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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 31, 2023 and August 21, 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because Figure 3, element 15, “secon location information” should read “second location information.”
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Invoked despite absence of “means”
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“pre-processing module” in claim 1
Claims 2 and 31 also contain the pre-processing module
“detection module” in claim 1
Claims 2-3 and 31 also contain the detection module
“post-processing module” in claim 1
Claims 8, and 31 also contain the post-processing module
“first sub detection module” in claim 3
Claims 4-5 also contain the first sub detection module
“second sub detection module” in claim 3
Claims 6-7 also contain the second sub detection module
“first neural network module” in claim 4
“second neural network module” in claim 4
“third neural network module” in claim 4
“fourth neural network module” in claim 6
“fifth neural network module” in claim 6
“sixth neural network module” in claim 6
“measurement module” in claim 9
“classification module” in claim 11
Claim 12 also contains the classification module
“tracking module” in claim 13
“malignancy prediction module” in claim 15
Claim 16 also contains the malignancy prediction module
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Examiner Note: it should be noted that based on the 35 USC 112(f) interpretation as noted above, corresponding 35 USC 112(b) and 35 USC 112(a) rejections were considered. However, upon analysis of the claims and specification it was determined structure and algorithm (as needed) were disclosed (see below). Thus, no 35 USC 112(b) and 35 USC 112(a) rejections were made.
Unit/Module
Element Number
Structure/Algorithm (when computer implemented)
*Note, all paragraph numbers are PGPub paragraphs
a network unit
Paragraph 0072: “a network unit 150.”
Structure: paragraph 0072, "The computing device 100 may include a processor 110, a memory 130, and a network unit 150;" paragraphs 0086-0087, “The network unit 150 according to several embodiments of the present disclosure may use various wired communication systems, such as a Public Switched Telephone Network (PSTN), an x Digital Subscriber Line (xDSL), a Rate Adaptive DSL (RADSL), a Multi Rate DSL (MDSL), a Very High Speed DSL (VDSL), a Universal Asymmetric DSL (UADSL), a High Bit Rate DSL (HDSL), and a local area network (LAN). The network unit 150 presented in the present specification may use various wireless communication systems, such as Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), Single Carrier-FDMA (SC-FDMA), and other systems.”
a pre-processing module
Figure 3, element 210
Structure: according to Figure 3, the pre-processing module is element 210, within element 200, and paragraph 0111 reads, "Referring to FIG. 3 , the computing device 100 according to an embodiment of the present disclosure may include a search module 200 that extracts information about a suspicious nodule present in a medical image 11." Thus, the search module 200 is within the computing device (computer implemented), and the pre-processing module is within the search module, thus also computer implemented
Algorithm: paragraph 0112, "The pre-processing module 210 may classify the input CT image into image group units based on a standard DICOM format. At this time, a classifier generally adopts a value suggesting the same series among type-1 attributes of DICOM, but may actually depend on an environment used by the computing device 100. As described above, the pre-processing module 210 may classify the CT images into image group units by itself or may receive already classified CT images as the input;" also all of paragraphs 0113-0114
detection module
Figure 3, element 220
Structure: according to Figure 3, the detection module is element 220, within element 200, and paragraph 0111 reads, "Referring to FIG. 3 , the computing device 100 according to an embodiment of the present disclosure may include a search module 200 that extracts information about a suspicious nodule present in a medical image 11." Thus, the search module 200 is within the computing device (computer implemented), and the detection module is within the search module, thus also computer implemented
Algorithm: paragraph 0111, " a detection module 220 that derives information on at least one region of interest based on the input image generated through the pre-processing module 210, and a post-processing module 230 that derives location information for the suspicious nodule based on the information on the region of interest derived through the detection module 220. In this case, the detection module 220 included in the search module 200 may include at least one neural network;" also all of paragraphs 0113-0114, and 0123-0132
post-processing module
Figure 3, element 230
Structure: according to Figure 3, the post-processing module is element 230, within element 200, and paragraph 0111 reads, "Referring to FIG. 3 , the computing device 100 according to an embodiment of the present disclosure may include a search module 200 that extracts information about a suspicious nodule present in a medical image 11." Thus, the search module 200 is within the computing device (computer implemented), and the post-processing module is within the search module, thus also computer implemented
Algorithm: paragraph 0111, " and a post-processing module 230 that derives location information for the suspicious nodule based on the information on the region of interest derived through the detection module 220;" also all of paragraph 0115-0121
a first sub detection module
Figure 4, element 221
Structure: paragraph 0043, "FIG. 5 is a block diagram illustrating a structure of a first sub detection module included in the search module according to an embodiment of the present disclosure;" paragraph 0111 reads, "Referring to FIG. 3 , the computing device 100 according to an embodiment of the present disclosure may include a search module 200 that extracts information about a suspicious nodule present in a medical image 11." Thus, the first sub detection module is in the search module, and the search module 200 is within the computing device (computer implemented), thus the first sub detection module is also computer implemented.
Algorithm: paragraph 0114, "Referring to FIG. 4 , the detection module 220 may include the first sub detection module 221 that generates a first probability value and first location information for at least one ROI based on the input image generated through the pre-processing module 210 and a second sub detection module 222 that estimates a second probability value for at least one ROI based on the input image generated through the pre-processing module 210 and the first location information. In this case, the first probability value and the second probability value may be numerical values representing probabilities that respective ROIs identified by the respective modules 221 and 222 will include the suspicious nodule;" also all of paragraphs 0115-0124
a second sub detection module
Figure 4, element 222
Structure: paragraph 0044, "FIG. 6 is a block diagram illustrating a structure of a second sub detection module included in the search module according to an embodiment of the present disclosure;" paragraph 0111 reads, "Referring to FIG. 3 , the computing device 100 according to an embodiment of the present disclosure may include a search module 200 that extracts information about a suspicious nodule present in a medical image 11." Thus, the second sub detection module is in the search module, and the search module 200 is within the computing device (computer implemented), thus the second sub detection module is also computer implemented.
Algorithm: paragraph 0114, "Referring to FIG. 4 , the detection module 220 may include the first sub detection module 221 that generates a first probability value and first location information for at least one ROI based on the input image generated through the pre-processing module 210 and a second sub detection module 222 that estimates a second probability value for at least one ROI based on the input image generated through the pre-processing module 210 and the first location information. In this case, the first probability value and the second probability value may be numerical values representing probabilities that respective ROIs identified by the respective modules 221 and 222 will include the suspicious nodule;" also all of paragraphs 0115 and 0129-0132
a first neural network module
Paragraph 0125, "a first neural network module 240"
Structure: paragraph 0123, "Referring to FIG. 5 , the first sub detection module 221 according to an embodiment of the present disclosure may include a first neural network module 240 that receives the 2D images generated by the pre-processing module 210 as the input and generates first feature maps having various sizes;" paragraph 0043, "FIG. 5 is a block diagram illustrating a structure of a first sub detection module included in the search module according to an embodiment of the present disclosure;" paragraph 0111 reads, "Referring to FIG. 3 , the computing device 100 according to an embodiment of the present disclosure may include a search module 200 that extracts information about a suspicious nodule present in a medical image 11." Thus, the first neural network module is within the first sub detection module which is within the search module in the computing device (computer implemented), thus the first neural network module is also computer implemented.
Algorithm: paragraph 0125, "A backbone structure may include a first neural network module 240 in which a bottleneck block 241 including skip-connection is repeated after a stem-cell block including a pooling layer. The bottleneck block 241 may enlarge or reduce the size of the feature map through a stride value. In the backbone structure, each of the first neural network modules 240 may generate first feature maps having a plurality of sizes for detecting nodules having different sizes. Specifically, the first feature maps having sizes of [68, 68], [68, 68], [34, 34], [34, 34], and [34, 34] may be output values of the first neural network module 240. "
a second neural network module
Paragraph 0126, "The second neural network module 250"
Structure: paragraph 0123, " The first sub detection module 221 may include a second neural network module 250 that generates second feature maps by concatenating at least some of the first feature maps based on the sizes of the first feature maps." paragraph 0043, "FIG. 5 is a block diagram illustrating a structure of a first sub detection module included in the search module according to an embodiment of the present disclosure;" paragraph 0111 reads, "Referring to FIG. 3 , the computing device 100 according to an embodiment of the present disclosure may include a search module 200 that extracts information about a suspicious nodule present in a medical image 11." Thus, the second neural network module is within the first sub detection module which is within the search module in the computing device (computer implemented), thus the second neural network module is also computer implemented.
Algorithm: paragraph 0126, "The neck structure may include a second neural network module 250 generating the second feature maps by combining the first feature maps generated by the first neural network module 240 of the backbone structure for each size. The second neural network module 250 may include at least one intermediate block 251 generating second feature maps of the next level by combining first feature maps of adjacent sizes with each other. In addition, the second neural network module 250 may generate the second feature maps by combining all first feature maps given as the input through at least one intermediate block 251. Specifically, one of the second neural network modules 250 may generate the second feature maps by combining first feature maps having sizes corresponding to [68, 68]. Specifically, the other one of the second neural network modules 250 may generate the second feature maps by combining first feature maps having sizes corresponding to [34, 34]. Further, the second neural network module 250 may generate the second feature maps by combining all of the first feature maps. The second feature maps generated through the second neural network module 250 may be used as an input of a head structure."
a third neural network module
Paragraph 0123, "In addition, the first sub detection module 221 may include a third neural network module 260"
Structure: Paragraph 0123, "In addition, the first sub detection module 221 may include a third neural network module 260;"paragraph 0043, "FIG. 5 is a block diagram illustrating a structure of a first sub detection module included in the search module according to an embodiment of the present disclosure;" paragraph 0111 reads, "Referring to FIG. 3 , the computing device 100 according to an embodiment of the present disclosure may include a search module 200 that extracts information about a suspicious nodule present in a medical image 11." Thus, the third neural network module is within the first sub detection module which is within the search module in the computing device (computer implemented), thus the third neural network module is also computer implemented.
Algorithm: paragraph 0127, "The head structure may include a third neural network module 260 including detection blocks 261 that individually receive the second feature maps generated through the second neural network module 250 of the neck structure as the input. That is, in the head structure, the third neural network module 260 may generate a probability value for a presence of a nodule value and location information in the ROI corresponding to the output value of the first sub detection module 221 based on the output of the second neural network module 250. Specifically, the detection block 261 of the third neural network module 260 may match the second feature map with a predefined anchor box and output a probability value that the ROI corresponding to the second feature map will include the nodule, and a size and location information of a nodule which is present in an ROI corresponding to an offset between an actual output value and the anchor box."
a fourth neural network module
Paragraph 0130, "Referring to FIG. 6 , the second sub detection module 222 according to an embodiment of the present disclosure may include a fourth neural network module 270 generating at least one third feature map by performing encoding based on the 3D patch extracted from an image output by the pre-processing module 210 based on the first location information. "
Structure: Paragraph 0130, "Referring to FIG. 6 , the second sub detection module 222 according to an embodiment of the present disclosure may include a fourth neural network module 270 generating at least one third feature map by performing encoding based on the 3D patch extracted from an image output by the pre-processing module 210 based on the first location information. " paragraph 0044, "FIG. 6 is a block diagram illustrating a structure of a second sub detection module included in the search module according to an embodiment of the present disclosure;" paragraph 0111 reads, "Referring to FIG. 3 , the computing device 100 according to an embodiment of the present disclosure may include a search module 200 that extracts information about a suspicious nodule present in a medical image 11." Thus, fourth neural network module is within the second sub detection module in the search module, and the search module 200 is within the computing device (computer implemented), thus the fourth neural network module is also computer implemented.
Algorithm: paragraph 0131, "The second sub detection module 222 may include a fourth neural network module 270 including one or more encoder blocks 271 for adjusting the size of the 3D patch and a fifth neural network module 280 including one or more decoder blocks 281. The 3D patch passing through the fourth neural network module 270 may be compressed up to a size of [3, 3, 3]. The compressed patch may be restored up to a size of [18, 18, 18] while passing through the fifth neural network module 280. In this restoration process, a combination (element-wise sum) with the third feature map of the fourth neural network module 270 having the same size is performed, and a more complex fourth feature map may be generated. "
a fifth neural network module
Paragraph 0130, "The second sub detection module 222 may include a fifth neural network module 280"
Structure: paragraph 0130, "The second sub detection module 222 may include a fifth neural network module 280;" paragraph 0044, "FIG. 6 is a block diagram illustrating a structure of a second sub detection module included in the search module according to an embodiment of the present disclosure;" paragraph 0111 reads, "Referring to FIG. 3 , the computing device 100 according to an embodiment of the present disclosure may include a search module 200 that extracts information about a suspicious nodule present in a medical image 11." Thus, fifth neural network module is within the second sub detection module in the search module, and the search module 200 is within the computing device (computer implemented), thus the fifth neural network module is also computer implemented.
Algorithm: paragraph 0130, "The second sub detection module 222 may include a fifth neural network module 280 generating at least one fourth feature map by performing decoding based on the third feature map.;" paragraph 0131, "a fifth neural network module 280 including one or more decoder blocks 281. The 3D patch passing through the fourth neural network module 270 may be compressed up to a size of [3, 3, 3]. The compressed patch may be restored up to a size of [18, 18, 18] while passing through the fifth neural network module 280. "
a sixth neural network module
Paragraph 0011, "a sixth neural network module included in the second sub detection module."
Structure: Paragraph 0011, "a sixth neural network module included in the second sub detection module;" paragraph 0044, "FIG. 6 is a block diagram illustrating a structure of a second sub detection module included in the search module according to an embodiment of the present disclosure;" paragraph 0111 reads, "Referring to FIG. 3 , the computing device 100 according to an embodiment of the present disclosure may include a search module 200 that extracts information about a suspicious nodule present in a medical image 11." Thus, sixth neural network module is within the second sub detection module in the search module, and the search module 200 is within the computing device (computer implemented), thus the sixth neural network module is also computer implemented.
Algorithm: paragraph 0130, "in FIG. 6 , the second sub detection module 222 may include a sixth neural network module generating a second probability value for at least one ROI based on the feature map generated by integrating the third feature map and the fourth feature map;" paragraph 0131, "The second sub detection module 222 may output a second probability value regarding the presence of the nodule in the ROI based on the fourth feature map using a sixth neural network module that performs 3D convolution"
measurement module
Figure 8 and 13, element 300
Structure: paragraph 0160, "Referring to FIG. 12 , the computing device 100 according to an embodiment of the present disclosure may include a measurement module 300 that generates a mask 65 of a region suspected of being a nodule from an input patch 61 on a medical image. "
Algorithm: paragraph 0167, "FIG. 14 is a flowchart illustrating the operation process of the measurement module according to an embodiment of the present disclosure."
classification module
Figure 8 and 15, element 400
Structure: paragraph 0174, "Referring to FIG. 15 , the computing device 100 according to an embodiment of the present disclosure may include a classification module 400 that generates class information 79 for the state of the region suspected of being the nodule based on the input patch 71 of the medical image and the mask 75 for the suspicious nodule. "
Algorithm: paragraph 0053, "FIG. 15 is a block diagram illustrating an operation process of a classification module included in the computing device according to an embodiment of the present disclosure."
tracking module
Figure 9, element 500
Structure: paragraph 0146, "the processor 110 may perform registration between the pre-analyzed image and the medical image 41 by using the pre-trained tracking module 500;" being that the processor 110 performs the actions of the tracking module 500, the tracking module is read as computer implemented
Algorithm: paragraph 0146, "The processor 110 may match the suspicious nodule present in the pre-analyzed image and the suspicious nodule present in the medical image 41, of which registration is completed by using the tracking module 500."
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.
Claims 1, 8-17 and 31-32 (and claims 2-7 for inheriting and failing to cure the deficiency of the base claims) 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.
Written description:
Claim limitation “using a pre- trained malignancy prediction module” in claim 15 and “by using a pre-trained malignancy prediction module” in claim 16 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Regarding “malignancy prediction module” in claims 15 and 16 applicant appears to describe only as in relation to element 600 (paragraph 0142). Applicant further describes the malignancy prediction module only in relation to the function it is configured to perform (paragraphs 0081 and 0142-0143). However, none of these paragraphs describe a structure for the malignancy prediction module. Therefore, in this instance “malignancy prediction module” is interpreted as a 112(f) limitation and the specification fails to disclose a specific structure for the malignancy prediction module.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Relative Terminology:
The term “suspicious” in claims 1, 8-17 and 31-32 is a relative term which renders the claim indefinite. The term “suspicious” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It appears applicant intends to claim the “suspicious nodule” is related to a level of probability value, but it is unclear what value that encompasses. For example, if the probability value is 0 regarding the nodule presence, it is unclear if the nodule is still suspicious in the same way as a nodule probability value of 100. For the sake of examination, the examiner will interpret a “suspicious nodule” as a subset of nodules in the region of interest from the initial nodules present with probability greater than 0.
35 USC § 112(a)
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 15-16 (and 17 for failing to cure the efficiency of the rejected base claim) 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.
Regarding claim 15 and 16, applicant claims, “using a pre- trained malignancy prediction module” and “using a pre-trained malignancy prediction module.”
As per MPEP § 2181(IV), “A means- (or step-) plus-function limitation that is found to be indefinite under 35 U.S.C. 112(b) based on failure of the specification to disclose corresponding structure, material or act that performs the entire claimed function also lacks adequate written description” (emphasis added). Furthermore, as per MPEP 2163.03(VI), “(s)uch a limitation also lacks an adequate written description as required by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because an indefinite, unbounded functional limitation would cover all ways of performing a function and indicate that the inventor has not provided sufficient disclosure to show possession of the invention.” Therefore, since applicant has not defined any particular structure for the “malignancy prediction module” in claims 15 and 16, the inventor has not provided sufficient disclosure to show possession of the invention. Applicant has not provided any specific definition for the structure that carry out the functions disclosed in claim 15 and 16. Additionally, the claimed invention as a whole may not be adequately described if the claims require an essential or critical feature which is not adequately described in the specification and which is not conventional in the art or known to one of ordinary skill in the art. It appears that these components and/or features are essential and critical features of the applicants invention because without them applicant' s invention wouldn' t work. In particular, the structure of the malignancy prediction module is not described in any detail. Therefore, since applicant has not adequately described a particularly structure for performing each of the functions, a person skilled in the art at the time the invention was filed would not have recognized that the inventor was in possession of the invention as claimed.
NOTE: Claim 17 is rejected based on the dependency on claim 15.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 32 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by KR 102209382B1 (hereinafter KR ‘382, of which a machine translation from Google Patents has been provided).
Regarding independent claim 32, the rejection of claim 1 applies directly. Additionally, KR ’382 further discloses A computing device for analyzing a lesion based on a medical image (page 3, “A method for providing information on lesion diagnosis and a device for providing information on lesion diagnosis using the same;” page 9, “Accordingly, based on the medical image received from the processor 150 receiving unit 110”), comprising:
a processor including at least one core (page 9, “Accordingly, based on the medical image received from the processor 150”);
a memory including program codes executable in the processor (page 14, “140: storage unit;” page 9, “to store an instruction of the device 100); and
a network unit receiving a medical image (page 9, “a medical image received through the receiving unit 110”),
wherein the processor,
generates, by using a pre-processing module, an input image of a pre-trained detection module from the medical image (page 9, “The storage unit 140 may be configured to store a medical image received through the receiving unit 110 and to store an instruction of the device 100 for providing information on lesion diagnosis set through the input unit 120;” page 12, “For example, referring to FIG. 3C, a medical image 312 may be input”),
generates, by using the detection module, a probability value regarding a presence of a nodule in at least one region of interest and first location information about the at least one region of interest, based on the input image (page 12, “According to a feature of the present invention, in the step of classifying a lesion (S330), a lesion probability for a feature is calculated by a plurality of classifiers, and a lesion for a target site is classified based on the lesion probability;” page 12, “For example, referring to FIG. 3D, in the step of classifying the lesion (S330), one combining feature 326 obtained as a result of the step of extracting a feature (S320) is input to each of the plurality of classifiers 332 Can be. Then, a predetermined probability of a lesion, that is, a lesion probability 334 for the binding feature 326 by each of the plurality of classifiers 332 may be calculated. Then, an average lesion probability 335 for a plurality of lesion probabilities 334 calculated by each of the plurality of classifiers 332 is calculated. Finally, if the average lesion probability 335 for a lesion is equal to or higher than a predetermined level, it can be classified as having a predetermined lesion for the target site;” the initial probability in the site is read as the presence of the nodule in an ROI at a first location in the image), and
determines, by using a post-processing module, second location information about a suspicious nodule present in the medical image from the first location information, based on the probability value regarding the presence of the nodule (page 12, “Finally, if the mean classification lesion probability 337 for the lesion is above a predetermined level, it can be finally classified as having a predetermined lesion for the target site. That is, information on the final lesion 338 determined as a result of classifying the lesion (S330) may be provided. At this time, the probability for the final lesion 338 may be provided together as information about lesion diagnosis;” comparing the probability to the threshold is read as determining a final classification of the location).
Allowable Subject Matter
Claims 1-17 and 31 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: the closest prior arts of record teach methods of analyzing lesions in medical images according to probability values. However, none of them alone or in any combination teaches the plurality of modules and unit algorithms for lesion analysis, in which the algorithms outlined in the specification are performed exactly and encompassing all described functions. The closest prior art being KR ‘382 discloses “the steps of: receiving a medical image for a region suspected of developing a disease of the individual, predicting a lesion on the individual using a classifier configured to predict a lesion based on the medical image, and determining the lesion in the medical image. Among the predicting processing, a method for providing information on lesion diagnosis, including the step of generating a lesion expression suspicious image indicating a degree of interest in predicting a lesion of a classifier, and providing a predicted lesion and a suspected lesion expression image, and the same It provides a device for providing information about the used lesion diagnosis.” However, KR ‘382 fails to disclose the plurality of modules and unit algorithms for lesion analysis, in which the algorithms outlined in the specification are performed exactly and encompassing all described functions.
Further, it should be noted that the claims are interpreted under 35 U.S.C. 112(f) thus, "Therefore, the broadest reasonable interpretation of a claim limitation that invokes 35 U.S.C. 112(f) is the structure, material or act described in the specification as performing the entire claimed function and equivalents to the disclosed structure, material or act. As a result, section 112(f) limitations will, in some cases, be afforded a more narrow interpretation than a limitation that is not crafted in "means plus function" format (See MPEP 2181)." The examiner further notes the 35 USC 112(f) section above clarifies the structures and algorithms being read on each module, which are then understood as being present within the claim, making the claim more narrow in scope.
Conclusion
The examiner would like to note the following considerations:
The examiner considered a 35 USC 101 rejection on this application in regard to claim 31. However, after MPEP review and discussion with SPE Villecco it was determined no 35 USC 101 rejection was necessary. Specifically, MPEP 2106.03 states, “software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment (emphasis added).” In this case, claim 31 is directed toward a “A computer program stored in a non-transitory computer- readable storage medium,” thus, the program is attached to a medium.
The examiner considered a non-statutory double patenting rejection on the claim set against U.S. Patent No. 12,283,051 (hereinafter US ‘051). Upon an analysis of the claim sets the rejection was not considered in that the current application and the patent in question differ in scope. US ‘051 does not determined second location information related to a suspicious nodule based on the first location data and the probability data. This key feature of the instant application prevents the non-statutory double patenting rejection.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Publication No. 2010/0111386 to El-Baz discloses, “A computer aided diagnostic system and automated method diagnose lung cancer through tracking of the growth rate of detected pulmonary nodules over time (abstract).”
U.S. Publication No. 2020/0160997 to Bagci discloses, “A method of detecting and diagnosing cancers characterized by the presence of at least one nodule/neoplasm from an imaging scan is presented (abstract).”
U.S. Publication No. 2005/0207630 to Chan discloses, “A computer assisted method of detecting and classifying lung nodules within a set of CT images includes performing body contour, airway, lung and esophagus segmentation to identify the regions of the CT images in which to search for potential lung nodules (abstract)”
Contact
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/COURTNEY JOAN NELSON/Primary Examiner, Art Unit 2661