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 .
Status of Claims
This office action for the 18/326004 application is in response to the communications filed February 06, 2026.
Claims 1, 3, 5, 8, 11, 13 and 18 were amended February 06, 2026.
Claims 2, 4-7, 12 and 14-17 were cancelled February 06, 2026.
Claims 1, 3, 5, 8-11, 13 and 18-20 are currently pending and considered below.
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, 3, 5, 8-11, 13 and 18-20 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.
As per claim 1,
Step 1: The claim recites subject matter within a statutory category as a process.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of a method for predicting persistent post-concussive neuropsychiatric symptoms based on thalamocortical coherence, the method comprising: receiving a first set of biomarkers of a plurality of thalamic sub-nuclei of a patient; wherein the first set of biomarkers are rs-fMRI (resting-state functional magnetic resonance imaging) waveforms obtained by measuring on each of the thalamic sub-nuclei of the patient; receiving a second set of biomarkers of a plurality of cortical regions of the patient, wherein the second set of biomarkers are second rs-fMRI waveforms obtained by measuring on each of the cortical regions of the patient; calculating first magnitude-squared coherence between the first rs-fMRI waveforms of every two of the thalamic sub-nuclei; calculating second magnitude-squared coherence between the second rs-fMRI waveforms of every two of the cortical regions; determining a disease-dominant frequency range based on the first magnitude-squared coherences and the second magnitude-squared coherences; arranging the first magnitude-squared coherences in the disease-dominant frequency range into a first two-dimensional array to obtain a first coherence matrix; arranging the second magnitude-squared coherences in the disease-dominant frequency range into a second two-dimensional array to obtain a second coherence matrix; and predicting a prolonged postconcussive symptom of the patient based on a postconcussive symptom score of the patient for a given time after at least one year, which is predicted based on the first coherence matrix and the second coherence matrix. These steps, as drafted, under the broadest reasonable interpretation recite:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. The identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recites a list of rules or instructions that a human person can follow in the course of their personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“through a machine learning-based predictive model” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0084] of the as-filed specification describes that the hardware implementing the steps of the abstract idea is at a level of a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, 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.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 3,
Claim 3 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 3 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the postconcussive symptom score is a score of Rivermead postconcussive symptom questionnaire.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 8,
Claim 8 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 8 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“further comprising: receiving information about age and sex of the patient; and predicting the postconcussive symptom score of the patient for the given time … based on the first coherence matrix, the second coherence matrix, and the received information” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“through the machine learning-based predictive model” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 9,
Claim 9 depends from claim 8 and inherits all the limitations of the claim from which it depends. Claim 9 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein a first average value of first elements in the first coherence matrix and a second average of second elements in the second coherence matrix are input … to predict the postconcussive symptom score of the patient” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“to the machine learning-based predictive model” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 10,
Claim 10 depends from claim 8 and inherits all the limitations of the claim from which it depends. Claim 10 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the first coherence matrix and the second coherence matrix are input … to predict the postconcussive symptom score of the patient” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“to the machine learning-based predictive model” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 11,
Claim 11 is substantially similar to claim 1. Accordingly, claim 11 is rejected for the same reasons as claim 1.
As per claim 12,
Claim 12 is substantially similar to claim 2. Accordingly, claim 12 is rejected for the same reasons as claim 2.
As per claim 13,
Claim 13 is substantially similar to claim 3. Accordingly, claim 13 is rejected for the same reasons as claim 3.
As per claim 14,
Claim 14 is substantially similar to claim 4. Accordingly, claim 14 is rejected for the same reasons as claim 4.
As per claim 15,
Claim 15 is substantially similar to claim 5. Accordingly, claim 15 is rejected for the same reasons as claim 5.
As per claim 16,
Claim 16 is substantially similar to claim 6. Accordingly, claim 16 is rejected for the same reasons as claim 6.
As per claim 17,
Claim 17 is substantially similar to claim 7. Accordingly, claim 17 is rejected for the same reasons as claim 7.
As per claim 18,
Claim 18 is substantially similar to claim 8. Accordingly, claim 18 is rejected for the same reasons as claim 8.
As per claim 19,
Claim 19 is substantially similar to claim 9. Accordingly, claim 19 is rejected for the same reasons as claim 9.
As per claim 20,
Claim 20 is substantially similar to claim 10. Accordingly, claim 20 is rejected for the same reasons as claim 10.
Subject Matter Free of Prior Art
Claims 1-20 are free of prior art. The Examiner has conducted a thorough search of the prior art and was not able to find a reference, or combination of references, with adequate rationale to combine, to teach the limitations of “calculating a first coherence matrix from the first set of biomarkers; and predicting a postconcussive symptom score of the patient for a given time through a machine learning-based predictive model based on the first coherence matrix” as claimed in claim 1. Claim 11 recites similar limitations. The closes prior art that the Examiner was able to find was:
Mortaheb et al. (Mortaheb, Sepehr, et al. “Neurophysiological biomarkers of persistent post-concussive symptoms: A scoping review.” Frontiers in Neurology, vol. 12, 9 Sept. 2021, https://doi.org/10.3389/fneur.2021.687197; herein referred to as Mortaheb.) discloses a method for predicting persistent post-concussive neuropsychiatric symptoms (the association between neuroimaging and/or neurophysiological biomarkers and the presence or severity of Persistent post-concussive symptoms (PCS) symptoms as measured cross-sectionally or longitudinally with the following scales (non-exhaustive list): Post-concussion Symptom Scale, Post-concussive Symptom Questionnaire, PCS-19, PCS-Negative Impression Management Scale, Sport Concussion Assessment Tool (all versions), International Classification of Diseases-10th edition Diagnostic Criteria for Post-concussion Syndrome Diagnostic and Statistical Manual for Mental Disorders-IV th edition Diagnostic Criteria for Post-concussional Disorder, Rivermead Post-concussion Symptoms Questionnaire, British Columbia Post- concussion Symptom Inventory, Neurobehavioral Symptom Inventory, and Immediate Post-concussion Assessment and Cognitive Testing, [Page 4, Left Column, Paragraph 3], EEG studies could provide an insightful prediction of PCS outcome that could be more easily implemented in clinical practice, [Page 15, Left Column, Paragraph 1]), the method comprising receiving a first set of biomarkers of a patient (Taking all these into account, our review aims to address which neuroimaging features of mTBI are relevant to the clinical expression of PCS and could therefore potentially be used as biomarkers for PCS diagnosis and/or prognosis whether cross-sectionally or longitudinally, [Page 2, Right Column, Paragraph 2]), but fails to teach calculating a first coherence matrix from the first set of biomarkers; and predicting a postconcussive symptom score of the patient for a given time through a machine learning-based predictive model based on the first coherence matrix.
Park (US 2020/0269053) discloses calculating a first coherence matrix (calculating a spectral coherency matrix for each of the at least one selected frequency band, and calculating an eigenvector centrality for each spectral coherency matrix to facilitate identifying a contact for stimulation, Paragraph [0006]), but fails to teach a method for predicting persistent post-concussive neuropsychiatric symptoms based on thalamocortical coherence; receiving a first set of biomarkers of a plurality of thalamic sub-nuclei of a patient; calculating a first coherence matrix from the first set of biomarkers; and predicting a postconcussive symptom score of the patient for a given time through a machine learning-based predictive model based on the first coherence matrix.
Bahado (US 2020/0165680) discloses persistent post-concussive symptoms (Post-concussion symptoms, and other issues that may be interfering with academic progress should be identified and monitored, Paragraph [0147]); receiving a first set of biomarkers of a patient (A large number of potential epigenomic biomarkers were identified for the detection of pediatric concussion, Paragraph [0226]); and predicting a postconcussive symptom of the patient through a machine learning-based model (Multiple AI techniques were used simultaneously to assess and confirm the robustness of epigenomic and metabolomic biomarkers for the detection of pediatric concussion, Paragraph [0262]), but fails to teach a method for predicting persistent post-concussive neuropsychiatric symptoms based on thalamocortical coherence; receiving a first set of biomarkers of a plurality of thalamic sub-nuclei of a patient; calculating a first coherence matrix from the first set of biomarkers; and predicting a postconcussive symptom score of the patient for a given time through a machine learning-based predictive model based on the first coherence matrix.
As it can be seen, no reference here either alone or in combination teaches the limitations of claims 1 or 11. Accordingly, claims 1-20 contain subject matter free of prior art.
Response to Arguments
Applicant's arguments filed February 06, 2026 have been fully considered.
Applicant’s arguments pertaining to objections made, obviate these objections. Accordingly, they are withdrawn.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 101 are not persuasive.
The Applicant argues that the pending claims do not recite an abstract idea because the limitations of claim 1 cannot practically be performed in the human mind.
The Examiner respectfully disagrees. The Examiner has not characterized the abstract idea as “mental processes” but rather as “certain methods of organizing human activity”. The Applicant’s argument here is not relevant to the rejection made.
The Applicant further argues that the pending claims provide an improvement in the function of a computer, or an improvement to other technology or technical field. The claimed limitations provide an improvement in medical imaging-based decision support systems by enabling earlier, data-driven triage and resource allocation for high-risk patients as opposed to merely reciting a generic ‘apply ML to biomarkers’ step. This improvement is provided in the improved accuracy of the predictive machine learning model.
The Examiner respectfully disagrees. Improvement in accuracy of a predictive model is not inherently an improvement to technology. Predictive models per se, are a series of steps that a human is able to perform in the course of their personal behavior. Applying this predictive model to the technical field of machine learning models does not necessarily transfer potential improvements from this abstract idea to a technical field. There has been no discussion about what technical deficits machine learning models themselves have with regard to predictive capacity. All that has been discussed is an alleged improvement in predictive capacity in a predictive model that is based on a particular mathematical process. Humans are more than capable of carrying out these steps without the intervening technology and the alleged improvement would have still been present, or at least this is what the understanding of one of ordinary skill in the art would have been. Without identifying what specific technology deficit is being addressed, there is no reason for the Examiner to conclude that the steps of the pending claims in any way improve technology.
The Applicant further argues that the steps of the pending claims provide something significantly more than the abstract idea because they are not well-known routine and conventional.
The Examiner respectfully disagrees. The analysis of Step 2B relies on additional elements to the abstract idea providing something significantly more than the abstract idea. That is to say if an element is in the abstract idea itself, it cannot also be an additional element to the abstract idea. The only additional element present in claim 1 is “through a machine learning-based predictive model”. The rest of the claim is tied up in the abstract idea itself. An abstract idea itself cannot also be a practical application of the abstract idea.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681
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