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 .
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.
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.
Claim(s) 1-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (U.S. PG-PUB NO. 2024/0185567) in view of Loewke (U.S. PG-PUB NO. 2024/0037743).
-Regarding claim 1, Zhang discloses a system for determining a predicted implantation potential of an embryo (see abstract), the system comprising: storage circuitry configured to store a first artificial neural network and a second artificial neural network (non-transitory computer readable storage media encoded with a program including instructions executable by the at least one processor, paragraph 45), wherein the first artificial neural network is trained to classify the embryo as euploid or aneuploid (3D neural networks to detect the embryo ploidy (euploid vs. aneuploid), paragraph 115), and the second artificial neural network is trained to generate a predicted implantation potential of the embryo (CNN-RNN; predict the live-birth probability of a transfer with single or multiple embryos in a IVF transplantation, paragraph 117); control circuitry configured to: train the first artificial neural network with training PGS data and training morphokinetic signatures (fine-tuned the classification head with embryo time-lapse videos for ploidy status prediction, morphological features and developmental kinetics, paragraph 116; PGT-A, paragraph 38); receive a first feature input, wherein a first feature input is based on a morphokinetic signature of an embryo (uniformly sampling per hour ... to capture morphological features and developmental kinetics of the embryo over the whole process of embryonic development, paragraph 116); input the first feature input into the first artificial neural network to generate a first feature output based on a classification of the embryo, wherein the first artificial neural network is trained to classify morphokinetic signatures of embryos as euploid or aneuploid (3D neural networks to detect the embryo ploidy (euploid vs. aneuploid), paragraph 115); train a second artificial neural network with training patient ages and associated known implantation data (integrate clinical metadata including maternal age, endometrial thickness, etc., to further improve prediction, paragraph 117); input a patient age into the second artificial neural network as part of determining the predicted implantation potential (integrate clinical metadata including maternal age, endometrial thickness, etc., to further improve prediction using methods such as logistic regression, paragraph 117).
Zhang is silent to teaching that input the first feature output into the second artificial neural network to generate a second feature output based on the predicted implantation potential of the embryo, wherein the second artificial neural network is trained to predict predicted implantation potentials of embryos based on classifications of morphokinetic signatures, patient ages, and known implantation data; and generate for display, on a user interface, a recommendation for implantation based on the second feature output; and input/output circuitry configured to generate for display on a display device the predicted implantation potential. However, the claimed limitation is well known in the art as evidenced by Loewke.
In the same field of endeavor, Loewke teaches input the first feature output into the second artificial neural network to generate a second feature output based on the predicted implantation potential of the embryo, wherein the second artificial neural network is trained to predict predicted implantation potentials of embryos based on classifications of morphokinetic signatures, patient ages, and known implantation data (combining the training data to indicate both the pregnancy outcome and the ploidy status, paragraph 154; feedforward neural network 801c may include layers with batch normalization, ReLU, and dropout. The feedforward neural network 801c may then generate a final score representing a likelihood of successful pregnancy for an embryo in the specific image that was cropped and classified, paragraph 107; the D-CNN may incorporate patient data 206 (e.g., patient-specific metadata) such as age, body mass index, donor status, etc., to improve the accuracy of the overall score assigned to the embryo, paragraph 79); and generate for display, on a user interface, a recommendation for implantation based on the second feature output; and input/output circuitry configured to generate for display on a display device the predicted implantation potential (the overall viability score indicating the likelihood of clinical pregnancy (e.g., successful outcome) may be displayed on one or more displays … a clinician (e.g., embryologist, reproductive endocrinologists, clinician, etc.) may select an embryo for transfer in real-time in consultation with a patient based on the overall viability score of the embryos and the ranks of the images generated by the D-CNN, paragraph 80).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Zhang with the teaching of Loewke in order to improve implantation prediction (KSR: applying a known feature for a predictable result).
-Regarding claim 2, Zhang discloses a non-transitory computer readable media for determining a predicted implantation potential of an embryo (see abstract; non-transitory computer readable storage media encoded with a program including instructions executable by the at least one processor, paragraph 45), comprising instructions that, when executed by one or more processors, cause operations comprising: receiving a first feature input, wherein a first feature input is based on a morphokinetic signature of an embryo (fine-tuned the classification head with embryo time-lapse videos for ploidy status prediction, morphological features and developmental kinetics, paragraph 116; PGT-A, paragraph 38); determining a first feature output based on a classification of the embryo as euploid or aneuploid (3D neural networks to detect the embryo ploidy (euploid vs. aneuploid), paragraph 115).
Zhang is silent to teaching that inputting the first feature output and the first feature input into a second artificial neural network to generate a second feature output based on a predicted implantation potential of the embryo, wherein the second artificial neural network is trained to predict predicted implantation potentials of embryos based on classifications of morphokinetic signatures and known implantation data; and generating for display, on a user interface, a recommendation for implantation based on the second feature output. However, the claimed limitation is well known in the art as evidenced by Loewke.
In the same field of endeavor, Loewke teaches that inputting the first feature output and the first feature input into a second artificial neural network to generate a second feature output based on a predicted implantation potential of the embryo, wherein the second artificial neural network is trained to predict predicted implantation potentials of embryos based on classifications of morphokinetic signatures and known implantation data (combining the training data to indicate both the pregnancy outcome and the ploidy status, paragraph 154; feedforward neural network 801c may include layers with batch normalization, ReLU, and dropout. The feedforward neural network 801c may then generate a final score representing a likelihood of successful pregnancy for an embryo in the specific image that was cropped and classified, paragraph 107; the D-CNN may incorporate patient data 206 (e.g., patient-specific metadata) such as age, body mass index, donor status, etc., to improve the accuracy of the overall score assigned to the embryo, paragraph 79); and generating for display, on a user interface, a recommendation for implantation based on the second feature output (the overall viability score indicating the likelihood of clinical pregnancy (e.g., successful outcome) may be displayed on one or more displays … a clinician (e.g., embryologist, reproductive endocrinologists, clinician, etc.) may select an embryo for transfer in real-time in consultation with a patient based on the overall viability score of the embryos and the ranks of the images generated by the D-CNN, paragraph 80).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Zhang with the teaching of Loewke in order to improve implantation prediction (KSR: applying a known feature for a predictable result).
-Regarding claim 3, the combination further discloses receiving PGS data that indicates whether the embryo is euploid or aneuploid, wherein the classification is based at least on the PGS data (Loewke, (PGT-A) results for embryos that may have been biopsied and tests, and patient data. After collecting the data, the microscopy images in the collected data may be split into two groups based on their ploidy status. For example, the microscopy images in the collected data may be split into euploid embryos and aneuploid embryos, paragraph 153); converting the PGS data into a first feature output having a format compatible for interfacing with the second artificial neural network (Loewke, by combining the training data to indicate both the pregnancy outcome and the ploidy status, the technology described herein may seamlessly integrate to accurately predict both the ploidy status of an embryo and the viability of the embryo based on the ploidy status, paragraph 154); and providing the first feature output to the second artificial neural network (Loewke, Patient data may be incorporated by concatenating each image score ... with the corresponding patient data, paragraph 107).
-Regarding claim 4, the combination further discloses training a first artificial neural network with training PGS data and training morphokinetic signatures (Zhang, AI based prediction on embryo ploidy (euploid vs. aneuploid) without a biopsy, paragraph 31); generating inferred PGS data from at least the morphokinetic signatures (Zhang, predict the ploidy status (euploid vs aneuploid) of an embryo given an embryo time-lapse video, paragraph 116); and inputting, to the first artificial network, the inferred PGS data, wherein the classification is further based at least on the inferred PGS data (Zhang, the CNNs described above may be trained to predict the ploidy status of the embryo ... analyzed and classified by the CNNs in real-time to generate a ploidy status, paragraph 149).
-Regarding claim 5, the combination further discloses the predicted implantation potential is a numerical score (Zhang, a probability, providing a prediction of the likelihood of an embryo leading to a successful pregnancy after implantation in the uterus, paragraph 32).
-Regarding claim 6, the combination further discloses training the second artificial neural network with training images of embryos that depict training morphological features and associated known implantation data (Zhang, CNNs were effective in extracting morphological features from embryo images, paragraph 118); and inputting a morphological feature from an image that includes the morphokinetic signatures as part of determining the predicted implantation potential (Zhang, Image features of the embryos were extracted from each embryo in a single transfer by a shared CNN, and then further fused ... to give an overall live-birth probability, paragraph 117).
-Regarding claim 7, the combination further discloses training the second artificial neural network with training videos of embryos that depict training morphokinetic signatures and associated known implantation data (Zhang, automatic evaluation system to detect embryo chromosomal ploidy and live birth outcome based on embryo still images and time-lapse videos, paragraph 114); and inputting a morphokinetic feature from a video that includes the morphokinetic signatures as part of determining the predicted implantation potential (Zhang, three-dimensional CNNs were adopted to predict the ploidy status (euploid vs aneuploid) of an embryo given an embryo time-lapse video, paragraph 116).
-Regarding claim 8, the combination further discloses training the second artificial neural network with training patient ages and associated known implantation data (Zhang, integrate clinical metadata including maternal age, endometrial thickness, etc., to further improve prediction using methods such as logistic regression, paragraph 117); and inputting a patient age as part of determining the predicted implantation potential (Loewke, the D-CNN may incorporate patient data 206 (e.g., patient-specific metadata) such as age, body mass index, donor status, etc., to improve the accuracy of the overall score assigned to the embryo, paragraph 79).
-Regarding claim 9, the combination further discloses training the second artificial neural network with training patient body mass indices and associated known implantation data (Loewke, the D-CNN may incorporate patient data 206 (e.g., patient-specific metadata) such as age, body mass index, donor status, etc., to improve the accuracy of the overall score assigned to the embryo, paragraph 79); and inputting a body mass index as part of determining the predicted implantation potential (Loewke, variables such as patient age, body mass index, and/or donor status may be obtained from electronic medical records, paragraph 107).
-Regarding claim 10, the combination further discloses training the second artificial neural network with training fertilization techniques used for embryos and associated known implantation data (Loewke, the D-CNN may incorporate patient data 206 (e.g., patient-specific metadata) such as age, body mass index, donor status, etc., to improve the accuracy of the overall score assigned to the embryo, paragraph 79); and inputting a fertilization technique for the embryo as part of determining the predicted implantation potential (Zhang, selecting a human embryo in an IVF/ICSI cycle, paragraph 44).
-Regarding claim 11, the combination further discloses the morphokinetic signature is obtained from a video of the development of the embryo (Zhang, fine-tuned the classification head with embryo time-lapse videos for ploidy status prediction, morphological features and developmental kinetics, paragraph 116).
-Regarding claim 12, Zhang discloses a method for determining a predicted implantation potential of an embryo (see abstract), comprising: receiving a first feature input, wherein a first feature input is based on a morphokinetic signature of an embryo (fine-tuned the classification head with embryo time-lapse videos for ploidy status prediction, morphological features and developmental kinetics, paragraph 116; PGT-A, paragraph 38); determining a first feature output based on a classification of the embryo as euploid or aneuploid (3D neural networks to detect the embryo ploidy (euploid vs. aneuploid), paragraph 115).
Zhang is silent to teaching that inputting the first feature output and the first feature input into a second artificial neural network to generate a second feature output based on a predicted implantation potential of the embryo, wherein the second artificial neural network is trained to predict predicted implantation potentials of embryos based on classifications of morphokinetic signatures and known implantation data; and generating for display, on a user interface, a recommendation for implantation based on the second feature output. However, the claimed limitation is well known in the art as evidenced by Loewke.
In the same field of endeavor, Loewke teaches that inputting the first feature output and the first feature input into a second artificial neural network to generate a second feature output based on a predicted implantation potential of the embryo, wherein the second artificial neural network is trained to predict predicted implantation potentials of embryos based on classifications of morphokinetic signatures and known implantation data (combining the training data to indicate both the pregnancy outcome and the ploidy status, paragraph 154; feedforward neural network 801c may include layers with batch normalization, ReLU, and dropout. The feedforward neural network 801c may then generate a final score representing a likelihood of successful pregnancy for an embryo in the specific image that was cropped and classified, paragraph 107; the D-CNN may incorporate patient data 206 (e.g., patient-specific metadata) such as age, body mass index, donor status, etc., to improve the accuracy of the overall score assigned to the embryo, paragraph 79); and generating for display, on a user interface, a recommendation for implantation based on the second feature output (the overall viability score indicating the likelihood of clinical pregnancy (e.g., successful outcome) may be displayed on one or more displays … a clinician (e.g., embryologist, reproductive endocrinologists, clinician, etc.) may select an embryo for transfer in real-time in consultation with a patient based on the overall viability score of the embryos and the ranks of the images generated by the D-CNN, paragraph 80).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Zhang with the teaching of Loewke in order to improve implantation prediction (KSR: applying a known feature for a predictable result).
-Regarding claim 13, the combination further discloses receiving PGS data that indicates whether the embryo is euploid or aneuploid, wherein the classification is further based at least on the PGS data (Loewke, (PGT-A) results for embryos that may have been biopsied and tests, and patient data. After collecting the data, the microscopy images in the collected data may be split into two groups based on their ploidy status. For example, the microscopy images in the collected data may be split into euploid embryos and aneuploid embryos, paragraph 153); converting the PGS data into a first feature output having a format compatible for interfacing with the second artificial neural network (Loewke, by combining the training data to indicate both the pregnancy outcome and the ploidy status, the technology described herein may seamlessly integrate to accurately predict both the ploidy status of an embryo and the viability of the embryo based on the ploidy status, paragraph 154); and providing the first feature output to the second artificial neural network (Loewke, Patient data may be incorporated by concatenating each image score ... with the corresponding patient data, paragraph 107).
-Regarding claim 14, the combination further discloses training a first artificial neural network with training PGS data and training morphokinetic signatures (Zhang, AI based prediction on embryo ploidy (euploid vs. aneuploid) without a biopsy, paragraph 31); generating inferred PGS data from at least the morphokinetic signatures (Zhang, predict the ploidy status (euploid vs aneuploid) of an embryo given an embryo time-lapse video, paragraph 116); and inputting, to the first artificial network, the inferred PGS data, wherein the classification is further based at least on the inferred PGS data (Zhang, the CNNs described above may be trained to predict the ploidy status of the embryo ... analyzed and classified by the CNNs in real-time to generate a ploidy status, paragraph 149).
-Regarding claim 15, the combination further discloses training the second artificial neural network with training videos of embryos that depict training morphokinetic signatures and associated known implantation data (Zhang, automatic evaluation system to detect embryo chromosomal ploidy and live birth outcome based on embryo still images and time-lapse videos, paragraph 114); and inputting a morphokinetic feature from a video that includes the morphokinetic signatures as part of determining the predicted implantation potential (Zhang, three-dimensional CNNs were adopted to predict the ploidy status (euploid vs aneuploid) of an embryo given an embryo time-lapse video, paragraph 116).
-Regarding claim 16, the combination further discloses training the second artificial neural network with training patient ages and associated known implantation data (Zhang, integrate clinical metadata including maternal age, endometrial thickness, etc., to further improve prediction using methods such as logistic regression, paragraph 117); and inputting a patient age as part of determining the predicted implantation potential (Loewke, the D-CNN may incorporate patient data 206 (e.g., patient-specific metadata) such as age, body mass index, donor status, etc., to improve the accuracy of the overall score assigned to the embryo, paragraph 79).
-Regarding claim 17, the combination further discloses training the second artificial neural network with training fertilization techniques used for embryos and associated known implantation data (Loewke, the D-CNN may incorporate patient data 206 (e.g., patient-specific metadata) such as age, body mass index, donor status, etc., to improve the accuracy of the overall score assigned to the embryo, paragraph 79); and inputting a fertilization technique for the embryo as part of determining the predicted implantation potential (Zhang, selecting a human embryo in an IVF/ICSI cycle, paragraph 44).
Conclusion
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/PING Y HSIEH/ Primary Examiner, Art Unit 2664
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