DETAILED ACTION
The present application is being examined under the pre-AIA first to invent provisions.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
2. Claims 1-8 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of Pat. No. 12159241.
Application:
1. A method for processing blepharometric data for a subject, the method including:
receiving a blepharometric data set including data representative of a subject’s eyelid position as a function of time;
processing the data representative of the subject’s eyelid position as a function of time thereby to:
(i) identify a plurality of events, wherein each event is defined by a set of time blepharometric data for a positive or negative change in eyelid amplitude that is greater than a given velocity threshold for a given time duration;
(ii) identify a plurality of individual blinks, wherein each individual blink is a pairing of an event corresponding to a positive change in amplitude position and a negative change in amplitude position that are within predefined relative amplitude and position limits; and
(iii) processing data representative of the identified events and the identified individual blinks thereby to a data artifact set, the data artifacts set including:
A. one or more data artifacts representative of statistical attributes of inter-event timings; and
B. one or more artifacts representative of statistical attributes individual for blink events including velocity.
2. A method according to claim 1 wherein the one or more data artifacts representative of statistical attributes of inter-event timings include a data artifact representative of duration of ocular quiescence.
3. A method according to claim 1 wherein the one or more data artifacts representative of statistical attributes of inter-event timings include a data artifact representative of blink eye closure duration.
4. A method according to claim 1 wherein the one or more data artifacts representative of statistical attributes of inter-event timings include a data artifact representative of time period between blink events.
5. A method according to claim 1 wherein the time period between blink events is measured between blink initiation times for consecutive individual blinks.
6. A method according to claim 1 including training a classifier based on training data, wherein the training data includes the data artifact set and a plurality of further the data artifacts sets each including, for further blepharometric data sets: A. one or more data artifacts representative of statistical attributes of inter-event timings; and B. one or more artifacts representative of statistical attributes individual for blink events including velocity.
7. A method according to claim 1 including using the artifact data set to predict a presence/absence of a defined physiological condition.
8. A method according to claim 1 including using the artifact data set to predict a presence/absence of a defined physiological condition using a classifier that is trained based on training data, wherein the training data includes, for further blepharometric data sets: A. one or more data artifacts representative of statistical attributes of inter-event timings; and B. one or more artifacts representative of statistical attributes individual for blink events including velocity.
Pat. No. 12159241:
1. A method for processing blepharometric data for a subject, the method including:
(i) receiving a set of blepharometric data for the subject; (ii) receiving input representative of an observed occurrence of a physiological event or condition; (iii) defining one or more blepharometric data sets based on the blepharometric data, wherein each data set is labelled with data representative of presence/absence of an observed occurrence of a physiological event or condition thereby to define labelled training data; and (iv) training an AI classifier based on the labelled training data;
such that the AI classifier is configured thereby to predict presence/absence of a current or future occurrence of a physiological event or condition based on a new set of blepharometric data, wherein that new set of blepharometric data is not labelled with data representative of presence/absence of an observed occurrence of a physiological event or condition;
wherein the step of defining one or more blepharometric data sets based on the blepharometric data includes processing data representative of the subject's eyelid position as a function of time thereby to:
(i) identify a plurality of events, wherein each event is defined by a set of time blepharometric data for a positive or negative change in eyelid amplitude that is greater than a given velocity threshold for a given time duration;
(ii) identify a plurality of individual blinks, wherein each individual blink is a pairing of an event corresponding to a positive change in amplitude position and a negative change in amplitude position that are within predefined relative amplitude and position limits; and
(iii) processing data representative of the identified events and the identified individual blinks thereby to define data artifacts which are blepharometric data sets, the data artifacts including:
A. one or more data artifacts representative of statistical attributes of inter-event timings; and
B. one or more artifacts representative of statistical attributes individual for blink events including velocity.
2. A method according to claim 1 wherein the input representative of an observed occurrence of a physiological event or condition includes timestamped data representative of an event or condition identified by a separate monitoring system.
3. A method according to claim 2 wherein the separate monitoring system includes a brain activity monitoring system.
4. A method according to claim 2 wherein the separate monitoring system includes a cardiovascular activity monitoring system.
5. A method according to claim 2 wherein the separate monitoring system includes a monitoring system that receives results from an interactive test.
6. A method according to claim 1 wherein the input representative of an observed occurrence of a physiological event or condition includes timestamped data representative of an event or condition identified manually and inputted via in input interface.
7. A method according to claim 1 wherein step (iii) includes caballing each data set with activity data representative of a current state of activity for the subject when the blepharometric data was obtained.
8. A method according to claim 7 wherein the activity data is derived from processing of accelerometer/IMU data from a wearable device worn by the subject.
9. A method according to claim 7 wherein the activity data is derived from processing of biometric data from a biometric monitoring device worn by the subject.
10. A method according to claim 1 wherein the blepharometric data is derived from one or more of the following sources: wearable configurations including infrared reflectance oculography components; vehicle operator configurations, in which an image capture device is positioned to capture blepharometric data for an operator of the vehicle; desktop/laptop computer configurations in which a webcam or other image capture device is used to monitor subject blepharometric data subject to: (i) a foreground software application; and/or (ii) a background software application, which collects blepharometric data whilst a subject engages in other activities on the computer; mass transport passenger configurations in which an image capture device operates in conjunction with a display screen, such that blepharometric data is collected concurrently with the subject observing content displayed via the display screen; vehicle passenger configurations in which an image capture device is positioned to capture blepharometric data for a passenger of the vehicle; smartphone/tablet configurations in which a front facing camera is used to monitor subject blepharometric data, subject to: (i) a foreground application; and/or (ii) a background application, which collects blepharometric data whilst a subject engages in other activities on the smartphone/tablet; and medical facility configurations.
11. A method according to claim 1, wherein the one or more data artifacts representative of statistical attributes of inter-event timings include a data artifact representative of duration of ocular quiescence.
12. A method according to claim 1, wherein the one or more data artifacts representative of statistical attributes of inter-event timings include a data artifact representative of blink eye closure duration.
13. A method according to claim 1 wherein the one or more data artifacts representative of statistical attributes of inter-event timings include a data artifact representative of a time period between blink events.
14. A method according to claim 13, wherein the time period between blink events is measured between blink initiation times for consecutive individual blinks.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vaughan et al. (US 20230092866 A1) teaches digital personalized medicine systems and methods described herein provide digital diagnostics and digital therapeutics to patients. The digital personalized medicine system uses digital data to assess or diagnose symptoms of a patient in ways that inform personalized or more appropriate therapeutic interventions and improved diagnoses.
Coles et al. (US 20210378568 A1) teaches devices and processing systems are configured to enable physiological event prediction based on blepharometric data analysis. For example, some embodiments provide methods and associated technology that enable retrospective analysis of blepharometric data driving subsequent hardware/software configuration, thereby to provide for personalized and/or generalized biomarker identification.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHAI MINH NGUYEN whose telephone number is (571)272-7923. The examiner can normally be reached 6-3.
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/KHAI M NGUYEN/Primary Examiner, Art Unit 2641
