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 .
Response to Amendment
Claims 1, 7, 12-13, 18, and 20 has been amended.
Claims 1-20 are still pending for consideration.
Claim Interpretation under 35 U.S.C. 112(f) has been withdrawn.
Response to Arguments
Applicant's arguments filed on Jan 28, 2026 have been fully considered but they are not persuasive.
Applicant on page 2 of the “Remarks” states “However, Peng fails to disclose a neural network model with a specific structure of a common portion, and multiple individual portions where each individual portion is trained with different training data. Hence, Peng fails to disclose, teach or suggest the features of "wherein the neural network model comprises: a first common portion configured to obtain first feature set based on the target eye image; a first individual portion configured to obtain the first diagnosis assistance information based on the first feature set; and a second individual portion configured to obtain the second diagnosis assistance information based on the first feature set, and wherein the first individual portion is trained based on first training data, and the second individual portion is trained based on second training data different from the first training data at least in part," recited in amended claim 1. Zhou also fails to remedy such deficient disclosure of Peng”.
Response: Examiner respectfully disagree with applicant’s argument. The rejection does not rely on Peng alone for “first common portion, first individual portion, second individual portion, and different training data” structure. Peng is relied upon for teaching a diagnosis assistance apparatus that receives fundus/eye images and use a trained machine learning model to generate health analysis data based on the fundus image. Peng discloses that the system receives fundus image data including images of the patient’s eye and generates health analysis data characterizing the patient’s health using machine learning model (see Abstract).
Peng further teaches that the output, may be condition-specific, may predict a future health-event risk may characterize overall health, or may predict values of risk factors. Thus, Peng is not limited to a single glaucoma likelihood output. Peng teaches condition-state score, follow-up scores, progression score, risk score, and predicted risk-factor values (see para [0006]).
Applicant’s “principle of operation” argument is not persuasive because Peng expressly permits its CNN to be arranged in various configurations, including multiple modules and multiple subnetworks (see para [0055] “a deep convolutional neural network…. arranged in various configurations, e.g., as multiple modules, multiple subnetworks, and so on”). Accordingly, implementing Peng’s fundus-image diagnosis system using a known shared common portion and multiple individual output options would not change Peng’s principle of operation, because Peng would still receive fundus image and generate health-analysis data using a trained machine-learning model.
Applicant’s arguments regarding the amended claim limitation have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-8, and 10-12, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Peng et al. (US 20190180441 A1) in view Shazeer et al. (US 20200089755 A1).
Regarding claim 1, Peng et al. teaches diagnosis assistance apparatus which uses a neural network model is configured to obtain diagnosis assistance information based on an eye image (see Abstract; “apparatus…..for processing fundus images using fundus image processing machine learning models. …. each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model, wherein the fundus image processing machine learning model is configured to process the model input comprising the one or more fundus image to generate a model output; and processing the model output to generate health analysis data” Note: health analysis data implies diagnosis assistance), the diagnosis assistance apparatus comprising: at least one processor (see para [0139]; “machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers…”); and at least one memory storing instructions that, when executed by the at least one processor (see para [0144]; “a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data”), cause the at least one processor to perform operations comprising; target eye obtaining a target eye image which is obtained from eyes of a subject (see para [0038]; “the fundus image data can include one or more images of the fundus in the patient's left eye and one or more images of the fundus in the patient's right eye”); and obtaining diagnosis assistance information based on the target eye image using the neural network model trained to obtain diagnosis assistance information based on an eye image (see para [0043]; “the fundus image processing machine learning model may be a deep convolutional neural network. An example of a deep convolutional neural network that can be trained to process a fundus image to generate the model outputs”, see also para [0102]; “the other patient data using a fundus image processing machine learning model to generate a predicted fundus image (step 704)….The predicted fundus image is an image of the fundus of the eye of the patient”), wherein the neural network model is configured to obtain first diagnosis assistance information (see para [0048]; “the model output is a prediction of the risk of a particular health event occurring in the future. A model output that is a prediction of the risk of a particular event occurring is described in more detail below with reference to FIG. 8”) and second diagnosis assistance information based on the target eye image, wherein the second diagnosis assistance information is different from the first diagnosis assistance information (see also para [0050]; “the model output is a prediction of values of factors that contribute to a particular kind of health-related risk. A model output that is a prediction of values of risk factors is described in more detail below with reference to FIG. 10”, and para [0069]; “in the case of glaucoma, the single score may represent a likelihood that the patient has glaucoma”, Note: Fig. 8 risk of a particular health event and Fig. 10 values of risk factors (i.e., glaucoma) are different diagnosis assistance information from the same fundus images). However, Peng et al. does not teach wherein the neural network model comprises: a first common portion configured to obtain first feature set based on the target eye image; a first individual portion configured to obtain the first diagnosis assistance information based on the first feature set; and a second individual portion configured to obtain the second diagnosis assistance information based on the first feature set, and wherein the first individual portion is trained based on first training data, and the second individual portion is trained based on second training data different from the first training data at least in part.
In the same field of endeavor, Shazeer et al. teaches comprising at least one neural network layer (see para [0004]; “a single machine learning model to perform multiple machine learning tasks from different machine learning domains”) and wherein the neural network model comprises: a first common portion configured to obtain first feature set based on the target eye image (see para [0005]; “an encoder neural network…. to generate respective encoder data outputs; a decoder neural network ….. to generate respective decoder data outputs from the unified representation space” Note: the encoder/decoder collectively generate the shared representation before output specific processing); a first individual portion configured to obtain the first diagnosis assistance information based on the first feature set; and a second individual portion configured to obtain the second diagnosis assistance information based on the first feature set (see para [0005]; “a plurality of multiple output modality neural networks…. configured to map decoder data outputs …to data outputs of the corresponding modality”, see also para [0043]; “multiple input modality neural networks 102a-102c, an encoder neural network 104, a decoder neural network 106, and multiple output modality neural networks 108a-108c….. Decoded data outputs, e.g., decoded data outputs 116 are provided to the multiple output modality neural networks 108a-108c”, Note: separate output neural networks use the shared decoder output to generate different outputs), and wherein the first individual portion is trained based on first training data, and the second individual portion is trained based on second training data different from the first training data at least in part (see para [0063]; “The training data may include multiple labeled training examples from different corpora, e.g., labeled training examples from a speech corpus, an image database, parsing dataset, or translation corpuses. The training data may be used to adjust the input modality neural networks 102a-c, encoder neural network 104, decoder neural network 106, and output modality neural networks 108a-c weights from initial values to trained values”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify an apparatus for processing fundus images using fundus image processing machine learning models of Peng et al. in view of a method to perform multiple machine learning tasks from multiple machine learning domains of Shazeer et al. in order to improve efficiency and generalization across related medical imaging tasks while enabling multiple outputs from a single image input (see para [0004]).
Regarding claim 2, the rejection of claim 1 is incorporated herein.
Peng et al. in the combination further teach wherein the first feature set comprises a plurality of feature values which are associated with the first diagnosis assistance information and the second diagnosis assistance information (see para [0056]; “the model output is a set of scores 170, with each score being generated by a corresponding node in the output layer 166….. the set of scores 170 are specific to particular medical condition… the scores in the set of scores 170 characterize the overall health of the patient”), wherein the first individual portion is configured to obtain the first diagnosis assistance information based on at least one feature value included in the first feature set (see para [0068]; “the set of condition state scores includes a single score that represents a likelihood that the patient has the medical condition….the single score may represent a likelihood that the patient has glaucoma”), and wherein the second individual portion is configured to obtain the second diagnosis assistance information based on at least one feature value included in the first feature set (see para [0110]; “the set of risk scores includes a single score that measures a particular kind of risk. For example, the score may measure a predicted cardiovascular risk of the patient, e.g., may be a predicted Framingham risk score that measures the 10-year cardiovascular risk of the patient”).
Regarding claim 3, the rejection of claim 2 is incorporated herein.
Peng et al. in the combination further teach wherein the first diagnosis assistance information comprises first information and second information (see para [0048]; “the model output is a prediction of the risk of a particular health event occurring in the future. A model output that is a prediction of the risk of a particular event occurring is described in more detail below with reference to FIG. 8”, see also para [0050]; “the model output is a prediction of values of factors that contribute to a particular kind of health-related risk. A model output that is a prediction of values of risk factors is described in more detail below with reference to FIG. 10” Note: Fig. 8 risk of a particular health event and Fig. 10 values of risk factors (i.e., glaucoma) are different diagnosis assistance information from the same fundus images).
Shazeer et al. in the combination further teach and wherein the first individual portion comprises: second common portion configured to obtain second feature set which comprises a plurality of feature values associated with the first information and the second information, based at least in part on the first feature set (see para [0043]; “Decoded data outputs, e.g., decoded data outputs 116 are provided to the multiple output modality neural networks 108a-108c”, see also para [0047]; “each output modality neural network of the multiple output modality networks 108a-c is configured to map data outputs of the unified representation space received from the decoder neural network”, and para [0061]; “A categorical output modality network is a neural network that is configured to reshape a one-dimensional decoder neural network output… and perform progressive down sampling”); first sub-portion configured to obtain the first information based at least in part on the second feature set and second sub-portion configured to obtain the second information based at least in part on the second feature set (see para [0025]; “processing the decoder output using the selected output modality neural network to generate data representing an output”).
Regarding claim 4, the rejection of claim 1 is incorporated herein.
Peng et al. in the combination further teach wherein the first diagnosis assistance information comprises at least one piece of diagnosis assistance information related to an eye disease (see para [0025]; “For a given patient, the fundus image analysis system 100 receives fundus image data 122 that includes one or more fundus images of the patient's eye and generates health analysis data 142 that characterizes the health of the patient”) and the second diagnosis assistance information comprises at least one piece of diagnosis assistance information related to a cerebral cardiovascular disease (see para [0124]; “the system processes the input fundus image data using a fundus image processing machine learning model to generate a respective predicted value for each of one or more risk factors (step 1004)… Each of the risk factors is a factor that contributes to the risk of one of a particular set of health-related events happening to the patient. For example, when the risk is cardiovascular risk”).
Regarding claim 5, the rejection of claim 1 is incorporated herein.
Peng et al. in the combination further teach wherein the first diagnosis assistance information comprises at least one piece of diagnosis assistance information related first eye disease, and the second diagnosis assistance information comprises at least one piece of diagnosis assistance information related to second eye disease which is different from the first eye disease (see para [0061]-[0064]; “Generally, the set of condition state scores are specific to a particular medical condition that the system has been configured to analyze…the medical condition is a particular eye-related condition. For example, the particular eye-related condition may be glaucoma. Generally, glaucoma is a condition in which the optic nerve is damaged, which can result in blindness. As another example, the particular eye-related condition may be age-related macular degeneration. Generally, age-related macular degeneration is a condition in which the macula, an area near the center of the retina, has deteriorated, which may cause partial or total vision loss” Note: glaucoma and macular degeneration are different eye diseases).
Regarding claim 6, the rejection of claim 1 is incorporated herein.
Peng et al. in the combination further teach wherein the first diagnosis assistance information comprises diagnosis assistance information related to glaucoma (see para [0063]; “the particular eye-related condition may be glaucoma. Generally, glaucoma is a condition in which the optic nerve is damaged, which can result in blindness”), and the second diagnosis assistance information comprises diagnosis assistance information related to a coronary artery disease (see para [0124]; “Each of the risk factors is a factor that contributes to the risk of one of a particular set of health-related events happening to the patient. For example, when the risk is cardiovascular risk, the particular set of health-related events can be a health event that is classified as a major cardiovascular health event, e.g., myocardial infarction, heart failure, percutaneous cardiac intervention, coronary artery bypass grafting”).
Regarding claim 7, the rejection of claim 1 is incorporated herein.
Peng et al. in the combination further teach wherein the at least one processor is configured to perform pre-processing for emphasizing a blood vessel included in the target eye image, and to obtain a blood vessel-emphasized eye image (see para [0042]; “prior to processing the fundus image data using the machine learning model, the system can pre-process the fundus images. For example, for a given image, the system can apply any of a variety of conventional image processing techniques to the image to improve the quality of the output generated by the machine learning model. As an example, the system may crop, scale, deskew or re-center the image. As another example, the system can remove distortion from the image, e.g., to remove blurring or to re-focus the image, using conventional image processing techniques”, see also para [066]; “the particular eye-related condition may be ocular occlusions. Generally, an ocular occlusion is the blockage or closing of a blood vessel that carries blood to or from some portion of the eye, e.g., to or from the retina”), and wherein the first common portion is configured to obtain the first feature set based on the blood vessel-emphasized eye image (see para [0066]; “an ocular occlusion is the blockage or closing of a blood vessel that carries blood to or from some portion of the eye, e.g., to or from the retina”).
Regarding claim 8, the rejection of claim 3 is incorporated herein.
Peng et al. in the combination further teach wherein the first information and the second information are diagnosis assistance information related to a disease related to first part of a human body (see para [0063]; “For example, the particular eye-related condition may be glaucoma. Generally, glaucoma is a condition in which the optic nerve is damaged, which can result in blindness…. As another example, the particular eye-related condition may be age-related macular degeneration. Generally, age-related macular degeneration is a condition in which the macula, an area near the center of the retina, has deteriorated, which may cause partial or total vision loss”), and the second diagnosis assistance information is diagnosis assistance information related to a disease related to second part of the human body, the second part being different from the first part (see para [0067]; “the specific condition is not an eye-related condition but is instead a neurodegenerative condition, e.g., Parkinson's or Alzheimer's, or another condition that can effectively be analyzed using fundus imagery”, see also para [0109]; “the other patient data using a fundus image processing machine learning model to generate a set of risk scores (step 804)…the set of risk scores includes a single score that measures a particular kind of risk. For example, the score may measure a predicted cardiovascular risk of the patient”).
Regarding claim 10, the rejection of claim 1 is incorporated herein.
Peng et al. in the combination further teach wherein the first feature set comprises at least one feature map (see para [0055]; “a set of convolutional neural network layers 162, followed by a set of fully connected layers 164, and an output layer 166”, see also para [0132]; “The initial convolutional layers process each fundus image in the fundus image data to extract a respective feature vector for each of multiple regions in the fundus image” Note: initial convolution layers inherently produce feature maps).
Regarding claim 11, the rejection of claim 3 is incorporated herein.
Peng et al. in the combination further teach wherein the first feature set comprises at least one feature map, and the second feature set comprises at least one feature value (see para [0132]; “the initial convolutional layers process each fundus image in the fundus image data to extract a respective feature vector for each of multiple regions in the fundus image. The attention mechanism determines an attention weight for each of the regions in the fundus image and then attends to the feature vectors in accordance with the corresponding attention weights to generate an attention output” see also para [0056]; “the model output is a set of scores 170, with each score being generated by a corresponding node in the output layer 166. As will be described in more detail below, in some cases, the set of scores 170 are specific to particular medical condition” Note: initial convolution layers inherently produce feature maps and output feature score “feature values”).
Regarding claim 12, the scope of claim 12 is fully encompassed by the scope of claim 1, accordingly, the rejection of claim 1 is fully applicable here.
Regarding claim 16, the rejection of claim 12 is incorporated herein.
Peng et al. in the combination further teach wherein the first diagnosis assistance information comprises at least one piece of diagnosis assistance information related first eye disease, and the second diagnosis assistance information comprises at least one piece of diagnosis assistance information related to second eye disease which is different from the first eye disease(see para [0069]; “in the case of glaucoma, the single score may represent a likelihood that the patient has glaucoma”, see also para [0072]; “in the case of ocular occlusions, the single score may represent a likelihood that the patient has one or more ocular occlusions”).
Regarding claim 17, the rejection of claim 12 is incorporated herein.
Peng et al. in the combination further teach wherein the first feature set comprises at least one feature map (see para [0131]; “the machine leaning model is a model that includes one or more initial convolutional layers followed by an attention mechanism, which in turn is followed by one or more additional neural network layers. The initial convolutional layers process each fundus image in the fundus image data to extract a respective feature vector for each of multiple regions in the fundus image”).
Regarding claim 18, the rejection of claim 12 is incorporated herein.
Peng et al. in the combination further teach wherein the processing unit further comprises a pre-processing unit configured to perform pre-processing for emphasizing a blood vessel included in the target eye image and to obtain a blood vessel-emphasized eye image (see para [0042]; “prior to processing the fundus image data using the machine learning model, the system can pre-process the fundus images. For example, for a given image, the system can apply any of a variety of conventional image processing techniques to the image to improve the quality of the output generated by the machine learning model. As an example, the system may crop, scale, deskew or re-center the image. As another example, the system can remove distortion from the image, e.g., to remove blurring or to re-focus the image, using conventional image processing techniques”, see also para [066]; “the particular eye-related condition may be ocular occlusions. Generally, an ocular occlusion is the blockage or closing of a blood vessel that carries blood to or from some portion of the eye, e.g., to or from the retina”), and wherein obtaining the first feature set comprises obtaining the first feature set based on the blood vessel-emphasized eye image through the first common portion (see para [0043]; “As shown in FIG. 4, common module F1 is shared for determining each of the outputs. Module F2 is an analysis-specific module”).
Regarding claim 19, the rejection of claim 12 is incorporated herein.
Peng et al. in the combination further teach wherein the first diagnosis assistance information comprises at least one piece of diagnosis assistance information related to an eye disease (see para [0069]; “in the case of glaucoma, the single score may represent a likelihood that the patient has glaucoma”), and the second diagnosis assistance information comprises at least one piece of diagnosis assistance information related to a cerebral cardiovascular disease (see para [0110]; “the set of risk scores includes a single score that measures a particular kind of risk. For example, the score may measure a predicted cardiovascular risk of the patient, e.g., may be a predicted Framingham risk score that measures the 10-year cardiovascular risk of the patient”).
Regarding claim 20, the rejection of claim 12 is incorporated herein.
Peng et al. in the combination further teach computer-readable recording medium having a program recorded thereon to perform the method (see para [0145]; “Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices”).
Claims 13-15 rejected under 35 U.S.C. 103 as being unpatentable over Peng et al. 9in view Shazeer et al. as applied in claim 12 above, and further in view of De Fauw et al. (US 20190005684 A1).
Regarding claim 13, the rejection of claim 12 is incorporated herein.
Peng et al. in the combination further teach wherein the first diagnosis assistance information comprises first information and second information (see para [0060]-[0061]; “the other patient data using a fundus image processing machine learning model to generate a set of condition state scores (step 304)..the set of condition state scores are specific to a particular medical condition that the system has been configured to analyze”, see also para [0084]-[0085]; “The set of follow-up scores includes a respective score for each of multiple possible follow-up actions that can be taken by the patient to treat a particular medical condition.. The system generates health analysis data from the follow-up scores (step 406). For example, the system can generate health analysis data that recommends that the patient take the follow-up action that has the highest follow-up score”), the first individual portion comprises second common portion (see para [0043]; “Decoded data outputs, e.g., decoded data outputs 116 are provided to the multiple output modality neural networks 108a-108c”). However, the combination of Peng et al. and Shazeer et al. as a whole does not teach a further claimed.
In the same field of endeavor, De Fauw teach and the first individual portion comprises second common portion, first sub-portion and second sub-portion (see para [0004]; “generate a segmentation map that segments the eye tissue in the input image into a plurality of tissue types…. receive a classification input derived from a segmentation map of eye tissue, and process the classification input to generate a classification output that characterizes the eye tissue”, wherein the obtaining diagnosis assistance information comprises: obtaining, by the second common portion, second feature set which is associated with the first information and the second information, based at least in part on the first feature set (see para [0046]; “The system 100 provides the classification input for each segmentation map as an input to each of one or more classification neural networks… generate a classification output”, see also para [0074]; “the classification output may include both referral scores and condition scores”); obtaining, by the first sub-portion, the first information based at least in part on the second feature set (see para [0048]; “the classification output …. includes a respective condition score for each of multiple medical conditions”, see also para [0071]; “the classification output …. includes a respective condition score for each of multiple medical conditions…. if the medical image is an OCT image of the patient's eye”); and obtaining, by the second sub-portion, the second information based at least in part on the second feature set (see para [0047]; “the classification output …. includes a respective referral score for each of multiple possible clinical referral decision. Each condition score may represent a predicted likelihood that the patient 104 has the medical condition”, see also para [0049]; “the classification output …. includes a respective progression score for each of multiple condition states”, see also para [0074]; “the classification output …. includes multiple different types of scores (e.g., the previously described scores). For example, the classification output may include both referral scores and condition scores”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify an apparatus for processing fundus images using fundus image processing machine learning models of Peng et al. in view of a method to perform multiple machine learning tasks from multiple machine learning domains of Shazeer et al. and for generating a final classification output for an image of eye tissue of De Fauw et al. in order to make a final classification and a false alarm rate for the final classification (see para [0004]).
Regarding claim 14, the rejection of claim 13 is incorporated herein.
Peng et al. in the combination further teach wherein the first feature set comprises at least one feature map, and the second feature set comprises at least one feature value (see para [0131]; “the machine leaning model is a model that includes one or more initial convolutional layers followed by an attention mechanism, which in turn is followed by one or more additional neural network layers… the initial convolutional layers process each fundus image in the fundus image data to extract a respective feature vector for each of multiple regions in the fundus image. The attention mechanism determines an attention weight for each of the regions in the fundus image and then attends to the feature vectors in accordance with the corresponding attention weights to generate an attention output” see also para [0056]; “the model output is a set of scores 170, with each score being generated by a corresponding node in the output layer 166. As will be described in more detail below, in some cases, the set of scores 170 are specific to particular medical condition” Note: initial convolution layers inherently produce feature maps and output feature score “feature values”).
Regarding claim 15, the rejection of claim 13 is incorporated herein.
Peng et al. in the combination further teach wherein the first information and the second information are diagnosis assistance information related to a disease related to first part of a human body, and the second diagnosis assistance information is diagnosis assistance information related to a disease related to second part of the human body, the second part being different from the first part (see para [0067]; “the specific condition is not an eye-related condition but is instead a neurodegenerative condition, e.g., Parkinson's or Alzheimer's, or another condition that can effectively be analyzed using fundus imagery”, see also para [0072]; “in the case of ocular occlusions, the single score may represent a likelihood that the patient has one or more ocular occlusions”).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Peng et al. in view of Shazeer et al. as applied in claims 1 and 3 above, and further in view of Zee et al. (US 20120257164 A1).
Regarding claim 9, the rejection of claim 3 is incorporated herein.
Peng et al. in the combination further teach wherein the first information is diagnosis assistance information indicating whether the eyes of the subject correspond to glaucoma (see para [0063]; “the particular eye-related condition may be glaucoma. Generally, glaucoma is a condition in which the optic nerve is damaged, which can result in blindness”), and wherein the second diagnosis assistance information is diagnosis assistance information indicating a degree of calcification of a coronary artery of the subject (see para [0124]; “when the risk is cardiovascular risk, the particular set of health-related events can be a health event that is classified as a major cardiovascular health event, e.g., myocardial infarction, heart failure, percutaneous cardiac intervention, coronary artery bypass grafting”). However, the combination of Peng et al. and Shazeer et al. as a whole does not teach and the second information is diagnosis assistance information indicating whether the eyes of the subject correspond to diabetic retinopathy.
In the same field of endeavor Zee et al. teaches and the second information is diagnosis assistance information indicating whether the eyes of the subject correspond to diabetic retinopathy (see para [0058]; “detecting and analyzing abnormal patterns related to diabetic retinopathy in the preprocessed image”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify an apparatus for processing fundus images using fundus image processing machine learning models of Peng et al. in view of a method to perform multiple machine learning tasks from multiple machine learning domains of Shazeer et al. and devices for diagnosing and/or predicting the presence, progression and/or treatment effect of a disease characterized by retinal pathological changes in a subject of Zee et al. in order to provide disease risk prediction based on their complexity of characteristics (see para [0058]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WINTA GEBRESLASSIE whose telephone number is (571)272-3475. The examiner can normally be reached Monday-Friday9:00-5:00.
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, Andrew Bee can be reached at 571-270-5180. 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.
/WINTA GEBRESLASSIE/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
Prosecution Insights