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 .
2. It would be of great assistance to the office if all incoming papers pertaining to a filed application carried the following items:
i. Application number (checked for accuracy, including series code and serial no.).
ii. Group art unit number (copied from most recent Office communication).
iii. Filing date.
iv. Name of the examiner who prepared the most recent Office action.
v. Title of invention.
vi. Confirmation number (See MPEP § 503).
Response to Arguments
3. Applicant's arguments with respect to claims have been considered but are moot in view of the new ground(s) of rejection.
4. The Examiner has pointed out particular references contained in the prior art of record within the body of this action for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages, paragraph and figures may apply. Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
5. Claim interpretation: When multiple limitations are connected with “OR”, one of the limitations doesn’t have any patentable weight since both of the limitations are optional.
Claim Rejection- 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 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.
Claims 1-4, 7-10 & 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Matsuda et al (Pub No. 2019/0351900) and further in view of Miyatani et al (Pub No. 2019/0220676) and further in view of Russel et al (US 2020/0175759).
Regarding claim 1, Matsuda et al discloses a method comprising: acquiring a moving route of a moving object (Para. 23: A movement route calculating unit calculates a planned route to travel when the own vehicle 2); generating a superimposed image by superimposing an image representing the moving object (Para. 58 & 73: superimposing an image representing the moving object) and an image representing an environment related to moving including the moving route on each other (Para. 24 & 73: an image representing an environment & abstract & Fig. 1 & 5); acquiring a label information about the location and position of the moving object (Para. 73-75: Text information of the surroundings vehicle stop position).
Matsuda is silent regarding generating a learning data set including the superimposed image and the label information.
Miyatani et al discloses generating a learning data set including the superimposed image and the label information (Para. 29 & 32-33: Learning model from input images, depth maps, information output from a learning model) & (Para. 64 & Para. 112).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the invention to use the generating learning model based on image data of of Miyatani’s disclosure with the driving assistance apparatus, as taught by Matsuda. Doing so would have resulted in effectively analyze vehicle environment and using a learning model to help vehicle for autonomous driving.
Matsuda is also silent regarding an image or three dimensional data data representing an appearance of the moving object and an image or three-dimensional data representing an appearance of an environment.
Russel et al disclose an image or three-dimensional data representing an appearance of the moving object (Para. 43: 3D environment with 3D non-stationary 3D object-human 308 appearance) & (Para. 32-33: 3D data 104 with dynamic object data) and an image or three-dimensional data representing an appearance of an environment (Para. 32-33: 3D data of the environment) (Fig. 3A: appearance of an environment) and data set including the superimposed image (Para. 60: a dynamic object is superimposed into the new image).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the invention to use the 3D environmental model to identify a surrounding environment and identify any moving item in the environment efficiently.
Regarding claim 15, Claim 15 corresponds to claim 1 and is analyzed accordingly.
Regarding claim 2, Matsuda et al discloses conducting a predetermined work on the image about the environment related to moving before the generating the superimposed image (Para. 73-74: superimposing an image representing the moving object on an environment).
Regarding claim 3, Matsuda et al discloses conducting a predetermined work on the image or the three-dimensional data about the moving object before the generating the superimposed image (Para. 73-74: superimposing an image).
Regarding claim 4, Matsuda et al discloses conducting a predetermined work on the superimposed image (Para. 73-74: superimposing an image).
Regarding claim 7, Matsuda et al discloses the generating the superimposed image is performed a plurality of times, and a difference is made in the location of the moving object in a direction parallel to the moving route between at least two of a plurality of the superimposed images generated by performing the generating the superimposed image the plurality of times (Para. 58 & 71-73: superimposed image is performed a plurality of times, and a difference is made in the location of the moving object).
Regarding claim 8, Matsuda et al discloses the generating the superimposed image is performed a plurality of times, and a difference is made in the location of the moving object in a direction vertical to the moving route between at least two of a plurality of the superimposed images generated by performing the generating the superimposed image the plurality of times (Para. 58 & 71-73: superimposed image is performed a plurality of times, and a difference is made in the location of the moving object).
Regarding claim 9, Matsuda et al discloses the generating the superimposed image is performed a plurality of times, and a difference is made in the position of the moving object relative to the moving route between at least two of a plurality of the superimposed images generated by performing the generating the superimposed image the plurality of times (Para. 58 & 71-73: superimposed image is performed a plurality of times, and a difference is made in the location of the moving object).
Regarding claim 10, Matsuda et al discloses the generating the superimposed image is performed a plurality of times, and a difference is made in a color of the moving object between at least two of a plurality of the superimposed images generated by performing the generating the superimposed image the plurality of times (Para. 58 & 71-73: superimposed image is performed a plurality of times, and a difference is made in the location of the moving object).
Regarding claim 12, Matsuda is silent regarding performing machine learning using a learning data set group including the learning data set.
Miyatani et al discloses performing machine learning using a learning data set group including the learning data set (Fig. 12 & Para. 32).
At the time of filling, it would have been obvious to use machine learning system to improve self-driving by using image data.
Regarding claim 13, Matsuda et al discloses acquiring information about the location and position of the moving object during a move in the environment related to moving using a learned model generated by the performing machine learning and a captured image of the moving object during the move in the environment related to moving (Abstract & Para. 73).
Regarding claim 14, Matsuda teaches the generating the superimposed image is performed a plurality of times (73: superimposing an image).
Matsuda is silent regarding the method further comprises performing machine learning using a learning data set group including a plurality of the learning data sets, determining a particular location among a plurality of locations on the moving route, the particular location is a location where accuracy of output of a learned model generated by the performing machine learning is comparatively low, and the number of the learning data sets corresponding to the particular location is increased compared to another location.
Miyatani et al discloses the method further comprises performing machine learning using a learning data set group including a plurality of the learning data sets (Fig. 12 & Para. 32), determining a particular location among a plurality of locations on the moving route (Para. 33), the particular location is a location where accuracy of output of a learned model generated by the performing machine learning is comparatively low (Para. 70-72 & Fig. 10: low accuracy), and the number of the learning data sets corresponding to the particular location is increased compared to another location (Para. 70-72 & Fig. 10: higher accuracy). Note: Increase training data in low accuracy areas. Target training for weak prediction is standard in machine learning system. In this way machine learning can be improved.
At the time of filling, it would have been obvious to use machine learning system to improve self-driving by using image data.
Claims 5, 6 & 11 are rejected under 35 U.S.C. 103 as being unpatentable over Matsuda et al (Pub No. 2019/0351900), in view of Miyatani et al (Pub No. 2019/0220676), in view of Russel et al (US 2020/0175759) and further in view of Rowell (Pub No. 2019/0220676).
Regarding claim 5, Matsuda is silent regarding the predetermined work includes at least one of addition or change of light to be applied to the moving object and addition or change of shading of the moving object.
Rowell discloses the predetermined work includes at least one of addition or change of light to be applied to the moving object and addition or change of shading of the moving object (Para. 47: during pre-processing an image may be reviewed to reduce noise in the image data, enhance contrast).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the invention to use the image adjusting system to determine and identify target item from the images.
Regarding claim 6, Matsuda is silent regarding the predetermined work includes change of at least one of lightness, saturation, and contrast.
Rowell discloses the predetermined work includes change of at least one of lightness, saturation, and contrast (Para. 47: during pre-processing an image may be reviewed to reduce noise in the image data, enhance contrast).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the invention to use the image adjusting system to determine and identify target item from the images.
Regarding claim 11, Matsuda is silent regard in the acquiring a label information, the label information is acquired from information associated with the image or the three-dimensional data about the moving object.
Rowell discloses in the acquiring a label information, the label information is acquired from information associated with the image or the three-dimensional data about the moving object (Para. 35: 3D information).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the invention to use the 3d data to determine and identify target item from the images.
Another Prior Art
7. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Another prior art, Fridman (US 2018/0025235) discloses a road surface information collection. An image representative of a portion of the road segment, identifying in the at least one image at least one road surface feature along the portion of the road segment, determining a plurality of locations associated with the road surface feature according to a local coordinate system of the vehicle, and transmitting the determined plurality of locations from the vehicle to a server. The determined locations configured to enable determination by the server of a line representation of the road surface feature extending along the road segment.
Another Prior art Nishio et al (2026/0006164) discloses object data corresponds to a real space and is associated with a physical quantity of an object based on the third object data in the real space. The object data is data that defines an appearance of the object, specifically, a shape, a color, a pattern, a surface state, a skin sensation or texture, a posture, and the like. The data of the appearance of the object is three-dimensional (3D) data. The data for displaying the image of the object based on the third object data in a superimposed manner on the image of the real space
CONCLUSION
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 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 date of this final action.
Any inquiry concerning this communication from the examiner should be directed to Patent Examiner Md Talukder whose telephone number is (571) 270-3222. The examiner can normally be reached on Mon-Th 8:00 am to 4:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisors, Wesley Kim can be reached on 571-272-7867.
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/MD K TALUKDER/Primary Examiner, Art Unit 2648
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