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 .
Response to Amendment
This Office Action is in response to applicant’s communication filed 8 October 2025, in response to the Office Action mailed 15 July 2025. The applicant’s remarks and any amendments to the claims or specification have been considered, with the results that follow.
Information Disclosure Statement
As required by M.P.E.P. 609(c), the applicant's submission of the Information Disclosure Statements, dated 19 September 2025 and 8 October 2025, are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy of the PTOL-1449 forms, initialed and dated by the examiner, are attached to the instant office action.
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 1-4 and 6-8 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 11-13 of copending Application No. 17/709195 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of Application No. 17/709195 anticipate those of the instant application as described below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
As per claim 1, the claim is compared with claim 11 of Application No. 17/709195—where any differences between them have been highlighted (in bold)—as follows:
Instant Application
Application No. 17/709195
A method of neurostimulation based on a clinical response estimate (CRE) biomarker of a patient having an implanted neurostimulation system
A method of modifying an operation of an implanted neurostimulation system of a patient… comprising: determining a clinical response estimate (CRE) biomarker indicative of the patient’s electrographic seizure rate
the method comprising: processing a plurality of key inputs included in a subject-patient dataset to determine an input dataset, wherein the subject-patient dataset includes a plurality of data types that are derived from records electrical activity of the patient’s brain that were sensed and stored by the implanted neurostimulation system, and a plurality of non-physiological features of the patient
A method of modifying an operation of an implanted neurostimulation system of a patient based on information included in a subject-patient dataset comprising ap plurality of patient features that are non-physiological and a plurality of data types that are based on electrical activity of the patient’s brain…applying a machine-learned model to the plurality of key inputs to derive an input dataset from the subject-patient dataset
processing the input dataset to obtain a plurality of model inputs, wherein the model inputs comprise at least one input from two or more of data included in the input dataset and at least one input derived from two or more non-physiological features included in the input dataset
based on information included in a subject-patient dataset comprising ap plurality of patient features that are non-physiological and a plurality of data types that are based on electrical activity of the patient’s brain…processing the input dataset to obtain a plurality of model inputs
and applying a machine-learned CRE model to the plurality of model inputs to determine the CRE biomarker
and applying a machine-learned CRE model to the plurality of model inputs to determine the CRE biomarker
comparing the CRE biomarker to a criterion; and responsive to the CRE biomarker not satisfying the criterion, delivering stimulation through the implanted neurostimulation system
comparing CRE biomarker to a seizure rate criterion; and responsive to the criterion not being met, adjusting the parameter set by changing a detection parameter; and responsive to detection of an electrographic event based on the adjusted detection parameter set, delivering a stimulation therapy to the patient
As per claim 2, see claim 12 of Application No. 17/709195.
As per claim 3, see claim 13 of Application No. 17/709195.
As per claim 4, see claim 11 of Application No. 17/709195.
As per claim 6, see claim 11 of Application No. 17/709195.
As per claim 7, see claim 11 of Application No. 17/709195.
As per claim 8, see claim 11 of Application No. 17/709195, wherein sensed electrical activity and a record of electrical activity comprise at least one data type that is sensed and stored over a time period, and that is characterized by respective values over the time period.
Claims 9-12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 11 of copending Application No. 17/709195 in view of Burton (US 2021/0169417).
This is a provisional nonstatutory double patenting rejection.
As illustrated above, claim 11 of Application ‘195 claims all of the limitations set forth in the instant application, except for displaying the plurality of CRE biomarkers as a function of time over the time period.
Burton teaches displaying the plurality of CRE biomarkers as a function of time over the time period [a wearable device to monitor and analyze various sensor signals for the patient (abstract, etc.) with a display can be used to display real-time biomarker data being monitored (paras. 0105-112, 0215, 0233-237, etc.)].
Application ‘195 and Burton are analogous art, as they are within the same field of endeavor, namely monitoring and analyzing patient-related sensor signals.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include a display device for displaying the monitored patient sensor signal data and associated analysis/markers, as taught by Burton, for the monitored sensor signal data and associated analysis/markers in the system taught by Application ‘195.
Burton provides motivation as [providing these signals to the user via the display provides analyzing and presenting complex scenarios that, when presented, allow optimal scheduling of the individual’s performance and or mood factors versus time efficiency, etc., by the user and provide appropriate alerts to the user (para. 0417, etc.)].
As per claim 10, Application ‘195/Burton teaches displaying the respective values of the at least one data type as a function of time over the time period [a wearable device to monitor and analyze various sensor signals for the patient (Burton: abstract, etc.) with a display can be used to display real-time biomarker data being monitored (Burton: paras. 0105-112, 0215, 0233-237, etc.); which real-time monitored signal data includes the data as a function of time over the time period].
As per claim 11, Application ‘195/Burton teaches displaying an occurrence of an event of interest [a wearable device to monitor and analyze various sensor signals for the patient to indicate events or health conditions of interest (Burton: abstract, etc.) with a display can be used to display real-time biomarker data being monitored as well as event/condition data (Burton: paras. 0105-112, 0215, 0222-224, 0233-242, etc.)].
As per claim 12, Application ‘195/Burton teaches wherein the at least one data type that is sensed and stored over the time period comprises a key input [based on a plurality of data types that are based on electrical activity of the patient’s brain sensed and stored by the implanted neurostimulation system….applying a machine-learned model to the subject-patient dataset to identify a plurality of key inputs comprising a first key input that is derivable from a record of electrical activity of the patient’s brain (Application ‘195: claim 11)].
Claim 13-16 and 26-27 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 11-13, 33, and 34 of copending Application No. 17/709195 in view of Arcot Desai (US 2019/0117978 – cited in an IDS).
This is a provisional nonstatutory double patenting rejection.
As per claim 13, see the rejection of claim 1, above, wherein claim 11 of Application ‘195 claims all of the limitations set forth in the instant application, except for: an apparatus for monitoring a clinical response estimate (CRE) biomarker of a patient having an implanted neurostimulation system, the apparatus comprising: a memory having a plurality of modules; and a processor coupled to the memory and configured to execute operations based on the plurality of modules to: [perform the method].
Arcot Desai teaches an apparatus comprising: a memory having a plurality of modules; and a processor coupled to the memory and configured to execute operations based on the plurality of modules to: [perform the method] [a computing device 1300 that includes the records processor 104 of FIG. 2. The computing device 1300 is specially configured to execute instructions related to the records processing described above with reference to FIGS. 3A, 3B and 3C, including the application of a deep learning algorithm to EEG records and the application of a similarities algorithm to feature vectors. The instructions are referred to below as “records processing instructions.” Computers capable of being specially configured to execute such instructions may be in the form of a laptop, desktop, workstation, or other appropriate computer capable of connecting to the system 100 of FIG. 1. For example, the computing device 1300 may correspond to a programmer 116 (para. 0094, fig. 13, etc.)].
Application ‘195 and Arcot Desai are analogous art, as they are within the same field of endeavor, namely providing therapies (including neurostimulation) in response to detected/historical patient data, via a machine learning model(s).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize a processing system including stored instructions, as taught by Arcot Desai, to implement the method of Application ‘195.
Because both Application ‘195 and Arcot Desai teach methods of training machine learning models to detect patient features/data from various sources in order to control neurostimulation, it would have been obvious to one of ordinary skill in the art to utilize a processing system including stored instructions, as taught by Arcot Desai, to implement the method of Application ‘195, to achieve the predictable result of providing a physical implementation of the claimed method that can be used in a real world application.
As per claim 14, see the rejection of claim 2, above.
As per claim 15, see the rejection of claim 3, above.
As per claim 16, see the rejection of claim 4, above.
As per claim 26, Application ‘195/Arcot Desai teaches wherein the plurality of data types that are derived from records of electrical activity of the patient’s brain comprise at least two of: counts of events in records, rates of events in records, distributions of events in records, classification of records, measures of power, measures of phase amplitude coupling, measures of coherence, spectrogram images, and time-series images [a plurality of data types that are derived from records of electrical activity of the patient’s brain that were sensed and stored (Application ‘195: claim 11; etc.), wherein processing the input dataset comprises filtering a record of electrical activity of the brain (Application ‘195: claim 33), wherein the filtering comprises extracting spectral power in specific frequency bands of the record (Application ‘195: claim 34, etc.) and, in an example operation of the system, one or more input records corresponding to electrical activity of the subject patient are obtained. For example, a clinician may select one or more input EEG records from the database, which may correspond to one or a few EEG records of the subject patient in a spectrogram form. A set of search EEG records stored in a database and corresponding to EEG records of patients other than the subject patient is also obtained. The obtained search EEG records are of the same type as the input EEG records (Arcot Desai: paras. 0028-31, etc.); which includes at least counts/rates of events, classification of records, measures of power, time series images, etc.].
Arcot Desai provides motivation as [The identified information may benefit the subject patient, for example, because it may reveal parameter values that were effectively used in therapy for one of the other patients that may also lead to a desired outcome for the subject patient. For example, the clinician may refer to the identified information in deciding whether to adjust the settings that determine how the subject patient receives a therapy or therapies (para. 0010, etc.)].
As per claim 27, Application ‘195/Arcot Desai teaches wherein the plurality of non-physiological features of the patient comprise at least two of patient demographics, patient diagnoses, patient treatments, implantable neurostimulation system detection settings, implantable neurostimulation system stimulation settings, lead implant locations, and lead type [a plurality of data types that are derived from records of electrical activity of the patient’s brain that were sensed and stored (Application ‘195: claim 11; etc.) In addition to information acquired from a patient's implanted neurostimulator and lead(s) (neurostimulator-reported information), the database 106 may contain a lot of other information about the patient from a variety of sources, e.g., other databases (electronic health records), data entered by a clinician, and the results of other algorithms run on data about the patient. For example, the patient's clinical history may be in the database, including that history which relates to the condition or disorder that led the patient to have the neurostimulation system 102 implanted in the first place (e.g., epilepsy). The database 106 may include information about drug therapy(ies) to which the patient has been subjected (during or before or after euromodulation therapy), such as type of drug, dose, and time of day of dose. The patient's clinical response to a form of therapy may also be in the database (Arcot Desai: para. 0043, etc.) and, for any patient actively being treated with the neuromodulation therapy delivered by an implanted neurostimulation system 102, the programmer 116 should be used to transmit current neurostimulator-reported information (stored EEGs, settings for programmable parameters, etc.) to the database (Arcot Desai: para. 0046, etc.) As mentioned above, clinical information may include a patient's clinical history, clinical response to neuromodulation therapies, past and current neurostimulation system detection parameter settings and electrical stimulation parameter settings, and past and current drug information (Arcot Desai: paras. 0051-52, etc.) and controllable parameters/thresholds for the implantable device (Burdick: paras. 0019, 0030, etc.); which includes at least patient diagnoses, patient treatments, implantable neurostimulation system detection/system stimulation settings, etc.].
Arcot Desai provides motivation as [The identified information may benefit the subject patient, for example, because it may reveal parameter values that were effectively used in therapy for one of the other patients that may also lead to a desired outcome for the subject patient. For example, the clinician may refer to the identified information in deciding whether to adjust the settings that determine how the subject patient receives a therapy or therapies (para. 0010, etc.)].
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 4, 8, 13, 16, 26 and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Genov (US 2019/0246989), in view of Burdick (US 2016/0310739), and further in view of Arcot Desai (US 2019/0117978 – cited in an IDS).
As per claim 1, Genov teaches a method of neurostimulation based on a clinical response estimate (CRE) biomarker of a patient having an implanted neurostimulation system [a system and method for classifying time series data for state identification (abstract, etc.) to detect transitions in intracranial electroencephalogram (iEEG) sensor data, using feature extraction and classification models, and providing an appropriate control waveform for a neurostimulator (para. 0059, fig. 3, etc.) and the system can learn patient-specific feature timescales/windows to maximize the performance of the classifier, to classify irregular neural recordings for the specific patient (paras. 0045-49, etc.) in order to classify brain states, (for example, seizures in epilepsy or tremors in Parkinson's disease) and provide responsive intervention with electrical stimulation on a patient-by-patient basis (paras. 0054-57, 0063, etc.); where the classified/categorized “irregular neural recordings” are learned CRE biomarkers of the patient with the implanted neurostimulator], the method comprising: processing a plurality of key inputs included in a subject-patient dataset to determine an input dataset [feature selection can be performed by an offline model, and provided to the feature extraction models/classifier (para. 0070, etc.); where selected features (by the feature selector(s)) are key inputs of the subject-patient dataset], wherein the subject-patient dataset includes a plurality of data types that are derived from records of electrical activity of the patient’s brain that were sensed and stored by the implanted neurostimulation system, and a plurality of features of the patient [an input stage can include an autoencoder NN for filtering input iEEG signals converted via the ADC, which are then fed to the feature extraction blocks, etc., and where signals are stored and processed by the NURIP processor(s) and associated memory (paras. 0056-60, figs. 3-4, etc.); and the system can learn patient-specific feature timescales/windows to maximize the performance of the classifier for the specific patient (para. 0049, etc.); where iEEG signals are the subject-patient dataset, the derived input dataset includes the converted digital data, the AE filtered/reduced data and the features extracted (as well as the learned patient-specific timescale/window) include at least one feature of the patient]; processing the input dataset to obtain a plurality of model inputs, wherein the model inputs comprise at least one input derived from two or more of data included in the input dataset [an input stage can include an autoencoder NN for filtering input iEEG signals converted via the ADC, which are then fed to the feature extraction blocks, to produce (the input dataset) which is passed to the classifier model to predict/classify seizures, which class/prediction data is passed to the control blocks of the neurostimulator (paras. 0054-56, figs. 3-4, etc.); and the system can learn patient-specific feature timescales/windows to maximize the performance of the classifier, to classify irregular neural recordings for the specific patient (paras. 0045-49, etc.); where the classifier processes the input dataset and its outputs to the neurostimulator control blocks are the “plurality of model inputs”]; comparing a biomarker to a criterion; and responsive to the biomarker not satisfying the criterion, delivering stimulation through the implanted neurostimulation system [a system and method for classifying time series data for state identification (abstract, etc.) to detect transitions in intracranial electroencephalogram (iEEG) sensor data, using feature extraction and classification models, and providing an appropriate control waveform for a neurostimulator (para. 0059, fig. 3, etc.) and the system can learn patient-specific feature timescales/windows to maximize the performance of the classifier, to classify irregular neural recordings for the specific patient (paras. 0045-49, etc.) in order to classify brain states, (for example, seizures in epilepsy or tremors in Parkinson's disease) and provide responsive intervention with electrical stimulation on a patient-by-patient basis (paras. 0054-57, 0063, etc.); where the irregular classification is the biomarker not satisfying a criterion].
While Genov teaches a method of monitoring a clinical response estimate (CRE) biomarker of a patient having an implanted neurostimulation system, where a classifier produces multiple inputs to be used by the control elements of the neurostimulator (see above), it has not been relied upon for teaching wherein the subject-patient dataset includes a plurality of data types including a plurality of non-physiological features of the patient; wherein the model inputs comprise at least one input derived from two or more non-physiological features included in the input dataset; applying a machine-learned CRE model to the plurality of model inputs to determine the CRE biomarker; comparing the CRE biomarker to a criterion; and responsive to the CRE biomarker not satisfying the criterion, delivering stimulation through the implanted neurostimulation system.
Burdick teaches applying a machine-learned CRE model to the plurality of model inputs to determine the CRE biomarker [the neurostimulator device can include a machine learning model/method (machine-learned CRE model) operable to learn/optimize one or more stimulation parameters based upon the electrical signals (CRE biomarkers) being received from a patient (paras. 0015, see also: 0027, 0192-199, 0203-207, etc.); using the plurality of model inputs from the classifier model of Genov, above], comparing the CRE biomarker to a criterion; and responsive to the CRE biomarker not satisfying the criterion, delivering stimulation through the implanted neurostimulation system [The at least one processor may be configured to perform a machine learning method (based on the signals received from the sensor interface) to determine a set of stimulation parameters. In such embodiments, the at least one processor may modify the at least one complex stimulation pattern based at least in part on the set of stimulation parameters, and the at least one processor may be configured to transmit the recorded electrical signals to the computing device and to receive information therefrom. The at least one processor may be configured to modify the at least one complex stimulation pattern based at least in part on the information received from the computing device (paras. 0015-19; see also: 0027, 0192-199, 0203-207, etc.); for the stimulation criteria of Genov, above].
Genov and Burdick are analogous art, as they are within the same field of endeavor, namely optimizing neurostimulation systems using machine learning methods/models.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the neurostimulator parameter optimization (machine learned) model, taught by Burdick, in the implanted neurostimulator control utilizing machine learning models for predicting specific seizures/events, of the system taught by Genov.
Burdick provides motivation as [research has shown a wide variability among patients in the most effective means for providing neurostimulation (para. 0055, 0192, etc.) and utilizing a machine learning model for optimization of the neurostimulator’s parameters allows the system to provide patient-customized stimuli, compensate for errors, and adapt the stimuli over time (paras. 0082, 0197, etc.)].
Arcot Desai teaches wherein the subject-patient dataset includes a plurality of data types that are derived from records of electrical activity of the patient’s brain that were sensed and stored by the implanted neurostimulation system [In an example operation of the system, one or more input records corresponding to electrical activity of the subject patient are obtained. For example, a clinician may select one or more input EEG records from the database, which may correspond to one or a few EEG records of the subject patient in a spectrogram form. A set of search EEG records stored in a database and corresponding to EEG records of patients other than the subject patient is also obtained. The obtained search EEG records are of the same type as the input EEG records (paras. 0028-31, etc.)], and a plurality of non-physiological features of the patient; wherein the model inputs comprise at least one input derived from two or more non-physiological features included in the input dataset [In addition to information acquired from a patient's implanted neurostimulator and lead(s) (neurostimulator-reported information), the database 106 may contain a lot of other information about the patient from a variety of sources, e.g., other databases (electronic health records), data entered by a clinician, and the results of other algorithms run on data about the patient. For example, the patient's clinical history may be in the database, including that history which relates to the condition or disorder that led the patient to have the neurostimulation system 102 implanted in the first place (e.g., epilepsy). The database 106 may include information about drug therapy(ies) to which the patient has been subjected (during or before or after euromodulation therapy), such as type of drug, dose, and time of day of dose. The patient's clinical response to a form of therapy may also be in the database (paras. 0028, 0043, etc.) and, for any patient actively being treated with the neuromodulation therapy delivered by an implanted neurostimulation system 102, the programmer 116 should be used to transmit current neurostimulator-reported information (stored EEGs, settings for programmable parameters, etc.) to the database (para. 0046, etc.) As mentioned above, clinical information may include a patient's clinical history, clinical response to neuromodulation therapies, past and current neurostimulation system detection parameter settings and electrical stimulation parameter settings, and past and current drug information (paras. 0051-52, etc.); which includes inputs derived from electrical activity as well as a number of non-physiological features].
Genov/Burdick and Arcot Desai are analogous art, as they are within the same field of endeavor, namely providing therapies (including neurostimulation) in response to detected/historical patient data, via a machine learning model(s).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include both electrical signal data and non-physiological features as patient data for inputs, as taught by Arcot Desai, as inputs to the model for classification and stimulation control in the system taught by Genov/Burdick.
Arcot Desai provides motivation as [The identified information may benefit the subject patient, for example, because it may reveal parameter values that were effectively used in therapy for one of the other patients that may also lead to a desired outcome for the subject patient. For example, the clinician may refer to the identified information in deciding whether to adjust the settings that determine how the subject patient receives a therapy or therapies (para. 0010, etc.)].
As per claim 4, Genov/Burdick/Arcot Desai teaches wherein processing the input dataset to obtain a plurality of model inputs comprises: applying a machine-learned model to records of electrical activity of the brain to obtain a model input corresponding to a numeric value [the feature extraction models form a feature vector out of features extracted from the converted iEEG inputs, for input to the classifier (Genov: paras. 0049-51, 0063; figs. 3, 5B; etc.), where the feature extraction blocks can be used to implement univariate (signal-band energy (SE)) and multivariate (phase locking value (PLV) and cross frequency coupling (CFC)) neural signal processing (Genov: paras. 0059-61, fig. 5B, etc.); where the feature vector used by the classifier is thus a combination of a plurality of data types and a combination of a plurality of patient features].
As per claim 8, Genov/Burdick/Arcot Desai teaches wherein: the subject-patient dataset and the input dataset derived therefrom comprise at least one data type that is sensed and stored over a time period, and that is characterized by respective values over the time period, and the processing and applying are performed a plurality of times for the time period to determine a corresponding plurality of CRE biomarkers [a system and method for classifying time series data for state identification (abstract, etc.) to detect transitions in intracranial electroencephalogram (iEEG) sensor data, using feature extraction and classification models, and providing an appropriate control waveform for a neurostimulator (Genov: para. 0059, fig. 3, etc.) and the system can learn patient-specific feature timescales/windows to maximize the performance of the classifier, to classify irregular neural recordings for the specific patient (Genov: paras. 0045-49, etc.); where the classified/categorized “irregular neural recordings” are learned CRE biomarkers from the time-series sensor data, and where iEEG data is characterized by respective values over the time period(s)].
As per claim 13, see the rejection of claim 1, above, wherein Genov/Burdick/Arcot Desai also teaches a system comprising: an implantable neurostimulation system; and an apparatus for monitoring a clinical response estimate (CRE) biomarker of a patient having an implanted neurostimulation system, the apparatus comprising: a memory having a plurality of modules; and a processor coupled to the memory and configured to execute operations based on the plurality of modules to: [perform the method] [a system and method for classifying time series data for state identification and neurostimulation (Genov: abstract, etc.) using one or more processors and one or more memory units containing instructions to be executed by the processor(s) (Genov: paras. 0013, 0038, 0054; fig. 3; Burdick: paras. 0033, 0101; etc.)].
As per claim 16, see the rejection of claim 4, above.
As per claim 26, Genov/Burdick/Arcot Desai teaches wherein the plurality of data types that are derived from records of electrical activity of the patient’s brain comprise at least two of: counts of events in records, rates of events in records, distributions of events in records, classification of records, measures of power, measures of phase amplitude coupling, measures of coherence, spectrogram images, and time-series images [the system can learn patient-specific feature timescales/windows (count/rate/distribution of the events) to maximize the performance of the classifier, to classify irregular neural recordings for the specific patient (Genov: paras. 0043-49, etc.) in order to classify brain states, (for example, seizures in epilepsy or tremors in Parkinson's disease) and provide responsive intervention with electrical stimulation on a patient-by-patient basis (Genov: paras. 0054-57, 0063, etc.); The definition of a performance function that characterizes human motor behavior (e.g. standing or stepping behavior) may depend upon at least two factors: (1) what kinds of motor performance data is available (e.g., video-based motion capture data, foot pressure distributions, accelerometers, EMG measurements, etc.); and (2) the ability to quantify motor performance. While more sensory data is preferable, a machine learning approach to parameter optimization can employ various types of sensory data related to motor performance (Burdick: para. 0203, etc.); which includes at least counts/rates of events, classification of records, measures of power, time series images, etc.].
As per claim 27, Genov/Burdick/Arcot Desai teaches wherein the plurality of non-physiological features of the patient comprise at least two of patient demographics, patient diagnoses, patient treatments, implantable neurostimulation system detection settings, implantable neurostimulation system stimulation settings, lead implant locations, and lead type [In addition to information acquired from a patient's implanted neurostimulator and lead(s) (neurostimulator-reported information), the database 106 may contain a lot of other information about the patient from a variety of sources, e.g., other databases (electronic health records), data entered by a clinician, and the results of other algorithms run on data about the patient. For example, the patient's clinical history may be in the database, including that history which relates to the condition or disorder that led the patient to have the neurostimulation system 102 implanted in the first place (e.g., epilepsy). The database 106 may include information about drug therapy(ies) to which the patient has been subjected (during or before or after euromodulation therapy), such as type of drug, dose, and time of day of dose. The patient's clinical response to a form of therapy may also be in the database (Arcot Desai: para. 0043, etc.) and, for any patient actively being treated with the neuromodulation therapy delivered by an implanted neurostimulation system 102, the programmer 116 should be used to transmit current neurostimulator-reported information (stored EEGs, settings for programmable parameters, etc.) to the database (Arcot Desai: para. 0046, etc.) As mentioned above, clinical information may include a patient's clinical history, clinical response to neuromodulation therapies, past and current neurostimulation system detection parameter settings and electrical stimulation parameter settings, and past and current drug information (Arcot Desai: paras. 0051-52, etc.) and controllable parameters/thresholds for the implantable device (Burdick: paras. 0019, 0030, etc.); which includes at least patient diagnoses, patient treatments, implantable neurostimulation system detection/system stimulation settings, etc.].
Claim(s) 2, 3, 6, 7, 14, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Genov (US 2019/0246989), in view of Burdick (US 2016/0310739), further in view of Arcot Desai (US 2019/0117978 – cited in an IDS), and further in view of Yuan et al. (Using Class-Specific Feature Selection for Cancer Detection with Gene Expression Profile Data of Platelets, March 2020, pgs. 1-16).
As per claim 2, Genov/Burdick/Arcot Desai teaches wherein processing the input dataset comprises applying a first machine-learned model to a first subset of the input dataset to obtain one or more of the plurality of model inputs [An input digital stage can include an autoencoder neural network for both iEEG spatial filtering and dimensionality reduction. Dedicated feature extraction blocks can be used to implement univariate (signal-band energy (SE)) and multivariate (phase locking value (PLV) and cross frequency coupling (CFC)) neural signal processing. A proceeding support vector machine (SVM) accelerator employs these features for brain state classification. A further processor can be used to facilitate additional custom feature extraction and system control, as suitable. In response to a detection of a pathological brain state, an appropriate modulation waveform is generated to control the operation of the current-mode neurostimulator… In an exemplary embodiment, an array of three configurable neural signal feature extractors, shown in FIG. 5B, can be used to enable custom patient-specific processing to maximize classifier performance (Genov: paras. 0054, 0059-61; figs. 3, 5B; etc.), and feature selection can be performed by an offline model, to be provided to the feature extraction models/classifier (Genov: para. 0070, etc.); where the SVM/classifier is the first machine-learned model and the features selected to be provided as input to the classifier are the first subset of the input dataset. Examiner’s Note: because any set is a subset of itself, the entire input dataset is also a first subset of the input dataset].
While Genov/Burdick/Arcot Desai teaches that a first machine-learned model is trained on datasets that include types derived from records of electrical activity of the brain including ictal and interictal data (see, e.g., Genov: paras. 0042-44, etc.), it has not been relied upon for teaching wherein the first machine-learned model is trained on datasets that include data types derived from records of electrical activity of the brain classified as ictal records, and exclude data types of records of electrical activity of the brain classified as interictal records.
Yuan teaches wherein processing the input dataset comprises applying a first machine-learned model to a first subset of the input dataset to obtain one or more of the plurality of model inputs [a number of elastic nets combined with binary classifiers are used to select and extract class-specific feature sets, which are sent to class-specific PSVM classifiers, which are then combined to produce and output prediction (pgs. 5-6, section 3 and fig. 2; etc.); where the PSVM 1 is the first machine-learned model processing the first subset of the input dataset (class-specific feature set 1)], wherein the first machine-learned model is trained on datasets that include data types derived from records of electrical activity of the brain classified as ictal records, and exclude data types of records of electrical activity of the brain classified as interictal records [a number of elastic nets combined with binary classifiers are trained and used to select and extract class-specific feature sets, which are sent to class-specific PSVM classifiers, which are then combined to produce and output prediction (pgs. 5-6, section 3 and fig. 2; etc.); where the PSVM 1 is the first-machine learned model processing the first subset of the input dataset (class-specific feature set 1), and where the class-specific feature set 1 is a data type derived from record of electrical activity of the brain of a specific class (ictal records of Genov/Burdick, above) and excludes data types (other class-specific feature sets) of electrical activity of the brain of other specific classes (interictal records of Genov/Burdick, above)].
Genov/Burdick/Arcot Desai and Yuan are analogous art, as they are within the same field of endeavor, namely imbalanced data classification for health condition detection/prediction.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize class specific feature extraction/selection- and classification models, as taught by Yuan, for ictal and interictal classes of electrical activity of the brain used for feature extraction/selection and classification in the system taught by Genov/Burdick/Arcot Desai.
Yuan provides motivation as [The multi-class classification problems can be decomposed into a series of binary classification problems by OVA strategy, where one class is treated as the positive class and the rest classes are treated as the negative class. These binary classification problems are solved with global features in general. However, for a special positive class, the global features may not be the best class-specific features. If the class-specific features of positive classes are similar, then using the global features are suitable for these binary classification problems. However, if the class-specific features of positive classes are very different from one another, then using class-specific features to solve these binary classification problems will achieve better results than using global features… the global features may not be the optimal features for the multi-class classification problem, and using the class-specific features of each binary classification problem obtained by the decomposition strategy would be easier to distinguish the positive class from the negative class. Therefore, if the effects of the binary classifiers can be improved by the class-specific features, then the classification result of the multi-class classification problem can be improved as well (pg. 5, section 3; etc.), where using this strategy with class-specific features is more resistant to the impact of class-imbalance than traditional methods using global features (pg. 11, section 5.2; etc.); for improving/fixing the class imbalance problem mentioned by Genov (see, e.g., Genov: para. 0042, etc.)].
As per claim 3, Genov/Burdick/Arcot Desai teaches wherein processing the input dataset comprises applying a second machine-learned model to a second subset of the input dataset to obtain one or more of the plurality of model inputs [An input digital stage can include an autoencoder neural network for both iEEG spatial filtering and dimensionality reduction. Dedicated feature extraction blocks can be used to implement univariate (signal-band energy (SE)) and multivariate (phase locking value (PLV) and cross frequency coupling (CFC)) neural signal processing. A proceeding support vector machine (SVM) accelerator employs these features for brain state classification. A further processor can be used to facilitate additional custom feature extraction and system control, as suitable. In response to a detection of a pathological brain state, an appropriate modulation waveform is generated to control the operation of the current-mode neurostimulator… In an exemplary embodiment, an array of three configurable neural signal feature extractors, shown in FIG. 5B, can be used to enable custom patient-specific processing to maximize classifier performance (Genov: paras. 0059-61; figs. 3, 5B; etc.), and feature selection can be performed by an offline model, to be provided to the feature extraction models/classifier (Genov: para. 0070, etc.); where the SVM/classifier is the second machine-learned model and the features selected to be provided as input to the classifier are the second subset of the input dataset. Examiner’s Note: because any set is a subset of itself, the entire input dataset is also a second subset of the input dataset].
While Genov/Burdick/Arcot Desai teaches that a first machine-learned model is trained on datasets that include types derived from records of electrical activity of the brain including ictal and interictal data (see, e.g., Genov: paras. 0042-44, etc.), it has not been relied upon for teaching wherein the second machine-learned model is trained on datasets that include data types derived from records of electrical activity of the brain classified as interictal records, and exclude data types of records of electrical activity of the brain classified as ictal records.
Yuan teaches wherein processing the input dataset comprises applying a second machine-learned model to a second subset of the input dataset to obtain one or more of the plurality of model inputs [a number of elastic nets combined with binary classifiers are used to select and extract class-specific feature sets, which are sent to class-specific PSVM classifiers, which are then combined to produce and output prediction (pgs. 5-6, section 3 and fig. 2; etc.); where the PSVM i or m is the second machine-learned model processing the second subset of the input dataset (class-specific feature set i or m)], wherein the second machine-learned model is trained on datasets that include data types derived from records of electrical activity of the brain classified as interictal records, and exclude data types of records of electrical activity of the brain classified as ictal records [a number of elastic nets combined with binary classifiers are trained and used to select and extract class-specific feature sets, which are sent to class-specific PSVM classifiers, which are then combined to produce and output prediction (pgs. 5-6, section 3 and fig. 2; etc.); where the PSVM i or m is the second machine-learned model processing the second subset of the input dataset (class-specific feature set i or m), and where the class-specific feature set i or m is a data type derived from record of electrical activity of the brain of a specific class (interictal records of Genov, above) and excludes data types (other class-specific feature sets) of electrical activity of the brain of other specific classes (ictal records of Genov, above)].
Genov/Burdick/Arcot Desai and Yuan are analogous art, as they are within the same field of endeavor, namely imbalanced data classification for health condition detection/prediction.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize class specific feature extraction/selection- and classification models, as taught by Yuan, for ictal and interictal classes of electrical activity of the brain used for feature extraction/selection and classification in the system taught by Genov/Burdick/Arcot Desai.
Yuan provides motivation as [The multi-class classification problems can be decomposed into a series of binary classification problems by OVA strategy, where one class is treated as the positive class and the rest classes are treated as the negative class. These binary classification problems are solved with global features in general. However, for a special positive class, the global features may not be the best class-specific features. If the class-specific features of positive classes are similar, then using the global features are suitable for these binary classification problems. However, if the class-specific features of positive classes are very different from one another, then using class-specific features to solve these binary classification problems will achieve better results than using global features… the global features may not be the optimal features for the multi-class classification problem, and using the class-specific features of each binary classification problem obtained by the decomposition strategy would be easier to distinguish the positive class from the negative class. Therefore, if the effects of the binary classifiers can be improved by the class-specific features, then the classification result of the multi-class classification problem can be improved as well (pg. 5, section 3; etc.), where using this strategy with class-specific features is more resistant to the impact of class-imbalance than traditional methods using global features (pg. 11, section 5.2; etc.); for improving/fixing the class imbalance problem mentioned by Genov (see, e.g., Genov: para. 0042, etc.)].
As per claim 6, Genov/Burdick/Arcot Desai teaches further comprising determining the plurality of key inputs by one or a combination of: applying a model to the subject-patient dataset to identify the plurality of key inputs; and applying a model to records of electrical activity of the brain to obtain a key input corresponding to a numeric value [feature selection can be performed by an offline model, and provided to the feature extraction blocks and classifier (Genov: para. 0070, etc.); where the feature selection model is applied to the subject-patient dataset/brain activity to identify the plurality of key inputs corresponding to numeric values of brain activity types].
While Genov/Burdick/Arcot Desai teaches performing feature selection and feature extraction to determine the plurality of key inputs (see above), it has not been relied upon for teaching wherein these models are machine-learned models.
Yuan teaches wherein determining the plurality of key inputs comprises one or a combination of: applying a machine-learned model to the subject-patient dataset to identify the plurality of key inputs; and applying a machine-learned model to records of electrical activity of the brain to obtain a key input corresponding to one of a brain activity type and a numeric value [elastic net and classifier (machine-learned) models are used for class-specific feature selection and extraction (pgs. 5-6, section 3 and fig. 2; etc.); for the feature selection and feature extraction blocks/models applied to records of electrical activity of the brain for determining the key inputs in Genov, above, so that the machine-learned feature selection models are the machine-learned model applied to the subject-patient dataset/records of electrical activity of the brain to identify selected features (key inputs)].
Genov/Burdick/Arcot Desai and Yuan are analogous art, as they are within the same field of endeavor, namely imbalanced data classification for health condition detection/prediction.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize machine-learned, class specific feature extraction and selection models, as taught by Yuan, for the feature selection and feature extraction blocks used for determining the key inputs from records of electrical activity of the brain in the system taught by Genov/Burdick/Arcot Desai.
Yuan provides motivation as [The multi-class classification problems can be decomposed into a series of binary classification problems by OVA strategy, where one class is treated as the positive class and the rest classes are treated as the negative class. These binary classification problems are solved with global features in general. However, for a special positive class, the global features may not be the best class-specific features. If the class-specific features of positive classes are similar, then using the global features are suitable for these binary classification problems. However, if the class-specific features of positive classes are very different from one another, then using class-specific features to solve these binary classification problems will achieve better results than using global features… the global features may not be the optimal features for the multi-class classification problem, and using the class-specific features of each binary classification problem obtained by the decomposition strategy would be easier to distinguish the positive class from the negative class. Therefore, if the effects of the binary classifiers can be improved by the class-specific features, then the classification result of the multi-class classification problem can be improved as well (pg. 5, section 3; etc.), where using this strategy with class-specific features is more resistant to the impact of class-imbalance than traditional methods using global features (pg. 11, section 5.2; etc.); for improving/fixing the class imbalance problem mentioned by Genov (see, e.g., Genov: para. 0042, etc.)].
As per claim 7, Genov/Burdick/Arcot Desai/Yuan teaches wherein processing a plurality of key inputs included in a subject-patient dataset to determine an input dataset comprises applying a machine-learned model to the plurality of key inputs [feature selection can be performed by an offline model, to be provided to the feature extraction blocks and classifier (Genov: para. 0070, etc.) by using elastic net and classifier (machine-learned) models for class-specific feature selection and extraction (Yuan: pgs. 5-6, section 3 and fig. 2; etc.); where the class-specific feature selection models are the machine-learned model producing selected features (key inputs) and the feature extraction models are the machine-learned model applied to the feature space of the selected features].
Examiner’s Note: the reasoning and motivation for the combination of these teaching of Genov and Yuan are provided in the rejection of claim 6, above.
As per claim 14, see the rejection of claim 2, above.
As per claim 15, see the rejection of claim 3, above.
Claim(s) 9-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Genov (US 2019/0246989), in view of Burdick (US 2016/0310739), further in view of Arcot Desai (US 2019/0117978 – cited in an IDS), and further in view of Burton (US 2021/0169417).
As per claim 9, Genov/Burdick/Arcot Desai teaches the method of claim 8, as described above.
While Genov/Burdick/Arcot Desai also teaches that the implanted processors/device may be connected to external processing devices (see, e.g., Genov: para. 0047, etc.), it has not been relied upon for teaching displaying the plurality of CRE biomarkers as a function of time over the time period.
Burton teaches displaying the plurality of CRE biomarkers as a function of time over the time period [a wearable device to monitor and analyze various sensor signals for the patient (abstract, etc.) with a display can be used to display real-time biomarker data being monitored (paras. 0105-112, 0215, 0233-237, etc.)].
Genov/Burdick/Arcot Desai and Burton are analogous art, as they are within the same field of endeavor, namely monitoring and analyzing patient-related sensor signals.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include a display device for displaying the monitored patient sensor signal data and associated analysis/markers, as taught by Burton, for the monitored sensor signal data and associated analysis/markers in the system taught by Genov/Burdick/Arcot Desai.
Burton provides motivation as [providing these signals to the user via the display provides analyzing and presenting complex scenarios that, when presented, allow optimal scheduling of the individual’s performance and or mood factors versus time efficiency, etc., by the user and provide appropriate alerts to the user (para. 0417, etc.)].
As per claim 10, Genov/Burdick/Arcot Desai/Burton teaches displaying the respective values of the at least one data type as a function of time over the time period [a wearable device to monitor and analyze various sensor signals for the patient (Burton: abstract, etc.) with a display can be used to display real-time biomarker data being monitored (Burton: paras. 0105-112, 0215, 0233-237, etc.); which real-time monitored signal data includes the data as a function of time over the time period].
As per claim 11, Genov/Burdick/Arcot Desai /Burton teaches displaying an occurrence of an event of interest [a wearable device to monitor and analyze various sensor signals for the patient to indicate events or health conditions of interest (Burton: abstract, etc.) with a display can be used to display real-time biomarker data being monitored as well as event/condition data (Burton: paras. 0105-112, 0215, 0222-224, 0233-242, etc.)].
As per claim 12, Genov/Burdick/Arcot Desai/Burton teaches wherein the at least one data type that is sensed and stored over the time period comprises a key input [feature selection can be performed by an offline model, to be provided to the feature extraction models/classifier (Genov: para. 0070, etc.); where the selected features are the key inputs from the time series sensor data over the time period].
Response to Arguments
The double patenting rejections have been updated based upon the amendments filed.
Applicant’s arguments, see the remarks, filed 8 October 2025, with respect to the rejections under 35 U.S.C. 101 have been fully considered and are persuasive, in view of the amendments made to the independent claims (providing a practical application for the judicial exception). The rejections of claims 1-16 have been withdrawn.
Applicant’s arguments, see the remarks, filed 8 October 2025, with respect to the rejection(s) of claim(s) 1-16 under 35 U.S.C. 103 have been fully considered and are persuasive, in view of the amendments made to the claims. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Arcot Desai, which has been relied upon for teaching utilization of multiple non-physiological features in the patient dataset (see above).
Conclusion
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i): claims 5 and 17-25 are cancelled; claims 1-4, 6-16, 26, and 27 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kabrams (US 2020/0194120 – cited in an IDS) – discloses a system for energy efficient brain monitoring including utilizing various filters on EEG data before passing to a machine-learned classifier to predict certain events/symptoms.
Sabesan (US 2016/0310070 – cited in an IDS) – discloses a system utilizing unsupervised training of machine learning models for predicting a response to neurostimulation.
Parhi (US 10,426,365) – discloses a system for detection/prediction of seizure activity from EEG signals, including utilizing feature extraction and feature selection models providing inputs to a machine learned classifier.
Schiff (US 2013/0102919) – discloses a system using machine learned models for control in patients with Parkinson’s disease, including using multiple observer models to represent interictal and ictal time periods.
Snyder (US 2008/0208074) – discloses a system using machine learned models for detection/prediction of seizures and providing alerts, all in a patient-specific manner, including predicting pro-ictal time periods and utilizing different combinations of ictal/interictal/other data types/periods for training.
Snyder (US 2008/0183096) – discloses a system/method using multiple, separate machine learned models for classifying/predicting contra-ictal and pro-ictal states, including using EEG signal data labeled by a physician/expert.
Pineau et al. (Treating Epilepsy via Adaptive Neurostimulation: A Reinforcement Learning Approach, Aug 2009, pgs. 227-240) – discloses a system/method utilizing RL models to optimize neurostimulation in epilepsy patient(s).
Hoppmann (US 2021/0304893 – cited in an IDS) – discloses tracking patient condition data over time, as well as comparing to other patients/baselines.
Osorio (US 10,993,652 – cited in an IDS) – discloses utilizing inputs including patient and environmental condition data.
The examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/GEORGE GIROUX/Primary Examiner, Art Unit 2128