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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/2/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
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.
Claims 1, 11 and 21 recite the limitation "the presence". There is insufficient antecedent basis for this limitation in the claim. For purposes of examination the indefinite limitation has been deemed to claim “presence” or “a presence”.
The term “limited-lead” in Claims 2-4, 8, 11, 12, 14, 18 and 21 is a relative term which renders the claim indefinite. The term “limited-lead” 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 fewer than 20 leads.
Regarding Claims 6, 12, 16 and 22, the limitation where “obtaining”, “inputting” and “receiving” render the claim indefinite because it is unclear if these are the same “obtaining”, “inputting” and “receiving” limitations as set forth in Claims 1 and 21. For purposes of examination the indefinite limitation has been deemed to claim “the obtaining”, “the inputting” and “the receiving”.
The term “critically ill” in Claims 5 and 15 is a relative term which renders the claim indefinite. The term “critically ill” 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 an individual with any ailment.
The term “user-friendly” in Claims 6, 16 and 22 is a relative term which renders the claim indefinite. The term “user-friendly” 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 where the obtaining, inputting and receiving is capable of being performed by a user.
Claims 7, 17 and 23 recites the limitation "the relationship". There is insufficient antecedent basis for this limitation in the claim. For purposes of examination the indefinite limitation has been deemed to claim “a relationship”.
Claims 7, 17 and 32 recites the limitation "the EEG-based data". There is insufficient antecedent basis for this limitation in the claim. For purposes of examination the indefinite limitation has been deemed to claim the “EEG test data” as set forth in Claim 3.
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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Each of Claims1-23 have been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1-23 recites at least one step or instruction for training and machine learning machine and then using machine learning to predict delirium, which is grouped as a mental process under the 2019 PEG or a certain method of organizing human activity under the 2019 PEG. Accordingly, each of Claims 1-23 recites an abstract idea.
Specifically, Claims 1-23 recite training a machine learning model to predict delirium and obtaining EEG data and inputting EEG data in the machine learning model to predict delirium. (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Step 2A, Prong 2
The above-identified abstract idea in each of Claims 1-23 are not integrated into a practical application under 2019 PEG because the additional elements, either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, the additional elements of: an EEG device and electrodes are generically recited computer elements the claims which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea identified above in independent Claims 1-23 are not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer (e.g., an EEG device and electrodes as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in Claims 1-23 are not integrated into a practical application under the 2019 PEG.
Accordingly, Claims 1-23 are each directed to an abstract idea under 2019 PEG.
Step 2B
None of Claims 1-23 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of: an EEG device and electrodes.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per Applicant’s specification, the electrodes and EEG device are described as a generic 10-electrode rrEEG device at [0065]. Accordingly, in light of Applicant’s specification, the claimed term EEG device and electrodes are reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
The recitation of the above-identified additional limitations in Claims 1-23 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the method/systems of Claims 1-23 are directed to applying an abstract idea as identified above on a general-purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 1-23 provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in Claims 1-23 do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1-23 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1-23 amount to significantly more than the abstract idea itself. Accordingly, Claims 1-23 are not patent eligible and rejected under 35 U.S.C. 101.
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.
Claim(s) 1, 2, 11 and 12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WO 2022/124862 A1 to Lee.
Regarding Claims 1 and 11, Lee discloses a system and method for predicting delirium in patients (ll. 22-23 “… a method for determining whether delirium has occurred…”) which integrates a 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…”), the method comprising inter alia:
training a machine-learned supervised deep learning method model to predict the presence of delirium in a patient based on training data (ll. 443-444 “…the artificial intelligence model is built by learning the predicted data and actual data related to the occurrence and progression of delirium for each patient.”) associated with at least a plurality of limited-lead rapid-response EEG training data sets from patients (ll. 445-447 “…the artificial intelligence model may be built by learning the predicted data and the actual data in consideration of information including the age, gender, severity of disease, etc. of each diagnosed patient.”);
obtaining EEG test data associated with a target patient to be tested for the presence of delirium (ll. 82-83 “…a measuring unit that measures the brain wave and electrocardiogram of the subject…”);
inputting the EEG test data into the machine-learned supervised deep learning method model (ll. 429-431 “…the controller 140 applies at least one data of the subject's EEG… to the artificial intelligence model…”); and
receiving, as output of the model, a positive or negative prediction of whether the target patient is experiencing the presence of delirium (ll. 433-434 “…the AI model, the control unit 140 can predict how advanced the delirium of the subject is and how it will proceed in the future.”).
Regarding Claims 2 and 12, Lee discloses the method according to claim 1, wherein the EEG test data is obtained using a limited-lead rapid-response EEG device (ll. 353=354 “…allowing EEG to be measured only by the first electrode sensor 1221 disposed in the corresponding area, EEG more quickly and precisely analysis is possible.”).
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.
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.
Claim(s) 3, 7, 13 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view “Multi-Channel Vision Transformer for Epileptic Seizure Prediction” to Hussein et al. (hereinafter, Hussein).
Lee teaches the claimed invention except for expressly disclosing where a vision transformer based model operates by slicing an image into a matrix of n×n sub-images, processing the sub-images as sequential data to measure the 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-based data.
However, Hussein teaches a device for EEG detection and classification (Abstract). Hussein teaches a vision transformer based model: slicing an image into a matrix of n×n sub-images (3.2.2. “Each 2D scalogram image… is first split into fixed-size non-overlapping 2D patches…”), processing the sub-images as sequential data to measure the relationship between pairs of sub-images (3.2.2. “The resulting patches are then flattened and mapped into lower-dimensional representations called “patch embeddings” using linear projection. The size of the patch embeddings is set to D, which also is the size of the latent vector used by the transformer through all of its layers.”) and then aggregating the relationship information for classification or for object detection, for analyzing sequential and spatial relationships in the EEG-based data (3.2.2. “Lastly, the output feature representations of the different transformer encoders are aggregated and used as an input to MLP for preictal/interictal EEG classification.”).
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify the supervised deep learning method to be the vision transformed based model as set forth in Hussein, as Hussein teaches in the Abstract that such methodology is simple and efficient and further teaches at 4.1 that prior attempts of EEG classification result in unreliable domain-features and are prone to domain shift.
Claim(s) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Hussein, and further in view of US 20230108267 A1 to Sughrue et al. (hereinafter, Sughrue).
Lee in view of Hussein teach the claimed invention including wherein the supervised deep learning method is trained with EEG delirium data corresponding to the plurality of limited-lead rapid-response EEG training data sets from patients (ll. 443-449), however, Lee does not expressly disclose wherein the supervised deep learning method is trained using ground truth.
However, Sughrue teaches processing EEG data related to the brain of a subject ([0002] “…processing data related to the brain of a subject… electroencephalogram (EEG) data.”), defines a deep learning training setup ([0123] “…the ground truth identification 704 represents what the machine learning model 710 should generate in response to processing the training EEG data 702.”) and generating a training input to train the deep learning with EEG training data sets from patients ([0164] “…generating (i) a training input comprising the channel data of the EEG data corresponding to the electrode and (ii) a ground-truth output identifying one or more parcellations whose brain activity was captured by the channel data of the EEG data corresponding to the electrode” and [0165] “…training a machine learning model using the training input and the ground-truth output, wherein the machine learning model is configured to process first channel data captured by an electrode and to generate a prediction of a parcellation represented by the first channel data.”).
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify the supervised deep learning method training using delirium data of Lee in view of Hussein with the training using ground truth of Sughrue, as Sughrue teaches that training against ground truth provides a highly specific advantage allowing the trained model to automatically identify the correct target on its own, which would have eliminated the need to run data through the original, complex processing system during future, meaning, ground truth training makes the diagnostic process faster an more autonomous ([0092] “That is, the training system can train the machine learning model to automatically identify the set 222 of parcellations corresponding to each set of channel data in the EEG data 202, without requiring the EEG data 202 (and/or the fMRI data 204) to be processed by the source localization system 200.”).
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of “Reporting of demographic data and representativeness in machine learning models using electronic health records” to Bozkurt et al. (hereinafter, Bozkurt”).
Lee discloses the claimed invention including where the EEG training data are data sets derived from patients, where age and severity of disease are taken into account and specifically mentions diagnosing a person in their 70s (ll. 443-449). Lee does not expressly disclose where the data sets are specifically derived from patients who are critically ill older adults.
However, Bozkurt teaches about the development of machine learning models (Abstract) and further teaches that machine learning models should be training on populations for which they will be applied (Introduction, p. 2). One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify the training data sets of Lee to be specifically from critically ill older adults, because Bozjurt teaches that if training data is not from the populations from which they are to be applied, disparities in these populations with the perpetuated and validity may be called into question. Therefore, since Lee already discloses the diagnosis of an individual in their 70s, it would have been obvious in light of Bozkurt to train the machine learning with data from other ill (and non-ill adults) in order to determine if they indeed suffer from a degree of delirium.
Claim(s) 6, 8-10, 16, 18-20 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of “Rapid Handheld Continuous Electroencephalogram (EEG) Has the Potential to Detect Delirium in Older Adults” to Mulkey et al. (hereinafter, Mulkey – cited in IDS).
Lee discloses the claimed invention except for expressly disclosing wherein obtaining and inputting the EEG test data and receiving the model output is conducted by a user operating a user-friendly preprogrammed handheld EEG device with rapid-response (rr) analytics and wherein the limited-lead rapid-response EEG device includes a headband with a plurality of electrodes that circumscribe the head of a target patient.
However, Mulkey teaches a manner to detect delirium using EEG patterns (Abstract). Mulkey teaches obtaining and inputting EEG test data by a user operating a user-friendly preprogrammed handheld EEG device wherein the limited-lead rapid-response EEG device includes a headband with a plurality of electrodes that circumscribe the head of a target patient (Introduction “The Ceribell is a handheld device that provides rapid continuous EEG (cEEG) monitoring that was Food and Drug Administration (FDA) approved for seizure monitoring in 2017. (See Figure 2) Unlike other limited lead devices, the Ceribell has 10 leads (8 waveforms) that are housed in a headband that circumscribe the head, allowing for monitoring of all five cerebral lobes of the brain.).
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modifying the obtaining and inputting of EEG data with the handheld headband with a plurality of electrodes that circumscribe the head of a target patient of Mulkey, as Mulkey teaches in the Introduction that the application of the headband would have been easily applied by non-experts (Introduction “Because leads are housed in the headband, the Ceribell headband can be easily applied by non-experts, such as nurses.”).
Regarding Claims 9, 10, 19 and 20, Lee discloses the claimed invention except for expressly disclosing 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.
However, Mulkey teaches a manner to detect delirium using EEG patterns (Abstract). Mulkey teaches where 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 (Measures “Prior to EEG data analysis, noise components were filtered out of all eight channels using traditional high- and low-pass filters and advanced independent component analysis filters.”), and the EEG test data are then divided into multiple discrete time epochs for inputting into the machine-learned supervised deep learning method model (Measures: “Evaluation of EEG data based on delirium status was conducted using power spectral density analysis to detect the relative presence of high- and low-frequency activity in each channel. Spectral density and independent component analyses in Matlab were used to evaluate EEG signals in 5-minute epochs.”
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify the EEG test data of Lee to be filtered and divided into epochs, as done so in Mulkey as Mulkey teaches filtering and the epoch division would have allowed for objective documentation of EEG characteristics (Measures “Computational algorithms allowed for objective and quantitative documentation of EEG characteristics.”).
Claim(s) 21 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view Hussein.
Lee discloses a system and method for predicting delirium in patients (ll. 22-23 “… a method for determining whether delirium has occurred…”) which integrates a 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…”), the method comprising inter alia:
training a machine-learned supervised deep learning method model to predict the presence of delirium in a patient based on training data (ll. 443-444 “…the artificial intelligence model is built by learning the predicted data and actual data related to the occurrence and progression of delirium for each patient.”) associated with at least a plurality of limited-lead rapid-response EEG training data sets from patients (ll. 445-447 “…the artificial intelligence model may be built by learning the predicted data and the actual data in consideration of information including the age, gender, severity of disease, etc. of each diagnosed patient.”);
obtaining EEG test data associated with a target patient to be tested for the presence of delirium (ll. 82-83 “…a measuring unit that measures the brain wave and electrocardiogram of the subject…”);
inputting the EEG test data into the machine-learned supervised deep learning method model (ll. 429-431 “…the controller 140 applies at least one data of the subject's EEG, EEG, pulse, and pulse wave to the artificial intelligence model…”); and
receiving, as output of the model, a positive or negative prediction of whether the target patient is experiencing the presence of delirium (ll. 433-434 “…the AI model, the control unit 140 can predict how advanced the delirium of the subject is and how it will proceed in the future.”).
Lee teaches the claimed invention except for expressly disclosing where a vision transformer based model operates by slicing an image into a matrix of n×n sub-images, processing the sub-images as sequential data to measure the 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-based data.
However, Hussein teaches a device for EEG detection and classification (Abstract). Hussein teaches a vision transformer based model: slicing an image into a matrix of n×n sub-images (3.2.2. “Each 2D scalogram image… is first split into fixed-size non-overlapping 2D patches…”), processing the sub-images as sequential data to measure the relationship between pairs of sub-images (3.2.2. “The resulting patches are then flattened and mapped into lower-dimensional representations called “patch embeddings” using linear projection. The size of the patch embeddings is set to D, which also is the size of the latent vector used by the transformer through all of its layers.”) and then aggregating the relationship information for classification or for object detection, for analyzing sequential and spatial relationships in the EEG-based data (3.2.2. “Lastly, the output feature representations of the different transformer encoders are aggregated and used as an input to MLP for preictal/interictal EEG classification.”).
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify the vision transformer based model to operator via the slicing, processing and aggregating of Hussein, as Hussein teaches in the Abstract that such methodology is simple and efficient and further teaches at 4.1 that prior attempts of EEG classification result in unreliable domain-features and are prone to domain shift.
Claim(s) 22 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view Hussein, and further in view of Mulkey.
Lee in view of Hussein teach the claimed invention except for expressly disclosing wherein obtaining and inputting the EEG test data and receiving the model output is conducted by a user operating a user-friendly preprogrammed handheld EEG device with rapid-response (rr) analytics and wherein the limited-lead rapid-response EEG device includes a headband with a plurality of electrodes that circumscribe the head of a target patient.
However, Mulkey teaches a manner to detect delirium using EEG patterns (Abstract). Mulkey teaches obtaining and inputting EEG test data by a user operating a user-friendly preprogrammed handheld EEG device wherein the limited-lead rapid-response EEG device includes a headband with a plurality of electrodes that circumscribe the head of a target patient (Introduction “The Ceribell is a handheld device that provides rapid continuous EEG (cEEG) monitoring that was Food and Drug Administration (FDA) approved for seizure monitoring in 2017. (See Figure 2) Unlike other limited lead devices, the Ceribell has 10 leads (8 waveforms) that are housed in a headband that circumscribe the head, allowing for monitoring of all five cerebral lobes of the brain.).
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modifying the obtaining and inputting of EEG data of Lee in view of Hussein with the handheld headband with a plurality of electrodes that circumscribe the head of a target patient of Mulkey, as Mulkey teaches in the Introduction that the application of the headband would have been easily applied by non-experts (Introduction “Because leads are housed in the headband, the Ceribell headband can be easily applied by non-experts, such as nurses.”).
Claim(s) 23 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view Hussein, and further in view of Mulkey.
Lee in view of Hussein teach (all the following citations are from Hussein) a device for EEG detection and classification (Abstract). Hussein teaches slicing an image into a matrix of n×n sub-images (3.2.2. “Each 2D scalogram image… is first split into fixed-size non-overlapping 2D patches…”), processing the sub-images as sequential data to measure the relationship between pairs of sub-images (3.2.2. “The resulting patches are then flattened and mapped into lower-dimensional representations called “patch embeddings” using linear projection. The size of the patch embeddings is set to D, which also is the size of the latent vector used by the transformer through all of its layers.”) and then aggregating the relationship information for classification or for object detection, for analyzing sequential and spatial relationships in the EEG-based data (3.2.2. “Lastly, the output feature representations of the different transformer encoders are aggregated and used as an input to MLP for preictal/interictal EEG classification.”).
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify the vision transformer-based model to operator via the slicing, processing and aggregating of Hussein, as Hussein teaches in the Abstract that such methodology is simple and efficient and further teaches at 4.1 that prior attempts of EEG classification result in unreliable domain-features and are prone to domain shift.
Lee in view of Hussein teach the claimed invention except for expressly disclosing where 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 vision transformer based model.
However, Mulkey teaches a manner to detect delirium using EEG patterns (Abstract). Mulkey teaches where 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 (Measures “Prior to EEG data analysis, noise components were filtered out of all eight channels using traditional high- and low-pass filters and advanced independent component analysis filters.”), and the EEG test data are then divided into multiple discrete time epochs for inputting into the machine-learned supervised deep learning method model (Measures: “Evaluation of EEG data based on delirium status was conducted using power spectral density analysis to detect the relative presence of high- and low-frequency activity in each channel. Spectral density and independent component analyses in Matlab were used to evaluate EEG signals in 5-minute epochs.”
One having an ordinary skill in the art at the time the invention was filed would have found it obvious to modify the EEG test data of Lee to be filtered and divided into epochs, as done so in Mulkey as Mulkey teaches filtering and the epoch division would have allowed for objective documentation of EEG characteristics (Measures “Computational algorithms allowed for objective and quantitative documentation of EEG characteristics.”).
Conclusion
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/SEAN P DOUGHERTY/Primary Examiner, Art Unit 3791