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 .
Response to Amendment
Applicant's response to the last office action, filed April 28, 2026 has been entered and made of record. Claims 1, 4-7, 10, and 12-13 are amended. Claims 1-13 are pending for examination.
Response to Arguments
Applicant’s arguments with respect to claims 1-13 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4, 7-13 are rejected under 35 U.S.C. 103 as being unpatentable over Yong et al, (KR 20280138107, “based on English machine translation”) in view of Suzuki et al, (“Computer-Aided Diagnostic Scheme for Distinction Between Benign and Malignant Nodules in Thoracic Low-Dose CT by Use of Massive Training Artificial Neural Network”, IEEE transaction on medical imaging, vol. 24, No. 9, September 2005); and further in view of AHN, (US-PGPUB 20210134442)
In regards to claim 1, Yong discloses an estimation apparatus, (CAD system), comprising:
at least one memory configured to store instructions, (see at least: Par. 0125,” ROM, RAM”); and at least one processor configured to execute the instructions, (see at least: Par. 0121, “processor”), to:
acquire input data serving as image data, (Fig. 2, and Par. 0056, implicit by acquiring the medical image (10), which may be an image or video, using at least one of MRI, CT, X-ray, PET, and EIT);
estimate information relating to an estimation object by inputting the input data into a trained model, (see at least: Fig. 1, and Par. 0037-0040, the lesion diagnosis unit (110), “i.e., estimation unit”, analyzes the medical image using a deep neural network, extracts feature information within the medical image based on the image analysis, and can diagnose whether there is a lesion, ”i.e., object”, based on the extracted feature information, [i.e., estimate information relating to an estimation object, “extracts feature information to determine whether there is a lesion”, by inputting the input data into a trained model, “implicitly by inputting the medical image to the deep neural network”]); and
a display unit, (121 in Fig. 2), configured to display the estimated information
Yong does not expressly disclose wherein the trained model includes a plurality of modularized networks constructed in such a manner that each of the modularized networks are trained in advance by different characteristics of the estimation object in image data for first training and estimation, and a fusion network for estimating information relating to the estimation object in input images constructed in such a manner that a plurality of output signals obtained by inputting image data for the second training and estimation into the plural modularized networks are inputted, and generating the reason why the estimated information is estimated according to the plurality of output signals.
However, Suzuki discloses wherein the trained model includes a plurality of modularized networks, (Fig. 3, “MTANNs”), constructed in such a manner that each of the modularized networks are trained in advance by different characteristics of the estimation object in image data for first training and estimation, (see at least: Page 1140, section III.C, the multi-MTANN consists of plural MTANNs that are arranged in parallel, where each MTANN is trained by use of benign nodules representing a different benign type, but with the same malignant nodules, [i.e., wherein the trained model includes a plurality of modularized networks, “multi-MTANN”], and each MTANN acts as an expert for distinguishing malignant nodules from a specific type of benign nodule, e.g., MTANN no. 1 is trained to distinguish malignant nodules from small benign nodules, and MTANN no. 2 is trained to distinguish malignant nodules from medium-sized benign nodules with fuzzy edges; and so on, [i.e., MTANN is implicitly constructed in such a manner that each of the modularized networks, “MTANN no. 1, MTANN no. 2…”, are trained in advance by different characteristics of the estimation object in image data for first training and estimation, “MTANN no. 1 is trained to distinguish malignant nodules from small benign nodules, and MTANN no. 2 is trained to distinguish malignant nodules from medium-sized benign nodules”]); and
a fusion network for estimating information relating to the estimation object in input images constructed in such a manner that a plurality of output signals obtained by inputting image data for the second training and estimation into the plural modularized networks are inputted, (see at least: Fig. 3, where the integration ANN corresponds to the fusion network; and Page 1141, section D, left-hand-column, the scores from the expert MTANNs in the multi-MTANN are combined by use of an integration ANN such that different types of benign nodules can be distinguished from malignant nodules, where scores of each MTANN are entered to each input unit in the integration ANN, “i.e., that a plurality of output signals obtained from the modularized networks MTANNs are input to the integration ANN (fusion network)”, and after training, the integration ANN is expected to output a higher value for a malignant nodule, and a lower value for a benign nodule, “estimating information relating to the estimation object”]).
Yong and Suzuki are combinable because they are both concerned with medical imaging diagnosis. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify Yong, to use the MTANNs and integration ANN, as though by Suzuki, with the Yong’s lesion diagnosis unit (110), in order to make a distinction between malignant and benign nodules, (see Abstract, and Page 1141, section D, left-hand-column).
The combine teaching Yong and Suzuki as whole does not expressly disclose displaying generating the reason why the estimated information is estimated, and generating the reason why the estimated information is estimated according to the plurality of output signals.
AHN discloses displaying and generating the reason why the estimated information is estimated according to the plurality of output signals, (see at least: Figs. 4-5, Par. 0099-0102, processor 420 may generate the display information 360 including diagnosis results for the endoscopic image frame 450 by applying the plurality of diagnosis application algorithms, “AI Algorithm 312 and AI algorithm 314”, to the endoscopic image frame 450, “i.e., diagnosis results are generated according to the plurality of output signals from plurality of AI Algorithms”. Specifically, Par. 0104, there may be provided a menu that allows a user to select a final diagnosis application artificial intelligence algorithm based on the above-described criteria. In order to help the user to make a selection, the menu may be displayed together with the diagnosis results of the plurality of AI algorithms and a description of the reason for displaying the diagnosis results, [i.e., displaying and generating the reason why the estimated information is estimated, “implicit by displaying the menu with the diagnosis results of the plurality of Al algorithms and a description of the reason for displaying the diagnosis results”, according to the plurality of output signals, “according to output signals from AI Algorithm 312 and AI algorithm 314”]).
Yong, Suzuki, and AHN are combinable because they are all concerned with medical imaging diagnosis. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify combine teaching Yong and Suzuki, to display the menu together with the diagnosis results of the plurality of AI algorithms and a description reason for the diagnosis results, as though by AHN, in order to allows a user to select a final diagnosis application artificial intelligence algorithm based on the described criteria reason for the diagnosis results, (AHN, Par. 0104).
In regards to claim 2, the combine teaching Yong and Suzuki as whole discloses the limitations of claim 1.
Yong further discloses wherein the information relating to the estimation object includes some or all of name, type, and attribute of the estimated object and a numerical value related to the estimated object, (Yong, see at least: Par. 0058, the diagnosis result may include numerical values and disease names, “i.e., some of diagnosis result including numerical values and disease names related to the object”).
In regards to claim 4, the combine teaching Yong and Suzuki as whole discloses the limitations of claim 1.
Yong further discloses wherein the at least one processor is further configured to execute the instructions to output information indicating the degree of influence of the plurality of output signals on a result of estimation, (Yong , see at least: Par. 0099, lesion diagnosis factor description information generation unit (522) can generate highly reliable lesion diagnosis factor description information …, and degree of spiculation, through a cost function (541) regarding the accuracy of the lesion diagnosis factor description information, “implicit the degree of influence of the plurality of output signals on a result of estimation”).
In regards to claim 7, the combine teaching Yong and Suzuki as whole discloses the limitations of claim 1.
Suzuki further discloses wherein, in estimating the information relating to the estimation object, the at least one processor is further configured to execute the instructions to:
construct the plurality of trained modularized networks by receiving the image data for the first training and estimation from the data acquisition unit, and inputting the image data for the first training and estimation into the plurality of modularized networks, (Suzuki, see at least: Page 1140, Fig. 3, where the MTANNs are constructed to receive the input image (nodule ROI) implicitly acquired from the data acquisition unit, and the image data (nodule ROI) is input to the MTANN’s components for first training), and construct the trained fusion network by receiving the image data for the second training and estimation and inputting the plurality of output signals obtained by the input of the image data for the second training and estimation into the plurality of modularized networks into the fusion network to implement supervised training, (Suzuki, see at least: Page 1140, Fig. 3, the integration ANN is constructed to receive the output scoring from the plurality of MTANNs components to perform a supervised second training, to determine likelihood of malignancy).
In regards to claim 8, the combine teaching Yong and Suzuki as whole discloses the limitations of claim 1.
Suzuki further discloses wherein the image data for the first training and estimation includes a plurality of data sets trained in each of the plurality of modularized networks, the plurality of data sets corresponding to the information relating to the estimation object, and the plurality of modularized networks, which have been trained, are constructed in such a manner that the plurality of data sets are input into the plurality of modularized networks to be subjected to training, respectively, (Suzuki, see at least: Page 1140, Fig. 3, where the image data nodule (ROI) is trained in plurality of each of MTANNs components with the nodule information, (implicitly information related to object), by inputting the dataset with respect to the image data nodule (ROI) to the MTANNs components).
In regards to claim 9, the combine teaching Yong and Suzuki as whole discloses the limitations of claim 1.
Suzuki further discloses wherein each of the plurality of output signals obtained by inputting the image data for the second training and estimation into the plurality of modularized networks is a signal corresponding to one type of the characteristics of the estimation object, (Suzuki, see at least: Page 1140, Fig. 3, where the output signals from the MTANN components are input to the integration ANN, where the multi-MTANN consists of plural MTANNs that are arranged in parallel. Each MTANN is trained by use of benign nodules representing a different benign type, “i.e., different characteristics of the estimation object”).
In regards to claim 10, the combine teaching Yong and Suzuki as whole discloses the limitations of claim 1.
Yong further discloses wherein the least one processor further configured to execute the instructions to estimate, as the information relating to the estimation object, discrimination information relating to a state of the estimation object discriminated based on a type of the characteristics of the estimation object, (Yong, see at least: Par. 0065, the diagnostic network (320) can classify malignant masses and benign masses according to the lesion characteristics, such as the margin and shape of a lesion (Mass) in the medical image, and a random noise vector).
In regards to claim 11, the combine teaching Yong and Suzuki as whole discloses the limitations of claim 1.
Suzuki further discloses wherein the fusion network constructed by training of the image data for the first training and estimation and the image data for the second training and estimation, including a boundary surface that is formed in a vector space, to which a multidimensional vector having the output signals from the plurality of modularized networks belongs, to discriminate the state of the estimation object, (Suzuki, see at least: Page 1140, Fig. 3, where the MTANNs are constructed to receive the input image (nodule ROI) implicitly acquired from the data acquisition unit, and the image data (nodule ROI) is input to the MTANN’s components for first training, ….and the integration ANN is constructed to receive the output scoring from the plurality of MTANNs components to perform a supervised second training, to determine likelihood of malignancy. Further, Page 1147, left hand-column, the segmentation was performed by use of the radial search of edge candidates based on edge magnitude and contour smoothness for determining the regions of the nodules, “i.e., implicitly determining the boundary surface of the lesion”, and from Page 1148, paragraph below Fig. 18, all 76 malignant nodules in the database in the principal component (PC) vector space, “i.e., the boundary surface of the lesion is implicitly formed in a vector space to implicitly discriminate the state of the estimation object”, …., where the MTANN can be considered as an 81-dimensional (81-D) input vector, “i.e., the multidimensional vector are output from the plurality of MTANNs components”).
Regarding claim 12, claim 12 recites substantially similar limitations as set forth in claim 1. As such, claim 12 is rejected for at least similar rational.
The Examiner further acknowledged the following additional limitation(s): “an estimation method”. However, Yong discloses the “estimation method”, (see at least: Par. 0001, “method for generating a diagnosis reason explanation”).
Regarding claim 13, claim 12 recites substantially similar limitations as set forth in claim 1. As such, claim 12 is rejected for at least similar rational.
The Examiner further acknowledged the following additional limitation(s): “a non-transitory computer readable medium storing a program”. However, Yong discloses the “non-transitory computer readable medium storing a program”, (see at least: Par. 0123, 0125, Software and data may be stored on one or more computer-readable recording media).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AMARA ABDI/Primary Examiner, Art Unit 2668 06/10/2026
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