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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 5, 2024, March 26, 2025, July 16, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant has amended claims 12-14, 16, and 18-24. Claim 17 is cancelled. Claims 12-16 and 18-25 are now being considered. Examiner has acknowledged the amended claims and withdraws the rejection under 35 U.S.C. 101. Applicant's arguments filed 7/23/2026 have been fully considered but they are not persuasive regarding the prior arts Shiino and Li for the rejections 35 U.S.C 103 for claims 13, 17-21, 23, 25.
Applicant’s arguments, filed 7/23/2026, with respect to the rejection(s) of claim(s) 12, 14-16, 22, 24 under 35 U.S.C 102(a)(2) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Li (United States Patent Application Publication US 2023/0260630 A1).
Applicant argues that Shiino fails to disclose the specific, integrated data retrieval and output framework, “accessing the prior knowledge database to read the explanatory text and the reliability rating of a predetermined prior data item corresponding to the specified data item of the subject data; and outputting (i) the prediction result of the brain disease of the subject, (ii) the specified data item of the subject data, and (iii) the explanatory text and the reliability rating of the predetermined prior data item read from the prior knowledge database, in association with each other.” In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Shiino and Li can be combined to arrive at the solution of the claimed invention.
Shiino does disclose “accessing the prior knowledge database to read the explanatory text and the reliability rating of a predetermined prior data item corresponding to the specified data item of the subject data; and outputting (i) the prediction result of the brain disease of the subject, (ii) the specified data item of the subject data,” in paragraphs [0173]-[0179], “the machine learning device is accessible to a diagnosis information database DB. The diagnosis information database DB stores brain images of multiple persons, and diagnosis results indicating whether each person is ADNC spectrum and whether each person is a healthy subject. In the present embodiment, the brain images are three-dimensional MRI images.” Shiino paragraph [0170] discloses “the machine learning device has the function of learning prediction algorithms for predicting the possibility that an ADNC subject will develop Alzheimer’s disease within a prescribed period (e.g. within 5 years).” Additionally, Shiino paragraph [0177] discloses “The learning unit comprises a first learning unit and a second learning unit. The first learning unit learns a prediction algorithm based on the teacher data, and stores the learned prediction algorithm in the auxiliary storage device.” Shiino fails to disclose (iii) the explanatory text and the reliability rating of the predetermined prior data item read from the prior knowledge database, in association with each other.
Li teaches (iii) the explanatory text and the reliability rating of the predetermined prior data item read from the prior knowledge database, in association with each other in Figures 11, 12, 19, 26, 37, and paragraphs [0019], [0117]-[0119]. The specific evidence-ranking framework utilized to interpret and validate the model’s output is essentially the knowledge gained from the physician’s training and prior literature research.
Thus, the prior arts have been reconsidered and applied to new grounds of rejection.
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.
Claim(s) 12-16 and 18-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shiino (United States Patent Application Publication US 2023/0162351 A1) in view of Li (United States Patent Application Publication US 2023/0260630 A1).
Regarding claim 12, Shiino discloses a diagnosis support device comprising: a processor: and a memory storing a prior knowledge database including a plurality of predetermined prior data items related to brain disease, each of which is associated with respective explanatory text and reliability rating; the processor configured to execute processing of acquiring subject data including both (i) a feature amount of a medical image related to a brain of a subject and (ii) subject information including neuropsychological test information of the subject; predicting a brain disease of the subject based on the subject data (Shiino Figure 1, 2, brain image acquisition unit, prediction algorithm); a specification unit specifying a data item corresponding to subject data, which contributes to a prediction result of the prediction unit, in the subject data (Shiino Figure 2, teacher data generation unit); and an output unit outputting the data item specified by the specification unit and prior knowledge related to the brain disease in association with each other (Shiino Figure 19, diagnosis assistance device with display); upon receiving a prediction result of the brain disease of the subject, calculating a degree of contribution of one or more data items of the subject data used as a basis for the prediction result; specifying a data item having the calculated degree of contribution higher than a predetermined contribution threshold value among the data items of the subject data, accessing the prior knowledge database to read the explanatory text and the reliability rating of a predetermined prior data item corresponding to the specified data item of the subject data (Shiino [0173]-[0179]: the machine learning device is accessible to a diagnosis information database DB. The diagnosis information database DB stores brain images of multiple persons, and diagnosis results indicating whether each person is ADNC spectrum and whether each person is a healthy subject. In the present embodiment, the brain images are three-dimensional MRI images.); and outputting (i) the prediction result of the brain disease of the subject, (ii) the specified data item of the subject data (Shiino [0170: the machine learning device has the function of learning prediction algorithms for predicting the possibility that an ADNC subject will develop Alzheimer’s disease within a prescribed period (e.g. within 5 years). [0177]: The learning unit comprises a first learning unit and a second learning unit. The first learning unit learns a prediction algorithm based on the teacher data, and stores the learned prediction algorithm in the auxiliary storage device.).
However, Shiino fails to disclose outputting (iii) the explanatory text and the reliability rating of the predetermined prior data item read from the prior knowledge database, in association with each other.
Li teaches to disclose outputting (iii) the explanatory text and the reliability rating of the predetermined prior data item read from the prior knowledge database, in association with each other (Li Figure 11, 12, 19, 26, 37, [0019], [0117]-[0119]).
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This is important to the claimed invention because the explanatory text allows the healthcare provider to view the diagnosis quickly and determine the next course of treatment based on the rated list. Since both Shiino and Li use MRI images as part of the learning algorithm for diagnosis prediction relating to brain disease, with prior knowledge gleaned from the doctor’s medical training and literature, it would have been obvious for one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Shiino and Li to arrive at the solution of the claimed invention.
Regarding claim 13, Shiino discloses a diagnosis support device according to claim 12. However, Shiino fails to disclose wherein the processor is configured to execute processing of: scaling the subject data, and predicting the brain disease of the subject based on the scaled subject data.
Li teaches wherein the processor is configured to execute processing of: scaling the subject data, and predicting the brain disease of the subject based on the scaled subject data (Li Figure 4, normalization unit performs normalizing processing of matching the head MRI image with the reference head MRI image). This step is important so that there is consistency between the image sizes for additional analysis. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Shiino and Li so that the device of Shiino has a scaling unit.
Regarding claim 14, Shiino discloses the diagnosis support device according to claim 12, wherein the processor is configured to acquire, as the subject data, an image feature amount calculated based on the medical image related to the brain of the subject (Shiino Figure 19, Diagnosis assistance device has MRI device image as input into the brain image acquisition unit).
Regarding claim 15, Shiino discloses the diagnosis support device according to claim 14, wherein the image feature amount includes a degree of atrophy of a part of the brain(Shiino [0126]-[0131], Figures 10-11).
Regarding claim 16, Shiino discloses the diagnosis support device according to claim 12, wherein the subject information includes at least one of test information and clinical information related to the brain of the subject (Shiino Figure 15, Machine learning device includes inputs from brain image and diagnosis result, with results then inputted to the diagnosis assistance device after prediction algorithm is applied).
Regarding claim 18, Shiino discloses the diagnosis support device according to claim 12. However, Shiino fails to disclose wherein the processing of outputting includes outputting (iv) a degree of association between the specified data item of the subject data and the predetermined prior data item in the prior knowledge database.
Li teaches wherein the processing of outputting includes outputting (iv) a degree of association between the specified data item of the subject data and the predetermined prior data item in the prior knowledge database (Li, [0119] Figure 12, derivation results in descending order of the first contribution are displayed at the display control unit). This is important to the claimed invention because the association between various images during the disease state progression and prior knowledge of the disease provides critical insight into the characteristic of the patient specific diagnosis and prognosis. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Shiino and Li so that the degree of association is noted in the device of Shiino.
Regarding claim 19, Li further discloses the diagnosis support device according to claim 18, wherein the processor is configured to: calculate the degree of association based on at least one of the calculated degree of contribution of the specified subject data and the reliability rating of the predetermined prior data item in the prior knowledge database (Li, [0116]-[0119], Figure 12).
Regarding claim 20, Shiino discloses the diagnosis support device according to claim 12. However, Shiino fails to disclose wherein the processing of outputting includes outputting a part of the medical image related to the brain of the subject corresponding to the specified data item of the subject data.
Li teaches wherein the processing of outputting includes outputting a part of the medical image related to the brain of the subject corresponding to the specified data item of the subject data (Li, Figure 29, output unit outputs first class data which is whether dementia is developed and second-class data which is the age of patient). This is important to the claimed invention because the output allows the physician to gather the data appropriately. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Shiino with the teachings of Li so that the output is reflected with the subject data.
Regarding claim 21, Shiino discloses the diagnosis support device according to claim 12. However, Shiino fails to disclose further comprising: a display displaying (i) the prediction result of the brain disease of the subject, (ii) the specified data item of the subject data, and (iii) the explanatory text and the reliability rating of the predetermined prior data item read from the prior knowledge database, in association with each other, wherein the specified data item includes two or more specified data items of the subject data which are displayed on the display in an order of the respective degree of contribution to the displayed prediction result of the brain disease of the subject.
Li teaches further comprising: a display displaying (i) the prediction result of the brain disease of the subject, (ii) the specified data item of the subject data, and (iii) the explanatory text and the reliability rating of the predetermined prior data item read from the prior knowledge database, in association with each other, wherein the specified data item includes two or more specified data items of the subject data which are displayed on the display in an order of the respective degree of contribution to the displayed prediction result of the brain disease of the subject (Li, Figure 26 diagnosis support application displays information related to the patient and contribution results). This is important to the claimed invention because the association between various images during the disease state progression and prior knowledge of the disease provides critical insight into the characteristic of the patient specific diagnosis and prognosis. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Shiino and Li so that the degree of association is noted in the device of Shiino.
Regarding claim 22, the rejection rationale of claim 12 is fully incorporated herein.
Shiino discloses a computer readable non-transitory recording medium recording a computer program causing a computer to execute a process comprising: acquiring subject data including both (i) a feature amount of a medical image related to a brain of a subject and (ii) subject information including neuropsychological test information of the subject; predicting a brain disease of the subject based on the subject data (Shiino Figure 1, 2, brain image acquisition unit, prediction algorithm); upon receiving a prediction result of the brain disease of the subject, calculating a degree of contribution of one or more data items of the subject data used as a basis for the prediction result (Shiino Figure 2, teacher data generation unit); specifying a data item having the calculated degree of contribution higher than a predetermined contribution threshold value among the data items of the subject data, accessing a prior knowledge database stored in a memory, the prior knowledge database including a plurality of predetermined prior data items related to brain disease, each of which is associated with respective explanatory text and reliability rating; reading the explanatory text and the reliability rating of a predetermined prior data item corresponding to the specified data item of the subject data; and outputting (i) the prediction result of the brain disease of the subject, (ii) the specified data item of the subject data, and (iii) the explanatory text and the reliability rating of the predetermined prior data item read from the prior knowledge database, in association with each other (Shiino Figure 19, diagnosis assistance device with display).
Regarding claim 23, Shiino discloses the computer readable non-transitory recording medium according to claim 22. However, Shiino fails to disclose wherein the computer program causes the computer to execute a process comprising: scaling the subject data; and predicting the brain disease of the subject based the scaled subject data.
Li teaches wherein the computer program causes the computer to execute a process comprising: scaling the subject data; and predicting the brain disease of the subject based the scaled subject data (Li Figure 4, normalization unit performs normalizing processing of matching the head MRI image with the reference head MRI image). This step is important so that there is consistency between the image sizes for additional analysis. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Shiino and Li so that the device of Shiino has a scaling unit.
Regarding claim 24, the rejection rationale of claim 12 is fully incorporated herein. Shiino discloses a diagnosis support method comprising: acquiring subject data including both (i) a feature amount of a medical image related to a brain of a subject and (ii) subject information including neuropsychological test information of the subject; predicting a brain disease of the subject based on the subject data (Shiino Figure 1, 2, brain image acquisition unit, prediction algorithm); upon receiving a prediction result of the brain disease of the subject, calculating a degree of contribution of one or more data items of the subject data used as a basis for the prediction result (Shiino Figure 2, teacher data generation unit); specifying a data item having the calculated degree of contribution higher than a predetermined contribution threshold value among the data items of the subject data, accessing a prior knowledge database stored in a memory, the prior knowledge database including a plurality of predetermined prior data items related to brain disease, each of which is associated with respective explanatory text and reliability rating; reading the explanatory text and the reliability rating of a predetermined prior data item corresponding to the specified data item of the subject data; and outputting (i) the prediction result of the brain disease of the subject, (ii) the specified data item of the subject data, and (iii) the explanatory text and the reliability rating of the predetermined prior data item read from the prior knowledge database, in association with each other (Shiino Figure 19, diagnosis assistance device with display).
Regarding claim 25, Shiino discloses the diagnosis support method according to claim 24. However, Shiino fails to disclose further comprising: scaling the subject data; and predicting the brain disease of the subject based on the scaled subject data.
Li teaches further comprising: scaling the subject data; and predicting the brain disease of the subject based on the scaled subject data (Li Figure 4, normalization unit performs normalizing processing of matching the head MRI image with the reference head MRI image). This step is important so that there is consistency between the image sizes for additional analysis. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Shiino and Li so that the device of Shiino has a scaling unit.
Conclusion
Response to Amendment
Examiner has acknowledged the amendments made to the claims and performed an updated search. The rejection under 35 U.S.C 101 has been withdrawn. However, further reconsideration of the prior arts Shiino and Li has produced new grounds of rejection under 35 U.S.C 103 as explained above.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 JESSICA YIFANG LIN whose telephone number is (571)272-6435. The examiner can normally be reached M-F 7:00am-6:15pm, with optional day off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JESSICA YIFANG LIN/Examiner, Art Unit 2668 August 26, 2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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