DETAILED ACTION
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 .
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 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.
Status of Claims
Claims 1-9 are pending and under consideration for patentability.
Information Disclosure Statement
The Information Disclosure Statements (IDS) submitted on 17 October 2024, 19 June 2025, and 07 January 2026 have been acknowledged and considered by the Examiner.
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 1-9 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Krishnan et al. (US 2005/0020903 A1).
Regarding claims 1 and 9, Krishnan describes a medical determination support apparatus and a non-transitory computer-readable storage medium ([0002]) comprising
an analysis unit that outputs each of a plurality of analysis results related to a determination target part of a subject for a plurality of different analysis items, on the basis of medical information related to the subject ([0020]: “…CAD (computer-aided diagnosis) systems and applications for cardiac imaging, which implement automated methods for extracting and analyzing relevant features/parameters from a collection of patient information (including image data and/or non-image data) of a subject patient to provide automated assistance to a physician for various aspects of physician workflow…”)
a comprehensive determination unit that determines a state of the determination target part on the basis of an output of a comprehensive determination learning model, which has been trained to predict the state of the determination target part on the basis of the analysis results for the plurality of analysis items related to the determination target part and to output the state, in a case where the plurality of analysis results output by the analysis unit are input to the comprehensive determination learning model ([0020]: “the exemplary CAD systems implement machine-learning techniques that use a set of training data that is obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to properly and accurately analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow”)
Regarding claim 2, Krishnan describes wherein the comprehensive determination unit determines a prognosis of the determination target part on the basis of a prognosis information database and the determined state of the determined target part ([0037]: “the CAD system (10) can be configured to make a determination, in view of a patient's clinical and family history, as to the likelihood that the patient has (or can develop) coronary artery disease”), in which state change information indicating a change in the state of the determination target part over time in a past is accumulated and stored ([0037]: “such determinations can be made using a training set as described above and machine-learning techniques).
Regarding claim 3, Krishnan describes wherein the medical information includes a plurality of medical images acquired by a plurality of different types of medical apparatuses ([0027]: “the input to the CAD system (10) comprises various sources of patient information including image data (1) in one or more imaging modalities (e.g., ultrasound image data, MRI data, nuclear medicine data, etc.)”).
Regarding claim 4, Krishnan describes wherein the analysis unit is configured to include a plurality of analysis learning models that correspond to the analysis items and that have been trained to output analysis results related to the determination target part for the corresponding analysis items on the basis of the medical information ([0012]: “CAD systems and methods for cardiac imaging implement machine-learning techniques that use a set of training data that is obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to properly and accurately analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow”).
Regarding claim 5, Krishnan describes
wherein the determination target part is a heart ([0020])
the analysis unit outputs a disease state related to each of a plurality of disease types related to the heart as the plurality of analysis results ([0020]: “automated assessment of regional myocardial function through wall motion analysis, automated diagnosis of heart diseases and conditions such as cardiomyopathy, coronary artery disease and other heart-related medical conditions”)
the comprehensive determination unit comprehensively determines a state of the heart of the subject on the basis of the disease state related to each of the plurality of disease types ([0020], [0022])
Regarding claim 6, Krishnan describes a display control unit that displays the medical information ([0040]), on which analysis by the analysis unit is based, and a determination result of the comprehensive determination unit on a display unit ([0045] - [0046]).
Regarding claim 7, Krishnan describes wherein the display control unit displays a screen for selecting ([0040]: “computer monitor with keyboard and mouse input devices”) either the plurality of pieces of medical information, on which analysis by the analysis unit is based, or the determination result of the comprehensive determination unit on the display unit ([0040] - [0041]).
Regarding claim 8, Krishnan describes wherein the display control unit displays the medical information in an aspect in which an importance of the medical information is capable of being discriminated on the basis of importance information indicating the importance of each type of the medical information in a case where the analysis result is output ([0028]: “the CAD system (10) can provide a confidence score or indicator of confidence for each regional assessment”; [0031]: “the CAD system (10) can determine and output a “score” (13) for each additional test or feature, which provides some measure or indication as to the potential usefulness of the particular imaging modality or feature(s) (including clinical data) that would improve the confidence of an assessment or diagnosis determined by the CAD system (10)”; [0046]: “in an exemplary embodiment wherein a scoring technique recommended by the ASE is used, the classification results (which include the ASE scores) can be displayed in a “bulls-eye” plot”).
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Conclusion
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Ankit D. Tejani, whose telephone number is 571-272-5140. The Examiner may normally be reached on Monday through Friday, 8:30AM through 5:00PM EST. 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, Carl Layno, can be reached by telephone at 571-272-4949. 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 at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (in USA or Canada) or 571-272-1000.
/Ankit D Tejani/
Primary Examiner, Art Unit 3796
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