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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 3-5, 11 and 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. While claim 15 is not found to be rejected under 101, said subject matter is not found in claims 1, 11, or 13. The 101 rejection of claims 1, 3-5, 11 and 13 are maintained. Newly amended subject matter further performs the new derivations on updated medical image regions. Performing derivations on the previous non-updated medical images were found to be an abstract idea as data gathering steps. Reiterating the claim language of deriving more data but instead updated disease regions does not solve the issues raised by the original 101 rejection and instead discloses additional data gathering steps.
Applicant argues the improve of medical image segmentation is reflected by the newly amended subject matter. The examiner respectfully disagrees. Segmentation in and of itself is a technology normal in the art in regards to model processing of images. Further, the recitation of updating regions of an image is not found to directly or indirectly correlate to the implied improvement.
New search was performed in view of the newly amended subject matter which provided new art for claims 1, 3-5, 11 and 13. Claim 2 is objected to allowance and claim 15 is allowed.
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, 3-5, 11 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims 1 and 13 recite to acquire a medical image and a first disease region in the medical image, derives a second disease region related to the first disease region in the medical image based on the medical image and the first disease region, updates the first disease region based on the medical image and the second disease region, updates the second disease region based on the medical image and the updated first disease region and repeats updates of the first and second disease regions until a predetermined end condition is satisfied and derives the updated first disease region and the updated second disease region respectively as a final first disease region and a final second disease region from the medical image in a case in which the predetermined end condition is satisfied.
The claim limitations of “derives” is a mental process of evaluation, a user can derive a second disease region from observing the medical image and the first disease region.
This judicial exception is not integrated into a practical application because the additional elements of “acquires a medical image and a first disease region” amounts to data gathering steps which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)). The element of “updates” and “repeats” are mere instructions to apply the exception using a generic computer component. All elements are recited to a high level of generality.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as disclosed do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea.
Claim 11 recite an information processing method that acquires a medical image and a first disease region, derives a second disease region relates to the first disease region and the medical image, updates the first disease region based on the medical image and the second disease region, updates second disease region based on medical image and the updated first disease region and repeats update of the first and second diseases regions until a predetermined end condition is satisfied and deriving the updated first disease region and the updated second disease region, respectively as a final first disease region and a final second disease region, from the medical image in a case in which the predetermined end condition is satisfied.
The claim limitations of “derives” is a mental process of evaluation, a user can derive a second disease region from observing the medical image and the first disease region.
This judicial exception is not integrated into a practical application because the additional elements of “acquires a medical image and a first disease region” amounts to data gathering steps which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)). The element of “updates” and “repeats” are mere instructions to apply the exception. See MPEP 2106.05(f).
The claims do not include additional elements that sufficient to amount to significantly more than the judicial exception because the additional elements as disclosed do not integrate the judicial exception into a practical application as they are mere instructions to apply the abstract idea, which cannot be inventive concept. See MPEP 2106.05(f).
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim recites the additional limitation of “processor performs update of the first disease region and derivation and update of the second disease region further based on at least one of information representing an anatomical region of an organ including the first and second disease regions or clinical information”, which is merely elaborating on the abstract idea, by further specifying an additional element recited at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of claim 1. The claim recites the additional limitation of “processor acquires the first disease region by extracting the first disease region from the medical image”, which is merely elaborating on the abstract idea, by further specifying an additional element recited at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of claim 1. The claim recites the additional limitation of “processor derives quantitative information for at least one of the first disease region or the second disease region, and displays the quantitative information”, which is merely elaborating on the abstract idea, by further specifying an additional element recited at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
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, 3-5, 11 and 13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yoshiara et al. (US 2010/0094133 A1).
With respect to Claim 1, Yoshiara’133 shows an information processing apparatus (figure 1 apparatus main body 11) comprising:
at least one processor (figure 1 processor 40), wherein the processor acquires a medical image (Figure 2 100 paragraph [0047] scan converter 24b acquires the ultrasound (medical) input and generates image data) and a first disease region (paragraphs [0057], [0082]-[0083], and [0089] describes the regions to regard diseases) in the medical image (Figure 2 100 paragraphs [0067]-[0068] image 100 includes regions A-C; paragraph [0096] sets each region based on pixel signal intensity reaching a threshold),
derives (paragraph [0086] 40d calculates/derives each pixel of the image including of each image) a second disease region related to the first disease region in the medical image based on the medical image and the first disease region (paragraph [0096] sets each region based on pixel signal intensity reaching a threshold),
updates the first disease region based on the medical image and the second disease region, updates the second disease region based on the medical image and the updated first disease region (Figures 2-3 paragraphs [0070]-[0075] the region of interest marker 110 is moved from region A to region B causing the color coding part to change/update each of the regions A-C (including first and second regions) with new color based on the ultrasound contrast agent detected in the new region of interested designated by the ROI marker (predetermined condition).),
repeats update of the first disease region and update of the second disease region until a predetermined end condition is satisfied (figure 5 and paragraph [0085] describes multiple ROI markers 110-112 which each are capable of moving (updating) and designating (condition) a region of interest. [0092] describes to automatically set the ROI markers instead of manually.), and
derives the updated first disease region and the updated second disease region, respectively as a final first disease region and a final second disease region, from the medical image in a case in which the predetermined end condition is satisfied (paragraph [0055] describes the clinical practice of updating the color codes based on an operator set reference time (another condition), such that regions A-C are continuously updated until the reference time period ends).
With respect to Claim 3, Yoshiara’133 shows the information processing apparatus according to claim 1, wherein the processor performs update of the first disease region and derivation and update of the second disease region further based on at least one of information representing an anatomical region of an organ including the first and second disease regions or clinical information (paragraph [0067] describes regions regard organs such as kidney).
With respect to Claim 4, Yoshiara’133 shows the information processing apparatus according to claim 1, wherein the processor acquires the first disease region by extracting the first disease region from the medical image (figure 1 doppler processor 23 paragraph [0045] extracts blood flow and other data utilized by image generator).
With respect to Claim 5, Yoshiara’133 shows the information processing apparatus according to claim 1, wherein the processor derives quantitative information for at least one of the first disease region or the second disease region, and displays the quantitative information (paragraph [0061] plurality of quantitative/measured information is derived/detected/calculated for the regions including: inflow, pixel luminance, and timing).
With respect to Claims 11 and 13, rejection analogous to those presented for claim 1, are applicable.
Allowable Subject Matter
Claim 2 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 15 is allowed.
The following is an examiner’s statement of reasons for allowance: none of the references either singularly or in combination teach or fairly suggest an information processing apparatus comprising: at least one processor, wherein the processor acquires a medical image, inputs the medical image into a first discriminative model and derive a first mask image representing a first disease region in the medical image outputted from the first discriminative model, inputs the medical image and the first mask image into a second discriminative model to derive a second mask image representing a second disease region outputted from the second discriminative model, wherein the second disease region is related to the first disease region in the medical image, inputs the medical image and the second mask image into a third discriminative model to derive an updated first mask image representing an updated first disease region in the medical image outputted by the third discriminative model, inputs the medical image and the updated first mask image to the second discriminative model to derive an updated second mask image representing an updated second disease region representing an updated second disease region in the medical image outputted by the second discriminative model, repeats update of the first disease region and update of the second disease region until a predetermined end condition is satisfied, and derives the updated first mask image and the updated second mask image, respectively as a final first disease region and a final second disease region, after the predetermined end condition is satisfied.
Akahori’996 shows paragraph [0050] acquiring CT image and discriminates a thrombus region or an infarction region in the CT image, paragraph [0023] discriminating a second disease region from the medical image, paragraph [0052] CT image B0, from which disease regions are to be extracted, from the image storage server 3 in order to extract the disease regions such as a thrombus region and an infarction region. Akahori’996 do not include all the detailed combined limitations included in the claim including an information processing apparatus comprising: at least one processor, wherein the processor acquires a medical image, inputs the medical image into a first discriminative model and derive a first mask image representing a first disease region in the medical image outputted from the first discriminative model, inputs the medical image and the first mask image into a second discriminative model to derive a second mask image representing a second disease region outputted from the second discriminative model, wherein the second disease region is related to the first disease region in the medical image, inputs the medical image and the second mask image into a third discriminative model to derive an updated first mask image representing an updated first disease region in the medical image outputted by the third discriminative model, inputs the medical image and the updated first mask image to the second discriminative model to derive an updated second mask image representing an updated second disease region representing an updated second disease region in the medical image outputted by the second discriminative model, repeats update of the first disease region and update of the second disease region until a predetermined end condition is satisfied, and derives the updated first mask image and the updated second mask image, respectively as a final first disease region and a final second disease region, after the predetermined end condition is satisfied, therefore this claim is allowable.
Yoshiara’133 shows calculates/derives each pixel of the image including of each image and sets each region based on pixel signal intensity reaching a threshold. Yoshiara’133 do not include all the detailed combined limitations included in the claim including an information processing apparatus comprising: at least one processor, wherein the processor acquires a medical image, inputs the medical image into a first discriminative model and derive a first mask image representing a first disease region in the medical image outputted from the first discriminative model, inputs the medical image and the first mask image into a second discriminative model to derive a second mask image representing a second disease region outputted from the second discriminative model, wherein the second disease region is related to the first disease region in the medical image, inputs the medical image and the second mask image into a third discriminative model to derive an updated first mask image representing an updated first disease region in the medical image outputted by the third discriminative model, inputs the medical image and the updated first mask image to the second discriminative model to derive an updated second mask image representing an updated second disease region representing an updated second disease region in the medical image outputted by the second discriminative model, repeats update of the first disease region and update of the second disease region until a predetermined end condition is satisfied, and derives the updated first mask image and the updated second mask image, respectively as a final first disease region and a final second disease region, after the predetermined end condition is satisfied, therefore this claim is allowable.
Kim et al. (US 2022/0207720 A1) shows in paragraph [0197] Diagnosis assistance information, e.g., an average value of each age, a disease index of a normal person, etc., to be compared with the diagnosis assistance information that may be obtained from the medical image may be obtained in advance, stored in the second memory 2400, and continuously updated. Kim do not include all the detailed combined limitations included in the claim including an information processing apparatus comprising: at least one processor, wherein the processor acquires a medical image, inputs the medical image into a first discriminative model and derive a first mask image representing a first disease region in the medical image outputted from the first discriminative model, inputs the medical image and the first mask image into a second discriminative model to derive a second mask image representing a second disease region outputted from the second discriminative model, wherein the second disease region is related to the first disease region in the medical image, inputs the medical image and the second mask image into a third discriminative model to derive an updated first mask image representing an updated first disease region in the medical image outputted by the third discriminative model, inputs the medical image and the updated first mask image to the second discriminative model to derive an updated second mask image representing an updated second disease region representing an updated second disease region in the medical image outputted by the second discriminative model, repeats update of the first disease region and update of the second disease region until a predetermined end condition is satisfied, and derives the updated first mask image and the updated second mask image, respectively as a final first disease region and a final second disease region, after the predetermined end condition is satisfied, therefore this claim is allowable.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
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 IRIANA CRUZ whose telephone number is (571)270-3246. The examiner can normally be reached 10-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Akwasi M. Sarpong can be reached at (571) 270-3438. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IRIANA CRUZ/Primary Examiner, Art Unit 2681
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