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 § 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.
Claims 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process using images (concept performed in a human mind, including as observation, evaluation, judgment, opinion, prediction, etc.), and mathematical calculations for producing the processed image data values. This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally and no additional features in the claims would preclude them from being performed as such.
According to the USPTO guidelines, claims are directed to non-statutory subject matter.
The following analysis is based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) published on January 7, 2019 (84 Fed. Reg. 50). See also MPEP 2106.04(a)(2)(II).
Regarding independent claims 1-6:
Step 1:
Claims 1-6 meets step 1 requirement as they are directed towards a process, machine, manufacture or composition of matter which is/are statutory subject matter. In this case, “A method’ and a “A non-transitory computer-readable recording medium” satisfy the criteria.
Step 2A, prong 1 test:
Independent claims 1-6 are analyzed to determine whether claims 1-6 are directed to any judicial exception.
Claims 1-6 recite (claim 1 as representative claim):
calculating, by using a first lesion detection process configured to detect a specific lesion region from three-dimensional volume data generated based on a plurality of tomographic images obtained by imaging an inside of a human body, a probability of being the specific lesion region for each of unit image areas included in each of the plurality of tomographic images;
and executing, based on one tomographic image of the plurality of tomographic images and the probability calculated for each of the unit image areas included in the one tomographic image, a second lesion detection process configured to detect the specific lesion region from the one tomographic image.
Do the independent claims 1-6 recite an abstract idea, mathematical concepts, law of nature, or natural phenomenon? Claims recite “calculating, by using a first lesion detection process configured to detect a specific lesion region from three-dimensional volume data generated based on a plurality of tomographic images obtained by imaging an inside of a human body, a probability of being the specific lesion region for each of unit image areas included in each of the plurality of tomographic images;
and executing, based on one tomographic image of the plurality of tomographic images and the probability calculated for each of the unit image areas included in the one tomographic image, a second lesion detection process configured to detect the specific lesion region from the one tomographic image”.
The limitations as recited, corresponds to calculation a probability and executing a second lesion detection. Claims 1-6 as whole recite mathematical relationship and solving mathematical relationship using a mental process using paper and pencil which corresponds to abstract idea as explained below. The dependent claims also merely recite “calculating” and “detecting”.
The limitations of the independent claims “calculating, by using a first lesion detection process configured to detect a specific lesion region from three-dimensional volume data generated based on a plurality of tomographic images obtained by imaging an inside of a human body, a probability of being the specific lesion region for each of unit image areas included in each of the plurality of tomographic images;
and executing, based on one tomographic image of the plurality of tomographic images and the probability calculated for each of the unit image areas included in the one tomographic image, a second lesion detection process configured to detect the specific lesion region from the one tomographic image.” as drafted, is a process that, under its broadest reasonable interpretation, covers solving of mathematical relationship using mental process i.e. abstract idea.
The Examiner notes that under MPEP 2106.04(A) (2) (Ill), the courts consider a mental process (thinking, human intelligence) that can be performed in the mind/intelligence using a paper and pencil to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[Mental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’”" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978). Further the Examiner note that that even if you combined the math with the mental process, a combination of abstract ideas don't make a claim eligible. See MPEP 2106.04(II)(A)(2): Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”").
With the exception of generic processor/computer components recited in independent claim 6 disclosed in the specification, that is, nothing in the claims 1-6 element preclude solving mathematical relationship for processing data using mental process merely using paper and pencil. (instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea). Accordingly, the claims 1-6 recite an abstract idea (Step 2A, prong 1 test Abstract idea= Yes).
Step 2A, prong 2 test:
Claims 1-6 are then analyzed if it requires an additional elements or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize
the exception —i.e., limitation that are indicative of integration into a practical application: improving to the functioning of a computer or to any other technology or technical field. Claims 1-6 recite “calculating, by using a first lesion detection process configured to detect a specific lesion region from three-dimensional volume data generated based on a plurality of tomographic images obtained by imaging an inside of a human body, a probability of being the specific lesion region for each of unit image areas included in each of the plurality of tomographic images;
and executing, based on one tomographic image of the plurality of tomographic images and the probability calculated for each of the unit image areas included in the one tomographic image, a second lesion detection process configured to detect the specific lesion region from the one tomographic image.
As noted above, nothing in the claims 1-6 element preclude solving mathematical relationship using mental process merely on paper and pencil using mental process. In the current claims, with the exception of generic processor/computer/memory components recited in independent claim 6 disclosed in the specification, that is, nothing in the claims 1-6 element preclude solving mathematical relationship for processing data using mental process merely using paper and pencil.
In the current claims 1-6 limitations recited are insignificant limitations and contain no additional elements that could constitute significantly more than abstract idea. Further in the current claims, there are no additional elements in the claims that would integrate the abstract idea into a practical application. The claims 1-6 do not mention any improvement to a computer or to any other technology or technical field. Therefore claims 1-6 are no more than abstract idea without significantly more (Step 2A: Prong Two Abstract Idea=Yes).
Step 2B:
Do the claims 1-6 recite additional elements that amount to significantly than judicial exception?
As noted above that nothing in the claims 1-6 other than generic computer/processor/memory recited in claim 6 the limitation covers a mental process using collected images (concept performed in a human mind, including as observation, evaluation, judgment, opinion, prediction, etc.), and mathematical calculations for processing image data using mathematical relationship. The generic computer/processor memory for processing image data using mathematical relationship recited in claims automate a human mental process using conventional/generic computer/processor/memory stand as to automate a human mental process and solving mathematical relationship. In the current claims 1-6 limitations recited are insignificant limitations and contain no additional elements that could constitute significantly more than abstract idea. Further there are no additional elements in the claims that would integrate the abstract idea into a practical application. Therefor limitations of claims 1-6 fail to add inventive concept to otherwise mental process for solving mathematical relationship.
Regarding dependent claims 2-5 also recite mathematical concepts at a higher level of generality such that they amount to no more than solving mathematical relationship made to model mathematical parameter function using a machine learning model with a generic neural network, to processing function to output image data values, amounts to no more than mere instructions applied by generic computer/processor/memory component, which does not provide an inventive concept, without any practical application. The claims only require the steps of “calculating” and “executing” and “detecting”. See MPEP 2106.05(f)). The “one or more machine learning models” which appears as recited in the claims further stands in to automate a human mental process using a generic machine learning models that has not been improved by the applicant.
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.
Claims 1-6 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Liu et al “Cascaded atrous dual attention U-net for Tumor”.
As to claims 1 representative of claim 6, Liu teaches a method implemented by a computer for detecting a lesion, the method comprising:
calculating, by using a first lesion detection process configured to detect a specific lesion region from three-dimensional volume data generated based on a plurality of tomographic images obtained by imaging an inside of a human body (see section 3 proposed method), a probability of being the specific lesion region for each of unit image areas included in each of the plurality of tomographic images (section 3.1, computing prediction maps for different areas, such as, liver and lesion tissues with probabilities (see equation 1)); and
executing, based on one tomographic image of the plurality of tomographic images and the probability calculated for each of the unit image areas included in the one tomographic image, a second lesion detection process configured to detect the specific lesion region from the one tomographic image (see figure 2 and section 3.1 second stage 2D segmentation).
As to claim 2, Liu teaches wherein the calculating includes obtaining the probability for each of the unit image areas from a processing result output during the first lesion detection process is executed (section 3.1, equation 1 shows the prediction map with a probability).
As to claim 3, Liu teaches calculating includes calculating the probability for each of the unit image areas by inputting the generated volume data to a first machine learning model configured to detect the specific lesion region from input volume data that has image data of a three-dimensional space (see figure 2 3D input, the specific region being the liver or lesion tissue).
As to claim 4, Liu teaches wherein the first machine learning model includes a neural network, and the probability for each of the unit image areas is output from an output layer of the neural network (see figure 2 and section 3.1 equation 1)
As to claim 5, Liu teaches the second lesion detection processing includes
detecting the specific lesion region from the one tomographic image by using a second machine learning model configured to output (section 3.1, 2D segmentation model), when an input image that has image data of a two-dimensional space and the probability of being the specific lesion region that corresponds to each of the unit image areas included in the input image are input to the second machine learning model, information that indicates whether or not each of the unit image areas included in the input image is the specific lesion region (output from 3D segmentation is sent to the 2D segmentation, see figure 2).
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/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667