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 .
Response to Amendment
This Office Action is in response to communications filed February 17, 2026. Claims 1, 2, 4-6, 8, and 11-14 have been amended. Claim 7 has been cancelled. Claims 1, 2, 4-6 and 8-15 are currently pending.
Allowable Subject Matter
Claim 8 is 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.
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.
Claim(s) 1, 2, 4-6, 9-11 and 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sicconi et al. (Sicconi; US Pub No. 2020/0057487 A1) in view of Kim et al. (Kim; US Pub No. 2018/0039392 A1) and Goldstein et al. (Goldstein; US Pub No. 2022/0061767 A1).
As per claim 1, Sicconi teaches an electronic system for monitoring the state of awareness of an operator in a control station of an aircraft, the monitoring system comprising:
a receiver (Fig. 3, Processing Unit 315, Camera 308, Biosensors 314; paragraph [0042], lines 10-12; paragraph [0050], lines 31-36), at least one of the sensors comprises a worn sensor being in physical contact with the operator (paragraph [0050], lines 31-36), [[and]] at least one of the sensors comprises an off-set sensor being at a distance from the operator (paragraph [0042], lines 6-12)…
a processor extracting from each datum, at least one parameter representative of the state of awareness of the operator (Fig. 3, Processing Module 315; paragraph [0039], lines 25-31)… and
a fuser receiving the representative parameters and implementing a machine learning method for determining, depending on the representative parameters, whether the operator is in a nominal state of awareness or in an altered state of awareness (paragraph [0039], lines 25-40).
Sicconi does not expressly teach and at least one of the worn sensors comprises a pressure sensor measuring at least one pressure applied by the operator to the pressure sensor… wherein the parameter associated with the pressure sensor is a duration during which the measured pressure is greater than a predetermined threshold… wherein each worn sensor is chosen from the group consisting of:
a cardiac sensor;
a pulse oximeter;
a respiration sensor;
an accelerometer;
a scalp electrode;
a pressure sensor arranged in a seat of the operator;
a pressure sensor arranged in a control system suitable for being actuated by the operator;
a sweating sensor for the operator;
a galvanic skin response sensor;
an internal temperature sensor for the operator; and
a near-infrared spectroscopy headband.
Kim teaches and at least one of the worn sensors comprises a pressure sensor measuring at least one pressure applied by the operator to the pressure sensor (paragraph [0041]: wearable device; paragraph [0276], lines 1-4)… wherein the parameter associated with the pressure sensor is a duration during which the measured pressure is greater than a predetermined threshold (paragraph [0276], lines 1-11).
It would have been obvious to one having ordinary skill in the art at the time the invention was effectively filed to implement the wearable device comprising a pressure sensor as taught by Kim, since Kim states in paragraph [0276] that such a modification would result in a user controlling different functions of a device based upon actuating different levels of pressure on a single control button.
Goldstein teaches wherein each worn sensor is chosen from the group consisting of:
a cardiac sensor (paragraph [0117]: ECG);
a pulse oximeter (paragraph [0117]: pulse oximetry);
a respiration sensor (paragraph [0117]: breathing rates sensor);
an accelerometer (paragraph [0112]: accelerometer);
a scalp electrode (paragraph [0129]: patch-like sensor/electrode attached to scalp);
a pressure sensor arranged in a seat of the operator (paragraph [0112]: pressure sensor);
a pressure sensor arranged in a control system suitable for being actuated by the operator (paragraph [0127]: user actuated switched);
a sweating sensor for the operator (paragraph [0132]: sweat monitoring device);
a galvanic skin response sensor (paragraph [0106]: galvanic skin response);
an internal temperature sensor for the operator (paragraph [0110]: measure core body temperature); and
a near-infrared spectroscopy headband (paragraph [0217]: headband; paragraph [0255]: spectroscopy).
It would have been obvious to one having ordinary skill in the art at the time the invention was effectively filed to implement the plurality of sensors as taught by Goldstein, since Goldstein states that it is well known in the art to use any one of a plurality of sensor type to measure different parameters associated with a user for determining a user condition.
As per claim 2, Sicconi in view of Kim and Goldstein further teaches the monitoring system according to claim 1, further comprising a warner issuing a warning signal when said fusion module determines that the operator is in an altered state of awareness (Sicconi, paragraph [0050], lines 19-21).
As per claim 4, Sicconi in view of Kim and Goldstein further teaches the monitoring system according to claim 1, wherein at least one of the worn sensors [[is]] comprises a cardiac sensor comprising an electrocardiograph (Goldstein, paragraph [0117]: ECG).
As per claim 5, Sicconi in view of Kim and Goldstein further teaches the monitoring system according to claim 1, wherein at least one of the worn sensors [[is]] comprises a pulse oximeter comprising a photoplethysmography sensor (Goldstein, paragraph [0207]: photoplethysmography).
As per claim 6, Sicconi in view of Kim and Goldstein further teaches the monitoring system according to claim 1, wherein at least one of the worn sensors [[is]] comprises a scalp electrode comprising an electroencephalograph (Goldstein, paragraph [0238]: electroencephalography).
As per claim 9, Sicconi in view of Kim and Goldstein further teaches the monitoring system according to claim 1, wherein each off-set sensor is chosen from the group consisting of:
a camera configured for taking at least one image including at least part of the operator (Sicconi, paragraph [0042], lines 6-8);
a microphone for picking up at least one sound emitted by the operator (Sicconi, paragraph [0050], lines 28-29) and
an infrared sensor for the skin temperature of the operator (Goldstein, paragraph [0247]).
As per claim 10, Sicconi in view of Kim and Goldstein further teaches the monitoring system according to claim 9, wherein the sound emitted by the operator is the operator’s voice or the operator’s respiration (Sicconi, paragraph [0063], lines 5-8).
As per claim 11, Sicconi in view of Kim and Goldstein further teaches the monitoring system according to claim 9, wherein at least one of the off-set sensors [[is]] comprises a camera capturing at least one image comprising at least a part of the operator (Sicconi, paragraph [0042], lines 6-8), each parameter processor being chosen from the group consisting of:
a movement of the operator (Sicconi, paragraph [0063], lines 5-8: gesture);
a position of the operator (Sicconi, paragraph [0063], lines 2-3);
an orientation of the head of the operator (Sicconi, paragraph [0063], line 3);
a direction of the glance of the operator (Sicconi, paragraph [0063], lines 3-4);
a partial opening of the eyes of the operator (Sicconi, paragraph [0089], lines 5-6);
a blink of the eyes of the operator (Sicconi, paragraph [0070], lines 44-46); and
information on the structure of the image wherein the operator appears (Sicconi, paragraph [0066], lines 10-11).
As per claim 13, Sicconi in view of Kim and Goldstein further teaches the monitoring system according to claim 1, wherein said processor extracts from each datum at least one parameter representative of the state of awareness of the operator by implementing (Sicconi, Fig. 3, Processing Module 315; paragraph [0039], lines 25-31), for each datum, an algorithm chosen from the group consisting of:
an extraction of a predetermined characteristic of the associated datum followed by a machine learning method (Sicconi, paragraph [0039], lines 48-50);
a deep learning method applied directly to the associated datum (Sicconi, paragraph [0048]); and
a predetermined modeling applied to the associated datum (Sicconi, paragraph [0037], lines 11-16).
As per claim 14, (see rejection of claim 1 above) a method for monitoring the state of awareness of an operator in a control station of an aircraft, the monitoring method comprising:
receiving data from at least two sensors onboard the aircraft, at least one of the sensors comprising a worn sensor being in physical contact with the operator, [[and]] at least one of the sensors comprising an off-set sensor being at a distance from the operator, and at least one of the worn sensors comprising a pressure sensor measuring at least one pressure applied by the operator to the pressure sensor;
extracting from each datum, at least one parameter representative of the state of awareness of the operator, wherein the parameter associated with the pressure sensor is a duration which the measured is greater than a predetermined threshold; and
receiving the representative parameters and implementing a machine learning method for determining, depending on the representative parameters, whether the operator is in a nominal state of awareness or in an altered state of awareness,
wherein each worn sensor is chosen from the group consisting of:
a cardiac sensor;
a pulse oximeter;
a respiration sensor;
an accelerometer;
a scalp electrode;
a pressure sensor arranged in a seat of the operator;
a pressure sensor arranged in a control system suitable for being actuated by the operator;
a sweating sensor for the operator;
a galvanic skin response sensor;
an internal temperature sensor for the operator; and
a near-infrared spectroscopy headband.
As per claim 15, Sicconi in view of Kim and Goldstein further teaches a non-transitory computer program including software instructions which, when executed by a computer, cause the computer to perform a method according to claim 14 (Sicconi, paragraph [0145]).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sicconi in view of Kim and Goldstein as applied to claim 1 above, and further in view of Hermina Martinez et al. (Hermina Martinez; US Pub No. 2019/0355178 A1).
As per claim 12, Sicconi in view of Kim and Goldstein teaches the monitoring system according to claim 1… and at least one of the off-set sensors [[is]] comprises a camera (Sicconi, paragraph [0042], lines 6-8).
Sicconi in view of Kim and Goldstein does not expressly teach wherein at least one of the worn sensors [[is]] comprises a pressure sensor, at least one of the worn sensors [[is]] comprises an accelerometer.
Hermina Martinez teaches wherein at least one of the worn sensors [[is]] comprises a pressure sensor, at least one of the worn sensors [[is]] comprises an accelerometer (paragraph [0032], lines 3-4 and lines 17-19).
It would have been obvious to one having ordinary skill in the art at the time the invention was effectively filed to implement the weight sensor and accelerometer as taught by Hermina Martinez, since Hermina Martinez states in paragraph [0032] that such a modification would result in monitoring the movements of a driver within a vehicle.
Response to Arguments
Applicant’s arguments with respect to the above claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAOMI J SMALL whose telephone number is (571)270-5184. The examiner can normally be reached Monday-Friday 8:30AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Quan-Zhen Wang can be reached at 571-272-3114. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NAOMI J SMALL/ Primary Examiner, Art Unit 2685
Prosecution Insights