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 § 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 (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.
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.
Claim(s) 1, 6, and 11 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shao et al. (Shao) (US 2023/0367380).
Regarding claim 1, the limitations of claim 1 are rejected in the analysis of claim 11 (please see claim 11 below). Sho further discloses anon-transitory recording medium storing a program that is executable by a computer to perform a machine learning process ([0024], [0025], a program stored in a memory is executed by a processor).
Regarding claims 6 and 11, Shao discloses a machine learning device comprising:
a memory ([0024], memory 130), and
a processor coupled to the memory, the processor being configured to execute
processing ([0024], [0025], a program stored in a memory is executed by a processor), the processing including:
based on route information ([0105], feature values and location information of vehicles at an intersection) indicating movement conditions of a plurality of respective moving bodies in a specific geographical range ([0092], [0093], segments EA, DE, EF, EJ, etc.) at each of a plurality of time points (FIG. 6, step 610, [0055], [0086], traffic information is collected over a time period), generating traffic flow information indicating a number of moving bodies ([0105], according to the image characteristics of each video frame, the traffic volume of the intersection in each direction is determined by the cyclic neural network…The cyclic neural network may…count the number of vehicle tracks in each direction of the intersection as the traffic volume of the intersection) located at respective route segments ([0092], [0093], segments EA, DE, EF, EJ, etc.) within the specific geographical range for each of the plurality of time points (FIG. 4, [0093], [0094], [0100], [0105], traffic volume including a number of vehicles passing through an intersection is generated); and
by using training data that includes the traffic flow information as input feature
values and includes information indicating a degree of congestion of traffic in the specific
geographical range at a time point corresponding to the traffic flow information as label
information ([0086], the prediction model is trained using historical congestion degree information for a location over a target time period), training a machine learning model for deriving a degree of congestion of traffic corresponding to traffic flow information ([0061], [0084]-[0088], the congestion at a target location and time period is determined by a training a prediction model based on input location information which includes historical congestion degree information).
Allowable Subject Matter
Claims 2-5, 7-10, and 12-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Karpov (US 2016/0124906) ([0015] [0016] a degree of congestion for an area is determined using a neural network).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEFFERY A WILLIAMS whose telephone number is (571)270-7579. The examiner can normally be reached M-F 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sath Perungavoor can be reached at 571-272-7455. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEFFERY A WILLIAMS/Primary Examiner, Art Unit 2488
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