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 .
Election/Restrictions
Claims 12-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on August 18, 2026.
Applicant’s election without traverse of Group I, claims 1-11 in the reply filed on August 18, 2026 is acknowledged.
Claim Objections
Claim 2 is objected to because of the following informalities:
In claim 2, in line 1, --- configured – should be inserted after “further”.
In claim 2, in line 2, --- the --- should be inserted before “portions”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3 and 9-11 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sackellares et al. (US Pub No. 2015/0088024).
With regards to claim 1, Sackellares et al. disclose a system for determining an acute brain function impairment of a subject caused by acute drug intoxication, the system comprising:
a sensor system (i.e. EEG recording module) to acquire data indicative of a function of a brain of the subject (paragraphs [0009], [0011]-[0013], referring to EEG data being recorded with an EEG recording module (106), wherein numerous scalp EEG electrodes are applied to the subject to obtain EEG data containing information from brain activity in both temporal and spatial domains and EEG provides direct information about brain functions through analysis of brain electrical activity; Figure 1);
a processor (400) to receive the data from the sensor system (paragraph [0042], referring to the processor system (400), wherein EEG electrodes (424) interface with the processor system (400) to allow for acquisition of EEG signals from a subject; Figures 1, 4) and to:
process the data to determine an amount of functional connectivity between portions of the brain of the subject (paragraphs [0017]-[0019], referring to extracted features of the multichannel EEG data from box (236) being used to provide a quantitative description of the spatiotemporal characteristics of the signals, including “local and global network connectivity characteristics”; paragraph [0142], referring to EEG channels being designated as the vertices of a graph representing a brain functional network, wherein an edge between two vertices signifies a “functional connection” between two EEG channels; paragraph [0138], referring to a brain being a structurally and functionally complex network of neurons, wherein the functional network reflects the connectedness among brain regions in terms of neuronal activity and applying graph theoretical analysis on the EEG data reveals the brain functional network features; Figures 1-2);
analyze the processed data by feeding the processed data into a trained machine learning algorithm (paragraph [0018], referring to the extracted features from box 236 [which includes the functional connectivity characteristics] may be provided as inputs for network modeling in box 239 and for classification of the cerebral condition in box 245 of the condition classification module 115 and wherein after the network model has been constructed in box 239, network features may be extracted in box 242 from the network model and provided to the condition classification module 115 for classification in box 245; paragraph [0104], referring to the use of machine learning techniques to analyze EEG data; paragraph [0133], referring to using classifiers [wherein paragraph [0104] describes classifiers such as support vector machine, convolutional neural network, etc.] to predict seizures, wherein patient-specific training and a test scheme can be implemented; Figures 1-2);
determine an acute impairment of the subject caused by acute drug intoxication (i.e. “drug or alcohol toxicity”) based on the analysis (paragraph [0019], referring to classification of the cerebral condition in box 245 may be based upon comparison of extracted features from boxes 236 and 242 by comparison with established norms to determine if they indicate a normal condition within normal limits or “an abnormal condition” (i.e. acute impairment); paragraph [0040], referring to the system being used for neurological assessment of acute and subacute encephalopathies including such disorders as those due to “toxic encephalopathies (e.g. drug or alcohol toxicity)”; Figures 1-2); and
generate a report indicating the acute impairment of the subject (paragraphs [0020]-[0021], referring to an indication of the classification results being generated in box 251 for rendering on the system display, wherein a warning may be generated when an abnormal condition has be indicated and wherein a summary (or report) of findings may be provided, wherein the report may provide an indication of the determined abnormal category classification, further referring to the display indicating the severity of the abnormalities/impairment; Figures 1-2).
With regards to claim 2, Sackellares et al. disclose that the processor is further configured to compare the amount of functional connectivity between portions of the brain of the subject to a reference amount (i.e. “established norms”/”normative comparison dataset”) of functional connectivity and determine the acute impairment (i.e. abnormality) of the subject when the comparison indicates that the amount of functional connectivity is significantly different (i.e. outside “normal” limits/range) than the reference amount of functional connectivity (paragraph [0019], referring to “Classification of the cerebral condition in box 245 may be based, at least in part, upon comparison of extracted features from boxes 236 and 242 by comparison with established norms to determine if they indicate a normal condition within normal limits or an abnormal condition”, and therefore if the extracted features are outside the normal limits, this indicates that the extracted features [which include functional connectivity] is significantly different than the reference amount (i.e. “established norms”) of functional connectivity; paragraph [0024]; paragraphs [0032]-[0038], referring to the comparison of the data to norms/normative comparison dataset in order to determine the degree of abnormality; Figures 1-3).
With regards to claim 3, Sackellares et al. disclose that the sensor system is to acquire the data from a prefrontal cortex of the subject (paragraphs [0112], [0157], [0165], referring to the “frontal” electrodes which would be positioned on the frontal cortex which includes the “prefrontal” cortex; note further that the placement of the electrodes dictates where the data is from, and the “frontal” electrodes have the capability to be positioned in any region of the frontal cortex, including the prefrontal cortex which is part of the frontal cortex).
With regards to claim 9, Sackellares et al. disclose that the system further comprises a display (412) configured to display the report (paragraphs [0020]-[0021], [0042], referring to the results/report being indicated on the system display, wherein the system includes a display device (412); Figures 1, 4).
With regards to claim 10, Sackellares et al. disclose that the report includes a quantitative indication of acute impairment of the subject (paragraphs [0020]-[0021], referring to the report of findings including an indication of the classification results; paragraphs [0030]-[0033], referring to quantitated measures of signal properties, wherein global characteristics, such as clustering coefficient and minimum path lenghs being compared to norms in box 324 to determine whether or not they are within the normal range, wherein if the values are outside of the normal range, the degree of abnormality (based on standard deviations from the mean) for each electrode channel is determined; paragraph [0155], referring to quantifying the degree of asymmetry of the brain which is regarded as a pathological consequence or unusual phenomenon, wherein the asymmetry can be used as an index (i.e. quantitative value) to quantify the abnormal states; Figures 1-3).
With regards to claim 11, Sackellares et al. disclose that the report includes a qualitative indication of acute impairment of the subject to distinguish between unimpaired and impaired (paragraph [0021], referring to “a default condition may provide a report labeled as normal or indicating the determined abnormal category classification (e.g., mildly abnormal, left hemisphere). In addition, a visual display of the anatomical location of the abnormalities may be provided graphically, using a color bar, grey scale or other graphic display to indicate the severity of the abnormalities”, wherein the visual/graphic display using colors or a gray scale to indicate the severity of the abnormalities provides a qualitative indication of acute impairment/abnormality).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sackellares et al. as applied to claim 1 above, and further in view of Geva et al. (US Pub No. 2016/0038049).
With regards to claim 4, as discussed above, Sackellares et al. meet the limitations of claim 1. Further, Sackellares et al. disclose that the trained machine learning algorithm is trained using impairment data (paragraphs [0120], [0126], [0133], referring to EEG recordings of patients with a seizure being included in a training dataset and thus the training dataset includes “impairment” data as a result of the seizure).
However, Sackellares et al. do not specifically disclose that the trained machine learning algorithm is trained from subjects having been administered an intoxicating drug.
Geva et al. disclose a method of analyzing neurophysiological data recorded from a subject, wherein a feature selection procedure is applied on a training set of the dataset in order to evaluate each feature characterizing the group’s dataset (Abstract; paragraphs [0218]-[0219]). A machine learning architecture includes an architecture trained on impairment data from subjects having been administered a drug (paragraph [0219], [0332], referring to the training set being of the dataset acquired from a group of subjects, such as diseased subjects, etc; paragraphs [0267], [0302]-[0306], [0465], referring to the data being acquired from subjects treated with a drug). Quantitaive assessment of the responsivity to treatment with the drug can thus be determined (paragraphs [0267]-[0271]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the trained machine learning algorithm be trained from subjects having been administered an intoxicating drug, as taught by Geva et al., in order to provide a quantitative assessment of the responsivity to the drug.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sackellares et al. in view of Geva as applied to claim 4 above, and further in view of Gevins et al. (US Pub No. 2003/0013981).
With regards to claim 5, as discussed above, the above combined references meet the limitations of claim 4. However, the above combined references do not specifically disclose that the intoxicating drug is THC.
Gevins et al. disclose an effective, objective testing method and system for evaluating changes in mental function by measuring an individual’s brain function using EEG during a brief cognitive battery and passive control conditions (Abstract; paragraphs [0004], [0014]-[0015]). The method and system is designed to assess an individual’s fundamental cognitive functions and whether those functions have been significantly affected by a variety of factors, including the use of marijuana (Abstract; paragraphs [0014]-[0015], [0211]-[0213]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to substitute the administered drug of the above combined references with an administered intoxicating drug in the form of THC (i.e. marijuana contains THC), as taught by Gevins et al., as the substitution of one known drug for another yields predictable results to one of ordinary skill in the art and further in order to assess whether an individual’s cognitive functions are affected by THC (Abstract; [0014]-[0015], [0211]-[0213]). One of ordinary skill in the art would have been able to carry out such a substitution and the results are reasonably predictable as it would be expected that THC would provide changes in brain activity.
Claim(s) 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sackellares et al. as applied to claim 1 above, and further in view of Sitaram et al. (US Pub No. 2020/0038653).
With regards to claims 6-8, as discussed above, Sackellares et al. meet the limitaitons of claim 1. However, Sackellares et al. do not specifically disclose that the sensor system includes a plurality of near infrared spectroscopy (NIRS) sensors configured to acquire the data, wherein the plurality of NIRS sensors includes sensors configured to be arranged along a forehead of the subject and the system further comprises a headband configured to arrange the sensor system to acquire the data from a prefrontal cortex of the subject.
Sitaram et al. discloses monitoring an impaired brain of a patient at targeted brain areas using a functional NIRS device and generating an output signal from the fNIRS device corresponding to brain activity in the targeted brain areas (paragraph [0007], [0083]-[0084]). fNIRS has benefits including ease of setup and lack of interference from movement artifacts due to associated hardware, as compared, for example, to EEG (paragraph [0036]). A plastic hood that holds the sensors is placed with elastic bands around the participant’s head (paragraph [0084], Figures 1-2, note, as seen in Figure 1, the system comprises a headband configured to arrange the sensor system to acquire the signals from a prefrontal cortex of the subject and further, as seen in Figures 1-2 and 4-5, there are at least eight sensors).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the sensor system of Sackellares et al. include a plurality of near infrared spectroscopy (NIRS) sensors configured to acquire the data, wherein the plurality of NIRS sensors includes sensors configured to be arranged along a forehead of the subject and the system further comprises a headband configured to arrange the sensor system to acquire the data from a prefrontal cortex of the subject, as taught by Sitaram et al., in order to provide an easier setup for measuring brain activity and avoid interference from movement artifacts (paragraph [0036]).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-8 of U.S. Patent No. 12,396,674.
Although the claims at issue are not identical, they are not patentably distinct from each other because although the claims at issue are not identical, they are not patentably distinct from each other because claim 1 of the instant application is generic to all that is recited in claims 1 and 6 of the Patent. That is, claims 1 and 6 of the Patent falls entirely within the scope of instant claim 1, or in other words, instant claim 1 is anticipated by claims 1 and 6 of the Patent. Specifically, because claims 1 and 6 of the Patent claims the same structure (i.e. sensor system, processor processing the data to determine/analyze an amount of functional connectivity, analyze the processed data by feeding the data into a trained machine learning algorithm [see Patent claim 6], determining an acute impairment (i.e. acute physical and/or mental performance deficiencies) caused by acute drug intoxication (i.e. “caused by consumption of intoxicants of the subject”), and generate a report indicating the acute impairment (i.e. “acute physical and/or mental performance deficiencies”) of the subject, etc.), as claimed in instant claim 1, the system of instant claim 1 is anticipated by claims 1 and 6 of the Patent.
With regards to instant claim 2, claim 1 of the Patent sets forth the same limitations.
With regards to instant claim 3, claim 1 of the Patent sets forth the same limitations.
With regards to instant claim 4, claims 6 and 7 of the Patent sets forth the same limitations.
With regards to instant claim 5, claim 7 of the Patent sets forth the same limitations.
With regards to instant claim 6, claim 2 of the Patent sets forth the same limitations.
With regards to instant claim 7, claim 3 of the Patent sets forth the same limitations.
With regards to instant claim 8, claim 4 of the Patent sets forth the same limitations.
With regards to instant claim 9, claim 5 of the Patent sets forth the same limitations.
With regards to instant claim 10, claim 8 of the Patent sets forth the same limitations.
With regards to instant claim 11, claim 1 of the Patent sets forth the same limitations.
Conclusion
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/KATHERINE L FERNANDEZ/Primary Examiner, Art Unit 3798
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