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.
(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) 317, 321-324, 328-331 and 335-338 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Comaniciu (20170357844).
Regarding claim 317, Comaniciu discloses a machine learning system, comprising (pars. 98-106, machine learning):
at least one nontransitory processor-readable storage medium that stores (a) at least one of processor-executable instructions or data, (b) a trained convolutional neural network (CNN) model, (c) a database that stores batches of image data that depict regions of interest and their associated features, and (d) query data for a query of one or more regions of interest, the query data including image data and a set of associated features (pars. 40-41, 50-51, 82-88, and 102-104, stores model images, features and instructions); and
at least one processor communicably coupled to the at least one nontransitory processor-readable storage medium, in operation, the at least one processor (pars. 98-106, processor and memory):
extracts features from the image data of the one or more regions of interest utilizing the trained CNN model and includes the extracted features in the set of associated features of the one or more regions of interest (pars. 38-41 and 84-88, extracts image features with CNN);
loads a trained machine learning model that has been designed to predict outcomes associated with the query, wherein the machine learning model is trained based at least partially on medical images (pars. 40-42, 84 and 94, patient image features);
calculates query features for a query subject from the set of associated features of the one or more regions of interest, based at least partially on medical images (pars. 40-42, 84 and 94, patient image features);
and uses the query features as at least a subset of inputs to the trained machine learning model to generate prediction results (pars. 40-45 and 94-95, predict results).
Regarding claim 321, see pars. 43 and 94-95.
Regarding claim 322, see pars. 43, 87 and 94-95.
Regarding claim 323, see par. 43 and 87-89 which discloses predicting malignancy from image patches.
Regarding claim 324, Comaniciu teaches extracting features from image data of a medical condition query utilizing a trained convolutional neural network (CNN) model (pars. 38-41 and 84-88, extracting CNN features);
loading a trained machine learning model that has been trained based at least partially on medical images that indicate changes of medical conditions over time, to output one or more predictions (pars. 82-88 and 95-106, storing and applying a trained model); and
feeding the extracted features, as at least part of inputs, to the trained machine learning model to generate at least one prediction associated with the medical condition query (pars. 40-45 and 94-95, predicting phenotype from extracted features).
Regarding claim 328, see pars. 43 and 94-95 of Comaniciu which teaches predicting outcomes.
Regarding claim 329, see pars. 43, 87 and 94-95, predicting recurrence and treatment responses.
Regarding claim 330, see pars. 43 and 87-89, predicting malignancy from image patches
Regarding claim 331, see the rejection of claim 324 and par. 104 of Comaniciu.
Regarding claims 335-337, see the rejection of claims 328-330 above.
Regarding claim 338, Comaniciu teaches a machine learning system, comprising: at least one nontransitory processor-readable storage medium that stores (a) at least one of processor-executable instructions or data, (b) a trained convolutional neural network (CNN) model, and (c) a database that stores batches of image data that depict regions of interest and their associated features (pars. 50-51, 82-88, and 102-104); and
at least one processor communicably coupled to the at least one nontransitory processor-readable storage medium, in operation, the at least one processor (pars. 98-106):
extracts features from target image data of one or more regions of interest utilizing a trained CNN model and includes the extracted features in a set of associated features of the one or more regions of interest (pars. 38-41 and 84-88, extracts image features with CNN); and
uses the set of associated features as at least a subset of inputs to the trained machine learning model to generate prediction results for a query subject (pars. 40-45 and 94-95).
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.
Claim(s) 318, 325 and 332 are rejected under 35 U.S.C. 103 as being unpatentable over Comaniciu in view of Katzman (“DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network”).
Regarding claim 318, Comaniciu does not teach the limitations of claim 318.
Katzman teaches wherein the at least one processor, in operation: identifies a selection of treatment options for the query subject from a list of available treatment options; and for each of one or more of the identified treatment options, uses the treatment option as at least another subset of the inputs to the trained machine learning model to generate the prediction results (Abstract and section 3.2, compares model risk across different models).
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Comaniciu the ability to have comparison of different model outputs as taught by Katzman. The reason is to select the option with the lower predicted risk.
Regarding claims 325 and 332, see the rejection of claim 318 above.
Claim(s) 319, 326 and 333 are rejected under 35 U.S.C. 103 as being unpatentable over Comaniciu in view of Roth (“Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation”).
Regarding claim 319, Roth teaches wherein the prediction results include one or more candidate lesions (see the abstract and section II.B-IID, classifies lesion candidates using a CNN).
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Comaniciu the ability to output the lesion candidates as taught by Roth. The reason is to help identify the candidates.
Regarding claims 326 and 333, see the rejection of claim 319 above.
Claim(s) 320, 327, and 334 are rejected under 35 U.S.C. 103 as being unpatentable over Comaniciu in view of Roth (“Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation”) in further view of Gokturk (20080144943).
Regarding claim 320, Gokturk teaches wherein the machine learning model includes a regression model whose output is a measure of similarity between the extracted features and image features of the one or more potentially cancerous lesions (see pars. 219-226, regression distance to similarity).
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Comaniciu and Roth the ability to use regression similarity model as taught Gokturk in order to improve similarity measure over multiple images.
Regarding claims 327 and 334, see the rejection of claim 320 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Rosenberg (8429173), teaches candidate images matching the query labels are identified. Visual similarity scores are generated for the candidate images. Each visual similarity score represents the visual similarity of a respective candidate image to the query image.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HADI AKHAVANNIK whose telephone number is (571)272-8622. The examiner can normally be reached 9 AM - 5 PM Monday to Friday.
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/HADI AKHAVANNIK/ Primary Examiner, Art Unit 2676
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