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 10/31/2023 and 01/02/2024 have been 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 (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, 3, 5-6, 8, 10-11 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sun (“Deep-Learning-Enabled Polarization-Sensitive Optical Coherence Tomography (OCT)”; already of record).
Regarding claim 1, Sun discloses, an information processing device comprising:
a learning unit (see “Introduction”, lines 1-7) configured to perform learning on a machine learning model having one or more non-polarization OCT images that are OCT images without polarization information as inputs and a pseudo polarization OCT image corresponding to a polarization OCT image that is an OCT image with polarization information as output (see “Data preparation”, lines 1-8); and
a storage unit (see “Introduction”, lines 1-7) configured to store a learning result of the learning unit;
wherein the one or more non-polarization OCT images and the polarization OCT image that serves as a training image of the learning are images acquired by one polarization OCT device (see “Training of the deep learning model”, lines 1-14); and
wherein, at a time of determination by using the learning result, a non-polarization OCT image acquired by a non-polarization OCT device different from the one polarization OCT device is used as an input (see “Training of the deep learning model”, lines 1-14).
Regarding claim 3, Sun discloses, an input image of the machine learning model is a normal OCT image, an OCTA image, an attenuation coefficient image, or an image of a combination of two or more of these images (see “Data preparation”, lines 1-8); and wherein an output image of the machine learning model is a pseudo image of a polarization phase difference, a local polarization phase difference, birefringence, polarization uniformity, depolarization, Shannon entropy, a polarization axis, or polarization axis uniformity (see “Data preparation”, lines 1-8).
Regarding claim 5, Sun discloses, an information processing device comprising
a determination unit (see “Introduction”, lines 1-7) having one or more non-polarization OCT images that are OCT images without polarization information as inputs and configured to determine, based on a learning result of a machine learning model and by using the machine learning model, a pseudo polarization OCT image corresponding to a polarization OCT image that is an OCT image with polarization information, according to a non-polarization OCT image that is an input image (see “Data preparation”, lines 1-8),
wherein the one or more non-polarization OCT images are images acquired by a non- polarization OCT device (see “Data preparation”, lines 1-8 and “Training of the deep learning model”, lines 1-14); and
wherein the learning result is obtained by learning having a non-polarization OCT image acquired by one polarization OCT device different from the non-polarization OCT device as an input and a polarization OCT image acquired by the one polarization OCT device as a training image (see “Training of the deep learning model”, lines 1-14).
Regarding claim 6, Sun discloses, a storage unit (see “Introduction”, lines 1-7) configured to store the learning result of the machine learning model having one or more non-polarization OCT images as inputs and a pseudo polarization OCT image as output, wherein the determination unit determines, based on the learning result stored in the storage unit and by using the machine learning model, a pseudo polarization OCT image according to a non-polarization OCT image that is the input image (see “Data preparation”, lines 1-8 and “Training of the deep learning model”, lines 1-14).
Regarding claim 8, Sun discloses, an input image of the machine learning model is a normal OCT image, an OCTA image, an attenuation coefficient image, or an image of a combination of two or more of these images (see “Data preparation”, lines 1-8; and wherein an output image of the machine learning model is a pseudo image of a polarization phase difference, a local polarization phase difference, birefringence, polarization uniformity, depolarization, Shannon entropy, a polarization axis, or polarization axis uniformity (see “Data preparation”, lines 1-8).
Regarding claim 10, Sun discloses, a non-transitory computer readable storage medium storing a program causing a computer to:
perform learning on a machine learning model (see “Introduction”, lines 1-7) having one or more non-polarization OCT images that are OCT images without polarization information as inputs and a pseudo polarization OCT image corresponding to a polarization OCT image that is an OCT image with polarization information as output (see “Data preparation”, lines 1-8); and
store a learning result in a storage units (see “Introduction”, lines 1-7),
wherein the one or more non-polarization OCT images and the polarization OCT image that serves as a training image of the learning are images acquired by one polarization OCT device (see “Training of the deep learning model”, lines 1-14); and
wherein, at a time of determination by using the learning result, a non-polarization OCT image acquired by a non-polarization OCT device different from the one polarization OCT device is used as an input (see “Training of the deep learning model”, lines 1-14).
Regarding claim 11, Sun discloses, a non-transitory computer readable storage medium storing a program causing a computer to:
acquire a learning result of a machine learning model (see “Introduction”, lines 1-7);
input one or more non-polarization OCT images that are OCT images without polarization information (see “Data preparation”, lines 1-8); and
determine, based on the learning result acquired and by using the machine learning model, a pseudo polarization OCT image corresponding to a polarization OCT image that is an OCT image with polarization information, according to a non-polarization OCT image that is an input image (see “Data preparation”, lines 1-8),
wherein the one or more non-polarization OCT images are images acquired by a non- polarization OCT device (see “Data preparation”, lines 1-8 and “Training of the deep learning model”, lines 1-14); and
wherein the learning result is obtained by learning having a non-polarization OCT image acquired by one polarization OCT device different from the non-polarization OCT device as an input and a polarization OCT image acquired by the one polarization OCT device as a training image (see “Data preparation”, lines 1-8 and “Training of the deep learning model”, lines 1-14).
Regarding claim 14, Sun discloses, an information processing device comprising:
a learning unit (see “Introduction”, lines 1-7) configured to perform learning on a machine learning model having one or more non-polarization OCT images that are OCT images without polarization information as inputs and a pseudo polarization OCT image corresponding to a polarization OCT image that is an OCT image with polarization information as output (see “Data preparation”, lines 1-8); and
a storage unit (see “Introduction”, lines 1-7) configured to store a learning result of the learning unit,
wherein the one or more non-polarization OCT images and the polarization OCT image that serves as a training image of the learning are images acquired by one polarization OCT device (see “Training of the deep learning model”, lines 1-14);
wherein the information processing device further includes a determination unit having, at a time of determination, one or more non-polarization OCT images that are OCT images without polarization information as inputs and configured to determine, based on the learning result of the machine learning model and by using the machine learning model, a pseudo polarization OCT image corresponding to a polarization OCT image that is an OCT image with polarization information, according to a non-polarization OCT image that is an input image (see “Data preparation”, lines 1-8 and “Training of the deep learning model”, lines 1-14); and
wherein the one or more non-polarization OCT images are images acquired by a non- polarization OCT device different from the one polarization OCT device (see “Data preparation”, lines 1-8 and “Training of the deep learning model”, lines 1-14).
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 2 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Sun (“Deep-Learning-Enabled Polarization-Sensitive Optical Coherence Tomography (OCT)”; already of record) as applied to claims 1 and 5 above, in view of Choi (US 2018/0014748).
Sun remains as applied to claims 1 and 5 above.
Sun does not disclose the machine learning model is a model of a convolutional neural network.
Choi teaches, from the same field of endeavor that in an information processing device that it would have been desirable to make the machine learning model is a model of a convolutional neural network (Para. 0004-0005).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to make the machine learning model is a model of a convolutional neural network as taught by the information processing device of Choi in the information processing device of Sun since Choi teaches it is known to include this feature in an information processing device for the purpose of providing an information processing device that allows accurate and improved image resolution.
Claims 12-13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Sun (“Deep-Learning-Enabled Polarization-Sensitive Optical Coherence Tomography (OCT)”; already of record) as applied to claims 1, 5 and 14 above, in view of Shemonski et al. (US 2019/0272631).
Sun remains as applied to claims 1, 5 and 14 above.
Sun does not disclose as the input, a combination of an OCT image and an OCTA image as a multichannel image is used as an input.
Shemonski teaches, from the same field of endeavor that in information processing device that it would have been desirable to make as the input, a combination of an OCT image and an OCTA image as a multichannel image is used as an input (Para. 0007 and 0027).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to make as the input, a combination of an OCT image and an OCTA image as a multichannel image is used as an input as taught by the information processing device of Shemonski in the information processing device of Sun since Shemonski teaches it is known to include this feature in an information processing device for the purpose of providing an information processing device with enhanced images for accurate and reliable diagnosis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAWAYNE A PINKNEY whose telephone number is (571)270-1305. The examiner can normally be reached M-F 9-5.
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, Pinping Sun can be reached at 571-270-1284. 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.
/DAWAYNE PINKNEY/Primary Examiner, Art Unit 2872 06/03/2026
Prosecution Insights