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 .
Election/Restrictions
Applicant’s election of species is acknowledged. Applicant’s amendment to the claims has now rendered the restriction to the robotic system aspect moot, and claims 1-7, and 9-14 are pending, with claim 8 withdrawn. An action on the merits is appended below.
Claim Rejections - 35 USC § 112
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 1-7 and 9-14 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.
Regarding claims 1 and 14, Applicant has recited the act of “training” a classifier to detect lesions of MR images and “training” a model to determine malignancy, with no additional details supplied.
While it is appreciated that such a method can potentially involve many different specifics in training, depth, truthing, etc, it is unclear what does and does not constitute training such models/classifiers to accomplish the task. Are they meant to supplement a diagnostician, oncologist, radiologist, or pathologist, or be fully automated sources of particular datasets which are input and acted upon in a sequence of processes and algorithms to “classify” or “determine malignancy”? What are the inputs and outputs required to constitute such “training”? Additionally, it is unclear if the “probability of malignancy” obtained with the classifier is actually the “determine malignancy of a lesion” of the second training, or if this is another step entirely, as the training recitation only recites the classifier to detect lesions, not to determine the probability of malignancy. This step and more specifically, this “identifying step” lacks support within the claim as the classifier is trained to “detect lesions” not to provide “probability of malignancy”.
Finally, the diagnosis portion is not specific to the AI model alone, but recited indefinitely such that an AI model can provide “assistance” to some sort of degree with this step in the method. The subsequent surgical treatment requires a robotic device to provide assistance in the treatment based on “the surgical treatment pathway”, which is also broadly recited and not defined. Does it include timing, resection targets, power, temperature (for cryotherapy), start and end protocols, etc? None of these elements are recited as being included or excluded, so for the purpose of examination, any method which trains data for identification of lesion(s), helps to determine if cancerous lesion(s) are present, and provides for guidance for a robotic system performing some sort of treatment in the event that a biopsy or treatment is warranted, appears to meet the limitations of the instant recitation, so long as MR image data is utilized for the assessment and the location of the lesion is established/maintained as a target. Determination steps are recited as including AI model(s), probability, diagnosis, but are not specifically recited as being performed by any particular set of data, constraint(s), or other specifics, and it’s overwhelmingly unclear what does and does not constitute the specifics of the scope of each element.
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.
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) 1-2, 5-7, and 9-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Perrin et al (US 2019/0156477) in view of Onik et al (US 2022/0265343).
Regarding claims 1 and 14, Perrin et al disclose and teach, a method and computer implemented instructions for training a classifier to identify lesions in MR image data of an organ, training another AI model on another set of data to determine if cancerous lesions are present, access and assess MR images of a specific patient in order to utilize the classifier and model to determine cancer presence, and, if a treatment is needed, provide treatment guidance/data for a treatment pathway to the lesion. Perrin et al specifically disclose the scoring of sites as benign or cancerous (0036) and provides treatment recommendations based on the analysis (0049-0050, 0055-0060).
Regarding claims 5-7, and 9, Perrin et al discloses the assessment of lesions, including the decision to treat or biopsy a site, and additionally discloses the implementation of guidance to target the site, but fails to disclose or teach cryoablation, nor the implementation disclosure with robotic devices (also applicable to claims 1 and 14) for the purpose of combined biopsy/treatment of cancerous-determined tissues/lesions.
Attention is hereby directed to the teaching reference to Onik et al which, in the same area of expertise, teaches such an implementation of robotic treatment with either a biopsy or cryoablation therapy on a cancer diagnosed portion or target lesion.
Onik et al teaches that algorithms, including models (0179, 0182-0183, 0190) can be utilized to identify biopsy sites (0179) and surgical treatment pathways are built from the tumor and imaging data (0182-0185), including MR imaging data (0172-0173). The treatment includes robotic placement (0184-0189) and/or localization, including cryoablation treatment (0159-0200 covers the biopsy needle tracking – treatment of biopsied tissues and should be utilized throughout, 0021, 0068, 0178, 0194-0199). The biopsy result is analyzed with the model (0179-0190) for probe placement and subsequent treatment.
It would have been obvious to one of ordinary skill in the art at the time of the invention to have utilized the robotic treatment teachings of Onik et al with the diagnostic, planning, and classification tools/methods of Perrin et al for the purpose of accurately treating and diagnosing cancerous lesions of a patient (abs, Onik, Perrin).
Regarding claims 2, 9 and 12-13, Perrin et al disclose and teach the target as a prostate, the generation of control instructions for guidance towards a region of interest, the analysis of MR images including segmented RADS scores for malignancy measurements (Claims 1, 4, Perrin). Perrin et al fair to disclose the robotic control with the instructions, but attention is again directed to the teachings of Onik et al, including cryoablation treatment (0159-0200 covers the biopsy needle tracking – treatment of biopsied tissues and should be utilized throughout, 0021, 0068, 0178, 0194-0199), which is implemented with a robotic control (0170-179, biopsy, robotic control, and implementation with a treatment pathway, 0169-0170). It would have been obvious to one of ordinary skill in the art at the time of the invention to have utilized the teachings of Onik et al for robotic control with the device and planning of Perrin et al for diagnostic measurement and treatment of cancerous tissues (0112, 0125, Perrin).
Regarding claims 10-11, Perrin et al utilize outcome generation sequences based on historical data and analysis of lesions compared to known quantities, including those with RADS scores (0035-0036, Claim 1-4, 0049, 0051, 0104-0105, 0111).
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Perrin et al (US 2019/0156477) in view of Onik et al (US 2022/0265343) as applied to claim 1 above, and in further view of Leng et al (US 2020/0250817).
With respect to claims 3-4, Perrin et al and Onik et al disclose assessments based on data and utilizing learning models, but do not specify Gleason scoring and histopathological modeling. While Onik et al does mention Gleason scoring for prostate cancer assessment (0020), it does not specify the machine learning model/AI is utilizing Gleason scoring, only that it can be a market for high risk prostate cancer.
Attention is hereby directed to the supplemental reference to Leng et al which specifically denotes that histopathological modeling and Gleason scoring is utilized to assess malignancy risk in AI-based modeling of MR imaging data for cancer diagnosis (0039-0041 (MR imaging data), 0052-0053, 0065-0067, training analysis with Gleason scoring). Outputs include segmented risk maps (Fig 7, 0054) and malignancy (0051-0053, 0063, 0051-0077 for the entire cancer detection process, including training, ground truthing, prediction mapping, and risk-based mapping of tissues/lesions with annotations). It would be obvious to one of ordinary skill at the time of the invention to have utilized the AI-based modeling and analysis of Leng et al for the purpose of facilitating a Gleason score-based predictive model for cancerous diagnosis of tissues of a patient (Abs, Claim 1-4, cited process for mapping/diagnosis of Leng et al).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOEL M. LAMPRECHT whose telephone number is (571)272-3250. The examiner can normally be reached Mon - Fri 9:00-5:30.
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/JOEL LAMPRECHT/ Primary Examiner, Art Unit 3798