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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Priority documents have been filed on 12/19/2018. However, based on the translated specification (provided by Examiner) it has been determined JP 2017-206696 does not support the claimed invention. Accordingly, the effective filing date is considered to be 10/25/2018.
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, 5-6, 9-10, 12-13, 21-22, 25-26, 37 and 41-42 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. Claim 1 is rejected because the specification, as originally filed, fails to disclose where the learning models of machine learning select an entry side of the patient for the lesion to be treated first as now claimed. As best understood, the learning models only select a probability order (see Table 1) and not the entry side. Claim 1 is also rejected because the specification, as originally filed, fails to disclose details of the machine learning to demonstrate possession of the claimed invention, particularly the limitation(s) of having the machine learning select an entry side for the lesion to be treated first, selecting the probability order based on Deep Q-learning method and by validation based on a noise imparting method as now claimed. Applicant asserts that support is found in Table 1 and Paragraphs [0290]-[0295] and [0461]-[0470] of the PG-Publication, Examiner respectfully disagrees. Table 1 depicts a selection probability for lesions changed with Deep-Learning and Q-Learning, not “Deep-Q-learning”. Also, the order is changed only for short lesions “c” and “d” and not “a” and “b” in the “first treatment”. Claim 1 is also rejected because it is unclear if the treatment in the “the lesion to be treated first among the plurality of lesions” involves multiple treatments as depicted in Table 1. Claim 10 appears to have the same issues. Claim 10 is also rejected because the specification, as originally filed fails to disclose validating based on a noise imparting method and a subsequent validating step to determine therapeutic effect.
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, 5-6, 9-10, 12-13, 21-22, 25-26, 37 and 41-42 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. Claim 1 is rejected because it is unclear if “limb arteries having lesions” are the same lesions previously identified. Claim 1 is also rejected because it is unclear if “the selection probability order based on a Deep-Q-learning method and by validation based on a noise imparting method” are different iterations of the aforementioned learning models as now claimed. For example, Table 1 appears to depict two different probability orders based on “Deep-Learning” and “Q-Learning” which appear to be different. Claim 1 is rejected because it is unclear how the noise imparting method provides validation. For example, Paragraph [0463] of the PG-Publication simply states that when noise was added in an axial direction of the lesion, determination of the side to be treated first was changed. However, Table 1 shows lesion a (left TRI entry) treated first each time. Claim 10 appears to have similar issues. Claim 10 is rejected because it is unclear if the last step of validating is the same validation based on noise imparting method as now claimed. Claim 10 is rejected because it is unclear how validating occurs after treatment to show a higher therapeutic effect. As best understood, the validating applies noise to the lesion before treatment to see if the length of the lesion has any basis on the treatment priority. Claim 12 is rejected because it is unclear which validating step is being further defined or if it’s a different validating step. Claim 13 is rejected because it is unclear which validation, “the validation” refers to. Claim 41 is rejected because it is unclear if the validating step is a new validation or referring to validations previously set forth in Claim 10.
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 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.
Claim(s) 1-3, 5-6, 9-10, 12-13, 21-22, 25-26, 37 and 41-42 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2014/0358123 to Ueda et al. “Ueda” in view of U.S. Publication No. 2021/0085397 to Passerini et al. “Passerini”, U.S. Publication No. 2016/0038248 to Bharadwaj et al. “Bharadwaj”, U.S. Patent No. 10,762,424 to Nazari et al. “Nazari” and U.S. Publication No. 2018/0315182 to Rapaka et al. “Rapaka”.
Note: Rejections below are based upon a best attempt to address the claimed subject matter in view of the various 35 U.S.C. 112(a) and 112(b) rejections above.
As for Claims 1-2, 5-6, and 37, Ueda discloses a method for diagnosing and treating a plurality of identified lesions (e.g. targets) in a plurality of bifurcated lumens connected to a biological lumen via a bifurcated from the main lumen including steps of identifying lesions (e.g. targets) and treating lesions (Abstract; Paragraphs [0006], [0007], [0011] and [0012]; also Fig. 1 and corresponding descriptions). Ueda explains that treating can include a step of introducing a catheter into the artery of the arm (radial artery or brachial artery) (Paragraph [0005]). Examiner notes that the method for treating described by Ueda inherently treats one of the lesions first in its broadest reasonable interpretation.
However, Ueda does not particularly disclose how the targets are identified (e.g. via diagnostic imaging or EM waves) and selecting a probability order (e.g. a lesion treatment order/priority) based on the identified lesion(s) utilizing output from learning models of machine learning to determine a lesion to be treated first as claimed.
Passerini teaches rom within a similar field of endeavor with respect to systems and methods to assess anatomy (e.g. bifurcations [0056]) and plan treatment with medical images (Abstract) where geometric features (Paragraph [0008] and [0059]) are extracted from medical image data (e.g. CT, X-ray, ultrasound, etc.; Paragraph [0050]) and using a first machine learned trained regression model, lesions are identified, ordered and labeled progressively with decreasing severity (Paragraphs [0009], [0011] and [0046]). Passerini explains where lesion length is measured (Paragraph [0056]). Next, Passerini utilizes another machine learning model to select candidate treatment options based on the geometric features and post treatment predictions (Paragraph [0016]). Passerini explains the aforementioned steps provides improved results for diagnosis and treatment planning for multiple lesions (Paragraph [0049]). Examiner notes that CT imaging, for example, is considered to read on detecting an EM wave obtained through a patient by irradiating the patient with EM waves and obtaining EM wave information indicative of a changed EM wave in its broadest reasonable interpretation.
Accordingly, one skilled in the art would have been motivated to have modified the system and method described by Ueda to incorporate machine learning to automatically identify and quantify lesions to set a treatment order as described by Passerini in order to improve results for diagnosis and treatment planning for multiple lesions.
However, it is unclear if the learning model is also based on an entry side of the patient for treatment and if the machine learning utilizes conventional types such as deep Q-learning and K-fold cross validation.
Bharadwaj teaches from within a similar field of endeavor with respect to planning treatment based on medical image data (Abstract; Paragraph [0005]) where a treatment planning module may automatically identify potential lesions (Paragraph [0076]) and determine a treatment zone and a route to the treatment zone (Paragraphs [0008]-[0010], [0059] and [0088]). Examiner notes that the route to the target would include an entry side of the patient in its broadest reasonable interpretation.
Nazari teaches from within a similar field of endeavor with respect to machine learning where Machine learning models may utilize k-fold cross validation and deep q-learning in order to provide a robust learning model (Column 26, Line 55-Column 27-15; Column 31, Lines 50-65).
Rapaka teaches from within a similar field of endeavor with respect to machine learning for medical diagnosis (Abstract) where noise is added to medical images used to train the model to increase robustness (Paragraph [0059]).
Accordingly, one skilled in the art would have been motivated to have modified the treatment system and method described by Ueda and Passerini, particularly the treatment recommendation means to take into account the optimized route to take including an entry side of the patient as described by Bharadwaj and use conventional machine learning techniques as described by Nazari and Rapaka in order to efficiently plan treatment with more than one lesion and improve machine learning model robustness. Such a modification merely involves combining prior art elements according to known techniques to yield predictable results (MPEP 2143).
Regarding Claim 3, Examiner notes that both Ueda and Passerini disclose imaging the claimed region(s) of the body to determine lesion information as described above.
As for Claim 9, Passerini explains where deep reinforcement learning can be used to learn over time most desirable post treatment scenarios (Paragraph [0087]).
As for Claims 10, 12-13, 41 and 42, the modified system and method described above discloses the steps of detecting, identifying, acquiring, measuring, selecting, outputting and treating as claimed in its broadest reasonable interpretation. Ueda explains that treating can include a step of introducing a catheter into the artery of the arm (radial artery or brachial artery) (Paragraph [0005]) and the modified system and method includes a noise imparting method as described above. Furthermore, Passerini explains where deep reinforcement learning can be used to learn over time most desirable post treatment scenarios (Paragraph [0087]). Nazari also discloses multiple validation steps (Column 26, Lines 55-65). Such a disclosure is considered to read on the subsequent verification steps in its broadest reasonable interpretations. Examiner also notes that the subsequent validation step of Claim 10 may be accomplished by merely repeating the lesion quantification steps to check and see if the lesion(s) have been reduced and/or repeating the severity assessment steps to see if the treatment was successful. Accordingly, one skilled in the art would have been motivated to have repeated the lesion measurements after treatment to assess the efficacy of and/or validate the applied treatment. Such a modification is merely considered to be a duplication of previously disclosed method steps to yield predictable results.
Regarding Claim 21-22 and 25-26, Passerini discloses wherein the machine learning model used to identify and quantify lesions is trained (Paragraphs [0009], [0011] and [0058]). Examiner notes the recurrent network would continue to build on previous inputs and iterations in its broadest reasonable interpretation.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-3, 5-6, 9-10, 12-13, 21-22, 25-26, 37 and 41-42 have been considered but are moot in view of the updated ground of rejection necessitated by amendment.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. U.S. Publication No. 2018/0103865 to Trayanova et al. which discloses simulating ablation effects before a patient undergoes an ablation procedure (Paragraph [0045]).
U.S. Publication No. 2007/0276777 to Krishnan et al. which discloses a treatment plan with priority (Paragraph [0069]).
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 CHRISTOPHER L COOK whose telephone number is (571)270-7373. The examiner can normally be reached M-F approximately 8AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ashley Buran can be reached on 571-270-05525284. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER L COOK/Primary Examiner, Art Unit 3793