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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 27 April 2026 has been entered.
Claim Rejections - 35 USC § 103
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.
Claims 1-6, 8, 9, 12, 16, 18, 21, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Greene’629 (US Pub No. 2007/0213629 – previously cited) in view of Osorio’580 (US Pub No. 2018/0116580 – previously cited) further in view of Armstrong et al.’188 (US Pub No. 2014/0074188 – previously cited) further in view of Das et al.’056 (US Pub No. 2019/0080056 – previously cited) further in view of Giftakis et al.’214 (US Pub No. 2010/0121214 – previously cited), as evidenced by Giftakis et al.’629 (US Pub No. 2011/0245629 – previously cited).
Regarding claims 1 and 8, Greene’629 discloses a system comprising: a memory (Figure 7, memory 516, sections [0089] and [0134]); a plurality of electrodes (Figure 4, electrodes 412,414, sections [0091] and [0109]); sensing circuitry configured to: sense, via at least two electrodes of the plurality of electrodes, electrical signals from a patient (see Figures 5 and 6 and descriptions thereof); and generate, based on the electrical signals, physiological information (see Figures 5 and 6 and descriptions thereof); processing circuitry configured to: receive, from the sensing circuitry, the physiological information (see Figure 7 and description thereof); periodically determine, based on the physiological information, a seizure metric indicative of a respective seizure status of the patient at an initial seizure detection frequency (see Figure 7 and description thereof; It is noted that the processing circuitry of Greene’629 inherently determines the seizure metric according to an initial seizure detection frequency); and store the seizure metric in the memory (sections [0089] and [0134]); and a housing carrying the plurality of electrodes and containing both of the sensing circuitry and the processing circuitry (Figure 2, housing 226, sections [0042-0043] and [0091]), wherein the housing is configured to be implanted within the patient subcutaneously (sections [0095-0096]).
Greene’629 discloses all of the elements of the current invention, as discussed above, except for the processing circuitry being configured to periodically determine, based on the physiological information and motion data received from an accelerometer, the seizure metric.
Osorio’580 teaches that a seizure metric may be determined based on a combination of brain activity signals and accelerometer signals (section [0043]). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the processing circuitry of Greene’629 to be configured to periodically determine, based on the physiological information and motion data acquired by an accelerometer within the housing, the respective seizure metric indicative of a respective seizure status of the patient at the initial seizure detection frequency, as it would merely be combining prior art elements according to known methods to yield predictable results.
Greene’629 in view of Osorio’580 discloses all of the elements of the current invention, as discussed above, except for responsive to determining an arrhythmia, increasing the seizure detection frequency to expediate a determination of a subsequent seizure metric indicative of a subsequent seizure status of the patient.
Armstrong et al.’188 discloses an implantable medical device that detects both seizures and cardiac arrhythmias, wherein the detection of seizures and arrhythmias is used to provide neurostimulation when it is determined that neurostimulation is needed (sections [0046] and [0055]). Armstrong et al.’188 further discloses that an arrhythmia is determined based on an electrocardiogram signal (electrical activity of the heart) acquired by an electrode coupled to a patient’s heart (section [0031]). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the processing circuitry of Greene’629 in view of Osorio’580 to be configured to also detect cardiac arrhythmias based on an electrocardiogram signal, as taught by Armstrong et al.’188, as it would allow the system of Greene’629 in view of Osorio’580 to provide neurostimulation for both seizures and cardiac arrhythmias. Official notice is being taken that it is well known in the art that seizures can cause the occurrence of cardiac arrhythmias.
As evidenced by Giftakis et al.’629, electrocardiogram information is capable of being acquired by sensing circuitry within an implantable housing (sections [0226-0227]). Therefore, it would have been obvious to one of ordinary skill in the art that the system and method of Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 would include sensing circuitry configured to generate physiological information comprising electrocardiogram information within its implantable housing.
Das et al.’056 teaches increasing the frequency by which a method is performed/a patient condition is obtained (i.e., a detection frequency), responsive to determining that one sensor produces data that indicates an abnormal medical condition. Das et al.’056 teaches that increasing the frequency of method execution allows a system to respond more quickly if a patient’s condition continues to deteriorate and an emergency is detected (sections [0062-0063]: “It should be appreciated that the increased sensor sampling rate may also cause an increase in the rate at which the method 400 is executed. For example, the edge device may obtain a patient condition at block 420 more frequently due to the increased amount of sensor data resulting from the increased sampling rate. Thus, the edge device 120 may be able to respond more quickly if the patient’s condition continues to deteriorate and an emergency is detected.” – emphasis added). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the processing circuitry of Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 to be configured to increase the seizure detection frequency responsive to determining an arrhythmia (an abnormal medical condition), as it would merely be applying a known technique to a known device/method ready for improvement to yield predictable results. The modification to Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 would allow the system to increase its seizure detection frequency responsive to an arrhythmia detection, thus allowing the system to respond more quickly if a medical emergency is detected.
Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 discloses all of the elements of the current invention, as discussed above, except for explicitly stating that the motion data comprises head motion data representative of head movement frequency of the patient.
As noted above, Osorio’580 teaches that a seizure metric may be determined based on a combination of brain activity signals and accelerometer signals (section [0043]). Osorio’580 further teaches that patient posture data may be used to determine a seizure metric (sections [0040], and [0043] in combination with [0063]: [0043] recites “neurologic (e.g., kinetic signals such as accelerometer signals and/or inclinometer signals”, and [0063] lists “posture” signals as a kinetic signal), and section [0128] specifically teaches using accelerometer signals to determine whether a patient’s posture has changed (“e.g., from a fall”). Giftakis et al.’214 teaches that accelerometer signals picked up from the head of a patient can detect head motion that can be used for seizure detection (sections [0058-0059]: section [0059] specifically teaches using the signals to determine whether a patient has fallen). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have configured the system of Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 to receive, from the accelerometer, motion data comprising head motion data of the patient, as Osorio’580 teaches that motion data representative of the patient’s posture change may be used to determine the seizure metric, and Giftakis et al.’214 teaches that motion data comprising head motion data can be used to determine posture changes. Any head motion data received from accelerometers placed within or on a head of a patient is inherently representative of head movement frequency of the patient.
In the combination of Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’056 further in view of Giftakis et al.’214, seizure metrics would periodically be determined based on the generated physiological information and received motion data at an initial seizure detection frequency. When an arrhythmia of the patient is detected (an abnormal medical condition), the frequency of the seizure detection method taught by Greene’629 in view of Osorio’580 would be increased (“an increase in the rate at which a method is executed” as taught by Das et al.’056) in order to allow the system to more quickly respond to the patient’s condition by providing subsequent seizure metrics more frequently.
Increasing a seizure detection frequency would result in expediting a determination of a subsequent seizure metric indicative of a subsequent seizure status of the patient (i.e., obtaining a patient condition more frequently), and the storing of subsequent seizure metrics.
Regarding claim 2, Greene’629 teaches that the physiological data comprises brain activity data (sections [0085] and [0089]).
Regarding claim 3, the system of Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 is capable of being implanted such that the plurality of electrodes are configured to detect brain activity data corresponding to activity in at least one of a P3, Pz, or P4 brain region.
Regarding claim 4, the housing of the system of Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 is capable of being implanted such that the housing is disposed at or adjacent to a rear portion of a neck or skull of the patient.
Regarding claim 5, the system of Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 is capable of being implanted such that the plurality of electrodes is configured to detect brain activity data corresponding to activity in at least one of a T3 or T4 brain region.
Regarding claim 6, the processing circuitry is configured to determine, based on the physiological information representative of hemisphere activity associated with respective electrodes at the T3 or T4 brain region, the seizure metric indicative of the seizure status of the patient (as the electrodes of Greene’629 are capable of being placed at the T3 or T4 locations, the processing circuity is capable of making the seizure index determination from signals obtained at the T3 or T4 brain region).
Regarding claim 9, Greene’629 teaches that the physiological data comprises electrical brain activity data (section [0085]), and as discussed above, Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 teaches that the physiological data comprises electrical heart activity data (the electrocardiogram information). Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 inherently requires circuitry to generate the electrical brain activity data and electrical heart activity data. While Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 does not teach separate first circuitry and second circuitry, it would have been obvious to one of ordinary skill in the art to use two separate circuits as the courts have held that the mere duplication of parts has no patentable significance unless a new and unexpected result is produced (MPEP 2144.04 VI. B.).
Regarding claim 12, Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 discloses all of the elements of the current invention, as discussed above, except for the processing circuitry being configured to select one seizure type representative of a seizure experienced by the patient based on the electrocardiogram (electrical heart activity) information. Osorio’580 teaches selecting, based on cardiovascular data (e.g. maximum heart rate, heart rate variability, electrocardiogram, electrocardiogram morphology) and from a plurality of seizure types, one seizure type representative of a seizure being experienced by a patient (sections [0045], [0052-0054]). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the processing circuitry of Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 to be configured to select one seizure type representative of a seizure experienced by the patient based on acquired electrocardiogram information, as taught by Osorio’580, as it would merely be combining prior art elements according to known methods to yield predictable results. The modification to Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 would allow the processing circuitry to determine a specific type of seizure being experienced by the patient. It is noted that the system of Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 determines, based on the seizure metrics (including any subsequent seizure metrics), that the patient has experienced a seizure.
Regarding claims 16, 18, 21, and 22, the sections of Greene’629 discussed above, as modified by Osorio’580, Armstrong et al.’188, Das et al.’056, and Giftakis et al.’214, disclose a method comprising the steps set forth in claims 16, 18, and 21, and a non-transitory computer-readable medium comprising instructions that, when executed, cause processing circuitry to perform the steps recited in claim 22.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214, as applied to claim 1, further in view of Drew’970 (US Pub No. 2006/0094970 – previously cited).
Greene’629 teaches that the system comprises telemetry circuitry within the housing, wherein the processing circuitry is configured to control the telemetry circuitry to transmit the seizure metric to an external device (sections [0102] and [0115]).
Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 discloses all of the elements of the current invention, as discussed in paragraph 4 above, except for the external device being configured to determine a geographic location of the patient, and transmit the geographic location and the subsequent seizure metric to an emergency service.
Drew’970 teaches determining a geographic location of a patient with an external device, and transmitting patient geographic location information and “necessary information to prepare for treatment and provide support after arrival on the scene” to an emergency service so that an emergency response team can be dispatched to the determined patient location (section [0084]). One of ordinary skill in the art would realize that “necessary information to prepare for treatment and provide support after arrival on the scene” would include physiological information about the patient, and with the system of Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214, the physiological information would include at least any determined seizure metrics. It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the external device of Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 to be configured to transmit determined geographic location information of the patient and the seizure metrics to an emergency service as it would allow an emergency response team to be dispatched to the patient’s location when treatment and support are needed.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214, as applied to claim 16, further in view of Savit et al.’038 (US Pub No. 2006/0200038 – previously cited).
Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 discloses all of the elements of the current invention, as discussed in paragraph 4 above, except for the plurality of electrodes being configured to detect brain activity data corresponding to activity in at least one of a P3, Pz, or P4 brain region. Savit et al.’038 teaches that those skilled in the art can determine a suitable placement of electrodes to best predict seizures in a particular subject (section [0017]). As seizures are known to originate in different parts of the brain for different patients, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have determined the optimum placement of the electrodes for seizure detection based on the particular subject.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059, as applied to claim 1, further in view of Carlson et al.’024 (US Pub No. 2008/0046024).
Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 discloses all of the elements of the current invention, as discussed in paragraph 4 above, except for the processing circuitry being configured to determine the seizure metric or the subsequent seizure metric by determining a short-term average of spectral power over a frequency band of the physiological information for a first period of time; determining a long-term average of the spectral power by averaging the short-term average of the spectral power over a second period of time, wherein the second period of time is greater than the first period of time; generating a ratio of the short-term average of the spectral power to the long-term average of the spectral power; and determining the seizure metric or the subsequent seizure metric based on the ratio.
Carlson et al.’024 teaches determining a seizure metric by determining a short-term average of spectral power over a frequency band of physiological information for a first period of time, determining a long-term average of the spectral power by averaging the short-term average of the spectral power over a second period of time, wherein the second period of time is greater than the first period of time; generating a ratio of the short-term average of the spectral power to the long-term average of the spectral power, and determining the seizure metric based on the ratio (sections [0063-0072]). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the processing circuitry of Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 to be configured to determine a short-term average of spectral power over a frequency band of the physiological information for a first period of time, determine a long-term average of the spectral power by averaging the short-term average of the spectral power over a second period of time, wherein the second period of time is greater than the first period of time; generate a ratio of the short-term average of the spectral power to the long-term average of the spectral power, and determine the seizure metric based on the ratio, as taught by Carlson et al.’024, as it would merely be combining prior art elements according to known methods to yield predictable results. Furthermore, the modification to Greene’629 in view of Osorio’580 further in view of Armstrong et al.’188 further in view of Das et al.’059 further in view of Giftakis et al.’214 would merely be substituting one known seizure metric determination method (the one taught by Carlson et al.’024) for another to yield predictable results.
Response to Arguments
Applicant’s arguments filed on 30 March 2026 have been fully considered.
Regarding the rejections of claims 2, 9, 12, 18, and 21 under 35 U.S.C. 112(b), the amendments to the claims have overcome the rejections.
Regarding the rejections of the claims in view of the previously cited prior art, Applicant’s arguments remain unpersuasive.
Applicant argues that the cited prior art of record does not teach "generating or analyzing motion data to determine a 'head movement frequency' as recited by amended independent claim 1". This argument is not persuasive as claim 1 does not require analyzing motion data to determine a head movement frequency. There is no positive recitation of determining a head movement frequency. Instead, the claim only requires generating motion data that comprises head motion data, wherein that head motion data is "representative of" head movement frequency. As noted in paragraph 4 above, any head motion data is "representative of" head movement frequency.
Applicant's arguments regarding whether or not Das teaches increasing a seizure detection frequency to expedite a determination of a subsequent seizure metric in response to determining an arrhythmia have already been addressed in previous Office actions and will not be addressed further.
Regarding Applicant's argument that modification of Greene by Das changes the principle of operation of the primary reference, Applicant's argument is not persuasive. There is no implication by Greene that adjusting its "settling time" would "destroy the core functionality of Greene" or "render the primary reference inoperable for its intended purpose of utilizing fixed time windows for waveform analysis". There is no need in Greene for a static detection frequency.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Giftakis et al.’629 teaches using head motion data obtained from an accelerometer to determine patient posture information, including fall information (sections [0044-0045], [0047]). Warwick et al.’117 (US Pub No. 2011/0270117 – previously cited) teaches that head motion data obtained from an accelerometer is useful in monitoring seizures (section [0040]). Chang et al.’769 (US Pub No. 2014/0364769 – previously cited) teaches tracking postural changes using an accelerometer attached to a user’s head (section [0006]). Arroyo’806 (US Pub No. 2015/0323806 – previously cited) teaches using head motion data obtained from an accelerometer to determine fall information (section [0029]). Aarts et al.’344 (US Pub No. 2021/0244344 – previously cited) teaches using head motion data obtained from an accelerometer to monitor postural changes (section [0023]). Williams et al.’730 (US Pub No. 2019/0053730 – previously cited) teaches that it is desirable to have a means for monitoring and detecting both seizures and strokes (section [0005]), and teaches analyzing brain activity signals to detect the presence of seizures and strokes (section [0087]). Naber et al.’299 (US Pub No. 2021/0030299 – previously cited) teaches improving an automated stroke detection control unit by incorporating patient fall information determined from accelerometer data, wherein the fall information is used as an additional data point on the severity of a stroke event (section [0067]). Wilkinson et al. (Application of the muse portable… – previously cited) teaches using motion data acquired from an accelerometer to build a classifier that analyzes brain activity data and movement data in order to detect and classify strokes (paragraph on page 15 that begins with “With the MuseTM, …” to the end of the paragraph that begins with “In the literature…“ on page 16, and first full paragraph on page 18, beginning with “Our initial results…”). Schalk et al.’154 (US Pub No. 2010/0094154 – previously cited) teaches analyzing EEG and ECoG signals in order to predict seizures and stroke (section [0044]). Besio et al.’298 (US Pub No. 2012/0310298 – previously cited) teaches analyzing EEG signals in order to detect and localize seizures and stroke (section [0144]). Waziri et al.’724 (US Pub No. 2015/0230724 – previously cited) teaches analyzing ECoG signals to monitor or detect seizure and stroke (section [0041]). Denison et al.’495 (US Pub No. 2017/0071495 – previously cited) discloses analyzing an EEG signal in order to determine both a seizure metric and a stroke metric (section [0073]). Orvis et al.’920 (US Pub No. 2019/0320920 – previously cited) teaches increasing the rate at which a method is performed in order to collect more accurate data (section [0076]). Gharieb et al.’249 (USPN 8,190,249 – previously cited) teaches determining an index that can be used to diagnose/monitor/classify epilepsy and seizure (col. 9, lines 47-60).
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/ETSUB D BERHANU/Primary Examiner, Art Unit 3791
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