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 08/13/2025 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Regarding the arguments against the rejection of claims under 35 USC 101, the Examiner respectfully disagrees. Applicant argues that the claims integrate the abstract idea into a practical application because the claimed technology improves over previous EEG processing systems. Examiner asserts that the training of the statistical model using annotated EEG data recites generic computer implementation as there is no specific training technique used with the annotated EEG data to modify the model. This is demonstrated in the Applicant’s Specification as noted in the updated Office Action rejection: [Page 35 lines 1-4, Page 19 lines 10-23] recites the generically configured statistical model and retrieving it from a storage device. [Page 34 lines 20-32, Page 35 lines 7-9, Page 36 lines 10-16] recites the training EEG data for patients in clinical trial with annotations with EEG signature descriptions. [Page 34 lines 20-32, Page 35 lines 7-9] recites the use of multiple methods for generically training the model using the annotated EEG data, where the annotated data is retrieved from storage as recited in [Page 34 lines 16-20]. The use of the trained model to computationally handle large volumes of data, automating the process of EEG signal interpretation, identifying novel features and signatures of EEG data, and speeding up the processing are merely the result of using the computing device, see MPEP 2106.05(f), specifically” "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).”
Regarding the arguments against the rejection of claims under 35 USC 102, Examiner agrees and therefore this rejection is withdrawn.
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 1-22 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
It is appropriate for the Examiner to determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance to the Subject Matter Eligibility Test as recited in the following Steps: 1, 2A, and 2B, see MPEP 2106(III.).
Patent Subject Matter Eligibility Test: Step 1:
First, the Examiner is to establish whether the claim falls within any statutory category including a process, a machine, manufacture, or composition of matter, see MPEP 2106.03(II.) and MPEP 2106.03(I).
Claims 1-7 and 8-13 are related to systems, and claims 14-22 are also related to a method (i.e., a process). Accordingly, these claims are all within at least one of the four statutory categories.
Patent Subject Matter Eligibility Test: Step 2A- Prong One:
Step 2A of the Subject Matter Eligibility Test demonstrates whether a clam is directed to a judicial exception, see MPEP 2106.04(I.). Step 2A is a two-prong inquiry, where Prong One establishes the judicial exception. Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes, see MPEP 2106.04(II.)(A.)(1.) and 2106.04(a)(2).
Independent claim 1 includes limitations that recite at least one abstract idea as underlined in the following limitations. Specifically, independent claim 1 recites:
An electroencephalography (EEG) processing system for training one or more statistical models to analyze neurophysiology associated with Alzheimer's disease, the system comprising:
an EEG detector device comprising an array of sensors;
one or more processors;
a computer-readable storage media coupled to the one or more processors; and
an analysis pipeline implemented by the one or more processor configured to ingest and process electrical signals from the EEG detector device wherein said one or more computer-readable storage media are further configured to store processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:
retrieving at least one trained statistical model from at least one storage device, wherein the trained statistical model is trained on a plurality of annotated EEG data, wherein the plurality of EEG training data includes at least one annotation describing an entity of interest selected from a group comprising:
specific dementia diagnosis, cognitive scores, behavioral scores, rate of cognitive decline, survival time, drug response, patient level phenotype, genetic mutations, protein biomarkers, imaging biomarkers, likelihood of adverse reaction, inclusion or exclusion criteria for a clinical trial;
processing, using the at least one trained statistical model, EEG data from a subject to generate relevant output labels for said entity of interest, by:
identifying one or more EEG signatures in the EEG data from the subject; and
generating the relevant output labels for said entity of interest based at least in part on the identified one or more EEG signatures; and
storing the predicted entity of interest on the at least one storage device.
The Examiner submits that the foregoing underlined limitations constitute a “mental process”, as the following abstract limitations are related to observations, evaluations and judgments that can be practically performed in the human mind:
“process” electrical signals from the EEG detector device, which is an abstract limitation related to an observation and evaluation of gathered EEG data,
“processing” EEG data from a subject to “generate” relevant output labels for said entity of interest by “identifying” one or more EEG signatures in the EEG data from the subject, which is an abstract limitation related to an observation and evaluation of gathered EEG data, evaluation of the EEG signatures in the EEG data, and further make an evaluation on output labels for entities of interest.
Accordingly, the claim recites the steps for analyzing neurophysiology associated with Alzheimer's disease that can practically be performed in the human mind.
Furthermore, dependent claims further define the at least one abstract idea, and thus fails to make the abstract idea any less abstract as noted below:
Claim 2 recites further abstract limitations of “processing” EEG data from a subject to “generate” relevant output labels for EEG features or signatures, “extracting” values for one or more features or signatures from the EEG data that is annotated, and “processing” the values for the one or more features extracted from the annotated EEG data to “predict” an entity of interest from a group of entities, further describing the abstract idea. Claim 4 recites further abstract limitations that further describe the values for one or more features or signatures, further describing the abstract idea.
Additionally, claim 8 recites:
An electroencephalography (EEG) processing system for training one or more statistical models to analyze neurophysiology associated with Alzheimer's disease, the system comprising:
an EEG detector device comprising an array of sensors;
one or more processors;
a computer-readable storage media coupled to the one or more processors; and
an analysis pipeline implemented by the one or more processor configured to ingest and process electrical signals from the EEG detector device,
wherein said one or more computer-readable storage media are further configured to store processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:
accessing a plurality of training annotated EEG recordings associated with a group of patients in a randomized controlled clinical trial or an observational study, wherein each of the plurality of training annotated EEG recordings is associated with diagnosis data for a respective patient, wherein each of the plurality of training EEG recordings includes at least one annotation describing an EEG feature signature or characteristic category for a portion of the recording, wherein the plurality of training annotated EEG recordings includes:
a first plurality of annotated EEG recordings associated with a first group of patients with Alzheimer's disease, and
a second plurality of annotated EEG recordings associated with a second group of patients belonging to a control group without Alzheimer's disease or with a specific alternate diagnosis;
training one or more statistical models based on said plurality of training EEG data and said plurality of annotations;
storing the one or more trained models on at least one storage device; and
processing, using the one or more trained models, EEG data to predict the diagnosis of a new individual or new group of patients not previously used to train said one or more statistical models by:
identifying one or more EEG signatures in the EEG data; and
generating a prediction of the diagnosis of the new individual or new group of patients.
The Examiner submits that the foregoing underlined limitations constitute a “mental process”, as the following abstract limitations are related to observations, evaluations and judgments that can be practically performed in the human mind:
“process” electrical signals from the EEG detector device, which is an abstract limitation related to an observation and evaluation of gathered EEG data,
“processing” EEG data to “predict” the diagnosis of a new individual or new group of patients not previously used to train said one or more statistical models by “identifying” one or more EEG signatures in the EEG data to generate the prediction, which is an abstract limitation of an observation, evaluation of the EEG for the EEG signatures, and evaluation using the gathered EEG data to make a judgment on the diagnosis of a new individual or group.
Accordingly, the claim recites the steps for analyzing neurophysiology associated with Alzheimer's disease that can practically be performed in the human mind.
Furthermore, dependent claims further define the at least one abstract idea, and thus fails to make the abstract idea any less abstract as noted below:
Claim 9 recites further abstract limitations of “processing” EEG data to “predict” accelerated cognitive decline in a new individual or new group of patients not previously used to train the models, further describing the abstract idea. Claim 10 recites further abstract limitations of “processing” EEG data to “predict” whether a new individual or new group of patients not previously used to train the model meet inclusion and exclusion criteria for a clinical trial, further describing the abstract idea. Claim 11 recites further abstract limitations of “processing” EEG data to “predict” whether a new individual or new group of patients not previously used to train the model is treatment with said therapy, further describing the abstract idea. Claim 12 recites further abstract limitations of “processing” EEG data to “predict” the clinical response of a new individual or new group of patients not previously sued to train the model to either said therapy used to treat the first group of patients or said therapy used to treat the second group of patients, further describing the abstract idea. Claim 13 recites further abstract limitations of “processing” EEG data to “predict” the likelihood of adverse events of a new individual or new group of patients not previously used to train said model to ether said therapy used to treat the first group of patient or said therapy used to treatment he second group of patients, further describing the abstract idea.
Additionally, claim 14 recites:
A method comprising acts of:
receiving electroencephalography (EEG) signals associated with a subject;
processing, by a statistical model, the EEG signals associated with the subject by:
identifying one or more EEG signatures in the EEG signals associated with the subject; and
performing a determination, by the statistical model, of an annotation of the EEG signals associated with the subject relevant to neurophysiology associated with Alzheimer's disease based on the identified one or more EEG signatures, wherein the statistical model is trained on a plurality of annotated EEG data.
The Examiner submits that the foregoing underlined limitations constitute a “mental process”, as the following abstract limitations are related to observations, evaluations and judgments that can be practically performed in the human mind:
“processing” the EEG signals associated with a subject by “identifying” one or more EEG sigs in the EEG signals, which is an abstract limitation of an observation and evaluation of the gathered EEG signals and evaluate to identify the signatures,
“determining” an annotation of the EEG signals associated with the subject relevant to a neurophysiology associated with Alzheimer’s disease based on the identified EEG signatures, which is an abstract limitation of an observation and judgment for annotations of the gathered EEG signals relevant for the neurophysiology for Alzheimer’s disease and the previous abstract limitation of the identified EEG signatures.
Accordingly, the claim recites the steps for analyzing neurophysiology associated with Alzheimer's disease that can practically be performed in the human mind.
Furthermore, dependent claims further define the at least one abstract idea, and thus fails to make the abstract idea any less abstract as noted below:
Claim 15 recites further abstract limitations of the determined annotation, further describing the abstract idea. Claim 16 recites further abstract limitations of “identifying”, responsive to the previous determination, whether the subject has Alzheimer’s disease, further describing the abstract idea. Claim 17 recites further abstract limitations of “identifying”, responsive to the previous determination, whether the subject is more likely to have a neurogenerative disease other than Alzheimer disease, further describing the abstract idea. Claim 18 recites further abstract limitations of “identifying”, responsive to the previous determination, whether the subject should be categorized in a subgroup of subjects having similar neurophysiology, further describing the abstract idea. Claim 19 recites further abstract limitations of “identifying”, responsive to the previous determination, whether the subject is predicted to have clinically relevant biomarkers, imaging features, or clinical assessments further describing the abstract idea. Claim 20 recites further abstract limitations of “identifying”, responsive to the previous determination, whether the subject is indicated for a therapeutic intervention, further defining the abstract idea. Claim 21 recites further abstract limitations of “identifying”, responsive to the previous determination, whether the subject is predicted to have a clinical benefit to one or more therapies, further describing the abstract idea. Claim 22 recites further abstract limitations of “identifying”, responsive to the previous determination, whether the subject is predicted to have an adverse event response to receiving or more therapies, further describing the abstract idea.
Any limitations not identified above as part of the abstract idea are deemed “additional elements” (i.e., processor) and will be discussed in further detail below.
Accordingly, the claims as a whole recite at least one abstract idea.
Patent Subject Matter Eligibility Test: Step 2A- Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrates the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exceptions into a “practical application,” see MPEP 2106.04(II.)(A.)(2.) and 2106.04(d)(I.).
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
Regarding claim 1:
An electroencephalography (EEG) processing system for training one or more statistical models to analyze neurophysiology associated with Alzheimer's disease, the system comprising (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)):
an EEG detector device comprising an array of sensors (merely data gathering steps as noted below, see MPEP 2106.05(g) and buySAFE, Inc. v. Google, Inc.);
one or more processors;
a computer-readable storage media coupled to the one or more processors; and (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f))
an analysis pipeline implemented by the one or more processor configured to ingest (merely data gathering steps as noted below, see MPEP 2106.05(g) and buySAFE, Inc. v. Google, Inc.) and process electrical signals from the EEG detector device wherein said one or more computer-readable storage media are further configured to store processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)):
retrieving at least one trained statistical model from at least one storage device, wherein the trained statistical model is trained on a plurality of annotated EEG data, wherein the plurality of EEG training data includes at least one annotation describing an entity of interest selected from a group comprising:
specific dementia diagnosis, cognitive scores, behavioral scores, rate of cognitive decline, survival time, drug response, patient level phenotype, genetic mutations, protein biomarkers, imaging biomarkers, likelihood of adverse reaction, inclusion or exclusion criteria for a clinical trial (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
processing, using the at least one trained statistical model (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)), EEG data from a subject to generate relevant output labels for said entity of interest by:
identifying one or more EEG signatures in the EEG data from the subject; and
generating the relevant output labels for said entity of interest based at least in part on the identified one or more EEG signatures; and
storing the predicted entity of interest on the at least one storage device (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)).
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitation of the overall EEG processing system that trains statistical models which includes processors, memory, a computer-readable storage media coupled to the one or more processors, an analysis pipeline or process EEG signals, retrieving at least one trained statistical model from at least one storage device, wherein the trained statistical model is trained on a plurality of annotated EEG data where the annotations describe entity of interests from a group, using the trained model to process the EEG data, and storing the predicted entity of interest on the at least one storage device, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [Page 45] of the Applicant’s Specification recites the overall generic computing system with processors that is used for the analysis pipeline. [Page 35 lines 1-4, Page 19 lines 10-23] recites the generically configured statistical model and retrieving it from a storage device. [Page 34 lines 20-32, Page 35 lines 7-9] recites the use of multiple methods for generically training the model using the annotated EEG data, where the annotated data is retrieved from storage as recited in [Page 34 lines 16-20]. [Page 37 lines 5-16] recites an example of using the generic trained model to perform steps. [Page 19 lines 6-23] recites the use of a generic storage layer to store data. The additional elements recite the use of generic computing components and computer implemented algorithms related to training machine learning models with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer.
Regarding the additional limitation of the use of an EEG detector device comprising an array of sensors that are used by the system to ingest EEG signals, this is merely pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)). [Page 23 lines 1-11, Page 11 lines 20-31] of the Applicant’s Specification recites the use of the head-mounted EEG detector device to gather the EEG signals that are used in the pipeline for the system and where the data is stored. The use of the sensors to generate EEG signals are used to perform actions for the system including data gathering for the abstract idea, and thus recites insignificant pre-solution activities.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set below:
Claim 2 recites additional elements of a first trained statistical model from storage device where the model is trained with annotated EEG data as described and is used to perform steps, and where there is a second model retrieved that is trained on features from annotated EEG data that is also used to perform steps, and where part of the abstract idea is stored on the storage device; because of the generic nature of the training, models, and storage, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components Claim 3 recites further additional elements that detail the EEG features or signatures that is used as the training data, however the use of this data as training for the model is recited generically, and the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components. Claim 5 recites an additional element of merely providing the predicted entity of interest as a companion diagnostic to indicate Alzheimer’s for a clinician, however this recites an insignificant impractical application by merely outputting part of the abstract idea, and thus recites insignificant post-solution activity. Claim 6 recites additional elements of describing the models as a convolutional neural net, however the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components. Claim 7 recites additional elements of the models including a generalized linear model, a random forest, a support vector machine, or a gradient boosted tree, however the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components.
Taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Regarding claim 8:
An electroencephalography (EEG) processing system for training one or more statistical models to analyze neurophysiology associated with Alzheimer's disease, the system comprising (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)):
an EEG detector device comprising an array of sensors (merely data gathering steps as noted below, see MPEP 2106.05(g) and buySAFE, Inc. v. Google, Inc.);
one or more processors;
a computer-readable storage media coupled to the one or more processors; and (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f))
an analysis pipeline implemented by the one or more processor configured to ingest (merely data gathering steps as noted below, see MPEP 2106.05(g) and buySAFE, Inc. v. Google, Inc.) and process electrical signals from the EEG detector device,
wherein said one or more computer-readable storage media are further configured to store processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)):
accessing a plurality of training annotated EEG recordings associated with a group of patients in a randomized controlled clinical trial or an observational study, wherein each of the plurality of training annotated EEG recordings is associated with diagnosis data for a respective patient, wherein each of the plurality of training EEG recordings includes at least one annotation describing an EEG feature signature or characteristic category for a portion of the recording, wherein the plurality of training annotated EEG recordings includes:
a first plurality of annotated EEG recordings associated with a first group of patients with Alzheimer's disease, and
a second plurality of annotated EEG recordings associated with a second group of patients belonging to a control group without Alzheimer's disease or with a specific alternate diagnosis (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
training one or more statistical models based on said plurality of training EEG data and said plurality of annotations (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
storing the one or more trained models on at least one storage device; and (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f))
processing, using the one or more trained models (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)), EEG data to predict the diagnosis of a new individual or new group of patients not previously used to train said one or more statistical models by:
identifying one or more EEG signatures in the EEG data; and
generating a prediction of the diagnosis of the new individual or new group of patients.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitation of the overall EEG processing system that trains statistical models which includes processors, memory, a computer-readable storage media coupled to the one or more processors, an analysis pipeline or process EEG signals, accessing a plurality of training annotated EEG recordings associated with a group of patients in a randomized controlled clinical trial or an observational study, wherein each of the plurality of training annotated EEG recordings is associated with diagnosis data for a respective patient, wherein each of the plurality of training EEG recordings includes at least one annotation describing an EEG feature signature or characteristic category for a portion of the recording, where the annotated EEG records includes groups of patients with and without Alzheimer’s, training one or more statistical models based on said plurality of training EEG data and said plurality of annotations, storing the one or more trained models on at least one storage device, and using the trained models for processing the EEG data, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [Page 45] of the Applicant’s Specification recites the overall generic computing system with processors that is used for the analysis pipeline. [Page 35 lines 1-4, Page 19 lines 10-23] recites the generically configured statistical model and retrieving it from a storage device. [Page 34 lines 20-32, Page 35 lines 7-9, Page 36 lines 10-16] recites the training EEG data for patients in clinical trial with annotations with EEG signature descriptions. [Page 34 lines 20-32, Page 35 lines 7-9] recites the use of multiple methods for generically training the model using the annotated EEG data, where the annotated data is retrieved from storage as recited in [Page 34 lines 16-20]. [Page 19 lines 10-23] recites storing the trained model on a generic storage device. [Page 37 lines 5-16] recites using the generic trained model to perform steps. The additional elements recite the use of generic computing components and computer implemented algorithms related to training machine learning models with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer.
Regarding the additional limitation of the use of an EEG detector device comprising an array of sensors that are used by the system to ingest EEG signals, this is merely pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)). [Page 23 lines 1-11, Page 11 lines 20-31] of the Applicant’s Specification recites the use of the head-mounted EEG detector device to gather the EEG signals that are used in the pipeline for the system and where the data is stored. The use of the sensors to generate EEG signals are used to perform actions for the system including data gathering for the abstract idea, and thus recites insignificant pre-solution activities.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set below:
Claims 9-13 recite additional elements of the training data further described to include a first and second plurality of annotated EEG records as described in each claim, and where the model is used to perform steps of the abstract idea, however the recitation of the data to be used for the training of the models are recited at a generic level, and the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components.
Taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Regarding claim 14:
A method comprising acts of:
receiving electroencephalography (EEG) signals associated with a subject (merely data gathering steps as noted below, see MPEP 2106.05(g) and buySAFE, Inc. v. Google, Inc.);
processing, by a statistical model (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)), the EEG signals associated with the subject by:
identifying one or more EEG signatures in the EEG signals associated with the subject; and
performing a determination, by the statistical model, of an annotation of the EEG signals associated with the subject relevant to neurophysiology associated with Alzheimer's disease based on the identified one or more EEG signatures, wherein the statistical model is trained on a plurality of annotated EEG data (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)).
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitation of the method with the use of a statistical model to carry out steps and wherein the statistical model is trained on a plurality of annotated EEG data, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [Page 35 lines 1-4, Page 19 lines 10-23] of the Applicant’s Specification recites the use of the generic statistical model. [Page 34 lines 20-32, Page 35 lines 7-9, Page 36 lines 10-16] recites the training EEG data for patients in clinical trial with annotations with EEG signature descriptions. [Page 34 lines 20-32, Page 35 lines 7-9] recites the use of multiple methods for generically training the model using the annotated EEG data, where the annotated data is retrieved from storage as recited in [Page 34 lines 16-20]. The additional elements recite the use of generic computing components such as statistical models with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer.
Regarding the additional limitation of receiving electroencephalography (EEG) signals associated with a subject, this is merely pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)). [Page 23 lines 1-11, Page 11 lines 20-31] of Applicant’s Specification recites the steps of receiving the EEG signals. The step of receiving the EEG data is used to perform an action for the system including data gathering for the abstract idea, and thus recites insignificant pre-solution activities.
Taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination for each independent claim adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to analyze neurophysiology associated with Alzheimer's disease, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception, see MPEP 2106.04(d), 2106.05(a), 2106.05(b).
Thus, taken alone and in ordered combination, the additional elements do not integrate the at least one abstract idea into a practical application.
Patent Subject Matter Eligibility Test: Step 2B:
Regarding Step 2B of the Subject Matter Eligibility Test, the independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application, see MPEP 2106.05(II.). Further, it may need to be established, when determining whether a claim recites significantly more than a judicial exception, that the additional elements recite well understood, routine, and conventional activities, see MPEP 2106.05(d).
Regarding claim 1:
Regarding the additional limitation of the overall EEG processing system that trains statistical models which includes processors, memory, a computer-readable storage media coupled to the one or more processors, an analysis pipeline or process EEG signals, retrieving at least one trained statistical model from at least one storage device, wherein the trained statistical model is trained on a plurality of annotated EEG data where the annotations describe entity of interests from a group, using the trained model to process the EEG data, and storing the predicted entity of interest on the at least one storage device, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f) and MPEP § 2106.05(d)(II), specifically “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93”). [Page 45] of the Applicant’s Specification recites the overall generic computing system with processors that is used for the analysis pipeline. [Page 35 lines 1-4, Page 19 lines 10-23] recites the generically configured statistical model and retrieving it from a storage device. [Page 34 lines 20-32, Page 35 lines 7-9] recites the use of multiple methods for generically training the model using the annotated EEG data, where the annotated data is retrieved from storage as recited in [Page 34 lines 16-20]. [Page 37 lines 5-16] recites an example of using the generic trained model to perform steps. [Page 19 lines 6-23] recites the use of a generic storage layer to store data. The additional elements recite the use of generic computing components and computer implemented algorithms related to training machine learning models with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer and does not recite significantly more than the judicial exception. Storing and retrieving the relevant annotations for the EEG data from storage recites well understood, routine, and conventional activity.
Regarding the additional limitation of the use of an EEG detector device comprising an array of sensors that are used by the system to ingest EEG signals, this is merely pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g) and MPEP § 2106.05(d)(II), specifically “buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)”). [Page 23 lines 1-11, Page 11 lines 20-31] of the Applicant’s Specification recites the use of the head-mounted EEG detector device to gather the EEG signals that are used in the pipeline for the system and where the data is stored. The use of the sensors to generate EEG signals are used to perform actions for the system including data gathering for the abstract idea, and thus recites insignificant pre-solution activities and does not recite significantly more than the judicial exception. The EEG device that is used to gather the EEG data sends the EEG data over a network to the device for processing the signals, where the use of the sensor, as recited in the Specification for gathering data, and the transmission of the data recite well understood, routine, and conventional activities.
Regarding claim 8:
Regarding the additional limitation of the overall EEG processing system that trains statistical models which includes processors, memory, a computer-readable storage media coupled to the one or more processors, an analysis pipeline or process EEG signals, accessing a plurality of training annotated EEG recordings associated with a group of patients in a randomized controlled clinical trial or an observational study, wherein each of the plurality of training annotated EEG recordings is associated with diagnosis data for a respective patient, wherein each of the plurality of training EEG recordings includes at least one annotation describing an EEG feature signature or characteristic category for a portion of the recording, where the annotated EEG records includes groups of patients with and without Alzheimer’s, training one or more statistical models based on said plurality of training EEG data and said plurality of annotations, storing the one or more trained models on at least one storage device, and using the trained models for processing the EEG data, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f) and MPEP § 2106.05(d)(II), specifically “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93”). [Page 45] of the Applicant’s Specification recites the overall generic computing system with processors that is used for the analysis pipeline. [Page 35 lines 1-4, Page 19 lines 10-23] recites the generically configured statistical model and retrieving it from a storage device. [Page 34 lines 20-32, Page 35 lines 7-9, Page 36 lines 10-16] recites the training EEG data for patients in clinical trial with annotations with EEG signature descriptions. [Page 34 lines 20-32, Page 35 lines 7-9] recites the use of multiple methods for generically training the model using the annotated EEG data, where the annotated data is retrieved from storage as recited in [Page 34 lines 16-20]. [Page 19 lines 10-23] recites storing the trained model on a generic storage device. [Page 37 lines 5-16] recites using the generic trained model to perform steps. The additional elements recite the use of generic computing components and computer implemented algorithms related to training machine learning models with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer and does not recite significantly more than the judicial exception. Storing and retrieving the relevant annotations for the EEG data from storage recites well understood, routine, and conventional activity.
Regarding the additional limitation of the use of an EEG detector device comprising an array of sensors that are used by the system to ingest EEG signals, this is merely pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g) and MPEP § 2106.05(d)(II), specifically “buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)”). [Page 23 lines 1-11, Page 11 lines 20-31] of the Applicant’s Specification recites the use of the head-mounted EEG detector device to gather the EEG signals that are used in the pipeline for the system and where the data is stored. The use of the sensors to generate EEG signals are used to perform actions for the system including data gathering for the abstract idea, and thus recites insignificant pre-solution activities and does not recite significantly more than the judicial exception. The EEG device that is used to gather the EEG data sends the EEG data over a network to the device for processing the signals, where the use of the sensor, as recited in the Specification for gathering data, and the transmission of the data recite well understood, routine, and conventional activities.
Regarding claim 14:
Regarding the additional limitation of the method with the use of a statistical model to carry out steps and wherein the statistical model is trained on a plurality of annotated EEG data, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [Page 35 lines 1-4, Page 19 lines 10-23] of the Applicant’s Specification recites the use of the generic statistical model. [Page 34 lines 20-32, Page 35 lines 7-9, Page 36 lines 10-16] recites the training EEG data for patients in clinical trial with annotations with EEG signature descriptions. [Page 34 lines 20-32, Page 35 lines 7-9] recites the use of multiple methods for generically training the model using the annotated EEG data, where the annotated data is retrieved from storage as recited in [Page 34 lines 16-20]. The additional elements recite the use of generic computing components such as statistical models with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer and does not recite significantly more than the judicial exception.
Regarding the additional limitation of receiving electroencephalography (EEG) signals associated with a subject, this is merely pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g) and MPEP § 2106.05(d)(II), specifically “buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)”). [Page 23 lines 1-11, Page 11 lines 20-31] of Applicant’s Specification recites the steps of receiving the EEG signals. The step of receiving the EEG data is used to perform an action for the system including data gathering for the abstract idea, and thus recites insignificant pre-solution activities. The receiving of EEG signals as recited in the Specification is for gathering data, and the transmission of the data recite well understood, routine, and conventional activities.
The dependent claims for each independent claim do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exceptions for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
For the reasons stated, the claims fail the Subject Matter Eligibility Test and therefore claims 1-22 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
The following references are significant, however do not teach the invention individually nor in combination:
US 2023/0255564 A1 to Pascual-Leone et al. teaches of a system for gathering EEG recordings and the use of a model that is trained using joint probability distributions