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 Status
Claims 1-6 are pending.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required:
Medical consultation text
Medical professional documents
Medical response
Medical language model
Rewritten medical consultation text
Specialty field of authors of the medical professional documents
wherein the calculation process includes referring to a result obtained by inputting, to the medical language model, the medical response generated in the generation process, the medical professional document extracted in the extraction process, and the medical consultation text
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) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anvari (US 11,031,106) in view of Park (US 2016/0022238).
Examiner Note: Hereafter, above references will be known as combination A.
a memory storing instructions; and at least one processor configured to execute the instructions to
perform processes comprising:
Anvari FIG. 12
an acquisition process of acquiring a medical consultation text;
an extraction process of extracting medical professional documents
related to the medical consultation text;
Anvari col 22 lines 24-48 At 2016 AI 103 retrieves patient's old and new medical data from database 102. AT 2017 AI 103 studies patient's medical information data from database 102 and present medical status and symptoms. At 2018 AI 103 consults medical library in database 102 and at 2019 decides if more information is needed. If the answer is positive at 2020 AI 103 communicates with patient and gets more information and goes to step 2018 to consult the medical library in database 102 and continues to step 2019 and repeats this loop until there is no need for more information.
Examiner Note: medical professional documents are inherent in “medical library.”
a rewriting process of rewriting the medical consultation text with use of the medical professional documents; a generation process of generating a medical response corresponding to the rewritten medical consultation text with use of a medical language model;
Anvari col 22 lines 24-48 At 2016 AI 103 retrieves patient's old and new medical data from database 102. AT 2017 AI 103 studies patient's medical information data from database 102 and present medical status and symptoms. At 2018 AI 103 consults medical library in database 102 and at 2019 decides if more information is needed. If the answer is positive at 2020 AI 103 communicates with patient and gets more information and goes to step 2018 to consult the medical library in database 102 and continues to step 2019 and repeats this loop until there is no need for more information. When AI 103 has all the information needed then at 2021 studies all medical information and proceeds to 2022. (195) FIG. 11E shows the procedure of deciding if an in person doctor visit or a video call with a doctor needed. At 2022 AI 103 consults medical library in database 102. Based on consultation with medical library at 2023 AI 103 decides if an in person visits to a doctor due to need for physical examination is required. If the answer is positive then at 2024 AI 103 arranges for patient a visit to a local doctor and sends patient's medical information data to the doctor. The doctor performs outpatient surgery, refers the patient to a psychologist, a neurologist or any other doctor as appropriate. At 2025 patient visits assigned doctor and all next steps will be recorded in database 102 for possible follow up if needed.
Anvari FIG. 7, illustrates an embodiment of a virtual medical system using artificial intelligence (VMAI) 700. In general, the VMAI 700 provides medical recommendation to a patient based on the patient's medical information data. The patient through application 701 embedded in IoT (UE) device 200 communicates with medical (biometric) monitoring devices/LABs 702 (410.sub.1 to 410.sub.n), a medical information database 703 (102), an artificial intelligence (AI) 704 (103), and a solution and sources recommendation 705. The patient through application 701 (210, 312, 412) controls the medical (biometric) monitoring devices (410.sub.1 to 410.sub.n) that are attached to IoT (UE) device 200 and makes various appointments with different test laboratories (stationary or mobile, 410.sub.1 to 410.sub.n) to perform various tests on the patient and sends the results from biometric monitoring devices and the test laboratories (Labs) to the medical information database 703 (102) for storage. The patient through application 701 (210, 312, 412) based on his or her medical status and condition contacts AI 704 (103) for recommendation and solutions. The patient also through application 701 (210, 312, 412) accesses the test results stored in the medical information database 703 (102) and based on the results asks the artificial intelligence (AI) 704 (103) for recommendation. Artificial intelligence (AI) 704 (103) uses the patient's test results stored in medical information database 703 (102) and the patient's inputs from application 701 (210, 312, 412) and through medical library, findings and research results database 705 (102) provides recommendations for solutions and sources in solution and sources recommendation 706. The patient through application 701 (210, 312, 412) accesses the recommendations in solution/sources recommendation 706 and performs the next step.
a calculation process of calculating medical reliability of the medical response;
a determination process of determining whether the medical reliability exceeds a threshold; and
acquired in the acquisition process.
Anvari discloses elements of the claimed invention as noted but does not disclose above limitation. However, Park discloses:
Park [0063] FIGS. 2A and 2B are diagrams explaining a process of displaying supplementary diagnosis information. FIG. 2A is a diagram illustrating features of medical images extracted from the medical images. FIG. 2B is a diagram illustrating an aspect of displaying supplementary diagnosis information, in which a threshold of reliability for displaying supplementary diagnosis information may configured to be at 70%. Medical image feature 1 211 and medical image feature 2 212 are medical image features related to benign tumors. Medical image feature 3 213 and medical image feature 4 214 are medical image features related to malignant tumors.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Anvari to obtain above limitation based on the teachings of Park for the purpose of display configured to display supplementary diagnosis information in response to a reliability of the determination being above a predetermined threshold, see abstract.
an output process of outputting a result obtained by adding
information identifying the medical professional document to the medical
response generated in the generation process, and of outputting the medical
reliability of the medical response generated in the generation process,
Park abstract, An apparatus and method for aiding imaging diagnosis is provided. The imaging diagnosis aiding apparatus includes: an extractor configured to extract at least one medical image feature from medical images, a diagnosis component configured to determine whether a lesion is malignant or benign based on the at least one extracted medical image feature, and a display configured to display supplementary diagnosis information in response to a reliability of the determination being above a predetermined threshold.
wherein the calculation process includes referring to a result obtained by inputting, to the medical language model, the medical response generated in the generation process, the medical professional document extracted in the extraction process, and the medical consultation text
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the above claim limitation is required. Patentable weight is not given to the above.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A
Combination A discloses wherein in a case where the medical reliability has been determined to
exceed the threshold, the output process includes outputting the result
obtained by adding the information identifying the medical professional
document as an optimized result.
Park [0063] FIGS. 2A and 2B are diagrams explaining a process of displaying supplementary diagnosis information. FIG. 2A is a diagram illustrating features of medical images extracted from the medical images. FIG. 2B is a diagram illustrating an aspect of displaying supplementary diagnosis information, in which a threshold of reliability for displaying supplementary diagnosis information may configured to be at 70%. Medical image feature 1 211 and medical image feature 2 212 are medical image features related to benign tumors. Medical image feature 3 213 and medical image feature 4 214 are medical image features related to malignant tumors.
Examiner Note: “as an optimized result” is drawn to intended use and is not given patentable weight.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A
Combination A discloses wherein the at least one processor is further configured to execute the
instructions to perform a contact provision process of providing contact information of medical specialists based on the medical response.
Anvari col 22 lines 24-48 At 2016 AI 103 retrieves patient's old and new medical data from database 102. AT 2017 AI 103 studies patient's medical information data from database 102 and present medical status and symptoms. At 2018 AI 103 consults medical library in database 102 and at 2019 decides if more information is needed. If the answer is positive at 2020 AI 103 communicates with patient and gets more information and goes to step 2018 to consult the medical library in database 102 and continues to step 2019 and repeats this loop until there is no need for more information. When AI 103 has all the information needed then at 2021 studies all medical information and proceeds to 2022. (195) FIG. 11E shows the procedure of deciding if an in person doctor visit or a video call with a doctor needed. At 2022 AI 103 consults medical library in database 102. Based on consultation with medical library at 2023 AI 103 decides if an in person visits to a doctor due to need for physical examination is required. If the answer is positive then at 2024 AI 103 arranges for patient a visit to a local doctor and sends patient's medical information data to the doctor. The doctor performs outpatient surgery, refers the patient to a psychologist, a neurologist or any other doctor as appropriate. At 2025 patient visits assigned doctor and all next steps will be recorded in database 102 for possible follow up if needed.
Anvari FIG. 7, illustrates an embodiment of a virtual medical system using artificial intelligence (VMAI) 700. In general, the VMAI 700 provides medical recommendation to a patient based on the patient's medical information data. The patient through application 701 embedded in IoT (UE) device 200 communicates with medical (biometric) monitoring devices/LABs 702 (410.sub.1 to 410.sub.n), a medical information database 703 (102), an artificial intelligence (AI) 704 (103), and a solution and sources recommendation 705. The patient through application 701 (210, 312, 412) controls the medical (biometric) monitoring devices (410.sub.1 to 410.sub.n) that are attached to IoT (UE) device 200 and makes various appointments with different test laboratories (stationary or mobile, 410.sub.1 to 410.sub.n) to perform various tests on the patient and sends the results from biometric monitoring devices and the test laboratories (Labs) to the medical information database 703 (102) for storage. The patient through application 701 (210, 312, 412) based on his or her medical status and condition contacts AI 704 (103) for recommendation and solutions. The patient also through application 701 (210, 312, 412) accesses the test results stored in the medical information database 703 (102) and based on the results asks the artificial intelligence (AI) 704 (103) for recommendation. Artificial intelligence (AI) 704 (103) uses the patient's test results stored in medical information database 703 (102) and the patient's inputs from application 701 (210, 312, 412) and through medical library, findings and research results database 705 (102) provides recommendations for solutions and sources in solution and sources recommendation 706. The patient through application 701 (210, 312, 412) accesses the recommendations in solution/sources recommendation 706 and performs the next step.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Krasadakis (US 11,106,718).
Combination A discloses elements of the claimed invention as noted but does not disclose wherein the at least one processor is further configured to execute the instructions to perform a decision making process of making a decision whether to provide the medical response based on the medical reliability and the specialty field of authors of the medical professional documents.
Krasadakis col 2 lines 28-45, In some implementations an author reliability score comprises a score representing a likelihood that electronic documents written by the author are reliable, and analyzing the generated data to determine an author reliability score comprises: identifying an author of the electronic document; identifying, from a database of stored electronic documents, one or more similar electronic documents and, for each similar electronic document, respective authors of the similar electronic document; identifying, for each similar electronic document, a respective content reliability score for the similar electronic document; identifying, for each identified author of a similar electronic document, a current respective author reliability score; computing an author reliability score for author of the electronic document based at least on the identified content reliability scores for the similar electronic documents and the identified author reliability scores for the authors of the similar electronic documents.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A to obtain above limitation based on the teachings of Krasadakis for the purpose of providing a computing device to generate, based on the received input data, data representing one or more features of the electronic document and analyzes the generated data representing one or more features of the electronic document to determine one or more reliability scores indicating respective measures of reliability of the electronic document. The reliability scores include one or more of (i) a content reliability score, (ii), an author reliability score, and (iii) a domain reliability score. The computing device indicates, based on one or more of the reliability scores, whether the electronic document is reliable or not, see abstract.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anvari (US 11,031,106) in view of Park (US 2016/0022238).
Examiner Note: Hereafter, above references will be known as combination A.
an acquisition process of acquiring a medical consultation text;
an extraction process of extracting medical professional documents
related to the medical consultation text;
Anvari col 22 lines 24-48 At 2016 AI 103 retrieves patient's old and new medical data from database 102. AT 2017 AI 103 studies patient's medical information data from database 102 and present medical status and symptoms. At 2018 AI 103 consults medical library in database 102 and at 2019 decides if more information is needed. If the answer is positive at 2020 AI 103 communicates with patient and gets more information and goes to step 2018 to consult the medical library in database 102 and continues to step 2019 and repeats this loop until there is no need for more information.
Examiner Note: medical professional documents are inherent in “medical library.”
a rewriting process of rewriting the medical consultation text with use of the medical professional documents; a generation process of generating a medical response corresponding to the rewritten medical consultation text with use of a medical language model;
Anvari col 22 lines 24-48 At 2016 AI 103 retrieves patient's old and new medical data from database 102. AT 2017 AI 103 studies patient's medical information data from database 102 and present medical status and symptoms. At 2018 AI 103 consults medical library in database 102 and at 2019 decides if more information is needed. If the answer is positive at 2020 AI 103 communicates with patient and gets more information and goes to step 2018 to consult the medical library in database 102 and continues to step 2019 and repeats this loop until there is no need for more information. When AI 103 has all the information needed then at 2021 studies all medical information and proceeds to 2022. (195) FIG. 11E shows the procedure of deciding if an in person doctor visit or a video call with a doctor needed. At 2022 AI 103 consults medical library in database 102. Based on consultation with medical library at 2023 AI 103 decides if an in person visits to a doctor due to need for physical examination is required. If the answer is positive then at 2024 AI 103 arranges for patient a visit to a local doctor and sends patient's medical information data to the doctor. The doctor performs outpatient surgery, refers the patient to a psychologist, a neurologist or any other doctor as appropriate. At 2025 patient visits assigned doctor and all next steps will be recorded in database 102 for possible follow up if needed.
Anvari FIG. 7, illustrates an embodiment of a virtual medical system using artificial intelligence (VMAI) 700. In general, the VMAI 700 provides medical recommendation to a patient based on the patient's medical information data. The patient through application 701 embedded in IoT (UE) device 200 communicates with medical (biometric) monitoring devices/LABs 702 (410.sub.1 to 410.sub.n), a medical information database 703 (102), an artificial intelligence (AI) 704 (103), and a solution and sources recommendation 705. The patient through application 701 (210, 312, 412) controls the medical (biometric) monitoring devices (410.sub.1 to 410.sub.n) that are attached to IoT (UE) device 200 and makes various appointments with different test laboratories (stationary or mobile, 410.sub.1 to 410.sub.n) to perform various tests on the patient and sends the results from biometric monitoring devices and the test laboratories (Labs) to the medical information database 703 (102) for storage. The patient through application 701 (210, 312, 412) based on his or her medical status and condition contacts AI 704 (103) for recommendation and solutions. The patient also through application 701 (210, 312, 412) accesses the test results stored in the medical information database 703 (102) and based on the results asks the artificial intelligence (AI) 704 (103) for recommendation. Artificial intelligence (AI) 704 (103) uses the patient's test results stored in medical information database 703 (102) and the patient's inputs from application 701 (210, 312, 412) and through medical library, findings and research results database 705 (102) provides recommendations for solutions and sources in solution and sources recommendation 706. The patient through application 701 (210, 312, 412) accesses the recommendations in solution/sources recommendation 706 and performs the next step.
a calculation process of calculating medical reliability of the medical response;
a determination process of determining whether the medical reliability exceeds a threshold; and
acquired in the acquisition process.
Anvari discloses elements of the claimed invention as noted but does not disclose above limitation. However, Park discloses:
Park [0063] FIGS. 2A and 2B are diagrams explaining a process of displaying supplementary diagnosis information. FIG. 2A is a diagram illustrating features of medical images extracted from the medical images. FIG. 2B is a diagram illustrating an aspect of displaying supplementary diagnosis information, in which a threshold of reliability for displaying supplementary diagnosis information may configured to be at 70%. Medical image feature 1 211 and medical image feature 2 212 are medical image features related to benign tumors. Medical image feature 3 213 and medical image feature 4 214 are medical image features related to malignant tumors.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Anvari to obtain above limitation based on the teachings of Park for the purpose of display configured to display supplementary diagnosis information in response to a reliability of the determination being above a predetermined threshold, see abstract.
an output process of outputting a result obtained by adding
information identifying the medical professional document to the medical
response generated in the generation process, and of outputting the medical
reliability of the medical response generated in the generation process,
Park abstract, An apparatus and method for aiding imaging diagnosis is provided. The imaging diagnosis aiding apparatus includes: an extractor configured to extract at least one medical image feature from medical images, a diagnosis component configured to determine whether a lesion is malignant or benign based on the at least one extracted medical image feature, and a display configured to display supplementary diagnosis information in response to a reliability of the determination being above a predetermined threshold.
wherein the calculation process includes referring to a result obtained by inputting, to the medical language model, the medical response generated in the generation process, the medical professional document extracted in the extraction process, and the medical consultation text
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the above claim limitation is required. Patentable weight is not given to the above.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anvari (US 11,031,106) in view of Park (US 2016/0022238).
Examiner Note: Hereafter, above references will be known as combination A.
an acquisition process of acquiring a medical consultation text;
an extraction process of extracting medical professional documents
related to the medical consultation text;
Anvari col 22 lines 24-48 At 2016 AI 103 retrieves patient's old and new medical data from database 102. AT 2017 AI 103 studies patient's medical information data from database 102 and present medical status and symptoms. At 2018 AI 103 consults medical library in database 102 and at 2019 decides if more information is needed. If the answer is positive at 2020 AI 103 communicates with patient and gets more information and goes to step 2018 to consult the medical library in database 102 and continues to step 2019 and repeats this loop until there is no need for more information.
Examiner Note: medical professional documents are inherent in “medical library.”
a rewriting process of rewriting the medical consultation text with use of the medical professional documents; a generation process of generating a medical response corresponding to the rewritten medical consultation text with use of a medical language model;
Anvari col 22 lines 24-48 At 2016 AI 103 retrieves patient's old and new medical data from database 102. AT 2017 AI 103 studies patient's medical information data from database 102 and present medical status and symptoms. At 2018 AI 103 consults medical library in database 102 and at 2019 decides if more information is needed. If the answer is positive at 2020 AI 103 communicates with patient and gets more information and goes to step 2018 to consult the medical library in database 102 and continues to step 2019 and repeats this loop until there is no need for more information. When AI 103 has all the information needed then at 2021 studies all medical information and proceeds to 2022. (195) FIG. 11E shows the procedure of deciding if an in person doctor visit or a video call with a doctor needed. At 2022 AI 103 consults medical library in database 102. Based on consultation with medical library at 2023 AI 103 decides if an in person visits to a doctor due to need for physical examination is required. If the answer is positive then at 2024 AI 103 arranges for patient a visit to a local doctor and sends patient's medical information data to the doctor. The doctor performs outpatient surgery, refers the patient to a psychologist, a neurologist or any other doctor as appropriate. At 2025 patient visits assigned doctor and all next steps will be recorded in database 102 for possible follow up if needed.
Anvari FIG. 7, illustrates an embodiment of a virtual medical system using artificial intelligence (VMAI) 700. In general, the VMAI 700 provides medical recommendation to a patient based on the patient's medical information data. The patient through application 701 embedded in IoT (UE) device 200 communicates with medical (biometric) monitoring devices/LABs 702 (410.sub.1 to 410.sub.n), a medical information database 703 (102), an artificial intelligence (AI) 704 (103), and a solution and sources recommendation 705. The patient through application 701 (210, 312, 412) controls the medical (biometric) monitoring devices (410.sub.1 to 410.sub.n) that are attached to IoT (UE) device 200 and makes various appointments with different test laboratories (stationary or mobile, 410.sub.1 to 410.sub.n) to perform various tests on the patient and sends the results from biometric monitoring devices and the test laboratories (Labs) to the medical information database 703 (102) for storage. The patient through application 701 (210, 312, 412) based on his or her medical status and condition contacts AI 704 (103) for recommendation and solutions. The patient also through application 701 (210, 312, 412) accesses the test results stored in the medical information database 703 (102) and based on the results asks the artificial intelligence (AI) 704 (103) for recommendation. Artificial intelligence (AI) 704 (103) uses the patient's test results stored in medical information database 703 (102) and the patient's inputs from application 701 (210, 312, 412) and through medical library, findings and research results database 705 (102) provides recommendations for solutions and sources in solution and sources recommendation 706. The patient through application 701 (210, 312, 412) accesses the recommendations in solution/sources recommendation 706 and performs the next step.
a calculation process of calculating medical reliability of the medical response;
a determination process of determining whether the medical reliability exceeds a threshold; and
acquired in the acquisition process.
Anvari discloses elements of the claimed invention as noted but does not disclose above limitation. However, Park discloses:
Park [0063] FIGS. 2A and 2B are diagrams explaining a process of displaying supplementary diagnosis information. FIG. 2A is a diagram illustrating features of medical images extracted from the medical images. FIG. 2B is a diagram illustrating an aspect of displaying supplementary diagnosis information, in which a threshold of reliability for displaying supplementary diagnosis information may configured to be at 70%. Medical image feature 1 211 and medical image feature 2 212 are medical image features related to benign tumors. Medical image feature 3 213 and medical image feature 4 214 are medical image features related to malignant tumors.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Anvari to obtain above limitation based on the teachings of Park for the purpose of display configured to display supplementary diagnosis information in response to a reliability of the determination being above a predetermined threshold, see abstract.
an output process of outputting a result obtained by adding
information identifying the medical professional document to the medical
response generated in the generation process, and of outputting the medical
reliability of the medical response generated in the generation process,
Park abstract, An apparatus and method for aiding imaging diagnosis is provided. The imaging diagnosis aiding apparatus includes: an extractor configured to extract at least one medical image feature from medical images, a diagnosis component configured to determine whether a lesion is malignant or benign based on the at least one extracted medical image feature, and a display configured to display supplementary diagnosis information in response to a reliability of the determination being above a predetermined threshold.
wherein the calculation process includes referring to a result obtained by inputting, to the medical language model, the medical response generated in the generation process, the medical professional document extracted in the extraction process, and the medical consultation text
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the above claim limitation is required. Patentable weight is not given to the above.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETIENNE PIERRE LEROUX whose telephone number is (571)272-4022. The examiner can normally be reached M-F 8:00 am to 4:30 pm.
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, Apu Mofiz can be reached at 571 272 4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ETIENNE P LEROUX/Primary Examiner of Art Unit 2161
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