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
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3, 5-10, 14-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Applicant has failed to show any possession of the algorithms and/or artificial neural network and/or similar machine learning or artificial intelligence technique required to perform the automated image analysis to obtain the assessment of the image to obtain a severity score. Applicant fails to describe any detail about a kinetic engineering model based on whole body movements, blood flow, fat, muscle and bone as well as capturing a single joint; multi-joints, and a combination of joints. Applicant fails to describe any detail about a biomechanical extremity model.
In response to Applicant’s argument that all of these are well-known, the Examiner disagrees. That’s merely Applicant’s opinion. Applicant has failed to provide any factual evidence to show that these well-known. Furthermore, references used by Examiner do not provide any evidence that Applicant has any possession on these methods and function. Specifically, any of these models could be a special model that require special algorithm to show how this algorithm is applied in this Spec. But Applicant has failed to show anything about these models.
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, 3, 5-10, 14-20 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.
In re claim 1, it is unclear what the metes and bounds of “performing assessment of images…. Using software to assess the image” are. So performing assessment method does not using software, but “assess the image scan to provide a severity score” is using software?
Furthermore, is there any difference between “performing assessment of images” and “using software to assess the image? Aren’t both the assessment of the images? Then, what’s the scope of wherein the assessment of the images are? In the clause of “wherein the assessment of images,” which assessment of images is it referring to? Is it the performed assessment images, or the software assessed images?
Furthermore, is the automated image analysis within the software? Or is another separated module or function or method or device? But it also “provides the severity score based on the assessment.” So the severity score is provided by both distinct methods?
Claim 1 has an artificial neural network, an artificial neural network-based software ore algorithm. Are they different in “software,” “algorithm,” or anything else?
It is also unclear what the metes and bounds of “wherein the dose of Rb-82 is calculated from at least one parameter selected from group consisting of subject parameters, imaging system parameters and radionuclide infusion system parameters using an artificial neural network-based software or algorithm” are.
Is it “calculated from at least one parameter selected from group consisting of”a) subject parameters, b) imaging system parameters, and c) radionuclide infusion system parameters using an artificial neural network-based software or algorithm; OR a) subject parameters using an artificial neural network-based software or algorithm, b) imaging system parameters using an artificial neural network-based software or algorithm, and c) radionuclide infusion system parameters using an artificial neural network-based software or algorithm.
The Examiner interprets: “calculated from at least one parameter selected from group consisting of a) subject parameters, b) imaging system parameters, and c) radionuclide infusion system parameters using an artificial neural network-based software or algorithm”
In re claim 7, it is unclear what is further limiting in this claim. Even though claim 7 claims “artificial neural network” which is already recited by claim 1. Claim 7 does not merely narrow down to “artificial neural network” only. Furthermore, is there anything different between artificial neural network and artificial neural network technique.
In re claim 8, it is unclear what is further limiting in this claim. Claim 1 d already recited this limitation.
In re claim 9, Is the artificial neural network and artificial neural network algorithms the same? Claim 1 has an artificial neural network, an artificial neural network-based software ore algorithm. Which one is claim 9 using?
In re claim 16, what is “its” referring to?
In re claim 17, is the software-based assessment referring to the “using software to assess the image scan” steps?
What is “models” referring to? Claim 1 does not have model, claim 7 has one model before “models.”
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3, 5, 7, 8, 9, 14, 15, 16, 17, 18, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gould (US 2008/0058642, hereinafter Gould ‘642) in view of Uber, III (US 2017/0172527, hereinafter Uber ‘527), and still further in view of De Bruin et al. (US 2011/0119212, hereinafter De Bruin ‘212).
In re claim 1, Gould ‘642 teaches a method of diagnosing and/or treating a peripheral arterial disease in a subject comprising: a) calculating a dose of Rb-82 to be administered to the subject (Applicant also admits that this is well-known, See Arg. Page 6-7);
b) administering the calculated dose of Rb-82 and a stress agent to a subject and scanning the region of interest (0039),
c) performing assessment of images obtained after scanning of step (b) (0040,-0057, 0084-0093);
d) using software to assess the image scan to provide a severity score based (0040, 0052, 0054, table 2, 0062, 0084, 0087, 0089); and
wherein the assessment of the images is obtained by an automated image analysis based on algorithms (0040, 0082, 0084, Applicant also admits that this is well-known, See Arg. Page 7); and
wherein the automated image analysis is based on one or more of a machine learning (0052, 0062, 0063, linear regression is a machine learning method), an artificial neural network, a simulated neural network (SNN) and a deep learning neural network, which provides the severity score based on the assessment (Applicant also admit that this is well-known, See Arg. Page 6-7); and
wherein the dose of Rb-82 is calculated from at least one parameter selected from group consisting of subject parameters (0111, 0119), imaging system parameters and radionuclide infusion system parameters using an artificial neural network-based software or algorithm. (Applicant also admits that this is well-known, See Arg. Page 6-7).
Uber ‘527 teaches wherein the Rb-82 chloride is administered by an automated generation and infusion system (0018, 0064, 0085, 0091, 0098, 0113, 0141, 0145, 0179); wherein the dose of Rb-82 is calculated from at least one parameter selected from group consisting of subject parameters (0018, 0064, 0085, 0091, 0098, 0113), imaging system parameters (0018, 0064, 0085, 0091, 0098, 0113) and radionuclide infusion system parameters using an artificial neural network-based software or algorithm.
It would have been prima facie obvious to one of ordinary skills in the art at the time of invention to modify the method/device of Gould ‘642 to include the features of Uber ‘527 in order to accurately deliver the required dose to human automatically.
Gould ‘642 and Uber ‘527 fail to teach e) suggesting a treatment plan (Applicant also admits that this is well-known, See Arg. Page 7); and f) following up with the patient using a telemedicine application.
De Bruin ‘212 teaches e) suggesting a treatment plan (0111); and f) following up with the patient using a telemedicine application (0081, 0083, 0087-0093).
It would have been prima facie obvious to one of ordinary skills in the art at the time of invention to modify the method/device of Gould ‘642 to include the features of Uber ‘527 in order to accurately deliver the required dose to human automatically, and to include the features of De Bruin ‘212 in order to select the optimal treatment choice for an individual.
In re claim 3, Gould ‘642 teaches wherein the dose of Rb-82 ranges from 0.01 mBq to 5000 mBq (0039).
In re claim 5, Gould ‘642 teaches wherein the scanning comprises positron emission tomography imaging (0077).
In re claim 7, De Bruin ‘212 teaches wherein the automated image analysis and assessment of the image is performed by artificial neural network technique (0075, 0076, 0079, 0082, 0083, 0107, 0120, 0182, etc.).
In re claim 8, Gould ‘642 teaches wherein the severity score provided by the software based on the assessment (0052, 0062, 0063).
In re claim 9, De Bruin ‘212 teaches wherein the treatment plan is based on the artificial neural network algorithms (0111, 0120, 0182).
In re claim 14, Uber ‘527 teaches wherein the software measures and delivers the required dose and/or volume within a specified time (0088, 0107, table 1, 0109, 0122).
In re claim 15, Gould ‘642 teaches wherein the software includes data acquisition (fig. 8, 0039, 0040), control (fig. 7, 0103), imaging (0035), reporting (0117-0119; fig. 10, 11). Gould ‘642 fails to teach telemedicine modules.
De Bruin ‘212 teaches software includes data acquisition (0081), control (0126), imaging (0070, 0206), reporting (0257), and telemedicine modules (0081, 0083, 0087-0093).
It would have been prima facie obvious to one of ordinary skills in the art at the time of invention to modify the method/device of Gould ‘642 to include the features of Uber ‘527 in order to accurately deliver the required dose to human automatically, and to include the features of De Bruin ‘212 in order to select the optimal treatment choice for an individual.
In re claims 16 and 17, Applicant admit that this is well-known, See Arg. Page 7. Hence, it would have been obvious to modify the method/device of Gould ‘642 to include the features of Uber ‘527 in order to accurately deliver the required dose to human automatically, and to include the features of De Bruin ‘212 in order to select the optimal treatment choice for an individual, and to include the well-known features here in order to defining disease signatures for automatic diagnosis.
In re claim 18, Uber ‘527 teaches wherein the software measures a dose delivery in real-time (0127, 0140, 0163, 0198).
In re claim 19, De Bruin ‘212 teaches wherein the software captures;
a) patient pre-screening historical data (0069);
b) symptoms (0030), and
c) risk factors suggestive of peripheral arterial disease comprising one or more of demographic information, previous serial imaging results, physician referrals, patient weakness and pain in lower extremities, patient fatigue, smoking habits, diabetes, dyslipidemia, blood sample history, comorbid conditions, height, weight, body mass index, waist and hip circumferences, cardiovascular risk factors, claudication pain history, brain injury, list of current medications (0030, 0031, 0069, 0074, 0214).
Claim(s) 6 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gould ‘642, Uber ‘527 and De Bruin ‘212 in view of Velazquez et al. (US 2020/0062820, hereinafter Velazquez ‘820).
In re claims 6 and 20, Gould ‘642 fails to teach wherein the region of interest comprises lower extremities; wherein the software provides an expert system module to score the severity of peripheral arterial disease based on imaging results or assessment.
Velazquez ‘820 teaches wherein the region of interest comprises lower extremities (0049, 0051, limb perfusion by PET imaging is a lower extremity by PET); wherein the software provides an expert system module to score the severity of peripheral arterial disease based on imaging results or assessment (0004, 0049, 0053, 0062).
It would have been prima facie obvious to one of ordinary skills in the art at the time of invention to modify the method/device of Gould ‘642, Uber ‘527 and De Bruin ‘212 to include the features of Velazquez ‘820 in order to diagnoses problems with ischemic tissue.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gould ‘642, Uber ‘527 and De Bruin ‘212 in view of Avinash et al. (US 2004/0122708, hereinafter Avinash ‘708).
In re claim 10, Gould ‘642 fails to teach wherein the following-up of the patient further comprises: capturing a data from a patient after diagnosis with a wearable or non wearable device including software application to provide the telemedicine application to track patient aerobic exercise, blood pressure and mental health.
Avinash ‘708 teaches wherein the following-up of the patient comprises: capturing the data from a patient after diagnosis with a wearable or non wearable device including software application to provide a telemedicine application to track patient aerobic exercise (0259), blood pressure (0250) and mental health (0254, 0261-0263, fig. 8).
It would have been prima facie obvious to one of ordinary skills in the art at the time of invention to modify the method/device of Gould ‘642, Uber ‘527 and De Bruin ‘212 to include the features of Avinash ‘708 in order to provide better medical management.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gould ‘642, Uber ‘527 and De Bruin ‘212 in view of Rousso et al. (US 2008/0033291, hereinafter Rousso ‘291).
In re claim 16, Gould ‘642 fails to teach wherein the software includes a kinetic engineering model based on whole body movements, blood flow, fat, muscle and bone and its joints. Applicant also admits that this is well-known, See Arg. Page 6-7.
Rousso ‘291 teaches wherein the software includes a kinetic engineering model (0027, 0284) based on whole body movements (0329), blood flow (0280), fat (0018, 0500, 0637), muscle (0290) and bone (0018, 0177, 0373, 0505, 0458, 0595, 0597, 0601, 0602), and its joints (0329).
It would have been prima facie obvious to one of ordinary skills in the art at the time of invention to modify the method/device of Gould ‘642, Uber ‘527 and De Bruin ‘212 to include the features of Rousso ‘291 in order to defining disease signatures for automatic diagnosis, preferably, based on a multi-parameter vector, preferably, based on kinetic radiopharmaceutical values.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gould ‘642, Uber ‘527 and De Bruin ‘212 in view of Gardner et al. (US 2013/0230454, hereinafter Gardner ‘454) and further in view of Front et al. (US 2001/0041835, hereinafter Front ‘835).
In re claim 17, Gould ‘642 teaches software includes anatomic segments and coordinates (figs. 8-9, 0120, 0098, note that fig. 8-9 is in a coordinates, 0113, 0113, 0117); but fails to teach wherein the software includes the biomechanical extremity model, and models representing anatomic segments, bony landmarks, joint motion, and number of markers. Applicant also admits that this is well-known, See Arg. Page 7.
Gardner ‘454 teaches wherein the software includes the biomechanical extremity model (0147, 0180), and models representing anatomic segments (0168, 0181, 0183, 0184, 0188), bony landmarks (0071, fig.2, all the white dots are landmarks; fig. 5, disc outlined, 0235), joint motion (0184-0186, 0188, 0241) and coordinates (0066).
It would have been prima facie obvious to one of ordinary skills in the art at the time of invention to modify the method/device of Gould ‘642, Uber ‘527 and De Bruin ‘212 to include the features of Gardner ‘454 in order to provide classifications of disc nutrition deficit, pathological conditions and/or associated back pain measured that can be based on specific parameters associated with hypoperfusion, hypoxia, and ischemia.
Gould ‘642 and Gardner ‘454 fails to teach software includes number of markers.
Front ‘835 teaches software includes number of markers (0046-0047, 0049, 0052, 0056).
It would have been prima facie obvious to one of ordinary skills in the art at the time of invention to modify the method/device of Gould ‘642, Uber ‘527 and De Bruin ‘212 to include the features of Gardner ‘454 in order to provide classifications of disc nutrition deficit, pathological conditions and/or associated back pain measured that can be based on specific parameters associated with hypoperfusion, hypoxia, and ischemia, and to include the features of Front ‘835 in order to accurately record the relative position of the frame and the internal tissue of patient in 3D in the structural image.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 3, 5-10, 14-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
In response to Applicant’s argument that the limitations, under rejection of 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, are well-known, the Examiner disagrees. That’s merely Applicant’s opinion. Applicant has failed to provide any factual evidence to show that these well-known. Furthermore, references used by Examiner do not provide any evidence that Applicant has any possession on these methods and function. Specifically, if it is likely that any of these models could be a special model that require special algorithm to show how this algorithm is applied in this Spec. But Applicant has failed to show anything about these models.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BO JOSEPH PENG whose telephone number is (571)270-1792. The examiner can normally be reached Monday thru Friday: 8:00 AM-5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANNE M KOZAK can be reached at (571) 270-0552. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BO JOSEPH PENG/Primary Examiner, Art Unit 3797