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 & 31 have been cancelled.
Claims 26, 32-33, 36-37, & 42-45 have been amended.
Claim 46 is new.
Claims 26-30 & 32-46 are pending.
Response to Arguments
Applicant’s arguments with respect to claim(s) 26-30 & 32-46 have been considered but are moot because of the new grounds of rejection. See Office Action below.
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) 26-30, 32-33, & 37-46 is/are rejected under 35 U.S.C. 103 as being unpatentable over LEE et al. (US Pub. No. 2020/0322775 A1) in view of Scott et al. (US Pub. No. 2022/0342583 A1).
In respect to Claim 26, LEE 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, wherein the set of attributes comprises one or more of an identifier for an analytics service to consume prepared data, (LEE teaches [0002, 0059, 0239] a data collection feature for retrieving prepared analytics data from a network function device.)
LEE does not explicitly disclose:
an identifier for an artificial intelligence (AI) model to use the prepared data, or an identifier for a machine learning (ML) model to use the prepared data;
selecting a data preparation action that updates a quality of a data set, wherein the data preparation action corresponds to the set of attributes;
and processing the data set by applying the data preparation action to generate the prepared data
However, Scott teaches:
an identifier for an artificial intelligence (AI) model to use the prepared data, or an identifier for a machine learning (ML) model to use the prepared data; (Scott teaches [0020] a DPP tool with modules that can be loaded or input through artificial intelligence or machine learning. Scott further teaches [0021] the tool may include a unique ID or identifier for the data.)
selecting a data preparation action that updates a quality of a data set, wherein the data preparation action corresponds to the set of attributes; (Scott teaches [0005] modules can be selectively applied and loaded [0020] via predetermined settings to address data specific problems.)
and processing the data set by applying the data preparation action to generate the prepared data (Scott teaches [0004, 0024] data preparation by removing outliers, filling in missing values, and formatting data such as a cleaned dates module.)
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 Scott into the system of LEE. One of ordinary skill in the art would be motivated to provide a system of data preprocessing that automates data cleaning tasks and provides cleaned data that can be more efficiently parsed and analyzed.
As per Claim 27, LEE 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 (LEE [0007] event ID identifier of a type of event; [0070] statistical information of past events, predicative information; [0239] various data sources)
As per Claim 28, LEE 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 (LEE [0258] estimation of level of accuracy before the time deadline; [0261] based on level of accuracy or based on the time when analytics are needed)
As per Claim 29, LEE 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 (LEE [0239])
As per Claim 30, Scott 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 (Scott teaches [0004, 0024] data preparation by removing outliers, filling in missing values, and formatting data such as a cleaned dates module.)
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 Scott into the system of LEE. One of ordinary skill in the art would be motivated to provide a system of data preprocessing that automates data cleaning tasks and provides cleaned data that can be more efficiently parsed and analyzed.
As per Claim 32, LEE 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 data preparation request (LEE teaches [0002, 0059, 0239] a data collection feature for retrieving prepared analytics data from a network function device.)
As per Claim 33, LEE 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 data associated with different data sets (LEE [0064, 0239, 0282] teaches various data sources, and identifiers for data which constitutes information for labeling data.)
Claims 37-43 are the network claims corresponding to method claims 26-30 & 32-33 respectively, therefore are rejected for the same reasons noted previously.
In respect to Claim 44, LEE 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,
wherein the set of attributes comprise one or more of an identifier for an analytics service to consume prepared data, (LEE teaches [0002, 0059, 0239] a data collection feature for retrieving prepared analytics data from a network function device.)
LEE does not explicitly disclose:
an identifier for an artificial intelligence (AI) model to use the prepared data, or an identifier for a machine learning (ML) model to use the prepared data,
and wherein a data preparation action corresponding to the set of attributes is selected to update a quality of a data set;
and receiving, from the first NF and in response to the data preparation request, the prepared data, wherein the prepared data is based at least in part on the data preparation request action being applied to the data set
However, Scott teaches:
an identifier for an artificial intelligence (AI) model to use the prepared data, or an identifier for a machine learning (ML) model to use the prepared data, (Scott teaches [0020] a DPP tool with modules that can be loaded or input through artificial intelligence or machine learning. Scott further teaches [0021] the tool may include a unique ID or identifier for the data.)
and wherein a data preparation action corresponding to the set of attributes is selected to update a quality of a data set; (Scott teaches [0005] modules can be selectively applied and loaded [0020] via predetermined settings to address data specific problems.)
and receiving, from the first NF and in response to the data preparation request, the prepared data, wherein the prepared data is based at least in part on the data preparation request action being applied to the data set (Scott teaches [0004, 0024] data preparation by removing outliers, filling in missing values, and formatting data such as a cleaned dates module.)
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 Scott into the system of LEE. One of ordinary skill in the art would be motivated to provide a system of data preprocessing that automates data cleaning tasks and provides cleaned data that can be more efficiently parsed and analyzed.
Claim 45 is the network claim corresponding to method claim 44, therefore is rejected for the same reasons noted above.
As per Claim 46, Scott teaches:
wherein the data preparation action comprises one or more of: a data recovery action to recover data missing from the data set; a data cleaning action to remove or reduce an impact of one or more outliers in the data set; formatting the data set into a format for use by the AI model or the ML model; or separating the data set into different data sets for one or more inference or training tasks (Scott teaches [0004, 0024] data preparation by removing outliers, filling in missing values, and formatting data such as a cleaned dates module.)
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 Scott into the system of LEE. One of ordinary skill in the art would be motivated to provide a system of data preprocessing that automates data cleaning tasks and provides cleaned data that can be more efficiently parsed and analyzed.
Allowable Subject Matter
Claims 34-36 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.
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA BULLOCK whose telephone number is (571)270-1395. The examiner can normally be reached 8:00 am - 4:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached at 571-272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOSHUA BULLOCK/Primary Examiner, Art Unit 2153 September 1, 2026
Prosecution Insights