DETAILED ACTION
This Action is in response to Application Number 19051202 received on 2/12/2025.
Claims 1-20 are presented for examination.-
This application claims foreign priority to 202211067195.2, filed 09/01/2022.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 5-6, 10, 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites the limitation, “the analytic data” in the preamble. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the limitation will be interpreted to recite, “the supported analytic data”, in order to refer to the same in claim 2.
Claim 3 recites the limitation " the network element " in the fourth limitation. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the limitation will be interpreted to recite “the at least one network element” to remain consistent with the other limitations of the claim.
Claim 5 recites the limitation, “the at least one network element” in the first limitation. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, claim 5 will be interpreted to depend from claim 2, as claim 2 initially recites the limitation “at least one network element”.
Claim 5 recites the limitation “the analytic operation” in the second limitation. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, claim 5 will be interpreted to depend from claim 2, as claim 2 initially recites the limitation “at least one network element”.
Claim 6 recites the limitation “the analytic operation” in the “sending” limitation. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, claim 6 will be interpreted to depend from claim 2, as claim 2 initially recites the limitation “at least one network element”.
Claim 6 recites the limitation, “information about an analytic status of the first management data analytic module, wherein the analytic status indicates a status of performing the analytic operation by the first management data analytic module”, which is found indefinite as it is not clear if “information about an analytic status” requires the analytic status itself, or if it requires broader information that has relation to the analytic status.
Claim 10 recites the limitation, “one or more of a first management data analytic modules”, which appears to include a minor typographical error, specifically by reciting “one or more of a…modules”. For examination purposes, the limitation will be interpreted to recite, “one or more of a first management data analytic module
Claim 12 recites the limitation “the analytic operation” in the “sending” limitation. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation, “information about an analytic status of the first management data analytic module, wherein the analytic status indicates a status of performing the analytic operation by the first management data analytic module”, which is found indefinite as it is not clear if “information about an analytic status” requires the analytic status itself, or if it requires broader information that has relation to the analytic status. For examination purposes, the limitation will be interpreted to cover broader embodiments.
Claim 16 recites the limitation “the analytic operation” in the “sending” limitation. There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites the limitation, “information about an analytic status of the first management data analytic module, wherein the analytic status indicates a status of performing the analytic operation by the first management data analytic module”, which is found indefinite as it is not clear if “information about an analytic status” requires the analytic status itself, or if it requires broader information that has relation to the analytic status.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Subramanya et al. (US 20250184241) in view of Gebert (US 20250031131).
Regarding claim 1, Subramanya disclosed a data analysis method, applied to a first communication apparatus, wherein the method comprises:
sending first information to a second communication apparatus, wherein the first information comprises capability information of the first management data analytic module in the first communication apparatus (Subramanya, [0080]-[0082] Subramanya disclosed cross-domain AI QoS – including discovering “performance capabilities (e.g., supported performance metrics, (re) configurable options such as model retraining, model reselection, model termination) of the domain-specific AI pipeline that the AI pipeline orchestrator is capable to configure”; See also [0089], Subramanya disclosed cross-domain APIs, including at [0090], “Cross-Domain AI Performance Capability Discovery API (Request/Response)”, which “allows the cross-domain AI pipeline orchestrator (entity), via (e.g.) PCD-1 interface, to discover AI reconfiguration methods and/or AI performance metrics that the domain-specific AI pipeline orchestrator (entity) is capable of configuring in the domain-specific AI pipeline belonging to the cross-domain network service.”; The domain-specific AI pipeline orchestrator provides its capabilities to the cross-domain AI pipeline orchestrator; [0065] Subramanya disclosed the Ai piplines amounting to network functions);
receiving second information from the second communication apparatus, wherein the second information comprises control information of the first management data analytic module, and the control information is used to configure a running parameter of the first management data analytic module (Subramanya, [0083], “Requesting the domain-specific AI pipeline orchestrator to (re) configure (e.g., retrain the model, reselect the model, terminate the model) the domain-specific AI pipeline belonging to the cross-domain network service and/or to configure the AI performance metrics to be measured in the domain-specific AI pipeline belonging to the cross-domain network service”; See also [0091], “2. Cross-Domain AI Performance Configuration API or Cross-Domain AI Performance Delegation API (Request/Response)—It allows the cross-domain AI pipeline orchestrator (entity), via (e.g.) PCD-1 interface, to configure/delegate the desired/updated AI QoS (derived from the cross-domain AI QoS) that the domain-specific AI pipeline orchestrator (entity) is required to meet in the domain-specific AI pipeline belonging to the cross-domain network service. Additionally, it allows the cross-domain AI pipeline orchestrator (entity) to request the domain-specific AI pipeline orchestrator (entity) to (re) configure (e.g., retrain the model, reselect the model, terminate the model) the domain-specific AI pipeline belonging to the cross-domain network service and/or to configure the AI performance metrics to be measured in the domain-specific AI pipeline belonging to the cross-domain network service”; The domain-specific AI pipeline receives control information from the cross-domain AI pipeline orchestrator for particular running metrics, for example, that the domain-specific AI pipeline orchestrator is capable of measuring/reporting/escalating in the domain-specific AI pipeline belonging to the cross-domain network service);
analyzing information about at least one network element by using the first management data analytic module, to obtain a first analytic result and sending the first analytic result to the second communication apparatus (Subramanya, [0084] “Requesting/querying AI performance report for the domain-specific AI pipeline belonging to the cross-domain network service from the domain-specific AI pipeline orchestrator(s), [0085] Verifying whether the cross-domain AI QoS and/or the domain-specific AI QOS requirements for the cross-domain network service are satisfied”; See also [0092], “3. Cross-Domain AI Performance Reporting API or Cross-Domain AI Performance Escalation API (Request/Response or Subscribe/Notify)—It allows the cross-domain AI pipeline orchestrator (entity), via (e.g.) PCD-1 interface, to request/subscribe for AI performance metrics that the domain-specific AI pipeline (entity) orchestrator is capable of measuring/reporting/escalating in the domain-specific AI pipeline belonging to the cross-domain network service.”; The domain-specific AI pipeline provides the report as the analytic result; See also Figure 13B, 23 “AI Performance Report Collection, and 24. Cross-Domain AI Performance Report Response/Notify).
While Subramanya explicitly disclosed the domain-specific AI pipeline orchestrator (entity) in request/response communication with the cross-domain AI pipeline orchestrator, Subramanya did not explicitly disclose the request/response communications to include an identity of a first management data analytic module, as claimed.
However, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to include an identity of the domain-specific AI pipeline orchestrator (entity) within the request/response communications of Subramanya, since utilizing identities of the entities (such as network addresses) is a fundamental aspect of computer networking since as such identifiers allow the devices to locate and communicate with each other across the network.
Gebert disclosed the utilization of an identity of the first management data analytic module in communications (Gebert, [0089], Gebert disclosed NF service consumers obtaining NF identifiers for requesting a service from a NF service producer, in which, [0090] Gebert disclosed the NWDAF providing network analytics information upon request from one or more network functions within the network; Gebert disclosed at [0092], the utilization of a Service Based Architecture in 3GPP communication networks, in which Network Functions can provide their services, using defined protocols and interfaces to other network functions, in which the interfaces are referred to as Service-based interfaces (SBI), and may comprise REST Application Protocol Interface (API)-based interfaces, in which communication between the network elements may be for example, the common HTTP/2 Internet protocol; REST packets contain both source and destination addresses and ports, and therefore contain the identity of the network functions in communication).
One of ordinary skill in the art would have been motivated to combine the teachings of Subramanya and Gebert, since Subramanya explicitly disclosed the AI pipeline orchestrators as network functions (Subramanya, [0065]) and as such, the teachings of both references are within similar 3GPP environments (Subramanya, [0054] and Gebert, [0084]).
Therefore, it would have been obvious to one or ordinary skill in the art at the time the invention was filed to incorporate the teachings of Gebert within the teachings of Subramanya in order to obtain predictable results, allowing the network functions of Subramanya to communicate over REST, thereby providing the teachings Subramanya over network configurations and deployments in relation to 3GPP, as explicitly suggested by Subramanya, and explicitly provided by Gebert.
Claim 10 recites a management method, applied to a second communication apparatus, with limitations that are substantially similar to the limitations of claim 1, the difference being the viewpoint from the opposite end of communication in which the same communications occur (i.e. claim 1 recites the first apparatus sending the first information to the second apparatus, whereas claim 10 recites the second apparatus receiving the first information). As shown by the rejection of claim 1 above, the combination of Subramanya and Gebert disclosed such limitations.
Claim 15 recites a communication system, comprising: a first communication apparatus, and a second communication apparatus, performing limitations that are substantially similar to the limitations of claim 1. Subramanya and Gebert disclosed the recited first and second apparatuses (Subramanya, Figures 1-3).
As such, claims 10 and 15 are rejected under the same rationale applied above to claim 1.
Regarding claims 2 and 18, Subramanya and Gebert disclosed the method according to claim 1 and system of claim 15, wherein the capability information of the first management data analytic module comprises one or more of the following: a capability identity that indicates a capability of the first management data analytic module; information about supported analytic data; or information about a supported analytic operation (Subramanya, [0080], “performance capabilities (e.g., supported performance metrics, (re) configurable options such as model retraining, model reselection, model termination) of the domain-specific AI pipeline that the AI pipeline orchestrator is capable to configure in the domain-specific AI pipeline belonging to the cross-domain network service”; [0089], “AI reconfiguration methods and/or AI performance metrics that the domain-specific AI pipeline orchestrator (entity) is capable of configuring in the domain-specific AI pipeline belonging to the cross-domain network service.”). See motivation to combine above.
Regarding claim 3, Subramanya and Gebert disclosed the method according to claim 2, wherein the analytic data comprises one or more of the following: coverage analytic data that comprises related data for analyzing coverage of a signal sent by the at least one network element; mobility analytic data that comprises related data for analyzing handover of a terminal device between network elements; energy saving analytic data that comprises related data for analyzing energy saving of the at least one network element; service quality analytic data that comprises related data for analyzing service quality of the network element; rate analytic data that comprises related data for analyzing a data transmission rate of the at least one network element; or capacity analytic data that comprises related data for analyzing a capacity of the at least one network element (Subramanya, [0084]-[0088], Subramanya disclosed requesting the AI performance report for the domain-specific pipeline and, verifying whether the cross-domain AI QoS requirements are satisfied, and if needed, updating the AI QoS requirements based on the AI performance reports; the analytic information therefore includes service quality analytic data that comprises related data for analyzing service quality of the network element). See motivation to combine above.
Regarding claim 4, Subramanya and Gebert disclosed the method according to claim 2, wherein the analytic operation comprises one or more of the following: a problem identification operation that is used to identify a problem existing in a network; a root cause analytic operation that is used to identify a cause of a problem occurring in a network; or an analytic solution adjustment operation that is used to analyze an adjustment method of network information (Subramanya, [0086] Subramanya disclosed, “Performing root-cause analysis of the AI performance reports received from the domain-specific AI pipeline orchestrator(s)”; The information in the report therefore includes information about an analytic operation such as a problem identification operation that is used to identify a problem existing in a network; It is noted that the entirety of the limitation recited in claim 4 is directed to a limitation that is recited in the alternative in claim 2. As the rejection of claim 2 above does not reject this alternative, the limitation of claim 4 is directed to a non-selected embodiment). See motivation to combine above.
Regarding claims 5 and 20, Subramanya and Gebert disclosed the method according to claim 1 and system of claim 15, wherein the control information comprises one or more of the following: information about an analytic scope, wherein the analytic scope indicates the at least one network element; information about analytic time, wherein the analytic time indicates time at which the first management data analytic module performs the analytic operation; or capability enabling information, wherein the capability enabling information is used to enable a part of or all capabilities of the first management data analytic module (Subramanya, [0082], “information concerning the performance capabilities (e.g., supported performance metrics, (re) configurable options such as model retraining, model reselection, model termination) of the domain-specific AI pipeline that the AI pipeline orchestrator is capable to configure in the domain-specific AI pipeline belonging to the cross-domain network service”). See motivation to combine above.
Regarding claims 6, 12, and 16, Subramanya and Gebert disclosed the method according to claims 1 and 10, and system of claim 15, further comprising: sending, to the second communication apparatus (or receiving from the first communication apparatus per claim 12), information about an analytic status of the first management data analytic module, wherein the analytic status indicates a status of performing the analytic operation by the first management data analytic module (Subramanya, “ [0083] Subramanya disclosed the cross-domain pipeline orchestrator “requesting the domain-specific AI pipeline orchestrator to (re) configure (e.g., retrain the model, reselect the model, terminate the model) the domain-specific AI pipeline belonging to the cross-domain network service and/or to configure the AI performance metrics to be measured in the domain-specific AI pipeline belonging to the cross-domain network service” and [0084]-[0086] obtaining the report and verifying the status of the AI pipeline that implemented the changes as requested; The report therefore includes information about a status). See motivation to combine above.
Regarding claims 7, 13 and 17, Subramanya and Gebert disclosed the methods according to claims 1 and 10, and system of claim15, further comprising: receiving a query request from the second communication apparatus (or sending to the first communication apparatus, per claim 13), wherein the query request comprises the identity of the first management data analytic module, and the identity of the first management data analytic module is used to request an analytic result of the first management data analytic module (The combined teachings of Subramanya and Gebert disclosed the limitations: Subramanya disclosed at [0084]-[0086] obtaining the report and verifying the status of the AI pipeline that implemented the changes as requested; The report therefore includes information about a status; As explained in the rejection of claim 1, the REST communication between network functions, as disclosed by Gebert includes source and destination IP addresses). See motivation to combine above.
Regarding claim 8, Subramanya and Gebert disclosed the method according to claim 7, wherein the query request further comprises an identity of a first type of analytic result or a second type of analytic result to which the first analytic result belongs, the first type of analytic result represents an analytic result currently obtained by the first management data analytic module, and the second type of analytic result represents an analytic result historically obtained by the first management data analytic module (Subramanya, [0084] Subramanya disclosed, “Requesting/querying AI performance report for the domain-specific AI pipeline belonging to the cross-domain network service from the domain-specific AI pipeline orchestrator(s), [0085] Verifying whether the cross-domain AI QoS and/or the domain-specific AI QOS requirements for the cross-domain network service are satisfied”; Querying for the performance report is querying for an analytic result that represents either an analytic results currently obtained by the domain-specific AI pipeline or historically, because the request may include start time and stop time as disclosed in [0109])
Regarding claims 9, 14, and 19, Subramanya and Gebert disclosed the method according to claim 1, wherein the first communication apparatus is a domain management unit in an autonomous network, the second communication apparatus is a cross domain management unit in the autonomous network, the domain management unit is configured to manage one or more network elements, and the cross domain management unit is configured to manage a plurality of network elements;
the first communication apparatus is a cross domain management unit in an autonomous network, the second communication apparatus is a service operation unit in the autonomous network, the cross domain management unit is configured to manage a plurality of network elements, and the service operation unit is configured to manage one or more services implemented by the plurality of network elements; or
the first communication apparatus is a service operation unit in an autonomous network, the second communication apparatus is a third-party system other than the autonomous network, and the service operation unit is configured to manage one or more services implemented by a plurality of network elements (Subramanya, [0078] “the cross-domain trustworthy AI/ML framework for cognitive autonomous networks”; Fig. 10, Cross-Domain Policy/Intent Manager and Cross-Domain MD; [0064] domain-specific resource/service requrements).
Regarding claim 11, Subramanya and Gebert disclosed the method according to claim 10, wherein the first information comprises identities of a plurality of management data analytic modules and capability information of the plurality of management data analytic modules, the plurality of management data analytic modules comprise the first management data analytic module, and the method further comprising: determining the first management data analytic module from the plurality of management data analytic modules (The combination of Subramanya and Gebert disclosed the limitation as claimed, Subramanya, [0079]-[0084] disclosed the cross-domain AI pipeline orchestrator supporting interaction with each domain-specific AI pipeline orchestrator as shown in Fig. 12, and therefore with respect to [0081]-[0084] in view of such, Subramanya disclosed receiving the capabilities for each domain-specific AI pipeline; As explained in the rejection of claim 1, the REST communication between network functions, as disclosed by Gebert includes source and destination IP addresses. The cross-domain AI pipeline orchestrator is able to differentiate between the separate domain-specific AI pipelines as shown by the disclosed communications).
Conclusion
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/JERRY B DENNISON/Primary Examiner, Art Unit 2409
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