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 amendments and remarks, filed on 01/15/2026, are acknowledged. Applicant’s arguments have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application. Rejections and/or objections not reiterated from the previous office actions are hereby withdrawn.
Status of Claims
Claims 1-20 are under presently under examination.
Priority
Applicant’s claim for the benefit of priority under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. The instant application claims the benefit of priority to US National Stage Application claiming priority to PCT/US2020/049751, filed 09/08/2020.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The following rejection is modified in view of applicant’s amendments.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The United States Patent and Trademark Office published revised guidance on the application of 35 U.S.C. § 101. USPTO’s 2019 Revised Patent Subject Matter Eligibility Guidance (“Guidance”). Under the Guidance, in determining what concept the claim is “directed to,” we first look to whether the claim recites:
(1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes) (Guidance Step 2A, Prong 1); and
(2) additional elements that integrate the judicial exception into a practical application (see MPEP § 2106.05(a)-(c), (e)-(h)) (Guidance Step 2A, Prong 2).
Only if a claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look to whether the claim contains an “‘inventive concept’ sufficient to ‘transform’” the claimed judicial exception into a patent-eligible application of the judicial exception. Alice, 573 U.S. at 221 (quoting Mayo, 566 U.S. at 82). In so doing, we thus consider whether the claim:
(3) adds a specific limitation beyond the judicial exception that are not “well-understood, routine and conventional in the field” (see MPEP § 2106.05(d)); or 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019).
(4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.(Guidance Step 2B). See Guidance, 84 Fed. Reg. at 54-56.
Guidance Step 1:
The instant invention (claim 1 being representative) is directed to a method, computer readable medium and device that performs a process. Thus, the claims are directed to one of the statutory categories of invention. MPEP 2106.03.
A. Guidance Step 2A, Prong 1
The Revised Guidance instructs us first to determine whether any judicial exception to patent eligibility is recited in the claim. The Revised Guidance identifies three judicially-excepted groupings identified by the courts as abstract ideas: (1) mathematical concepts, (2) certain methods of organizing human behavior such as fundamental economic practices, and (3) mental processes. Regarding claim(s) 1, the claimed steps that are part of the abstract idea are as follows:
processing, by the processing system, the group of pre-treatment images according to an imaging model that is a machine learning model based on...:
generating a plurality of predicted variables…via a 3D convolutional neural network trained upon longitudinal patient data, wherein the…variables include…risk scores….;
generating…event estimation curves based on predicted variables…;
Mental Processes
Under the BRI, the above italicized steps are all nominally recited and include limitations that generally encompass performing analysis and/or calculations. Applicant is also reminded that the Office's eligibility guidance does not set limit on the number of calculations that can or cannot be performed mentally. MPEP § 2106.04(a)(2)III. For these reasons, but for the recitation of a processing system, the above steps fall within the “mental processes” grouping of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III [Step 2A, Prong 1: YES].
Mathematical Concept
In this case, at least some of the above steps require generating prediction variables (using a neural network), and calculating scores. Accordingly, these steps amount to mathematical calculations and/or mathematically relating data. The specification also teaches specific mathematical models for achieving the above limitations [0024-0034]. Therefore, when read in light of applicant’s own specification, the claims are directed to mathematical concepts. See MPEP 2106.04 and 2106.05(II). [Step 2A, Prong 1: YES].
B. Guidance Step 2A, Prong 2
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional steps/elements recited in the claim beyond the judicial exception, and (2) evaluating those additional steps/elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
In this case, the additional steps/elements recited in the claim beyond the judicial exception are as follows:
obtaining, by a processing system including a processor, a pre-treatment image for each candidate of a group of candidates for a clinical trial resulting in a group of pre- treatment images, the pre-treatment image capturing at least an organ that is to be subject to treatment for a disease in the clinical trial, the group of pre-treatment images being captured prior to the treatment;
resampling…computed tomography scan volumes…;
generating a plurality of 3D organ box data…based on: applying at least one segmentation mask…; and extracting imaging features…;
With regards to the obtaining step, this is not limited to any particular techniques or devices and results in collecting data for use by the abstract idea. Therefore, this step amounts to insignificant extra-solution activity and is not indicative of an integration into a practical application. See MPEP 2106.05(g).
With regards to the preprocessing steps of resampling, generating, applying, and extracting, these are not limited to any particular techniques and generally result in obtaining specific data for use by the abstract idea. Therefore, these steps amount to insignificant extra-solution activity and are not indicative of an integration into a practical application. See MPEP 2106.05(g).
With regards to the claimed processing system, processor, computer readable medium, and memory (claims 1, 15, 19), these limitations are recited at high level of generality and read on a generic computer. Accordingly, these features are merely being used as tools to perform generic computer functions or the abstract idea, and therefore amount to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application. [Step 2A, Prong 2: NO].
C. Guidance Step 2B:
This part of the eligibility analysis evaluates whether the claim as a whole amount to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed above, the non-abstract steps/elements amount to nothing more than insignificant extra-solution activity. A review of the specification teaches a plurality of routine and conventional computational tools and systems for obtaining and processing data [Figure 1, para. 00146-53]. In addition, Perumal et al. (International Journal of Pure and Applied Mathematics Volume 118 No. 18 2018, 3681-3688) teaches methods for preprocessing medical images (CT-scans) that include resampling (to size images), segmentation masking (to obtain regions of interest), and extraction [See entire]. Therefore, even upon reconsideration, there is nothing unconventional with regards to the above non-abstract elements/steps. See MPEP 2106.05(d)(Part II). Thus, the independent claim(s) as a whole do not amount to significantly more than the exception itself. Therefore, the claim(s) is/are not patent eligible. [Step 2B: NO].
D. Dependent Claims
Dependent claims 2-14, 16-18, and 20 have also been considered under the two-part analysis but do not include additional steps/elements appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception(s) for the following reasons. In particular, claims 2-13, 16-18, and 20 are directed to limitations that further limit the specificity of the abstract idea or the nature of the data being used by the abstract idea. Accordingly, these claims are also directed to an abstract idea for the reasons set forth above (Step 2A, prong 1 analysis) and because data is abstract. Regarding claim(s) 14, this claim further limits the GUI recited at high level of generality and reads on a generic computer. Accordingly, these features are merely being used as tools to perform generic computer functions or the abstract idea, and therefore amount to no more than mere instructions to apply the exception using a generic computer. Accordingly, these claims are not drawn to eligible subject matter as they are directed to insignificant extra-solution activity and/or generically linking the use of the judicial exception to a particular technological environment or field of use. See MPEP 2106.05(f) and 2106.05(h). Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea (and/or natural correlation) without significantly more.
Response to Arguments
Applicant’s arguments have been fully considered but are moot in view of the newly applied rejection set forth above which is necessitated by amendment.
Claim rejections - 35 USC § 112, 1st paragraph
The following is a quotation 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 35 U.S.C. 112 (pre-AIA ), first paragraph:
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.
The following rejection is modified in view of applicant’s amendments.
Claims 1-20 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
The written description requirement is separate and distinct from the enablement requirement. The specification must: (1) describe the claimed invention in a manner understandable to a person of ordinary skill in the art, and (2) show that the inventor actually invented the claimed subject matter.
Regarding claim(s) 1, 15, 19, the specification fails to provide written description support for the following steps:
obtaining, by a processing system including a processor, a pre-treatment image for each candidate of a group of candidates for a clinical trial resulting in a group of pre- treatment images, the pre-treatment image capturing at least an organ that is to be subject to treatment for a disease in the clinical trial, the group of pre-treatment images being captured prior to the treatment;
processing, by the processing system, the group of pre-treatment images according to an imaging model that is a machine learning model based on...:
resampling…computed tomography scan volumes…;
generating a plurality of 3D organ box data…based on: applying at least one segmentation mask…; and extracting imaging features…;
generating a plurality of predicted variables…via a 3D convolutional neural network trained upon longitudinal patient data, wherein the…variables include…risk scores….;
generating…event estimation curves based on predicted variables…;
In this case, the claimed invention purports to be able to generate “event estimation curves” based on generically recited “prediction variables” (for the intended use of an investigational arm and a control arm of a clinical trial). While the method is limited to the use of based on 3D pre-treatment CT scan data corresponding to a 3D organ box, the claimed patients are not limited to any particular “events” (e.g. a disease a medical condition) that would necessitate a treatment or clinical investigation or any particular type of 3D organ data. The specification does discuss using the claimed invention for breast cancer screening [0034], as well as liver or lung cancer [0060]. However, such features are not commensurate in scope with what is claimed and it is improper to import narrowing limitations into the claims. MPEP 2111.01. Moreover, one of ordinary skill in the art would recognize that segmentation difficulty depends on the type of organs and medical condition being imaged. For example, Asma-Ull et al. (IEEE Access, Data Efficient Segmentation of Various 3D Medical Images Using GGANs, May 29, 2000, Vol. 8, pp. 102022-31) teaches methods for data efficient segmentation of 3D images. In particular, unlike the claimed method, Asma-Ull teaches difficulties for segmenting different types of 3D organ image data, e.g. segmenting aortic valve (AV) images due to thin myocardial walls and ventricles) and that shapes and appearance of images vary depending on the nature of the medical condition (e.g. brain tumor). As such, there is no evidence that applicant has actually disclosed the requisite prediction variables and estimation curves for the full scope of organs and medical conditions presently embraced by the claims (which is literally any organ and every known and unknown medical event that can be captured via CT scans).
With regards to the “generating” steps, it is unclear who the generating of predicted variables based on 3D organ box data using a neural network is achieved. In this case, the neural network is merely trained upon generically recited “longitudinal patient data”. However, one of ordinary skill in the art would recognize that the nature of data used for training neural network for survival analysis is not trivial. For example, Zhu et al. (IEEE International Conference on Bioinformatics and Biomedicine, December 2016, pp. 544-547, entire and Figure 1) teaches methods for modeling survival analysis based on convolutional neural networks and medical image data (CT-scans). Unlike the instant claims, Zhu teaches training these neural networks using pathological image data (not longitudinal patient data or pre-treatment image data (Section III). As such, it is unclear how the claimed method achieves the intended function because there is nothing in the claims or specification to suggest that the longitudinal data has anything to do with pathological data. Moreover, it is unclear if the neural network recited in this step is intended to be the same as the “imaging model” or an entirely different model altogether. As such, there is no evidence that applicant has actually disclosed the requisite neural network for generating predicted variables for the full scope of what is presently embraced by the claims (which is literally any and every known and unknown medical event that can be captured via CT scans). Therefore, the specification does not establish a reasonable structure-function correlation (wherein the structure is broadly interpreted as the neural network and the function is generating event curves). “[A] sufficient description of a genus . . . requires the disclosure of either a representative number* of species falling within the scope of the genus or structural features common to the members of the genus so that one of skill in the art can 'visualize or recognize' the members of the genus” (AbbVie, 759 F.3d at 1297, reiterating Eli Lilly, 119 F.3d at 1568-69)(emphasis added). Accordingly, one of ordinary skill in the art would not have understood applicant to have invented a method/system of performing the claimed functions with no more than routine experimentation.
For the reasons discussed above, the specification does not satisfy the written description requirement with respect to the full scope of what is being claimed. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b).
Response to Arguments
Applicant’s arguments have been fully considered but are moot in view of the newly applied rejection set forth above which is necessitated by amendment.
Claim rejections - 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.
The following rejection is modified in view of applicant’s amendments.
Claims 1-20 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 pre-AIA the applicant regards as the invention. Claims that depend directly or indirectly from claim(s) 1, 15, 19 is/are also rejected due to said dependency.
Claims 1, 15, 19 recite “processing, by the processing system, the group of pre-treatment images according to an imaging model that is a machine learning model based on: resampling…; generating…”. Firstly, it remains unclear as to the metes and bounds of the claimed imaging model. The specification does not provide any limiting definition, equations, or prose equivalent that would serve to clarify the scope of this term (i.e. what mathematical structure is encompassed). The specification discloses a generic list of machine learning models [0038], but fails to provide any parameters that would account for the use of machine learning model being claimed. Clarification is requested via amendment. As a result, it is also unclear limiting effect is intended by the phrase “according to an imaging model that is a machine learning model”, i.e. is the processing ‘based on’ resampling and generating, or does the imaging model perform the resampling and generating steps, or otherwise. Clarification is requested via amendment. In the interest of advancing prosecution, the examiner suggests deleting this phrase if it is not a critical aspect of the claimed invention.
Claim 9 recites “The method of claim 1, comprising: generating…a graphical user interface; obtaining…images for a third subset of candidates..; processing…the group of on-treatment images…”. In this case, the language of claim 9 appears to reiterate limitations that are already recited in parent claim 1 (e.g. obtaining, processing). Therefore, it is unclear in what way claim 9 further limits the subject matter of parent claim 1, i.e. where do the limitations of claim 9 occur with respect to the steps of parent claim 1 (e.g. before, after, or otherwise). To obviate this rejection, applicant is encouraged to amend the claim to recite, for example, "The method of claim 1, further comprising...". This is only a suggestion, as support for such amendments must be found in the specification.
Claim 9 recites “processing, by the processing system, the group of on-treatment images according to the imaging model based on: resampling…; generating…”. It is unclear as to the metes and bounds of the claimed imaging model. The specification does not provide any limiting definition, equations, or prose equivalent that would serve to clarify the scope of this term (i.e. what mathematical structure is encompassed). The specification discloses a generic list of machine learning models [0038], but fails to provide any parameters that would account for the use of machine learning model being claimed. Clarification is requested via amendment. As a result, it is also unclear limiting effect is intended by the phrase “according to an imaging model that is a machine learning model”, i.e. is the processing ‘based on’ resampling and generating, or does the imaging model perform the resampling and generating steps, or otherwise. Clarification is requested via amendment. In the interest of advancing prosecution, the examiner suggests deleting this phrase if it is not a critical aspect of the claimed invention.
Claim 14 recites “The method of claim 9, comprising: wherein the patient portion of the graphical user interface that is related to the candidate includes a predicted image of the organ at a future time that is generated based on the analyzing the group of pre-treatment images, the analyzing the group of on-treatment images, the predicted variables, the predicted on-treatment variables, or a combination thereof.” This claim is problematic for the following reasons: (1) The phrase “comprising: wherein” is confusing and should be amended to recite “The method of claim 9, wherein…”. (2) Claim 14 appears to further limit the structure of a product (i.e. a portion of the GUI) by further limiting the data it includes. However, a method cannot “comprise” a product, and further limiting the nature of the data included in the GUI, per se, does not further limit the method as claimed. As a result, it is unclear what limiting effect of the claimed method is intended because the method as claimed does not require "using" a GUI (e.g. to display information). Clarification is requested via amendment. Applicant is also reminded that a single claim which claims both an apparatus/system and the method steps of using the apparatus/system is indefinite. See MPEP 2173.05(p). If applicant intends for claimed GUI to be USED by the method, the claims should be amended accordingly.
Claim rejections - 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.
The following rejection is modified in view of applicant’s amendments.
Claim 9 is 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 discussed above, the language of claim 9 appears to reiterate limitations that are already recited in parent claim 1 (e.g. obtaining, processing). Therefore, it is unclear in what way claim 9 further limits the subject matter of parent 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 14 is 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 discussed above, claim 14 appears to further limit the structure of a product (i.e. GUI) by further limiting the data it includes. However, a method cannot “comprise” a product, and further limiting the nature of the data included in the GUI, per se, does not further limit the method as claimed. As a result, it is unclear in what way claim 14 further limits the subject matter of parent claims 1 or 9. 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.
Examiner' s Comment – Prior Art Rejection of Indefinite Claims
In view of the indefiniteness and lack of clarity in the instant claims, as set forth in the 35 USC 112(b) rejections above, the Examiner has had difficulty in properly interpreting instant claims. However, to give applicant a better appreciation for relevant prior art if the claims are redrafted to avoid the 35 USC 112(b) rejections, the Examiner has broadly interpreted the claims for purposes of applying the following prior art rejections.
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.
The following rejection is necessitated by amendment.
Claims 1, 4, 5, 9, 15, 16, 17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Cha et al. (Tomography, 2016, Vol. 2, No. 4, pp.421-429).
Regarding claim(s) 1, 15, 19, Cha teaches methods for evaluating bladder cancer treatment response using deep convolution neural networks for segmentation of 3D medical imaging data [Abstract]. In particular, Cha teaches obtaining 3D CT-scan data from patients before (i.e. pre-treatment) and after treatment [pp. 422, Col. 2]; reconstructing images (i.e. resampling) [pp. 422, col. 2]; generating boxes around the organ in both pre-treatment and post-treatment images [pp.422, col. 2 and pp.423, col. 1, and Figure 1]; identifying regions of interest (i.e. segmenting) [ pp.423, col. 1, and Figure 1]; and extracting image features from the ROI [pp.423, col. 1, and Figure 1].
With regards to generating predicted variables, Cha additionally teaches training a convolution neural network model using the extracted ROI from pre-treatment images [pp.423, col. 1 and Figure 1], wherein the model is associated with likelihood variables (i.e. predicted variables) for predicting bladder cancer [pp. 423, col. 1 and 2, and pp.424, entire].
With regards to generating event estimation curves based on predicted variables, Cha does not specifically teach generating event estimation curves based on the predicted variables. However, Cha reasonably suggests this limitation by generating operating characteristics (ROC) analysis and area under the receiver operating characteristic curve (i.e. event curves) for estimating the accuracy for predicting disease response after surgery based on changes between pre- and post-treatment CT scans using the DL CNN, the AI-CALS, and the manual segmentation methods [pp. 425, col. 2, Figures 1-3], and since one of ordinary skill in the art would understand that ROC and AUC functions are necessarily associated with curves. In this case bladder cancer is broadly interpreted as an “event” given the breadth of the claims.
With regards to event estimation curves “for an investigational trial and control trial arms” of a clinical trial, Cha is silent to this intended use recitation. However, applicant is reminded that intended use recitations are not given patentable weight as they have no limiting effect on the method as claimed.
Regarding claim(s) 4, Cha teaches training their CNN model on a plurality of pre-treatment scans to recognize patterns both inside and outside of the bladder, wherein the scans encompass overlapping ROI regions [page 423, col. 1], as claimed. In this case, the ingesting is broadly construed as overlapping images.
Regarding claim(s) 5, Cha teaches segmentation that includes the entire VOI (i.e. total volume) and training based in part on areas both inside and outside of the bladder (i.e. surrounding regions) as set forth above.
Regarding claim(s) 9, Cha additionally teaches obtaining 3D CT-scan data from patients before (i.e. pre-treatment) and after treatment [pp. 422, Col. 2] and generating graphical images associated with segmentation [Figures 1-3], which broadly reads on a GUI absent any limiting definition to the contrary. Cha additionally teaches or suggests the claimed processing and generating steps as discussed above. Regarding claim(s) 16, 17, Cha teaches predictive tumor response variables, as set forth above; CT images, as set forth above; and a 3DCNN, as set forth above [pp. 425, col. 2, Figures 1-3].
The following rejection is necessitated by amendment.
Claims 1, 2, 3, 15, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (2016 IEEE International Conference on Bioinformatics and Biomedicine, December 2016, pp. 544-547) in view of Cha et al. (Tomography, 2016, Vol. 2, No. 4, pp.421-429)
Regarding claim(s) 1, 15, 19, Zhu teaches methods for modeling survival analysis based on convolutional neural networks. In particular, Zhu teaches obtaining pathological (i.e. pre-treatment) medical images from lung cancer patients; and generating boxes around the organ images based on randomly sampling smaller sections from the image (i.e. re-sampling), identifying regions of interest (i.e. segmenting), and extracting image features from the ROI [See at least Section B, Data Preprocessing, and Figure 3].
With regards to generating predicted variables, Zhu additionally teaches generating training a convolution neural network model using the extracted ROI in pathological images, wherein the model includes a loss function comprising specific variables (i.e. predicted variables) that represent survival prediction [pp. 545, Section III]. Zhu does not specifically teach generating event estimation curves (i.e. survival curves) based on the predicted variables. However, Zhu suggests this limitation because their survival analysis model is based on Cox proportional hazard model and associated with a specific survival function [Section II.A], and one of ordinary skill in the art would understand that such a function is necessarily associated with a curve. With regards to event estimation curves “for an investigational trial and control trial arms” of a clinical trial, Zhu is silent to this intended use recitation. However, applicant is reminded that intended use recitations are not given patentable weight as they have no limiting effect on the method as claimed.
Zhu does not specifically teach the use of 3D computed tomography scan images or a 3D convolutional neural network, as in claims 1, 15, 19.
However, Cha teaches methods for evaluating bladder cancer treatment response using deep convolution neural networks for segmentation of 3D medical imaging data [Abstract and pp.421-23, entire].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to alter the method of Zhu by alternatively using 3D computed tomography scan images or a 3D convolutional neural network, as taught by Cha, since the dimensionality of the data set is considered are arbitrary design choice and since Zhu already uses convolution neural network models. The motivation to have used 3D volumetric images would have been to obtained improved estimates of changes in tumor size.
Regarding claim(s) 2, 3, Zhu teaches random extraction of data from ROIs [Section B] and randomization of prediction parameters [Section C], as well as generating survival scores (as set forth above). Furthermore, any limitations directed to “investigational” trial arm and control trial arm are intended use recitations and therefore are not given patentable weight.
Conclusion
No claims are allowed.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PABLO S WHALEY whose telephone number is (571)272-4425. The examiner can normally be reached between 1pm-9pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Anita Coope can be reached at 571-270-3614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PABLO S WHALEY/Primary Examiner, Art Unit 3619
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