DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
2. Claim 15 is objected to because of the following informalities: limitation “classifying the determination result using the correct label based on a user input received with respect to the displayed screen” is missing. Appropriate correction is required.
Claim Rejections - 35 USC § 102
3. 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 (i.e., changing from AIA to pre-AIA ) 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.
4. 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)(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.
5. Claims 1, 3-6, 8-11, 13-15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by WAKE U.S. publication No.2022/0309400 (hereinafter WAKE).
Regarding claim 1, WAKE discloses an information processing system (a position estimation system, Figure 2) comprising:
at least one memory storing a computer program (a CPU or a processor reading out and executing a software program recorded on a hard disk or on a recording medium such as a semiconductor memory; paragraphs 91, 155); and
at least one processor configured to execute the computer program to (a CPU or a processor reading out and executing a software program recorded on a hard disk; paragraph 155):
acquire a plurality of determination results for data in chronological order, the plurality of determination results being output from a learning model (acquiring the object detection result and calculates an evaluation value on the basis of a difference between the acquired object detection result and the correct information. The object detection result includes the detected class, the detected class indicating the class of the object obtained by inputting the learning image into the learning model that receives input of an image and outputs the object detection result; paragraph 101, Figures 6A-6C illustrating an arrangement order of deviation events, and see the rest of reference);
detect deviation of a defemination result among the plurality of determination results (calculating the evaluation value such that a deviation of a specific target to be detected, among targets to be detected, has a relatively greater influence on the evaluation value than deviations of the other targets to be detected have on the evaluation value; paragraphs 102-104, Figures 6A-6C); and
acquire a correct label associated with the data in which the deviation is detected (a plurality of labels is included in the class, learning device 40 is capable of generating a trained model with improved accuracy of detecting a specific label; paragraphs 138, paragraph 144 indicating that calculating an evaluation value such that a deviation of the class of importance, among a plurality of detected classes).
Regarding claim 3, WAKE discloses the information processing system according to claim 1, wherein the data is at least one image (acquiring a learning image and correct information; paragraphs 94, 101 and see the rest of reference).
Regarding claim 4, WAKE discloses the information processing system according to claim 1, wherein the at least one processor is further configured to execute the instructions to: detect deviation in the determination result based on a previous determination result before the determination result and a subsequent determination result after the determination result (adjusting parameters of a learning model so as to reduce an evaluation value that quantifies a deviation of the estimated box (e.g., see FIG. 6B) from the correct box (e.g., see FIG. 6A), and a difference between a detected class and a correct class. The evaluation value indicates object detection performance of the learning model; paragraphs 83-112, Figures 6A-6C).
Regarding claim 5, WAKE discloses the information processing system according to claim 1, wherein the at least one processor is further configured to execute the instructions to: generate data to control a displayed screen for selection of the correct label from among multiple predetermined labels; and classify the determination result using the correct label based on a user input received with respect to the displayed screen paragraphs 83-112, Figures 6A-6C, and see the rest of reference).
Regarding claims 6, 8, 9, 10, claims 6, 8, 9, 10 are the method claims of device claims 1, 3, 4, 5, respectively. Therefore, method claims 6, 8, 9, 10 are rejected for the reason given in device claims 1, 3, 4, 5.
Regarding claims 11, 13, 14, 15, claim 11, 13, 14, 15 are directed to a non-transitory computer-readable recording medium, and recites identical features as claims 1, 3, 4, 5. Thus, claim 11, 13, 14, 15 are rejected for the same reasons discussed in claims 1, 3, 4, 5 above.
Claim Rejections - 35 USC § 103
6. 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.
7. Claims 2, 7, 12 are rejected under 35 U.S.C. 103 as being unpatentable over WAKE in view of NAVRATIL et al. U.S. publication No. 2019/0213502 (hereinafter NAVRATIL)
Regarding claim 2, WAKE discloses the information processing system according to claim 1, wherein the at least one processor execute the instructions to: calculate a deviation based on the plurality of the determination results (paragraphs 34, 83, Figure 6C).
WAKE does not explicitly disclose wherein the detecting the deviation comprises detecting the deviation based on the metric.
However, NAVRATIL teaches wherein the detecting the deviation comprises detecting the deviation based on the metric (Applying to detect the types of deviation metrics that result in some contexts of machine learning environments; paragraphs 30-31).
In view of the above, it would have been obvious to one having ordinary skill in the art at the time of the invention was made to combine the system of WAKE as taught by NAVRATIL to include wherein the detecting the deviation comprises detecting the deviation based on the metric. By doing so, the combined system of NAVRATIL would have generated an optimized feature space that yielded the highest confidence from the learning model for all metrics of deviations.
Regarding claim 7, claim 7 is the method claims of device claim 2. Therefore, method claim 7 is rejected for the reason given in device claim 2.
Regarding claim 12, claim 12 is directed to a non-transitory computer-readable recording medium, and recites identical features as claim 2. Thus, claim 12 is rejected for the same reasons discussed in claim 2 above.
Information Disclosure Statement
8. The information disclosure statement (IDS) submitted on 11/13/2024 and 02/20/2025 were filed in compliance with the provisions of 37 CFR 1.97 and 1.98. Accordingly, the information disclosure statement is being considered by the examiner.
Cited Art
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kunio et al. (US 2022/0346885) discloses an artificial intelligence detection apparatus comprising: one or more processors that operate to: acquire or receive angiography image data; receive a trained model or load a trained model from a memory; apply the trained model to the acquired or received angiography image data; select one angiography frame; detect a marker location on the selected angiography frame with the trained model, the detected marker location defining detected results; check whether the marker location is correct or accurate; in an event that the marker location is not correct or accurate, then modify the detected results or the detected marker location, and repeat the check as to whether the marker location is correct or accurate, or in an event that the marker location is correct or accurate, then check whether all of the angiography frames have been checked for correctness or accuracy; and in an event that all of the angiography frames have not been checked for correctness or accuracy, then select another angiography frame and repeat the detection of a marker location and the check of whether the marker location is correct or accurate or not for the another angiography frame, wherein the one or more processors further operate to one or more of the following: (i) in an event that all of the angiography frames have been checked for correctness or accuracy, then perform registration based on the detected marker location; (ii) display the detected marker location on a display; (iii) display the detected marker location on the display such that the detected marker location is overlayed on angiography data; (iv) display the modified detected results and/or the modified marker location on the display; (v) insert an intravascular imaging catheter that has a marker or radiopaque marker into an object or sample; and/or (vi) acquire or receive the angiography image data during a pullback operation of the intravascular imaging catheter, wherein the one or more processors further operate to use one or more neural networks or convolutional neural networks to one or more of: load the trained model, select the angiography frame, detect the marker location for each frame, determine whether the detected marker location is accurate or correct, modify the detected results or the detected marker location for each frame, display the detected marker location on the display, perform the registration, insert the intravascular image, and/or acquire or receive the angiography image data during the pullback operation.
Singh et al. (US 2022/0293219) discloses a method, comprising: sampling a video of a set of gametes; for individual gametes in the set of gametes: tracking the gamete through the video; extracting a sub-video of the gamete from the video; and automatically determining a specialist selection probability score and a DNA fragmentation index (DFI) score for the gamete based on the sub-video using a set of trained machine learning models; and selecting a target gamete from the set of gametes based on the specialist selection probability score and the DFI score for the target gamete, wherein selecting the target gamete comprises: selecting a subset of gametes from the individual gametes based on the specialist selection probability score for each gamete in the subset; and selecting the target gamete from the subset of gametes based on the DFI score for the target gamete.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLEN H NGUYEN whose telephone number is (571)270-1229. The examiner can normally be reached M-F 7 am-4 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ABDERRAHIM MEROUAN can be reached at (571) 270-5254. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALLEN H NGUYEN/Primary Examiner, Art Unit 2683
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