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 § 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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhou et al (EP 3293736 B1).
Regarding claim 1, Zhou et al discloses a method for decision support in a medical therapy system, the method comprising: acquiring a medical scan of a patient (page 3); generating a prediction of survival post therapy for the patient (survival time) (page 11), the survival generated by a machine-learned multi-task generator having been trained based on at least two losses including a survival loss; (pages 11, 13, 18) and displaying an image of the survival (i.e. monitor, workstation, printer, handheld, or computer) (page 12).
Regarding claim 2, Zhou et al discloses wherein acquiring comprises scanning the patient with a computed tomography scanner (medical diagnostic imaging scanner, i.e. ultrasound, computed tomography (CT), x-ray, fluoroscopy, positron emission tomography, single photon emission computed tomography) (page 12).
Regarding claim 3, Zhou et al discloses wherein acquiring comprises acquiring voxel data representing a three-dimensional distribution of locations in a volume of the patient, and wherein generating comprises generating based on input of the voxel data for a segmented three-dimensional region (tissue may also be classified very locally e.g., independent classification of every voxel) (page 12).
Regarding claim 4, Zhou et al discloses wherein generating comprises generating with the machine-learned multi-task generator comprises a convolutional neural network (page 19).
Regarding claim 5, Zhou et al discloses wherein generating comprises generating with the convolutional neural network comprising an encoder (auto-encoding) (page 10) network trained as part of an encoder and decoder network, and the convolutional neural network (page 10) comprising a neural network configured to receive bottleneck features of the encoder network, the neural network generating the survival (page 11).
Regarding claim 6, Zhou et al discloses wherein the at least two losses include an image feature loss (See Fig.4, page 10) (loss function), and wherein generating comprises generating with the machine-learned multi-task generator having been trained with deep learning to create features compared to handcrafted radiomics (CT) features for the image feature loss (page 18).
Regarding claim 7, Zhou et al discloses wherein generating comprises generating with the machine-learned multi-task generator having been trained with a greater number of training data samples (feature values Gw) for an image feature loss of the at least two losses than for the survival loss (page 10).
Regarding claim 8, Zhou et al discloses wherein generating comprises generating the survival as a time to event (pages 2, 18).
Regarding claim 9, Zhou et al discloses wherein generating comprises generating with the machine-learned multi-task generator having been trained with the survival loss comprising a maximum likelihood (page 11, first paragraph).
Regarding claim 10, Zhou et al discloses wherein generating comprises generating with the machine-learned multi-task generator having been trained with deep learning including non-linear relationships between the survival and image features (pages 9-10).
Regarding claim 11, Zhou et al discloses wherein generating comprises generating the survival as a likelihood as a function of time (prediction of survival time) (page 18).
Regarding claim 12, Zhou et al discloses further comprising stratifying the survival (categories) (page 10).
Regarding claim 13, Zhou et al discloses further comprising treating the patient based on the stratification (page 10, last paragraph).
Regarding claim 14, Zhou et al discloses a method for machine training decision support in a medical therapy system (page 3), the method comprising: defining a multi-task network with an output layer for survival estimation (survival time) (page 11) and an output layer for image feature estimation (page 18); machine training the multi-task network to estimate image features and to estimate survival from input medical imaging volumes (pages 3-4), the training being based on ground truth survivals and ground truth image features (page 10); and storing the machine-trained multi-task network (page 12).
Regarding claim 15, Zhou et al discloses wherein machine training comprises machine training with a loss function (Ew) comprising a weighted combination of an image feature loss and a survival loss comprising a maximum likelihood (pages 9-10).
Regarding claim 16, Zhou et al discloses wherein the machine training comprises training with training data samples for the ground truth survivals and training data samples for the ground truth image features, the training data samples for the ground truth survivals being fewer in number than the training data samples for the ground truth image features by an order of magnitude (page 10, paragraph 5).
Regarding claim 17, Zhou et al discloses wherein defining comprises defining the multi- task network as an encoder and decoder with a neural network receiving bottleneck features of the encoder and decoder (auto-encoding) (page 10) as input, and wherein machine training comprises comparing the ground truth image features compared to an output of the decoder and comparing the ground truth survivals to an output of the neural network (page 11).
Regarding claim 18, Zhou et al discloses a method for decision support in a medical therapy system, the method comprising: acquiring a medical scan of a patient (page 3); generating a prediction of survival post therapy for the patient (survival time) (page 11), the survival generated by a machine-learned multi-task generator having been trained based on at least two losses including a survival loss; and displaying an image of the survival (pages 11, 13, 18).
Regarding claim 19, Zhou et al discloses wherein the medical imager comprises a computed tomography imager (page 8), and wherein the multi-task trained network was trained using a first loss for image features based on handcrafted radiomics (CT) and using a second loss for the time to event as survival (page 18).
Regarding claim 20, Zhou et al discloses wherein the multi-task trained network comprises a machine-learned encoder for image features and a neural network (deep-learning neural network) (page 7) for the prediction of the time-to-event or failure risk after the therapy (predicted survival time) (page 11).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Huang et al (“Survival Prediction After Transarterial Chemoembolization for Hepatocellular Carcinoma: a Deep Multitask Survival Analysis Approach”) predicts survival for patients with Barcelona Clinic Liver Cancer (BCLC) stage B hepatocellular carcinoma (HCC) who undergo transarterial chemoembolization (TACE).
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/F.P.B./Examiner, Art Unit 2884
/UZMA ALAM/Supervisory Patent Examiner, Art Unit 2884
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