Detailed Action
Notice of AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant is reminded that in order for a patent issuing on the instant application to obtain priority under 35 U.S.C. 119(a)-(d) or (f), 365(a) or (b), or 386(a) or (b), based on priority papers filed in a parent or related Application No. FR2403808 (to which the present application claims the benefit under 35 U.S.C. 120, 121, 365(c), or 386(c) or is a reissue application of a patent issued on the related application), a claim for such foreign priority must be timely made in this application. To satisfy the requirement of 37 CFR 1.55 for a certified copy of the foreign application, applicant may simply identify the parent nonprovisional application or patent for which reissue is sought containing the certified copy. A certified copy of FR2403808 has been received, however, an English translation is required to confirm priority.
Specification
Objection to the specification, specifically page 12: “the amount of nap taken” should be changed to “the amount of naps taken” (Add “s” to “nap” making it plural). Appropriate correction is highly recommended.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
“Claims 1-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Grube et al. (U.S. Patent No. 9,771,081 B2; Hereinafter referred to as Grube). For clarity, all claim limitations are bolded and the direct cites to the prior art are illustrated below. The entirety of the cited prior art reference(s) are relevant, however, the directly cited portions contain the best teachings of the claim limitations.
Regarding claim 1, Grube teaches A method for characterizing aeronautic routes, each aeronautic route presenting a sequence of flights performed by the same operator, each flight being defined by a departure airport and an arrival airport, the method comprising: (Abstract; See "A system, method, and computer program product for detecting, in real time, a fatigue level of a vehicle operator. Multiple physiological indicators of an individual vehicle operator can be monitored at different instances when the operator's level of fatigue is known and can be quantified. A statistical model of the vehicle operator's fatigue can be developed from the data from the multiple instances. In future instances, the statistical model can be applied to real-time physiological data collected from the operator during operation of the vehicle to determine a fatigue level. A warning can be provided when the statistically-calculated fatigue level exceeds a threshold level." All successful flights are defined by a departure airport and an arrival airport.) acquiring a plurality of evaluation data relative to a population of operators, the evaluation data being determined from physiological data of the operators; (Col. 2, ll. 17-27; See "The memory 102 can also store one or more vehicle operator profiles 104. Various embodiments of the system 100 can also include a remote computer system 114 that can store various statistical models of fatigue for respective vehicle operators, historical biometric data for respective vehicle operators, and/or historical statistical indicators of fatigue levels for respective vehicle operators. The remote computer system 114 can also store various statistical models of fatigue based on groups of operators having common characteristics ( e.g., age, gender, experience, and body mass index).") preprocessing the evaluation data; (Col. 2 ll. 38-45; See "In various other embodiments, the system 100 can be at least partially integrated in the vehicle. In such embodiments, at least portions of the system, such as the memory 102 and the processor 110 can be integrated into the vehicle. In such systems, the memory 102 can include selected vehicle operator profiles 104 for each operator, for example each operator who has operated and/or who may operate the vehicle." & Col. 4, ll. 4-9; See "The memory 102 and biometric sensors 108 can be in communication with the processor 110. The processor 110 can process the gathered biometric data in the statistical fatigue model of the vehicle operator profile 104 and output an indication of fatigue level to a fatigue warning indicator 112.") acquiring a plurality of specific context data relative to the specific evaluation context of the operators; (Col. 2, ll. 32-38; See "For example, the system 100, except for the remote computer system 114, described in greater detail below, may be worn by the vehicle operator, and the system 100 may move among different vehicles with the operator. In such embodiments, the memory 102 may only store the vehicle operator profile 104 for the particular vehicle operator that uses the system 100." & Col. 2, ll. 53-61; See "The system 100 can also include a plurality of biometric sensors 108 that detect behaviors and/or activities of the vehicle operator during operation of the vehicle that may be associated with fatigue. For example, the biometric sensors 108 can include one or more digital cameras that can measure facial expressions, head posture, and/or body posture of the vehicle operator. The biometric sensors 108 can also include one or more gyroscopes, solid state position sensors, or the like to measure head and/or body posture.") extracting from the specific context data, data on flights performed by the operators; (Col. 4, ll. 39-65; See "As shown in FIG. 2, the data from the various biometric sensors 108 can be gathered during a plurality of instances of operation of a vehicle when the vehicle operator's fatigue level is known and/or can be quantified. Blocks 202a and 204a illustrate gathering data points from the various biometric sensors 108 and the vehicle operator's fatigue level, respectively, for a first instance. Blocks 202b and 204b illustrate gathering the data from the various biometric sensors 108 and the vehicle operator's fatigue level, respectively, for a second instance. Blocks 202n and 204n illustrate gathering the data from the various biometric sensors 108 and the vehicle operator's fatigue level, respectively, for an nth instance. One or more instances can occur during a single operation of vehicle. For example, for an airline pilot, an operation may be defined as a single flight. In various embodiments, each mission can include several instances at which biometric data and associated fatigue levels are gathered. For example, several data points may be gathered during a long-distance flight (e.g., a flight from New York City to Los Angeles). In blocks 204a-204n, the fatigue level of the vehicle operator in each instance may be determined in several different ways. In some instances, the fatigue level may be known and/or assumed. For example, a pilot flying his first mission after several days of rest may be considered to be fully rested (i.e., not fatigued). By contrast, a pilot flying his last mission at the end of his duty period may be considered to be fully fatigued.") identifying a plurality of corresponding aeronautic routes; and for each identified aeronautic route, determining a fatigue indicator based on the evaluation data of the operators having performed this route. (Col. 8, ll. 50-67; See "As discussed above, the indication of fatigue level, warnings, and/or alarms can also be forwarded to a remote computer system 114. In various instances, the remote computer system 114 can receive periodically-updated indication of fatigue level. For example, the processor 110 may periodically send the indication of fatigue level for a vehicle operator once every half hour ( or other time interval) while the operator is operating the vehicle. The remote computer system 114 may analyze the received indications of fatigue level for trends and proactively take action to prevent a fatigued operator from continuing to operate the vehicle. For example, a remote computer system 114 may be monitoring the fatigue level of various pilots for an airline. If a particular pilot's fatigue is trending higher such that he is predicted to be unacceptably fatigued before his next-scheduled flight concludes, then the remote computer system 114 can flag the pilot's status and recommend to a scheduler that the pilot be replaced." The aeronautic routes are defined by the operators i.e. pilots taking said routes and their fatigue level is constantly monitored which may lead to being replaced for future routes.)
Regarding claim 2, Grube teaches The method according to claim 1, wherein the evaluation data comprises at least one type of data chosen from the group consisting of: objective operator fatigue level; and subjective operator fatigue level. (Col. 5,ll. 2-6; See "Each instance in which the vehicle operator rates his fatigue level may be imprecise because the rating is subjective. For example, a vehicle operator may be queried to rate his fatigue level at three separate instances in which his fatigue level is the same." Plenty of objective identifiers are also identified, See Col. 4, ll. 39-48; "As shown in FIG. 2, the data from the various biometric sensors 108 can be gathered during a plurality of instances of operation of a vehicle when the vehicle operator's fatigue level is known and/or can be quantified. Blocks 202a and 204a illustrate gathering data points from the various biometric sensors 108 and the vehicle operator's fatigue level, respectively, for a first instance. Blocks 202b and 204b illustrate gathering the data from the various biometric sensors 108 and the vehicle operator's fatigue level, respectively, for a second instance.")
Regarding claim 3, Grube teaches The method according to claim 2, wherein said preprocessing comprises implementing at least one element chosen from the group consisting of: normalization of the objective fatigue level; normalization of the subjective fatigue level; identification of an objective fatigue class according to the objective fatigue level; and identification of a subjective fatigue class according to the subjective fatigue level. (Col. 6, ll. 25-38; See "In various embodiments, the determined fatigue level of the operator can be normalized based on the operator's measured responses becoming too slow and/or inaccurate. For example, on a scale of zero to ten, where zero is fully rested and ten is fully fatigued, an operator's fatigue level can be determined to be ten when his responses become unacceptably slow, incorrect, and/or inaccurate. For example, the operator's fatigue level can be determined to be zero when his responses are equal to his personal fastest response times. Fatigue levels between zero and ten can be determined based on the operator's response times being between his fastest time and slowest time (e.g., using a linear interpolation method).")
Regarding claim 4, Grube teaches The method according to claim 2, wherein, for each identified aeronautic route, said determining a fatigue indicator comprises determining a weighted sum of the operator fatigue levels having performed this route. (Cols. 4-5, ll. 48-2; ll. "Blocks 202n and 204n illustrate gathering the data from the various biometric sensors 108 and the vehicle operator's fatigue level, respectively, for an nth instance. One or more instances can occur during a single operation of vehicle. For example, for an airline pilot, an operation may be defined as a single flight. In various embodiments, each mission can include several instances at which biometric data and associated fatigue levels are gathered. For example, several data points may be gathered during a long-distance flight (e.g., a flight from New York City to Los Angeles). In blocks 204a-204n, the fatigue level of the vehicle operator in each instance may be determined in several different ways. In some instances, the fatigue level may be known and/or assumed. For example, a pilot flying his first mission after several days of rest may be considered to be fully rested (i.e., not fatigued). By contrast, a pilot flying his last mission at the end of his duty period may be considered to be fully fatigued. In various other instances, the fatigue level of the vehicle operator may be determined by querying the vehicle operator to rate his fatigue level (e.g., on a scale of one to ten) when biometric data is gathered (at blocks 202a-202n)." The amount of fatigue may be analyzed and summed up numerically for each route based on the operators using that route.)
Regarding claim 5, Grube teaches The method according to claim 4, wherein the weighting coefficients are determined according to predetermined rules. (Cols. 5-6, ll. 66-4; See "A vehicle operator may be fully fatigued when his reaction time reaches or exceeds a predetermined threshold ( e.g., a threshold reaction time that may be considered unsafe). Successively faster response times may be associated with successively lower fatigue levels." Further, fatigue levels may be determined using many more measurements and other data points, See Col. 5, ll. 47-60; "As another example, the new data point may replace a data point (e.g., the oldest data point). Thereafter, the statistical analysis, discussed below with reference to block 206, can be re-run to provide an updated statistical model. For example, over time (e.g., as an operator ages), an operator's biological measurements in response to fatigue may change. By replacing the oldest data points with newly-acquired data points, the updated statistical model may continue to accurately calculate fatigue for the operator as the operator's biological measurements in response to fatigue change. In various other instances, the fatigue level of the vehicle operator can be measured relative to other metrics, such as vehicle operator reaction time.")
Regarding claim 6, Grube teaches The method according to claim 1, further comprising correlating data acquired/determined during different collection phases carried out relative to the same operator, each collection phase being chosen from among an initial collection phase implemented before the mission, an intermediate collection phase implemented during the mission, and a final collection phase implemented after the mission. (Col. 4, ll. 39-60; See "As shown in FIG. 2, the data from the various biometric sensors 108 can be gathered during a plurality of instances of operation of a vehicle when the vehicle operator's fatigue level is known and/or can be quantified. Blocks 202a and 204a illustrate gathering data points from the various biometric sensors 108 and the vehicle operator's fatigue level, respectively, for a first instance. Blocks 202b and 204b illustrate gathering the data from the various biometric sensors 108 and the vehicle operator's fatigue level, respectively, for a second instance. Blocks 202n and 204n illustrate gathering the data from the various biometric sensors 108 and the vehicle operator's fatigue level, respectively, for an nth instance. One or more instances can occur during a single operation of vehicle. For example, for an airline pilot, an operation may be defined as a single flight. In various embodiments, each mission can include several instances at which biometric data and associated fatigue levels are gathered. For example, several data points may be gathered during a long-distance flight (e.g., a flight from New York City to Los Angeles). In blocks 204a-204n, the fatigue level of the vehicle operator in each instance may be determined in several different ways." The fatigue data from the operator is gathered in real-time, thus at the start, middle, and end of a flight operation, See Col. 1, ll. 22-24; "In various embodiments, real-time data about a vehicle operator is received from a plurality of biometric sensors during operation of the vehicle.")
Regarding claim 7, Grube teaches The method according to claim 1, further comprising: acquiring a plurality of general context data relative to the general evaluation context of the operators; and preprocessing the general context data. (Col. 2, ll. 17-45; See "The memory 102 can also store one or more vehicle operator profiles 104. Various embodiments of the system 100 can also include a remote computer system 114 that can store 20 various statistical models of fatigue for respective vehicle operators, historical biometric data for respective vehicle operators, and/or historical statistical indicators of fatigue levels for respective vehicle operators. The remote computer system 114 can also store various statistical models of fatigue based on groups of operators having common characteristics ( e.g., age, gender, experience, and body mass index). As described in greater detail below, a vehicle operator profile 104 can include a statistical model of fatigue for a particular vehicle operator, based on a plurality of different biometric information. In various embodiments, the system 100 can be personal to a particular vehicle operator. For example, the system 100, except for the remote computer system 114, described in greater detail below, may be worn by the vehicle operator, and the system 100 may move among different vehicles with the operator. In such embodiments, the memory 102 may only store the vehicle operator profile 104 for the particular vehicle operator that uses the system 100. In various other embodiments, the system 100 can be at least partially integrated in the vehicle. In such embodiments, at least portions of the system, such as the memory 102 and the processor 110 can be integrated into the vehicle. In such systems, the memory 102 can include selected vehicle operator profiles 104 for each operator, for example each operator who has operated and/or who may operate the vehicle." & Col. 4, ll. 4-9; See "The memory 102 and biometric sensors 108 can be in communication with the processor 110. The processor 110 can process the gathered biometric data in the statistical fatigue model of the vehicle operator profile 104 and output an indication of fatigue level to a fatigue warning indicator 112.")
Regarding claim 8, Grube teaches The method according to claim 7, wherein the general context data comprises at least one type of data chosen from the group consisting of: operator data relative to the environment; and operator physiological data. (Col. 2, ll. 17-27; See "The memory 102 can also store one or more vehicle operator profiles 104. Various embodiments of the system 100 can also include a remote computer system 114 that can store various statistical models of fatigue for respective vehicle operators, historical biometric data for respective vehicle operators, and/or historical statistical indicators of fatigue levels for respective vehicle operators. The remote computer system 114 can also store various statistical models of fatigue based on groups of operators having common characteristics ( e.g., age, gender, experience, and body mass index)." & Col. 7, ll. 2-5; See "These methods may optionally use the full dataset of physiological fatigue factors or some reduced dimension dataset resulting from an application of a principal components and/or partial least squares analysis.")
Regarding claim 9, Grube teaches The method according to claim 8, wherein said preprocessing the general context data comprises implementing at least one element chosen from the group consisting of: definition of the evaluation location; definition of the local time; identification of an early, normal, or late session; identification of the position occupied by the operator; and extrapolation of information relative to the sleep of the operator. (Col. 2, ll. 53-66; See "The system 100 can also include a plurality of biometric sensors 108 that detect behaviors and/or activities of the vehicle operator during operation of the vehicle that may be associated with fatigue. For example, the biometric sensors 108 can include one or more digital cameras that can measure facial expressions, head posture, and/or body posture of the vehicle operator. The biometric sensors 108 can also include one or more gyroscopes, solid state position sensors, or the like to measure head and/or body posture. Deviations from a vehicle operators baseline head and/or body posture, such as slouching and/or a head tilted toward the chest, may indicate fatigue. The biometric sensors 108 can also include one or more digital cameras that can detect information about the eyes of the vehicle operator." & Col. 1, ll. 22-24; See "In various embodiments, real-time data about a vehicle operator is received from a plurality of biometric sensors during operation of the vehicle." Which analyzes the fatigue of an operator in various times, including local time. Further, every single airplane is equipped with many sensors identifying the location and the evaluation sensors may be integrated into the vehicle itself, See Col. 2, ll. 38-45 "In various other embodiments, the system 100 can be at least partially integrated in the vehicle. In such embodiments, at least portions of the system, such as the memory 102 and the processor 110 can be integrated into the vehicle. In such systems, the memory 102 can include selected vehicle operator profiles 104 for each operator, for example each operator who has operated and/or who may operate the vehicle.")
Regarding claim 10, Grube teaches The method according to claim 7, wherein, for each identified aeronautic route, said determining a fatigue indicator comprises determining different fatigue indicators associated with the aeronautic route based on different general context data forming one or more filtering criteria. (Col. 2, ll. 17-32; See "The memory 102 can also store one or more vehicle operator profiles 104. Various embodiments of the system 100 can also include a remote computer system 114 that can store various statistical models of fatigue for respective vehicle operators, historical biometric data for respective vehicle operators, and/or historical statistical indicators of fatigue levels for respective vehicle operators. The remote computer system 114 can also store various statistical models of fatigue based on groups of operators having common characteristics ( e.g., age, gender, experience, and body mass index). As described in greater detail below, a vehicle operator profile 104 can include a statistical model of fatigue for a particular vehicle operator, based on a plurality of different biometric information. In various embodiments, the system 100 can be personal to a particular vehicle operator." & Col. 8, ll. 50-67; See "As discussed above, the indication of fatigue level, warnings, and/or alarms can also be forwarded to a remote computer system 114. In various instances, the remote computer system 114 can receive periodically-updated indication of fatigue level. For example, the processor 110 may periodically send the indication of fatigue level for a vehicle operator once every half hour ( or other time interval) while the operator is operating the vehicle. The remote computer system 114 may analyze the received indications of fatigue level for trends and proactively take action to prevent a fatigued operator from continuing to operate the vehicle. For example, a remote computer system 114 may be monitoring the fatigue level of various pilots for an airline. If a particular pilot's fatigue is trending higher such that he is predicted to be unacceptably fatigued before his next-scheduled flight concludes, then the remote computer system 114 can flag the pilot's status and recommend to a scheduler that the pilot be replaced." The aeronautic routes are defined by the operators i.e. pilots taking said routes and their fatigue level is constantly monitored which may lead to being replaced for future routes thus filtering out severely fatigued operators.)
Regarding claim 11, Grube teaches The method according to claim 1, further comprising determining a fatigue indicator for each flight of the same aeronautic route based on objective/subjective fatigue levels determined for different operators for this flight. (Col. 5,ll. 2-6; See "Each instance in which the vehicle operator rates his fatigue level may be imprecise because the rating is subjective. For example, a vehicle operator may be queried to rate his fatigue level at three separate instances in which his fatigue level is the same." Plenty of objective identifiers are also identified, See Col. 4, ll. 39-48; "As shown in FIG. 2, the data from the various biometric sensors 108 can be gathered during a plurality of instances of operation of a vehicle when the vehicle operator's fatigue level is known and/or can be quantified. Blocks 202a and 204a illustrate gathering data points from the various biometric sensors 108 and the vehicle operator's fatigue level, respectively, for a first instance. Blocks 202b and 204b illustrate gathering the data from the various biometric sensors 108 and the vehicle operator's fatigue level, respectively, for a second instance.")
Regarding claim 12, Grube teaches A system for characterizing aeronautic routes, comprising calculating modules configured to implement the method according to claim 1. (Cols. 1-2, ll. 67-4; See "a system 100 can use a statistical model, specific to a particular vehicle operator, to calculate an indication of fatigue level for the operator that may be represented in a signal that is sensed by the operator, another person, or a machine." Also see the teachings shown above with reference to claim 1.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Heneghan at al. (U.S. Patent No. 11,197,633 B2) teaches a fatigue monitoring and management system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTHONY M GARTRELLE whose telephone number is (571) 270-0639. The examiner can normally be reached during the hours of 7:00am-5:00pm Mo-Fr (EST).
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/A.M.G./ Examiner, Art Unit 3661
/RAMYA P BURGESS/Supervisory Patent Examiner, Art Unit 3661
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