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 .
DETAILED ACTION
Art Unit – Location
The Art Unit location of your application in the USPTO may have changed. To aid in correlating any papers for this application, all further correspondence regarding this application should be directed to Art Unit 2682.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/18/2026 has been entered.
Drawings
The drawings are accepted.
Claims
Claims 12 and 13 have been cancelled.
Claims 1-11 and 14-20 are presented.
Please refer to:
Iida (US 2021/0166378)
Golay (US 2020/0100724)
Regarding independent claims 1 and 14:
The Applicant argues: Iida only describes using the model for inference on a patient image, and does not describe updating, retraining, or modifying the deep learning model after deployment. Neither does Golay.
The Examiner responds: Iida teaches “learning” [0071]. The Applicants as-filed specification defines “modification” as “learning” [0112]. The Examiner understands the Applicant is arguing that the Applicant’s deep learning model is updated. Patent Application US 2018/0341747 A1 to Bernard et al. teaches that a model is retrained or updated “medical scan natural language analysis function can be updated” or “when the performance score data 630 indicates that the medical scan natural language analysis function needs to be retrained.” [0293]. Bernard is applied to the amendments in this Office Action.
The Applicant argues: Iida and Golay do not teach a hospital terminal where a first and second data set are collected from a first hospital and a second hospital different from the first hospital.
The Examiner responds: The reference of Bernard is applied to the Applicants claim amendment. Bernard teaches a “ client input devices” [0045]. Bernard teaches data from a first and second hospital “a first medical scan inference function can be directed to characterizing CT scans from a first hospital, and a second medical scan image analysis function can be directed to characterizing CT scans from a second hospital.” [0209].
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.
Claims 1, 2, 4, 5, 6, 9, 11, 14. 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Iida et al. (US 2021/0166378 A1) “Iida” in view of Golay (US 2020/0100724 A1) “Golay” and further in view of Bernard (US 2018/0341747 A1) “Bernard”.
1. Iida a teaches: A non-transitory recording medium storing a computer-readable program ("a non-transitory storage medium reflecting one aspect of the present invention stores a computer readable program" [0013]) for modifying a dynamic analysis algorithm that performs dynamic analysis on a dynamic image obtained by performing dynamic imaging with radiation on a subject ("when the medical image of the diagnosis target patient is a dynamic image(s), the deep learning uses dynamic images as the input data." [0072]. The dynamic analysis algorithm can be modified by deep learning of the input images.) , the program causing a computer to ("a computer readable program that causes a computer to" [0013]) perform: a process of receiving, from a first data collection device ("The controller 41 receives the medical image obtained by radiographing of the chest of the patient from the radiographic imaging apparatus 10 via the communication unit 44" [0078] via bus “47” [0064]) , a first data set ("acquire a medical image obtained by imaging of a patient to be diagnosed" [0014]) that is an anonymized data set ("PATIENT ID" [FIG. 3]. The patient ID is anonymous.) including a first dynamic image obtained by dynamic imaging with radiation on a first subject ("In dynamic imaging, images are obtained as radiation such as X-rays is emitted at predetermined time intervals (pulse emission) or continuously emitted at a low dose without interruption (continuous emission) " [0042]) information obtained by a first test other than the dynamic imaging on the first subject ("ACQUIRE PAST MEDICAL IMAGES AND LUNG AGES OF CONCERNING PATIENT S14" [FIG. 8]);
Iida does not explicitly teach: a process of receiving, from a second data collection device , a second data set that is an anonymized data set including a second dynamic image obtained by dynamic imaging with radiation on a second subject and information obtained by a second test other than the dynamic imaging on the second subject; and a process of modifying the dynamic analysis algorithm based on the first data set and the second data set.
However, Golay teaches: a process of receiving, from a second data collection device ("imaging device 72" [0068]) , a second data set that is an anonymized data set including a second dynamic image obtained by dynamic imaging with radiation on a second subject (plurality of "images" [0035]; “scans” [0036] for “other patients” [ABSTRACT]) and information obtained by a second test (“blood pressure” [0067]) other than the dynamic imaging on the second subject (blood pressure is different than the plurality of images and "storing the non-imaging data in a storage medium containing stored non-imaging data and existing imaging data for this patient and for a plurality of other patients” [ABSTRACT]); and a process of modifying the dynamic analysis algorithm based on the first data set and the second data set ("applying non-real time and non-user attended algorithms to the stored non-imaging data and the existing imaging data of this patient and other patients" [ABSTRACT]) .
The dynamic image of X-rays and the image information obtained other than the dynamic image of the X-ray of Iida can be modified by Golay to include dynamic imaging and non-dynamic imaging tests of a second patient.
The motivation for the combination is provided by Golay “it would be beneficial to patients and health care professionals alike to develop an individual patient self-generated, fully controlled and censored, centralized electronic medical and biographical records and medical diagnostic system that may be accessed by patients and health care professionals regardless of their affiliation with a particular hospital, clinic, or other health care provider.” [0019].
Iida teaches a hospital terminal device: "viewer terminal 60" [0040]. Golay teaches a hospital terminal device “a computer owned by a computer” [0011] which functions as a hospital terminal.
The combination of Iida and Golay do not explicitly teach:
a process of providing a hospital terminal device with a service using the modified dynamic analysis algorithm, wherein the first data collection device collects the first data set from a first hospital, and wherein the second data collection device collects the second data set from a second hospital which is different from the first hospital.
However, Bernard teaches:
a process of providing a hospital terminal device ("client input devices" [0045]) with a service using the modified dynamic analysis algorithm ("medical scan subsystem" [0041]), wherein the first data collection device collects the first data set from a first hospital ("characterizing CT scans from a first hospital" [0209]) , and wherein the second data collection device collects the second data set from a second hospital ("a second medical scan image analysis function can be directed to characterizing CT scans from a second hospital." [0209]) which is different from the first hospital (A first hospital is different than a second hospital).
The deep learning algorithm of Iida serving as the dynamic analysis algorithm and computer as a hospital terminal can be modified by Bernard to include terminals from which to enter data from a first hospital and a second hospital.
The motivation for the combination is provided by Bernard. The patent application is related to “medical imaging devices and knowledge-based systems used in conjunction with client/server network architectures” [0004]
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
2. The recording medium according to claim 1, wherein the first test is different from the second test (The first test is an image of a lung in Iida and the second test is a blood pressure test of Golay).
The first test of Iida can be modified to include a second test of Golay.
The motivation for the combination is provided by Golay “it would be beneficial to patients and health care professionals alike to develop an individual patient self-generated, fully controlled and censored, centralized electronic medical and biographical records and medical diagnostic system that may be accessed by patients and health care professionals regardless of their affiliation with a particular hospital, clinic, or other health care provider.” [0019].
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
4. The recording medium according to claim 1, wherein each of the first test and the second test includes at least one of a pulmonary function test, a cardiac function test, a scintigraphy test, a CT scan, a plain X-ray test, an MRI test, and an ultrasound test ("in dynamic imaging, images are obtained as radiation such as X-rays is emitted at predetermined time intervals (pulse emission) or continuously emitted at a low dose without interruption (continuous emission). A cyclic dynamic state of the chest such as change in shape of the lungs by expansion and contraction with breathing and pulsation of the heart is continuously imaged in dynamic imaging, for example. Images obtained by such continuous imaging is called a dynamic image." [0042] of Iida, which teaches a cardiac function test.) .
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
5. The recording medium according to claim 1, wherein the dynamic analysis algorithm includes at least one of an algorithm by analysis target and a disease diagnosis support algorithm ("The present invention has been conceived in view of the problems in the prior art as described hereinbefore, and has objects of developing early detection and prevention of COPD and reducing a burden to patients." [0008] of Iida. COPD is a disease.) .
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
6. The recording medium according to claim 1, wherein, in the processes of receiving the first data set and the second data set, at least one of the first data set and the second data set is received via a dedicated line ("bus" of Iida “34” and “47” shown in FIGs 2 and 4 of Iida [0051], [0064]. The bus is a dedicated line.) .
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
9. The recording medium according to claim 1, wherein the program causes the computer to perform: a process of inputting a third dynamic image obtained by performing dynamic imaging with radiation on a third subject; and a process of outputting diagnosis support information based on the modified dynamic analysis algorithm, for the third dynamic image ("applying non-real time and non-user attended algorithms to the stored non-imaging data and the existing imaging data of this patient and other patients" [ABSTRACT] of Golay.) .
The dynamic image of X-rays and the image information obtained from the X-rays of Iida can be augmented by Golay to include dynamic imaging and non-dynamic imaging tests of other patients.
The motivation for the combination is provided by Golay “it would be beneficial to patients and health care professionals alike to develop an individual patient self-generated, fully controlled and censored, centralized electronic medical and biographical records and medical diagnostic system that may be accessed by patients and health care professionals regardless of their affiliation with a particular hospital, clinic, or other health care provider.” [0019].
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
11. The recording medium according to claim 9, wherein, in the process of outputting the diagnosis support information, classification information regarding the dynamic image is output as the diagnosis support information ("ABNORMALITY DETECTED? S17", “NOTIFY DETECTION OF ABNORMALITY S18”, AND “SEND LUNG AGE TO DATA MANAGEMENT SERVER S19” [FIG. 8] [0121-0122] of Iida.) .
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
The dynamic medical analysis system of Iida and Golay can be modified by Golay to include a hospital terminal.
The motivation for the combination is provided by Golay “it would be beneficial to patients and health care professionals alike to develop an individual patient self-generated, fully controlled and censored, centralized electronic medical and biographical records and medical diagnostic system that may be accessed by patients and health care professionals regardless of their affiliation with a particular hospital, clinic, or other health care provider.” [0019].
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
14. The dynamic analysis device of claim 14 has been analyzed in view of the dynamic imaging [0041] of the “IMAGE ANALYSIS DEVICE 40”, having “STORAGE 45”, and a “COMMUNICATION UNIT 44” in [FIG. 4] of Iida and further in view of claim 1. Claim 14 is rejected in a similar manner to claim 1.
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
19. A dynamic analysis system ("medical image analysis system 100" [0040] of Iida.) comprising: the dynamic analysis device according to claim 14; the first data collection device ("height, weight, blood pressure, pulse, and SpO.sub.2." [Claim 16] of Iida) ; and the second data collection device (dynamic imaging, images are obtained as radiation such as X-rays " [0042] of Iida.) .
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
20. The combination of Iida and Golay teach: A dynamic analysis system ("medical image analysis system 100" [0040] of Iida.) comprising: the dynamic analysis device according to claim 14; wherein the hospital terminal device that transmits ( “a radiographic imaging apparatus 10” transmits via "network N for data communication." [0040] of Iida.) , to the dynamic analysis device ("an image analysis device 40 " [0040] of Iida), a third dynamic image obtained by performing dynamic imaging with radiation on a third subject ("PATIENT ID", e.g. KM3 [FIG. 3] of Iida) ; wherein the dynamic analysis device ("an image analysis device 40 " [0040] of Iida.) receives the third dynamic image transmitted from the hospital terminal device("viewer terminal 60" [0040] of Iida.) , and outputs diagnosis support information based on the modified dynamic analysis algorithm ("a data management server 30" [0040] of Iida), for the third dynamic image (e.g. Image number "65435" [FIG. 3] of Iida) , and wherein the hospital terminal device ("viewer terminal 60" [0040] of Iida.) receives the diagnosis support information from the dynamic analysis device ("an image analysis device 40 " [0040] of Iida. All communication via the network “N” [0040] shown in FIG. 1 of Iida.).
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
Claims 3, 10 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Iida et al. (US 2021/0166378 A1) “Iida” in view of Golay (US 2020/0100724 A1) “Golay”, in view of Bernard (US 2018/0341747 A1) “Bernard” and further in view of Lyman et al. (US 2020/0352518 A1) “Lyman”.
3. Iida, Golay, and Bernard teach: The recording medium according to claim 1, having a first and second data set.
Iida, Golay, and Bernard do not explicitly teach: wherein a data set includes a correct answer label, and the correct answer label includes at least one of a reading result for the dynamic image and a diagnosis result based on the reading result.
However, Lyman teaches: wherein a data set includes a correct answer label, and the correct answer label includes at least one of a reading result for the dynamic image and a diagnosis result based on the reading result ("supervised … learning model" [0124]) .
The deep learning of Iida can be modified by Lyman to include a supervised learning model having a correct answer label.
The motivation for the combination is provided by Lyman: “This invention relates generally to medical imaging devices and knowledge-based systems used in conjunction with client/server network architectures.“ [0004]. “The medical scan artifact detection system 3100 can automatically detect if motion artifacts, nipple shadows and/or other artifacts are present in a scan to alert radiologist/users of these artifacts in their review of the scan to help ensure they don't mischaracterize motion artifacts, nipple shadows and/or other artifacts as abnormalities and can help user's order a rescan if necessary” [0306].
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
10. Iida, Golay, and Bernard teach: having a first and second data set having a diagnosis result based on the reading result, and in the process of outputting the diagnosis support information, information regarding diagnosis of the dynamic image is output as the diagnosis support information ("ABNORMALITY DETECTED? S17", “NOTIFY DETECTION OF ABNORMALITY S18”, AND “SEND LUNG AGE TO DATA MANAGEMENT SERVER S19” [FIG. 8] [0121-0122] of Iida.) .
Iida, Golay, and Bernard do not explicitly teach where a data set includes a correct answer label.
However, Lyman teaches where a data set includes a correct answer label ("supervised … learning model" [0124]. Supervised learning includes a correct answer.) .
The deep learning of Iida having a diagnosis can be modified by Lyman to include a supervised learning model have a correct answer label for the diagnosis.
The motivation for the combination is provided by Lyman: “This invention relates generally to medical imaging devices and knowledge-based systems used in conjunction with client/server network architectures.“ [0004]. “The medical scan artifact detection system 3100 can automatically detect if motion artifacts, nipple shadows and/or other artifacts are present in a scan to alert radiologist/users of these artifacts in their review of the scan to help ensure they don't mischaracterize motion artifacts, nipple shadows and/or other artifacts as abnormalities and can help user's order a rescan if necessary” [0306].
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
15. Iida, Golay, and Bernard teach: The recording medium according to claim 1,
Iida, Golay, and Bernard do not explicitly teach:
wherein the first data set includes a correct answer label, the correct answer label including at least one of a reading result for the first dynamic image and the diagnosis result based on said reading result.
However, Lyman teaches: wherein the first data set includes a correct answer label, the correct answer label including at least one of a reading result for the first dynamic image and the diagnosis result based on said reading result.
("supervised … learning model" [0124]. Supervised learning includes a correct answer.) .
The deep learning of Iida having a diagnosis can be modified by Lyman to include a supervised learning model have a correct answer label for the diagnosis.
The motivation for the combination is provided by Lyman: “This invention relates generally to medical imaging devices and knowledge-based systems used in conjunction with client/server network architectures.“ [0004]. “The medical scan artifact detection system 3100 can automatically detect if motion artifacts, nipple shadows and/or other artifacts are present in a scan to alert radiologist/users of these artifacts in their review of the scan to help ensure they don't mischaracterize motion artifacts, nipple shadows and/or other artifacts as abnormalities and can help user's order a rescan if necessary” [0306].
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
16. Iida, Golay, and Bernard teach: The recording medium according to claim 1,
Iida, Golay, and Bernard do not explicitly teach:
wherein the second data set includes a correct answer label, the correct answer label including at least one of a reading result for the second dynamic image and the diagnosis result based on said reading result.
However, Lyman teaches: wherein the second data set includes a correct answer label, the correct answer label including at least one of a reading result for the second dynamic image and the diagnosis result based on said reading result.
("supervised … learning model" [0124]. Supervised learning includes a correct answer.) .
The deep learning of Iida having a diagnosis can be modified by Lyman to include a supervised learning model have a correct answer label for the diagnosis.
The motivation for the combination is provided by Lyman: “This invention relates generally to medical imaging devices and knowledge-based systems used in conjunction with client/server network architectures.“ [0004]. “The medical scan artifact detection system 3100 can automatically detect if motion artifacts, nipple shadows and/or other artifacts are present in a scan to alert radiologist/users of these artifacts in their review of the scan to help ensure they don't mischaracterize motion artifacts, nipple shadows and/or other artifacts as abnormalities and can help user's order a rescan if necessary” [0306].
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
17. Iida, Golay, and Bernard teach: The recording medium according to claim 14,
Iida, Golay, and Bernard do not explicitly teach:
wherein the first data set includes a correct answer label, the correct answer label including at least one of a reading result for the first dynamic image and the diagnosis result based on said reading result.
However, Lyman teaches: wherein the first data set includes a correct answer label, the correct answer label including at least one of a reading result for the first dynamic image and the diagnosis result based on said reading result.
("supervised … learning model" [0124]. Supervised learning includes a correct answer.) .
The deep learning of Iida having a diagnosis can be modified by Lyman to include a supervised learning model have a correct answer label for the diagnosis.
The motivation for the combination is provided by Lyman: “This invention relates generally to medical imaging devices and knowledge-based systems used in conjunction with client/server network architectures.“ [0004]. “The medical scan artifact detection system 3100 can automatically detect if motion artifacts, nipple shadows and/or other artifacts are present in a scan to alert radiologist/users of these artifacts in their review of the scan to help ensure they don't mischaracterize motion artifacts, nipple shadows and/or other artifacts as abnormalities and can help user's order a rescan if necessary” [0306].
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
18. Iida, Golay, and Bernard teach: The recording medium according to claim 14,
Iida, Golay, and Bernard do not explicitly teach:
wherein the second data set includes a correct answer label, the correct answer label including at least one of a reading result for the second dynamic image and the diagnosis result based on said reading result.
However, Lyman teaches: wherein the second data set includes a correct answer label, the correct answer label including at least one of a reading result for the second dynamic image and the diagnosis result based on said reading result.
("supervised … learning model" [0124]. Supervised learning includes a correct answer.) .
The deep learning of Iida having a diagnosis can be modified by Lyman to include a supervised learning model have a correct answer label for the diagnosis.
The motivation for the combination is provided by Lyman: “This invention relates generally to medical imaging devices and knowledge-based systems used in conjunction with client/server network architectures.“ [0004]. “The medical scan artifact detection system 3100 can automatically detect if motion artifacts, nipple shadows and/or other artifacts are present in a scan to alert radiologist/users of these artifacts in their review of the scan to help ensure they don't mischaracterize motion artifacts, nipple shadows and/or other artifacts as abnormalities and can help user's order a rescan if necessary” [0306].
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Iida et al. (US 2021/0166378 A1) “Iida” in view of Golay (US 2020/0100724 A1) “Golay”, in view of Bernard (US 2018/0341747 A1) “Bernard” and further in view of Zhou (WO 2023/070284 A1) “Zhou”.
7. Iida, Golay, and Bernard teach; The recording medium according to claim 1 having the first data set and the second data set.
Iida, Golay and Bernard do not explicitly teach where the first data set or the second data set is received by a virtual private network.
However, Zhou teaches at least one of the first data set and the second data set is received via a virtual private network ("Network 120 may include any suitable network capable of facilitating the exchange of information and/or data for medical data processing system 100"; “Network 120 may include …virtual private networks VPN [Middle Page 9]).
The data over a Network “N” in FIG. 1 of Iida can be modified by Zhou to send the data using a virtual private network.
The motivation for the combination is provided by Zhou. “In recent years, with the vigorous development of cloud storage, cloud sharing and other technologies, the demand for medical data sharing is also increasing. Therefore, it is necessary to provide an efficient privacy information processing method” [Background].
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
8. Iida, Golay and Bernard teach: The recording medium according to claim 1, wherein, in the processes of receiving the first data set and the second data set, the first data set is received via a dedicated line ("a bus” “34" [0051] “47” [0064] of Iida shown in FIGs 2 and 4).
Iida, Golay, and Bernard do not explicitly teach where the second data set is received via a virtual private network.
However, Zhou teaches: the second data set is received via a virtual private network ("VPN" [Middle Page 9].) .
The data of Iida transmitted over the Network “N” in FIG. 1 can be modified by Zhou to send the data using a virtual private network.
The motivation for the combination is provided by Zhou. “In recent years, with the vigorous development of cloud storage, cloud sharing and other technologies, the demand for medical data sharing is also increasing. Therefore, it is necessary to provide an efficient privacy information processing method” [Background]. There are a finite number of identified predictable potential solutions to the need of transmitting data. The data can be transmitted over a dedicated line or a VPN. A person having ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success because some data can be confidentially shared within an institution (dedicated line) and other data may be shared outside the institution with a requirement of privacy (VPN).
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
Relevant Prior Art
Kohli et al. US 2019/0156921 A1)
Abstract
Systems, methods, and apparatus provide facilitate detection, processing, and relevancy analysis of clinical data including imaging related clinical context are disclosed and described herein. An example imaging related clinical context apparatus includes a processor to: analyze a plurality of documents to identify a subset of relevant documents in the plurality of document by: applying natural language processing to identify terms in the plurality of documents, a subset of the identified terms forming tagged concepts; processing the identified terms using a machine learning model with respect to a relevancy criterion for an examination to select the subset of relevant documents; and adding an emphasis to the tagged concepts found in the subset of relevant documents. The processor is to output the subset of relevant documents including emphasized tagged concepts.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TED W BARNES whose telephone number is (571)270-1785. The examiner can normally be reached Mon-Fri. 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benny Tieu can be reached at 571-272-7490. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TED W. BARNES/ Ph.D. Electrical Engineering
Primary Examiner
Art Unit 2682
/TED W BARNES/Primary Examiner, Art Unit 2682
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