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 .
Priority
Acknowledgement is made of Applicant’s claim of the present application being a continuation of PCT International Patent Application No. PCT/JP2022/014071, filed on March 24, 2022, which claims priority under 35 U.S.C. 119(a) to Japanese Patent Application No. JP2021-100612, filed on June 17, 2021.
Information Disclosure Statement
The information disclosure statements (“IDS”) filed on 03/05/2024, 08/13/2024, and 10/07/2025 were reviewed and the listed references were noted.
Drawings
The 18-page drawings have been considered and placed on record in the file.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Status of Claims
Claims 1-12 are pending.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
a. Determining the scope and contents of the prior art.
b. Ascertaining the differences between the prior art and the claims at issue.
c. Resolving the level of ordinary skill in the pertinent art.
d. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2 and 7-12 are rejected under 35 U.S.C. 103 as being unpatentable over Chung II Ahn (US 2020/0202523 - IDS) in view of Lee et al. (US 2014/0101080 - IDS).
Consider Claim 1 (and similarly method Claim 11 and CRM Claim 12), Ahn discloses “A medical image diagnostic system comprising: a processor; and a memory that stores one or more commands executed by the processor” (Ahn, Paragraph [0018], the processor and Paragraph [0079], the computer-readable storage medium), “wherein the processor acquires a first discrimination result from a first discrimination device that executes first discrimination intended for a predetermined site and a predetermined lesion, on a medical image” (Ahn, Paragraph [0018] and [0020], the first diagnostic algorithm, first discrimination device, diagnosing suspicious lesion locations), “acquires a second discrimination result from a second discrimination device that executes second discrimination intended for the predetermined site and the predetermined lesion, which are the same as those for which the discrimination of the first discrimination device is intended, on the medical image” (Ahn, Paragraph [0018] and [0020], the second diagnostic algorithm, second discrimination device, diagnosing suspicious lesion locations), “discrimination device to the integrated discrimination result and a degree of contribution of the second discrimination device to the integrated discrimination result based on the integrated discrimination result and a definitive diagnosis result by a doctor” (Ahn, Paragraph [0048] discloses generation of scores for each of the plurality of diagnosis application algorithm; Paragraph [0049] discloses “The processor of the computing system 100 extracts a diagnosis requirement for the medical image by analyzing the medical image at step S210. The processor selects the plurality of diagnosis application algorithms based on the diagnosis requirement from among the plurality of medical image diagnosis algorithms to analyze the medical image at step S220”; and finally, Paragraph [0053] discloses “In the medical image diagnosis assistant apparatus according to the present invention, the I-scores 170, i.e., evaluation scores, are transferred from the computing system 100 to the reading computing terminal of the medical staff at step S180. Final diagnosis reports may be generated by incorporating the I-scores 170, i.e., evaluation scores, into the generation of the diagnosis results at step S130. According to an embodiment of the present invention, the computing system 100 may generate diagnosis reports together with the I-scores 170, i.e., evaluation scores, and transfer them to the computing system of the medical staff at step S180. In this case, the diagnosis reports generated by the computing system 100 may be written using the diagnosis results based on diagnosis application algorithms having higher I-scores 170, i.e., higher evaluation scores.”) and a definitive diagnosis result by a doctor (Ahn, Paragraph [0055] discloses “when different diagnosis results are obtained by applying different diagnosis application algorithms to the same medical image, this plurality of diagnosis results are displayed such that they can be compared with each other, and the user may select any one of the first diagnosis result and the second diagnosis result and generate it as a final diagnosis result.” In addition, Paragraphs [0081]-[0082] discloses a user’s (doctor’s) comparison of the results and verify his/her diagnosis through this comparison) Although Ahn discloses “The processor may assign weights to the confidence scores of each of the plurality of medical image diagnosis algorithms, the accuracy scores of each of the plurality of medical image diagnosis algorithms, and the evaluation confidence scores of each of the plurality of medical image diagnosis algorithms by the user” (Ahn, Paragraph [0064]), Ahn does not explicitly disclose “derives an integrated discrimination result in which the first discrimination result and the second discrimination result are integrated”. However, in an analogues field of endeavor, Lee discloses “an integrated model generation unit configured to generate an integrated diagnostic model based on the selected one or more diagnostic model”.
Accordingly, before the effective date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Ahn with teachings of Lee to generate a diagnosis model by integrating the results from the two discriminator devices. One of ordinary skill in the art would be motivated to combine Ahn and Lee to place additional weight to the more accurate results when combining the generated two results. Therefore, it would have been obvious to combine Ahn and Lee to obtain the invention of Claim 1.
Consider Claim 2, the combination of Ahn and Lee discloses “The medical image diagnostic system according to claim 1, wherein the processor derives the integrated discrimination result by using an integrated discrimination device, which is a trained learning model that has trained using a set of the medical image and a correct answer image in which a lesion is detected from the medical image, as learning data” ( , Paragraph [0010] discloses “The model generation unit may include: a data collection unit configured to collect the learning data from prior patients or medical database; a data categorization unit configured to categorize the learning data into the one or more categories according to a predefined set of rules; a model learning unit configured to generate the categorized diagnostic models by learning the categorized learning data; and a model storage unit configured to store the categorized diagnostic models”).
Accordingly, before the effective date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Ahn with teachings of Lee to train the learning model with a set of medical images with the known answers regarding identified lesions. One of ordinary skill in the art would be motivated to combine Ahn and Lee to create a robust automatic diagnostic engine for detection and classification of lesions in an image. Therefore, it would have been obvious to combine Ahn and Lee to obtain the invention of Claim 2.
Consider Claim 7, the combination of Ahn and Lee discloses “The medical image diagnostic system according to claim 1, wherein the processor causes a display device to display at least one of the first discrimination result or the second discrimination result” (Ahn, Paragraph [0076] discloses “the results obtained by applying artificial intelligence algorithms (the diagnosis application algorithms) to the medical image may be displayed on the reading computing terminal of the medical staff at step S120”).
Consider Claim 8, the combination of Ahn and Lee discloses “The medical image diagnostic system according to claim 1, wherein the processor acquires input information representing the definitive diagnosis result by the doctor, which is input by using an input device” (Ahn, Paragraphs [0084]-[0085] discloses: “The evaluation of recommended artificial intelligence algorithms by a user may be related to the charging system of an evaluation system inside the diagnosis recommendation system.” And, “a user may obtain information about the clinical usefulness of medical image diagnosis algorithms in the process of generating a final diagnosis result, and the information about the clinical usefulness may be fed back to the diagnosis recommendation system of the present invention” (emphasis added), interpreted as the input by the doctor).
Consider Claim 9, the combination of Ahn and Lee discloses “The medical image diagnostic system according to claim 1, further comprising: an image storage device that stores the medical image, wherein the processor acquires the medical image from the image storage device” (Ahn, Claim 1 discloses “a plurality of medical image diagnosis algorithms each having a diagnostic function of a medical image in memory or a database” (emphasis added)).
Consider Claim 10, the combination of Ahn and Lee discloses “The medical image diagnostic system according to claim 1, wherein the medical image diagnostic system includes the first discrimination device and the second discrimination device” (Ahn, Paragraph [0017] discloses “The processor is further configured to select a plurality of diagnosis application algorithms based on the diagnosis requirement to analyze the medical image from among the plurality of medical image diagnosis algorithms” (emphasis added)).
Allowable Subject Matter
Claims 3-6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The following is the examiner’s stated reason for indication of allowable subject matter: none of the cited prior art references, alone or in combination, provides a motivation to teach the ordered combination of the limitations recited in Claims 3-6.
Conclusion and Contact
The prior art references made of record and not relied upon are considered pertinent to Applicant’s disclosure: Morita et al. (US 2004/0100476) and Sadato Akahori (US 2020/0104996).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Siamak HARANDI whose telephone number is (571)270-1832. The examiner can normally be reached Monday - Friday 9:30 - 6:00 ET.
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, Amandeep Saini can be reached on (571)272-3382. 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.
/Siamak Harandi/Primary Examiner, Art Unit 2662