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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/08/2024 and 07/23/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 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 1-4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al.(CN 113066080 A) in view of Fujimoto et al. (US Pub No. 20160349240 A1).
Regarding Claim 1,
Zhou discloses An evaluation method acquiring a photographic image; (Zhou, Contents of the Invention, discloses the embodiment of the invention claims a slice tissue identification method, comprising: obtaining a slice image to be identified; inputting the to-be-identified slice image into the cell identification model; obtaining the identified cell type and the position information of the identified cell; inputting the to-be-identified slice image into a tissue segmentation model; generating a first image; wherein the first image includes an identified tissue region; combining the identified cell type and the position information of the identified cell with the first image, generating a second image; wherein the second image includes the identified tissue region and a cell type within the identified tissue region; according to the cell type in the tissue region identified in the second image, classifying the tissue region identified in the second image; image is obtained of the entire tissue)
a region extraction step of extracting a localization region in which one kind of cell is localized, from the photographic image; (Zhou, Contents of the Invention, discloses the embodiment of the invention claims a slice tissue identification method, comprising: obtaining a slice image to be identified; inputting the to-be-identified slice image into the cell identification model; obtaining the identified cell type and the position information of the identified cell; inputting the to-be-identified slice image into a tissue segmentation model; generating a first image; wherein the first image includes an identified tissue region; combining the identified cell type and the position information of the identified cell with the first image, generating a second image; wherein the second image includes the identified tissue region and a cell type within the identified tissue region; according to the cell type in the tissue region identified in the second image, classifying the tissue region identified in the second image; tissue region is extracted from the image)
an analysis step of analyzing the localization region; and an evaluation step of evaluating a condition of the spheroid, wherein the spheroid is a spheroid including multiple kinds of liver-derived cells including a hepatocyte, and the analysis step includes: a first calculation step of calculating an area of the entire spheroid in the photographic image; (Zhou, Contents of the Invention, discloses the embodiment of the invention claims a slice tissue identification method, comprising: obtaining a slice image to be identified; inputting the to-be-identified slice image into the cell identification model; obtaining the identified cell type and the position information of the identified cell; inputting the to-be-identified slice image into a tissue segmentation model; generating a first image; wherein the first image includes an identified tissue region; combining the identified cell type and the position information of the identified cell with the first image, generating a second image; wherein the second image includes the identified tissue region and a cell type within the identified tissue region; according to the cell type in the tissue region identified in the second image, classifying the tissue region identified in the second image; image is obtained of the entire tissue; liver derived cell region being hepatocyte and its type are determined of the liver image)
a second calculation step of calculating an area of the localization region in the photographic image; (Zhou, Description, discloses In the embodiment of the invention, the second image identified in the tissue region of the cell type for pixel filling, further determining the second image comprises diseased cell type tissue region; then calculating the ratio of diseased cell type tissue area and the identified whole tissue area, the ratio can be used for determining the lesion level. Through the above method, further reducing the workload of the pathological doctor, assisting the doctor to judge the slice tissue; slice tissue identification method can be used for specific pathology, such as the method can be used for identifying intestinal epithelial in the pathology of gastritis. Of course, the system also can be applied to liver cancer, breast cancer, prostate cancer, tongue cancer, endometrial cancer and other tissue identification, the invention is not limited; the slice tissue identification system 400 can be used for specific pathology, such as the system can be used for identifying gastritis intestinal epithelial. Of course, the system also can be applied to liver cancer, breast cancer, prostate cancer, tongue cancer, endometrial cancer and other tissue identification, the invention is not limited; area of cell and tissue is determined)
a third calculation step of calculating a ratio of the localization region on the basis of the area of the entire spheroid and the area of the localization region; (Zhou, Description, discloses In the embodiment of the invention, the second image identified in the tissue region of the cell type for pixel filling, further determining the second image comprises diseased cell type tissue region; then calculating the ratio of diseased cell type tissue area and the identified whole tissue area, the ratio can be used for determining the lesion level. Through the above method, further reducing the workload of the pathological doctor, assisting the doctor to judge the slice tissue; slice tissue identification method can be used for specific pathology, such as the method can be used for identifying intestinal epithelial in the pathology of gastritis. Of course, the system also can be applied to liver cancer, breast cancer, prostate cancer, tongue cancer, endometrial cancer and other tissue identification, the invention is not limited; the slice tissue identification system 400 can be used for specific pathology, such as the system can be used for identifying gastritis intestinal epithelial. Of course, the system also can be applied to liver cancer, breast cancer, prostate cancer, tongue cancer, endometrial cancer and other tissue identification, the invention is not limited; area of cell region and tissue regions are determined) and
a fourth calculation step of calculating an evaluation parameter on the basis of the ratio of the localization region. (Zhou, Description, discloses the second image identified in the tissue region of the cell type for pixel filling, further determining the second image comprises diseased cell type tissue region; then calculating the ratio of diseased cell type tissue area and the identified whole tissue area, the ratio can be used for determining the lesion level. Through the above method, further reducing the workload of the pathological doctor, assisting the doctor to judge the slice tissue; slice tissue identification method can be used for specific pathology, such as the method can be used for identifying intestinal epithelial in the pathology of gastritis. Of course, the system also can be applied to liver cancer, breast cancer, prostate cancer, tongue cancer, endometrial cancer and other tissue identification, the invention is not limited; the slice tissue identification system 400 can be used for specific pathology, such as the system can be used for identifying gastritis intestinal epithelial. Of course, the system also can be applied to liver cancer, breast cancer, prostate cancer, tongue cancer, endometrial cancer and other tissue identification, the invention is not limited; ratio of cell region and tissue regions are determined and parameter to determine of which type of cell is determined)
Zhou does not explicitly disclose for a spheroid used in a bio 3D printer, comprising: an image acquisition step of imaging the spheroid by optical coherence tomography
Fujimoto discloses for a spheroid used in a bio 3D printer, comprising: an image acquisition step of imaging the spheroid by optical coherence tomography (Fujimoto, [0039], [0044], [0052], discloses an imaging unit 20 is disposed above the well plate WP which is held by the holder section 10. The imaging unit 20 is capable of imaging tomographic images of a target object in a non-contact non-destructive (non-invasive) manner. As an example, use of an optical coherence tomography (OCT) apparatus will be described. The imaging unit 20 which is an OCT apparatus comprises a light source 21 which emits illumination light for a target object, a beam splitter 22 which splits light from the light source 21, an object lens 23, a reference mirror 24, a photo-detector 25 and a housing 26 which holds and houses them as one unit, as described in detail later; FIGS. 2A and 2B are drawings for describing the principle of imaging in this image processing apparatus. More specifically, FIG. 2A is a drawing which shows optical paths inside the imaging unit 20, and FIG. 2B is a schematic drawing which shows tomographic imaging of a spheroid. For easy understanding of the principle, FIG. 2A omits the housing 26 and the object lens 23 which is equivalent to an ordinary object lens generally used in an imaging optical system among the respective structure elements of the imaging unit 20. As described earlier, the imaging unit 20 works as an optical coherence tomography (OCT) apparatus; based on thus obtained image data, the 3D restoration section 33 generates 3D image data corresponding to the stereoscopic image of the spheroid Sp (Step S103). Describing more specifically, as tomographic image data sporadically acquired along the Y direction are interpolated in the Y direction for instance, the 3D image data can be obtained. A technique of generating 3D image data from tomographic image data has already been practiced and will not be described in detail. optical coherence tomographic imaging technique is used to capture spheroid of the object)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Zhou in view of Fujimoto having a method of extracting tissue region, and a cell region and calculating ratio of both, with the teachings of Fujimoto having obtaining optical tomography imaging of the object or tissue region to improve the efficiency of extraction of each regions and their precise areas for accurate classification of regions within imaging tissue including abnormal cell regions in medical imaging.
Regarding Claim 2,
The combination of Zhou and Fujimoto further discloses wherein the evaluation step includes a determination step of determining a quality of the spheroid on the basis of the evaluation parameter. (Zhou, Contents of the invention, discloses pre-constructing cell identification model for identifying cell type and cell position information and for identifying tissue segmentation model of the tissue region, when obtaining the to-be-identified slice image, the image respectively input to the trained two models; obtaining the cell type identified in the image; identifying the position information of the cell and the identified tissue area, finally combining the three kinds of data to obtain the second image identifying the tissue area and identifying the cell type in the tissue area, finally classifying the tissue area according to the cell type, namely finishing the identification of the slice tissue. Through the above method, reducing the workload of the pathological doctor, improving the identification efficiency of the slice tissue and the accuracy of identification; identification efficiency (quality) is obtained by evaluating parameters (slice cell classification by cell type)). Additionally, the rational and motivation to combine the references Zhou and Fujimoto as applied in rejection of claim 1 apply to this claim.
Regarding Claim 3,
The combination of Zhou and Fujimoto further discloses wherein the evaluation parameter is an estimated value of a secretion amount of predetermined protein in the spheroid. (Zhou, [0009], discloses it is desired that a technique would be established for realizing simple and convenient observation of changes in a three-dimensionally cultured cell aggregate caused by administration of a chemical substance. However, there have not been techniques which satisfy this requirement, including the conventional technique described above; changes in cultured cell aggregate of chemical substance (protein segregation) in image is extracted and detected). Additionally, the rational and motivation to combine the references Zhou and Fujimoto as applied in rejection of claim 1 apply to this claim.
Regarding Claim 5,
The combination of Zhou and Fujimoto further discloses wherein the region extraction step includes: a first extraction step of extracting an entire region corresponding to the spheroid, from the photographic image; a second extraction step of extracting a high- intensity region of which intensity value is higher than a predetermined threshold value in the entire region; and a third extraction step of extracting a region that is left after the high-intensity region and a region inner than the high- intensity region are excluded from the entire region, as the localization region. Zhou, Description, discloses the second image identified in the tissue region of the cell type for pixel filling, further determining the second image comprises diseased cell type tissue region; then calculating the ratio of diseased cell type tissue area and the identified whole tissue area, the ratio can be used for determining the lesion level. Through the above method, further reducing the workload of the pathological doctor, assisting the doctor to judge the slice tissue; slice tissue identification method can be used for specific pathology, such as the method can be used for identifying intestinal epithelial in the pathology of gastritis. Of course, the system also can be applied to liver cancer, breast cancer, prostate cancer, tongue cancer, endometrial cancer and other tissue identification, the invention is not limited; the slice tissue identification system 400 can be used for specific pathology, such as the system can be used for identifying gastritis intestinal epithelial. Of course, the system also can be applied to liver cancer, breast cancer, prostate cancer, tongue cancer, endometrial cancer and other tissue identification, the invention is not limited; ratio of cell region and tissue regions are determined and parameter to determine of which type of cell is determined). Additionally, the rational and motivation to combine the references Zhou and Fujimoto as applied in rejection of claim 1 apply to this claim.
Claim 6 recite method with steps corresponding to the method steps recited in Claim 1. Therefore, the recited steps of the method Claim 6 are mapped to the proposed combination in the same manner as the corresponding steps of Claim 1. Additionally, the rationale and motivation to combine the Zhou and Fujimoto references presented in rejection of Claim 1, apply to this claim.
Furthermore, the combination of Zhou and Fujimoto further discloses An evaluation method for a spheroid used in a bio 3D printer, comprising: a training step of creating a trained model configured to receive a photographic image of the spheroid imaged by optical coherence tomography, as input information, and produce a localization region in which one kind of cell is localized, as output information, by deep learning; an image acquisition step of imaging the spheroid by optical coherence tomography and acquiring a photographic image; a region extraction step of inputting the photographic image to the trained model, to extract the localization region output from the trained model; (Zhou, pre-constructing cell identification model for identifying cell type and cell position information and for identifying tissue segmentation model of the tissue region, when obtaining the to-be-identified slice image, the image respectively input to the trained two models; obtaining the cell type identified in the image; identifying the position information of the cell and the identified tissue area, finally combining the three kinds of data to obtain the second image identifying the tissue area and identifying the cell type in the tissue area, finally classifying the tissue area according to the cell type, namely finishing the identification of the slice tissue. Through the above method, reducing the workload of the pathological doctor, improving the identification efficiency of the slice tissue and the accuracy of identification; combining the technical solution provided by the first aspect, in some possible implementation, obtaining the cell identification model by the following steps, the step comprises: obtaining the first training sample data; wherein the first training sample data comprises a plurality of sample slice image and each sample slice image corresponding to the label data, the label data comprises cell type and position information of the cell; based on the first training sample data, training the initial model to convergence to obtain the cell identification model).
Allowable Subject Matter
Claim 4 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20240303811 A1 ([0107-0108], the enzyme activity of protein CYP3A4 secreted from each of the multiple liver spheroids having been imaged by optical coherence tomography was measured. Note that, for each of the liver spheroids, either imaging by optical coherence tomography or measurement of the enzyme activity of protein CYP3A4 may be performed earlier; [0108] FIG. 13 shows a result of Experiment 2, and is a graph showing a relationship between the area ratio of the localization region calculated from the OCT image and the enzyme activity of CYP3A4, for the liver spheroids. As shown in FIG. 13, Pearson's product-moment correlation coefficient between the area ratio of the localization region in each liver spheroid, calculated from the OCT image, and the enzyme activity of CYP3A4, was −0.885. Between the area ratio of the localization region in each liver spheroid, calculated from the OCT image, and the enzyme activity of CYP3A4, a high (negative) correlation can be found. Therefore, it can be said that, when the enzyme activity of CYP3A4 is estimated on the basis of an area ratio of a localization region calculated from an OCT image for a liver spheroid, the resultant estimated value has sufficient reliability)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PINALBEN V PATEL whose telephone number is (571)270-5872. The examiner can normally be reached M-F: 10am - 8pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wills-Burns Chineyere can be reached at 571-272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Pinalben Patel/Examiner, Art Unit 2673