# US 2026/0086989 A1 · Apparatus and method for data preparation analytics, preprocessing and control in a wireless communications network > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0086989 A1 - Document ID: US20260086989A1 - Status: Published application - Kind: A1 - Application number: 19/112,321 - Filed: 2022-11-10 - Published: 2026-03-26 - Earliest claimed: 2022-10-03 - Assignees: Lenovo (Singapore) Pte. Ltd. - Applicants: Lenovo (Singapore) Pte. Limited - Inventors: Konstantinos Samdanis; Emmanouil Pateromichelakis; Dimitrios Karampatsis - Web page: https://patentgrep.com/patent/US20260086989A1 ## CPC - G06F 16/215: Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors - H04L 41/0853: by actively collecting configuration information or by backing up configuration information - G06F 11/1474: in transactions (updating of structured data in databases G06F16/23) - H04L 41/16: using machine learning or artificial intelligence - H04L 41/0654: using network fault recovery (ring fault isolation or reconfiguration in loop networks without recovery actions by a network management system H04L12/437) - H04L 41/5009: Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF] - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract There is provided a data preparation function in a wireless communication network, the data preparation function comprising: one or more processors arranged to: collect data from one or more data sources in the wireless communication network; analyse the collected data to derive one or more data characteristics and to identify whether the collected data face one or more quality issues or irregularities; and prepare the collected data based on the analysis, including performing one or more of the following: data recovery to recover data missing from the collected data; data cleaning of the collected data; formatting of the collected data; or separation of the collected data into different data sets for one or more training tasks. ## Claims ### Claim at position 1 1-25. (canceled) ### Claim 26 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, an identifier for an artificial intelligence (AI) model or an identifier for a machine learning (ML) model to use the prepared data; and processing, based at least in part on the data preparation request, a data set to generate the prepared data. ### Claim 27 (depends on 26) The method of claim 26, 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. ### Claim 28 (depends on 26) The method of claim 26, 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. ### Claim 29 (depends on 26) The method of claim 26, 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. ### Claim 30 (depends on 26) The method of claim 26, 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. ### Claim 31 (depends on 26) The method of claim 26, wherein the second NF comprises a network data analytics function (NWDAF). ### Claim 32 (depends on 26) The method of claim 26, further comprising: 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. ### Claim 33 (depends on 32) The method of claim 32, 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. ### Claim 34 (depends on 26) The method of claim 26, further comprising: 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. ### Claim 35 (depends on 34) The method of claim 34, wherein the control request is transmitted to a data preparation function controller, and the control information is received from the data preparation function controller. ### Claim 36 (depends on 34) The method of claim 34, wherein the control request is transmitted to a network exposure function (NEF), and the control information is received from the NEF. ### Claim 37 A first network function (NF) for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and operable to cause the first NF to: receive, 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, an identifier for an artificial intelligence (AI) model or an identifier for a machine learning (ML) model to use the prepared data; and process, based at least in part on the data preparation request, a data set to generate the prepared data. ### Claim 38 (depends on 37) The first NF of claim 37, 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. ### Claim 39 (depends on 37) The first NF of claim 37, 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. ### Claim 40 (depends on 37) The first NF of claim 37, wherein to process the data set, the at least one processor is operable to cause the first NF to: derive 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. ### Claim 41 (depends on 37) The first NF of claim 37, wherein to process the data set, the at least one processor is operable to cause the first NF to: perform 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. ### Claim 42 (depends on 37) The first NF of claim 37, wherein the at least one processor is operable to cause the first NF to: receive, from a data preparation control function, control information associated with preparation of the data set, wherein the data set is processed based at least in part on the received data preparation request. ### Claim 43 (depends on 42) The first NF of claim 42, 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. ### Claim 44 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, an identifier for an artificial intelligence (AI) model or an identifier for a machine learning (ML) model to use the prepared data; and receiving, from the first NF, the prepared data, wherein the prepared data is based at least in part on the data preparation request. ### Claim 45 A second network function (NF) for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and operable to cause the second NF to: transmit, 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, an identifier for an artificial intelligence (AI) model or an identifier for a machine learning (ML) model to use the prepared data; and receive, from the first NF, the prepared data, wherein the prepared data is based at least in part on the data preparation request.