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 .
Response to Amendment
The amendment was filed on 1/12/2026.
Claims 1, 3-12, and 14-17 are pending. Claim 2 and 13 are canceled.
Response to Arguments
Applicants’ arguments filed under Remarks on pages 8-11 on 1/12/2026 have been fully considered but they are not persuasive. Applicants, in summarizing the entirety of the Remarks, on page 10 state that:
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The Examiner respectfully disagrees. Tatsuki discloses “identify a treatment method performed in a past case extracted based on a degree of similarity with the estimated diagnosis result as a candidate for a treatment method to be applied to the estimated diagnosis result”. Tatsuki discloses on page 9 first full paragraph that “the storage unit 23 stores patient information regarding the patient 42. FIG. 3 is a conceptual diagram showing an example of the contents of patient information. Patient information includes the age, gender, clinically identifiable symptoms, or test history of the patient 42. The patient information may include treatment history.” Therefore, we have the patient’s information including treatment history, which inherently includes treatment method being performed.
Tatsuki also discloses “output (a) the candidate for the treatment method” (Display unit 25 on page 8, output device 2, page 14 first full paragraph where in a case that the test is positive for influenza, treatment information is included, such as “prescribing and selection of anti-influenza drugs such as Tamiflu, Relenza, Inavir, Zofluza, or Rapiacta, the need for close examination, or the need for isolation.”) “and (b) an evaluation value for the candidate for the treatment method” (top of page 11 where likelihood and possibilities are assigned depending on a condition, “If the number of outbreaks of the disease by gender or age to which the patient 42 belongs is high, the possibility that the patient 42 is affected is high. If the number of outbreaks of the disease is high in the facility that the patient 42 usually uses, the possibility that the patient 42 is affected is high. In addition, if the time-series change in the number of occurrences tends to increase, the possibility that the patient 42 has a disease is high, and if the number of occurrences tends to decrease, the possibility that the patient 42 has a disease is low. When a particular disease is an infectious disease, there is a higher correlation between the number of occurrences or time-series changes in the number of occurrences of the disease in the area and the likelihood that patient 42 is affected”), as recited in claim 1.
Claim Rejections - 35 USC § 102
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.
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.
Claims 1, 4-9, 11-12 and 14-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by JP2020-160590A to Tatsuki.
With regard to claim 1 Tatsuki discloses an information processing apparatus (Fig. 1) comprising: one or more processors connected to one or more memories storing a program including instructions, which when executed by the one or more processor, cause the information processing apparatus (page 3 sixth paragraph, processor and memory, learning model 15, processing device 1, storage device 3, third paragraph on page 10) to function as: an estimation unit configured to estimate a diagnosis result for a medical image of a subject by using a learning model learned by using training data including a set of a medical image and a diagnosis result of the medical image (starting at top of page 8; “The diagnostic system 100 executes the learning model generation method.”; page 8 second full paragraph – “The first test gives the first test result indicating that patient 42 may be ill.” Last paragraph on page 9; page 12 first full paragraph, etc. throughout the reference); an identification unit configured to identify a treatment method performed in a past case extracted based on a degree of similarity with the estimated diagnosis result as a candidate for a treatment method to be applied to the diagnosis result (first and fourth full paragraph on page 9; last two paragraphs on page 10; Fig. 11, step S23; bottom of page 12; etc. throughout the reference; on page 9 first full paragraph that “the storage unit 23 stores patient information regarding the patient 42. FIG. 3 is a conceptual diagram showing an example of the contents of patient information. Patient information includes the age, gender, clinically identifiable symptoms, or test history of the patient 42. The patient information may include treatment history.”); and an output unit configured to (a) output the candidate for the treatment method and (b) an evaluation value for the candidate for the treatment method (output device 2, display unit 25, page 8 last four paragraphs; page 9 first full paragraph; Fig. 8 and it’s respective description on page 10; top two paragraphs on page 11, page 12 second to last paragraph and so on throughout the reference).
With regard to claim 4 Tatsuki discloses wherein the diagnosis result is an identification result for at least one of following: presence or absence of a disease; a severity of the disease; a type of the disease; presence or absence of metastasis; a location of the metastasis; a location of a tumor; a size of the tumor; and a number of tumors (page 8 first three paragraphs).
With regard to claim 5 Tatsuki discloses wherein the learning model is constructed so as to include a neural network that performs deep learning using training data including a set of a medical image and a diagnosis result of the medical image (Fig. 9, second and fourth paragraph on page 11).
With regard to claim 6 Tatsuki discloses a correction unit configured to correct the diagnosis result estimated by the estimation unit by inputting the medical image of the subject captured by a first imaging apparatus into a first learning model, using an output result obtained by inputting a medical image captured by a second imaging apparatus into a second learning model (re-learning model for example, Fig. 14, last two paragraphs on page 13; top of page 14).
With regard to claim 7 Tatsuki discloses a correction unit configured to correct the diagnosis result estimated by the estimation unit based on at least one of following information: a diagnosis result for a medical image captured by an imaging apparatus different from the imaging apparatus by which the medical image is captured; subject information of the subject; and an imaging condition under which the image of the subject is captured (re-learning model for example, Fig. 14, last two paragraphs on page 13; top of page 14).
With regard to claim 8, claim 8 is rejected same as claim 1 and the arguments similar to that presented above for claim 1 are equally applicable to claim 8. Tatsuki discloses display control unit (display 25, page 8, bottom of page 12 to top of page 13), and all of the other limitations similar to claim 1 are not repeated herein, but incorporated by reference.
With regard to claim 9 Tatsuki discloses wherein the identification unit identifies, as candidates for the treatment method to be applied to the diagnosis result, a first treatment method and an evaluation value for the first treatment method, and a second treatment method and an evaluation for the second treatment method, and the display control unit displays the evaluation value for the first treatment method that is one of candidates for the treatment method and the evaluation value for the second treatment method that is one of candidate for the treatment method on the display unit in a parallel manner, a superimposed manner, a superimposed manner, or a switchable manner (last two paragraphs on page 10; Fig. 9 page 11; top of page 15 where score value is disclosed; and second test result and treatment information, non-numerical information is converted into a numerical value are processed).
With regard to claim 11 Tatsuki discloses wherein the display control unit further displays supplementary information related to the evaluation value on the display unit (display 25 outputs the value; Fig. 2; Fig. 9, pages 10-15).
With regard to claims 12 and 14, claims 12and 14 are rejected same as claims 1 and 8 and the arguments similar to that presented above for claims 1 and 8 are equally applicable to claims 12 and 14, and all of the other limitations similar to claims 1 and 8 are not repeated herein, but incorporated by reference.
With regard to claim 15 Tatsuki discloses wherein the display control unit displays on a display unit an evaluation value for at least one of following indicators: a survival rate; a cost; a side effect; an effect on appearance; an effect on fertility; and a low recurrence rate (Fig. 9, second and third full paragraphs page 11; bottom paragraphs on page 12, and throughout the reference).
With regard to claims 16-17, claims 16-17 are rejected same as claims 1 and 8 and the arguments similar to that presented above for claims 1 and 8 are equally applicable to claims 16-17, and all of the other limitations similar to claims 1 and 8 are not repeated herein, but incorporated by reference.
Claim Rejections - 35 USC § 103
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.
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 of this title, 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.
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 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over JP2020-160590A to Tatsuki in combination with JP2020-48685A to Yoshimasa.
With regard to claims 3 and 10, Tatsuki teaches an information processing/display apparatus as discussed above in claims 1 and 8. However, Tatsuki does not expressly teach that wherein in a case where there are two or more candidates for the treatment method, the output unit outputs at least two candidates for the treatment method, while in a case where there is only one candidate for the treatment method, the output unit outputs the only one candidate for the treatment method together with information indicating that there are no other candidates for the treatment method, as recited in claim 3 and corresponding features in claim 10.
Tatsuki starting on page 10, Fig. 6, teaches teacher database that contains multiple teacher data. One teacher data includes the results of the first test and the second test of the second test performed on a patient 42, the patient information of the same patient 42, and the medical facility 4 visited by the patient 42.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify Tatsuki’s reference to have the condition knowing that having multiple data to output multiple but only do it for one if there is only one data. The suggestion/motivation for doing so would have been to efficiently output data for each patient for example and provide treatment plans accordingly.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to obtain the invention as specified in claims 3 and 10.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHEFALI D. GORADIA whose telephone number is (571)272-8958. The examiner can normally be reached on Monday-Thursday 8AM-6PM, Friday 8AM-12PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached on 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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SHEFALI D. GORADIA
Primary Patent Examiner
Art Unit 2676
/SHEFALI GORADIA/
Primary Examiner, Art Unit 2665
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