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 1-3, 6-14, 17-24 are presented for examination.
Claims 4, 5, 15, 16 are cancelled.
IDS filed on 6/19/2025 is considered.
Claim Objections
Claim 14 is objected to because of the following informalities:
Claim 14 recites the limitation “AI/ML”, the limitation should be recited as “Artificial Intelligence (AI)/Machine learning (ML)”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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, 6-14, 17-19, 22-24 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Luebke et al. WO 2020/219685 A1, hereinafter Luebke.
Referring to claim 1, Luebke discloses a data preparation service ([0003][0151])in a wireless communication network ([0032], wireless communication network), comprising:
at least one memory ([0031], memory 2150); and
at least one processor (processor 2450) coupled with the at least one memory (memory 2150) and configured to cause the data preparation service to ([0031]):
receive, via at least one of a network automation or optimization tool (collector 2720), configuration information (input data; [0039], input data could be configuration data)from at least one of an application or service provider (the input data sources and/or data stores (3060))([0151], the machine learning (ML) training data preparation program (5512) retrieves input data from one or more of the input data sources and/or data stores (3060), via the collector (2720)) ; and
configure a data preparation process using the configuration information ([0150][0151], The ML training data preparation program processes the retrieved data to generate machine learning model training, validation, and testing data formatted as a data frame suitable for use in training one or more ML models. Processing of the retrieved data includes cleaning the data to remove outliers, interpolating or otherwise filling in missing data points, and removing erroneous or otherwise unneeded data and formatting the data in a data frame).
Referring to claim 6, Luebke discloses the data preparation service of claim 1, any preceding claim, wherein, based at least in part on the configuration information, the at least one processor is configured to cause the data preparation service to collect data from one or more data sources in the wireless communication network ([0030][0039][0150], DLAD processor is configured to collect data from one or more data sources in the wireless communication network).
Referring to claim 7, Luebke discloses the data preparation service of claim 1, wherein, based at least in part on the configuration information, the at least one processor is configured to cause the data preparation service to analyze collected data to derive one or more data characteristics and to identify whether the data includes one or more quality issues or irregularities ([0042][0045-0047], analyzing collected data to detect/identify anomalies).
Referring to claim 8, Luebke discloses the data preparation service of claim 7, wherein the at least one processor is configured to cause the data preparation service to derive the one or more data characteristics including at least one of a central tendency of the collected data; a variation of the collected data; a relative effect among variables or features; or an amount of the collected data being adequate for a requested task ([0042][0045-0047], Newly collected DLAD data values are compared to predicted DLAD data values and newly collected data values that fall outside of either the upper or lower bounds of the predicted data values are considered to be “anomalous”).
Referring to claim 9, Luebke discloses the data preparation service of claim 7, wherein the at least one processor is configured to cause the data preparation service to identify whether the data includes the one or more quality issues or irregularities based at least in part on whether the collected data comprise includes one or more of an anomaly; a missing value; irregular cardinality; or an outlier ([0042][0045-0047], analyzing collected data to detect/identify anomalies).
Referring to claim 10, Luebke discloses the data preparation service of claim 1, wherein, based at least in part on the configuration information, the at least one processor is configured to cause the data preparation service to prepare and perform at least one of data recovery to recover data missing from the data; data cleaning of the data; formatting of the data; labeling of the data; or separation of the data into different data sets for one or more training tasks ([0151], processing of the retrieved data includes cleaning the data to remove outliers, interpolating or otherwise filling in missing data points, and removing erroneous or otherwise unneeded data and formatting the data in a data frame).
Referring to claim 11, Luebke discloses the data preparation service of claim 10, wherein the configuration information specifies one or more of: at least one of a type of data recovery or a cleaning method to be implemented by the data preparation service; a type of data formatting usable by the data preparation service to format the data; one or more data sources; how to separate the data into data sets; or how to label the data sets ([0148-0151], cleaning, formatting and data sources).
Referring to claim 12, Luebke discloses the data preparation service of claim 10, wherein the data recovery comprises the at least one processor is configured to cause the data preparation service to one or more of: recover missing data from a data source different than a data source from which the data was initially requested; replace the missing data by other data; or augment existing data to account for the missing data ([0151], Processing of the retrieved data includes cleaning the data to remove outliers, interpolating or otherwise filling in missing data points, and removing erroneous or otherwise unneeded data and formatting the data in a data frame).
Referring to claim 13, Luebke discloses the data preparation service of claim 10, wherein the data cleaning comprises the at least one processor is configured to cause the data preparation service to execute a policy to mitigate against outliers and random errors from the data by at least one of removing data or introducing one or more weights to reduce an impact of the outliers and random errors in the collected data ([0151], Processing of the retrieved data includes cleaning the data to remove outliers, interpolating or otherwise filling in missing data points, and removing erroneous or otherwise unneeded data and formatting the data in a data frame).
Referring to claim 14, Luebke discloses the data preparation service of any of claim 10, wherein the preparing the data comprises the at least one processor is configured to cause the data preparation service to determine at least one of an expected performance impact or a confidence level on an AI/ML model, wherein the prepared data is as an input to the AI/ML model ([0044-0047], [0107-0113], The ML training data preparation program determines the anomaly with the received input is viewed as the data preparation service to determine at least one of an expected performance impact or a confidence level on an AI/ML model, wherein the prepared data is as an input to the AI/ML model).
Referring to claims 17-19, 22-24, the claims encompass the same scope of the invention as that of the claims 1, 6-9. Therefore, claims 17-19, 22-24 are rejected on the same ground as the claims 1, 6-9.
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 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 2, 3, 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Luebke in view of Tsui, US Patent Publication Number 2022/0279535, hereinafter Tsui.
Referring to claims 2, 3, 20 and 21, Luebke discloses the invention as described in claims 1 and 18. Luebke wherein the data preparation service operates in a virtualized radio access network environment for application- based configuration of one or more service provider capabilities, and wherein the at least one of network automation or optimization tool is one of an xApp or an rApp related to an open radio access network (O-RAN) system.
Tsui discloses wherein the data preparation service operates in a virtualized radio access network environment for application- based configuration of one or more service provider capabilities ([0051], the system 160 may include a RAN 162a communicatively coupled to a core network 190. The core network 190 can include a 5G network, an evolved packet core (EPC) network, a higher generation network, or any combination thereof. In various embodiments, the RAN 162a may be, or may include, a vRAN (e.g., in an Open RAN (O-RAN) implementation), in which software is decoupled from hardware, and implementation thereof is in accordance with principles of network function virtualization (NFV)), and wherein the at least one of network automation or optimization tool is one of an xApp or an rApp related to an open radio access network (O-RAN) system ([0037][0059][0146], the network implementation operates in Open Radio Access Network (O-RAN) standards with the use of xApp and rApp).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to implement the machine-learning-based network discovery and management functions of Luebke within the virtualized Open Radio Access Network (O-RAN) architecture taught by Tsui, including implementation as an xApp or rApp. Tsui teaches that network automation and optimization functions may be deployed in a virtualized O-RAN environment using xApps and rApps, thereby providing a standardized and flexible framework for deploying network management applications.
A person with ordinary skill in the art would have been motivated to incorporate Tsui’s O-RAN implementation into Luebke because doing so would allow Luebke’s machine-learning management functions to operate within a modern virtualized RAN architecture, improving scalability, interoperability, and deployment flexibility while yielding the predictable result of performing the same management and optimization functions within an O-RAN-compliant environment.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is reminded that in amending in response to a rejection of claims, the patentable novelty must be clearly shown in view of the state of the art disclosed by the references cited and the objection made. Applicant must show how the amendments avoid such references and objections. See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANGCHE A WANG whose telephone number is (571)272-3992. The examiner can normally be reached M-F 10:00am to 6:30pm.
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Liang-che Alex Wang
July 27, 2026
/LIANG CHE A WANG/Primary Examiner, Art Unit 2447
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