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 .
Applicant’s arguments filed in the reply on July 20, 2026 were received and fully considered. Claims 1 and 10 were amended. Please see below for more detail.
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.
Claim(s) 1, 3-6, and 11-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Taylor (US 2012/0053918) in view of Russell et al (US 2015/0094545) (“Russell”) and further in view of Grodzki et al (US 2013/0267827) (“Grodzki”) and further in view of Pittenger et al (WO 2017/218288) (“Pittenger”).
Regarding Claim 1, while Taylor teaches a brain function measurement device (Abstract, [0348]-[0350] blood flow modeling can be of cerebral perfusion specifically, to create a prediction model of blood flow dynamics in brain, [0363] an example modeling is shown in Fig. 41, [0376] and the modeling quantifies plaque vulnerability) comprising:
a brain blood flow information acquirer configured to acquire brain blood flow information of a subject using imaging sensing (Fig. 41, [0363]-[0364] brain blood flow information acquired of a subject as an input including medical imaging data 1153 such as CCTA data, additional physiological data 1154, and brain perfusion data 1155);
an information acquirer configured to acquire heartbeat information of the subject ([0364] heartrate information acquired of a subject as an input as physiological data 1154);
a storage ([0109] a non-transitory computer readable medium that stores relevant data and instructions for performance of the invention) configured to store
a predetermined condition for analysis performed by the system ([0135], [0298], [0373] relevant predetermined conditions for performance of the invention’s analyses)
a controller ([0106], [0109] processor performs processing of system data) configured to
determine whether the user input confirms user is in a resting state ([0366])
when it is determined that the user input confirms a resting state, gather data with the resting state tag indicating that the subject is in the resting state ([0366] brain blood flow information acquired under several physical conditions, including a physical condition of rest, where the patient being in a rest condition must be based on satisfaction of a criteria, [0109] this data will be stored in the storage);
Taylor fails to teach
a rest information acquirer configured to acquire heartbeat information of the subject as rest information for determining whether or not the subject is in a resting state;
Storing
a predetermined condition indicating that the brain of the subject is in a relaxed and resting state when the predetermined condition is satisfied; and
a controller configured to
determine whether the heart beat information satisfies the predetermined condition based on the heartbeat information and the predetermined condition; and
when it is determined that the heartbeat information satisfies the predetermined condition, store in the storage resting state ON information indicating that the subject is in the resting state;
wherein the controller is further configured to;
sequentially accumulate and acquire, as resting brain blood flow measurement data, the brain blood flow information in a state in which the resting state ON information is stored in the storage.
However Russell teaches a physiological monitor utilizing automated at-rest sensing (Abstract) comprising:
a rest information acquirer configured to acquire heartbeat information of the subject as rest information to determine whether or not the subject is at rest ([0019] use physiological markers and mechanical markers of rest together to confirm rest state, [0024] these markers may be judged by three sensors, the second sensor of which may be operable to detect a physiological monitor of heartbeat information, [0041]-[0042] where this information is used to automatically identify rest state of subject, [0048] heart rate is heartbeats per minute and is thus heartbeat information);
Storing a predetermined condition indicating that the subject is in a relaxed and resting state when the predetermined condition is satisfied ([0045]-[0050] various datasets have specific thresholds, subject is determined at rest when both a mechanical at-rest threshold and a physiological at-rest threshold is met under certain conditions);
a controller ([0028], [0030]) configured to
determine whether the heart beat information satisfies the predetermined condition based on the heartbeat information and the predetermined condition ([0045]-[0050] rest condition confirmed by measured heart rate satisfying physiological at-rest threshold that have been predetermined); and
when it is determined that the heartbeat information satisfies the predetermined condition, store in the storage resting state ON information indicating that the subject is in the resting state ([0021] collected data is stored [0047] data collected when the rest thresholds are appropriately met is labeled as resting data);
wherein the controller is further configured to;
sequentially accumulate and acquire, as resting physiological measurement data, the physiological information in a state in which the resting state ON information is stored in the storage ([0021], [0045]-[0050], [0063]);
notes the utility of the invention when acquiring second patient information tagged as resting second patient information based on the rest information acquired by the rest information acquirer satisfying a predetermined condition ([0006]-[0007], [0019] both physiological and mechanical measurements must confirm the patient is in a rest condition); and
further teaches that the transmitting of data can be limited so the healthcare provider accumulates only the data tagged as at-rest data ([0033], [0091]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to specify the resting state determination steps in Taylor as specifically the resting state determination steps given by Russell as this provides a consistent standardized framework that can be applied across applications of the invention. Furthermore, Russell’s rest determining steps is envisioned as being applied to contextualize secondary data, an application that synergizes with Taylor’s brain blood flow data being contextualized by its occurrence during a patient’s rest. Even further, Taylor’s system gathers heartbeat information through heart rate as well and thus can be seen as already suited for identifying the physiologically at-rest state in the subject. In sum, Russell’s teachings applied to Taylor would motivate storing a predetermined condition indicating that the subject is in a relaxed and resting state with the predetermined condition calibrated for a resting brain, sequentially acquiring the brain blood flow information as resting brain blood flow information when the heart beat based rest flag is satisfied, and understanding the sequential acquisition based on the flag will have the dataset only accumulate brain blood flow information in a state in which the resting state ON information for the storage.
Yet their combined efforts fail to teach
wherein the storage is further configured to store a predetermined measurement time at which acquisition of the brain blood flow information is to be ended,
wherein the controller is further configured to;
acquire a resting brain blood flow measurement data accumulation time, which is a measurement time of the accumulated resting brain blood flow measurement data;
compare the acquired resting physiological measurement data accumulation time with the predetermined measurement time, and
terminate acquisition of the brain blood flow information when the resting brain blood flow measurement data accumulation time reaches the predetermined measurement time.
However Grodzki teaches a brain-based physiological measurement system (Abstract) comprising
a brain information acquirer (Abstract, Fig. 1, [0038] magnetic resonance imaging system 5)
a rest information acquirer (Abstract, Fig. 1 [0038] electroencephalograph 30, [0019] EEG data measured to identify resting state by whether the frequency spectrum of the EEG data acquired in this time interval is situated predominantly in a desired frequency band that was previously established);
wherein the storage is further configured to store a predetermined measurement time at which acquisition of the brain information is to be ended ([0021]-[0022] a predetermined time interval for MR data is predefined by the system, [0030] memory for guiding the performance of the invention through a computer),
wherein the controller ([0030]) is further configured to;
acquire a resting brain measurement data accumulation time, which is a measurement time of the accumulated resting brain measurement data;
compare the acquired resting physiological measurement data accumulation time with the predetermined measurement time, and
terminate acquisition of the brain information when the resting brain measurement data accumulation time reaches the predetermined measurement time ([0021] “For each time interval a decision is made as to whether the frequency spectrum of the EEG data acquired in this time interval is situated predominantly in a desired frequency band that was previously established. Only if this is the case are the MR data of the corresponding time interval evaluated; otherwise, these MR data are discarded. Only if the sum of time intervals in which the MR data of the evaluation were supplied (meaning that the frequency spectrum of the EEG data acquired in this time interval was predominantly situated in the desired frequency band) is larger than a predetermined time interval does the method end.”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to generate cumulative resting patient measurement information from separate instances of resting patient measurements as taught by Grodzki for the resting cerebral blood flow data of Taylor and Russell because the accumulated and curated output of resting-based information can provide an optimized amount of rest-related data for a healthcare provider to review ([0032]). Furthermore, it would be obvious to have a predefined end to the monitoring period to limit the amount of data a healthcare provider must review. Finally, it would be obvious that the predefined limit in data can be set by the practitioner based on the desired time interval of data to review ([0022]).
Yet their combined efforts fail to teach the brain blood flow information acquirer configured to acquire brain blood flow information of a subject by imaging using a light transmitter that irradiates measurement light in a near-infrared wavelength region and a light receiver.
However Pittenger teaches a brain-based near-infrared spectroscopy (NIRS) measurement device (Abstract) comprising:
A brain blood flow information acquirer acquiring brain blood flow information of a subject using a light transmitter that irradiates measurement light in a near-infrared wavelength region and a light receiver (p8, L. 23 – p9, L. 19, NIRS imaging performed on a region of interest in the brain, the NIRS performed using a light transmitter/emitter optode and a light receiver/detector optode, p9, L. 25 – p10, L. 3, “A NIRS cap can be positioned over the subject's frontal lobes using the international 10-20 system. Measurements of cortical perfusion can be obtained at 10 Hz using a fifty-two-channel near-infrared spectroscopy machine (ETG-4000, Hitachi Medical). To standardize the placement of the optode lattice, a source probe can be placed directly above the right ear in all participants.” Brain blood flow information acquired with the optode measurements).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to set the brain blood flow acquisition of Taylor to be performed with near-infrared imaging as taught by Pittenger as an example of brain perfusion data that enables the development of a perfusion calculation from each cerebral branch into each segmented volume. Specifically, to make a simulation of blood flow and pressure in cerebral arteries requires contextual perfusion information as shown in Fig. 41, and the development of perfusion data for the 3D model of the cerebral arteries is only possible with such measured blood flow perfusion data.
Regarding Claim 3, Taylor, Russell, Grodzki, and Pittenger teach the brain function measurement device according to claim 1, wherein the controller is further configured to acquire one or a plurality of pieces of the resting brain blood flow measurement data and combine the resting brain blood flow measurement data in order of acquisition to generate one piece of the cumulative resting brain blood flow measurement data (See Claim 1 Rejection¸ Grodzki teaches reviewing resting patient information and combining the duration of the resting patient information from individual episodes in order to generate one piece of cumulative duration of the resting patient information, where this would be applied to the patient information of brain blood flow when applied to Taylor).
Regarding Claim 4, Taylor, Russell, Grodzki, and Pittenger teach the brain function measurement device according to claim 1, and Taylor teaches wherein
the brain blood flow information acquirer is further configured to continuously acquire the brain blood flow information ([0024], [0048]-[0049] for example, a continuous collection over a 24 hour period) during acquisition of the rest information (See Claim 1 Rejection); and
the controller is further configured to:
extract information in a resting state as the resting brain blood flow measurement data from the continuously acquired brain blood flow information; and
generate the cumulative resting brain blood flow measurement data based on the extracted resting brain blood flow measurement data (See Claim 1 Rejection).
Regarding Claim 5, Taylor, Russell, Grodzki, and Pittenger teach the brain function measurement device according to claim 4, wherein the controller is further configured to perform, on the continuously acquired brain blood flow information, a process to enable distinction between the resting state and a non-resting state based on the rest information (See Claim 1 Rejection).
Regarding Claim 6, Taylor, Russell, Grodzki, and Pittenger teach the brain function measurement device according to claim 4, wherein the controller is further configured to acquire the brain blood flow information as the resting brain blood flow measurement data when the rest information satisfies the predetermined condition (See Claim 1 Rejection), and Russell teaches the predetermined condition is satisfied for a predetermined time or longer ([0048]-[0049]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to consider the resting state data of Taylor, Russell, Grodzki, and Pittenger to be achieved under a satisfied predetermined condition for a predetermined time or longer taught by Russell as this applies a measurable standard to confirm the desired resting in Taylor is occurring.
Regarding Claim 11, while Taylor teaches a method for measuring a brain function (Abstract, [0348]-[0350] blood flow modeling can be of cerebral perfusion specifically, to create a prediction model of blood flow dynamics in brain, [0363] an example modeling is shown in Fig. 41, [0376] and the modeling quantifies plaque vulnerability) comprising:
storing a predetermined condition for analysis performed by the system ([0135], [0298], [0373] relevant predetermined conditions for performance of the invention’s analyses);
acquiring brain blood flow information of a subject (Fig. 41, [0363]-[0364] brain blood flow information acquired of a subject as an input including medical imaging data 1153 such as CCTA data, additional physiological data 1154, and brain perfusion data 1155);
acquiring heartbeat information of the subject ([0364] heartrate information acquired of a subject as an input as physiological data 1154);
determine whether the user input confirms user is in a resting state ([0366])
when it is determined that the user input confirms a resting state, gather data with the resting state tag indicating that the subject is in the resting state ([0366] brain blood flow information acquired under several physical conditions, including a physical condition of rest, where the patient being in a rest condition must be based on satisfaction of a criteria, [0109] this data will be stored in the storage);
Taylor fails to teach
Storing a predetermined condition indicating that the brain of the subject is in a relaxed and resting state when the predetermined condition is satisfied;
acquiring heartbeat information of the subject as rest information for determining whether or not the subject is in a resting state;
determine whether the heart beat information satisfies the predetermined condition based on the heartbeat information and the predetermined condition;
storing resting state ON information indicating that the subject is in the resting state when it is determined that the heartbeat information satisfies the predetermined condition,;
sequentially accumulating and acquiring, as resting brain blood flow measurement data, the brain blood flow information in a state in which the resting state ON information is stored in the storage.
However Russell teaches a physiological monitor utilizing automated at-rest sensing (Abstract) comprising:
a rest information acquirer configured to acquire heartbeat information of the subject as rest information to determine whether or not the subject is at rest ([0019] use physiological markers and mechanical markers of rest together to confirm rest state, [0024] these markers may be judged by three sensors, the second sensor of which may be operable to detect a physiological monitor of heartbeat information, [0041]-[0042] where this information is used to automatically identify rest state of subject, [0048] heart rate is heartbeats per minute and is thus heartbeat information);
Storing a predetermined condition indicating that the subject is in a relaxed and resting state when the predetermined condition is satisfied ([0045]-[0050] various datasets have specific thresholds, subject is determined at rest when both a mechanical at-rest threshold and a physiological at-rest threshold is met under certain conditions);
a controller ([0028], [0030]) configured to
determine whether the heart beat information satisfies the predetermined condition based on the heartbeat information and the predetermined condition ([0045]-[0050] rest condition confirmed by measured heart rate satisfying physiological at-rest threshold that have been predetermined); and
when it is determined that the heartbeat information satisfies the predetermined condition, store in the storage resting state ON information indicating that the subject is in the resting state ([0021] collected data is stored [0047] data collected when the rest thresholds are appropriately met is labeled as resting data);
wherein the controller is further configured to;
sequentially accumulate and acquire, as resting physiological measurement data, the physiological information in a state in which the resting state ON information is stored in the storage ([0021], [0045]-[0050], [0063]);
notes the utility of the invention when acquiring second patient information tagged as resting second patient information based on the rest information acquired by the rest information acquirer satisfying a predetermined condition ([0006]-[0007], [0019] both physiological and mechanical measurements must confirm the patient is in a rest condition); and
further teaches that the transmitting of data can be limited so the healthcare provider accumulates only the data tagged as at-rest data ([0033], [0091]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to specify the resting state determination steps in Taylor as specifically the resting state determination steps given by Russell as this provides a consistent standardized framework that can be applied across applications of the invention. Furthermore, Russell’s rest determining steps is envisioned as being applied to contextualize secondary data, an application that synergizes with Taylor’s brain blood flow data being contextualized by its occurrence during a patient’s rest. Even further, Taylor’s system gathers heartbeat information through heart rate as well and thus can be seen as already suited for identifying the physiologically at-rest state in the subject. In sum, Russell’s teachings applied to Taylor would motivate storing a predetermined condition indicating that the subject is in a relaxed and resting state with the predetermined condition calibrated for a resting brain, sequentially acquiring the brain blood flow information as resting brain blood flow information when the heart beat based rest flag is satisfied, and understanding the sequential acquisition based on the flag will have the dataset only accumulate brain blood flow information in a state in which the resting state ON information for the storage.
Yet their combined efforts fail to teach
acquiring a resting brain blood flow measurement data accumulation time, which is a measurement time of the accumulated resting brain blood flow measurement data;
compare the acquired resting physiological measurement data accumulation time with a predetermined measurement time stored in the storage, and
terminating acquisition of the brain blood flow information when the resting brain blood flow measurement data accumulation time reaches the predetermined measurement time.
However Grodzki teaches a brain-based physiological measurement system (Abstract) comprising
a brain information acquirer (Abstract, Fig. 1, [0038] magnetic resonance imaging system 5)
a rest information acquirer (Abstract, Fig. 1 [0038] electroencephalograph 30, [0019] EEG data measured to identify resting state by whether the frequency spectrum of the EEG data acquired in this time interval is situated predominantly in a desired frequency band that was previously established);
wherein the storage is further configured to store a predetermined measurement time at which acquisition of the brain information is to be ended ([0021]-[0022] a predetermined time interval for MR data is predefined by the system, [0030] memory for guiding the performance of the invention through a computer),
wherein the controller ([0030]) is further configured to;
acquire a resting brain measurement data accumulation time, which is a measurement time of the accumulated resting brain measurement data;
compare the acquired resting physiological measurement data accumulation time with the predetermined measurement time, and
terminate acquisition of the brain information when the resting brain measurement data accumulation time reaches the predetermined measurement time ([0021] “For each time interval a decision is made as to whether the frequency spectrum of the EEG data acquired in this time interval is situated predominantly in a desired frequency band that was previously established. Only if this is the case are the MR data of the corresponding time interval evaluated; otherwise, these MR data are discarded. Only if the sum of time intervals in which the MR data of the evaluation were supplied (meaning that the frequency spectrum of the EEG data acquired in this time interval was predominantly situated in the desired frequency band) is larger than a predetermined time interval does the method end.”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to generate cumulative resting patient measurement information from separate instances of resting patient measurements as taught by Grodzki for the resting cerebral blood flow data of Taylor and Russell because the accumulated and curated output of resting-based information can provide an optimized amount of rest-related data for a healthcare provider to review ([0032]). Furthermore, it would be obvious to have a predefined end to the monitoring period to limit the amount of data a healthcare provider must review. Finally, it would be obvious that the predefined limit in data can be set by the practitioner based on the desired time interval of data to review ([0022]).
Yet their combined efforts fail to teach the brain blood flow information acquirer by imaging using a light transmitter that irradiates measurement light in a near-infrared wavelength region and a light receiver.
However Pittenger teaches a brain-based near-infrared spectroscopy (NIRS) measurement device (Abstract) comprising:
A brain blood flow information acquirer acquiring brain blood flow information of a subject using a light transmitter that irradiates measurement light in a near-infrared wavelength region and a light receiver (p8, L. 23 – p9, L. 19, NIRS imaging performed on a region of interest in the brain, the NIRS performed using a light transmitter/emitter optode and a light receiver/detector optode, p9, L. 25 – p10, L. 3, “A NIRS cap can be positioned over the subject's frontal lobes using the international 10-20 system. Measurements of cortical perfusion can be obtained at 10 Hz using a fifty-two-channel near-infrared spectroscopy machine (ETG-4000, Hitachi Medical). To standardize the placement of the optode lattice, a source probe can be placed directly above the right ear in all participants.” Brain blood flow information acquired with the optode measurements).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to set the brain blood flow acquisition of Taylor to be performed with near-infrared imaging as taught by Pittenger as an example of brain perfusion data that enables the development of a perfusion calculation from each cerebral branch into each segmented volume. Specifically, to make a simulation of blood flow and pressure in cerebral arteries requires contextual perfusion information as shown in Fig. 41, and the development of perfusion data for the 3D model of the cerebral arteries is only possible with such measured blood flow perfusion data.
Regarding Claim 12, Taylor, Russell, Grodzki, and Pittenger teach the method of claim 11, further comprising: acquiring one or a plurality of pieces of the resting brain blood flow measurement data and combine the resting brain blood flow measurement data in order of acquisition to generate one piece of the cumulative resting brain blood flow measurement data (See Claim 11 Rejection, Grodzki teaches reviewing resting patient information and combining the duration of the resting patient information from individual episodes in order to generate one piece of cumulative duration of the resting patient information, where this would be applied to the patient information of brain blood flow when applied to Taylor).
Regarding Claim 13, Taylor, Russell, Grodzki, and Pittenger teach the method of claim 12, and Taylor teaches the method further comprising:
continuously acquiring the brain blood flow information during acquisition of the rest information (See Claim 12 Rejection, [0024], [0048]-[0049] for example, a continuous collection over a 24 hour period);
extracting information in a resting state as the resting brain blood flow measurement data from the continuously acquired brain blood flow information; and
generating the cumulative resting brain blood flow measurement data based on the extracted resting brain blood flow measurement data (See Claim 12 Rejection).
Regarding Claim 14, Taylor, Russell, Grodzki, and Pittenger teach the method of claim 13, further comprising: performing, on the continuously acquired brain blood flow information, a process to enable distinction between the resting state and a non-resting state based on the rest information (See Claim 13 Rejection).
Regarding Claim 15, Taylor, Russell, Grodzki, and Pittenger teach the method of claim 13, acquiring the brain blood flow information as the resting brain blood flow measurement data when the rest information satisfies the predetermined condition (See Claim 13 Rejection), and Russell teaches the predetermined condition is satisfied for a predetermined time or longer ([0048]-[0049]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to consider the resting state data of Taylor, Russell, Grodzki, and Pittenger to be achieved under a satisfied predetermined condition for a predetermined time or longer taught by Russell as this applies a measurable standard to confirm the desired resting in Taylor is occurring.
Claim(s) 8-9 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Taylor in view of Russell and further in view of Grodzki and further in view of Pittenger and further in view of Kettunen et al (US 2005/0256414) (“Kettunen”).
Regarding Claim 8, while Taylor, Russell, Grodzki, and Pittenger teach the brain function measurement device according to claim 1, and Russell teaches collecting fluctuation in a heartbeat time interval of the subject as rest data (See Claim 1 Rejection), their combined efforts fail to teach the controller is further configured to acquire parasympathetic nerve activity based on a fluctuation in a heartbeat time interval of the subject and to determine a resting state of the subject's brain.
However Kettunen teaches a stress monitor (Abstract) that notes that a resting state of a subject can be acquired through parasympathetic nerve activity based on a fluctuation in a heartbeat time interval of the subject ([0007], [0011], [0053], [0056]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to further perform the resting state determination of Russell through the high frequency power of the heart rate variability (i.e. a fluctuation in a heartbeat time interval) of the subject as taught by Kettunen as another specific and reliable method of judging a subject’s relaxation.
Regarding Claim 9, Taylor, Russell, Grodzki, Pittenger, and Kettunen teach the brain function measurement device according to claim 8, comprising acquiring brain blood flow information as the resting brain blood flow information when the resting state is determined (See Claim 1 Rejection) and Kettunen further teaches wherein
the controller is further configured to:
perform power spectrum analysis on the fluctuation in the heartbeat time interval of the subject to acquire an HF component, which is an indicator of the parasympathetic nerve activity ([0053], [0056]); and
determine that the subject’s brain is in a resting state based on a state in which an intensity of the HF component exceeds a predetermined intensity ([0053]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to perform the resting state determination of Russell through the high frequency power of the heart rate variability (i.e. a fluctuation in a heartbeat time interval) of the subject as taught by Kettunen and acquire the brain blood flow information upon a resting state determination to fulfill the desired selective brain blood flow evaluation of Taylor.
Regarding Claim 17, while Taylor, Russell, Grodzki, and Pittenger teach the method of claim 11, and Russell teaches collecting fluctuation in a heartbeat time interval of the subject as rest data (See Claim 11 Rejection), their combined efforts fail to teach further comprising acquiring parasympathetic nerve activity based on a fluctuation in a heartbeat time interval of the subject and to determine a resting state of the subject's brain.
However Kettunen teaches a stress monitor (Abstract) that notes that a resting state of a subject can be acquired through parasympathetic nerve activity based on a fluctuation in a heartbeat time interval of the subject ([0007], [0011], [0053], [0056]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to further perform the resting state determination of Russell through the high frequency power of the heart rate variability (i.e. a fluctuation in a heartbeat time interval) of the subject as taught by Kettunen as another specific and reliable method of judging a subject’s relaxation.
Regarding Claim 18, Taylor, Russell, Grodzki, Pittenger, and Kettunen teach the method of claim 17, comprising acquiring brain blood flow information as the resting brain blood flow information when the resting state is determined (See Claim 17 Rejection) and Kettunen further teaches the method comprising:
performing power spectrum analysis on the fluctuation in the heartbeat time interval of the subject to acquire an HF component, which is an indicator of the parasympathetic nerve activity ([0053], [0056]); and
determining that the subject's brain is in a resting state based on a state in which an intensity of the HF component exceeds a predetermined intensity ([0053]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to perform the resting state determination of Russell through the high frequency power of the heart rate variability (i.e. a fluctuation in a heartbeat time interval) of the subject as taught by Kettunen and acquire the brain blood flow information upon a resting state determination to fulfill the desired selective brain blood flow evaluation of Taylor.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Taylor in view of Russell and further in view of Grodzki and further in view of Pittenger and further in view of Alailima et al (US 2019/0159716) (“Alailima”).
Regarding Claim 10, while Taylor teaches a brain function measurement device (Abstract, [0348]-[0350] blood flow modeling can be of cerebral perfusion specifically, to create a prediction model of blood flow dynamics in brain, [0376] and to quantify plaque vulnerability) comprising:
a brain blood flow information acquirer configured to acquire brain blood flow information of a subject using imaging sensing (Fig. 41, [0363]-[0364] brain blood flow information acquired of a subject as an input including medical imaging data 1153 such as CCTA data, additional physiological data 1154, and brain perfusion data 1155);
an information acquirer configured to acquire heartbeat information of the subject ([0364] heartrate information acquired of a subject as an input as physiological data 1154);
a storage ([0109] a non-transitory computer readable medium that stores relevant data and instructions for performance of the invention) configured to store
a predetermined condition for analysis performed by the system ([0135], [0298], [0373] relevant predetermined conditions for performance of the invention’s analyses)
a controller ([0106], [0109] processor performs processing of system data) configured to
determine whether the user input confirms user is in a resting state ([0366]),
when it is determined that the user input confirms a resting state, gather data with the resting state tag indicating that the subject is in the resting state ([0366] brain blood flow information acquired under several physical conditions, including a physical condition of rest, where the patient being in a rest condition must be based on satisfaction of a criteria, [0109] this data will be stored in the storage);
Taylor fails to teach
a rest information acquirer configured to acquire heartbeat information of the subject as rest information for determining whether or not the subject is in a resting state;
Storing a predetermined condition indicating that the brain of the subject is in a relaxed and resting state when the predetermined condition is satisfied;
the controller configured to
determine whether the heart beat information satisfies the predetermined condition based on the heartbeat information and the predetermined condition; and
repeat:
a first control of starting acquisition of the brain blood flow information as resting measurement data when it is determined that the heartbeat information satisfies the predetermined condition, and
a second control of stopping acquisition of the brain blood flow information when it is determined that the heartbeat information does not satisfy the predetermined condition;
sequentially accumulate, as accumulated resting measurement data, the resting measurement data acquired when it is determined that the heartbeat information satisfies the predetermined condition, and store the accumulated resting measurement data in the storage.
However Russell teaches a physiological monitor utilizing automated at-rest sensing (Abstract) comprising:
a rest information acquirer configured to acquire heartbeat information of the subject as rest information to determine whether or not the subject is at rest ([0019] use physiological markers and mechanical markers of rest together to confirm rest state, [0024] these markers may be judged by three sensors, the second sensor of which may be operable to detect a physiological monitor of heartbeat information, [0041]-[0042] where this information is used to automatically identify rest state of subject, [0048] heart rate is heartbeats per minute and is thus heartbeat information);
Storing a predetermined condition indicating that the subject is in a relaxed and resting state when the predetermined condition is satisfied ([0045]-[0050] various datasets have specific thresholds, subject is determined at rest when both a mechanical at-rest threshold and a physiological at-rest threshold is met under certain conditions);
a controller ([0028], [0030]) configured to
determine whether the heart beat information satisfies the predetermined condition based on the heartbeat information and the predetermined condition ([0045]-[0050] rest condition confirmed by measured heart rate satisfying physiological at-rest threshold that have been predetermined); and
repeatedly acquiring physiological information as resting measurement data when it is determined that the heartbeat information satisfies the predetermined condition ([0021] collected data is stored [0047] data collected when the rest thresholds are appropriately met is labeled as resting data) and repeatedly acquiring physiological information as non-resting measurement data when it is determined that the heartbeat information does not satisfy the predetermined condition ([0047], [0050]),
sequentially accumulate, as accumulated resting measurement data, the resting measurement data acquired when it is determined that the heartbeat information satisfies the predetermined condition, and store the accumulated resting measurement data in the storage ([0021], [0045]-[0050], [0063]);
notes the utility of the invention when acquiring second patient information tagged as resting second patient information based on the rest information acquired by the rest information acquirer satisfying a predetermined condition ([0006]-[0007], [0019] both physiological and mechanical measurements must confirm the patient is in a rest condition); and
further teaches that the transmitting of data can be limited so the healthcare provider accumulates only the data tagged as at-rest data ([0033], [0091]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to specify the resting state determination steps in Taylor as specifically the resting state determination steps given by Russell as this provides a consistent standardized framework that can be applied across applications of the invention. Furthermore, Russell’s rest determining steps is envisioned as being applied to contextualize secondary data, an application that synergizes with Taylor’s brain blood flow data being contextualized by its occurrence during a patient’s rest. Even further, Taylor’s system gathers heartbeat information through heart rate as well and thus can be seen as already suited for identifying the physiologically at-rest state in the subject. In sum, Russell’s teachings applied to Taylor would motivate storing a predetermined condition indicating that the subject is in a relaxed and resting state with the predetermined condition calibrated for a resting brain, sequentially acquiring the brain blood flow information as resting brain blood flow information when the heart beat based rest flag is satisfied, and understanding the sequential acquisition based on the flag will have the dataset only accumulate brain blood flow information in a state in which the resting state ON information for the storage.
Yet their combined efforts fail to teach repeat a second control of stopping acquisition of the brain blood flow information when it is determined that the heartbeat information does not satisfy the predetermined condition.
However Grodzki teaches a brain-based physiological measurement system (Abstract) comprising
a brain information acquirer (Abstract, Fig. 1, [0038] magnetic resonance imaging system 5)
a rest information acquirer (Abstract, Fig. 1 [0038] electroencephalograph 30, [0019] EEG data measured to identify resting state by whether the frequency spectrum of the EEG data acquired in this time interval is situated predominantly in a desired frequency band that was previously established);
wherein the controller ([0030]) is further configured to;
acquire a resting brain measurement data and resting brain measurement data accumulation time, which is a measurement time of the accumulated resting brain measurement data;
a second control of discarding intervals of the brain information when it is determined that the resting information does not satisfy the predetermined condition ([0021] “For each time interval a decision is made as to whether the frequency spectrum of the EEG data acquired in this time interval is situated predominantly in a desired frequency band that was previously established. Only if this is the case are the MR data of the corresponding time interval evaluated; otherwise, these MR data are discarded. Only if the sum of time intervals in which the MR data of the evaluation were supplied (meaning that the frequency spectrum of the EEG data acquired in this time interval was predominantly situated in the desired frequency band) is larger than a predetermined time interval does the method end.”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to generate cumulative resting patient measurement information from separate instances of resting patient measurements as taught by Grodzki for the resting cerebral blood flow data of Taylor and Russell because the accumulated and curated output of resting-based information can provide an optimized amount of rest-related data for a healthcare provider to review ([0032]). Furthermore, it would be obvious to have a predefined end to the monitoring period to limit the amount of data a healthcare provider must review. Finally, it would be obvious that the predefined limit in data can be set by the practitioner based on the desired time interval of data to review ([0022]).
Yet their combined efforts fail to teach the brain blood flow information acquirer by imaging using a light transmitter that irradiates measurement light in a near-infrared wavelength region and a light receiver.
However Pittenger teaches a brain-based near-infrared spectroscopy (NIRS) measurement device (Abstract) comprising:
A brain blood flow information acquirer acquiring brain blood flow information of a subject using a light transmitter that irradiates measurement light in a near-infrared wavelength region and a light receiver (p8, L. 23 – p9, L. 19, NIRS imaging performed on a region of interest in the brain, the NIRS performed using a light transmitter/emitter optode and a light receiver/detector optode, p9, L. 25 – p10, L. 3, “A NIRS cap can be positioned over the subject's frontal lobes using the international 10-20 system. Measurements of cortical perfusion can be obtained at 10 Hz using a fifty-two-channel near-infrared spectroscopy machine (ETG-4000, Hitachi Medical). To standardize the placement of the optode lattice, a source probe can be placed directly above the right ear in all participants.” Brain blood flow information acquired with the optode measurements).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to set the brain blood flow acquisition of Taylor to be performed with near-infrared imaging as taught by Pittenger as an example of brain perfusion data that enables the development of a perfusion calculation from each cerebral branch into each segmented volume. Specifically, to make a simulation of blood flow and pressure in cerebral arteries requires contextual perfusion information as shown in Fig. 41, and the development of perfusion data for the 3D model of the cerebral arteries is only possible with such measured blood flow perfusion data.
Yet their combined efforts fail to teach a second control of stopping acquisition of the brain blood flow information when it is determined that the heartbeat information does not satisfy the predetermined condition.
However Alailima teaches a brain function measurement device (Abstract) comprising selectively collecting data of a desired category, where selective collection may be achieved by either activating and deactivating sensing equipment or disregarding data based on whether it reflects data of the desired category ([0240]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to substitute Russell’s selective data acquisition, discarding gathered under undesired patient state, for a deactivation of data gathering during an undesired patient state as taught by Alailima as Alailima teaches that both steps are applicable when avoiding the gathering of undesired data. Thus, it is a simple substitution of one form of avoiding gathering irrelevant data (Russell: discarding) for another (Alailima: deactivating) to obtain predictable results of reliably restricting patient data collection to relevant data.
Response to Arguments
Applicant’s arguments filed 7/20/2026 with respect to the 35 USC 103 rejection of Claims 1, 10, and 11 and the application of Grodzki have been fully considered, but are not persuasive.
Applicant argues on page 10 that Grodzki fails to teach “Active Termination” of acquisition based on cumulative time. Specifically, Grodzki post-processes the MR and EEG data, only to then discard MR data. This is distinct from the claim’s active control to stop the sensor’s hardware acquisition once significant data is gathered. Examiner respectfully disagrees. Examiner notes that the rejection as a whole must be considered. While Grodzki’s discarding may be after the fact, the reference of Russell is what is relevant here. And Russell teaches an active termination of acquisition. What Grodzki provides is a relevant metric with which to make the termination decision in Russell, based on the fact that it is already being used in this way in Grozki.
Applicant argues on page 11 that Grodzki fails to motivate an active stopping as Grodzki’s MRI system require continuous operation for synchronization and stopping equipment would introduce startup delays and data loss in the system. Examiner respectfully disagrees. Again, Examiner that the rejection as a whole must be considered. Russell is providing the basis for an active discarding step based on data classification and Grodzki provides a relevant metric with which to judge when to end acquisition. And Examiner will argue that Grodzki’s metric does provide power saving because without a metric to judge when measurements should end, the method would continue measuring successive time intervals.
Applicant’s remaining amendments and arguments filed 7/20/2026 with respect to the 35 USC 103 rejection of Claims 1 and 11 have been fully considered and are persuasive. The rejection(s) is/are withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Taylor, Russell, Grodzki, and Pittenger.
Applicant’s remaining amendments and arguments filed 7/20/2026 with respect to the 35 USC 103 rejection of Claim 10 have been fully considered and are persuasive. The rejection(s) is/are withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Taylor, Russell, Grodzki, Pittenger, and Alailima.
Consequently, claims 3-6, 8-9, 12, 15, and 17-18 remain rejected due to their dependency on rejected independent claims.
Conclusion
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/JAIRO H. PORTILLO/
Examiner
Art Unit 3791
/PUYA AGAHI/Primary Examiner, Art Unit 3791
Prosecution Insights