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 § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
2. Claims 1 and 8 are rejected under 35 U.S.C. 103(a) as being unpatentable over U.S. Pub. 2008/0298249 to Baker in view of U.S. Pub. 2016/0323903 to Fujishiro and U.S. Pub. 2021/0368432 to Tsuda.
Regarding claim 1, Baker teaches a cell selection method adapted to a terminal device, comprising:
during performing a cell selection operation on a plurality of reference base stations, obtaining a signal indicator and a network congestion indicator corresponding to each of the reference base stations (see section [0045] and [0052], which teach that each access point (AP) broadcasts its congestion parameter and where section [0045] teach that the UE receives the congestion parameter and measures the signal strength of each AP (where RSS stands for “received signal strength”); and
selecting a serving base station from the reference base stations based on the signal indicator and the network congestion indicator corresponding to the each of the reference base stations (see sections [0045], [0052] and claim 13 of Baker which teach selecting the appropriate AP based on the values of congestion and signal strength stored in a table in the UE).
As Baker teaches the UE measuring the signal strength of each AP (again where RSS stands for “received signal strength), as RSS is not a “parameter/indicator” per se, Fujishiro is added.
In an analogous art, Fujishiro teaches a UE performing handover operations. As shown in Fig. 11, the UE 100 provides a measurement report of all detected APs, where each AP is identified by SSID, and each AP has an associated RSSI (where RSSI stands for “received signal strength indicator”). Therefore, the RSSI parameter in Fujishiro may be the “obtained signal indicator”. See also Fig. 7 (sections [0088] to [0093] which describe “signal strength selection parameters”) and see sections [0100] to [0102] and [0137] to [0147], which teach using the RSSI signal indicator and a congestion (load status) parameter/indicator received from each AP, which are also used to determine or select the next AP.
Therefore, as both Baker and Fujishiro teach receiving congestion parameters (and Baker also teaches measuring signal strength), and as Fujishiro explicitly teaches obtaining and/or measuring signal strength indicator per se, it would have been obvious to one of ordinary skill to modify Baker to obtain a signal indicator per se, as Fujishiro teaches the conventionality of using all of RSSI indicators, signal strength selection parameters and load indicators, as these factors are useful to consider in handovers.
Regarding the amendment to claim 1 which now “wherein in response to the terminal device being in an idle state or an inactive state, one of intra-frequency cell information and inter-frequency cell information is obtained from a current serving base station to perform the cell selection operation”, Tsuda is added.
In an analogous art, Tsuda teaches a system for performing handovers and/or cell selection (see Abstract and Figs.4-6). As described in sections [0019] and [0136] Tsuda teaches “Furthermore, in this first aspect, in a state of an idle mode or an inactive mode, the above-described wireless communication unit may acquire, via the above-described system information, a parameter for cell selection/reselection, intra-frequency neighboring cell information, inter-frequency neighboring cell information…”, as recited.
Therefore, as Baker/Fujishiro teach performing cell selection and/or cell reselection, and as Tsuda explicitly teaches the newly recited feature of receiving intra-frequency or inter-frequency cell information (in the idle or inactive state), it would have been obvious to one of ordinary skill to modify Baker/Fujishiro to obtain the newly recited cell information, as Tsuda also teaches the conventionality of using the frequency information in cell selection and/or reselection processes.
Regarding claim 8, which teaches similar steps as in claim 1 (from the base station perspective), see the base station (APs) of Baker and Fujishiro, as described in the cited sections above relating to the rejection of claim 1.
Claims 2 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over the references as applied to claims 1 and 8 above, and further in view of U.S. Pub. 2022/0232445 to Shu.
Regarding claims 2 and 9, which recite “wherein the signal indicator corresponding to the each of the reference base stations comprises signal strength and signal quality, and the network congestion indicator corresponding to the each of the reference base stations comprises a user network throughput determined by the each of the reference base stations”, as Fujishiro teaches broadcasting throughput rates (see [0090] and [0137] to [0142]), but not a “signal quality parameter” per se, Shu is added.
In an analogous art, Shu teaches a UE which selects wireless systems based on signal strength and signal quality (further based on the type of application currently running on the UE). As described in section [0139], Shu teaches that a UE selects one AP at a time (and while connected to that AP) receives its signal strength parameter and QoS parameter, where [0030] teaches that the QoS parameter includes latency, jitter, error rate, etc. See also sections [0129] and [0145] to [0151] and the Tables (5-7) which show the UE storing a signal strength parameter (RSSI or RSRP) along with the QoS parameter associated with each base station (AP), where sections [0052], [0100] to [0101], [0129] to [0130] and [0147], also teach a user throughput parameter.
Therefore, as Baker/Fujishiro teach using quality of connections (but not a QoS parameter per se), and as Shu explicitly teaches obtaining both a signal strength and a quality/QoS parameter/indicators from each AP, it would have been obvious to one of ordinary skill to modify Baker to also obtain a QoS indicator per se, as Shu teaches the reasons that quality/QoS is also an important factor in AP selection for handovers.
Regarding claims 3 and 10, which recite “wherein the user network throughput is a current user network throughput determined by a first reference base station”, as the congestion and/or load parameters in Baker calculated by each AP (see sections [0034] to [0043]) is performed with real-time or current parameters/conditions and the QoS parameters in Shu relate to throughput and/or bandwidth (see sections [0052] and [0100] to [0101]), the combination of references would teach and/or render obvious this feature, as recited.
Claims 4 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over the references as applied to claims 2 and 9 above, and further in view of either one of U.S. Pubs. 2014/0274064 to Al-Shalash or 2016/0037304 to Dunkin.
Regarding claims 4 and 11, which recite “wherein the user network throughput is a future user network throughput predicted by a first reference base station”, as Baker and Fujishiro do not explicitly teach future throughput, Al-Shalash or Dunkin is added.
In an analogous art, Al-Shalash teaches a wireless system which uses past handover histories to determine future load (overload) conditions. See for example, sections [0010], [0027]-[0029], [0037]-[0043], [0052]-[0055]. Also in an analogous art, Dunkin also teaches a wireless system which uses a database and stored handover histories to determine future load (overload) conditions. See for example, sections [0007] to [0010], [0025]-[0029], and [0076] to [0077].
Therefore, as Baker/Fujishiro/Shu teach throughput in real time current (but not in the future), and as either Al-Shalash or Dunkin explicitly teach predicting future loads and throughputs, it would have been obvious to modify the Baker combination to also predicted future loads, as either one of Al-Shalash or Dunkin teach the reasons for this future anticipation, as they teach that the historical data once analyzed may be used to accurately predict future results and that these future results are useful in avoiding congestion in networks, as is desired.
Regarding claim 12, which recites “further comprising: feeding at least one historical user network throughput into a predictive model, wherein the predictive model predicts the future user network throughput in response to the at least one historical user network throughput”, see the cited sections of Al-Shalash or Dunkin which teach using historical data to predict the future load. It is noted that these references do not explicitly use the word “model” per se, however, the calculations described therein are using a formula which is a “mathematical model” which is equivalent to the recited “predictive model”.
Claims 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over the references as applied to claim 2 above, and further in view of either one of U.S. Pubs. 2022/0038974 to Roy or 2020/0322861 to Ozturk.
Regarding claim 5, which recites “wherein selecting the serving base station from the reference base stations based on the signal indicator and the network congestion indicator corresponding to the each of the reference base stations comprises:
finding at least one candidate base station from the reference base stations based on the signal indicator corresponding to the each of the reference base stations, wherein the signal indicator corresponding to each of the candidate base station satisfies a preset condition; selecting the serving base station from the at least one candidate base station based on the network congestion indicator corresponding to the each of the candidate base station”, as Baker/Fujishiro do not explicitly teach the recited “preset condition”, either one of Roy or Ozturk is added.
In an analogous art, Roy teaches a UE which selects wireless systems based on signal strength and load. As described in sections [0023]-[0034] and [0040]-[0051], Roy teaches the use of “preset threshold conditions” which are used to select the next cell and/or base station. Also in an analogous art, Ozturk teaches a UE which selects cells based on signal strength and load and received thresholds associated with strength and load (as in sections [0053] to [0056] and [0059] to [0062]).
Therefore, as Baker/Fujishiro teach selecting a base station based for handoffs, and as either Roy or Ozturk teach using and comparing signal and load parameters to “preset condition thresholds”, it would have been obvious to modify the Baker/Fujishiro combination to compare those parameters to preset thresholds, as Roy or Ozturk teach the reasons and conventionality that different base stations/cells which have different signals strengths and/or capacities each have specific parameters associated with that base station, which provides for the best use of network resources, as is desired.
Regarding claim 7, which recites “wherein the reference base stations comprise a first reference base station, the signal indicator corresponding to the first reference base station comprises signal strength and signal quality, and the method further comprises: in response to determining that the signal strength corresponding to the first reference base station is not lower than a signal strength threshold and the signal quality corresponding to the first reference base station is not lower than a signal quality threshold, determining that the signal indicator corresponding to the first reference base station satisfies the preset condition; and in response to determining that the signal strength corresponding to the first reference base station is lower than the signal strength threshold or the signal quality corresponding to the first reference base station is lower than the signal quality threshold, determining that the signal indicator corresponding to the first reference base station does not satisfy the preset condition”, as described above in the cited sections of Roy and Ozturk, the combination of these references teach these features. Roy/Ozturk teach that if the cells’ strength and/or load parameter is less than the “preset threshold condition”, that cell is not used for handover (as recited), and if the target cell’s strength and/or load parameters are above received cell preset cell selection threshold parameters, that cell is selected for the handover.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over the references as applied to claim 5 above, and further in view of U.S. Pub. 2019/0380076 to Wang.
Regarding claim 6, which recites “wherein the network congestion indicator corresponding to the each of the reference base stations comprises a user network throughput determined by the each of the reference base stations, and the serving base station has a highest user network throughput of the at least one candidate base station”, although the throughput selection of (the smallest load) in Fujishiro/Baker would result in the “highest throughput” as it is not explicitly mentioned, Wang is added.
In an analogous art, Wang teaches that UE cell selection includes using signal strength and quality parameters along with throughput parameters of each cell base station. See for example, Figs. 7-9 and sections [0097] to [0108] and see section [0113], which explicitly teaches selecting the cell which provides the highest throughput.
Therefore, as Baker/Fujishiro/Roy or Ozturk teach selecting a base station based on comparing signal and load parameters and as Wang also considers the same parameters and explicitly teaches selecting the base station with the “highest throughput”, it would have been obvious to modify the Baker combination to select that base station, for the reasons and conventionality discussed in Wang, as it is desirable for the user to select the base station with the highest throughput.
Claims 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over the references as applied to claim 12 above, and further in view of U.S. Pub. 2023/0239784 to Zhang.
Regarding claim 13, which recites “wherein the predictive model comprises a long short-term memory model, and the future user network throughput is expressed as a “long short-term memory” or LSTM formula”, Zhang is added.
In an analogous art, Zhang a wireless system which predicts traffic loads on target cells. See for example, the process described in Fig. 2 (step 240), which uses AI to create a model to predict the future load on the base station. As described in the Table in section [0062], Zhang teaches that the modeling algorithm which uses the historical load information may be a “long short-term memory” LSTM model. See also Table 3 in section [0073], which also refers to the LSTM model. See also the discussion from sections [0097] to [0099], which teaches that when the base station’s real load is deviating from the predicted load using the current model, the base station may change the parameters of the current model and/or create a completely different model (which render obvious changing/creating any parameter in an LSTM model), as recited.
Therefore, as Baker/Fujishiro/Al-Shalash or Dunkin teach selecting a base station based on the predicted traffic models, and as Zhang teaches using the LSTM model, it would have been obvious to modify the Baker combination to use the LSTM type of model, as Zhang teaches the reasons and conventionality of using AI to create the most accurate model, as is desired.
Regarding claim 14, which recites “wherein the predictive model comprises a formula expressed as a “regression model”, as described above, Zhang teaches the conventionality of using AI to create the most accurate base station traffic model. See for example, sections [0072] to [0073] and Table 3 in section [0073] which teach using a regression model algorithm, sections [0115], [0169], [0189] and [0214], which teach regression time series models and sections [0097]-[0099], which render obvious changing any variable.
Response to Arguments
Applicant’s arguments have been considered but are moot because of the new grounds of rejection.
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.
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/STEVEN S KELLEY/Primary Examiner, Art Unit 2646