DETAILED ACTION
Claims 14, 16 and 18-22 are pending in this 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 .
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.
Claims 14, 16, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (CN-102819023-A) [hereinafter “Chen”] in view of Yu et al. (WO-2010032297-A1) [hereinafter “Yu”] in further view of Rogan (US PGPUB No. 2019/0378282)
As per claim 14, Chen teaches an information processing apparatus comprising at least one processor, the at least one processor carrying out: an obtaining means for process of obtaining input data which includes at least one of image data ([0014], using image data as input) and point cloud data ([0004] and [0037], using point cloud data to remove vegetation from input data); and an estimating means for process of estimating levels of importance with respect to a respective plurality of characteristic which are included in a frame indicated by the input data ([0092], estimating levels of importance in characteristic of an image and changing associated pixels to create training set see [0012]), the estimating means having with use of an inference model which has been trained with reference to replaced data that has been obtained by replacing at least one of the plurality of characteristic, which are included in the input data, with alternative data ([0012], changing elements of a pixel data set associated with a characteristic to create a training set used to train whether a image is landslide or non-landslide) in accordance with the levels of importance ([0011], pixels chosen based on importance determination see [0092]).
Chen does not explicitly teach estimating levels of importance with respect to a respective plurality of regions which are included in a frame. Yu teaches estimating levels of importance with respect to a respective plurality of regions which are included in a frame (Abstract, marking areas of an image with levels of importance).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Chen with the teachings of Yu, estimating levels of importance with respect to a respective plurality of regions which are included in a frame, to directly mark areas in an image that are of interest for training purposes.
The combination of Chen and Yu does not explicitly teach wherein the alternative data includes at least one of noise and the image data or the point cloud data that has a large quantization error, and the inference model has been trained by optimizing an evaluation value derived with reference to an output from a predetermined controller into which the replaced data has been inputted. Rogan teaches wherein the alternative data includes at least one of noise and the image data ([0059], noise data associated with data from camera sensor, i.e. image) or the point cloud data that has a large quantization error (Examiner Note: this is an optional feature and might overcome the current rejection – Examiner notes that point tracking via motion sensor data is taught by Rogan which might be relevant), and the inference model has been trained by optimizing an evaluation value derived with reference to an output from a predetermined controller into which the replaced data has been inputted ([0059], parameters based on a updated state based on received sensor data).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Chen and Yu with the teachings of Rogan, wherein the alternative data includes at least one of noise and the image data or the point cloud data that has a large quantization error, and the inference model has been trained by optimizing an evaluation value derived with reference to an output from a predetermined controller into which the replaced data has been inputted, to directly mark areas in an image that are of interest for training purposes.
As per claim 16, the substance of the claimed invention is identical or substantially similar to that of claim 14. Accordingly, this claim is rejected under the same rationale.
As per claim 18, the substance of the claimed invention is identical or substantially similar to that of claim 14. Accordingly, this claim is rejected under the same rationale.
As per claim 20, the combination of Chen, Yu and Rogan teaches the information processing apparatus as set forth in claim 14, wherein: the at least one processor further carries out an evaluating process of deriving an evaluation value by referring to the replaced data; and in the evaluating process, the at least one processor derives the evaluation value with reference to an output obtained from a controller of a movable body into which the replaced data has been inputted (Chen; [0012], generated training set is inputted back into system along with LiDAR component, [0004], which is airborne radar and thus has a moveable body).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Chen, Yu and Rogan in further view of Jiang et al. (CN-112257647-A) [hereinafter “Jiang”].
As per claim 19, the combination of Chen, Yu and Rogan teaches the information processing apparatus as set forth in claim 14.
The combination of Chen, Yu and Rogan does not explicitly teach wherein, in the estimating process, the inference model estimates the levels of importance with use of a self-attention module. Jiang teaches wherein, in the estimating process, the inference model estimates the levels of importance with use of a self-attention module (Abstract, using self-attention mechanism to evaluate importance of image data).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Chen, Yu and Rogan with the teachings of Jiang, wherein, in the estimating process, the inference model estimates the levels of importance with use of a self-attention module, to improve the accuracy and relevance of the input image data.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Chen and Yu in further view of Jiang et al. (CN-112257647-A) [hereinafter “Jiang”].
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Chen, Yu and Rogan in further view of Yeh et al. (WO-2019128971-A1) [hereinafter “Yeh”].
As per claim 21, the combination of Chen, Yu and Rogan teaches the information processing apparatus as set forth in claim 20.
The combination of Chen, Yu and Rogan does not explicitly teach wherein: the at least one processor further carries out a training process of training the inference model with reference to the evaluation value; the evaluation value includes a reward value derived from the output; and in the training process, the at least one processor trains the inference model so that the reward value becomes high. Yeh teaches wherein: the at least one processor further carries out a training process of training the inference model with reference to the evaluation value (Page 6, para. 1, using a reward function to train a neural network in manipulating cell image data); the evaluation value includes a reward value derived from the output; and in the training process, the at least one processor trains the inference model so that the reward value becomes high (Page 6, para. 1, reward is maximized to train model).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Chen, Yu and Rogan with the teachings of Yeh, wherein: the at least one processor further carries out a training process of training the inference model with reference to the evaluation value; the evaluation value includes a reward value derived from the output; and in the training process, the at least one processor trains the inference model so that the reward value becomes high, to improve the accuracy and relevance of the input image data.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Chen, Yu and Rogan in further view of Shao et al. (WO-2018121690-A1) [hereinafter “Shao”].
As per claim 22, the combination of Chen, Yu and Rogan teaches the information processing apparatus as set forth in claim 20.
The combination of Chen, Yu and Rogan does not explicitly teach wherein: the at least one processor further carries out a training process of training the inference model with reference to the evaluation value; the evaluation value is a loss value derived from the output; and in the training process, the at least one processor trains the estimating means so that the loss value becomes low. Shao teaches wherein: the at least one processor further carries out a training process of training the inference model with reference to the evaluation value (Page 8, para. 4-6, training model based on determined loss value); the evaluation value is a loss value derived from the output see id; and in the training process, the at least one processor trains the estimating means so that the loss value becomes low (Page 8, para. 6, minimizing loss value below average value).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Chen, Yu and Rogan with the teachings of Shao, wherein: the at least one processor further carries out a training process of training the inference model with reference to the evaluation value; the evaluation value is a loss value derived from the output; and in the training process, the at least one processor trains the estimating means so that the loss value becomes low, to improve the accuracy and relevance of the input image data.
Response to Arguments
Applicant’s arguments with respect to the rejection of claims 14, 16 and 18-22 under 35 U.S.C. 103 have been fully considered and in light of the new amendments, a new prior art reference, Rogan has been introduced and cited to.
To expedite prosecution, Examiner is open to an after-final interview to discuss claim amendments to overcome the current rejection and/or place the application in condition for allowance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhang et al. (US PGPUB No. 2024/0005626), Temple et al. (US PGPUB No. 2024/0221172), Matus et al. (US PGPUB No. 2018/0077538), Yang et al. ("Point cloud data enhancement based on layer connected region," 2014 International Conference on Audio, Language and Image Processing, Shanghai, China, 2014, pp. 600-604, doi: 10.1109/ICALIP.2014.7009865), Zhong et al. ("Optimisation of continuous overlapping point cloud data for laser cladding," 2024 7th International Conference on Data Science and Information Technology (DSIT), Nanjing, China, 2024, pp. 1-5, doi: 10.1109/DSIT61374.2024.10881872) and Zhang et al. ("Research on Point Cloud Data Preprocessing for Unmanned LiDAR," 2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 2024, pp. 617-621, doi: 10.1109/IAEAC59436.2024.10504008) all disclose various aspects of the claimed invention including determining importance levels in an image using point cloud data.
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 PETER C SHAW whose telephone number is (571)270-7179. The examiner can normally be reached Max Flex.
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/PETER C SHAW/Primary Examiner, Art Unit 2493 July 17, 2026
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