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 .
Response to Amendment
The amendment filed on 08/17/2026 has been entered. Claims 1-18 remain pending in this application. Claims 1, 3-4, 12-13, and 16 have been amended. Claim 2 has been cancelled. No claims are new. Applicant's amendments to the claims have overcome each and every 112(b) rejection set forth in the Non-Final Office Action dated 03/17/2026.
Response to Arguments
Applicant’s arguments filed 03/17/2026 regarding prior art rejections of claims 1 and 3-18 have been fully considered and are persuasive. All previous prior art rejections are overcome in consideration of amendments and arguments, however, additional prior art rejections are 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 3-10 are rejected under 35 U.S.C. 103 as being unpatentable over Kesavareddigari (US 20230325706 A1), hereinafter Kesavareddigari, in view of Logan (US 3460140 A), hereinafter Logan.
Regarding claim 1, Kesavareddigari, as shown below, discloses a communication network (See at least Fig. 1, [0049] “FIG. 1 is a diagram illustrating an example of a wireless communications system and an access network 100”), comprising multiple nodes, in which a node includes (See at least Fig. 1, [0057] “The base station 180 and the UE 104 may each include a plurality of antennas” Kesavareddigari discloses a plurality of nodes including base stations and UEs.):
one or more antennas (See at least Fig. 1, [0057] “The base station 180 and the UE 104 may each include a plurality of antennas”);
one or more input ports configured to receive communication signals from the antenna from other nodes in the network (See at least Fig. 1, [0051] “The communication links 120 may use multiple-input and multiple-output (MIMO) antenna technology”);
one or more output ports configured to transmit signals through the antenna to other nodes in the network (See at least Fig. 1, [0051] “The communication links 120 may use multiple-input and multiple-output (MIMO) antenna technology”);
a memory to store data associated with the communication signals (See at least Fig. 3, Item 360, [0077] “The controller/processor 359 can be associated with a memory 360 that stores program codes and data.”); and
one or more processors configured to execute code to cause the one or more processors to (See at least Fig. 3, Item 359, [0077] “The controller/processor 359 can be associated with a memory 360 that stores program codes and data.”):
gather local data about an environment in which the node operates (See at least [0036] “vehicles can be equipped with UEs and onboard sensors (e.g., RADARs, LIDARs, cameras, etc.) to provide a radio network with information about moving obstacles that may ultimately degrade signal quality by causing beam blockage.”);
communicate with one or more other nodes as needed to send local data (See at least [0042] “the control or management function of the ML service entity of the base station or ML server may adaptively request UEs to communicate their confidence for each feature extraction or inference or to apply a model capable of communicating such confidence levels.” Kesavareddigari discloses a collaborative system where a UE may communicate local data including generated features and confidence levels.); and
use the local data to determine optimized operational settings for the node (See at least [0045] “the ML service entity may adaptively instruct the UE to switch between feature extraction models, trigger sensor reconfiguration, and the like not only to predict and prevent LOS beam blockages as previously described, but also or alternatively to predict best beams for communication with a UE, perform beam management (beamforming training or refinement), optimally schedule resources to the UE”);
wherein at least one node comprises a sensor node, and the one or more processors in the sensor node are further configured to execute code to cause the one or more processors to (See at least Fig. 3, [0112] “The vehicle/UE 704a may be equipped with sensors, such as cameras, RADARs, LIDARs, or other sensing equipment”):
determine a position of the sensor node (See at least [0150] “vehicle sensor settings and configurations, sensor availability, sensor selection, location changes”);
receive return signals from the input ports (See at least [0112] “Sensing information may include, for example, raw sensor data or inference data. Raw sensor data may include, for example, RADAR or LIDAR point clouds”); and
determine if the return signals indicate that a blockage exists in the communications network (See at least [0149] “If the UE has obstructed views, for example, from its front RADAR that can complement the left window radar of an adjacent vehicle UE”)
Kesavareddigari does not explicitly disclose emit pulses having unique correlations with spherical orientation. However, Logan, in the same or in a similar field of endeavor, discloses:
emit pulses having unique correlations with spherical orientation (See at least Col. 2 Lines 21-25 “a helical shaped pulse is transmitted during each radar transmitting period of a radar having provision for steering the beam pointing direction through the use of phase modulation, to establish a conical pulse in time and space” Logan discloses helical pulses which by definition have unique correlations with spherical orientation)
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the sensor system disclosed by Kesavareddigari with the pulse disclosed by Logan. One would have been motivated to do so in order to advantageously increase reliability and flexibility (See at least Col. 2 Lines 16-20 “the reliability and flexibility inherent in a radar system having meam steering capability are retained while obtaining the benefits derived from the sequential lobing and simultaneous lobing systems.”).
Regarding claim 3, The combination of Kesavareddigari and Logan, as shown above, discloses all of the limitations of claim 1. Kesavareddigari additionally discloses
the one or more processors in the sensor node are further configured to execute code that causes the one or more processors to determine optimized angles for unblocked ports of the sensor node (See at least [0150] “if the UE which previously had a mostly unobstructed RADAR has an occlusion (or approaching occlusion) to the RADAR, the UE can preemptively reconfigure the FoV or switch to another RADAR having an unobstructed view and send the gNB/ML server a reconfiguration update message including reconfigured parameters accordingly.” See also [0077] regarding processors/code)
Regarding claim 4, The combination of Kesavareddigari and Logan, as shown above, discloses all of the limitations of claim 1. Kesavareddigari additionally discloses
the code that causes the one or more processors to communicate with one or more nodes comprises code that causes the one or more processors to: communicate with a central node (See at least Fig. 3, Item 310, [0091] “the ML service entity may be physically or logically deployed in a separate network node than those of a disaggregated base station. For example, the base station may include multiple units or network nodes, such as a central or centralized unit (CU)” Kesavareddigari discloses a base station 310 which may operate as a central unit/node in communication with a UE);
reduce data derived from the return signal to produce a reduced data set; and transmit the reduced data set to the central node (See at least [0149] “if the gNB/ML server determines a high network traffic load, the gNB/ML server may configure the UE to lower its RADAR measurement update rate, or to lower the resolution” further details of transmission present in at least [0149])
Regarding claim 5, The combination of Kesavareddigari and Logan, as shown above, discloses all of the limitations of claim 1. Kesavareddigari additionally discloses
the code that causes the one or more processors to gather local data and communicate with the one or more other nodes comprises code that causes the one or more processors to: communicate with a central node (See at least Fig. 3, Item 310, [0091] “the ML service entity may be physically or logically deployed in a separate network node than those of a disaggregated base station. For example, the base station may include multiple units or network nodes, such as a central or centralized unit (CU)” Kesavareddigari discloses a base station 310 which may operate as a central unit/node in communication with a UE);
receive one or more machine learning models from the central node (See at least Figs. 11-12, [0133] “Referring back to FIG. 11, the ML service entity 1104 may request the UE 1102 to switch between the different models illustrated in FIG. 12 according to the ML service entity 1104's training or inference need”);
use the local data to train at least one of the one or more models on the node; and send only updated models to the central node (See at least Fig. 11, [0134] “receiving the training or extracted features (e.g., OBBs 1206) from the UE 1102 at 8.1, at 8.2, the ML service entity 1104”)
Regarding claim 6, The combination of Kesavareddigari and Logan, as shown above, discloses all of the limitations of claims 1 and 5. Kesavareddigari additionally discloses
the code that causes the one or more processors to receive one or more machine learning models from the central node comprises code that causes the one or more processors to receive only a local part of the one or more machine learning models (See at least Fig. 11, [0133] “Referring back to FIG. 11, the ML service entity 1104 may request the UE 1102 to switch between the different models illustrated in FIG. 12 according to the ML service entity 1104's training or inference need”)
Regarding claim 7, The combination of Kesavareddigari and Logan, as shown above, discloses all of the limitations of claim 1. Kesavareddigari additionally discloses
the node comprises a central node, and the code that causes the one or more processors to communicate with other nodes comprises code that causes the one or more processors to (See at least Fig. 3, Item 310, [0091] “the ML service entity may be physically or logically deployed in a separate network node than those of a disaggregated base station. For example, the base station may include multiple units or network nodes, such as a central or centralized unit (CU)” Kesavareddigari discloses a base station 310 which may operate as a central unit/node in communication with a UE):
operate upon a global portion of the one or more machine learning models (See at least [0136] “the ML service entity 1302 here may include multiple ML models which perform different functions”);
receive updated data from local nodes (See at least [0136] “The ML service entity 1302 may apply these models to more efficiently perform beam management. For example, based on sensing information provided by the UE”);
update the global portion of the one or more machine learning models (See at least [0138] “the sensor data 1310 or extracted features 1312 which serve as training or inference data 1314 for one ML model of the ML service entity”); and
transmit the updated global portion of the one or more machine learning models as needed (See at least [0138] “the ML service entity 1302 may request the UE to switch to one of its models”)
Regarding claim 8, The combination of Kesavareddigari and Logan, as shown above, discloses all of the limitations of claim 1. Kesavareddigari additionally discloses
each node of the multiple nodes comprises at least one of: a communications device, a sensing device, a test and measurement instrument, and a reconfigurable intelligent surface (See at least Fig. 1, [0051] “The base stations 102 may wirelessly communicate with the UEs 104.” Kesavareddigari discloses a communications network system wherein base stations and UEs are communications devices.)
Regarding claim 9, The combination of Kesavareddigari and Logan, as shown above, discloses all of the limitations of claim 1. Kesavareddigari additionally discloses
the information about the local environment comprises physical layer information about a device under test residing at the node (See at least [0152] “For example, the UE 1602 may indicate a list of RADAR sensor/detector reconfigurable parameters such as FoV, orientation, range, resolution, update rate, and the like, as well as other RADAR sensor parameters including but not limited to sensor identification (e.g., number of RADAR sensors and associated IDs), sensor mounting on the vehicle (e.g., positions relative to the center of the ego vehicle and the mounting rotation angle [roll, pitch, yaw]), the detector configuration (e.g., angular field of view, range limit (min and max detection range), range rate limit (min and max range rate), detection probability, false alarm rate, range resolution, angle central band frequency, and the like), and the measurement resolution and bias (e.g., azimuth, elevation, range, range rate resolutions, and the like).” Kesavareddigari discloses information about the local environment includes information about sensors attached to the node such as details relating to the electrical and mechanical systems of equipped radar sensors which is under a ‘blockage’ test.)
Regarding claim 10, The combination of Kesavareddigari and Logan, as shown above, discloses all of the limitations of claims 1 and 9. Kesavareddigari additionally discloses
the physical layer comprises at least one of electrical, mechanical, optical, acoustic, and thermal (See at least [0152] “For example, the UE 1602 may indicate a list of RADAR sensor/detector reconfigurable parameters such as FoV, orientation, range, resolution, update rate, and the like, as well as other RADAR sensor parameters including but not limited to sensor identification (e.g., number of RADAR sensors and associated IDs), sensor mounting on the vehicle (e.g., positions relative to the center of the ego vehicle and the mounting rotation angle [roll, pitch, yaw]), the detector configuration (e.g., angular field of view, range limit (min and max detection range), range rate limit (min and max range rate), detection probability, false alarm rate, range resolution, angle central band frequency, and the like), and the measurement resolution and bias (e.g., azimuth, elevation, range, range rate resolutions, and the like).” Kesavareddigari discloses information about the local environment includes information about sensors attached to the node such as details relating to the electrical and mechanical systems of equipped radar sensors which is under a ‘blockage’ test.)
Claims 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Kesavareddigari, in view of Logan, in further view of Sakhnini (US 20240192311 A1), hereinafter Sakhnini.
Regarding claim 11, Kesavareddigari, as shown below, discloses a sensor system comprising the following limitations:
one or more antennas (See at least Fig. 1, [0057] “The base station 180 and the UE 104 may each include a plurality of antennas”) to allow the device to receive communication signals from other nodes in a communication network (See at least Fig. 1, [0051] “The communication links 120 may use multiple-input and multiple-output (MIMO) antenna technology” , [0057] “The base station 180 and the UE 104 may each include a plurality of antennas” Kesavareddigari discloses a plurality of nodes including base stations and UEs.);
one or more input ports configured to receive the communication signals (See at least Fig. 1, [0051] “The communication links 120 may use multiple-input and multiple-output (MIMO) antenna technology”);
one or more output ports configured to transmit communication signals to other nodes in the network through the one or more antennas (See at least Fig. 1, [0051] “The communication links 120 may use multiple-input and multiple-output (MIMO) antenna technology”);
a memory to store data associated with the communication signals (See at least Fig. 3, Item 360, [0077] “The controller/processor 359 can be associated with a memory 360 that stores program codes and data.”); and
one or more processors configured to execute code to cause the one or more processors to (See at least Fig. 3, Item 359, [0077] “The controller/processor 359 can be associated with a memory 360 that stores program codes and data.”): determine a position of the sensor device (See at least [0036] “vehicles can be equipped with UEs and onboard sensors (e.g., RADARs, LIDARs, cameras, etc.) to provide a radio network with information about moving obstacles that may ultimately degrade signal quality by causing beam blockage.” The Examiner notes that position has a broadest reasonable interpretation that includes relative position to obstacles);
receive return signals through the one or more input ports (See at least [0036] “vehicles can be equipped with UEs and onboard sensors (e.g., RADARs, LIDARs, cameras, etc.) to provide a radio network with information about moving obstacles that may ultimately degrade signal quality by causing beam blockage.”);
process the return signal data with a machine learning system to identify unblocked ports (See at least Fig. 11, [0153] “Following completion of session discovery and indication of the reconfigurable sensor parameters, at 5, the UE 1602 and ML server/gNB may establish a session between the devices for training, inference or performance optimization. Afterwards, either the gNB/ML server or the UE may trigger adaptive sensor reconfiguration.” Kesavareddigari discloses a collobroative system of nodes where a central node controlling a global machine learning system operates to adaptively configure radars/ports based on obstruction/blockage. See also [0150]-[0154])
Kesavareddigari does not explicitly disclose transmit signals through the output ports, each signal having a unique spherical orientation identifier. However, Logan, in the same or in a similar field of endeavor, discloses:
transmit signals through the output ports, each signal having a unique spherical orientation identifier (See at least Col. 2 Lines 21-25 “a helical shaped pulse is transmitted during each radar transmitting period of a radar having provision for steering the beam pointing direction through the use of phase modulation, to establish a conical pulse in time and space” Logan discloses helical pulses which by definition have unique correlations with spherical orientation)
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the sensor system disclosed by Kesavareddigari with the pulse system disclosed by Logan. One would have been motivated to do so in order to advantageously increase reliability and flexibility (See at least Col. 2 Lines 16-20 “the reliability and flexibility inherent in a radar system having meam steering capability are retained while obtaining the benefits derived from the sequential lobing and simultaneous lobing systems.”).
The combination of Kesavareddigari and Logan does not explicitly disclose separate return signals from other received signals and to produce return signal data. However, Sakhnini, in the same or in a similar field of endeavor, discloses:
separate return signals from other received signals and to produce return signal data (See at least Fig. 1, [0018] “the separation of the radar and communication pilot signals, i.e., the separation of the radar and communication channels, can be established at the at least one receiver unit (e.g., using Doppler-domain orthogonality at the at least one receiver unit)”); and
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the sensor system disclosed by Kesavareddigari with the pulse system disclosed by Logan with the return signal separation system disclosed by Sakhnini. One would have been motivated to do so in order to advantageously utilize radar and communication signals in a low-complexity and cost effective integration (See at least [0005] “joint communication and radar sensing to facilitate a low-complexity and cost-effective integration of radar functionalities into wireless communication systems without compromising the communication performance.”).
Regarding claim 12, the combination of Kesavareddigari, Logan, and Sakhnini, as shown in the rejection above, discloses all of the limitations of claim 11. Kesavareddigari further discloses
the one or more processors are further configured to execute code to cause the one or more processors to determine optimized beam directions for the one or more antennas (See at least [0150] “if the UE which previously had a mostly unobstructed RADAR has an occlusion (or approaching occlusion) to the RADAR, the UE can preemptively reconfigure the FoV or switch to another RADAR having an unobstructed view and send the gNB/ML server a reconfiguration update message including reconfigured parameters accordingly.” See also [0077] regarding processors/code)
Regarding claim 13, The combination of Kesavareddigari, Logan, and Sakhnini, as shown above, discloses all the limitations of claim 11. The combination of Kesavareddigari and Logan does not explicitly disclose the codes that causes the one or more processors to separate the return signals comprises code that causes the one or more processors to: use Doppler analysis to identify signals from neighboring nodes; and remove the identified signals from neighboring nodes from the return signals. However, Sakhnini, in the same or in a similar field of endeavor, discloses
the codes that causes the one or more processors to separate the return signals comprises code that causes the one or more processors to: use Doppler analysis to identify signals from neighboring nodes; and remove the identified signals from neighboring nodes from the return signals (See at least Fig. 1, [0018] “the separation of the radar and communication pilot signals, i.e., the separation of the radar and communication channels, can be established at the at least one receiver unit (e.g., using Doppler-domain orthogonality at the at least one receiver unit)”)
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the sensor system disclosed by Kesavareddigari with the pulse system disclosed by Logan with the return signal separation system disclosed by Sakhnini. One would have been motivated to do so in order to advantageously utilize radar and communication signals in a low-complexity and cost effective integration (See at least [0005] “joint communication and radar sensing to facilitate a low-complexity and cost-effective integration of radar functionalities into wireless communication systems without compromising the communication performance.”).
Regarding claim 14, the combination of Kesavareddigari, Logan and Sakhnini, as shown in the rejection above, discloses all of the limitations of claim 11. Kesavareddigari further discloses
the code that causes the one or more processors to process the return signals comprises code that causes the one or more processors to: determine a direction of each return signal received (See at least [0035] “Blockages can occur frequently, and the received power at the user device can drop significantly if an LOS path is blocked by moving obstacles such as vehicles, pedestrians, or the like.”, [0036] “onboard sensors (e.g., RADARs, LIDARs, cameras, etc.) to provide a radio network with information about moving obstacles that may ultimately degrade signal quality by causing beam blockage” Kesavareddigari discloses mapping obstacles (such as by point clouds) for obtaining blocked and unblocked directions/angles/fovs.); and
translate the direction of each return signal in an angle of the return signal to identify that angle as an unblocked angle (See at least [0150] “if the UE which previously had a mostly unobstructed RADAR has an occlusion (or approaching occlusion) to the RADAR, the UE can preemptively reconfigure the FoV or switch to another RADAR having an unobstructed view and send the gNB/ML server a reconfiguration update message including reconfigured parameters accordingly.” See also [0077] regarding processors/code)
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Kesavareddigari, in view of Logan, in further view of Sakhnini, in further view of Elwart (US 9453910 B2), hereinafter Elwart.
Regarding claim 15, The combination of Kesavareddigari, Logan, and Sakhnini, as shown above, discloses all the limitations of claim 11. The combination of Kesavareddigari, Logan, and Sakhnini does not explicitly disclose the code that causes the one or more processors to process the return signals causes the one or more processors to: identify ports for which no return signal was returned; and identify the ports for which no return signal was returned as blocked ports. However, Elwart, in the same or in a similar field of endeavor, discloses
the code that causes the one or more processors to process the return signals causes the one or more processors to: identify ports for which no return signal was returned; and identify the ports for which no return signal was returned as blocked ports (See at least Col. 8 Lines 8-12 “when the return signal magnitude is below a predetermined threshold, some data may be indicative of a blocked condition, in which case, a sensor blockage should be detected”).
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the sensor system disclosed by Kesavareddigari with the pulse system disclosed by Logan with the return signal separation system disclosed by Sakhnini with the port blockage identifying system disclosed by Elwart. One would have been motivated to do so in order to advantageously evaluate data reliability (See at least Col. 5 Lines 23-25 “As the radar signal cannot provide acceptable range data, the signal may not be reliable enough for control or notification functions”).
Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kesavareddigari, in view of Liu (US 20260254521 A1), hereinafter Liu.
Regarding claim 16, Kesavareddigari, as shown below, discloses a method of operating a communication network having multiple nodes and a central node, comprising:
transmitting, from the central node (See at least Figs. 3, Item 310, [0091] “the ML service entity may be physically or logically deployed in a separate network node than those of a disaggregated base station. For example, the base station may include multiple units or network nodes, such as a central or centralized unit (CU)” Kesavareddigari discloses a base station 310 which may operate as a central unit/node in communication with a UE), a (See at least Figs. 11-12, [0133] “Referring back to FIG. 11, the ML service entity 1104 may request the UE 1102 to switch between the different models illustrated in FIG. 12 according to the ML service entity 1104's training or inference need”);
receiving the (See at least Figs. 11-12, [0133] “Referring back to FIG. 11, the ML service entity 1104 may request the UE 1102 to switch between the different models illustrated in FIG. 12 according to the ML service entity 1104's training or inference need”);
collecting data local to the at least one remote node about an environment in which the remote node operates (See at least Fig. 13, [0136] “UE may provide training or extracted features to an ML model at the ML service entity 1302 as previously described in FIG. 11 at 8.1”);
using the data local to the at least one remote node to train the (See at least Fig. 13, [0136] “UE may provide training or extracted features to an ML model at the ML service entity 1302 as previously described in FIG. 11 at 8.1”); and
sending updated models to the central node (See at least [0141] “the UE 1102 similarly provide training or inference data to an ML model at the ML service entity 1104” Kesavareddigari discloses updating/training a local model and sending update/training data to a central model.)
Kesavareddigari does not explicitly disclose bootstrap
bootstrap (See at least [0120] “The AI/ML estimation model may be configured with a default set of parameters generated as a part of the hardware design […] the parameters or coefficients of the AI/ML model may be generated by a network entity and transmitted to the UE for implementation”)
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the sensor system disclosed by Kesavareddigari with the model system disclosed by Liu. One would have been motivated to do so in order to advantageously provide improvements including reliability, security, scalability, and low latency (See at least [0003] “These improvements may also be applicable to other multi-access technologies and the telecommunication standards that employ these technologies”).
Regarding claim 17, The combination of Kesavareddigari and Liu, as shown above, discloses all of the limitations of claim 16. Kesavareddigari additionally discloses
clustering the at least one remote node with other remote nodes; and sending updates to the central node from the cluster (See at least Fig. 10, [0129] “UE 1 and UE 2 may transmit these output features to an ML service entity 1014 to serve as inputs […]. The ML service entity 1014 may aggregate, for example through a summation or concatenation process, the features 1006, 1012” Kesavareddigari discloses an aggregation (clustering) of at least two UE nodes data at the central node.)
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Kesavareddigari, in view of Liu, in further view of Ahmed (US 11729636 B1), hereinafter Ahmed.
Regarding claim 18, The combination of Kesavareddigari and Liu, as shown above, discloses all the limitations of claims 16 and 17. The combination of Kesavareddigari and Liu does not explicitly disclose the clustering comprises clustering the at least one node and the other remote nodes is based upon one or more of a type of node, a localized geographic region, and a type of data local to the at least one remote node and other remote nodes. However, Ahmed, in the same or in a similar field of endeavor, discloses
the clustering comprises clustering the at least one node and the other remote nodes is based upon one or more of a type of node, a localized geographic region, and a type of data local to the at least one remote node and other remote nodes (See at least Figs. 1, 3, Col. 6 Lines 41-44 “FIG. 1 can implement the clustering component 128 to generate a first cluster 302 associated with the first geographic area 108 and a second cluster 304 associated with the second geographic area 110”).
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the sensor system disclosed by Kesavareddigari with the model system disclosed by Liu with the clustering system disclosed by Ahmed. One would have been motivated to do so in order to advantageously improve performance (See at least Col. 3 Lines 27-30 “In some examples, data output by the clustering component and/or the analysis component can be used generate recommendations that improve performance of a cell or network.”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH W GOOD whose telephone number is (571)272-4186. The examiner can normally be reached Mon - Thu 7:30 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Resha H Desai can be reached at (571) 270-7792. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KENNETH W GOOD/Examiner, Art Unit 3648
