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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/26/2026 has been entered.
Response to Amendment
In response to the amendments received in the Office on 05/26/2026, the Office acknowledges the current status of the claims: claims 1, 13, 27, and 34 have been amended, claims 2-3, 5-6, 8-12, 16-19, and 21-23 remain the same, claims 4, 7, 14-15, 20, 24-26, 28-33, and 35-41 have been canceled, and no new matter appears to be included.
In response to the amendments received in Office on 05/26/2025, the rejection of claims 1-2, 6, 12, 27, and 34 under 35 U.S.C. § 103 have been withdrawn.
Response to Arguments
Applicant’s arguments with respect to claim 1 has 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.
The remaining arguments regarding independent claims 27 and 34 and all dependent claims 2-3, 5-6, 8-13, 16-19, and 21-23, generally recite the same reasonings as for claim 1 and are moot.
However, the amendments have necessitated a new grounds of rejection presented below.
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.
Claims 1-2, 6, 12, 27, and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Albert et al. (US20220053345 hereinafter Albert) in view of Lee et al. (US20130142183 hereinafter Lee), in view of Hoykinpuro (US20250007152), in further view of Mazlum et al. (US20080188244 hereinafter Mazlum).
Regarding claims 1 and 34. Albert teaches the method and apparatus for a network node configured to estimate, for an antenna of a base station (fig. 7 and pars. 0094-0096, teaches the CNE 135 is further configured to utilize a calibration of signal strength prediction tool 760. Whereas, par. 0027, teaches the signal strength or coverage prediction tool is utilized to perform site selection for BS deployment; and for reconfiguring network parameters, such as BS antenna, tilt, and the like. Apart from planning and management, such a prediction tool can also be used to estimate and correct misaligned BS parameters, a task often referred to as site audit correction),
variations in received signal strength at User Equipments (UEs) (fig. 7 and pars. 0094-0097, teaches the CNE 135 receiving the measurement reports and BS configuration parameters. Whereas, par. 0099, teaches the CNE 135 can preprocess the collected reports and the corresponding BS configuration parameters and save them in a data table. The table attributes can include a time stamp or expiration timer for the data, the received signal strength of users, the location of users, BS configuration parameters, and the like)
the network node comprising processing circuitry and a memory containing instructions executable by the processing circuitry (fig.1 and par. 0048, teaches the core network 130 may further include a core network entity (CNE) 135, which responsible for the task of site audit correction, as described herein below. In certain embodiments, the CNE 135 is a base station, such as gNB 103. Wherein, fig. 2 and pars. 0050-0059, teaches the gNBs 101 and 103 of FIG. 1 could have the same or similar configuration as the gNB 102 illustrated in FIG. 2. Furthermore, the gNB 102 includes controller/processor 225 can include one or more processors or other processing devices that control the overall operation of the gNB 102), whereby network node is operable to:
receive, from the base station measurement data (fig. 7 and pars. 0094-0097, teaches the CNE 135 receiving the measurement reports and BS configuration parameters. Whereas, par. 0099, teaches the CNE 135 can preprocess the collected reports and the corresponding BS configuration parameters and save them in a data table. The table attributes can include the received signal strength of users and the location of users. Wherein, the “measurement reports and BS configuration parameters which include the data table comprising the received signal strength of users” reads as measurement data. Furthermore, fig. 7 and pars. 0094-0097, teaches the UE 116 transmits the measurement reports directly to the CNE 135, via the gNB 102), wherein the measurement data comprises signal strength measurements indicating received reference signal strength of the reference signal at UEs , and positional information of the UEs (fig. 7 and pars. 0094-0097, teaches the CNE 135 receiving the measurement reports and BS configuration parameters. Whereas, par. 0099, teaches the CNE 135 can preprocess the collected reports and the corresponding BS configuration parameters and save them in a data table. The table attributes can include the received signal strength of users and the location of users); …
generate model coefficients by processing the measurement data in a training model (fig. 7 and pars. 0094-0096, teaches the CNE 135 is further configured to utilize a calibration of signal strength prediction tool 760. Wherein, the prediction tool can be a prediction model configured to predict reference signal received power (RSRP) values for the BS. Whereas, the language “predict reference signal received power (RSRP) values for the BS” reads as generating model coefficients. Moreover, par. 0101, teaches these user reports along with the BS parameters can be stored in a data table at CNE 135 and herein will be referred to as the training data. Furthermore, par. 0116, teaches CNE 135 can initiate the calibration of the signal strength prediction tool 760 for a class of BSs using the collected data tables. Whereas, the language “CNE 135 can initiate the calibration of the signal strength prediction tool 760 using the collected data tables” reads as generate model coefficients by processing the measurement data in a training model); and
estimate, for a first potential electrical tilt change, variations in received signal strength of the received reference signal received at the UEs (figs. 7-8 and pars. 0094-0099, teaches the CNE 135 utilizing the signal strength prediction tool 760 with the collected reports and the corresponding BS configuration parameters in a data table to predict reference signal received power (RSRP) values for the BS. Whereas, par. 0133, teaches the output of the prediction can be the mean received signal strength. Wherein, par. 0027, teaches the signal strength or coverage prediction tool is utilized to perform site selection for BS deployment; and for reconfiguring network parameters, such as BS antenna, tilt, and the like. Wherein par. 0089, teaches the configuration of a gNB can involve electrical tilt (E-tilt), which reads as an electrical tilt change. Furthermore, it would be obvious to one of the ordinary skill in the art that the signal strength prediction tool that is utilized to perform site selection for BS deployment; and for reconfiguring network parameters, such as BS antenna, tilt, and the like, has some sort of estimation process for the collected reports in the data table in order to output the mean received signal strength, thus there has to be a first potential electrical tilt change),
wherein estimating, for the first potential electrical tilt change, variations in received signal strength of the received reference signal received at the UEs comprises: processing, in a prediction model (figs. 7-8 and pars. 0094-0099, teaches the CNE 135 utilizing the signal strength prediction tool 760 with the collected reports and the corresponding BS configuration parameters in a data table to predict reference signal received power (RSRP) values for the BS. Whereas, par. 0133, teaches the output of the prediction can be the mean received signal strength), (i) the generated model coefficients (fig. 7 and pars. 0094-0096, teaches the CNE 135 is further configured to utilize a calibration of signal strength prediction tool 760. Wherein, the prediction tool can be a prediction model configured to predict reference signal received power (RSRP) values for the BS. Whereas, the language “predict reference signal received power (RSRP) values for the BS” reads as generating model coefficients), (ii) a first … increment which defines the first potential electrical tilt change (par. 0027, teaches the signal strength or coverage prediction tool is utilized to perform site selection for BS deployment; and for reconfiguring network parameters, such as BS antenna, tilt, and the like. Wherein par. 0089, teaches the configuration of a gNB can involve electrical tilt (E-tilt), which reads as an electrical tilt change. Furthermore, it would be obvious to one of the ordinary skill in the art that in order to change the electric tilt there has to be a first increment of change), and (iii) the positional information received from the UEs (par. 0099, teaches the CNE 135 can preprocess the collected reports and the corresponding BS configuration parameters and save them in a data table. The table attributes can include the received signal strength of users and the location of users. Wherein, fig. 7 and pars. 0094-0099, teaches the CNE 135 utilizing the signal strength prediction tool 760 with the collected reports and the corresponding BS configuration parameters in a data table to predict reference signal received power (RSRP) values for the BS).
However, although Albert teaches processing, in a prediction model… a first … increment which defines the first potential electrical tilt change (pars. 0029 and 0089), the apparatus and methods of Albert explicitly fails to disclose, processing, …a first Remote Electrical Tilt (RET) increment which defines the first potential electrical tilt change.
Lee disclosed apparatus, systems, and methods for Remote Electrical Tilt (RET), so Lee is analogous to Albert. Furthermore, Lee teaches processing, …a first Remote Electrical Tilt (RET) increment which defines the first potential electrical tilt change (fig 4 and pars. 0066-0068, teaches implementation component 410 can be configured to direct the optimal set of RET values, to the respective access points. Wherein, the optimal set of RET values reads as a first Remote Electrical Tilt (RET) increment which defines the first potential electrical tilt change due to being used for directions to change respective access points. Furthermore, par. 0065, teaches remote electrical tilt (RET) values, e.g., tilt values, for respective access points).
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the invention to utilize processing, …a first Remote Electrical Tilt (RET) increment which defines the first potential electrical tilt change, as disclosed by Lee with the method and apparatus of Albert. The motivations for doing so would be to improve wireless coverage. (see Lee par. 0009)
However, the combination of Albert and Lee explicitly fails to disclose, receiving, … measurement data, wherein the measurement data comprises …antenna height information indicating a height of the antenna above ground elevation level.
Hoykinpuro disclosed apparatus, systems, and methods for antenna height information, so Hoykinpuro is analogous to Albert. Furthermore, Hoykinpuro teaches receiving, … measurement data, wherein the measurement data comprises …antenna height information indicating a height of the antenna above ground elevation level (fig. 3 and pars. 0051-0052, teaches obtaining data related to the base station. Wherein, the data related to the base station comprises user equipment distance data, antenna height data, antenna tilt angle data, and antenna radiation pattern data. Whereas, par. 0061, teaches the antenna height data comprises the height of the first antenna from the ground level).
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the invention to utilize receiving, … measurement data, wherein the measurement data comprises …antenna height information indicating a height of the antenna above ground elevation level, as disclosed by Hoykinpuro with the combination of Albert and Lee. The motivations for doing so would be to improve signal quality. (see Hoykinpuro par. 0075)
However, the combination of Albert, Lee, and Hoykinpuro explicitly fails to disclose, processing, in a prediction model, … (iv) the antenna height information.
Mazlum disclosed apparatus, systems, and methods for a prediction model, so Mazlum is analogous to Albert. Furthermore, Mazlum teaches processing, in a prediction model, … (iv) the antenna height information (par. 0098, teaches the RF software planning tool or model may utilize antenna heights for location accuracy estimation. Par. 0004 teaches radio frequency ("RF"))
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the invention to utilize processing, in a prediction model, … (iv) the antenna height information, as disclosed by Mazlum with the combination of Albert, Lee, and Hoykinpuro. The motivations for doing so would be to improve estimates. (see Mazlum par. 0059)
Regarding claim 2. Albert, Lee, Hoykinpuro, and Mazlum teaches the method for claim 1. Albert further teaches configuring electrical tilt of the antenna based on the estimated variations in received signal strength (par. 0027, teaches the signal strength or coverage prediction tool is utilized to perform site selection for BS deployment; and for reconfiguring network parameters, such as BS antenna, tilt, and the like. Wherein, fig. 7 and pars. 0094-0099, teaches the CNE 135 utilizing the signal strength prediction tool 760 with the collected reports and the corresponding BS configuration parameters in a data table to predict reference signal received power (RSRP) values for the BS. Moreover, table attributes can include a time stamp or expiration timer for the data, the received signal strength of users, the location of users, BS configuration parameters, and the like. Whereas, par. 0133, teaches the output of the prediction can be the mean received signal strength). Thus, the mean of the received signal strength of users and the location of users within the data table for the signal strength or coverage prediction tool is utilized to perform reconfiguring network parameters, such as BS antenna and tilt, reads as configuring electrical tilt of the antenna based on the estimated variations in received signal strength); and/or configuring electrical tilt of a plurality of adjacent antennas each corresponding to a different base station based on the estimated variations in received signal strength (interpreted as alternative language/disposition limitation and therefore not required to be disclosed by the art made of record).
Regarding claim 6. Albert, Lee, Hoykinpuro, and Mazlum teaches the method for claim 1. Albert further teaches the UEs are served by the antenna (fig. 1 and par. 0048, teaches the gNB 102 serves multiple UEs 111-116 via wireless interfaces respectively. Furthermore, pars. 0089-0090, teaches the gNB 102 antenna parameters impact the service to UEs 111-116. Thus, the UEs are served by the antenna).
Regarding claim 12. Albert, Lee, Hoykinpuro, and Mazlum teaches the method for claim 1. Albert further teaches generating model coeffects by processing the measurement data in a training model comprises: generating modified versions of the prediction model by inputting a range of model coefficients into the prediction model (fig. 7 and pars. 0094-0096, teaches the CNE 135 is further configured to utilize a calibration of signal strength prediction tool 760. Wherein, the prediction tool can be a prediction model configured to predict reference signal received power (RSRP) values for the BS. Whereas, the language “predict reference signal received power (RSRP) values for the BS” reads as generating model coefficients. Moreover, par. 0101, teaches these user reports along with the BS parameters can be stored in a data table at CNE 135 and herein will be referred to as the training data. Furthermore, par. 0116, teaches CNE 135 can initiate the calibration of the signal strength prediction tool 760 for a class of BSs using the collected data tables. Thus, generating RSRP values (model coefficients) by imputing the collected RSRP values received from the UEs stored in the data table, reads as generating modified versions of the prediction model by inputting a range of model coefficients into the prediction model),
comparing the modified versions of the prediction model to the measurement data (fig. 10 and pars. 0131-0133, teaches CNE 135 determines the BS class and appropriate calibrated tool to use. Furthermore, pars. 0115-0116, teaches selecting the Model and Calibration Procedure. Wherein, CNE 135 can first perform a “goodness of fit” test, such as the Chi-squared test, Kolmogorov-Smirnov test, or the like, to validate if the newly collected data fits the existing calibrated model for that class. When a sufficient fit is not found, CNE 135 can signal an alarm for re-calibration of the signal strength prediction tool 760 for that particular BS class after including the newly collected data. Whereas, the signal strength prediction tool can be raytracing, a statistical channel model, or any other model. Thus, comparing the modified versions of the prediction model to the measurement data), and
selecting model coefficients from among the range of model coefficients that generate a modified version of the prediction model which best fits the measurement data (fig. 10 and pars. 1031-0133, teaches the output of the prediction can be generated. Wherein, the output of the prediction can be the mean received signal strength in the area surrounding CNE 135. In certain embodiments, a set of user locations can be given as an input and the mean received signal strength can be predicted at those locations. Furthermore, a set of observations (user RSRPs and locations) can be given as an input and the output of the algorithm can be a score or a likelihood that the observations satisfy the prediction tool. Whereas, one of the ordinary skill would understand that the “mean received signal strength” depends on the range of the input measurement data (RSRPs and location) which reads as selecting model coefficients from among the range of model coefficients. Thus, the resulting output of the mean received signal strength reads as a model coefficients from among the range of model coefficients that generate a modified version of the prediction model which best fits the measurement data).
Regarding claim 27. Albert teaches the apparatus for a base station configured to estimate, for an antenna of the base station (fig. 7 and pars. 0094-0096, teaches the CNE 135 is further configured to utilize a calibration of signal strength prediction tool 760. Whereas, par. 0027, teaches the signal strength or coverage prediction tool is utilized to perform site selection for BS deployment; and for reconfiguring network parameters, such as BS antenna, tilt, and the like. Apart from planning and management, such a prediction tool can also be used to estimate and correct misaligned BS parameters, a task often referred to as site audit correction. Furthermore, par. 0098, teaches the CNE 135 is a base station, such as gNB 103 itself),
variations in received signal strength at User Equipments (UEs) (fig. 7 and pars. 0094-0097, teaches the CNE 135 receiving the measurement reports and BS configuration parameters. Whereas, par. 0099, teaches the CNE 135 can preprocess the collected reports and the corresponding BS configuration parameters and save them in a data table. The table attributes can include a time stamp or expiration timer for the data, the received signal strength of users, the location of users, BS configuration parameters, and the like) the base station comprising processing circuitry and a memory containing instructions executable by the processing circuitry (fig. 2 and pars. 0051-0060, teaches the gNB 102 includes controller/processor 225 and memory 230. Wherein, the controller/processor 225 is also capable of executing programs and other processes resident in the memory 230), whereby the base station is operable to:
transmit, to a plurality of UEs, a reference signal (figs. 1 & 7 and par. 0097, teaches gNB 102 broadcasts a reference signal (RS) to enable users in its neighborhood to measure the signal strength via an RSRP measurement. Wherein, both served UEs 111-114 and non-served UEs 115-116 in the neighborhood of gNB 102 may correspondingly measure the RSRP from gNB 102);
obtain measurement data (fig. 7 and pars. 0094-0097, teaches the CNE 135 receiving the measurement reports and BS configuration parameters. Whereas, par. 0099, teaches the CNE 135 can preprocess the collected reports and the corresponding BS configuration parameters and save them in a data table. The table attributes can include the received signal strength of users and the location of users. Wherein, the “measurement reports and BS configuration parameters which include the data table comprising the received signal strength of users” reads as measurement data. Furthermore, fig. 7 and pars. 0094-0097, teaches the UE 116 transmits the measurement reports directly to the CNE 135, via the gNB 102), wherein the measurement data comprises signal strength measurements indicating received reference signal strength of the reference signal at UEs, positional information of the UEs (fig. 7 and pars. 0094-0097, teaches the CNE 135 receiving the measurement reports and BS configuration parameters. Whereas, par. 0099, teaches the CNE 135 can preprocess the collected reports and the corresponding BS configuration parameters and save them in a data table. The table attributes can include the received signal strength of users and the location of users)…;
generate model coefficients by processing the measurement data in a training model (fig. 7 and pars. 0094-0096, teaches the CNE 135 is further configured to utilize a calibration of signal strength prediction tool 760. Wherein, the prediction tool can be a prediction model configured to predict reference signal received power (RSRP) values for the BS. Whereas, the language “predict reference signal received power (RSRP) values for the BS” reads as generating model coefficients. Moreover, par. 0101, teaches these user reports along with the BS parameters can be stored in a data table at CNE 135 and herein will be referred to as the training data. Furthermore, par. 0116, teaches CNE 135 can initiate the calibration of the signal strength prediction tool 760 for a class of BSs using the collected data tables. Whereas, the language “CNE 135 can initiate the calibration of the signal strength prediction tool 760 using the collected data tables” reads as generate model coefficients by processing the measurement data in a training model); and
estimate, for a first potential electrical tilt change, variations in received signal strength of the received reference signal received at the UEs (figs. 7-8 and pars. 0094-0099, teaches the CNE 135 utilizing the signal strength prediction tool 760 with the collected reports and the corresponding BS configuration parameters in a data table to predict reference signal received power (RSRP) values for the BS. Whereas, par. 0133, teaches the output of the prediction can be the mean received signal strength. Wherein, par. 0027, teaches the signal strength or coverage prediction tool is utilized to perform site selection for BS deployment; and for reconfiguring network parameters, such as BS antenna, tilt, and the like. Wherein par. 0089, teaches the configuration of a gNB can involve electrical tilt (E-tilt), which reads as an electrical tilt change. Furthermore, it would be obvious to one of the ordinary skill in the art that the signal strength prediction tool that is utilized to perform site selection for BS deployment; and for reconfiguring network parameters, such as BS antenna, tilt, and the like, has some sort of estimation process for the collected reports in the data table in order to output the mean received signal strength, thus there has to be a first potential electrical tilt change),
wherein estimating, for the first potential electrical tilt change, variations in received signal strength of the received reference signal received at the UEs comprises: processing, in a prediction model (figs. 7-8 and pars. 0094-0099, teaches the CNE 135 utilizing the signal strength prediction tool 760 with the collected reports and the corresponding BS configuration parameters in a data table to predict reference signal received power (RSRP) values for the BS. Whereas, par. 0133, teaches the output of the prediction can be the mean received signal strength), (i) the generated model coefficients (fig. 7 and pars. 0094-0096, teaches the CNE 135 is further configured to utilize a calibration of signal strength prediction tool 760. Wherein, the prediction tool can be a prediction model configured to predict reference signal received power (RSRP) values for the BS. Whereas, the language “predict reference signal received power (RSRP) values for the BS” reads as generating model coefficients), (ii) a first … increment which defines the first potential electrical tilt change (par. 0027, teaches the signal strength or coverage prediction tool is utilized to perform site selection for BS deployment; and for reconfiguring network parameters, such as BS antenna, tilt, and the like. Wherein par. 0089, teaches the configuration of a gNB can involve electrical tilt (E-tilt), which reads as an electrical tilt change. Furthermore, it would be obvious to one of the ordinary skill in the art that in order to change the electric tilt there has to be a first increment of change), and (iii) the positional information received from the UEs (par. 0099, teaches the CNE 135 can preprocess the collected reports and the corresponding BS configuration parameters and save them in a data table. The table attributes can include the received signal strength of users and the location of users. Wherein, fig. 7 and pars. 0094-0099, teaches the CNE 135 utilizing the signal strength prediction tool 760 with the collected reports and the corresponding BS configuration parameters in a data table to predict reference signal received power (RSRP) values for the BS).
However, although Albert teaches processing, in a prediction model… a first … increment which defines the first potential electrical tilt change (pars. 0029 and 0089), the apparatus and methods of Albert explicitly fails to disclose, processing, …a first Remote Electrical Tilt (RET) increment which defines the first potential electrical tilt change.
Lee disclosed apparatus, systems, and methods for Remote Electrical Tilt (RET), so Lee is analogous to Albert. Furthermore, Lee teaches processing, …a first Remote Electrical Tilt (RET) increment which defines the first potential electrical tilt change (fig 4 and pars. 0066-0068, teaches implementation component 410 can be configured to direct the optimal set of RET values, to the respective access points. Wherein, the optimal set of RET values reads as a first Remote Electrical Tilt (RET) increment which defines the first potential electrical tilt change due to being used for directions to change respective access points. Furthermore, par. 0065, teaches remote electrical tilt (RET) values, e.g., tilt values, for respective access points).
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the invention to utilize processing, …a first Remote Electrical Tilt (RET) increment which defines the first potential electrical tilt change, as disclosed by Lee with the method and apparatus of Albert. The motivations for doing so would be to improve wireless coverage. (see Lee par. 0009)
However, the combination of Albert and Lee explicitly fails to disclose, obtaining, … measurement data, wherein the measurement data comprises …antenna height information indicating a height of the antenna above ground elevation level.
Hoykinpuro disclosed apparatus, systems, and methods for antenna height information, so Hoykinpuro is analogous to Albert. Furthermore, Hoykinpuro teaches obtaining, … measurement data, wherein the measurement data comprises …antenna height information indicating a height of the antenna above ground elevation level (fig. 3 and pars. 0051-0052, teaches obtaining data related to the base station. Wherein, the data related to the base station comprises user equipment distance data, antenna height data, antenna tilt angle data, and antenna radiation pattern data. Whereas, par. 0061, teaches the antenna height data comprises the height of the first antenna from the ground level).
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the invention to utilize obtaining, … measurement data, wherein the measurement data comprises …antenna height information indicating a height of the antenna above ground elevation level, as disclosed by Hoykinpuro with the combination of Albert and Lee. The motivations for doing so would be to improve signal quality. (see Hoykinpuro par. 0075)
However, the combination of Albert, Lee, and Hoykinpuro explicitly fails to disclose, processing, in a prediction model, … (iv) the antenna height information.
Mazlum disclosed apparatus, systems, and methods for a prediction model, so Mazlum is analogous to Albert. Furthermore, Mazlum teaches processing, in a prediction model, … (iv) the antenna height information (par. 0098, teaches the RF software planning tool or model may utilize antenna heights for location accuracy estimation. Par. 0004 teaches radio frequency ("RF"))
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the invention to utilize processing, in a prediction model, … (iv) the antenna height information, as disclosed by Mazlum with the combination of Albert, Lee, and Hoykinpuro. The motivations for doing so would be to improve estimates. (see Mazlum par. 0059)
Allowable Subject Matter
Claims 3, 5, 8-11, 13, 16-19, and 21-23 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/TRACY LAUREN RAIMONDO/Examiner, Art Unit 2474
/Michael Thier/Supervisory Patent Examiner, Art Unit 2474
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