DETAILED 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 .
Claims 1-25 have been cancelled.
Claims 26-45 are new.
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.
Claim(s) 26-29, 31-40, & 42-45 is/are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Kodaypak et al. (US Pub. No. 2022/0141111 A1).
In respect to Claim 26, Koday teaches:
a method performed by a first network function (NF), the method comprising: receiving, from a second NF, a data preparation request comprising a set of attributes (Koday teaches [0030-0031] multiple network functions with associated attributes.), wherein the set of attributes comprises one or more of an identifier for an analytics service to consume prepared data (Koday teaches [0030-0031] identifiers associated the attributes.), an identifier for an artificial intelligence (AI) model or an identifier for a machine learning (ML) model to use the prepared data; (Koday teaches [0054-0056] utilization of an artificial intelligence component and a machine learning model.)
and processing, based at least in part on the data preparation request, a data set to generate the prepared data (Koday teaches [0034, 0036] generation of data corresponding to a data request.)
As per Claim 27, Koday teaches:
wherein the set of attributes further comprises one or more of: time scheduling information associated with a time window associated with the prepared data; one or more identifiers of one or more data sources associated with the data set collected as input to process data; one or more identifiers related to a statistical property of the data set used as input to process the data set; or a type of data sources for the one or more data sources associated with the data set used as input to process the data set (Koday teaches [0031, 0049] attributes associated with data sources.)
As per Claim 28, Koday teaches:
wherein the set of attributes further comprises one or more of: a waiting time bound associated with processing the prepared data; an indication of a type of processing that the prepared data is expected to undergo when input into one or more of the AI model or the ML model; or accuracy level information for the prepared data (Koday teaches [0054-0056] utilization of an artificial intelligence component and a machine learning model.)
As per Claim 29, Koday teaches:
wherein processing the data set comprises: deriving one or more data characteristics of the data set, wherein the one or more data characteristics comprise one or more of: an effect among variables or features of the data set; or an amount of data adequate for a requested task (Koday teaches [0031, 0049] attributes associated with data sources.)
As per Claim 31, Koday teaches:
wherein the second NF comprises a network data analytics function (NWDAF) (Koday [0026, 0030])
As per Claim 32, Koday teaches:
receiving, from a data preparation control function, control information associated with processing the data set, wherein processing the data set is based at least in part on the received data preparation request (Koday [0025, 0042])
As per Claim 33, Koday teaches:
wherein the control information comprises one or more of: a type of data recovery rules or logic for the data set; a type of data cleaning rules or logic for the data set; a type of data formatting rules or logic for formatting the data set; one or more additional data sources to complement the data set; or information for labeling the data associated with different data sets (Koday [0047, 0050])
As per Claim 34, Koday teaches:
transmitting, to the second NF, a control request comprising one or more of: an indication of one or more data characteristics of the data set; an indication of one or more missing data values from the data set; an indication of one or more outliers in the data set; an indication of a data simplification method; or an indication of missing or erroneous data labels for characterizing the data set; and receiving, based at least in part on the control request, control information comprising one or more of: an indication of a type of problem associated with the control information; information for handling the one or more missing data values from the data set; information for handling the one or more outliers in the data set; an indication of an accuracy level for processing the data set; or an indication of a data labeling method for processing the data set (Koday [0034, 0089])
As per Claim 35, Koday teaches:
wherein the control request is transmitted to a data preparation function controller, and the control information is received from the data preparation function controller (Koday [0020, 0034, 0089])
As per Claim 36, Koday teaches:
wherein the control request is transmitted to a network exposure function (NEF), and the control information is received from the NEF (Koday [0048, 0066, 0068])
Claims 37-40 & 42-43 are the network claims corresponding to method claims 26-29 & 32-33 respectively, therefore are rejected for the same reasons noted previously.
In respect to Claim 44, Koday teaches:
a method performed by a second network function (NF), the method comprising: transmitting, to a first NF, a data preparation request comprising a set of attributes, (Koday teaches [0030-0031] multiple network functions with associated attributes.) wherein the set of attributes comprise one or more of an identifier for an analytics service to consume prepared data, (Koday teaches [0030-0031] identifiers associated the attributes.) an identifier for an artificial intelligence (AI) model or an identifier for a machine learning (ML) model to use the prepared data; (Koday teaches [0054-0056] utilization of an artificial intelligence component and a machine learning model.)
and receiving, from the first NF, the prepared data, wherein the prepared data is based at least in part on the data preparation request (Koday teaches [0034, 0036] generation of data corresponding to a data request.)
Claim 45 is the network claim corresponding to method claim 44, therefore is rejected for the same reasons noted above.
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.
Claim(s) 30 & 41 is/are rejected under 35 U.S.C. 103 as being unpatentable over Koday in view of YUI (US Pub. No. 2024/0295859 A1).
As per Claim 30, Koday does not explicitly disclose:
wherein processing the data set comprises: performing data recovery for the data set, wherein the data recovery comprises one or more of: recovering missing data from a data source or a data production tool; identifying and replacing invalid data with other data; or augmenting existing data to account for the missing data
However, YUI teaches:
wherein processing the data set comprises: performing data recovery for the data set, wherein the data recovery comprises one or more of: recovering missing data from a data source or a data production tool; identifying and replacing invalid data with other data; or augmenting existing data to account for the missing data (YUI teaches [0092] data recovery of missing data.)
It would have been obvious to one of ordinary skill in the art at the time of the filing date of the invention to incorporate the teachings of YUI into the system of Koday. One of ordinary skill in the art would be motivated to provide a system for reducing the burden on data collection processing in a case where a plurality of users have requested the same data. (YUI [0008])
Claim 41 is the network claim corresponding to method claim 30, therefore is rejected for the same reasons noted previously.
Conclusion
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/JOSHUA BULLOCK/Primary Examiner, Art Unit 2153 February 20, 2026