DETAILED ACTION
Response to Amendment
This action is in response to the amendment after non-final filed 20 November 2025. Claims 1-16 are pending, wherein claims 3-6, 13, and 15-16 are withdrawn from consideration.
Response to Arguments
Applicant’s arguments filed with respect to rejection under 35 USC 101 as being directed to an abstract idea have been fully considered and are persuasive. As the claimed invention claims initiating a therapeutic action in response to the analysis techniques, the claims are eligible. Therefore, the rejection has been withdrawn.
Applicant's arguments filed with respect to the rejection under 35 USC 103 as being unpatentable over Gommesen et al. (WO 2011/072684) in view of Quigg et al. (US Publication no. 2011/0230730), further in view of Adkins et al. (US Patent no. 5,928,272), and further in view of Dorfmeister et al. (US Patent no. 7,630,757). have been fully considered but they are not persuasive.
Applicant argues that none of the cited references teach the limitation of claim 1 reciting:
analyzing via the one or more processors a second extra-cerebral body signal of the patient where the second extra-cerebral body signal is one of: the autonomic signal. the non-electrocortical neurologic signal, the metabolic signal, the endocrine signal, and the tissue stress marker signal.
The Examiner respectfully disagrees. The Office action pointed to Gommesen et al. page 10 lines 23-27 for this disclosure. This section teaches that the to achieve more precise recording of preictal phase of epileptic seizure, the heart rate (e.g., electrocardiographic signal) “…may be combined with other measurements including breathing, temperature, perspiration, muscular tensions, tremors/convulsions, or neural response. The measurement of at least a second signal may be carried out by means of at least a second sensor in the sensor unit 1, or at least a second sensor unit which may be connected to the processor unit 2. The second sensor or sensor unit may be an electroencephalographic sensor, an electromyographic sensor, an electrocardiographic sensor, a gyrometer, an accelerometer or another type of sensor intended to perform the desired measurement.” This teaching is considered explicit of the limitation above. The types of signals and sensors for those signals may be properly construed to comprise “the second extra-cerebral body signal is one of: the autonomic signal. the non-electrocortical neurologic signal, the metabolic signal, the endocrine signal, and the tissue stress marker signal”. Further, processor 2 analyzes these signals. For this reason, the rejections under 35 USC 103 have been maintained. Rejections are repeated below.
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 7, and 9-12 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Gommesen et al. (WO 2011/072684) in view of Quigg et al. (US Publication no. 2011/0230730), further in view of Adkins et al. (US Patent no. 5,928,272).
In regard to claim 1, Gommesen et al. discloses a method of seizure detection in a patient, comprising:
detecting via one or more processors 2 (page 8 line 21-25) the epileptic seizure in the patient based on a first extra- cerebral body signal of the patient where the first extra-cerebral body signal is one of: an autonomic signal, a non-electrocortical neurologic signal, a metabolic signal, an endocrine signal, and a tissue stress marker signal (page 8 line 31 – page 9 line 10, page 9 line 28 – page 10 line 9 and lines 23-31; sensor 2 collects signals associated with an epileptic seizure, wherein the signals include electrocardiographic related signals which are considered to comprise autonomic signals as defined by the present specification) ;
analyzing via the one or more processors 2 (para 49) a second extra-cerebral body signal of the patient where the second extra-cerebral body signal is one of: the autonomic signal, the non-electrocortical neurologic signal, the metabolic signal, the endocrine signal, and the tissue stress marker signal (page 10 lines 23-27, the electrocardiographic signal may be processed with other signals such as breathing, temperature, perspiration etc., one sensor is for galvanic skin response; this additional signal may also be electromyographic signal related to kinetic properties of the a patient such as muscularly created signals like a tremor or tension (page 10 lines 11-21) obtained from a gyrometer or accelerometer (page 16 lines 11-20)).
Gommesen et al. collect and analyze the first and second signals to achieve more precise recording of seizure phases that occur just before an epileptic seizure. However, Gommesen et al. does not disclose the following steps:
determining via the one or more processors a first classification index comprising at least one of an epileptic seizure index and a non-epileptic seizure index based on the second extra-cerebral body signal;
classifying via the one or more processors as one of the non-epileptic seizure or the epileptic seizure based on the first classification index;
generating via the one or more processors a seizure classification signal;
and initiating one or more therapeutic actions based on the seizure classification signal;
wherein the first classification index comprises at least one kinetic index.
Quigg et al. describe a system and method for classifying seizures into epileptic and non-epileptic types (para 13). Quigg et al. disclose:
determining via the one or more processors (para 49) a first classification index comprising at least one of an epileptic seizure index and a non-epileptic seizure index based on the second extra-cerebral body signal (para 23, a processor accumulates data representing motor activity (i.e., a kinetic index) to distinguish seizures that may epileptic in nature or non-epileptic (e.g., epileptic pseudoseizures);
classifying via the one or more processors as one of the non-epileptic seizure or the epileptic seizure based on the first classification index (para 23, a processor accumulates data representing motor activity (i.e., a kinetic index) to distinguish seizures that may epileptic in nature or non-epileptic (e.g., epileptic pseudoseizures);
generating via the one or more processors a seizure classification signal (para 52, the distinguished seizure classes are used for diagnostic purposes from which clinical inferences may be made; the generation of the seizure classification signal is considered a logical outcome of the distinguishment steps);
wherein the first classification index comprises at least one kinetic index (e.g., motor activity data), the motor activity in Quigg et al. obtained from sensors that may by indicative of at least an expected range of velocity of motor activity (para 24), expected range of acceleration of motor activity (para 46), expected frequency of motor activity (para 26).
One of ordinary skill in the art at the time of filing would have found it obvious to combine the teachings of Gommesan using autonomic and motor activity signals with the kinetic activity processing of Quigg et al. to distinguish seizures between epileptic and non-epileptic classes to better diagnose and treat a patient’s needs. The modification considered to comprise the combination of complementary diagnostic information regarding seizure onset and type to improve diagnostic clinical inferences.
Neither Gommesan nor Quigg et al. teach using the outcome of the analysis to initiate one or more therapeutic actions based on the seizure classification signal.
Adkins et al. teaches that a neurostimulation device may be automatically activated based on the detection of an epileptic seizure (col 3 lines 42-49).
Modification to utilize the described by the combination of Commesan in view of Quigg et al. in an implantable medical device to treat epilepsy is considered to have been obvious to one of ordinary skill at the time of filing to enhance the seizure detection capability and improve therapeutic efficacy. Moreover, it is obvious to one of ordinary skill in the art to activate a therapy upon detection of epileptic seizures since Adkins et al. expressly teaches that this increases the likelihood of inhibited, if not aborting, the seizure relative soon after onset.
In regard to claim 2, in Quigg et al., an epileptic seizure classification is based on the epileptic seizure index reaching or exceeding a first threshold indicative of the epileptic seizure; or a non-epileptic seizure classification is based on the non-epileptic seizure index reaching or exceeding a second threshold indicative of the non-epileptic seizure (para 81, thresholds are used to distinguish ES from PNES).
In regard to claim 7, neither Gommesen et al., nor Quigg et al. nor Adkins et al. expressly teach further comprising at least one of: issuing a notification of a non-epileptic seizure event, based upon the first classification index indicating a non-epileptic state; logging at least one of a date of the non-epileptic seizure event, a time of occurrence of the non-epileptic seizure event, or a severity of the non-epileptic seizure event; or administering a therapy for the non-epileptic seizure event. However, a step such as issuing a notification of a non-epileptic seizure event is considered implied by the teachings of Quigg et al. as a logical outcome of the of the classification. Additionally, Quigg et al. teach that distinguishing between ES and PNES ensure that appropriate actions for treatment of the subject are made, which is considered to suggest that an appropriate therapy for a non-epileptic seizure vs for an epileptic seizure is administered.
In regard to claim 9, Quigg et al. as relied on teaches classifying the non-epileptic seizure based on the first classification index comprises: classifying the non-epileptic seizure based on the first classification index being within non- epileptic seizure reference values (para 81, thresholds are used to distinguish ES from PNES; the thresholds are considered to comprise reference values).
In regard to claim 10, Gommesen et al. describe a medical device system, comprising: at least one sensor configured to receive at least one of an autonomic signal indicative of an autonomic activity of a patient, a non-electrocortical signal indicative of a neurologic activity of the patient, a metabolic signal indicative of a metabolic activity of the patient, an endocrine signal indicative of an endocrine activity of the patient, or a tissue stress marker signal indicative of a tissue stress marker activity of the patient (page 8 line 31 – page 9 line 10, page 9 line 28 – page 10 line 9 and lines 23-31; sensor 2 collects signals associated with an epileptic seizure, wherein the signals include electrocardiographic related signals which are considered to comprise autonomic signals as defined by the present specification, additionally at page 10 lines 23-27, the electrocardiographic signal may be processed with other signals such as breathing, temperature, perspiration etc., one sensor is for galvanic skin response; this additional signal may also be electromyographic signal related to kinetic properties of the a patient such as muscularly created signals like a tremor or tension (page 10 lines 11-21) obtained from a gyro meter or accelerometer (page 16 lines 11-20));
a seizure detection unit including a first processor and a first storage device with seizure detection instructions configured to detect a seizure in the patient based on the at least one of the autonomic signal, the non-electrocortical signal indicative of the neurologic activity, the metabolic signal, the endocrine signal, or the tissue stress marker signal (i.e., analysis unit 5 with processor 2, page 11 lines 1-15 and page 11 line 14 – page 13 line 28, the analysis unit analyzes collected signals to detect seizures).
Gommesen et al. does not teach:
a classification unit including a second processor and a second storage device with seizure classification instructions configured to classify the seizure as one of an epileptic seizure and a non-epileptic seizure; generating a seizure classification signal.
Quigg et al. describe a system and method for classifying seizures into epileptic and non-epileptic types (para 13). Quigg et al. disclose:
a classification unit including a second processor and a second storage device with seizure classification instructions configured to classify the seizure as one of an epileptic seizure and a non-epileptic seizure; (para 23, a processor accumulates data representing motor activity (i.e., a kinetic index) to distinguish seizures that may epileptic in nature or non-epileptic (e.g., epileptic pseudoseizures);
and generating a seizure classification signal (para 52, the distinguished seizure classes are used for diagnostic purposes from which clinical inferences may be made; the generation of the seizure classification signal is considered a logical outcome of the distinguishment steps).
One of ordinary skill in the art at the time of filing would have found it obvious to combine the teachings of Gommesan using autonomic and motor activity signals with the kinetic activity processing of Quigg et al. to distinguish seizures between epileptic and non-epileptic classes to better diagnose and treat a patient’s needs. The modification considered to comprise the combination of complementary diagnostic information regarding seizure onset and type to improve diagnostic clinical inferences.
Neither Gommesen et al. nor Quigg et al. teach:
initiating one or more therapeutic actions based on the seizure classification signal. However, Quigg et al. does teach that the improved diagnosis may guide appropriate actions for treatment of the subject (para 41).
Adkins et al. teaches that a neurostimulation device may be automatically activated based on the detection of an epileptic seizure (col 3 lines 42-49).
Modification to utilize the described by the combination of Commesan in view of Quigg et al. in an implantable medical device to treat epilepsy is considered to have been obvious to one of ordinary skill at the time of filing to enhance the seizure detection capability and improve therapeutic efficacy. Moreover, it is obvious to one of ordinary skill in the art to activate a therapy upon detection of epileptic seizures since Adkins et al. expressly teaches that this increases the likelihood of inhibited, if not aborting, the seizure relative soon after onset.
In regard to claim 11, Gommesen et al. teach further comprising a signal module capable of collecting signals indicative of a motor activity of the patient based on a non-electrocortical neurological signal, and wherein the classification unit is configured to classify the epileptic seizure based on: at least one kinetic index indicating one of an onset of generalized motor activity based on the motor activity signals (page 10 lines 23-27, the electrocardiographic signal may be processed with other signals such as breathing, temperature, perspiration etc., one sensor is for galvanic skin response; this additional signal may also be electromyographic signal related to kinetic properties of the a patient such as muscularly created signals like a tremor or tension (page 10 lines 11-21) obtained from a gyro meter or accelerometer (page 16 lines 11-20)).
In regard to claim 12, Quigg et al. as relied on, discloses that the first classification index comprises at least one kinetic index (e.g., motor activity data), the motor activity in Quigg et al. obtained from sensors that may by indicative of at least an expected range of velocity of motor activity (para 24), expected range of acceleration of motor activity (para 46), expected frequency of motor activity (para 26).
In regard to claim 14, Quigg et al. teach that therapies effective for epileptic seizures may not be effective for non-epileptic pseudoseizures (para 3). Therefore it would be obvious to enable a therapy unit to withhold an epileptic therapy in the event that the seizure is non-epileptic in order to ensure that the patient receives the proper therapy since Quigg et al. expressly teach that a patient with a non-epileptic seizure would not benefit from epileptic therapies.
Claims 8 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Gommesen et al. (WO 2011/072684) in view of Quigg et al. (US Publication no. 2011/0230730) and Adkins et al. (US Patent no. 5,928,272), further in view of Dorfmeister et al. (US Patent no. 7,630,757).
In regard to claim 8, Gommesen et al. in view of Quigget al. and Adkins et al. are considered to substantially suggest the invention as claimed, however do not teach that a second classification index that includes a metabolic characteristic is additionally used to classify between epileptic and non-epileptic seizures.
Dorfmeister et al. describe a technique that recognizes changes in the cerebral state which may be useful in the detection, prediction, and validation of seizures. Such signals acquired to determine the cerebral state changes useful in the detection, prediction, and validation of seizures include non-electrical cerebral signals signal a concentrations of glucose, free-radicals, metabolic by-products, indices of metabolic activity, and substances such as lactic acid (col 9 lines 29-65).
Modification of the cited prior art to include a second classification index related to metabolic characteristics is considered to have been obvious to one of ordinary skill in the art at the time of the invention since Dorfmeister et al. expressly teach that metabolic characteristics are useful in the detection, prediction, and validation of seizures, wherein combining the cited prior art with the metabolic characteristic would optimize diagnostic capability.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN T GEDEON whose telephone number is (571)272-3447. The examiner can normally be reached M-F 8:00 am to 5:30 PM ET.
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/BRIAN T GEDEON/Primary Examiner, Art Unit 3796
4 December 2025
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