/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 .
Amendment filed on 07/13/2026 has been acknowledged. Claims 1-10, 12, 14-18 and 20-23, as originally filed, are currently pending and have been considered below. Claim 1, 14 and 20 are independent claim. Claim 11, 13 and 19 are cancelled. Claim 21-23 are added new. Claims 1, 5, 14 and 20 have been amended.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/13/2026 has been entered.
Priority
No priority claimed.
Remarks and Response
Applicant’s arguments filed in the amendments on 07/13/2026 have been fully considered but moot in view of new ground of rejection. The reasons set forth below.
Claim Rejections - 35 USC § 103
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.
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.
Claim 1-10, 12, 14-18 20 and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Pathania (US Patent Application No 2025/0119748 A1) in view of Ryan (US Patent Application Publication No 2019/0132787 A1).
Regarding Claim 1, Pathania discloses a computer-implemented method comprising:
obtaining, by one or more processors, radio frequency (RF) signal scan data indicative of RF conditions in a cell of an operator base station (Pathania, ¶[0032], receiver is responsible for capturing and processing incoming wireless signals from cell sites such as legitimate cell site or cell site simulator. ¶[0034], the signal measurement component is responsible for measuring the signals transmitted between the user device and a cell site. ¶[0035], the signal characteristics data includes cell site identifier, timestamp data, signal information block (SIB), signal strength data, signal to noise ratio data, signal frequency data, error rate data (bit error and packet error), signal bandwidth data, carrier to interference ratio, geolocation data);
inputting, by the one or more processors, the RF signal scan data into a machine learning model trained at least in part using historical RF signal scan data indicative of base station operation in an absence of a rogue base station (Pathania, ¶[0020], the method leverage historical signal characteristics data obtained from signal transmissions between user devices and known legitimate cell sites to construct a model aimed at distinguishing legitimate from suspicious cell sites. ¶[0043], the model may include the determined acceptable range and the algorithms used for feature engineering. The model includes machine learning models. ¶[0045], the feature engineering module can generate device specific models for each user device, based on the historical signal characteristics data between the user device with a known legitimate cell site);
detecting, by the one or more processors, an output of the machine learning model indicative of a presence of a rogue base station (Pathania, ¶[0048], based on the outcome of comparison of the actual features against the expected features and the determination of the actual clusters against the acceptable range, the determination module can further determine whether the cell site is a suspicious cell site. ¶[0049], the verification application is responsible to confirm the legitimacy of the suspicious cell. ¶[0051], the protection application initiates an immediate disconnection of the user device from cell site simulator); and
generating, by the one or more processors, an alert indicative of the presence of the rogue base station (Pathania, ¶[0036], providing visual or auditory alerts to inform users about changes in network conditions, signal characteristics and the detection of suspicious signal characteristics and allowing the user to provide feedback or report issues related to signal characteristics. ¶[0051], user may be notified of the detected anomaly, threat of the cell simulator and the protective action is taken).
Pathania does not explicitly teach the following limitation that Ryan teaches:
from at least one of the operator base station or a neighbor base station (Ryan, ¶[0100], the statistical approach to RBSR detection and identification also checks to ensure that the value associated with a given parameter varies in the rogue direction (statistically significant low neighbor count, statistically significant high cell reselect order) versus the non-rogue direction (statistically significant high neighbor count, statistically significant low cell reselect order);
the historical RF signal scan data including RF signal scan data from the at least one of the operator base station or the neighbor base station (Ryan, ¶[0100], the statistical approach to RBSR detection and identification also checks to ensure that the value associated with a given parameter varies in the rogue direction (statistically significant low neighbor count, statistically significant high cell reselect order) versus the non-rogue direction (statistically significant high neighbor count, statistically significant low cell reselect order);
Pathania in view of Ryan are analogous art because they are from the “same field of endeavor” and are from the same “problem solving area”. Namely, they pertain to the field of “detecting rogue base station”. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the invention of Pathania in view of Ryan to include the idea of detecting and identifying a cellular rogue base station router via statistical algorithms (Ryan, ¶[0002]).
Regarding Claim 2, Pathania in view of Ryan discloses the computer-implemented method of claim 1, wherein the RF signal scan data includes indications of random access channel (RACH) failures, standalone dedicated control channel (SDDCH) failures, signal to noise ratio (SINR) values, received signal strength indicator (RSSI) values, or received total wideband power (RTWP) values generated by the operator base station (Pathania, ¶[0019], analyzing signal characteristics beyond cell ID and frequency. These characteristics may encompass parameters like signal strength, signal to noise ratio, interference levels. ¶[0035], signal characteristics data include cell ID, timestamp, signal information block (SIB) data, signal strength data, signal to noise ratio data, signal frequency data, bit error rate, packet error rate, signal bandwidth data (the width of the frequency spectrum occupied by the signal in a specific period of time), signal latency data, carrier to interference ratio data, geolocation data).
Regarding Claim 3, Pathania in view of Ryan discloses the computer-implemented method of claim 1, wherein the RF signal scan data includes indications of bit error rate, block error rate, power levels at carrier frequencies, out-of-band power levels, subcarrier spacing, or channel allocation data generated by performing a frequency-domain analysis of signals received at the operator base station (Pathania, ¶[0019], analyzing signal characteristics beyond cell ID and frequency. These characteristics may encompass parameters like signal strength, signal to noise ratio, interference levels. ¶[0035], signal characteristics data include cell ID, timestamp, signal information block (SIB) data, signal strength data, signal to noise ratio data, signal frequency data, bit error rate, packet error rate, signal bandwidth data (the width of the frequency spectrum occupied by the signal in a specific period of time), signal latency data, carrier to interference ratio data, geolocation data).
Regarding Claim 4, Pathania in view of Ryan discloses the computer-implemented method of claim 1, wherein the RF signal scan data includes indications of reference signal receive power (RSRP), reference signal received quality (RSRQ), reference signal SINR (RS-SINR), serving cell RF information, neighbor cell RF information, cell ID, or serving cell ID generated by (i) user equipment (UE) served by the operator base station or (ii) a neighboring cell to the cell of the operator base station (Pathania, ¶[0019], analyzing signal characteristics beyond cell ID and frequency. These characteristics may encompass parameters like signal strength, signal to noise ratio, interference levels. ¶[0035], signal characteristics data include cell ID, timestamp, signal information block (SIB) data, signal strength data, signal to noise ratio data, signal frequency data, bit error rate, packet error rate, signal bandwidth data (the width of the frequency spectrum occupied by the signal in a specific period of time), signal latency data, carrier to interference ratio data, geolocation data).
Regarding Claim 5, Pathania in view of Ryan the computer-implemented method of claim 1, further comprising:
obtaining, by one or more processors, a plurality of RF signal scan data from a plurality of operator base stations, including the operator base station and the neighboring base station (Pathania, ¶[0044], the database is in connection with security management system and may include various data sources provided by third party or public, such as geolocation data, crime statistics data and law enforcement data. Also Ryan, ¶[0100]); and
storing, by the one or more processors, the plurality of RF signal scan data in a database (Pathania, ¶[0044], the database is in connection with security management system and may include various data sources provided by third party or public, such as geolocation data, crime statistics data and law enforcement data).
Regarding Claim 6, Pathania in view of Ryan discloses the computer-implemented method of claim 5, further comprising:
validating, by the one or more processors, the presence of the rogue base station by:
obtaining, from the database, RF signal scan data associated with a neighboring cell (Pathania, ¶[0044], the geolocation data may include geolocation of cell sites, user devices or potential anomalies which can be used for spatial analysis and geofencing. The data stored in database may be retrieved by the security management system and used to develop and validate the model. Ryan, ¶[0100], the statistical approach to RBSR detection and identification also checks to ensure that the value associated with a given parameter varies in the rogue direction (statistically significant low neighbor count, statistically significant high cell reselect order) versus the non-rogue direction (statistically significant high neighbor count, statistically significant low cell reselect order); and
inputting the RF signal scan data associated with the neighboring cell into the machine learning model (Pathania, ¶[0044], the geolocation data may include geolocation of cell sites, user devices or potential anomalies which can be used for spatial analysis and geofencing. The data stored in database may be retrieved by the security management system and used to develop and validate the model. Also Ryan, ¶[0100], the statistical approach to RBSR detection and identification also checks to ensure that the value associated with a given parameter varies in the rogue direction (statistically significant low neighbor count, statistically significant high cell reselect order) versus the non-rogue direction (statistically significant high neighbor count, statistically significant low cell reselect order).
Regarding Claim 7, Pathania in view of Ryan discloses the computer-implemented method of claim 6, wherein generating the alert comprises:
detecting, by the one or more processors, an output of the machine learning model indicative of the presence of the rogue base station when the RF signal scan data associated with the neighboring cell (Pathania, ¶[0048], based on the outcome of comparison of the actual features against the expected features and the determination of the actual clusters against the acceptable range, the determination module can further determine whether the cell site is a suspicious cell site. ¶[0049], the verification application is responsible to confirm the legitimacy of the suspicious cell. ¶[0051], the protection application initiates an immediate disconnection of the user device from cell site simulator);
estimating, by the one or more processors, a location of the rogue base station based on a coverage area associated with the cell and the neighboring cell (Pathania, ¶[0055], the geographic area where the cell site simulator located is identified based on simultaneous identification of the cell site simulator. A positioning technique such as bilateral or triangulation may be employed. The security management system compares angles measured by different user devices and identifies an intersection point where lines of sight from the user devices converge. The intersection point may represent the estimated location of the cell site simulator. ¶[0104]); and
generating, by the one or more processors, the alert such that the alert indicates the estimated location (Pathania, ¶[0051], user may be notified of the detected anomaly, threat of the cell simulator and the protective action is taken. ¶[0104], the suspicious cell site is located based on the geolocation data and position data included in the signal characteristics data associated with each user device. The location of the suspicious cell site may be reported to a third party such as law enforcement).
Regarding Claim 8, Pathania in view of Ryan discloses the computer-implemented method of claim 1, wherein:
the machine learning model is an anomaly detection model (Pathania, ¶[0045], the feature engineering module can generate device specific models for each user device based on the historical signal characteristics data between the user device with a known legitimate cell site. The device specific models can enhance anomaly detection. ¶[0046], most appropriate model for detection and determination of anomaly); and
the machine learning model is (i) trained using historical RF signal scan data generated by the operator base station to generate a baseline for operation of operator base station, and (ii) output the indication of the presence of the rogue base station in response to detecting a deviation between the obtained RF signal scan data and the baseline (Pathania, ¶[0020], the method leverages historical signal characteristics data obtained from signal transmissions between user devices and known legitimate cell sites to construct a model aimed at distinguishing legitimate from suspicious cell sites. ¶[0045]).
Regarding Claim 9, Pathania in view of Ryan discloses the computer-implemented method of claim 1, wherein:
the machine learning model is a classification model (Pathania, ¶[0045], the feature engineering module can generate device specific models for each user device based on the historical signal characteristics data between the user device with a known legitimate cell site. The device specific models can enhance anomaly detection. ¶[0046], most appropriate model for detection and determination of anomaly); and
the machine learning model is (i) trained using labeled historical RF signal scan data indicative of base station operation in an absence of a rogue base station and labeled historical RF signal scan data indicative of base station operation in a presence of a rogue base station, and (ii) output the indication of the presence of the rogue base station by classifying the RF signal scan data with a label indicative of the presence of the rogue base station (Pathania, ¶[0020], the method leverages historical signal characteristics data obtained from signal transmissions between user devices and known legitimate cell sites to construct a model aimed at distinguishing legitimate from suspicious cell sites. ¶[0045]).
Regarding Claim 10, Pathania in view of Ryan discloses the computer-implemented method of claim 1, wherein:
the machine learning model is a time series analysis model (Pathania, ¶[0045], the feature engineering module can generate device specific models for each user device based on the historical signal characteristics data between the user device with a known legitimate cell site. The device specific models can enhance anomaly detection. ¶[0046], most appropriate model for detection and determination of anomaly); and
the machine learning model is (i) trained using historical RF signal scan data indicative of base station operation over time in an absence of a rogue base station, (ii) accepts a plurality of RF signal scan data obtained from the operator base station generated across an interval of time, and (iii) output the indication of the presence of the rogue base station in response to detecting an anomalous pattern of operation (Pathania, ¶[0020], the method leverages historical signal characteristics data obtained from signal transmissions between user devices and known legitimate cell sites to construct a model aimed at distinguishing legitimate from suspicious cell sites. ¶[0045]).
Regarding Claim 12, Pathania in view of Ryan discloses the computer-implemented method of claim 1, wherein the operator base station captures the RF signal scan data during a period of low activity (Pathania, ¶[0032], receiver is responsible for capturing and processing incoming wireless signals from cell sites such as legitimate cell site or cell site simulator. ¶[0034], the signal measurement component is responsible for measuring the signals transmitted between the user device and a cell site. ¶[0035], the signal characteristics data includes cell site identifier, timestamp data, signal information block (SIB), signal strength data, signal to noise ratio data, signal frequency data, error rate data (bit error and packet error), signal bandwidth data, carrier to interference ratio, geolocation data).
Regarding Claim 14, Pathania discloses a system comprising:
one or more transceivers communicatively coupled to an operator base station (Pathania, Fig-7);
one or more processors (Pathania, Fig-7); and
one or more non-transitory memories storing processor-executable instructions that, when executed by the one or more processors, cause the system to (Pathania, Fig-7):
obtain radio frequency (RF) signal scan data indicative of RF conditions in a cell of an operator base station (Pathania, ¶[0032], receiver is responsible for capturing and processing incoming wireless signals from cell sites such as legitimate cell site or cell site simulator. ¶[0034], the signal measurement component is responsible for measuring the signals transmitted between the user device and a cell site. ¶[0035], the signal characteristics data includes cell site identifier, timestamp data, signal information block (SIB), signal strength data, signal to noise ratio data, signal frequency data, error rate data (bit error and packet error), signal bandwidth data, carrier to interference ratio, geolocation data);
input the RF signal scan data into a machine learning model trained at least in part using historical RF signal scan data indicative of base station operation in an absence of a rogue base station (Pathania, ¶[0020], the method leverage historical signal characteristics data obtained from signal transmissions between user devices and known legitimate cell sites to construct a model aimed at distinguishing legitimate from suspicious cell sites. ¶[0043], the model may include the determined acceptable range and the algorithms used for feature engineering. The model includes machine learning models. ¶[0045], the feature engineering module can generate device specific models for each user device, based on the historical signal characteristics data between the user device with a known legitimate cell site);
detect an output of the machine learning model indicative of a presence of a rogue base station (Pathania, ¶[0048], based on the outcome of comparison of the actual features against the expected features and the determination of the actual clusters against the acceptable range, the determination module can further determine whether the cell site is a suspicious cell site. ¶[0049], the verification application is responsible to confirm the legitimacy of the suspicious cell. ¶[0051], the protection application initiates an immediate disconnection of the user device from cell site simulator); and
generate an alert indicative of the presence of the rogue base station (Pathania, ¶[0036], providing visual or auditory alerts to inform users about changes in network conditions, signal characteristics and the detection of suspicious signal characteristics and allowing the user to provide feedback or report issues related to signal characteristics. ¶[0051], user may be notified of the detected anomaly, threat of the cell simulator and the protective action is taken).
Pathania does not explicitly teach the following limitation that Ryan teaches:
from at least one of the operator base station or a neighbor base station (Ryan, ¶[0100], the statistical approach to RBSR detection and identification also checks to ensure that the value associated with a given parameter varies in the rogue direction (statistically significant low neighbor count, statistically significant high cell reselect order) versus the non - rogue direction (statistically significant high neighbor count, statistically significant low cell reselect order);
the historical RF signal scan data including RF signal scan data from the at least one of the operator base station or the neighbor base station (Ryan, ¶[0100], the statistical approach to RBSR detection and identification also checks to ensure that the value associated with a given parameter varies in the rogue direction (statistically significant low neighbor count, statistically significant high cell reselect order) versus the non - rogue direction (statistically significant high neighbor count, statistically significant low cell reselect order);
Pathania in view of Ryan are analogous art because they are from the “same field of endeavor” and are from the same “problem solving area”. Namely, they pertain to the field of “detecting rogue base station”. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the invention of Pathania in view of Ryan to include the idea of detecting and identifying a cellular rogue base station router via statistical algorithms (Ryan, ¶[0002]).
Regarding Claim 15, Pathania in view of Ryan discloses the system of claim 14, wherein:
the machine learning model is an anomaly detection model (Pathania, ¶[0045], the feature engineering module can generate device specific models for each user device based on the historical signal characteristics data between the user device with a known legitimate cell site. The device specific models can enhance anomaly detection. ¶[0046], most appropriate model for detection and determination of anomaly); and
the machine learning model is (i) trained using historical RF signal scan data generated by the operator base station to generate a baseline for operation of operator base station, and (ii) output the indication of the presence of the rogue base station in response to detecting a deviation between the obtained RF signal scan data and the baseline (Pathania, ¶[0020], the method leverages historical signal characteristics data obtained from signal transmissions between user devices and known legitimate cell sites to construct a model aimed at distinguishing legitimate from suspicious cell sites. ¶[0045]).
Regarding Claim 16, Pathania in view of Ryan discloses the system of claim 14, wherein:
the machine learning model is a classification model (Pathania, ¶[0045], the feature engineering module can generate device specific models for each user device based on the historical signal characteristics data between the user device with a known legitimate cell site. The device specific models can enhance anomaly detection. ¶[0046], most appropriate model for detection and determination of anomaly); and
the machine learning model is (i) trained using labeled historical RF signal scan data indicative of base station operation in an absence of a rogue base station and labeled historical RF signal scan data indicative of base station operation in a presence of a rogue base station, and (ii) output the indication of the presence of the rogue base station by classifying the RF signal scan data with a label indicative of the presence of the rogue base station (Pathania, ¶[0020], the method leverages historical signal characteristics data obtained from signal transmissions between user devices and known legitimate cell sites to construct a model aimed at distinguishing legitimate from suspicious cell sites. ¶[0045]).
Regarding Claim 17, Pathania in view of Ryan discloses the system of claim 14, wherein:
the machine learning model is a time series analysis model (Pathania, ¶[0045], the feature engineering module can generate device specific models for each user device based on the historical signal characteristics data between the user device with a known legitimate cell site. The device specific models can enhance anomaly detection. ¶[0046], most appropriate model for detection and determination of anomaly); and
the machine learning model is (i) trained using historical RF signal scan data indicative of base station operation over time in an absence of a rogue base station, (ii) accepts a plurality of RF signal scan data obtained from the operator base station generated across an interval of time, and (iii) output the indication of the presence of the rogue base station in response to detecting an anomalous pattern of operation (Pathania, ¶[0020], the method leverages historical signal characteristics data obtained from signal transmissions between user devices and known legitimate cell sites to construct a model aimed at distinguishing legitimate from suspicious cell sites. ¶[0045]).
Regarding Claim 18, Pathania in view of Ryan discloses the system of claim 14, wherein:
the machine learning model is an ensemble machine learning model that includes two or more of an anomaly detection model, a classification model, and a time series analysis model (Pathania, ¶[0045], the feature engineering module can generate device specific models for each user device based on the historical signal characteristics data between the user device with a known legitimate cell site. The device specific models can enhance anomaly detection. ¶[0046], most appropriate model for detection and determination of anomaly. ¶[0076]- ¶[0077], machine learning classifier learns to differentiate. ¶[0080], machine learning models employ machine learning algorithms such as nearest neighbor algorithm, statistical algorithm, replicating reservoir networks (for non-linear models typically for time series)).
Regarding Claim 20, Pathania discloses a non-transitory storage medium storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to:
obtain radio frequency (RF) signal scan data indicative of RF conditions in a cell of an operator base station (Pathania, ¶[0032], receiver is responsible for capturing and processing incoming wireless signals from cell sites such as legitimate cell site or cell site simulator. ¶[0034], the signal measurement component is responsible for measuring the signals transmitted between the user device and a cell site. ¶[0035], the signal characteristics data includes cell site identifier, timestamp data, signal information block (SIB), signal strength data, signal to noise ratio data, signal frequency data, error rate data (bit error and packet error), signal bandwidth data, carrier to interference ratio, geolocation data);
input the RF signal scan data into a machine learning model trained at least in part using historical RF signal scan data indicative of base station operation in an absence of a rogue base station (Pathania, ¶[0020], the method leverage historical signal characteristics data obtained from signal transmissions between user devices and known legitimate cell sites to construct a model aimed at distinguishing legitimate from suspicious cell sites. ¶[0043], the model may include the determined acceptable range and the algorithms used for feature engineering. The model includes machine learning models. ¶[0045], the feature engineering module can generate device specific models for each user device, based on the historical signal characteristics data between the user device with a known legitimate cell site);
detect an output of the machine learning model indicative of a presence of a rogue base station (Pathania, ¶[0048], based on the outcome of comparison of the actual features against the expected features and the determination of the actual clusters against the acceptable range, the determination module can further determine whether the cell site is a suspicious cell site. ¶[0049], the verification application is responsible to confirm the legitimacy of the suspicious cell. ¶[0051], the protection application initiates an immediate disconnection of the user device from cell site simulator); and
generate an alert indicative of the presence of the rogue base station (Pathania, ¶[0036], providing visual or auditory alerts to inform users about changes in network conditions, signal characteristics and the detection of suspicious signal characteristics and allowing the user to provide feedback or report issues related to signal characteristics. ¶[0051], user may be notified of the detected anomaly, threat of the cell simulator and the protective action is taken);
wherein the machine learning model is an ensemble machine learning model that includes two or more of an anomaly detection model, a classification model, and a time series analysis model (Pathania, ¶[0045], the feature engineering module can generate device specific models for each user device based on the historical signal characteristics data between the user device with a known legitimate cell site. The device specific models can enhance anomaly detection. ¶[0046], most appropriate model for detection and determination of anomaly. ¶[0076]- ¶[0077], machine learning classifier learns to differentiate. ¶[0080], machine learning models employ machine learning algorithms such as nearest neighbor algorithm, statistical algorithm, replicating reservoir networks (for non-linear models typically for time series)).
Pathania does not explicitly teach the following limitation that Ryan teaches:
from at least one of the operator base station or a neighbor base station (Ryan, ¶[0100], the statistical approach to RBSR detection and identification also checks to ensure that the value associated with a given parameter varies in the rogue direction (statistically significant low neighbor count, statistically significant high cell reselect order) versus the non - rogue direction (statistically significant high neighbor count, statistically significant low cell reselect order);
the historical RF signal scan data including RF signal scan data from the at least one of the operator base station or the neighbor base station (Ryan, ¶[0100], the statistical approach to RBSR detection and identification also checks to ensure that the value associated with a given parameter varies in the rogue direction (statistically significant low neighbor count, statistically significant high cell reselect order) versus the non - rogue direction (statistically significant high neighbor count, statistically significant low cell reselect order);
Pathania in view of Ryan are analogous art because they are from the “same field of endeavor” and are from the same “problem solving area”. Namely, they pertain to the field of “detecting rogue base station”. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the invention of Pathania in view of Ryan to include the idea of detecting and identifying a cellular rogue base station router via statistical algorithms (Ryan, ¶[0002]).
Regarding Claim 22, Pathania in view of Ryan discloses the system of claim 14, wherein the processor-executable instructions further cause the system to validate the presence of the rogue base station by:
obtaining, from a database, RF signal scan data associated with a neighboring cell (Pathania, ¶[0044], the data stored in database may be retrieved by the security management system and used to develop and validate the model. ¶[0080], the machine learning models employ machine learning algorithm such as nearest neighbor (NN) algorithm. ¶[0082], the K-Nearest neighbors algorithm may receive each data point within the signal characteristic data and compare each to k closest data points), and
inputting the RF signal scan data associated with the neighboring cell into the machine learning model (Pathania, ¶[0045], the feature engineering module can generate device specific models for each user device based on the historical signal characteristics data between the user device with a known legitimate cell site. The device specific models for each user device based on the historical signal characteristics data between the user device with a known legitimate cell site. The device specific models can enhance anomaly detection. ¶[0046], most appropriate model for detection and determination of anomaly. ¶[0076]- ¶[0077], machine learning classifier learns to differentiate. ¶[0080], machine learning models employ machine learning algorithms such as nearest neighbor algorithm, statistical algorithm, replicating reservoir networks (for non-linear models typically for time series)).
Regarding Claim 23, Pathania in view of Ryan discloses the non-transitory storage medium of claim 20, wherein the machine learning model is an ensemble machine learning model that includes two or more of an anomaly detection model, a classification model, and a time series analysis model (Pathania, ¶[0045], the device specific models can enhance anomaly detection. ¶[0046], most appropriate model for detection and determination of anomaly. ¶[0076]- ¶[0077], machine learning classifier learns to differentiate. ¶[0080], machine learning models employ machine learning algorithms such as nearest neighbor algorithm, statistical algorithm, replicating reservoir networks (for non-linear models typically for time series)).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Pathania (US Patent Application No 2025/0119748 A1) in view of Ryan (US Patent Application Publication No 2019/0132787 A1) and further in view of Koral (US Patent Application Publication No 2023/0308878 A1).
Regarding Claim 21, Pathania in view of Ryan discloses the following limitation
the computer-implemented method of claim 1, further comprising:
determining, by the one or more processors, a serving cell ID associated with the rogue base station (Pathania, ¶[0019], analyzing signal characteristics beyond cell ID and frequency. These characteristics may encompass parameters like signal strength, signal to noise ratio, interference levels. ¶[0035], signal characteristics data include cell ID, timestamp, signal information block (SIB) data, signal strength data, signal to noise ratio data, signal frequency data, bit error rate, packet error rate, signal bandwidth data (the width of the frequency spectrum occupied by the signal in a specific period of time), signal latency data, carrier to interference ratio data, geolocation data); and
transmitting, by the one or more processors, the serving cell ID to user equipment and refrains from attaching to one or more base stations associated with the serving cell ID (Pathania, ¶[0098], a protective action is taken by the user device to cause a disconnection from the suspicious cell site. Data transmission between the user device and the suspicious cell site is ceased).
Pathania in view of Ryan do not explicitly teach the following limitation that Koral teaches:
such that the user equipment adds the serving cell ID to a blacklist (Koral, ¶[0047], the vendor and model analyzer sub-module can maintain a blacklist for vendors and models of access point that are known for being popular for attacker. In response to determining that the vendor or model is on the blacklist, the vendor and model analyzer sub-module can determine that the access point has a higher likelihood of being malicious).
Pathania in view of Ryan and Koral are analogous art because they are from the “same field of endeavor” and are from the same “problem solving area”. Namely, they pertain to the field of “access point impersonation protection”. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the invention of Pathania in view of Ryan and Koral to include the idea of incorporating an access point impersonation protection system that can determine whether the vendor or the model of the access point is on a blacklist (Koral, ¶[0009)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (see PTO-Form 892).
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/WASIKA NIPA/ Primary Examiner, Art Unit 2433
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