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 .
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.
Response to Arguments
Applicant’s arguments, see pages 7-8 of applicant’s response, filed 11/21/25, with respect to Karla have been fully considered and are persuasive. The rejections of claims 1-5, 8-17 have been withdrawn.
Applicant's arguments filed 11/21/25, regarding the rejections under Brisebois and Liao, have been fully considered but they are not persuasive.
Brisebois discloses that the selection of “best fit” parameters is based on another femtocell (FAP) which is expected/likely to have similar radio conditions (paragraph 25, 30, 52). This clearly meets the current claim limitations. This femtocell (and its corresponding parameters) are predicted to be the best fit for the expected conditions at the newly installed cell (paragraph 30).
The further arguments that the “the initial values of a self-provisioned FAP are responsive to a measurement during commissioning, not before, and that those initial values are not a prediction.” is incorrect. The cited portion within paragraph 47 indicates that the operating parameters utilized from the second FAP are “better than those the FAP.sub.A 102 can derive on its own during an initial measurement”, as merely an indication of the reasoning for utilizing the optimized parameters from another device. Brisebois does not disclose performing a measurement during commissioning and to generate initial values, as applicant suggests.
Paragraph 25 indicates that measurements performed at initialization are convention and “may not be accurate”. Similarly, paragraph 39 discloses “during a configuration mode, the FAP 102 can be provisioned with an initial set of operating parameters received from a nearby FAP (that has already been provisioned).” Further measurements are taken over time to “optimize the initial set of operating parameters”.
Therefore, applicant’s arguments are not convincing.
In response to applicant’s arguments regarding Liao obtaining base station configuration data comprising parameter values, Liao specifically discloses receiving pre-trained models received from already active base stations (paragraph 140, 149-151, 159-164), those models comprising parameters values (“the mapping between the network environment measurements as inputs and the optimization actions/decisions as outputs” paragraph 97, with the model output including parameters of “power, frequency, MIMO mode”; paragraph 111-115).
Regarding the limitation of “predicting autoconfiguration parameters”, Liao discloses where the model for the new base station (mapping network conditions to parameter values) is adapted based upon similarity data from other base stations determined to be “similar” (paragraph 92, 131-135, 146, 156, 170).
The selection and use of parameter models most “similar” constitutes a calculated prediction those values would be most relevant (paragraph 92, 133-134).
Therefore, applicant’s arguments are not convincing.
Claim Rejections - 35 USC § 102
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.
Claims 1-8, 11-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Brisebois et al. (Brisebois) (US 2012/0252423) (of record).
As to claim 1, Brisebois discloses a computer implemented method for autoconfiguration of a first base station of a communications network (paragraph 23, 27, 42), the method comprising:
obtaining from the communications network, base station configuration data, wherein the base station configuration data comprises parameter values from a plurality of active base stations and/or cells of the communications network (operating parameters of plural nearby FAPs; paragraph 30, 43);
obtaining network planning data (FAP location information and performance values; paragraph 44-47);
predicting autoconfiguration parameters for the first base station based on the obtained base station configuration data and the obtained network planning data (other FAP predicted to have most similar operating conditions/location; paragraph 45-47), wherein the predicted autoconfiguration parameters are initial parameters predicted before commissioning the first base station (initial configuration/power-up; paragraph 28, 42); and
configuring the first base station using the predicted autoconfiguration parameters (selecting best FAP profile to provision new FAP; paragraph 28, 46-47).
As to claim 2, Brisebois discloses configuring the first base station using at least some of the predicted autoconfiguration parameters and pre-set configuration parameters (best fit parameters from FAP and required parameters; paragraph 46).
As to claim 3, Brisebois discloses wherein the predicting autoconfiguration parameters further comprises:
using an algorithm comprising artificial intelligence (Fig. 5, paragraph 48-52);
training the algorithm with the obtained base station configuration data Fig. 5, paragraph 48-52); and
predicting autoconfiguration parameters using the trained algorithm (Fig. 5, paragraph 45-47, 52).
As to claim 4, Brisebois discloses wherein, the artificial intelligence comprises: neural network and/or machine learning based methods (Fig. 5, paragraph 48-52).
As to claim 5, Brisebois discloses wherein the predicting autoconfiguration parameters further comprises predicting autoconfiguration parameters separately for at least two cells of the first base station (paragraph 39-40, 65).
As to claim 6, Brisebois discloses wherein the predicting autoconfiguration parameters further comprises weighting some base stations in the prediction based on similarity with the first base station (paragraph 25, 44-45, 55).
As to claim 7, Brisebois discloses wherein the similarity is based on any one or more of: site location (paragraph 43-44, 46); manufacturer; vendor; cell technology; antenna type; antenna direction; antenna height; hardware; system-module; radio-module; and software version.
As to claim 8, Brisebois discloses wherein the predicted autoconfiguration parameters comprise parameters related to one or more of: base station parameters (paragraph 27, 34-35); cell parameters (paragraph 27, 34-35); and antenna parameters (paragraph 27, 34-35);
As to claim 11, Brisebois discloses an apparatus (402) comprising:
a processor (Fig. 4-5; paragraph 20-21, 42); and
a memory including computer program code (Fig. 4-5; paragraph 20-21, 42);
the memory and the computer program code configured to, with the processor, cause the apparatus to perform a method of autoconfiguration of a first base station of a communications network, the method comprising at least the following steps:
obtaining from the network, base station configuration data, wherein the base station configuration data comprises parameter values from a plurality of active base stations and/or cells of the communications network (operating parameters of plural nearby FAPs; paragraph 30, 43);
obtaining network planning data (FAP location information and performance values; paragraph 44-47);
predicting autoconfiguration parameters for the first base station based on the obtained base station configuration data and the obtained network planning data (other FAP predicted to have most similar operating conditions/location; paragraph 45-47), wherein the predicted autoconfiguration parameters are initial parameters predicted before commissioning the first base station (initial configuration/power-up; paragraph 28, 42); and
configuring the first base station using the predicted autoconfiguration parameters (selecting best FAP profile to provision new FAP; paragraph 28, 46-47).
As to claim 12, Brisebois discloses a computer program comprising a non-transitory computer readable medium having computer executable program code which when executed by a processor causes an apparatus to perform a method (network node server; Fig. 4-5; paragraph 20-21, 42) of autoconfiguration of a first base station of a communications network, the method comprising at least the following steps:
obtaining from the communications network, base station configuration data, wherein the base station configuration data comprises parameter values from a plurality of active base stations and/or cells of the communications network (operating parameters of plural nearby FAPs; paragraph 30, 43);
obtaining network planning data (FAP location information and performance values; paragraph 44-47);
predicting autoconfiguration parameters for the first base station based on the obtained base station configuration data and the obtained network planning data (other FAP predicted to have most similar operating conditions/location; paragraph 45-47), wherein the predicted autoconfiguration parameters are initial parameters predicted before commissioning the first base station (initial configuration/power-up; paragraph 28, 42); and
configuring the first base station using the predicted autoconfiguration parameters (selecting best FAP profile to provision new FAP; paragraph 46-47).
As to claim 13, Brisebois discloses wherein the predicting autoconfiguration parameters further comprises:
using an algorithm comprising artificial intelligence (Fig. 5, paragraph 48-52);
training the algorithm with the obtained base station configuration data Fig. 5, paragraph 48-52); and
predicting autoconfiguration parameters using the trained algorithm (Fig. 5, paragraph 45-47, 52).
As to claim 14, Brisebois discloses wherein, the artificial intelligence comprises: neural network and/or machine learning based methods (Fig. 5, paragraph 48-52).
As to claim 15, Brisebois discloses wherein the predicting autoconfiguration parameters further comprises predicting autoconfiguration parameters separately for at least two cells of the first base station (paragraph 39-40, 65).
As to claim 16, Brisebois discloses wherein the predicting autoconfiguration parameters further comprises predicting autoconfiguration parameters separately for at least two cells of the first base station (paragraph 39-40, 65).
As to claim 17, Brisebois discloses wherein the predicting autoconfiguration parameters further comprises predicting autoconfiguration parameters separately for at least two cells of the first base station (paragraph 39-40, 65).
As to claim 18, Brisebois discloses wherein the predicting autoconfiguration parameters further comprises weighting some base stations in the prediction based on similarity with the first base station (paragraph 25, 44-45, 55).
As to claim 19, Brisebois discloses wherein the predicting autoconfiguration parameters further comprises weighting some base stations in the prediction based on similarity with the first base station (paragraph 25, 44-45, 55).
As to claim 20, Brisebois discloses wherein the predicting autoconfiguration parameters further comprises weighting some base stations in the prediction based on similarity with the first base station (paragraph 25, 44-45, 55).
As to claim 21, Brisebois discloses wherein the predicted autoconfiguration parameters are selected from: location area code (paragraph 43), base station controller identifier, physical cell identity, radio network controller, scrambling code (paragraph 35), routing zone, and neighbour cell count.
As to claim 22, Brisebois discloses wherein the network planning data comprises at least information about location of the first base station (paragraph 33, 39, 43) and information about vendor and cell technology of the first base station (paragraph 34-35, 73).
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-8, 11-22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Liao et al. (Liao) (US 2021/0219384) (of record).
As to claim 1, Liao discloses a computer implemented method for autoconfiguration of a first base station of a communications network (paragraph 28-29), the method comprising:
obtaining from the communications network, base station configuration data (pre-trained models; paragraph 140, 149-151, 159-164), wherein the base station configuration data comprises parameter values (paragraph 97-124) from a plurality of active base stations and/or cells of the communications network (pre-trained models received from already active base stations; paragraph 140, 149-151, 159-164);
obtaining network planning data (network data indicating similarity; paragraph 141-142, 152, 165-169);
predicting autoconfiguration parameters for the first base station based on the obtained base station configuration data and the obtained network planning data, wherein the predicted autoconfiguration parameters are initial parameters predicted before commissioning the first base station (adapting model based upon similarity data; paragraph 61, 92, 131-135, 146, 156, 170); and
configuring the first base station using the predicted autoconfiguration parameters (new base stations provided with configuration models derived from other base stations; paragraph 61, 91, 147, 157, 171).
As to claim 2, Liao discloses configuring the first base station using at least some of the predicted autoconfiguration parameters and pre-set configuration parameters (paragraph 157, 199-200).
As to claim 3, Liao discloses wherein the predicting autoconfiguration parameters further comprises:
using an algorithm comprising artificial intelligence (deep reinforcement learning models; Fig. 4, paragraph 83-91);
training the algorithm with the obtained base station configuration data (paragraph 81-91, 132, 150-152); and
predicting autoconfiguration parameters using the trained algorithm (adapting model based upon similarity data; paragraph 92, 131-135, 146, 156, 170);
As to claim 4, Liao discloses wherein, the artificial intelligence comprises: neural network and/or machine learning based methods (Fig. 4, paragraph 83-91).
As to claim 5, Liao discloses wherein the predicting autoconfiguration parameters further comprises predicting autoconfiguration parameters separately for at least two cells of the first base station (paragraph 77, 89).
As to claim 6, Liao discloses wherein the predicting autoconfiguration parameters further comprises weighting some base stations in the prediction based on similarity with the first base station (paragraph 129-135).
As to claim 7, Liao discloses wherein the similarity is based on any one or more of: site location (paragraph 43, 132); manufacturer; vendor; cell technology; antenna type; antenna direction; antenna height; hardware; system-module; radio-module; and software version (paragraph 43, 132).
As to claim 8, Liao discloses wherein the predicted autoconfiguration parameters comprise parameters related to one or more of: base station parameters (paragraph 110-124); cell parameters (paragraph 110-124); and antenna parameters (antenna tuning directions; paragraph 110-124).
As to claim 11, Liao discloses an apparatus (Fig. 1, 20/60) comprising:
a processor (Fig. 7-8; paragraph 181-185); and
a memory including computer program code (Fig. 7-8; paragraph 181-185);
the memory and the computer program code configured to, with the processor, cause the apparatus to perform a method of autoconfiguration of a first base station of a communications network, the method comprising at least the following steps:
obtaining from the communications network, base station configuration data (pre-trained models; paragraph 140, 149-151, 159-164), wherein the base station configuration data comprises parameter values (paragraph 97-124) from a plurality of active base stations and/or cells of the communications network (pre-trained models received from already active base stations; paragraph 140, 149-151, 159-164);
obtaining network planning data (network data indicating similarity; paragraph 141-142, 152, 165-169);
predicting autoconfiguration parameters for the first base station based on the obtained base station configuration data and the obtained network planning data, wherein the predicted autoconfiguration parameters are initial parameters predicted before commissioning the first base station (adapting model based upon similarity data; paragraph 61, 92, 131-135, 146, 156, 170); and
configuring the first base station using the predicted autoconfiguration parameters (new base stations provided with configuration models derived from other base stations; paragraph 61, 91, 147, 157, 171).
As to claim 12, Liao discloses a computer program comprising a non-transitory computer readable medium having computer executable program code which when executed by a processor causes an apparatus to perform a method (Fig. 7-8; paragraph 181-185) of autoconfiguration of a first base station of a communications network, the method comprising at least the following steps:
obtaining from the communications network, base station configuration data (pre-trained models; paragraph 140, 149-151, 159-164), wherein the base station configuration data comprises parameter values (paragraph 97-124) from a plurality of active base stations and/or cells of the communications network (pre-trained models received from already active base stations; paragraph 140, 149-151, 159-164);
obtaining network planning data (network data indicating similarity; paragraph 141-142, 152, 165-169);
predicting autoconfiguration parameters for the first base station based on the obtained base station configuration data and the obtained network planning data, wherein the predicted autoconfiguration parameters are initial parameters predicted before commissioning the first base station (adapting model based upon similarity data; paragraph 92, 131-135, 146, 156, 170); and
configuring the first base station using the predicted autoconfiguration parameters (new base stations provided with configuration models derived from other base stations; paragraph 91, 147, 157, 171).
As to claim 13, Liao discloses wherein the predicting autoconfiguration parameters further comprises:
using an algorithm comprising artificial intelligence (deep reinforcement learning models; Fig. 4, paragraph 83-91);
training the algorithm with the obtained base station configuration data (paragraph 81-91, 132, 150-152); and
predicting autoconfiguration parameters using the trained algorithm (adapting model based upon similarity data; paragraph 92, 131-135, 146, 156, 170);
As to claim 14, Liao discloses wherein, the artificial intelligence comprises: neural network and/or machine learning based methods (Fig. 4, paragraph 83-91).
As to claim 15, Liao discloses wherein the predicting autoconfiguration parameters further comprises predicting autoconfiguration parameters separately for at least two cells of the first base station (paragraph 77, 89).
As to claim 16, Liao discloses wherein the predicting autoconfiguration parameters further comprises predicting autoconfiguration parameters separately for at least two cells of the first base station (paragraph 77, 89).
As to claim 17, Liao discloses wherein the predicting autoconfiguration parameters further comprises predicting autoconfiguration parameters separately for at least two cells of the first base station (paragraph 77, 89).
As to claim 18, Liao discloses wherein the predicting autoconfiguration parameters further comprises weighting some base stations in the prediction based on similarity with the first base station (paragraph 129-135).
As to claim 19, Liao discloses wherein the predicting autoconfiguration parameters further comprises weighting some base stations in the prediction based on similarity with the first base station (paragraph 129-135).
As to claim 20, Liao discloses wherein the predicting autoconfiguration parameters further comprises weighting some base stations in the prediction based on similarity with the first base station (paragraph 129-135).
As to claim 21, Liao discloses wherein the predicted autoconfiguration parameters are selected from: location area code (paragraph 132), base station controller identifier, physical cell identity, radio network controller, scrambling code, routing zone (paragraph 132), and neighbour cell count.
As to claim 22, Liao discloses wherein the network planning data comprises at least information about location of the first base station (paragraph 33, 39, 43) and information about vendor and cell technology of the first base station (paragraph 92).
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James R Sheleheda whose telephone number is (571)272-7357. The examiner can normally be reached M-F 8 am-5 pm CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Bruckart can be reached at (571) 272-3982. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/James R Sheleheda/ Primary Examiner, Art Unit 2424