed by Heneghan, to provide user data related to the user’s self-perceived fatigue state for 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 training of the neural network, provide one or more parameter values for the model, the one or more parameter values being used by the model to predict one or more future fatigue events for the particular person ([0025], “the present invention correlates measurable parameters of operator performance with … stages indicative of fatigue”).
Heneghan discloses
wherein the one or more processors are further configured to, in association with training of ([0066], “FIG. 7 contains a plot of a psychomotor vigilance test (PVT) average reaction time predictions on both training and test data against the … fatigue index”) the neural network ([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, the one or more parameter values being used by the model to predict one or more future fatigue events for the particular person, 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 to execute, wherein the one or more processors are further configured to, in association with training of the neural network, as disclosed by Heneghan, to provide average reaction time, training and test data, a fatigue index, 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 average reaction time, training and test data, a fatigue index, and a neural network to improve technology that relates to monitoring and managing fatigue.
In regards to claim 19, Atlas discloses
wherein the one or more processors are further configured to associate the model with the particular person using an identifying code ([0025], “the present invention correlates measurable parameters of operator performance with … stages indicative of fatigue” Examiner notes that parameters associated with a particular person may include an identifying code.).
In regards to claim 20, Atlas discloses the following limitation with the exception of the underlined limitation.
wherein the one or more processors are further configured to, subsequent to training of the neural network, store the model 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.).
Heneghan discloses
the neural network ([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, wherein the one or more processors are further configured to, subsequent to training of store the model in memory accessible by the one or more processors, 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 to execute, as disclosed by Heneghan, to provide average reaction time, training and test data, a fatigue index, 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 average reaction time, training and test data, a fatigue index, and a neural network to improve technology that relates to monitoring and managing fatigue.
Response to Remarks
Applicant's arguments filed February 24, 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 argues that “Applicant's claim 1 integrates the purported abstract idea into a practical application” (See Amendment, Remarks, Claim Rejections Under 35 USC § 101, page 9, paragraph 1), “the features of claim 1, as amended, are not a mental process” (See Amendment, Remarks, Claim Rejections Under 35 USC § 101, page 9, paragraph 2), “the rejection fails to apply this standard and thus necessarily fails to set forth a proper prima facie case for the rejection as an abstract idea based on mental processes” (See Amendment, Remarks, Claim Rejections Under 35 USC § 101, page 11, paragraph 1), and “the claimed invention is a judicial exception under the second prong of Step 2A because the features of claim 1 have been integrated into a practical application” (See Amendment, Remarks, Claim Rejections Under 35 USC § 101, page 12, paragraph 2). Examiner acknowledges Applicant’s remarks. claim 1 recites a method, comprising: accessing, by one or more server computing devices, 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, the particular person being tasked with monitoring a vehicle operating in an autonomous driving mode; determining, by the one or more server computing devices based on the results, respective scores for each of the plurality of first sets 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 person at different times; determining, by the one or more server computing devices, whether the information indicates that the particular person experienced one or more first fatigue events; and training a neural network, by the one or more server computing devices, to execute 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 training the model is 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. 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 information, and therefore falls within the scope of a method for organizing human activity, (i.e., an abstract idea). 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 generic computer and/or mere instructions to implement the abstract idea on a generic 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 generic 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 general-purpose 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 generic 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 generic computer and/or mere instructions to implement the abstract idea on a generic 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.
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.
As to establishing a prima facie case for the rejection as an abstract idea based on mental processes, upon review, the examiner’s rejection satisfied the requirements for MPEP 2106.04(a). 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). Applicant’s argument is not persuasive because, upon review, the rejection does make a prima facie case using MPEP 2106.04(a). Therefore, the rejections of claims 1-20, under 35 USC 101, are maintained.
With respect to claim rejections under 35 USC § 103, Applicant argues that “the Office Action fails to establish a prima facie case of obvious for independent claims 1 and 15” (See Amendment, Remarks, Claim Rejections Under 35 USC § 103, page 13, paragraph 5), “the Office Action fails to provide any motivation for the whole combination of Atlas, Wunderlich and Heneghan” (See Amendment, Remarks, Claim Rejections Under 35 USC § 103, page 18, paragraph 3), and “the rejections of claims 2-14 and 16-20 are deficient as well” (See Amendment, Remarks, Claim Rejections Under 35 USC § 103, page 19, paragraph 2). Examiner acknowledges Applicant’s remarks. Regarding 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 first sets of psychomotor vigilance tests (PVTs) administered to a particular person ([0031], “there is provided a method for conducting a Psychomotor Vigilance Test of an operator”) at different points in time, the results including respective response times for the particular person ([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 first sets 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 person 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 person experienced one or more first fatigue events ([0030], “there is provided a method for detecting the physiological onset of fatigue in an operator”); to execute 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 training the model is based on (i) the scores ([0031], “there is provided a method for conducting a Psychomotor Vigilance Test of an operator” Examiner notes that testing typically includes 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 ([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 the particular person being tasked with monitoring a vehicle operating in an autonomous driving mode ([0025], “a person ... is able to ... monitor the ... autonomous drive”), and Heneghan discloses and training a neural network ([0148], “the fatigue monitoring module ... could use ... neural networks”), 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 ([0066], “FIG. 7 contains a plot of a psychomotor vigilance test (PVT) average reaction time predictions on both training and test data against the … fatigue index”).
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.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lisa Antoine whose telephone number is (571)272-4252. The examiner can normally be reached Monday - Thursday 8:30 am - 6:30 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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LISA H ANTOINE
Examiner
Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715