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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/26/2026 has been entered.
Response to Amendment
The Amendment filed 01/26/2026 has been entered.
Claim 8 has been amended.
Claims 2-15 remain pending in the application.
Response to Arguments
Applicant’s arguments with respect to claims 2-15 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.
Claim Rejections - 35 USC § 103
5. 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.
6. Claims 2-4, 10, 12 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Agrawal et al. (US 20130072213 A1) in view of Raley et al. (US 20190260879 A1).
Regarding claim 2, Agrawal teaches a system (system of Fig. 1) for improving quality of experience (QoE) with a wireless communication system (system for determining the effects of resource allocations on the quality of service experienced by its served UEs, with one aspect of such quality of service being the channel gain experienced by UEs, see [0022]), the system comprising:
a first access node (network controller 114 of Fig. 1) within a plurality of access nodes (nodes 108, 110, 112) the first access node collects data related to performance of one or more channels in the wireless communication system (The network controller 114 may suitably receive gain information from the base stations, see [0025] and One mechanism for determining this relative channel gain is through inverting Shannon's capacity formula:
g i g i ' = g k 0 i UL k .noteq. k 0 g ki UL .apprxeq. S CQI := P k 0 g k 0 i DL k .noteq. k 0 P k g ki DL + N T = 1 b CQI ( 2 MPR ( CQI ) a CQI - 1 ) . ##EQU00010##, see [0050]); a processor coupled to the first access node (processor 228 of Fig. 2B), the processor being capable of performing steps comprising: receiving the collected data (The network controller 114 may suitably receive gain information from the base stations, see [0025]) and information to assess QoE (The base station 108 may comprise various elements directed toward determining the effects of resource allocations on the quality of service experienced by its served UEs, with one aspect of such quality of service being the channel gain experienced by UE, [0022]).
However, Agrawal does not teach the processor performing an ergodic analysis using at least some of the collected data to determine a policy that comprises at least one network adjustment, the at least one network adjustment being correlated to at least one network change by the ergodic analysis; and providing the policy to at least one access node within the plurality of access nodes, the at least one access node uses the policy to improve the (QoE) with the wireless communication system.
In an analogous art, Raley teaches the processor performing an ergodic analysis using at least some of the collected data to determine a policy that comprises at least one network adjustment (Service Controller 122 collects information from Usage Report Store 136 including usage data or other statistics relating to the usage or consumption of services by one or more devices 100 on the network, [0164]; Service controller 122 may be further configured to evaluate the usage information received from the network elements (e.g., gateways 410, 420, or usage report, store 136) to determine where a given user or device stands with respect to consumptions of its allocations of one or more network services, and to determine whether a notification should be sent to the one or more devices 100 concerning usage of service allocations in accordance with a service policy, [0165]) the at least one network adjustment being correlated to at least one network change by the ergodic analysis (, Service Controller 122 communicates with various network elements and the device Service Processor 115 to perform functions such as, for example, updating network elements with new or modified policies and removing deleted policies , [0163]); and
providing the policy to at least one access node within the plurality of access nodes (the adaptive service classification policy updater provides a classification policy update automatically to one or more policy provisioning elements that then provide the classification policy to the traffic gateway, [0108]; Service controller 122 may also be configured to communicate that policy to device 100 such as, for example, via service control device link 1691, [0167]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the optimization of wireless networks of Agrawal with the policy of Raleigh to provide a method and a system that improves the service capabilities for various levels of service cost or for various types of devices or groups as suggested, Raleigh [0087].
Regarding claim 3, Agrawal as modified by Raleigh teaches the system according to claim 1 wherein the collected data comprises data relating to at least one channel gain within the wireless communication system (The network controller 114 may suitably receive gain information from the base stations, see Agrawal [0025]).
Regarding claim 4, Agrawal as modified by Raleigh teaches the system according to claim 2 wherein a probability distribution of channel gains is generated using at least a portion of the data relating to the at least one channel gain (One mechanism for determining this relative channel gain is through inverting Shannon's capacity formula:
g i g i ' = g k 0 i UL k .noteq. k 0 g ki UL .apprxeq. S CQI := P k 0 g k 0 i DL k .noteq. k 0 P k g ki DL + N T = 1 b CQI ( 2 MPR ( CQI ) a CQI - 1 ) . ##EQU00010##, see Agrawal [0050]).
Regarding claim 10, Agrawal as modified by Raleigh teaches the system according to claim 1. Raleigh further teaches wherein the policy is based at least in part on an outage-probability metric (a service usage activity that attempts to communicate via the network with a specified QoS level that is not available under the access policies of the current service plan or due to network congestion, Raleigh [0224]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the optimization of wireless networks of Agrawal with the policy of Raleigh to provide a method and a system that improves the service capabilities for various levels of service cost or for various types of devices or groups as suggested, Raleigh [0087].
Regarding claim 12, Agrawal as modified by Raleigh teaches the system according to claim 1 wherein the collected data comprises at least one of a geometric average channel gain, reference signal received power, reference signal received quality, interference data, and noise data (The network controller 114 may suitably receive gain information from the base stations 108-112 and compute interference penalty information and perform resource allocation for the network 100 as a whole, or each of the base stations 108-112 may receive or compute gain information for its users and compute interference penalty information and perform resource allocation for its users, Agrawal [0025]).
Regarding claim 15, Agrawal as modified by Raleigh teaches the system according to claim 1. V further teaches wherein the processor estimates QoE parameters as part of the ergodic analysis (For example, this approach generally leads to service management or traffic shaping where certain aspects of a service are controlled based on service policies to provide lower levels of quality of service, Raleigh [0098]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the optimization of wireless networks of Agrawal with the policy of Raleigh to provide a method and a system that improves the service capabilities for various levels of service cost or for various types of devices or groups as suggested, Raleigh [0087].
7. Claims 5-8 are rejected under 35 U.S.C. 103 as being unpatentable over Agrawal in view of Raleigh and further in view of Bennis et al. (US 20140269300 A1).
Regarding claim 5, Agrawal as modified by Raleigh teaches the system according to claim 1. However, Agrawal and Raleigh do not teach wherein the processor performs the ergodic analysis using a learn-ed resource manager (LRM).
In an analogous art, Bennis teaches wherein the processor performs the ergodic analysis using a learn-ed resource manager (LRM) (Hence, each SCBS 120 learns over time how to select sub-bands and corresponding power levels in licensed and unlicensed bands, see Bennis [0038] and step 440 of Fig. 4).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the optimization of wireless networks of Agrawal and Raleigh with the system of Bennis to provide a method and a system that define QoE target parameter to enforce and ensure a certain quality of experience for applications as suggested.
Regarding claim 6, Agrawal as modified by Raleigh and Bennis teaches the system according to claim 4. Bennis further teaches wherein the LRM comprises an artificial intelligence process (the proposed approach was able to steer users' traffic in an intelligent and dynamic manner over both the licensed and unlicensed spectrums, Bennis [0067]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the optimization of wireless networks of Agrawal and Raleigh with the system of Bennis to provide a method and a system that define QoE target parameter to enforce and ensure a certain quality of experience for applications as suggested.
Regarding claim 7, Agrawal as modified by Raleigh and Bennis teaches the system according to claim 5. Bennis further teaches wherein the artificial intelligence process comprises at least one of a machine learning process, a deep learning process, a neural network, a generalized linear model, a gradient boosting method, a hidden Markov model, a logistic regression model, a rectified linear unit (ReLU), and any combination thereof (a network model of M=1 macrocell base stations (mBS) operates over a set S={1, . . . , S', . . . S} of S frequency bands out of which S' are over the licensed spectrum. A set K={1, . . . , K} of K SCBSs underlay the mBS, Bennis [0033]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the optimization of wireless networks of Agrawal and Raleigh with the system of Bennis to provide a method and a system that define QoE target parameter to enforce and ensure a certain quality of experience for applications as suggested.
Regarding claim 8, Agrawal as modified by Raleigh and Bennis teaches the system according to claim 4. Bennis further teaches wherein the LRM performs at least one of a feature extraction, creates a generalized linear model gradient, utilizes a boosting method, or implements a hidden Markov model (In order to cope with peak data traffic demands, mobile service providers (MSPs) are compelled to support the growth in mobile data traffic by finding new ways to boost network capacity for their customers, providing better coverage, and easing network congestion, [0021]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the optimization of wireless networks of Agrawal and Raleigh with the system of Bennis to provide a method and a system that define QoE target parameter to enforce and ensure a certain quality of experience for applications as suggested.
8. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Agrawal in view of Raleigh and further in view of Budka et al. (US 20030202574 A1).
Regarding claim 9, Agrawal as modified by Raleigh teaches the system according to claim 1.
However, Agrawal and Raleigh do not teach wherein the policy comprises ergodic spectrum management (ESM) guidance with QoE-influenced functional-choice specification of the modulation and coding-system (MCS) parameters.
In an analogous art Budka teaches wherein the policy comprises ergodic spectrum management (ESM) guidance with QoE-influenced functional-choice specification of the modulation and coding-system (MCS) parameters (The sample average of BEP.sub.t values in the received sequence is equal to the time average bit error probability of the channel under the assumption that the channel is stationary and ergodic, [0047] and Channel quality is fairly static over short periods of time. As a result of this high correlation, back-to-back downlink (uplink) TBFs are likely to experience similar airlink quality. The PCU 18 takes advantage of this high level of correlation to properly select the starting MCS for a new TBF, based on quality information from the old one, [0133]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the optimization of wireless networks of Agrawal and the policy of Raleigh with the link adaptation of Budka to provide a method and a system Enhanced General Packet Radio Service Networks; and more particularly, link adaptation in such networks as suggested, Budka [0002].
9. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Agrawal in view of Raleigh and further in view of Hennacy et al. (US 20200045353 A1).
Regarding claim 11, Agrawal as modified by Raleigh teaches the system of claim 9.
However, Agrawal and Raleigh do not teach wherein the outage-probability metric is based upon an adaptively learned Markov model.
In an analogous art, Hennacy teaches wherein the outage-probability metric is based upon an adaptively learned Markov model (The Markov model can represent probabilities of a watermark outage occurring at any time in the watermark encoding data based on a previous watermark outage or an elapsed time in a media signal (e.g., fifteen minutes into a television program, etc.). In some examples, the Markov model is established based on an initial training period, with an occurrence of a new state (e.g., a watermark outage that does not correspond to an existing state in the model, etc.) resulting in a watermark outage alarm, [0022]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the optimization of wireless networks of Agrawal and Raleigh with the model of Hennacy to provide a method and a system for accurate metrics of exposure to the media corresponding to the media signals as suggested, Hennacy [0002].
10. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Agrawal in view of Raleigh and further in view of Lundqvist et al. (US 20180242191 A1).
Regarding claim 13, Agrawal as modified by Raleigh teaches the system according to claim 1.
However, Agrawal and Raleigh do not teach wherein the collected data comprises at least one of a modulation and coding-system (MCS) parameter, an energy parameter, a beamforming parameter, a precoder parameter, a transmission's duration in symbol periods, a channel- frequency index, and a code-rate parameter.
In an analogous art, Lundqvist teaches wherein the collected data comprises at least one of a modulation and coding-system (MCS) parameter, an energy parameter, a beamforming parameter, a precoder parameter, a transmission's duration in symbol periods, a channel- frequency index, and a code-rate parameter (A network node 531, 532, 533, 534, e.g. a base station or a control node in the access network 530, or the network device 120, can make a prediction of the channel quality Q.sub.pred for a user for a certain specified time period. The predicted channel quality Q.sub.pred can be translated to a modulation and coding scheme ( MCS), see Lundqvist [0167]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the optimization of wireless networks of Agrawal and Raleigh with the prediction of Lundqvist to provide a method and a system for prediction radio channel quality improve the performance in a communication network as suggested, see Lundqvist [0005].
11. Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Agrawal in view Raleigh and further in view of Dey et al. (US 20140280679 A1).
Regarding claim 14, Agrawal as modified by Raleigh teaches the system according to claim 1.
However, Agrawal and Raleigh do not teach wherein the processor determines the policy by using at least one of an ergodic water filling method, an ergodic iterative water filling method, an ergodic spectrum management (ESM) Stage 1 iterative water filling, an ESM Stage 2 optimum spectrum balancing, an ESM Stage 2 orthogonal dimension division (ODD) method, an ESM Stage 3 method, a gradient descent method, and any other form of an iterative optimization method.
In an analogous art, Dey teaches wherein the processor determines the policy by using at least one of an ergodic water filling method, an ergodic iterative water filling method, an ergodic spectrum management (ESM) Stage 1 iterative water filling, an ESM Stage 2 optimum spectrum balancing, an ESM Stage 2 orthogonal dimension division (ODD) method, an ESM Stage 3 method, a gradient descent method, and any other form of an iterative optimization method (The first phase is to attempt to assign enough subcarriers to satisfy R.sub.min of each user, assuming equal power assignment per subcarrier and starting with the video request that has the best channel condition. The second phase is to re-allocate power, first to ensure R.sub.min of each user that was allocated in the first step, and then using water filling to assign the remaining power optimally to the users that were given subcarriers. Optionally, the first step may be executed again to reevaluate the subcarrier assignment based on the power allocation of the second phase of the previous iteration and subsequently assign power; i.e., repeating the first and second steps multiple times to get improved power and subcarrier assignment, see [0092]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the optimization of wireless networks Agrawal and Raleigh with the water filling algorithm of Dey to provide a method and a system to allocate power and bandwidth to maximize overall cell throughput while satisfying the ith user's minimum rate requirements, which is the rate that, if sustained, guarantees a video session without stalling as suggested, see Dey [0091].
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Perl et al. (US 11562435 B2) discloses: Proposed is a mobile automotive car system, and method thereof, for a dynamic, telematics-based connection search engine and telematics data aggregator, wherein risk-transfer profiles are captured and categorized in a results list from a plurality of first risk-transfer systems based on dynamically generated driving score parameters by means of appropriately triggered automotive data. As a variant, during a predefined trial period, the automotive and driving behavior data can be collected, which are transmitted together with the generated driving score parameters to multiple automated first risk-transfer systems for quotation. The user is able to dynamically select the best-fitting first risk-transfer system for risk-transfer by means of the results list, which is provided and updated in real-time for display to and selection by a user of a mobile telecommunication apparatus by means of a mobile telematics application of the mobile telecommunications apparatus.
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICOLE M LOUIS-FILS whose telephone number is (571)270-0671. The examiner can normally be reached Monday-Friday.
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, Charles Appiah can be reached at 571-272-7904. 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.
/NICOLE M LOUIS-FILS/ Examiner, Art Unit 2641
/JINSONG HU/ Supervisory Patent Examiner, Art Unit 2643