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 Arguments
Applicant’s arguments with respect to claim(s) 1-23 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.
Priority
The claims in the instant application have an effective priority date of 3/8/2024 and a grace period of 3/8/2023. The provision application (63/489,032) does not provide the details as set forth in independent claims 1, 11 and 21. The newly applied prior art of Mulkey was first available publicly on 11/8/2022
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.
Claims 1-23 are 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(s) 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 inventor(s), at the time the application was filed, had possession of the claimed invention.
At the time the invention was filed, the applicant did not have possession of the limitation “less than 20-lead rapid response EEG device”. Paragraph [0043] of the instant application states that there are electrodes numbered 1-10, not that there are less than 20-leads. Furthermore, there is a discussion of the number of electrodes, not leads.
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.
Claims 1-23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “localized” in claims is a relative term which renders the claim indefinite. The term “localized” 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. For purposes of examination the indefinite limitation has been deemed to claim that the visual alarm occurs on the display screen.
The term “active monitoring” in claims 1, 11 and 21 is a relative term which renders the claim indefinite. The term “active” 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. For purposes of examination the indefinite limitation has been deemed to claim that the display state changes.
Regarding claims 1, 11 and 21, the limitation “an array of a continuous number of rows of data” renders the claims indefinite. “Continuous number” is inconsistent, a count of rows is discrete, not continuous. It’s unclear whether “continuous” is meant to modify the contiguity of the rows of the numbers itself. For purposes of examination the indefinite limitation has been deemed to claim that there are just a number of rows of data.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vision Transformer Prediction of Delirium to Mulkey et al. (hereinafter, Mulkey) in view of WO 2022124862 A1 to Lee.
Mulkey discloses a method and a system for predicting delirium in patients which integrates a deep learning-based model with electroencephalogram (EEG) data (Abstract “deep learning methods with vision transformer to predict delirium”), the method and system comprising inter alia:
a processor (Introduction “To evaluate rrEEG waveforms, signal parameters are extracted and analyzed using computer based statistical algorithms.”) (Machine Leanring “support vector machine (computer builds a model to provide the greatest difference between categories”) and a non-transitory computer readable media that store instructions that, when executed by the process, cause the processor to perform the operations (Discussion “Use of pre-programmed algorithms, such as the one described here…”) (Introduction “preprogrammed handheld EEG devices with recording accuracy equivalent to traditional EEG and rapid-response (rr) analytics” comprising:
training a machine-learned supervised deep learning method model to predict a presence of delirium in a patient based on training data (Introduction “supervised machine learning where the ground truth is known to the researchers and labeled in the training dataset”) associated with at least a plurality of less than 20-lead rapid-response EEG training data sets from patients (Discussion “10-electrode rrEEG device”) (Machine Learning “readings from 10 sensors … an 8xn array”)”;
obtaining EEG test data using a less than 20-lead rapid-response EEG device comprising a preprogrammed handheld EEG device associated with a target patient to be tested for the presence of delirium (Introduction “preprogrammed handheld EEG devices with recording accuracy equivalent to traditional EEG and rapid-response (rr) analytics”), wherein the EEG test data is organized into a data slice comprising an array of a continuous number of rows of data, and wherein the array is resized using bilinear interpolation to be treated as an image (Machine Learning “continuous of number of rows of data are organized into a data slice, which is an 8×n array, where n represents the number of rows. These arrays are resized to 224×224 using bilinear interpolate and treated as images to feed into the ViT model”);
inputting the image of the EEG test data into the machine-learned supervised deep learning method model (Machine Learning “treated as images to feed into the ViT model. See figure 2.”);
receiving, as output of the model, a positive or negative prediction of whether the target patient is experiencing the presence of delirium (Discussion “patients were accurately classified as delirium positive or delirium negative”);
wherein the less than 20-lead rapid-response EEG device is a 10-electrode rapid-response EEG device (Mulkey Discussion “10-electrode rrEEG device”) (Mulkey Machine Learning “readings from 10 sensors … an 8xn array”);
wherein the supervised deep learning method is vision transformer based (Mulkey Abstract “supervised deep learning with vision transformer and a rapid-response EEG device for predicting delirium”);
wherein the supervised deep learning method is trained using ground truth of delirium (Mulkey Introduction “the ground truth is known to the researchers and labeled in the training dataset”) (Mulkey Measures/Label the Data “The CAM-ICU is based on the gold standard for delirium identification”) corresponding to the plurality of 10-electrode rapid-response EEG training data sets from patients (Mulkey Discussion “10-electrode rrEEG device”) (Machine Learning “readings from 10 sensors … an 8xn array”);
wherein the EEG training data sets are derived from patients who are older adults requiring mechanical ventilation (Mulkey Setting/Sample “All participants 50 years old or older, required mechanical ventilation for greater than 12 hours”);
wherein the obtaining and the inputting the EEG test data and the receiving the model output is conducted by a user operating the preprogrammed handheld EEG device with rapid-response (rr) analytics (Mulkey Introduction “user-friendly preprogrammed handheld EEG devices with recording accuracy equivalent to traditional EEG and rapid-response (rr) analytics … these devices offer rapid setup by anyone with limited training, providing rapid EEG data”);
wherein the vision transformer based model operates by slicing an image into a matrix of nxn sub-images, processing the sub-images as sequential data to measure a relationship between pairs of sub-images, and then aggregating the relationship information for classification or for object detection, for analyzing sequential and spatial relationships in the EEG test data (Mulkey Introduction “image into a matrix of n×n sub-images. These sub-images are treated as sequential data so self-attention mechanisms can be applied to measure the relationship between pairs of sub-images. Because ViT can maintain spatial and temporal information it is ideal for analyzing sequential and spatial relationship in EEG.”);
wherein the 10-electrode rapid-response EEG device includes a headband with a plurality of electrodes that circumscribe the head of a target patient (Mulkey Physiologic Assessment for Delirium “Rapid-response EEG (rrEEG) headbands that circumscribe the head were applied to participants daily. Accuracy of placement is based on headband location (fastener in the center of the forehead, electrodes are numbered 1-10”);
wherein the EEG test data is processed for inputting into the machine-learned supervised deep learning method model by removing artifact signals from the EEG test data (Mulkey rrEEG Processing “rrEEG data were processed to remove artifacts such as muscle movement in the face and interference from nearby devices such as ventilators and cardiac monitors using high and low frequencies filters. Data are then re-referenced to estimate physiological noise and divided into multiple discrete time periods called epochs.”); and
wherein the EEG test data are filtered using high and low frequencies filters to remove artifacts from movement of the target patient or interference from nearby medical devices, and the EEG test data are then divided into multiple discrete time epochs for inputting into the machine-learned supervised deep learning method model (Mulkey rrEEG Processing “rrEEG data were processed to remove artifacts such as muscle movement in the face and interference from nearby devices such as ventilators and cardiac monitors using high and low frequencies filters. Data are then re-referenced to estimate physiological noise and divided into multiple discrete time periods called epochs.”).
Mulkey discloses the claimed invention as set forth and cited above including a display screen of the preprogrammed handheld EEG device (see Figure 1). Mulkey does not expressly disclose automatically modifying an active monitoring display state of the less than 20-lead rapid-response EEG device by generating a localized visual alarm sequence directly on the display screen of the preprogrammed handheld EEG device in response to a received positive prediction of delirium.
However, Lee teaches predicting delirium in patients (ll. 22-23 “… a method for determining whether delirium has occurred…”) by integrating deep learning based model with electroencephalogram (EEG) data (ll. 429-431 “…the controller… applies at least one data of the subject’s EEG… to the artificial intelligence model to predict the progress rate of delirium…”). Lee teaches that EEG is used to determine the presence or absence (e.g., positive or negative prediction) of delirium (ll. 161-163) and a delirium detection device that includes a display (ll. 216-218). Lee teaches that their controller may calculate the possibility of the occurrence of delirium as to whether there is a EEG abnormality and may display the determined result on the screen (ll. 411-421, 426-427). One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify the display of Mulkey to provide the modification of display in response to a positive prediction of delirium of Lee, as Less teaches this would have allowed important management of delirium in ICU settings (Lee, ll. 50-53) and would have avoided late or misdiagnosed instances of delirium (Lee, ll. 54-60).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SEAN P DOUGHERTY/ Primary Examiner, Art Unit 3791
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