DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
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 of this title, 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.
Claims 1-2, 5 and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170202000 A1 (Fu), in view of Raca, D., Zahran, A.H., Sreenan, C.J., Sinha, R.K., Halepovic, E., Jana, R. and Gopalakrishnan, V., 2020. On leveraging machine and deep learning for throughput prediction in cellular networks: Design, performance, and challenges. IEEE Communications Magazine, 58(3), pp.11-17 (Raca) and in further view of US 20160269932 A1 (Nemavat).
Regarding Claims 1 and 7-8:
A user data rate estimation device, comprising: circuitry configured to execute: acquiring a reception power in a user; receiving input of information regarding a communication environment of the user; calculating a wireless resource to be allocated to the user on the basis of information regarding a bandwidth, a scheduling method, and the number of users included in the information regarding the communication environment; calculating an overhead on the basis of information regarding a communication standard, a frequency band, and a bandwidth included in the information regarding the communication environment; determining a communication mode on the basis of the reception power or communication quality information converted from the reception power; and calculating a user data rate on the basis of the wireless resource, the overhead, the communication mode, and information regarding the number of spatial streams included in the information regarding the communication environment (Fu: Figs. 1-7, a system and device configuration that obtains info on a past scheduling, a radio quality measure; predict load of cell and throughput for uplink data, optimize a quality service (i.e. calculate or estimate an user data rate) based on the prediction and etc. as detailed in Fig. 2; par. 44-47, 84-86, radio quality measure for SINR, MCS (communication mode), CQI, MIMO rank, where SINR is derived from received signal power; par. 47 throughput depends on cell load, par. 57 scheduling freq. reflects $ of users, and par. 69-74 scheduling freq. as load indicator, where scheduling freq., PRB allocation and buffer status indicate bandwidth (total PRBs), schedule behavior and number of user (cell load); par. 69-74, logs PRBs per TTI, par. 56-58 load estimated from scheduling freq., par. 103-106 throughput predicted from relationship between scheduling and past throughput, Claim 20 predicting current/future cell load (i.e., calculating a wireless resource); par. 42-43, 23, and 36-37 for SRS overhead, MAC control signaling overhead, avoids communication overhead, and OFDM resource block structure; par. 80, 119, CQI and Rank used in prediction, and throughput model input are CQI, rank, and etc.. where CQI determines modulation, coding scheme and effective spectral efficiency, and rank determines # of spatial streams in MIMO, where Raca Fig. 1 provides various implementations, including using AI modeling, for throughput prediction).
It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Fu with system implementations as further taught by Raca. The advantage of doing so is to provide accurate TP that can significantly improve performance of highly dynamic wireless communication environment (Roca: Abstract).
Fu does not teach explicitly on calculating an overhead based on communication standard, frequency band, bandwidth. However, Nemavat teaches (Nemavat: par. 50 and 58-63 overhead estimation may include protocol overhead, pretention frames overhead, security overhead etc.. where protocol overhead means communication standard and frequency band overheads, e.g., 802.11 operating in 2.4 or 5Ghz bands; par. 60 frame and symbol duration depend on channel bandwidth).
It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Fu with calculating an overhead based on communication standard, frequency band, bandwidth as further taught by Nemavat. The advantage of doing so is to provide systems, methods and apparatuses for capacity estimation to maximize coverage and throughput performance of the network (Nemavat: Background).
Regarding Claim 2, Fu as modified further teaches:
Claim 2. (Currently Amended) The user data rate estimation device according to claim 1,wherein the circuitry is further configured to execute: executing collecting information regarding the communication environment from a wireless communication system operating in an area of the user (Fu: Fig. 1, Roca: Fig. 1).
Regarding Claim 5, Fu as modified further teaches:
The user data rate estimation device according to claim 1,wherein:the acquiring the reception power includes acquiring a reception power in each unit band over an entire band of a frequency channel used by the user, and the calculating the user data rate includes calculating a data rate for each unit band on the basis of the reception power in each unit band for the entire band of the frequency channel, and calculating, as the user data rate, a sum of the data rate for each unit band with respect to the entire band of the frequency channel (Fu: par. 44-47, 84-86, radio quality measure for SINR, MCS (communication mode), CQI, MIMO rank, where SINR is derived from received signal power, where measurement choice between an entire band or unit band depends on the specific needs of the measurement task and the characteristics of the channel being analyzed).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over US 20170202000 A1 (Fu), in view of Raca, D., Zahran, A.H., Sreenan, C.J., Sinha, R.K., Halepovic, E., Jana, R. and Gopalakrishnan, V., 2020. On leveraging machine and deep learning for throughput prediction in cellular networks: Design, performance, and challenges. IEEE Communications Magazine, 58(3), pp.11-17 (Raca) and in further view of US 20160269932 A1 (Nemavat) and CN 103929268 A (Tan).
Regarding Claim 3, Fu as modified does not teach explicitly on calculating user data rate based on info of uplinkdownlink ratio. However. Tan teaches:
The user data rate estimation device according to claim 1, wherein: the calculating the user data rate further includes calculating the user data rate on the basis of information regarding an uplinldownlink ratio included in the information regarding the communication environment (Tan: par. 76-81 and Claim 7, throughput prediction based on ratios of uplink/downlink service resources, where reception power in this configuration uses partial band).
It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Fu as modified with calculating user data rate based on info of uplinkdownlink ratio as further taught by Tan. The advantage of doing so is to provide a network that is dynamically configured with uplink and downlink sub-frame matching ratio of each cell in cell set of the identical downlink sub-frame matching ratio of device and method (Tan: background).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over US 20170202000 A1 (Fu), in view of Raca, D., Zahran, A.H., Sreenan, C.J., Sinha, R.K., Halepovic, E., Jana, R. and Gopalakrishnan, V., 2020. On leveraging machine and deep learning for throughput prediction in cellular networks: Design, performance, and challenges. IEEE Communications Magazine, 58(3), pp.11-17 (Raca) and in further view of US 20160269932 A1 (Nemavat) and CN 101325786 A (Zhao).
Regarding Claim 4, Fu as modified does not teach explicitly on calculating user data rate based on info partial band. However. Zhao teaches:
The user data rate estimation device according to claim 1, wherein: the acquiring the reception power includes acquiring a reception power in a partial band of a frequency channel used by the user, and the calculating the user data rate includes calculating the user data rate by using the reception power in the partial band as a representative reception power in an entire band of the frequency channel (Zhao: Figs. 1-2, frequency band is shared by UMTS 900 and UMTS 2000, where the throughput for either protocols is based on partial frequency band).
It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Fu as modified with calculating user data rate based on info partial band as further taught by Zhao. The advantage of doing so is to provide a network with UMTS900 and UMTS2000, which gives attention to both the characteristics of the two frequency range and the proper service, takes the limit of actual available site resource and frequency resource into consideration, and builds network simple and pleasant via introducing in dynamic feedback regulating mechanism (Zhao: Abstract).
Allowable Subject Matter
The Claim 6 is objected to as being dependent upon a rejected base claim, but are potentially allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHITONG CHEN whose telephone number is (571) 270-1936. The examiner can normally be reached on M-F 9:30am - 5pm.
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/ZHITONG CHEN/
Primary Examiner, Art Unit 2649