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 .
Claim Rejections - 35 USC § 112
35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors. Claims 1-12 are also rejected at least by virtue of dependency upon a rejected base claim.
Claim 1 recites the limitation “derive a predetermined parameter indicating a body state of the subject based on the acquired sensor information”; similarly, claim 13 recites the limitation “deriving a predetermined parameter indicating a body state of the subject based on the acquired sensor information”; rendering the claims indefinite. The limitation is indefinitely broad, because the claim does not clearly define the ‘predetermined parameter’ or ‘body state’ of the subject. Upon review, the instant specification provides:
“Subsequently, the information processing apparatus 2 derives the predetermined parameter based on the sensor information acquired from each of the non-ultrasonic sensor I and the ultrasonic sensor 3 in step S103 (step S106). For example, the information processing apparatus 2 derives blood vessel resistance of the subject using the sensor information acquired from the non-ultrasonic sensor l and the physiological information of the subject estimated in step S105. Thereafter, the information processing apparatus 2 outputs information regarding the derived predetermined parameter to the output device 22 or the like (step S107), and then ends the process.”
[0049] (emphasis added)
“FIG. 10 illustrates an example of parameters derived by the information processing system 100 according to Modification Example I. The information processing system 100 derives a pulse wave transit time (PWTT) based on the sensor information acquired from each of the non-ultrasonic sensor 1 and the ultrasonic sensor 3. In other words, a predetermined parameter derived by the non-ultrasonic sensor 1 and the ultrasonic sensor 3 is a pulse wave transit time. Except for such point, the information processing system 100 has a similar configuration to the information processing system 100 described in the foregoing embodiment, and similar operational effects are obtained.”
[0095] (emphasis added)
“The information processing system 100 according to Modification Example 2 derives an oxygen transport rate based on the sensor information acquired from each of the non-ultrasonic sensor 1 and the ultrasonic sensor 3. In other words, a predetermined parameter derived by the information processing system 100 is an oxygen transport rate. The oxygen transport rate is, for example, an amount of oxygen arriving at a peripheral tissue. Except for such point, the information processing system 100 has a similar configuration to the information processing system 100 described in the foregoing embodiment, and similar operational effects are obtained.”
[0109] (emphasis added)
“The derivation unit 2015 derives a predetermined parameter indicating a body state of the subject based on the sensor information acquired from each of the ultrasonic sensors 3A and 3B. For example, the derivation unit 2015 derives the predetermined parameter using the first physiological information and the second physiological information estimated by the first estimation unit 2014 and the second estimation unit 2017. When the first estimation unit 2014 estimates the renal blood flow rate of the subject and the second estimation unit 2017 estimates the urinary volume change rate of the subject, the derivation unit 2015 derives, for example, a glomerular filtration rate (GFR) of the subject using the renal blood flow rate and the urinary volume change rate of the subject at the same time. The glomerular filtration rate is an index for measuring performance for producing urine by filtering blood made by a glomerulus of a kidney for 1 minute and is an important index used for a doctor or the like to determine a kidney function state or a dosage."
[0124] (emphasis added)
The written description fails to clearly link the structure (i.e., the ‘controller’) for deriving the predetermined parameter with the predetermined parameter itself (e.g., the GFR, pulse wave transit time, blood vessel resistance, oxygen transport rate, etc.); distinct structures (e.g., derivation unit, non-ultrasonic and ultrasonic sensors, and information processing apparatus) are recited as deriving distinct parameters. For the purposes of examination, any of the GFR, pulse wave transit time, oxygen transport rate, or blood vessel resistance fall within the broadest reasonable interpretation of the claim language. It is suggested to amend the claim to clearly point out what the ‘predetermined parameter’ actually is and how it is derived.
Claim 2 recites the limitation “the controller is configured to derive the parameter over time” which renders the claim indefinite. There is insufficient antecedent basis for this limitation in the claim. It is unclear what ‘the parameter’ is particularly pointing to; in an interpretation it may refer to the ‘predetermined parameter’ derived in claim 1, and in another interpretation it may refer to a distinct new ‘parameter’. For the purposes of examination the broadest reasonable interpretation of the claim language is any type of ‘parameter’.
Claim 3 recites the limitation “the controller is configured to derive the parameter using the estimated physiological information” which renders the claim indefinite. There is insufficient antecedent basis for this limitation in the claim. It is unclear what ‘the parameter’ is particularly pointing to; in an interpretation it may refer to the ‘predetermined parameter’ derived in claim 1, and in another interpretation it may refer to a distinct new ‘parameter’. For the purposes of examination the broadest reasonable interpretation of the claim language is any type of ‘parameter’.
Claim 5 recites the limitation “wherein the plurality of sensors includes the ultrasonic sensor and a non-ultrasonic sensor detecting physical information other than an ultrasonic wave” which renders the claim indefinite. The language doesn’t clearly indicate what information is detected by which specific sensor(s); in an interpretation the claim may indicate that both the ‘ultrasonic sensor and a non-ultrasonic sensor’ detect physical information other than an ultrasonic wave. It is suggested to amend the claim to clearly define what properties the ‘non-ultrasonic sensor’ is detecting, and that the ultrasonic sensor detects an ultrasonic wave.
Claim 7 recites the limitation “wherein the derived parameter includes at least one of” which renders the claim indefinite. There is insufficient antecedent basis for this limitation in the claim. It is unclear what ‘the derived parameter’ is particularly pointing to; in an interpretation it may refer to the ‘predetermined parameter’ derived in claim 1, and in another interpretation it may refer to a distinct newly derived ‘parameter’. For the purposes of examination the broadest reasonable interpretation of the claim language is any type of ‘parameter’.
Claim 9 recites the limitation “the controller is configured to derive the parameter using the estimated first physiological information and the estimated second physiological information” which renders the claim indefinite. There is insufficient antecedent basis for this limitation in the claim. It is unclear what ‘the parameter’ is particularly pointing to; in an interpretation it may refer to the ‘predetermined parameter’ derived in claim 1, and in another interpretation it may refer to a distinct newly derived ‘parameter’. For the purposes of examination the broadest reasonable interpretation of the claim language is any type of ‘parameter’.
Claim 11 recites the limitation “wherein the derived parameter includes a glomerular filtration rate” which renders the claim indefinite. There is insufficient antecedent basis for this limitation in the claim. It is unclear what ‘the derived parameter’ is particularly pointing to; in an interpretation it may refer to the ‘predetermined parameter’ derived in claim 1, and in another interpretation it may refer to a distinct newly derived ‘parameter’. For the purposes of examination the broadest reasonable interpretation of the claim language is any type of ‘parameter’.
35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim(s) 12 is/are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
As drafted, claim 12 recites the limitations “an information processing system comprising: a plurality of sensors including an ultrasonic sensor; and the information processing apparatus according to claim 1.” The ‘information processing apparatus’ of claim 1 already includes ‘a plurality of sensors including an ultrasonic sensor’ – the instant claim 12 fails to further limit the subject matter of claim 1. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7 and 12-13 is/are rejected under 35 U.S.C. 102(a)(2) as being clearly anticipated by Itu et al. (US20160166209A1, 2016-06-16; hereinafter “Itu”).
Regarding claim 1, Itu teaches an information processing apparatus (“An apparatus for personalized non-invasive assessment of renal artery stenosis for a patient […] means for computing a hemodynamic index for one or more locations of interest in the patient-specific renal arterial geometry” [clm 20]; [fig. 10]) comprising:
a controller (“Computer 1002 contains a processor 1004, which controls the overall operation of the computer 1002 by executing computer program instructions which define such operation. […] the processor 1004 executing the computer program instructions” [0074]; [fig. 10]) configured to:
acquire sensor information from each of a plurality of sensors including an ultrasonic sensor coming into contact with or in proximity to a subject (“means for receiving medical image data of the patient;” [clm 20]; “Medical image data from one or multiple imaging modalities can be received. […] The medical image data can be received directly from one or more image acquisition devices, such as a CT scanner, MR scanner, Angiography scanner, Ultrasound device, etc.,” [0053]; [fig. 10]); and
derive a predetermined parameter indicating a body state of the subject based on the acquired sensor information (“means for extracting features from the patient-specific renal arterial geometry of the patient; and means for computing a hemodynamic index for one or more locations of interest in the patient-specific renal arterial geometry based on the extracted features” [clm 20]; “In addition to using the patient-specific geometry to personalize the patient-specific multi-scale model of renal arterial circulation, other non-invasive measurements such as renal biomarkers of the patient may be used as well. […] In a possible implementation, the microvascular resistance used to implement the RAAS and renal microvasculature model 214 can be derived as a function of any one or combination of the above described biomarkers.” [0024]; “patient-specific renal arterial geometry is extracted from the medical image data of the patient.” [0055]; “The trained surrogate model inputs the extracted features, including the geometric features, non-invasive measurement features, and demographic data features, and calculates hemodynamic indices (such as rFFR or trans-stenotic pressure gradient) for particular locations in the patient-specific renal arterial geometry based on the extracted features.” [0059]; Medical image data (i.e., sensor information) from multiple imaging modalities including an ultrasound device is used to derive renal arterial geometry, wherein features are extracted for calculating hemodynamic indices (i.e., predetermined parameter) of the patient [0025-0060, 0073-0074], [fig. 10]).
Regarding claim 2, Itu teaches the information processing apparatus according to claim 1, Itu further teaching wherein
the controller is configured to acquire the sensor information over time from each of the plurality of sensors (“the medical image data can include, computed tomography (CT), Dyna CT, magnetic resonance (MR), Angiography, Ultrasound, […] The medical image data can be 2D, 3D, or 4D (3D+time) medical image data.” [0053]; [fig. 10]), and
the controller is configured to derive the parameter over time (“renal blood flow simulations are performed for the synthetic renal arterial anatomical models […] The blood flow simulations result in blood flow and pressure values at various locations in the renal arterial circulation model for each of a plurality of time steps.” [0030]; Blood flow and pressure values may be collected for a plurality of time steps [0025-0060, 0073-0074], [fig. 10], [see claim 1 rejection]).
Regarding claim 3, Itu teaches the information processing apparatus according to claim 1, Itu further teaching wherein:
the controller is configured to estimate physiological information of the subject based on the sensor information acquired from the ultrasonic sensor (“Renal Artery Stenosis (RAS) is a cardiovascular pathology consisting of narrowing of the renal artery, and is typically caused by either atherosclerosis or fibromuscular dysplasia. […] RAS can be identified using several medical imaging modalities, such as computed tomography angiography (CTA), magnetic resonance imaging (MRI), abdominal x-ray (AXR), and Doppler ultrasound,” [0003]; [see claim 1 rejection]), and
the controller is configured to derive the parameter using the estimated physiological information (“hemodynamic indices are calculated for the synthetic renal arterial anatomical models from the blood flow simulations.” [0031]; Doppler ultrasound generates blood flow information for the simulations, which is used to calculate hemodynamic indices [0025-0060, 0073-0074], [see claim 1 rejection]).
Regarding claim 4, Itu teaches the information processing apparatus according to claim 3,
Itu further teaching wherein the physiological information includes at least one of a flow quantity of a blood vessel, a pulse-wave waveform, and a cardiac output of the subject (“the hemodynamic index is renal fractional flow reserve (rFFR) at rest or at hyperemia” [clm 26]; “renal fractional flow reserve (rFFR) can be calculated for multiple sampling points along the renal artery centerline in each of the synthetic renal arterial anatomical models.” [0031]; “the trained surrogate model (or models) is used to predict a patient-specific hemodynamic index (such as rFFR) based on patient-specific features extracted from medical image data and non-invasive characteristics of a patient.” [0053]; [0025-0060, 0073-0074], [see claim 1 rejection]).
Regarding claim 5, Itu teaches the information processing apparatus according to claim 1,
Itu further teaching wherein the plurality of sensors includes the ultrasonic sensor and a non-ultrasonic sensor detecting physical information other than an ultrasonic wave (“the trained surrogate model (or models) is used to predict a patient-specific hemodynamic index (such as rFFR) based on patient-specific features extracted from medical image data and non-invasive characteristics of a patient. […] Medical image data from one or multiple imaging modalities can be received.” [0053]; “In addition to the medical image data, non-invasive measurements of the patient can be acquired or input as well. For example, cuff-based blood pressure measurements and heart rate measurements of the patient can be received.” [0054]; Non-invasive measurements (i.e., physical information) may be obtained from a cuff (i.e., non-ultrasonic sensor) in addition to ultrasound imaging [0025-0060, 0073-0074], [see claim 1 rejection]).
Regarding claim 6, Itu teaches the information processing apparatus according to claim 5,
Itu further teaching wherein the sensor information acquired from the non-ultrasonic sensor includes at least one of information regarding a blood pressure, information regarding electrocardiogram, and information regarding an arterial oxygen saturation “In addition to the medical image data, non-invasive measurements of the patient can be acquired or input as well. For example, cuff-based blood pressure measurements and heart rate measurements of the patient can be received.” [0054]; [0025-0060, 0073-0074], [see claim 5 rejection].
Regarding claim 7, Itu teaches the information processing apparatus according to claim 6,
Itu further teaching wherein the derived parameter includes at least one of blood vessel resistance, a pulse wave transit time, and an oxygen transport rate (“means for extracting features from the patient-specific renal arterial geometry of the patient; and means for computing a hemodynamic index for one or more locations of interest in the patient-specific renal arterial geometry based on the extracted features” [clm 20]; “In addition to using the patient-specific geometry to personalize the patient-specific multi-scale model of renal arterial circulation, other non-invasive measurements such as renal biomarkers of the patient may be used as well. […] In a possible implementation, the microvascular resistance used to implement the RAAS and renal microvasculature model 214 can be derived as a function of any one or combination of the above described biomarkers.” [0024]; “patient-specific renal arterial geometry is extracted from the medical image data of the patient.” [0055]; “The trained surrogate model inputs the extracted features, including the geometric features, non-invasive measurement features, and demographic data features, and calculates hemodynamic indices (such as rFFR or trans-stenotic pressure gradient) for particular locations in the patient-specific renal arterial geometry based on the extracted features.” [0059]; [0025-0060, 0073-0074], [fig. 10], [see claim 1 rejection]).
Regarding claim 12, Itu teaches an information processing system ([fig. 10], [see claim 1 rejection]) comprising:
a plurality of sensors including an ultrasonic sensor (“means for receiving medical image data of the patient;” [clm 20]; “Medical image data from one or multiple imaging modalities can be received. […] The medical image data can be received directly from one or more image acquisition devices, such as a CT scanner, MR scanner, Angiography scanner, Ultrasound device, etc.,” [0053]; [fig. 10], [see claim 1 rejection]); and
the information processing apparatus according to claim 1 (“An apparatus for personalized non-invasive assessment of renal artery stenosis for a patient […] means for computing a hemodynamic index for one or more locations of interest in the patient-specific renal arterial geometry” [clm 20]; [fig. 10], [see claim 1 rejection]).
Regarding claim 13, Itu teaches a non-transitory computer readable storage medium storing an information processing program causing a computer to perform processes (“A non-transitory computer readable medium storing computer program instructions for personalized non-invasive assessment of renal artery stenosis for a patient, the computer program instructions when executed by a processor cause the processor to perform operations” [clm 30]; [fig. 3, 10], [see claim 1 rejection]) of:
acquiring sensor information from each of a plurality of sensors including an ultrasonic sensor coming into contact with or in proximity to a subject (“receiving medical image data of the patient;” [clm 30]; “Medical image data from one or multiple imaging modalities can be received. […] The medical image data can be received directly from one or more image acquisition devices, such as a CT scanner, MR scanner, Angiography scanner, Ultrasound device, etc.,” [0053]; [fig. 10], [see claim 1 rejection]); and
deriving a predetermined parameter indicating a body state of the subject based on the acquired sensor information (“extracting features from the patient-specific renal arterial geometry of the patient; and computing a hemodynamic index for one or more locations of interest in the patient-specific renal arterial geometry based on the extracted features” [clm 30]; “In addition to using the patient-specific geometry to personalize the patient-specific multi-scale model of renal arterial circulation, other non-invasive measurements such as renal biomarkers of the patient may be used as well. […] In a possible implementation, the microvascular resistance used to implement the RAAS and renal microvasculature model 214 can be derived as a function of any one or combination of the above described biomarkers.” [0024]; “patient-specific renal arterial geometry is extracted from the medical image data of the patient.” [0055]; “The trained surrogate model inputs the extracted features, including the geometric features, non-invasive measurement features, and demographic data features, and calculates hemodynamic indices (such as rFFR or trans-stenotic pressure gradient) for particular locations in the patient-specific renal arterial geometry based on the extracted features.” [0059]; [0025-0060, 0073-0074], [fig. 10], [see claim 1 rejection]).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 8-11 is/are rejected under 35 U.S.C. 103 as being obvious over the teachings of Itu as applied to claim 1 above, in view of Toth et al. (US20170231490A1, 2017-08-17; hereinafter “Toth”).
Regarding claim 8, Itu teaches the information processing apparatus according to claim 1,
Itu further teaching wherein the plurality of sensors includes first and second ultrasonic sensors (“means for receiving medical image data of the patient;” [clm 20]; “Medical image data from one or multiple imaging modalities can be received. […] The medical image data can be received directly from one or more image acquisition devices, such as a CT scanner, MR scanner, Angiography scanner, Ultrasound device, etc.,” [0053]; Multiple imaging modalities (e.g., first and second ultrasound devices) may be utilized [0025-0060, 0073-0074], [fig. 10], [see claim 1 rejection]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify the information processing apparatus taught by Itu to utilize two ultrasound devices, since it has been held that mere duplication of the essential working parts of a device involves only routine skill in the art. St. Regis Paper Co. v. Bemis Co., 193 USPQ 8.
Although Itu appears to teach all the limitations of claim 8 as shown above, if in an interpretation, one argues (or interprets differently) that Itu does not teach the first and second ultrasound sensors, the following reference may be applied to supplement the teaches of Itu above.
In the same field of endeavor, Toth teaches an information processing apparatus (“A system for monitoring one or more physiologic signals, autonomic neural signals, and/or electrophysiological signals from a subject” [clm 95]; [fig. 1a-2]);
Toth further teaching wherein the plurality of sensors includes first and second ultrasonic sensors (“a subject is positioned in a supine posture, and instrumentation for monitoring one or more physiologic parameters is/are coupled to the subject. […] In aspects, a renal blood flow monitoring apparatus (e.g. an ultrasound imaging system, a Doppler flow meter, etc.) is coupled to the subject during the test to register a renal blood flow reading therefrom. In aspects, a urinary flow monitor (e.g. a urinary flow catheter, a bladder filling ultrasound imaging system, etc.) is coupled to the subject during the test to register or establish a urinary flow rate” [0140]; “Some non-limiting examples of physiologic parameters that may be measured during the stress test may include one or more […] renal functional parameters (e.g. renal blood flow (e.g. via renal artery Doppler ultrasound), urinary flow and/or bladder filling rate/volume (e.g. via bladder volume ultrasound),” [0180]; “the system may include a device with a diagnostic and/or therapeutic sonography component, configured to provide an ultrasonic signal to and/or receive a sonographic signal from an adjacent tissue upon engagement with the skin. The sonography component may be configured so as to image, and/or capture a metric from an adjacent tissue upon engagement with the skin.” [0252]; [0086-0141, 0176-0201, 0245-0268], [fig. 1a-2]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify the information processing apparatus taught by Itu with the first and second ultrasonic sensors as taught by Toth. In recent years, renal revascularization has evolved from both a procedural and technical point of view. A higher discriminatory power can be reached through the use of function indices, such as trans-stenotic pressure gradients, and renal fractional flow reserve (rFFR), but such functional indices typically require invasive and costly measures, which increase patient risk and strain healthcare budgets. Accordingly, non-invasive techniques for personalized function assessment of renal artery are desirable (Itu [0004]). New treatments and remote monitoring of patients with cardiovascular diseases (heart failure, post stroke, etc.), diabetes, kidney failure, chronic obstructive pulmonary disease (COPD), obesity, neurological disorders (depression, Alzheimer's disease, migraines, stress disorders, etc.), arthritis, among other ailments, for purposes of treatment or prevention of such diseases may substantially improve patient outcomes (Toth [0005]).
Regarding claim 9, Itu and Toth teach the information processing apparatus according to claim 8,
Toth further teaching wherein: the controller (“a processor electrically coupled and/or wirelessly coupled to the visual input device and the back-facing imaging sensor, the processor configured to deliver the output images, accept the feedback images, and analyze the images to determine one or more of the physiologic, autonomic neural, and/or electrophysiological signals.” [clm 95]; “The module 201 includes one or more of interconnects, sensors, […] a sensor communication circuit, a signal conditioning circuit, a processor, a memory device, a controller,” [0268]; [0086-0141, 0176-0201, 0245-0268], [fig. 1a-2]) is configured to:
estimate first physiological information of the subject based on the sensor information acquired from the first ultrasonic sensor (“In aspects, a renal blood flow monitoring apparatus (e.g. an ultrasound imaging system, a Doppler flow meter, etc.) is coupled to the subject during the test to register a renal blood flow reading therefrom.” [0140]; [0086-0141, 0176-0201, 0245-0268], [fig. 1a-2], [see claim 8 rejection]); and
estimate second physiological information of the subject based on the sensor information acquired from the second ultrasonic sensor (“In aspects, a urinary flow monitor (e.g. a urinary flow catheter, a bladder filling ultrasound imaging system, etc.) is coupled to the subject during the test to register or establish a urinary flow rate therefrom.” [0140]; [0086-0141, 0176-0201, 0245-0268], [fig. 1a-2], [see claim 8 rejection]), and
the controller is configured to derive the parameter using the estimated first physiological information and the estimated second physiological information (“Each patch or patch/module pair may be configured to monitor one or more local physiologic and/or physical parameters of the attached subject (e.g. local to the site of attachment, etc.), […] and to relay such information in the form of signals to a host device” [0088]; “the host device may be configured to coordinate information exchange to/from each module and/or patch, and to generate one or more physiologic signals, physical signals,” [0089]; “The system may include an algorithm (e.g. either incorporated into a processor on a patch/module pair, the HMD, in a processor coupled thereto, on a server, a virtual server, etc.), configured to analyze one or more images, ocular images, retinal images, facial images, physiologic signals,” [0185]; [0086-0141, 0176-0201, 0245-0268], [fig. 1a-2]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify the information processing apparatus taught by Itu with the first and second ultrasonic sensors as taught by Toth. Non-invasive techniques for personalized function assessment of renal artery are desirable (Itu [0004]). New treatments and remote monitoring of patients with cardiovascular diseases (heart failure, post stroke, etc.), diabetes, kidney failure, chronic obstructive pulmonary disease (COPD), obesity, neurological disorders (depression, Alzheimer's disease, migraines, stress disorders, etc.), arthritis, among other ailments, for purposes of treatment or prevention of such diseases may substantially improve patient outcomes (Toth [0005]).
Regarding claim 10, Itu and Toth teach the information processing apparatus according to claim 9,
Toth further teaching wherein the first physiological information of the subject includes a renal blood flow quantity of the subject (“In aspects, a renal blood flow monitoring apparatus (e.g. an ultrasound imaging system, a Doppler flow meter, etc.) is coupled to the subject during the test to register a renal blood flow reading therefrom.” [0140]; [0086-0141, 0176-0201, 0245-0268], [fig. 1a-2], [see claim 9 rejection]), and
the second physiological information of the subject includes a urinary volume change rate of the subject (“In aspects, a urinary flow monitor (e.g. a urinary flow catheter, a bladder filling ultrasound imaging system, etc.) is coupled to the subject during the test to register or establish a urinary flow rate therefrom.” [0140]; [0086-0141, 0176-0201, 0245-0268], [fig. 1a-2], [see claim 9 rejection]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify the information processing apparatus taught by Itu with the first and second ultrasonic sensors as taught by Toth. In recent years, renal revascularization has evolved from both a procedural and technical point of view. Non-invasive techniques for personalized function assessment of renal artery are desirable (Itu [0004]). New treatments and remote monitoring of patients with cardiovascular diseases (heart failure, post stroke, etc.), diabetes, kidney failure, chronic obstructive pulmonary disease (COPD), obesity, neurological disorders (depression, Alzheimer's disease, migraines, stress disorders, etc.), arthritis, among other ailments, for purposes of treatment or prevention of such diseases may substantially improve patient outcomes (Toth [0005]).
Regarding claim 11, Itu and Toth teach the information processing apparatus according to claim 10,
Toth further teaching wherein the derived parameter includes a glomerular filtration rate (“Some non-limiting examples of processes that may be monitored include renal nerve traffic, […] glomerular filtration rate,” [0141]; [0086-0141, 0176-0201, 0245-0268], [fig. 1a-2]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify the information processing apparatus taught by Itu with the first and second ultrasonic sensors as taught by Toth. In recent years, renal revascularization has evolved from both a procedural and technical point of view. A higher discriminatory power can be reached through the use of function indices, such as trans-stenotic pressure gradients, and renal fractional flow reserve (rFFR), but such functional indices typically require invasive and costly measures, which increase patient risk and strain healthcare budgets. Accordingly, non-invasive techniques for personalized function assessment of renal artery are desirable (Itu [0004]). New treatments and remote monitoring of patients with cardiovascular diseases (heart failure, post stroke, etc.), diabetes, kidney failure, chronic obstructive pulmonary disease (COPD), obesity, neurological disorders (depression, Alzheimer's disease, migraines, stress disorders, etc.), arthritis, among other ailments, for purposes of treatment or prevention of such diseases may substantially improve patient outcomes (Toth [0005]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James F. McDonald III whose telephone number is (571)272-7296. The examiner can normally be reached M-F; 8AM-6PM EST.
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JAMES FRANKLIN MCDONALD III
Examiner
Art Unit 3797
/JOSEPH M SANTOS RODRIGUEZ/ Primary Examiner, Art Unit 3797
Prosecution Insights