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 .
Election/Restrictions
Claim 25 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected group, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 11/27/2025. Therefore, the restriction is made final herein.
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 1-24 are considered eligible subject matter. Even if the claims were considered an abstract idea, the claims contain limitations that provide a practical application, i.e. facial state classifying.
Claim Rejections - 35 USC § 102
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 1-4, 6, 9,10,12,13-16,18,20-22 and 24 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by U.S. Patent Application Publication No. 20210241011 (Cronje et al).
Regarding claim 1, Cronje et al discloses a method of predicting a state of a human subject, the method including: capturing an image frame including a facial region of the subject (fig. 1, item 114, page 3, paragraph 76); and providing the image frame to an image classifier (fig. 1, item 120, 130), wherein the image classifier is configured to process the image frame thereby to output a result representative of a predicted state (fig. 1, item 139); wherein the image classifier is trained via a process including: gathering monitoring data, i.e. a large set of data (page 3, paragraph 79) from a plurality of subjects, i.e. operators (page 4, paragraph 91), wherein the monitoring data includes time correlated data representative of: (i) eyelid movement, i.e. PERCLOS information (fig. 1, item 134); and (ii) facial image data (fig. 1, item 114), processing the data representative of eyelid movement as a function of time, i.e. eye closure in each frame from the baseline (page 4, paragraph 86) thereby to predict respective states at a plurality of times (T1 to Tn), times of each frame of fig. 1, based on eyelid movement analysis (fig. 1, item 139 based on 134); labelling facial image data corresponding to the plurality of times (T1 to Tn) with a value representative of the respective state predicted for each of the plurality of times (T1 to Tn), the value that indicates state of drowsiness for the frame and camera (page 3, paragraph 79, fig. 1, item 139), thereby to define labelled facial image data, defining the facial data by the label (page3, paragraph 79), and providing the labelled facial image data to the image classifier as training data by providing the labelled facial image data to the trained network (page 3, paragraph 79).
Regarding claim 2, Cronje et al discloses the data representative of eyelid movement is representative of eyelid position as a function of time, since the eye positioning is analyzed for each frame which is a function of time (page4, paragraph 86).
Regarding claim 3, Cronje et al discloses eyelid movement analysis is blepharometric artefact analysis, since the eyelid position and movement is measured and a pattern in blink data is measured (page 4, paragraph 86).
Regarding claim 4, Cronje et al discloses the states are states relating to a condition of alertness or drowsiness (fig. 1, item 139).
Regarding claim 6, Cronje et al discloses the monitoring data is collected from subjects engaged in a predefined activity, i.e. operators operating, i.e. a vehicle (page 4, paragraph 91, page 5, paragraph 100) and wherein the step of capturing an image frame including a facial region of the subject is performed in respect of a subject engaging in the same form of predefined activity, since the subject is also an operator (page 5, paragraph 100).
Regarding claim 9, Cronje et al discloses the data representative of eyelid movement is data representative of eyelid movement as a function of time, since the eye positioning is analyzed for each frame which is a function of time (page 4, paragraph 86), and includes video data (page 5, paragraph 99), from which eyelid position as a function of time is extracted via image processing techniques (page 4, paragraph 86).
Regarding claim 10, Cronje et al discloses the data representative of eyelid movement is data representative of eyelid movement as a function of time, since the eye positioning is analyzed for each frame which is a function of time (page 4, paragraph 86), and includes data derived from eyelid monitoring hardware (page 2, paragraph 23, page 4, paragraph 92).
Regarding claim 12, Cronje et al discloses the analysis of eyelid movement is blepharometric artefact analysis, since the eyelid position and movement is measured and a pattern in blink data is measured (page 4, paragraph 86) and makes use of a subset of the following blepharometric artefacts: Amplitude to velocity ratio (AVRs);Negative Inter-Event-Duration (IED); Positive IED; Negative AVR; Positive AVR; Negative AVR * positive AVR; Negative AVR divided by positive AVR; BECD (blink eye closure duration), i.e. closing eyes for extended period of time (page 4, paragraph 86);4 Negative DOQ (duration of ocular quiescence); Positive DOQ; Relative Amplitude, i.e. percentage eye closure (page 4, paragraph 86); Relative Position; Max Amplitude; Max Velocity: Negative ZCI (zero crossing index); Positive ZCI Blink start position; Blink end position; Blink start time; Blink end time; and Trends and changes in any of the above artefacts over a defined period (page 4, paragraph 86).
Claim 13 is rejected for the same reasons as claim 1. Thus, the arguments analogous to that presented above for claim 1 are equally applicable to claim 13. Claim 13 distinguishes from claim 1 only in that claim 13 has a different preamble, stating that method trains a system, the method including the method of claim 1. Cronje et al teaches further this feature, i.e. a system that is trained (fig. 1, page 3, paragraph 79, fig. 3).
Claims 14-16, 18, 21,22 and 24 are rejected for the same reasons as claims 2-4, 6, 9, 10 and 12, respectively Thus, the arguments analogous to that presented above for claims 2-4, 6, 9, 10 and 12 are equally applicable to claims 14-16, 18, 21,22 and 24. Claims 14-16, 18, 21,22 and 24 distinguishes from claims 2-4, 6, 9, 10 and 12 only in that they have different dependencies, both of which have been previously rejected. Therefore, prior art applies.
Regarding claim 20, Cronje et al discloses the predefined activity is operating a vehicle (page 4, paragraph 91, page 5, paragraph 100).
Claim Rejections - 35 USC § 103
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 23 is rejected under 35 U.S.C. 103(a) as being unpatentable over Cronje et al in view of U.S. Patent Application Publication No. 20210345937 (Coles et al).
Regarding claim 23, Cronje et al discloses all of the claimed elements as set forth above, and incorporated herein by reference. Cronje et al further discloses using the eyelid monitoring hardware utilizes infrared reflectance in imaging (page 4, paragraph 93).
Cronje et al does not disclose expressly utilizing infrared reflectance oculography.
Coles et al discloses utilizing infrared reflectance oculography (page 2, paragraph 27).
Cronje et al and Coles et al are combinable because they are from the same field of endeavor, i.e. eyelid analysis.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use IRC.
The suggestion/motivation for doing so would have been to provide a more robust method by gathering precise data.
Therefore, it would have been obvious to combine the method of Cronje et al with use of IRC to obtain the invention as specified in claim 23.
Conclusion
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/KATHLEEN Y DULANEY/Primary Examiner, Art Unit 2666 12/5/2025