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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4-9, 12, 16, and 19-22 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Min et al. CN 114040321, hereinafter, ‘Min’.
Consider Claims 1 and as applied to claim 16 , Min teaches A method performed by a managing unit for predicting a serving Access Point, AP, among one or more APs comprised in a subset of APs, to serve a User Equipment, UE, in a communications network, wherein the UE is within a radio range of the one or more APs in the subset of APs(e.g., see at least 0046 “ …”the channel for each AP is shared by each UD …the server can obtain the status feedback…” ), the method comprising: obtaining a model associated to the subset of APs, for predicting the serving AP, which model is obtained based on training the model over a first training period (e.g., see at least 0055 –“…the reinforcement learning model is trained by using historical communication data, so the at the trained model can decided the target AP the UD needs to switch based on the RSS between the UD and the AP.” – see also 0061 – the reinforcement learning model …rounds of training), obtaining a predicted AP from the subset of APs to serve the UE, which predicted AP is obtained based on invoking the model with INPUT data for prediction, which INPUT data for prediction comprises radio signal measurements from a set of reporting APs comprised in the subset of APs, communicating a first indication to at least the predicted AP, which first indication indicates the AP that is predicted to serve the UE, and that only the predicted AP shall forward signals received from the UE to a Central Processing Unit, CPU, via a fronthaul (e.g., see at least 0058 – “ since the RSS is closely related to the communication rate the UD generates corresponding status feedback according to the RSS from different APs during the movement process and send it to the server.”- i.e., INPUT data, - see also figure 3 explanation in 0071-0077).
Consider Claims 4 and 19, Min teaches the claimed invention further comprising: communicating a second indication to each respective one or more APs in the subset of APs, except for the predicted AP, which second indication indicates that this AP is not a predicted AP to serve the UE, and that this AP do not need to forward signals received from the UE to the CPU (e.g., see indication to Target AP – 0071-0077- note: as best understood by the Examiner the indication is implicit based on the context of the Applicant’s original disclosure publication paragraph 0078-0079) .
Consider Claims 5 and 20, Min teaches further comprising: retraining the model continuously over one or more subsequent training periods, when updated training INPUT data is received, wherein the model is updated when the retraining has been performed (e.g., see updates in 0058 and 0077).
Consider Claims 6 and 21, Min teaches wherein the of the model over the one or more subsequent training periods, is performed continuously whenever anyone out of: a data traffic load drops below a threshold, a fronthaul capacity is available, a confidence level drops below a threshold, or. a measured hit rate drops below a threshold(i.e., the confidence level and measured hit rate are met based on the RSS status notes in 0071).
Consider Claims 7 and 22, Min teaches wherein the training INPUT data for the model continuously with a certain periodicity is received from one or more APs of the APs in the subset of APs (e.g., see at least 0072- the central coordinator runs an algorithm).
Consider Claim 8, Min teaches further comprising: determining the one or more APs to be comprised in the subset of APs, by selecting the largest possible number of APs, or a part thereof, that include APs which are potentially capable of receiving a transmission of the same UE (e.g., this is met based on the “different APs” noted in at least 0072).
Consider Claims 9, Min teaches further comprising: when detecting that an AP subset change is required, updating the subset of APs to comprise one or more second APs, by selecting the largest possible number of APs, or a part thereof, that include APs which are potentially capable of receiving a transmission of the same UE, and obtaining an updated model for the updated subset of APs comprising the one or more second APs, which updated model shall replace the model (i.e., this is met based on e.g., this is met based on the “different APs” noted in at least 0072, 0055 – “trained using historical data” ).
Consider Claims 12, Min teaches wherein any one or more out of: the first indication indicating the AP that is predicted to serve the UE, further indicates a confidence of the prediction of the predicted AP, and the second indication indicating that this AP is not a predicted AP to serve the UE, further indicates a confidence of not being predicted of the predicted AP(e.g., see indication to Target AP – 0071-0077- note: as best understood by the Examiner the indication is implicit based on the context of the Applicant’s original disclosure publication paragraph 0078-0079).
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.
Claim(s) 2-3, 10-11 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Min et al. CN 114040321, hereinafter, ‘Min’ in view of Chen et al. US Patent Pub. No.: 2025/0031230 A1, hereinafter, ‘Chen’.
Consider Claims 2 and 17, Min teaches the claimed invention wherein: the model is invoked with the INPUT data for prediction continuously with a certain periodicity, and the INPUT data for prediction comprises measurement data from each respective AP in the set of reporting APs(e.g., see at least 0058 – “ since the RSS is closely related to the communication rate the UD generates corresponding status feedback according to the RSS from different APs during the movement process and send it to the server.”- i.e., INPUT data, - see also figure 2 explanation in 0071-0077).
However, Min does not specifically teach using channel gain data in the model(s).
In analogous art, Chen teaches – in paragraph 0006 “… a base station (BS) may include a processor and a wireless transceiver coupled to the processor. The processor is configured to: obtain a number N and a first channel gain threshold, wherein the number N and the first channel gain threshold are determined based at least in part on uplink channel state information between the BS and multiple UEs; transmit with the wireless transceiver, a scheduling indicator report configuration to each of the multiple UEs; receive, with the wireless transceiver, multiple scheduling indicators; and select the number N of UEs for participating in local model training according to the multiple scheduling indicators…” paragraph 0009 - “the first channel gain threshold is a value of a K-th largest channel gain among channel gains received from the multiple UEs, wherein K is an integer not less than the number N”.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try using channel gain data as an input parameter to a ML model for the purpose of enabling predictive features as suggested by Chen.
Consider Claims 3 and as applied to claim 18, Min teaches wherein the training of the model during the first training period is performed by: receiving training INPUT data for the model continuously with a certain periodicity, which training INPUT data comprises first channel measurement data from each respective AP in the subset of APs, which first channel measurement data is measured by the particular AP on Uplink, UL, reference symbols transmitted by the UE, and is measured continuously with the certain periodicity within the first training period, and which received training INPUT data is used for the training of the model during the first training period(e.g., see at least 0058 – “ since the RSS is closely related to the communication rate the UD generates corresponding status feedback according to the RSS from different APs during the movement process and send it to the server.”- i.e., INPUT data, - see also figure 3 explanation in 0071-0077).
However, Min does not specifically teach using channel gain data in the model(s).
In analogous art, Chen teaches – in paragraph 0006 “… a base station (BS) may include a processor and a wireless transceiver coupled to the processor. The processor is configured to: obtain a number N and a first channel gain threshold, wherein the number N and the first channel gain threshold are determined based at least in part on uplink channel state information between the BS and multiple UEs; transmit with the wireless transceiver, a scheduling indicator report configuration to each of the multiple UEs; receive, with the wireless transceiver, multiple scheduling indicators; and select the number N of UEs for participating in local model training according to the multiple scheduling indicators…” paragraph 0009 - “the first channel gain threshold is a value of a K-th largest channel gain among channel gains received from the multiple UEs, wherein K is an integer not less than the number N”.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try using channel gain data as an input parameter to a ML model for the purpose of enabling predictive features as suggested by Chen.
Consider Claim 10, Min teaches the claimed invention except wherein the managing unit is located in any one out of: the CPU), or in each AP of the comprised in the subset of APs or updated subset of APs, such that each AP obtains its own model by performing its own training and obtains its own predicting of AP.
In analogous art, Chen teaches local model training – see at least 0006.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try local model training to arrive at the predictable result wherein the managing unit is located in any one out of: the CPU), or in each AP of the comprised in the subset of APs or updated subset of APs, such that each AP obtains its own model by performing its own training and obtains its own predicting of AP for the purpose of federated learning across multiple APs.
Consider Claim 11, Min teaches the claimed invention except wherein a first part of the managing unit is located the CPU and a second part of the managing unit is located in each AP comprised in the subset of APs or updated subset of APs, and wherein the first part of the managing unit is obtaining the model and sends it to each AP of one or more APs, and each second part of the managing unit performs its own training and obtains its own predicting of AP.
In analogous art, Chen teaches local model training and global model – see at least 0006 and a global model 0019-0020.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try local model and global model training to arrive at the predictable result wherein a first part of the managing unit is located the CPU and a second part of the managing unit is located in each AP comprised in the subset of APs or updated subset of APs, and wherein the first part of the managing unit is obtaining the model and sends it to each AP of one or more APs, and each second part of the managing unit performs its own training and obtains its own predicting of AP for the purpose of federated learning across multiple APs.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Min et al. CN 114040321, hereinafter, ‘Min’ in view of Chen et al. US Patent Pub. No.: 2025/0031230 A1, hereinafter, ‘Chen’ and further in view of Well Known art.
Consider Claim 13, Min as modified by Chen teaches the claimed invention except wherein each AP in the respective the subset of APs and/or updated subset of APs, comprises a respective set of multiple antennas and per antenna out of the multiple antennas of the particular AP, and a subsequent channel gain measurement data is measured continuously with the certain periodicity within a respective subsequent training period on UL reference symbols transmitted by the UE and per antenna out of the multiple antennas of the particular AP.
However, the Examiner takes official notice that MIMO multiple-input multiple-output (MIMO) techniques. Multi-antenna techniques can significantly increase the data rates and reliability of a wireless communication system.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try MIMO to arrive at the predictable result wherein each AP in the respective the subset of APs and/or updated subset of APs, comprises a respective set of multiple antennas and per antenna out of the multiple antennas of the particular AP, and a subsequent channel gain measurement data is measured continuously with the certain periodicity within a respective subsequent training period on UL reference symbols transmitted by the UE and per antenna out of the multiple antennas of the particular AP for the purpose of increasing data rates.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20180152849 A1 teaches set of training fingerprints may be access, in which each of the training fingerprints may specify an access point of a plurality of access points to which a mobile device made a successful connection and a cellular signal strength of a cellular tower near the client device when the successful connection was made. An interim model may be generated from the accessed set of training fingerprints, in which the interim model may contain a subset of the information in the set of training fingerprints to enable a destination device to generate a prediction model to predict an availability of an access point. The generated interim model may be transferred to the destination device.
US 20170325138 teaches a method and a first radio network node (110) for managing input parameters to a set of models for prediction of a quality of service of a user equipment (120) are disclosed. The first radio network node (110) operates the set of models for prediction of the quality of service. The quality of service relates to when the user equipment (120) is served by a second radio network node (130) after a handover from the first radio network node (110) to the second radio network node (130). The first radio network node (110) configures (205) the second radio network node (130) to report the input parameters at least once before the handover. The input parameters are usable by the first radio network node (110) when predicting, by use of the set of models, the quality of service. A corresponding computer program and a carrier therefor are also disclosed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES TERRELL SHEDRICK whose telephone number is (571)272-8621. The examiner can normally be reached 8A-5P.
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, Matthew D Anderson can be reached at 571 272 4177. 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.
/CHARLES T SHEDRICK/Primary Examiner, Art Unit 2646
Prosecution Insights