DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 2-21 are pending for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Regarding claims 11 and 21, recite the limitation “analyzing the deviations to identify patterns associated with different stages of fatigue onset”. The limitation is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventors, at the time the application was filed, had possession of the claimed invention.
A review of the specification does not appear to reveal disclosure describing how the system identifies patterns associated with different stages of fatigue onset. The term “patten” appears in paragraphs [0049], [0082], and [0089] in the context of “sleep pattern”, which differs from the patterns associated with different stages of fatigue. Additionally, the term “stage” does not appear anywhere in the specification.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Regarding claims 6 and 15, recite the term “rapid” in the last line. The term is a relative term which renders the claim indefinite. The term “rapid” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Regarding claims 8 and 16, recite the term “sudden” in the last line. The term is a relative term which renders the claim indefinite. The term “sudden” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 4, 6, 10, 12, 14-15, 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Grube (Pub. No.: US 2016/0090097 A1) in view of Sone (Pub. No.: US 2010/0137748 A1).
Regarding claim 2, Grube teaches an in-flight risk management system (Abstract, Fig. 1, fatigue detection for airline pilot 100) comprising:
one or more body movement sensors positioned in an aircraft cockpit (Fig. 1, biometric sensor 108, para [0010], “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.”);
one or more in-flight risk management (IRM) computers in communication with the one or more body movement sensors (Fig. 1, processor 110); and
one or more data stores in communication with the one or more IRM computers and storing instructions that, when executed, cause the one or more IRM computers to perform operations (Fig. 1, memory 102 stores fatigue algorithm 106) comprising:
receiving sensor data from the one or more body movement sensors (Fig. 3A, step 302, para [0017], “In block 302, during vehicle operation, the processor 110 can constantly or nearly-continuously monitor the biometric sensors 108 to detect biometric data related to the vehicle operator.”);
processing the sensor data to determine body movements indicative of fatigue for a flight crew member (Fig. 3A, steps 304-308), wherein processing comprises:
analyzing the body movements over time;
identifying body movements associated with increasing fatigue;
comparing the determined body movements to predetermined fatigue indicating body movement profiles (para [0010], “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 system continuously monitors the user’s body movements to determine deviations from the baseline body posture. If the system identifies the user’s head is tilted in comparison to the baseline body posture, then the system determines the user is fatigue.); and
calculating a fatigue measurement based on the comparison (Fig. 3a, step 306, the system calculates the fatigue level);
generating, based on the fatigue measurement, a real-time fatigue assessment for the flight crew member (The system determines the pilot is fatigue or not based on the fatigue level); and
updating a predicted fatigue profile for the flight crew member based on the real- time fatigue assessment (Fig. 2, Fig. 3B, para [0016], “In block 206, the gathered biometric data (from blocks 202a-n) and the determined fatigue levels (from blocks 204a-n) from the multiple operations can be statistically analyzed. For example, a linear regression analysis may be performed to determine which biometric data provide the maximum discriminatory capability between fatigue levels. As other examples, a non-linear regression analysis, a machine learning analysis, neural networks trained on known fatigue/non-fatigued instances from training data, or other statistical models can be used. In block 208, the method 200 can output the resulting statistical model for the vehicle operator to the vehicle operator profile 104.”. The system updates and tracks the pilot fatigue level over a period of time).
Grube teaches the fatigue detection system determines the level of fatigue based on body postures and other parts of the body but fails to teach the system determines fatigue based on leg movement.
However, in the same field fatigue detection, Sone teaches a fatigue detection system that measures whole body acceleration, including leg movements, to estimate a fatigue level. See para [0139], “For example, the wrist normally does not move during sleep, but when a person is on a vehicle, the wrist may be moved due to shaking of the vehicle. It is meaningless to estimate a fatigue level on the basis of acceleration due to the shaking. In order not to mistake shaking due to external factors for an activity level, it is preferable that by measuring acceleration of an entire body (e.g. waist, leg, trunk and head), acceleration due to shaking of a vehicle is offset by the acceleration of an entire body.” and para [0140], “Further, when a fatigue level cannot be estimated from acceleration of the wrist, a fatigue level can be estimated from movements of the second and third candidates for acceleration measurement (e.g. waist, leg, trunk and head).”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Grube’s fatigue detection system to monitor the leg movements of the driver/pilot as taught by Sone to accurately determine fatigue level.
Regarding claim 4, Grube in the combination teaches the system of claim 2, wherein the operations further comprise:
detecting, based on the fatigue assessment, that a fatigue level of the flight crew member exceeds a threshold; and
initiating a fatigue mitigation action comprising at least one of:
generating an alert (Fig. 3A, step 308, the system outputs a fatigue warning);
modifying a flight plan;
engaging an autopilot system; or
transferring control to a remote operator.
Regarding claim 6, Grube in the combination teaches the system of claim 4, further comprising an eye tracking sensor incorporated into a control panel of the aircraft (para [0010], “The biometric sensors 108 can also include one or more digital cameras that can detect information about the eyes of the vehicle operator.” and para [0011], “In embodiments in which the system is at least partially integrated into the vehicle or the vehicle operating platform, the biometric sensors 108 may be incorporated into the vehicle and/or worn by the vehicle operator. For example, in an aircraft flight deck, the pilots may wear headsets that incorporate brain activity sensors and eye tracking sensors may be incorporated into an instrument panel in the flight deck.”), wherein the operations further comprise:
analyzing the eye tracking data to calculate an eye fatigue measurement;
correlating the eye fatigue measurement with the fatigue measurement to enhance accuracy of the fatigue assessment (Fig. 2, step 206, the fatigue model of the user is determined based on a combination of biometric data that includes eye tracking data and body/leg movements); and
detecting sleep patterns based on rapid changes in eyelid closure patterns (para [0010], “For example, the digital cameras can detect various eye metrics, such as eye blink rate (i.e., how often the vehicle operator blinks), eye movement (i.e., how much the vehicle operator is looking around rather than staring in one direction), and/or eye closure amount (e.g., how much of the vehicle operator's eyes are covered by his eye lids) of the vehicle operator. Deviations in blink rate and/or a change in blink rate from a vehicle operator's baseline measurements may indicate fatigue.”).
Regarding claim 10, Grube in the combination teaches the system of claim 2, wherein processing the sensor data comprises applying a machine learning model trained to detect fatigue indicators from historical leg movement data correlated with confirmed fatigue incidents, the machine learning model being continuously updated with new flight data to improve fatigue prediction accuracy (para [0014], “For example, a pilot may be prompted to occasionally self-report his fatigue level during a flight operation, and a data point can be created from the self-reported fatigue level and the measurements from the biometric sensors 108 at the time of the pilot's self report. The new data point can be added to the data points and/or replace one or more data points that were used to generate the statistical model. For example, the new data point can be added to the existing data points. 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.” and para [0016], “In block 206, the gathered biometric data (from blocks 202a-n) and the determined fatigue levels (from blocks 204a-n) from the multiple operations can be statistically analyzed. For example, a linear regression analysis may be performed to determine which biometric data provide the maximum discriminatory capability between fatigue levels. As other examples, a non-linear regression analysis, a machine learning analysis, neural networks trained on known fatigue/non-fatigued instances from training data, or other statistical models can be used.”. The fatigue model constantly updates itself by using existing data and new data, such as historical leg/body movements and pilot’s self-report).
Regarding claim 12, recites a method for the system of claim 2. Therefore, it is rejected for the same reasons.
Regarding claim 14, recites a method for the system of claim 4. Therefore, it is rejected for the same reasons.
Regarding claim 15, recites a method for the system of claim 6. Therefore, it is rejected for the same reasons.
Regarding claim 17, recites a CRM for the system of claim 2. Therefore, it is rejected for the same reasons.
Regarding claim 18, recites a CRM for the system of claim 4. Therefore, it is rejected for the same reasons.
Regarding claim 20, recites a CRM for the system of claim 10. Therefore, it is rejected for the same reasons.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Grube (Pub. No.: US 2016/0090097 A1) in view of Sone (Pub. No.: US 2010/0137748 A1) as applied to claim 2, and further in view of Kato (Pub. No.: US 2011/0178680 A1).
Regarding claim 3, Grube in view of Sone teaches the system of claim 2, wherein the fatigue detection system determines body and/or leg movements with gyroscope and/or accelerometer.
Grube in view of Sone fails to teach wherein the one or more leg movement sensors are incorporated into a seat cushion of a chair in the aircraft cockpit, the one or more leg movement sensors comprising a plurality of pressure sensors distributed across the seat cushion to detect spatial and temporal changes in leg pressure.
However, in the same field of leg movement sensors, Kato teaches a vehicle seat includes pressure sensors 111 configured to detect leg movements of the user. See Fig. 4, Fig. 12 and para [0249], “The angle between the pelvis and the right femoral region is greatly changed, thus as the preparatory movement for maintaining the balance on the right and left, not only the movement of the left femoral region which appears on the seating surface in FIG. 12, but also the tension of the back muscle of the opposite side of the body from the leg lifted by the driver (i.e., left side back muscle in the case of the brake operation with the right foot) cause an increase of the pressure on the lower left of the back surface of the seat.”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Grube in view of Sone’s driver/pilot seat with a plurality of pressure sensors to detect leg movements of the driver/pilot. Such modification would accurately capture the leg movements of the driver/pilot for the purpose of fatigue determination.
Regarding claim 13, recites a method for the system of claim 3. Therefore, it is rejected for the same reasons.
Claims 7, 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Grube (Pub. No.: US 2016/0090097 A1) in view of Sone (Pub. No.: US 2010/0137748 A1) as applied to claim 2, and further in view of Serovy (Pat. No.: US 9,826,941 B1).
Regarding claim 7, Grube in the combination teaches the system of claim 2, further comprising a wearable human physiology sensor incorporated into a flight suit or jacket worn by the flight crew member (para [0011], “In embodiments in which the system is personal to a particular vehicle operator, all of the biometric sensors 108 may be worn by the operator (e.g., incorporated into clothing, headwear, jewelry, watches, eyewear, or the like).”), wherein the wearable human physiology sensor comprises:
a heart rate sensor (para [0010], “For example, the biometric sensors 108 can include sensors to detect the heart rate of the vehicle operator by measuring electrical signals in the body that regulate heart beats.”);
a galvanic skin response sensor (para [0010], “For example, the biometric sensors 108 can include a chest strap and/or a head band with various contact sensors in contact with the vehicle operator's skin.” and claim 2);
a skin temperature sensor (claim 2, body temperature); and
a gyroscopes for detecting body posture and movement (para [0010], “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.”).
Grube teaches a plurality of biometric sensors but fails to teach blood oxygen level sensor and accelerometer.
However, in the same field of pilot monitoring system, Servoy teaches an oximeter that measures the oxygen level of the pilot. See Col. 2 line 38 -54, “
Embodiments of the inventive concepts disclosed herein include monitoring pilot oxygen levels while in flight so as to reduce or eliminate the occurrence of hypoxia in pilots. Some embodiments include a wearable device, which may include an oximeter that measures a person's arterial oxygen saturation (SpO.sub.2) levels. In one embodiment, the wearable device may be implemented as … clothing wearable device (e.g., a sleeve device, a legging device, shirt device, or the like),”.
Also, in the same field of fatigue detection, Sone teaches the use of accelerometer to detect body movements. See para [0139], “For example, the wrist normally does not move during sleep, but when a person is on a vehicle, the wrist may be moved due to shaking of the vehicle. It is meaningless to estimate a fatigue level on the basis of acceleration due to the shaking. In order not to mistake shaking due to external factors for an activity level, it is preferable that by measuring acceleration of an entire body (e.g. waist, leg, trunk and head), acceleration due to shaking of a vehicle is offset by the acceleration of an entire body.”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Grube’s biometric sensors to further include an oximeter to detect blood oxygen of the pilot to prevent hypoxia and an accelerometer to accurately detect body and leg movements.
Regarding claim 8, Grube in the combination teaches the system of claim 7, wherein the operations further comprise:
continuously monitoring heart rate variability, galvanic skin response, skin temperature, and body movement patterns (para [0017],
“In block 302, during vehicle operation, the processor 110 can constantly or nearly-continuously monitor the biometric sensors 108 to detect biometric data related to the vehicle operator.”);
calculating a physiological fatigue measurement based on deviations from baseline measurements for each physiological parameter (para [0010], “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.”);
correlating the physiological fatigue measurement with the leg movement fatigue score to provide a fatigue assessment (Fig. 2, para [0014], “For example, the new data point can be added to the existing data points. 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.” the fatigue model is determined based on a combination of biometric data that includes leg movement, heart rate, galvanic skin response, body temperature and body movements); and
detecting sudden changes in physiological parameters indicative of fatigue onset (Fig. 3, the system uses the fatigue model at step 304 to determine whether the pilot is fatigue based on the received biometric data at step 302.).
Regarding claim 16, recites a method for the system of claims 7 and 8. Therefore, it is rejected for the same reasons.
Allowable Subject Matter
Claims 5, 9 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/ZHEN Y WU/Primary Examiner, Art Unit 2685