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
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claims 1-2, 4-5, 7-8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jin et al. (CN 114974522 A), hereinafter Jin.
Regarding claim 1, Jin teaches A dynamic image processing device comprising: (Abstract see "The invention relates to a medical image processing method and device, electronic equipment and a storage medium, and relates to the field of medical images." Para. 103 see "the disease progression animation is a plurality of target medical images sorted by image acquisition time, and the target medical images are the medical images of the same image part of the target case. The medical image dynamic map is generated by arranging multiple medical images of the same image part in chronological order." Examiner note: The phrase 'dynamic image' is not used explicitly but is described functionally.). a hardware processor, wherein the hardware processor, acquires a dynamic image, judges a disease candidate based on the dynamic image, (Para. 229 see "FIG. 10 is a block diagram of an electronic device 1000 according to an exemplary embodiment. As shown in FIG. 10, the electronic device 1000 may include: a processor 1001 and a memory 1002." Para. 73 see "a memory on which a computer program is stored" Para. 74 see "A processor, configured to execute the computer program in the memory, to implement the steps of the method" Para. 98 see "The medical image information includes a medical image and a predicted diagnosis result corresponding to the medical image, and the predicted diagnosis result is a diagnosis result obtained by inputting the medical image into a pre-trained image classification model."). and determines a storage location of the dynamic image based on the disease candidate. (Para. 29 see "the preset hierarchical structure includes physiological systems, diseases, diseases, and cases, and the medical image information to be stored and the predicted diagnosis results are stored in the database according to the preset hierarchical structure." Para. 162 see "In step S105, when the medical image information to be stored includes a doctor's diagnosis result, the medical image information to be stored and the predicted diagnosis result are stored in the knowledge base according to the doctor's diagnosis result according to a preset hierarchical structure. " Para. 164 see "In step S106, if the medical image information to be stored does not include the doctor's diagnosis result, the medical image information to be stored and the predicted diagnosis result are stored in the knowledge base according to the predicted diagnosis result according to a preset hierarchical structure.").
Regarding claim 2, Jin teaches The dynamic image processing device according to claim 1. wherein the hardware processor judges the disease candidate based on an analysis result of the dynamic image. (Para. 10 see "Obtain medical image information from a preset knowledge base according to the received operation instruction; the medical image information includes a medical image and a predicted diagnosis result corresponding to the medical image, and the predicted diagnosis result is input of the medical image Diagnostic results obtained after a pre-trained image classification model" Para. 98 see "The medical image information includes a medical image and a predicted diagnosis result corresponding to the medical image, and the predicted diagnosis result is a diagnosis result obtained by inputting the medical image into a pre-trained image classification model.").
Regarding claim 4, Jin teaches The dynamic image processing device according to claim 1. wherein the hardware processor judges the disease candidate using machine learning. (Para. 98 see "The medical image information includes a medical image and a predicted diagnosis result corresponding to the medical image, and the predicted diagnosis result is a diagnosis result obtained by inputting the medical image into a pre-trained image classification model." Para. 157 see "Step 2: Train the preset classification model according to the second training sample to obtain an image classification model. " Para. 158 see "The preset classification model is trained according to the second training sample. In the training stage, the loss function is calculated between the prediction output of the image classification model and the corresponding doctor's diagnosis result, and optimization methods such as gradient descent can be used to complete the image classification model. parameters for training. ").
Regarding claim 5, Jin teaches The dynamic image processing device according to claim 1. wherein the hardware processor determines that the dynamic image is stored in the storage location storing the dynamic image of the same disease candidate. (Para. 172 see "Step 3. In the case that the knowledge base includes the target disease of the target case, store the medical image information and predicted diagnosis result of the target case in the knowledge base according to a preset hierarchical structure." Para. 173 see "a case identifier (such as a case ID) corresponding to the medical image information to be stored can be determined according to the second subsidiary information of the medical image information to be stored, and whether the knowledge base includes the target case is determined by the predicted diagnosis result or the doctor's diagnosis result. Target disease, when the knowledge base includes the target disease of the target case, the medical image information to be stored in the knowledge base can be saved in the knowledge base according to the image acquisition time, so as to avoid the same medical image information being repeatedly stored in the knowledge base, and at the same time It can also provide the medical imaging information of the target case and the target disease with different changes over time." Para. 229 see "The electronic device 1000 may also include one or more of a multimedia component 1003, an input/output interface 1004, and a communication component 1005.").
Claim 7 is rejected under the same analysis as claim 1 above.
Claim 8 is rejected under the same analysis as claim 1 above.
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 3 is rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (CN 114974522 A), hereinafter Jin, in view of Steigauf et al. (US 20160350919 A1), hereinafter Steigauf.
Regarding claim 3, Jin teaches The dynamic image processing device according to claim 2.
Jin does not teach wherein the hardware processor judges the disease candidate based on imaging order information.
However, Steigauf teaches wherein the hardware processor judges the disease candidate based on imaging order information. (Abstract see "for medical imaging and diagnostic workflows involving the use of machine learning techniques such as deep learning, artificial neural networks, and related algorithms that perform machine recognition of specific features and conditions in imaging data." Para. 33 see "the condition detection logic 232 may perform a review of certain conditions based on the type of preliminary medical inquiries, known conditions, or findings indicated within the information of the order data 210." Para. 61 see "The workflow may further include processing non-image data associated with the medical imaging study using the deep learning model (operation 740) or an evaluative algorithm related to the image recognition model. For example, non-image data which indicates certain parameters of the medical imaging procedure may be provided as an input to lower levels of the deep learning algorithm. This non-image data may be processed with use of natural language processing, keyword detection, or like analysis, upon metadata, clinical history and reports, request or order data, and other machine-readable information.").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jin to incorporate the teachings of Steigauf to use imaging order information to determine the disease in the image. Doing so would predictably increase accuracy of detection by guiding the model's attention toward the most relevant features associated with a disease by providing context.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (CN 114974522 A), hereinafter Jin, in view of Lyman et al. (US 20200160966 A1), hereinafter Lyman.
Regarding claim 6, Jin teaches The dynamic image processing device according to claim 1. further comprising a transmitter (Para. 229 see " The electronic device 1000 may also include one or more of a multimedia component 1003 , an input/output interface 1004 , and a communication component 1005." Para. 230 see "The communication component 1005 is used for wired or wireless communication between the electronic device 1000 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or one or a combination of them , which is not limited here. Therefore, the corresponding communication component 1005 may include: Wi-Fi module, Bluetooth module, NFC module and so on.").
Jin does not teach that transmits the dynamic image to the outside, wherein the hardware processor determines a transmission destination to which the dynamic image is to be transmitted based on the disease candidate.
However, Lyman teaches that transmits the dynamic image to the outside, wherein the hardware processor determines a transmission destination to which the dynamic image is to be transmitted based on the disease candidate. (Abstract see "A triage routing system is operable to receive a medical scan via a receiver. Inference data for the medical scan is generated by performing an inference function, where the inference function utilizes a computer-vision model trained on a plurality of medical scans. One of a plurality of medical professionals is selected to review the medical scan based on the inference data. Triage routing data that indicates the medical scan and the one of the plurality of medical professionals is generated. The medical scan is transmitted to a client device associated with the one of the plurality of medical professionals for display via a display device in accordance with the triage routing data." Para. 242 see "FIG. 12A presents an embodiment of a triage routing system 3002. The triage routing system 3002 can automatically select at least one medical professional from a plurality of medical professionals to view a medical scan, based on inference data generated by performing an inference function on the medical scan... Triage routing data can be generated indicating one or more selected medical professionals to view the scan. The medical scan can be transmitted to the one or more client devices of the one or more medical professionals for display on a display device." Para. 275 see "the triage routing system transmits the medical scan, via a transmitter, to the client device in accordance with the triage routing data.").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jin to incorporate the teachings of Lyman to automatically determine a transmission destination and transmit the image. Doing so would predictably save cost and time by automatically determining where to send the image data for review as opposed to employing a human to view images and manually send data to the correct destination.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhang et al. (US 20210042916 A1) discloses systems, methods, devices, and media for carrying out medical diagnosis of diseases and conditions using artificial intelligence or machine learning approaches.
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/ALEXANDER VAUGHN/Examiner, Art Unit 2675
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
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