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 .
Response to Amendment
Applicant’s amendment, filed 26 February 2026, has been entered and carefully considered.
Claims 1-9 and 12-19 are amended.
Claims 1-19 are currently pending.
The outstanding objection to Claims 1-9 and 12-19 is withdrawn in light of Applicant’s amendment.
The outstanding rejection of Claims 1-19 under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) is withdrawn in light of Applicant’s amendment to Claims 1, 2 and 7.
Response to Arguments
Applicant’s arguments with respect to claims 1 and 7, specifically with regards to the newly added limitations, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. However, the Office notes that certain remarks provided by Applicant in the response filed 26 February 2026 with regards to the claim language and the Akman reference are not persuasive.
First, the Applicant states, at page 14 of the response, that (emphasis added by the Office) “the present application requires that the first base station acquire optimization policy information and parameter optimization models originating from a completed optimization by a second base station, compute SLA indicators, and adjust its own configuration accordingly.” Looking to claim 1, the preamble of the claim recites “(a) method for network slice self-configuration, applied to a first base station connected with a server that is in turn connected with a second base station”; however, the claim does not clearly set forth that the first base station performs the method, as alleged by Applicant. As the preamble recites that the method is applied to the elements, not performed by the elements either in combination or individually, Applicant’s statement that the application “requires” the first base station to perform certain steps is not commensurate in scope with the claim language, as currently drafted. Further, the claimed acquiring steps are not further defined in the claim language so as to require a specific operation or manner in which the optimization policy information or SLA indicator is “acquired.”
Applicant further states that “there is no disclosure of the server selecting specific base stations to act in distinct roles” and that “the server identifies the base station having the full set of data to share with another base station that lacks effective data”. However, although the claims now state “wherein the first base station and the second base station are selected by the server”, the claim does not indicate that the server selects the base stations for any recited reason or purpose. Further, the claims do not indicate from what the base stations are selected. Therefore, Applicant’s remarks are not commensurate in scope with the claim language, as currently drafted.
Lastly, Applicant states that “according to Akman, KPIs are raw performance measurements collected for central processing, while the gNodeBs do not generate optimization models or resource configuration information.” The Office notes that the claimed “parameter optimization models” and “network slice resource configuration information” are not further defined in the claim language so as to require a certain interpretation or format of the claimed “models” or “information.” A review of Applicant’s specification does not yield a special definition for either term. Therefore, it is submitted that the exchange of SLA slice configuration information and reconfiguration parameters (paragraphs 0040 and 0075, for example) discloses in Akman reasonably reads on these claim limitations.
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.
Claim 2 is 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 2 has been amended to recite the following (emphasis added by the Office): “utilizing, by the first base station, the first network slice resource configuration information and the parameter optimization model from the second base station, for subsequent optimization, so as to realize the lateral federated learning between the first base station and the second base station; and the second base station is selected by the base station for a desired quality and quantity of data samples.” With regards to the first highlighted limitation, the claim language expresses an intended result of the “utilizing” step, but it is not clear what the “utilizing” would comprise such that “subsequent optimization” results in a realized lateral federated learning between the two base stations. As such, the phrase “so as to realize the lateral federated learning between the first base station and the second base station” is indefinite and further is not accorded patentable weight (refer to MPEP 2111.04). Regarding “a desired quality and quantity of data samples”, the Office interprets this phrase as a relative term which renders the claim indefinite. The term “desired quality or quantity” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For these reasons, Claim 2 is found to be indefinite.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Akman et al (United States Pre-Grant Publication 20210160153), hereinafter Akman, in view of Li et al (United States Pre-Grant Publication 2023/0244952), hereinafter Li.
Regarding Claim 1, Akman discloses a method for network slice self-optimization, applied to a first base station connected with a server that is in turn connected with a second base station (Figure 4B and paragraphs 0061-0064 – multiple gNodeBs (base stations) are connected to a service management and orchestration (SMO) system comprising a slice assurance function (SAF), Network Slice Management Function (NSMF) and Network Slice Subnet Management Function (NSSMF)), the method comprising,
acquiring optimization policy information and indicator threshold sent by the server, wherein the optimization policy information comprises first network slice resource configuration information and a parameter optimization model, and the first network slice resource configuration information and the parameter optimization model are both from the second base station (paragraphs 0033, 0040, and 0042 – the SAF collects KPIs from each of the gNodeBs, the number and types of slices supported by each Tracking area and associated RAN-specific slice SLAs; paragraph 0035 – each gNodeB comprises a SAF client to receive reconfiguration commands and to enable activation of configuration changes; Figure 7 (paragraphs 0073-0075) shows more specific operation of the SAF, collecting slice KPIs from all gNodeBs, reoptimizing slice parameters among flows, and programming new slice configuration parameters for one or more base stations);
acquiring a service-level agreement (SLA) indicator according to the first network slice resource configuration information and the parameter optimization model (paragraphs 0073-0075 – the SAF collects KPIs associated with each slice from all base stations and determines if prediction-based SLA adjustment should be performed; paragraphs 0066-0070 – the SAF sends a request to the base station to reconfigure resources); and
adjusting the first network slice resource configuration information, in response to a dissatisfaction of the SLA indicator with the indicator threshold, such that the SLA indicator satisfies the indicator threshold (paragraph 0069 – the SAF client in the gNodeB can respond to the SAF indicating successful implementation of the reconfiguration).
However, while Akman discloses first and second base stations (Figure 4B – gNodeBs comprising SAF clients) and a server (Figure 4B – SAF 400 as part of the service management and orchestration system), Akman does not disclose the first and section base station are selected by the server or both the first network slice resource configuration information and the parameter optimization model are data originated from a completed optimization by the second base station. In an analogous art, Li renders both limitations obvious for the following reasons. Specifically, Li discloses federated learning AI model aggregation in a wireless network (see Figures 3 and 4), where a base station representing an edge server communicates with edge devices that transmit parameters of local models (or gradients used to derive the local models) to the edge server to update the global model (paragraph 0069). As further shown in Figure 4, the edge device (UE) determines quantized parameters based on AI modeling (step 450), then generates and transmits a message that includes quantized parameters or gradients to the base station comprising the edge server (steps 455 and 460). Further, the other (e.g., a first) edge devices receive an updated global model after the edge server receives the quantized parameters received from the second edge device. As such, Li renders obvious the limitation of selection of first/second base stations (communications with edge devices absent further limitation in the claim language), and data originated by the second base station (as the second edge device generates parameters and gradients for use in updating a global model then delivered to a first edge device). Thus, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine Akman and Li. One would have been motivated to do so in order to reduce the amount of data transmitted across the network in order to train AI models (refer to paragraphs 0068 and 0069 of Li).
Regarding Claim 10, Akman discloses a base station (paragraph 0044 - gNodeB), comprising a memory (paragraph 0044 – the gNodeB comprises memory), a processor (paragraph 0044 – the gNodeB comprises a processor), and a computer program stored in the memory and executable by the processor (paragraph 0044 – the memory (medium stores instructions for execution by the processors of the device) which, when executed by the processor causes the processor to carry out the method as claimed in claim 1 (refer to rejection of Claim 1 above).
Regarding Claim 11, Akman discloses a non-transitory computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to carry out the method as claimed in claim 1 (paragraph 0044 – the memory (medium stores instructions for execution by the processors of the device; refer to rejection of Claim 1 above).
Regarding Claim 2, the combination of Akman and Li further discloses acquiring the SLA indicator according to the first network slice resource configuration information and the parameter optimization model comprises, inputting the first network slice resource configuration information into the parameter optimization model for optimization and prediction, to acquire the SLA indicator (Figure 7, steps 602-605 and paragraphs 0073-0075 – the SAF collects all slice KPIs to determine if all tracking area slice SLAs are met), the method further comprises utilizing, by the first base station, the first network slice resource configuration information and the parameter optimization model from the second base station, for subsequent optimization, so as to realize the lateral federated learning between the first base station and the second base station (Figure 4 – the first edge device (UE 430) receives a global model that includes an indication of what is to be updated in the local model as a result of the parameters provided by a second edge device (UE 420) (paragraph 0084)); and the second base station is selected by the base station for a desired quality and quantity of data samples (paragraph 0073 – the base station (with edge server) configures edge devices to use MAC layers and PHY layers to better support federated learning model aggregation, including quantized parameters or gradients). Thus, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to further combine Akman and Li. One would have been motivated to do so in order to reduce the amount of data transmitted across the network in order to train AI models (refer to paragraphs 0068 and 0069 of Li).
Regarding Claims 3, 13 and 17, Akman discloses adjusting the first network slice resource configuration information comprises, acquiring second network slice resource configuration information which is local (Figure 7, step 602 and paragraph 0073 – the SAF acquires slice KPIs from all gNodeBs serving a tracking area); and
adjusting the first network slice resource configuration information according to the second network slice resource configuration information (Figure 7, step 642 and paragraph 0075 – the SAF sends new slice configuration parameters to the gNodeBs).
Regarding Claims 4, 14 and 18, Akman discloses after acquiring the SLA indicator according to the first network slice resource configuration information and the parameter optimization model, the method further comprises, reporting the first network slice resource configuration information and the parameter optimization model to the server, in response to a satisfaction of the SLA indicator with the indicator threshold (Figure 7, step 609 and paragraphs 0073-0075 – if all tracking area slice SLAs are met and prediction-based SLA adjustment is not necessary, then the process iterates to a next time interval to repeat the process (of collecting slice KPIs from all gNodeBs)).
Regarding Claims 5, 15 and 19, Akman discloses the optimization policy information is acquired by, acquiring a task threshold sent by the server (paragraph 0071 - If a slice SLA cannot be met, the SAF generates Performance Management jobs (tasks) to be executed by each gNodeB so that the slice KPIs are collected and reported for the consumption of SAF (the goal of the task corresponding to the network slice));
acquiring local network slice resource configuration information and the parameter optimization model (Figure 7, step 602 and paragraph 0073-0075 – the SAF collects slice KPIs from all gNodeBs serving the tracking area);
acquiring a candidate SLA indicator according to the local network slice resource configuration information and the parameter optimization model (Figure 7, step 604 and paragraphs 0073-0075 - the SAF determines whether the tracking areas total slice SLAs are met over a certain period);
adjusting the local network slice resource configuration information, in response to a dissatisfaction with the candidate SLA indicator with the task threshold, such that the candidate SLA indicator satisfies the task threshold (Figure 7, steps 605 and 618 and paragraphs 0073-0075 – the SAF re-optimizes slice parameters among flows in the slice SLAs are not met);
determining the adjusted local network slice resource configuration information as the first network slice resource configuration information, in response to a satisfaction of the candidate SLA indicator with the task threshold (Figure 7, steps 604, 609 and 618 and paragraphs 0073-0075 – a prediction-based SLA adjustment (e.g., preemptively reallocate slices resources to compute new slice configuration parameters for the slice) can be made if all slice SLAs are met); and
acquiring the optimization policy information according to the first network slice resource configuration information and the parameter optimization model (Figure 7, step 642 and paragraphs 0073-0075 – the SAF can reconfigure the base stations to implement the new slice configuration parameters).
Regarding Claim 6, Akman discloses the first network slice resource configuration information comprises at least one of: a resource reservation policy, a quality-of-service configuration policy, or a 5G quality of service indicator (Figure 1 shows a 5G RAN architecture; paragraph 0006 – the network SLA defines quality of service requirements; paragraph 0066 – the SAF calculates weights to apportion each tracking area’s SLA configuration, therefore meeting the claimed alternative limitation).
Regarding Claim 7, Akman discloses a method for network slice self-optimization, applied to a second base station connected with a server that is in turn connected with a first base station Figure 4B and paragraphs 0061-0064 – multiple gNodeBs (base stations) are connected to a service management and orchestration (SMO) system comprising a slice assurance function (SAF), Network Slice Management Function (NSMF) and Network Slice Subnet Management Function (NSSMF)), the method comprising,
acquiring a task threshold sent by the server (paragraph 0071 - If a slice SLA cannot be met, the SAF generates Performance Management jobs (tasks) to be executed by each gNodeB so that the slice KPIs are collected and reported for the consumption of SAF (the goal of the task corresponding to the network slice));
acquiring local network slice resource configuration information and a parameter optimization model (Figure 7, step 602 and paragraph 0073-0075 – the SAF collects slice KPIs from all gNodeBs serving the tracking area and reconfigures the base stations to implement the new slice configurations for the slice via commands described in paragraphs 0066-0070);
acquiring first network slice resource configuration information according to the task threshold, the local network slice resource configuration information and the parameter optimization model (paragraphs 0073-0075 – the SAF collects KPIs associated with each slice from all base stations and determines if prediction-based SLA adjustment should be performed; paragraphs 0066-0070 – the SAF sends a request to each base station to reconfigure resources);
sending the first network slice resource configuration information and the parameter optimization model to the first base station through the server, to cause the first base station to acquire a service-level agreement (SLA) indicator according to the first network slice resource configuration information and the parameter optimization model (paragraphs 0073-0075 – the SAF collects KPIs associated with each slice from all base stations and determines if prediction-based SLA adjustment should be performed; paragraphs 0066-0070 – the SAF sends a request to each base station to reconfigure resources), and
to cause the first base station to adjust the first network slice resource configuration information, in response to a dissatisfaction of the SLA indicator with an indicator threshold, such that the SLA indicator satisfies the indicator threshold (Figure 7, steps 604, 609 and 618 and paragraphs 0073-0075 – a prediction-based SLA adjustment (e.g., preemptively reallocate slices resources to compute new slice configuration parameters for the slice) can be made if all slice SLAs are met).
However, while Akman discloses first and second base stations (Figure 4B – gNodeBs comprising SAF clients) and a server (Figure 4B – SAF 400 as part of the service management and orchestration system), Akman does not disclose the first and section base station are selected by the server or both the first network slice resource configuration information and the parameter optimization model are data originated from a completed optimization by the second base station. In an analogous art, Li renders both limitations obvious for the following reasons. Specifically, Li discloses federated learning AI model aggregation in a wireless network (see Figures 3 and 4), where a base station representing an edge server communicates with edge devices that transmit parameters of local models (or gradients used to derive the local models) to the edge server to update the global model (paragraph 0069). As further shown in Figure 4, the edge device (UE) determines quantized parameters based on AI modeling (step 450), then generates and transmits a message that includes quantized parameters or gradients to the base station comprising the edge server (steps 455 and 460). Further, the other (e.g., a first) edge devices receive an updated global model after the edge server receives the quantized parameters received from the second edge device. As such, Li renders obvious the limitation of selection of first/second base stations (communications with edge devices absent further limitation in the claim language), and data originated by the second base station (as the second edge device generates parameters and gradients for use in updating a global model then delivered to a first edge device). Thus, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine Akman and Li. One would have been motivated to do so in order to reduce the amount of data transmitted across the network in order to train AI models (refer to paragraphs 0068 and 0069 of Li).
Regarding Claim 8, Akman discloses acquiring the first network slice resource configuration information according to the task threshold, the local network slice resource configuration information and the parameter optimization model comprises,
acquiring a candidate SLA indicator according to the local network slice resource configuration information and the parameter optimization model (Figure 7, step 604 and paragraphs 0073-0075 - the SAF determines whether the tracking areas total slice SLAs are met over a certain period);
adjusting the local network slice resource configuration information, in response to a dissatisfaction with the candidate SLA indicator with the task threshold, such that the candidate SLA indicator satisfies the task threshold (Figure 7, steps 605 and 618 and paragraphs 0073-0075 – the SAF re-optimizes slice parameters among flows in the slice SLAs are not met); and
determining the adjusted local network slice resource configuration information as the first network slice resource configuration information, in response to a satisfaction of the candidate SLA indicator with the task threshold (Figure 7, steps 604, 609 and 618 and paragraphs 0073-0075 – a prediction-based SLA adjustment (e.g., preemptively reallocate slices resources to compute new slice configuration parameters for the slice) can be made if all slice SLAs are met).
Regarding Claim 9, Akman discloses the local network slice resource configuration information comprises at least one of: a resource reservation policy, a quality-of-service configuration policy, or a 5G quality-of-service indicator (Figure 1 shows a 5G RAN architecture; paragraph 0006 – the network SLA defines quality of service requirements; paragraph 0066 – the SAF calculates weights to apportion each tracking area’s SLA configuration, therefore meeting the claimed alternative limitation).
Regarding Claims 12 and 16, Akman discloses acquiring the SLA indicator according to the first network slice resource configuration information and the parameter optimization model comprises, inputting the first network slice resource configuration information into the parameter optimization model for optimization and prediction, to acquire the SLA indicator (Figure 7, steps 602-605 and paragraphs 0073-0075 – the SAF collects all slice KPIs to determine if all tracking area slice SLAs are met).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW W. CHRISS whose telephone number is (571)272-1774. The examiner can normally be reached Monday-Friday, 8am-4pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kevin Bates can be reached at (571) 272-3980. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW W CHRISS/Primary Examiner, Art Unit 2472
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