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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. § 119 (a)-(d). The certified copy has been filed in parent Application No. JP2021-124772, filed on 07/29/2021.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1–2, 6 and 12 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by Yamada et al. (U.S. 11,276,000 B2).
Regarding claim 1, Yamada discloses a cell analysis method comprising: a step of acquiring a cell image including a cell; (Per Fig. 1, Yamada discloses a training image to analyze a cell image. Yamada col. 9 lines 1–13. This image is obtained from a sample stained so that the tissue structure can be recognized by microscopic observation.)
a step of inputting the cell image to a learned model that has learned classification of the cell into one of two or more types; (Per Fig. 11 at step S14, Yamada’s processing unit 10A inputs the training image to dissect a type of tissue in the machine learning model. Id. col. 24 lines 44–63. In step S14, the processing unit 10A receives input of the type of tissue for learning from the operator on the side of the deep learning apparatus 100A via the input unit 16.)
a step of acquiring an index value (an index value construed as an estimated value) indicating accuracy of the classification of the cell that is included in the cell image into one of two or more types1 based on an analysis result of each of pixels of the cell image output from the learned model; and (Per Fig. 6, Yamada discloses a trained model where an estimated value of the pixel in his analysis data 80 is an output to analyze the structure of the target in the image. Id. col. 19 line 43 – col. 20 line 6. That is, the estimated value 82 output from the output layer 60b of the neural network 60 is a label value generated for each pixel of the analysis target image, and is a datum indicating the layer structure in the analysis target image.)
a step of displaying (a step of displaying construed as extracting data at the window size) the acquired index value. (Per Fig. 2, Yamada retrieves his data 80 at the window size such that the value is indicated in the target image. Id. col. 20 lines 7–44. Thereafter, the analyzing data 80 is extracted at the window size while moving the window W2 by one pixel unit so that the center of the window W2 scans all the pixels of the analysis target data.)
Regarding claim 2, Yamada discloses the cell image analysis method,
wherein the learned model has been learned to output a probability value(s) that is/are an estimation value(s) of the classification as the analysis result; and (Per Fig. 6, Yamada discloses a trained model where an estimated value of the pixel in his analysis data 80 is an output to analyze the structure of the target in the image. Yamada col. 19 line 43 – col. 20 line 6. That is, the estimated value 82 output from the output layer 60b of the neural network 60 is a label value generated for each pixel of the analysis target image, and is a datum indicating the layer structure in the analysis target image.)
a representative value (a representative value construed as a threshold value) of the probability value(s) obtained based on the probability value(s) output by the learned model is acquired as the index value in the step of acquiring an index value. (While training the model, Yamada discloses a threshold value in the binarization processing to analyze color density of each pixel. Id. col. 13 lines 13–37. The determination of whether a region is a region of a cell nucleus or other region by binarization processing can be accomplished by comparing the color density value of each pixel in the image with a predetermined condition (for example, a color density threshold value).)
Regarding claim 6, it has been rejected in the same manner as claim 2.
Regarding claim 12, Yamada discloses the cell image analysis method further comprising a step of determining whether the index value is greater than a threshold. (Per Fig. 2, Yamada discloses whether the label value is greater or not than other nucleus regions. Yamada col. 14 lines 43–63. For example, the label value indicating the nucleus region of the second layer structure is “2”, and the label value indicating the other region is “0”.)
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4–5 are rejected under 35 U.S.C. § 103 as being unpatentable over Yamada in view of Xu (CN109671049A).
Regarding claim 4, Yamada fails to specifically disclose the cell image analysis method, wherein the learned model is produced by learning classification of the cell whether the cell is suitable for analysis whether the cell is a normal or abnormal cell; and
a value representing a suitability degree for analysis whether the cell that is included in the cell image is a normal or abnormal cell is acquired based on the probability value(s) as the index value in the step of acquiring an index value.
In related art, Xu discloses the cell image analysis method, wherein the learned model is produced by learning classification of the cell whether the cell is suitable for analysis whether the cell is a normal or abnormal cell; and (Xu discloses scoring generation step to determine whether how much cells are abnormal. Xu para. ¶59. [i]f the degree of image abnormality includes two types, normal and abnormal, then the scoring generation step involves obtaining the probability that the lesion image belongs to normal or abnormal, thus obtaining two first abnormality probabilities,)
a value representing a suitability degree (a value representing a suitability degree construed as different image abnormality degree) for analysis whether the cell that is included in the cell image is a normal or abnormal cell is acquired based on the probability value(s) as the index value in the step of acquiring an index value. (Xu discloses different degrees of image abnormality with different weighting coefficient. Id. para. ¶62. This method enables the analysis of the abnormality degree of medical images, improves the processing and analysis efficiency of medical images,)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Xu into the teachings of Yamada to overcome low efficiency while analyzing images on the naked eye. Id.
Regarding claim 5, it has been rejected in the same manner as claim 4.
Allowable Subject Matter
Claims 3, and 7–11 are 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ji et al. (U.S. 2020/0293934 A1) discloses a technique capable of adequately presenting the product specification of a material sample.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENEDICT LEE whose telephone number is (571)270-0390. The examiner can normally be reached 10:00-16:00 (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen R. Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BENEDICT E LEE/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
1 Examiner construes the accuracy of the classification as determining the layer structure based on the estimated value as Yamada differentiates a region of the structure based on the estimated value. See his col. 19 line 43 – col. 20 line 6. For example, when the estimated value is 1, it indicates that it is a nucleus region of the first layer structure, when the estimated value is 2, it indicates that it is the nucleus region of the second layer structure, when the estimated value is 3, it indicates that it is a nucleus region of the third layer structure, and when the estimated value is 0, it indicates that it is a region other than the cell nucleus.