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 .
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 1-20 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. Claims 1-10 are drawn to a method, and claims 11-20 are drawn to a system, each of which is within the four statutory categories.
Step 2A(1)
Claim 1 recites, in part, performing the steps of:
analyzing a brain-wave data, wherein the brain-wave data corresponds to a graphical representation of an electrical activity of a brain of an individual, wherein the individual is receiving at least one therapeutic for at least one brain disorder;
generating at least one output data based on the analyzing, wherein the at least one output data is indicative of a response of the individual to the at least one therapeutic, and an influence of the at least one therapeutic on the individual.
These functions amount to concepts performed in the human mind, and therefore fall within the scope of an abstract idea in the form of a mental process. Fundamentally the process is that of observing and evaluating graphical brain-wave data of an individual receiving a therapeutic for a brain disorder, and making a determination indicative of a response of the individual to the at least one therapeutic and an influence of the at least one therapeutic on the individual. Each of these functions are capable of being performed mentally by an individual, such as by a clinician evaluating EEG data of a patient being treated with a therapeutic.
Independent claim 11 recites similar limitations and also recites an abstract idea under the same analysis.
Step 2A(2)
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
Claims 1 and 11 recite additional elements including a) a communication device used to receive the brain wave data and transmit the at least one data, b) at least one device used to receive the transmitted at least one data, c) a diagnostic device recited as providing the brain wave data, d) a processing device used to perform the analysis based on an AI model and generate the output data, and e) the AI model trained on a brain-wave dataset associated with a mental disorder, a psychiatric disorder, a neurological disorder, and a neurodegenerative disorder, and at least one additional data comprising a clinical data, a demographic data, an EMR (electronic medical record)/EHR (electronic health record) data, of the individual.
Paragraphs 221 and 236 describe a communication device performing the corresponding recited functions, and the diagnostic device as an EEG capturing device. However, the disclosure does not expressly link a particular form of device or computer hardware with the communication device. Paragraphs 51 and 56 describe the disclosed functions as performable on a variety of computing devices such as servers. Paragraphs 208-211 and 214-215 describe a system architecture including a server communicating with client devices including various mobile devices, and discloses such computing devices as having processing units and system memory as well as the disclosure being practiced “within a general-purpose computer or in any other circuits or systems.” The communication device, processing device, and device receiving the transmitted at least one data are each therefore construed as encompassing generic computing devices. The diagnostic device is similarly construed as encompassing general EEG capture devices or sources of brain wave data.
Paragraphs 221 and 236 describe an AI model as being trained on a brain-wave dataset associated with a mental disorder, a psychiatric disorder, a neurological disorder, and a neurodegenerative disorder, and at least one additional data comprising a clinical data, a demographic data, an EMR (electronic medical record)/EHR (electronic health record) data, of the individual. Paragraphs 203-205 describe the use of models, including pre-trained models, such as Deep Convolutional Neural Networks and Generative Adversarial Networks. Paragraphs 160-164 describe further models in terms of their functions of classifying patients and identifying placebo responders or non-responders, but do not further describe the models or training process.
The above elements constitute mere instructions to implement steps of the abstract idea using computing devices and machines as tools. Each of the communication device and processing device are recited at a high level of generality as used to perform the respective data receipt, transmission, and processing functions, and are disclosed broadly as encompassing general forms of computing devices. The diagnostic device is likewise recited simply as providing the brain wave data and disclosed broadly. The AI model is similarly only recited at a high level of generality in which the brain wave data is analyzed “based on” the AI model, and only as trained on the recited forms of data. Given that the model is only recited as an “AI model” and merely as trained on the brain-wave dataset and the at least one additional data, the AI model only constitutes the general use of such a trained AI model to perform the data analysis.
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Claims 1 and 11 further recite additional elements of a) receiving the brain-wave data, and b) transmitting the at least one data. However, these only amount to insignificant extra-solution activity in the form of data gathering and application following performance of the abstract idea.
The above elements are therefore not sufficient to integrate the abstract idea into a practical application, and the above claims, as a whole, are directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claims 1 and 11 only recite the communication device, at least one receiving device, diagnostic device, processing device, and trained AI model as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f)
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Claims 1 and 11 further recite additional elements of a) receiving the brain-wave data, and b) transmitting the at least one data. As noted above however, these only amount to insignificant extra-solution activity in the form of data gathering and application following performance of the abstract idea.
C. Well-Understood, Routine and Conventional Activities. MPEP 2106.05(d)
In addition to amounting to insignificant extra-solution activity, the elements of receiving the brain-wave data, and transmitting the at least one data constitute well-understood routine and conventional activity in the form of receiving or transmitting data over a network.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Depending Claims
Claims 2 and 12 recite pre-processing the brain-wave data using at least one algorithm to obtain a pre-processed brain-wave data, wherein the pre-processing removes a noise-wave data from the brain-wave data, wherein the brain-wave data comprises the noise-wave data; and analyzing the pre-processed brain-wave data, wherein the generating of the at least one output data is further based on the analyzing of the pre-processed brain-wave data.
These limitations fall within the scope of the abstract idea as set out above, as well as an abstract idea in the form of mathematical calculations. Specifically, pre-processing brain-wave data using at least one algorithm to remove noise-wave data and obtain a pre-processed brain-wave data constitutes performance of mathematical calculations on the brain-wave data.
Claims 2 and 12 further recite additional elements of the processing device as performing the pre-processing and analysis steps, and the pre-processing and analysis steps being “based on the AI model.”
Paragraphs 51 and 56 describe the disclosed functions as performable on a variety of computing devices such as servers. Paragraphs 208-211 and 214-215 describe a system architecture including a server communicating with client devices including various mobile devices, and discloses such computing devices as having processing units and system memory as well as the disclosure being practiced “within a general-purpose computer or in any other circuits or systems.” The processing device is therefore construed as encompassing generic computing devices.
Paragraph 224 reflects the language of the claim, stating that the brain-wave data is preprocessed and that the preprocessing “may be based on the AI model.”
These elements constitute mere instructions to implement steps of the abstract idea using computing devices and machines as tools. The processing device is recited at a high level of generality as used to perform the data processing functions, and is disclosed broadly as encompassing general forms of computing devices. The AI model is similarly only recited at a high level of generality in which the brain wave data is pre-processed and analyzed “based on” the AI model. The AI model is only recited as an “AI model” applied to data processing functions at a high level of generality and disclosed broadly. These elements are not sufficient to integrate the abstract idea into a practical application or amount to significantly more.
Claims 3 and 13 recite wherein the brain-wave data comprises a plurality of brain-wave data corresponding to a plurality of individuals, wherein the at least one output data comprises a plurality of output data corresponding to the plurality of individuals, wherein the plurality of individuals is associated with a clinical trial of the at least one therapeutic, analyzing each of the plurality of output data based on a predefined therapeutic response, wherein the predefined therapeutic response influences an outcome of the clinical trial; determining a plurality of targeted individuals from the plurality of individuals based on the analyzing of each of the plurality of output data; and generating a report data based on the determining of the plurality of targeted individuals, wherein the report data corresponds to a report associated with the plurality of target individuals. These limitations fall within the scope of the abstract idea(s) as set out above.
Claims 3 and 13 further recite additional elements of a) the processing device as performing the analysis, determining, and generating steps, b) the analysis steps being “based on the AI model,” and c) the communication device and the at least one device as used to transmit and receive the report data.
Paragraphs 221 and 236 describe a communication device performing the corresponding recited functions. However, the disclosure does not expressly link a particular form of device or computer hardware with the communication device. Paragraphs 51 and 56 describe the disclosed functions as performable on a variety of computing devices such as servers. Paragraphs 208-211 and 214-215 describe a system architecture including a server communicating with client devices including various mobile devices, and discloses such computing devices as having processing units and system memory as well as the disclosure being practiced “within a general-purpose computer or in any other circuits or systems.” The communication device, processing device, and device receiving the transmitted at least one data are each therefore construed as encompassing generic computing devices.
Paragraph 226 reflects the language of the claim, stating that the analysis of the output data “may be based on the AI model.”
These elements constitute mere instructions to implement steps of the abstract idea using computing devices and machines as tools. The processing device and communication device are each recited at a high level of generality as used to perform the data processing and transmission functions, and are disclosed broadly as encompassing general forms of computing devices. The AI model is similarly only recited at a high level of generality in which the brain wave data is analyzed “based on” the AI model. The AI model is only recited as an “AI model” applied to data processing functions at a high level of generality and disclosed broadly.
The claims further recite the additional element of transmitting the report data. However, transmitting the report data following its generation only amounts to insignificant extra-solution activity in the form of application following performance of the abstract idea.
In addition to amounting to insignificant extra-solution activity, the element of transmitting the report data constitutes well-understood routine and conventional activity in the form of receiving or transmitting data over a network.
These elements are not sufficient to integrate the abstract idea into a practical application or amount to significantly more.
Claims 4 and 14 recite wherein the analyzing of each of the plurality of output data comprises assigning a value to each of the plurality of individuals based on the plurality of output data, wherein the value corresponds to a coordinate of a multi-dimensional space; and representing each of the plurality of individuals as the value in the multi-dimensional space based on the assigning of the value, wherein the determining of the plurality of targeted individuals is based on a proximity between a plurality of values associated with the plurality of individuals in the multi-dimensional space. These limitations fall within the scope of the recited abstract idea as an abstract idea in the form of mathematical calculations.
Claims 5 and 15 recite wherein the plurality of targeted individuals comprises at least one of a placebo responder and a non-placebo responder, wherein the predefined therapeutic response comprises at least one of a placebo response and a non-placebo response, wherein the placebo responder experiences a placebo-recovery from the at least one brain disorder, wherein the placebo-recovery is associated with a placebo effect, wherein the non-placebo responder experiences a therapeutic-recovery from the at least one brain disorder, wherein the therapeutic-recovery is associated with an active treatment of the at least one therapeutic, wherein the report comprises at least one of a placebo responder report and a non-placebo responder report. These limitations fall within the scope of the abstract idea as set out above.
Claims 6 and 16 recite wherein the analyzing of the brain-wave data comprises identifying a biological-characteristic of the individual, wherein the electrical activity of the brain is based on the biological-characteristic, wherein the at least one brain disorder is associated with the biological-characteristic, wherein the biomarker is associated with the biological-characteristic. These limitations fall within the scope of the abstract idea as set out above.
Claims 7 and 17 recite the additional elements of wherein the brain-wave dataset comprises each of a target-labelled brain-wave data and a target-unlabeled brain-wave data, wherein each of the target-labelled brain-wave data and the target-unlabeled brain-wave data is recorded from a plurality of drug-trail participants, wherein the plurality of drug-trail participants is associated with a clinical trial of the at least one therapeutic comprising at least one of a placebo treatment and an active treatment, wherein the target-labelled brain-wave data comprises an indicator indicating the at least one therapeutic associated with each of the plurality of drug-trail participants, wherein the target-unlabeled brain-wave data lacks the indicator.
Paragraphs 231 and 243 describe the brain wave dataset in the language of the claims as including each of a target-labelled brain-wave data and a target-unlabeled brain-wave data, where each of the target-labelled brain-wave data and the target-unlabeled brain-wave data is recorded from a plurality of drug-trail participants, the plurality of drug-trail participants are associated with a clinical trial of the at least one therapeutic comprising at least one of a placebo treatment and an active treatment, the target-labelled brain-wave data comprising an indicator indicating the at least one therapeutic associated with each of the plurality of drug-trail participants, and that the target-unlabeled brain-wave data lacks the indicator.
The above data types are construed as additional elements given that the brain-wave dataset is recited as the data on which the AI model is trained. However, as noted above the brain wave dataset is only recited in claims 1 and 11 at a high level of generality as used to train the AI model without further detail of that process. The additional recitation of the data encompassing the above types of data, still only amounts to a broad description of data falling within that broad training step. The above limitations therefore also amount to mere instructions to implement the abstract idea using computing elements, i.e. training of the AI model, under the same rationale above, given that the claims do not recite any special manner in which the data is actually used to train the model. These elements are not sufficient to integrate the abstract idea into a practical application or amount to significantly more.
Claims 8 and 18 recite clustering a plurality of individuals in a multi-dimensional space. These limitations fall within the scope of the recited abstract idea as an abstract idea in the form of mathematical calculations.
Claims 8 and 18 further recite additional elements of wherein the AI model comprises a plurality of AI models comprising each of the first AI model, a second AI model, a third AI model, and a fourth AI model, wherein the first AI model is configured to be trained on a target-unlabeled brain-wave data based on a self-supervised learning technique, wherein the brain-wave dataset comprises the target-unlabeled brain-wave data, wherein the second AI model is configured for performing the clustering of the plurality of individuals, and wherein the plurality of individuals is associated with the target-unlabeled brain-wave data.
Paragraph 244 describes the recited plurality of AI models comprising each of the first AI model, a second AI model, a third AI model, and a fourth AI model, wherein the first AI model is configured to be trained on a target-unlabeled brain-wave data based on a self-supervised learning technique, wherein the brain-wave dataset comprises the target-unlabeled brain-wave data, and wherein the plurality of individuals is associated with the target-unlabeled brain-wave data.
Examiner references the analyses provided above with respect to recitation of particular contents of the brain wave dataset. The brain wave dataset is only recited in claims 1 and 11 at a high level of generality as used to train the AI model without further detail of that process. The additional recitation of the data encompassing the above types of data, still only amounts to a broad description of data falling within that broad training step.
The above limitations amount to mere instructions to implement the abstract idea using computing elements. For example, each of the first, second, third, and fourth AI models are recited at a high level of generality as used to implement data analysis functions or broadly as “trained” using a self-supervised technique, and are broadly disclosed. These elements are not sufficient to integrate the abstract idea into a practical application or amount to significantly more.
Claims 9 and 19 recite predicting a biological characteristic associated with each of the plurality of individuals, predicting a response of the plurality of individuals to a therapy, predicting a therapeutic response of the plurality of individuals to the at least one therapeutic. These limitations fall within the scope of the abstract idea as set out above.
Claims 9 and 19 further recite additional elements of a) the second AI model used to predict the biological characteristic, b) the third AI model used to predict the response of the plurality of individuals, c) the fourth AI model used to predict the therapeutic response of the individuals, and d) wherein the fourth AI model is configured to be trained using a supervised learning.
Paragraph 245 reflects the language of the claim, broadly describing the second, third, and fourth AI models as used to perform the respective data analysis tasks, as well as that the fourth AI model “may be configured to be trained using a supervised learning.”
The above limitations amount to mere instructions to implement the abstract idea using computing elements. Each of the second, third, and fourth AI models are recited at a high level of generality as used to implement data analysis functions or broadly as “trained” using a supervised technique, and are broadly disclosed. Merely reciting AI models as used to implement data processing steps or at a high level of generality as being trained is not sufficient to integrate the abstract idea into a practical application or amount to significantly more.
Claims 10 and 20 recite the additional element of wherein the AI model is based on at least one of a deep convolutional neural network and a generative adversarial network.
As cited above, paragraphs 203-205 describe the use of models, including pre-trained models, such as Deep Convolutional Neural Networks and Generative Adversarial Networks. However, reciting the AI model as based on at least one of a deep convolutional neural network and a generative adversarial network only constitutes mere instructions to implement steps of the abstract idea using computing devices and machines as tools. As noted above, the model is only recited in claims 1 and 11 as an “AI model” and merely as trained on the brain-wave dataset and the at least one additional data, the AI model only constitutes the general use of such a trained AI model to perform the data analysis. Further broad recitation of the model being “based on” one of multiple general model types, i.e. a deep convolutional neural network or a generative adversarial network, is not sufficient to integrate the abstract idea into a practical application or amount to significantly more.
Claims 1-20 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 1-20 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 and 11 are indefinite because Examiner is unable to determine the metes and bounds of the claims based on the recitation of “EMR (electronic medical record)/EHR (electronic health record)” in line 11 of claim 1 and line 15 of claim 11. Specifically, it is not clear from the structure of the claim language whether the terms EMR and EHR are intended to be recited in the alternative, and also whether the terms are intended to be construed as having identical or different scopes. Examiner requests that Applicant clarify the intended scope and interpretation of these terms.
Claims 1 and 11 are further indefinite because Examiner is unable to determine the metes and bounds of the claims based on the recitation of “analyzing… the brain-wave data based on an artificial intelligence (AI) model, wherein the AI model is trained on a brain-wave dataset associated with a mental disorder, a psychiatric disorder, a neurological disorder, and a neurodegenerative disorder, and at least one additional data comprising a clinical data, a demographic data, an EMR (electronic medical record)/EHR (electronic health record) data, of the individual”. Specifically, the structure of the above limitations makes it unclear whether the portion reciting “and at least one additional data comprising a clinical data, a demographic data, an EMR (electronic medical record)/EHR (electronic health record) data, of the individual” corresponds to the function of analyzing the brain-wave data or the training of the AI model. Effectively, it is unclear whether the claim is intended to recite analyzing the brain-wave data based on an AI model “and at least one additional data comprising a clinical data, a demographic data, an EMR (electronic medical record)/EHR (electronic health record) data, of the individual,” or the AI model being trained on a brain-wave dataset “and at least one additional data comprising a clinical data, a demographic data, an EMR (electronic medical record)/EHR (electronic health record) data, of the individual.”
Claims 2-10 and 12-20 inherit the deficiencies of claims 1 and 11 through dependency and are likewise rejected.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5 and 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Oakley et al EEG Biomarkers to Predict Response to Sertraline and Placebo Treatment in Major Depressive Disorder (hereinafter Oakley) in view of Lih Oh et al Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals (hereinafter Lih Oh).
With respect to claim 1, Oakley discloses the claimed method of EEG-based biomarker discovery and commercialization for personalized diagnosis and treatment of brain disorders, the method comprising:
receiving, using a communication device, a brain-wave data from a diagnostic device, wherein the brain-wave data corresponds to a graphical representation of an electrical activity of a brain of an individual, wherein the individual is receiving at least one therapeutic for at least one brain disorder (Abstract, §II(A)-(B), and Figure 1 describe recording EEG data from a plurality of patients receiving using an EEG device comprising a plurality of electrodes, where each of the patients is receiving either Sertraline or a placebo therapeutic as part of a clinical trial);
analyzing, using a processing device, the brain-wave data based on an artificial intelligence (AI) model, wherein the AI model is trained on a brain-wave dataset associated with a mental disorder, a psychiatric disorder, a neurological disorder, and at least one additional data comprising a clinical data, a demographic data, an EMR (electronic medical record)/EHR (electronic health record) data, of the individual (§II(A) and (D) describe training machine learning models using the clinical study data containing brainwave data associated with Major Depressive Disorder, which is a form of mental disorder, psychiatric disorder, and neurological disorder, as well as whether the individual was a Sertaline responder/non-responder or Placebo responder/non-responder);
generating, using the processing device, at least one output data based on the analyzing, wherein the at least one output data is indicative of a response of the individual to the at least one therapeutic, and an influence of the at least one therapeutic on the individual and transmitting, using the communication device, the at least one data to at least one device (§III describes applying the model to classify individuals according to Sertaline responder/non-responder or Placebo responder/non-responder status);
but does not expressly disclose:
the AI model trained on a brain-wave dataset associated with a neurodegenerative disorder.
However, Lih Oh teaches that it was old and well known in the art of EEG analysis before the effective filing date of the claimed invention to train an AI model on a brainwave dataset associated with a neurodegenerative disorder (§§2, 3, and 3.3 describe training an AI model using an EEG dataset from patients having Schizophrenia).
Therefore it would have been obvious to one of ordinary skill in the art of EEG analysis before the effective filing date of the claimed invention to modify the system of Oakley to train an AI model on a brainwave dataset associated with a neurodegenerative disorder as taught by Lih Oh since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case Oakley already discloses training an AI model on data including a neurological disorder, and including a neurodegenerative disorder as taught by Lih Oh would serve that same function in Oakley, making the results predictable to one of ordinary skill in the art (MPEP 2143).
With respect to claim 2, Oakley/Lih Oh teach the method of claim 1. Oakley further discloses:
pre-processing, using the processing device, the brain-wave data using at least one algorithm to obtain a pre-processed brain-wave data, wherein the pre-processing removes a noise-wave data from the brain-wave data, wherein the brain-wave data comprises the noise-wave data, wherein the pre-processing is based on the AI model (§II(B) describes pre-processing the EEG data to remove noise such as 60Hz AC noise); and
analyzing, using the processing device, the pre-processed brain-wave data based on the AI model, wherein the generating of the at least one output data is further based on the analyzing of the pre-processed brain-wave data (§§II(B) and §III describe applying the model to the processed EEG data to classify the individuals according to Sertaline responder/non-responder or Placebo responder/non-responder status).
With respect to claim 3, Oakley/Lih Oh teach the method of claim 1. Oakley further discloses:
wherein the brain-wave data comprises a plurality of brain-wave data corresponding to a plurality of individuals, wherein the at least one output data comprises a plurality of output data corresponding to the plurality of individuals, wherein the plurality of individuals is associated with a clinical trial of the at least one therapeutic (§II(A) describe the brainwave dataset being from individuals associated with a clinical trial for Sertraline),
wherein the method further comprises:
analyzing, using the processing device, each of the plurality of output data based on a predefined therapeutic response, wherein the predefined therapeutic response influences an outcome of the clinical trial, wherein the analyzing of each of the plurality of output data is based on the AI model (§III describes applying the model to classify individuals according to Sertaline responder/non-responder or Placebo responder/non-responder status, i.e. predefined responses influencing the outcome of the trial);
determining, using the processing device, a plurality of targeted individuals from the plurality of individuals based on the analyzing of each of the plurality of output data (§III describe applying the model to identify individuals who are Sertaline responders/non-responders or Placebo responders/non-responders);
generating, using the processing device, a report data based on the determining of the plurality of targeted individuals, wherein the report data corresponds to a report associated with the plurality of target individuals and transmitting, using the communication device, the report data to the at least one device (§III describe applying the model to identify individuals who are Sertaline responders/non-responders or Placebo responders/non-responders).
With respect to claim 4, Oakley/Lih Oh teach the method of claim 3. Oakley further discloses:
wherein the analyzing of each of the plurality of output data comprises:
assigning a value to each of the plurality of individuals based on the plurality of output data, wherein the value corresponds to a coordinate of a multi-dimensional space; and representing each of the plurality of individuals as the value in the multi-dimensional space based on the assigning of the value, wherein the determining of the plurality of targeted individuals is based on a proximity between a plurality of values associated with the plurality of individuals in the multi-dimensional space (§III and Figure 3 describe clustering the individuals in 2-dimensions).
With respect to claim 5, Oakley/Lih Oh teach the method of claim 3. Oakley further discloses:
wherein the plurality of targeted individuals comprises at least one of a placebo responder and a non-placebo responder, wherein the predefined therapeutic response comprises at least one of a placebo response and a non-placebo response, wherein the placebo responder experiences a placebo-recovery from the at least one brain disorder, wherein the placebo-recovery is associated with a placebo effect, wherein the non-placebo responder experiences a therapeutic-recovery from the at least one brain disorder, wherein the therapeutic-recovery is associated with an active treatment of the at least one therapeutic, wherein the report comprises at least one of a placebo responder report and a non-placebo responder report (§II(A) and (D) describe the individuals being Sertaline responders/non-responders or Placebo responders/non-responders).
With respect to claim 11, Oakley discloses the claimed system of EEG-based biomarker discovery and commercialization for personalized diagnosis and treatment of brain disorders, the system comprising:
a communication device configured for: receiving a brain-wave data from a diagnostic device, wherein the brainwave data corresponds to a graphical representation of an electrical activity of a brain of an individual, wherein the individual is receiving at least one therapeutic for at least one brain disorder and transmitting at least one output data to at least one device (Abstract, §II(A)-(B), and Figure 1 describe recording EEG data from a plurality of patients receiving using an EEG device comprising a plurality of electrodes, where each of the patients is receiving either Sertraline or a placebo therapeutic as part of a clinical trial); and
a processing device communicatively coupled with the communication device, wherein the processing device is configured for: analyzing the brain-wave data based on an artificial intelligence (AI) model, wherein the AI model is trained on a brain-wave dataset associated with a mental disorder, a psychiatric disorder, and a neurological disorder, and at least one additional data comprising a clinical data, a demographic data, an EMR (electronic medical record)/EHR (electronic health record) data, of the individual (§II(A) and (D) describe training machine learning models using the clinical study data containing brainwave data associated with Major Depressive Disorder, which is a form of mental disorder, psychiatric disorder, and neurological disorder, as well as whether the individual was a Sertaline responder/non-responder or Placebo responder/non-responder); and
generating the at least one output data based on the analyzing, wherein the at least one output data is indicative of a response of the individual to the at least one therapeutic, and an influence of the at least one therapeutic on the individual (§III describes applying the model to classify individuals according to Sertaline responder/non-responder or Placebo responder/non-responder status);
but does not expressly disclose:
the AI model trained on a brain-wave dataset associated with a neurodegenerative disorder.
However, Lih Oh teaches that it was old and well known in the art of EEG analysis before the effective filing date of the claimed invention to train an AI model on a brainwave dataset associated with a neurodegenerative disorder (§§2, 3, and 3.3 describe training an AI model using an EEG dataset from patients having Schizophrenia).
Therefore it would have been obvious to one of ordinary skill in the art of EEG analysis before the effective filing date of the claimed invention to modify the system of Oakley to train an AI model on a brainwave dataset associated with a neurodegenerative disorder as taught by Lih Oh since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case Oakley already discloses training an AI model on data including a neurological disorder, and including a neurodegenerative disorder as taught by Lih Oh would serve that same function in Oakley, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Claim 12 recites limitations similar to those recited in claim 2, and is rejected on the same grounds set out above with respect to claim 2.
Claim 13 recites limitations similar to those recited in claim 3, and is rejected on the same grounds set out above with respect to claim 3.
Claim 14 recites limitations similar to those recited in claim 4, and is rejected on the same grounds set out above with respect to claim 4.
Claim 15 recites limitations similar to those recited in claim 5, and is rejected on the same grounds set out above with respect to claim 5.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM G LULTSCHIK whose telephone number is (571)272-3780. The examiner can normally be reached 9am - 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, Fonya Long can be reached at (571) 270-5096. 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.
/Gregory Lultschik/Examiner, Art Unit 3682
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