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
The amendment filed 2/3/2026 has been entered.
Claims 1-7 and 9-14 are currently pending and an Office Action on the merits follows.
Response to Arguments
Applicant’s arguments with respect to claim rejection under 35 USC 102 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.
Applicant’s arguments, see pages 8-9, filed 2/3/2026, with respect to the rejection under 35 USC 101 have been fully considered but they are not persuasive.
Particularly, the applicant argued that with the newly added limitations of trained models to perform the determinations cannot be performed mentally and they improve the technical operations of a medical image diagnosis system. While the examiner recognizes that the recited steps require a machine/computer to performed the steps, the recited computer and its parts are merely recited to generally linked to the Judicial Exception of mental process. The recitation of “a first trained model” and “a second trained model” in these claims do not negate the mental nature of these limitations because the claims here merely use the “machine” as a tool to perform the otherwise mental process. See MPEP 2106.04(a)(2), subsection III.C. The applicant is also advised to refer to 2024 Subject Matter Eligibility example 47 that involves trained neural networks.
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As noted in the example above, merely applying the judicial exception on a computer does not make the claim eligible. The recited limitations of “trained models” are used to generally apply the abstract idea without placing any limits on how the trained models function. Rather, these limitations only recite the outcome of “detecting the presence of abnormality” and do not include any details about how the “detecting” is accomplished. See MPEP 2106.05(f). Therefore, the previous rejection under 35 USC 101 is maintained.
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 1-7 and 9-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., abstract idea – mental processes) without significantly more.
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Claim 1 is used as an example. Claim 1 recites:
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With regard to Step (1), the instant claims recite a method, an apparatus and a computer program recording medium, therefore the answer is “yes”.
With regard to Step (2A), Prong One: Yes. When viewed under the broadest most reasonable interpretation, the instant claims are directed to a Judicial Exception – an abstract idea belong to the group of mental process. The limitations a) and b) of performing the first determining of whether the medical image is normal and the second determining of presence of an abnormality from the medical image are generically recited because there is no description of how they are accomplished. It can be interpreted as merely a physician deciding whether the obtained medical image belongs to a sick patient or a non-sick patient and deciding whether further image analysis should be performed by a medical profession. Human mind can perform the step of determining a medical image and then detecting objects candidates as abnormal like cancer cells. There is nothing in these limitations that require more than an operation that a human, armed with the appropriate apparatus cannot process. Hence, limitations a) and b) are interpreted as a mental step by having a person who is analyzing a medical image for detecting an object like cancer cell. Claims 2-7 and 9-14 having the “determining” and detecting abnormalities limitations are similarly analyzed as a mental step.
With regard to Step (2A), Prong Two: No.
The claim as a whole does not integrate the judicial exception into a practical application.
Steps b) and c), “determining” are recited as being performed by trained models. The “trained models” and “computer” are recited at a high level of generality and amount to no more than mere instruction to apply the exception using a generic computer (See MPEP 2106.05(f)). Similarly, each step uses “determining”, but provides nothing more than mere instruction to implement an abstract idea on a generic computer. Furthermore, “determining by a trained model” and “retraining” by the machine learning model (claims 4-6 and 8) are also recited at a high level of generality and amounts to no more than mere instruction to apply the exception using a generic computer and “displaying”/”outputting” of dependent claims also considered to be insignificant extra solution activities that are merely added to the judicial exception (See MPEP 2106.05(g)).
The “trained models” and “computer” are used to generally apply the abstract idea without limiting how the trained models function. The model running on a computer is described at a high level such that it amounts to using a computer with a generic machine learning model to apply the abstract idea. These limitations only recite the outcomes of “detecting objects” and without any details about how the outcomes are accomplished. Please refer to Example 47 of July 2024 Subject Matter Eligibility Examples.
With regard to Step (2B): No.
The pending claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above in Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer. Each of the additional elements are generic computer features which perform generic computer functions that are well-understood, routine, and conventional and do not amount to more than implementing the abstract idea with a computerized system.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation, and mere implementation on a generic computer does not add significantly more to the claims. Accordingly, the analysis under step 2B of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 and 9-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitations of “wherein the first determination is performed by a first trained model” and “wherein the second determination is performed by a second trained model”. However, the previous steps state that both first and second determinations are performed by “at least one processor”. It is unclear if the trained models can be interpreted as the processor.
Claim 13 is similarly analyzed and rejected as claim 1.
Claim 3 recites that the “second determination” is performed when the first determines the image to be normal whereas its independent claim 1 recites that “second determination” is performed when the first determines the image to be not normal. It is unclear whether this “second determination” performs in both situations or not.
Claim Rejections - 35 USC § 103
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 1-7 and 9-14 are rejected under 35 U.S.C. 103 as being unpatentable over Kiyuna et al. USPGPUB 2013/0301900 (hereinafter Kiyuna) in view of Im USPGPUB 2018/0350467.
With respect to claim 1, Kiyuna discloses a medical image diagnosis system (figs. 1 & 9) comprising:
at least one processor; and at least one memory that stores a command to be executed by the at least one processor, wherein the at least one processor (fig. 1)
performs first determination of determining whether or not a medical image obtained by imaging a subject is normal (S907 of fig. 9 and paragraph 153 determining if the image data is a low-magnification or a high-magnification image wherein the HM image is said to be related to image containing a cancer cell candidate in paragraph 113. Therefore, LM image is considered to be ‘normal’ image), and
performs second determination of determining presence or absence of an abnormality from the medical image in a case in which it is determined that the medical image is not normal in the first determination (performing feature analysis in paragraph 154).
However, Kiyuna does not explicitly discloses two trained models for performing the first and the second determinations.
Im, the same field of endeavor of detecting abnormalities in a medical image, discloses a system wherein the first determination is performed by a first trained model (paragraphs 39 and 46),
wherein in a case in which the medical image is input to the first trained model, the first trained model outputs whether or not the medical image is normal (paragraphs 39, 46 and 48),
wherein the second determination is performed by a plurality of second trained models, wherein in a case in which the medical image is respectively input to each of the plurality of second trained models, each of the plurality of second trained models detects a different lesion from the medical image, and wherein the presence of the abnormality is determined in a case in which a lesion is detected by at least one of the plurality of second trained models (paragraphs 53 and 55-57).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify the system of Kiyuna to incorporate the method of updating/downloading trained models as taught by Im.
The suggestion/motivation for doing so would have been to improve the accuracy of the lesion detection performance as taught by Im.
With respect to claim 2, Kiyuna discloses the medical image diagnosis system according to claim 1, wherein the at least one processor performs third determination of determining the presence or absence of the abnormality from the medical image in a case in which it is determined that the medical image is normal in the first determination (performing tissue structure analysis in paragraph 153).
Im further teaches performing an additional determination by the downloaded trained models (paragraphs 53 and 55-57).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify the system of Kiyuna to incorporate the method of updating/downloading trained models as taught by Im.
The suggestion/motivation for doing so would have been to improve the accuracy of the lesion detection performance as taught by Im.
With respect to claim 3, Kiyuna discloses the medical image diagnosis system according to any claim 2, wherein the at least one processor performs the third determination at a timing different from a timing of the second determination (since determination only picks one route, the second and the third determination cannot be performed simultaneously). Furthermore, Im discloses the further detection being done after the new trained models are downloaded/updated (paragraphs 55-57 of Im). Therefore, the determinations are done at different timings.
With respect to claim 4, Kiyuna discloses the medical image diagnosis system according to claim 2, wherein the at least one processor performs the first determination with a probability that the medical image is normal, and performs the third determination in a case in which the medical image is determined to be normal with a probability lower than a first threshold value in the first determination (as noted in claim 1, either image being LM or HM indicates ‘normal’ or not which can be interpreted as 100% or 0% probability).
With respect to claim 5, the combination of Kiyuna and Im discloses the system of claim 2, wherein the processor retrains the first trained model by using the medical image which is determined to have the abnormality in the third determinations (paragraphs 52 and 53 of Im).
With respect to claim 6, Im discloses the system wherein the first trained model is a trained model that has been trained by using combinations of an abnormal medical image, a normal medical image, and labels indicating the presence or absence of the abnormality, as a training data set (paragraphs 55-57 of Im).
With respect to claim 7, Kiyuna discloses the medical image diagnosis system according to claim 1, wherein the at least one processor performs fourth determination of determining the presence or absence of the abnormality from the medical image in a case in which it is determined that the abnormality is absent in the second determination (when no absolute cancer cell determination is made in S909, it moves to perform high-magnification process for further analysis), and in the fourth determination, determines the presence or absence of the abnormality from the medical image with a sensitivity relatively higher than a sensitivity in the second determination (performing feature analysis in figs. 9 &12). Furthermore, Im discloses the further detection being done after the new trained models are downloaded/updated (paragraphs 55-57 of Im). Therefore, the determinations are done at different timings.
With respect to claim 9, Kiyuna discloses the medical image diagnosis system according to claim 1, wherein, for a first case in which it is determined that the medical image is normal in the first determination (s907 in fig. 9), a second case in which it is determined that the medical image is not normal in the first determination and it is determined that the abnormality is present in the second determination (performing LM image analysis to detect any abnormality in fig. 9), and a third case in which it is determined that the medical image is not normal in the first determination and it is determined that the abnormality is absent in the second determination (performing LM image analysis to detect any abnormality in fig. 9), the at least one processor displays a determination result of the medical image on a display differently for the second and third cases than for the first case (S1223 in fig. 12). Furthermore, Im discloses the further detection being done after the new trained models are downloaded/updated (paragraphs 55-57 of Im). Therefore, the determinations are done at different timings.
With respect to claim 10, Kiyuna discloses the medical image diagnosis system according to claim 9, wherein the at least one processor displays the determination result of the medical image on the display differently between the second case and the third case (displaying different results based on the analysis in S1223 in fig. 12).
With respect to claim 11, Kiyuna discloses the medical image diagnosis system according to claim 9, wherein the at least one processor performs different types of post-processing on the medical image for the second and third cases than for the first case (fig. 9 and fig. 13 shows different steps being performed based on the decision).
With respect to claim 12, Kiyuna discloses the medical image diagnosis system according to claim 1, wherein the at least one processor performs the first determination and the second determination for each organ of the subject from the medical image (image processing performed on a different tissue area in fig. 13).
With respect to claims 13 and 14, arguments analogous to those presented in claim 1, are applicable.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAN S PARK whose telephone number is (571)272-7409. The examiner can normally be reached Monday-Friday 8:30am-5:00pm.
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/CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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