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 .
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Election/Restrictions
Applicant’s election without traverse of invention-I (claims 1-7) in the reply filed on 05/11/2026 is acknowledged. The non-elected inventions II and III, claims 8-21 are withdrawn.
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) 1, 6 and 7 rejected under 35 U.S.C. 103 as being unpatentable over Wahaj Arshad et al. (US 2022/0104055, “Arshad”) in view of Byun et al. (US 2026/0067172, “Byun”, provisional application 63395873 (“873”)).
Examiner’s note: in what follows, references are drawn to Arshad unless otherwise mentioned.
Arshad comprises the following features:
With respect to independent claim:
Regarding claim 1, apparatus comprising:
one or more processors ([0177 and Fig. 4] “In FIG. 4, network node 160 includes processing circuitry 170”), and memory storing instructions that, when executed by the one or more processors ([0180 and Fig. 4] “processing circuitry 170 may execute instructions stored in device readable medium 180 or in memory within processing circuitry 170”), cause the apparatus to perform:
monitoring whether an action related to machine learning of a machine learning model requires data from a terminal ([0105] “In FIG. 1, the RAN node determines in step 1 whether a condition to trigger data collection has been fulfilled. Once the RAN node determines the necessity to trigger a data collection procedure within the RAN, the RAN node proceeds to step 2 with transmitting a data collection request message to a network control entity to trigger a configuration for a data collection procedure.” Note that machine learning of a machine learning model will be discussed in view of Byun.);
checking whether, according to stored configuration data, the machine learning model is embedded at a radio access network ([0148] “the (preferred) data collection configuration could be determined based on an existing data collection configuration used by the RAN node.”);
configuring the terminal such that the terminal collects the data in response to monitoring that the action requires the data and to checking that the machine learning model is embedded at a radio access network ([0148] “the RAN node may determine a modification to the data collection procedure, such as type of measurements to be configured for UEs and to be reported, timing of data measurements (e.g., starting time, duration of measurements, periodicity, etc.), target UEs to be used for measurements, type of area to be used for measurements, etc.”, and [0107] “Upon receiving a data collection ACK command, in step 4, the RAN node transmits a data collection configuration message to one or more UE(s) to initialize data measurements and reporting based on the data collection acknowledgement response message.”), irrespective of whether or not, according to stored context information, consent has been given to collect the data by the terminal ([0127] “a specific UE, a group of UE or type of UEs that the RAN node wants to configure data collection to”, and [0128] “may include a request to get UE consent”).
It is noted that while disclosing data collection from UEs, Arshad does not specifically teach about a ML model. It, however, had been known in the art before the effective date of the instant application as shown by Byun as follows;
machine learning of a machine learning model ([Byun, 0197 and Fig. 10] “Referring to FIG. 10, in step S1001, NG-RAN node 2 is assumed to optionally have an AI/ML model” See [873, Fig. 5.3.2.3-1 and Section 4]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of instant application to modify Arshad by using the features of Byun in order to effectively optimize network performance such that “optimization can be performed using artificial intelligence (AI)/machine learning (ML) techniques. For this optimization, an AI/ML model can be used. For example, the AI/ML model can include network energy saving, load balancing, and/or mobility optimization.” [Byun, 0006].
With respect to dependent claims:
Regarding claim 6, the apparatus according to claim 1,
wherein either
the radio access network comprises an operation and maintenance system for at least one of operating or maintaining at least one node of the radio access network ([0094] “the RAN node (a node within the RAN) requests data collection initialization. FIGS. 1-3 each illustrate examples of signaling for a RAN node to trigger a data collection function managed by a network control entity, such as an element manager (EM), access and mobility management function (AMF), MME, Management Function, operations and maintenance (O&M), operations support system (OSS), SON, or positioning node, according to certain embodiments.”); or
the radio access network does not comprise the operation and maintenance system for the at least one of the operating or the maintaining the at least one node of the radio access network (This alternative is not examined.).
Regarding claim 7, the apparatus according to claim 1, wherein the action comprises at least one of the following:
performing a first training of the machine learning model by the machine learning (See [Byun, Fig. 10]); or
executing a first inference from the machine learning model after a second training of the machine learning model by the machine learning was performed; or evaluating a feedback to an executed second inference from the machine learning model after a third training of the machine learning model by the machine learning was performed (These alternatives are not examined.).
Claim(s) 2-3 and 5 rejected under 35 U.S.C. 103 as being unpatentable over Wahaj Arshad et al. (US 2022/0104055, “Arshad”) in view of Byun et al. (US 2026/0067172, “Byun”, provisional application 63395873 (“873”)) and further in view of Racz et al. (US 2015/0249932, “Racz”).
Examiner’s note: in what follows, references are drawn to Arshad unless otherwise mentioned.
Regarding claim 2, it is noted that while disclosing data collection from UEs, Arshad does not specifically teach about inhibiting checking or bypassing. It, however, had been known in the art before the effective date of the instant application as shown by Racz as follows;
the apparatus according to claim 1, wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform inhibiting checking the context information whether or not consent has been given to collect the data by the terminal ([Racz, 0097] “Configuring the privacy matrix may be optional, meaning that when no privacy matrix is given, or in case entries for some measurements are missing from the privacy matrix, a default interpretation may apply. The default interpretation may be that when no privacy matrix is given or when entries for some measurements are missing from the privacy matrix, the interpretation should be that no user consent applies (i.e., the corresponding measurements can be collected without user consent)”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of instant application to modify Arshad by using the features of Racz in order to effectively collect user data such that An advantage of such a method is that it is possible to implement any privacy management policy in an operator's network with regards to collection of MDT data.” [Racz, 0016].
Regarding claim 3, the apparatus according to claim 1, wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform checking whether or not, according to the context information, consent has been given to collect the data by the terminal at least for the action;
the configuring the terminal such that the terminal collects the data a) in response to checking that according to the context information, consent has not been given to collect the data at least for the action, and b) in response to checking that, according to the context information, consent has been given to collect the data at least for the action (See [Racz, 0071] “IF (privacylndicator(Mx)==1 AND userConsent(UEy)==1) OR privacylndicator(Mx)=0 THEN Start measurement Mx for that UE”, and [0072] “ELSE DO NOT Start measurement Mx for UEy”.)
The rational and motivation for adding this teaching of Racz are the same as for claim 2.
Regarding claim 5, the apparatus according to claim 1, wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform
monitoring whether, according to the stored configuration data, the data do not leave the radio access network for the machine learning ([0022] “certain aspects of the present disclosure may provide solutions to challenges presented by existing LTE MDT procedures in which all MDT traces collected from the UE in the RAN are forwarded to the OAM and are, thus, outside the RAN.”); inhibiting the configuring the terminal such that the terminal collects the data in response to monitoring that, according to the stored configuration data, the data may leave the radio access network for the machine learning and that, according to the context information, consent has not been given to collect the data by the terminal ([Racz, 0075] “If the user consent is required for a measurement according to the MDT privacy matrix, but user consent is not given by the UE, then at S6 it may be determined that the UE cannot be selected for MDT measurements in general.”).
The rational and motivation for adding this teaching of Racz are the same as for claim 2.
Claim(s) 4 rejected under 35 U.S.C. 103 as being unpatentable over Wahaj Arshad et al. (US 2022/0104055, “Arshad”) in view of Byun et al. (US 2026/0067172, “Byun”, provisional application 63395873 (“873”)) and further in view of Johansson (US 2012/0208503) and Racz et al. (US 2015/0249932, “Racz”).
Examiner’s note: in what follows, references are drawn to Arshad unless otherwise mentioned.
Regarding claim 4, it is noted that while disclosing data collection from UEs, Arshad does not specifically teach about inhibiting checking or bypassing, and without use consent. It, however, had been known in the art before the effective date of the instant application as shown by Johansson and Racz as follows;
the apparatus according to claim 1, wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform receiving the context information from a core network ([Johansson, 0033] “When UE 401 establishes data signaling connection with its serving eNB in E-UTRAN 402, in step 416, the user consent information of UE 401 is then provided from MME 403 to E-UTRAN 402.”); informing the core network that the terminal is configured to collect the data in response to checking that, according to the context information, consent has not been given to collect the data ([Racz, 0064] “the measurement can be collected even without user consent (privacy indicator=0).”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of instant application to modify Arshad by using the features of Johansson in order to effectively collect user data such that “It is desirable to provide a solution that fulfills the new system requirements related to managing user consent for MDT measurement collection with maximum simplicity and minimum impact to the current system.” [Johansson, 0016].
The rational and motivation for adding this teaching of Racz are the same as for claim 2.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Harry H. Kim whose telephone number and email address are as follows; 571-272-5009, harry.kim2@uspto.gov.
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/HARRY H KIM/ Primary Examiner, Art Unit 2411
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