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 .
DETAILED ACTION
Claims 1-19 are pending.
This action is in response to the application filed on June 17, 2025.
CROSS-REFERENCE TO RELATED APPLICATIONS
The present application claims under 35 U.S.C. §119(a) the benefit of Korean Patent Application No. 10-2024-0165597 filed on November 19, 2024, the entire contents of which are incorporated by reference herein.
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lee et al (US 20200192587 A1).
With respect to claims 1, 9 and 17, Lee et al teaches
preprocessing, by a processor, data collected while the vehicle is driving based on an abnormal data list ([0021] FIG. 1, a smart vehicle includes a smart car system 10, [0026] error detection signal EDS, data DQ_B and error detection data EDC_B received. That is, when the error detection signal EDS is received form the host 100, the storage device 200 may transmit and receive the error detection data EDC_B, needed for error correction, to and from the host 100);
when abnormal data is detected among the preprocessed data, and when the abnormal data is determined to be new, adding, by the processor, the abnormal data to the abnormal data list ([0028] the error detection signal EDS is activated such that at least one error contained in the data is detected.[0037] 120 may receive data HDQ_B and error detection data HEDC_B from the controller 110, and may transmit data DQ_B and error detection data EDC_B to the second interface 140);
removing, by the processor, the abnormal data from the preprocessed data ([0070] error correction circuit 263 may perform error correction using the received error detection data EDCB. [0096] error detection and correction technologies can be applied to the error correction circuit 263. the error correction circuit 263 may use the error correction code (ECC) in dirty or rogue data, and may use the error detection code (EDC) in clean data);
collating, by the processor, normal data in which the abnormal data is removed ([0096] error detection and correction technologies can be applied to the error correction circuit 263. error correction circuit 263 may use the error correction code (ECC) in dirty or rogue data, and may use the error detection code (EDC) in clean data); and
transmitting, by the processor, the normal data to a server ([0086] FIG. 6, the first mode may refer to an operation mode in which data is normally transmitted to a destination).
With respect to claims 2,10 and 18, Lee et al teaches abnormal data is detected by applying an artificial intelligence teaming model ([0005] The smart vehicle has been developed by combining traditional mechanical-based vehicle technology with modern technologies, for example, next-generation electrical and electronics technologies, information communication technologies, intelligence control technologies, artificial intelligence technologies, etc.).
With respect to claims 3,11 and 19, Lee et al teaches to update the artificial intelligence learning model through learning ([0005] The smart vehicle has been developed by combining traditional mechanical-based vehicle technology with modern technologies, for example, next-generation electrical and electronics technologies, information communication technologies, intelligence control technologies, artificial intelligence technologies, etc.).
With respect to claims 4 and 12, Lee et al teaches artificial intelligence learning model is an isolation forest model (([0005] The smart vehicle has been developed by combining traditional mechanical-based vehicle technology with modern technologies, for example, next-generation electrical and electronics technologies, information communication technologies, intelligence control technologies, artificial intelligence technologies).
With respect to claims 5 and 13, Lee et al teaches to preprocess the data by performing an automation script ([0006] The smart car, or any other vehicle, may store in-vehicle information in a storage device such as a memory, and may control the vehicle operation in response to or using information stored in the storage device).
With respect to claims 6 and 14, Lee et al teaches to perform a process of cleansing data based on the abnormal data list upon the preprocessing ([0096] error detection and correction technologies can be applied to the error correction circuit 263. error correction circuit 263 may use the error correction code (ECC) in dirty or rogue data may use the error detection code (EDC) in clean data).
With respect to claims 7 and 15, Lee et al teaches abnormal data includes data having a value of FFFF, NULL, or NaN ([0006] The smart car, or any other vehicle, may store in-vehicle information in a storage device such as a memory, and may control the vehicle operation in response to or using information stored in the storage device).
With respect to claims 8 and 16, Lee et al teaches abnormal data includes a minus value, over-collected data, or mis-collected data ([0096] error detection and correction technologies can be applied to the error correction circuit 263. error correction circuit 263 may use the error correction code (ECC) in dirty or rogue data, and may use the error detection code (EDC) in clean data).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wyatt; Timothy M. (US-20120240236-A1) CRAWLING MULTIPLE MARKETS AND CORRELATING.
Considered for teachings related generally to crawler program collects and stores application programs including application binaries and associated metadata from any number of sources such as official application marketplaces and alternative application marketplaces. An analysis including comparisons and correlations are performed among the collected data in order to detect and warn users about pirated or maliciously modified applications
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/ISAAC M WOO/ Primary Examiner, Art Unit 2163
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