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 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.
Status of Claims
This action is in reply to the application filed on 02/08/2024.
Claims 1-18 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/09/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 and therefore its dependent claims 2-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the node in the communications network". There is insufficient antecedent basis for this limitation in the claim. It is unclear to the Examiner whether “the node in the communications network” is referring to the initial node, which communicates with other nodes, or whether “the node in the communications network” is meant to reference another node such as a recipient of the communication. For the purposes of this examination, the Examiner will interpret “the node in the communications network” as referencing the initial node. Appropriate correction is required.
Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites the limitation "the device". There is insufficient antecedent basis for this limitation in the claim. It is unclear to the Examiner whether “the device” is referring to a specific node or other object. For the purposes of this examination, the Examiner will interpret “the device” as referencing the initial node. Appropriate correction is required.
Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 13 recites “those signals”. It is unclear to the Examiner whether the claims are intended to refer to the “identified signals from neighboring nodes” or other signals such as other received portions from the return signals. For the purposes of this examination, the Examiner will interpret “those signals” as ‘the identified signals from neighboring nodes’. Appropriate correction is required.
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.
(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 1-10 and 16-17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kesavareddigari (US 20230325706 A1), hereinafter Kesavareddigari.
Regarding claim 1, Kesavareddigari 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, each node comprising (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 in the communications network. (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”)
Regarding claim 2, Kesavareddigari, as shown above, discloses all of the limitations of claim 1. Kesavareddigari additionally discloses
at least one node comprises a sensor node, and the one or more processors in the sensor node are further configured 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”);
emit pulses having unique correlations with spherical location (See at least [0150] “related to location changes, the UE may determine to change its FoV and range in response to determining that the UE is located on a highway or at an intersection”, [0086] “These sensors may include, among others, one or more cameras, Radio Detection and Ranging systems (RADARs),” See also [0152]);
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”)
Regarding claim 3, Kesavareddigari, as shown above, discloses all of the limitations of claims 1 and 2. Kesavareddigari additionally discloses
the one or more processors are further configured to execute code that causes the one or more processors to determine optimized angles for unblocked ports of the device (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, Kesavareddigari, as shown above, discloses all of the limitations of claims 1 and 2. Kesavareddigari additionally discloses
the code that causes the one or more processors to communicate with one or mode 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, Kesavareddigari, 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, Kesavareddigari, 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, Kesavareddigari, 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, Kesavareddigari, 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, Kesavareddigari, 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, Kesavareddigari, 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.)
Regarding claim 16, Kesavareddigari discloses a method of operating a communication network having multiple nodes and a central node, comprising:
transmitting, from a 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 bootstrap model for a machine learning system (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 bootstrap model by at least one remote 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”);
collecting data local to the at least one remote mode 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 mode to train the bootstrap model (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.)
Regarding claim 17, Kesavareddigari, 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 Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Kesavareddigari, in 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);
transmit signals through the output ports, each signal having a unique spherical orientation identifier (See at least [0150] “related to location changes, the UE may determine to change its FoV and range in response to determining that the UE is located on a highway or at an intersection”, [0086] “These sensors may include, among others, one or more cameras, Radio Detection and Ranging systems (RADARs),” See also [0152]);
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 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 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 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 execute code to cause the one or more 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 and Sakhnini, as shown above, discloses all the limitations of claim 11. Kesavareddigari 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 those signals 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 those signals 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 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 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 Sakhnini, in further view of Elwart (US 9453910 B2), hereinafter Elwart.
Regarding claim 15, The combination of Kesavareddigari and Sakhnini, as shown above, discloses all the limitations of claim 11. The combination of Kesavareddigari 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 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”).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Kesavareddigari, in view of Ahmed (US 11729636 B1), hereinafter Ahmed.
Regarding claim 18, Kesavareddigari, as shown above, discloses all the limitations of claims 16 and 17. Kesavareddigari 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 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lund (US 20230396958 A1) - Aspects presented herein may improve the performance of mobile/computer applications. Aspects presented herein may enable mobile/computer applications to differentiate entities that are associated with UEs or entities running the navigations applications, thereby enabling the mobile/computer applications (or their associated servers) to have a more accurate understanding of the conditions surrounding the UEs and their users. In one aspect, a network node obtains first information including at least one feature associated with a plurality of devices. The network node selects a first subset of the plurality of devices for a measurement based on the at least one feature associated with the plurality of devices (or the network node may exclude a second subset of the plurality of devices from the measurement).
Edge (US 20230079636 A1) - Methods and techniques are described for supporting satellite wireless by a user equipment (UE) using satellite acquisition information. A UE may obtain (e.g., from an AMF or gNB) acquisition information for satellite cells supporting access to a PLMN. The UE may enter an inactive state with no radio access, may later leave the inactive state, find a preferred satellite cell based on the acquisition information and access the satellite cell (e.g., camp on the cell or connect to the PLMN using the cell). The acquisition information may indicate satellite cells available at one or more predefined times for a known location of the UE or may enable a satellite cell to be found for any UE location at any time. The acquisition information may also provide timing, frequency and other information to enable a UE to access a satellite cell with reduced latency and reduced power consumption.
Niesen (US 20190369233 A1) - Techniques are described herein for allowing one or more vehicles or radar systems in an environment to passively detect radar signals from other vehicles or other radar systems and determine spatial parameters of objects based on the passively received radar signals. A primary vehicle (or user equipment (UE) associated with the primary vehicle) may be configured to receive one or more radar signals from one or more secondary vehicles (or UEs associated with the secondary vehicles). The primary vehicle may be configured to determine one or more spatial parameters of the secondary vehicle based on the passively received radar signals. In some cases, the primary vehicle may receive an indication that identifies at least some communication resources to be used by the secondary vehicle to transmit the radar signals. The primary vehicle may determine one or more driving operations based on determining the spatial parameter.
Tao (US 20190302275 A1) - Provided are a vehicle positioning method, an apparatus and a device. The method includes: sending an auxiliary positioning request to an auxiliary positioning device within a preset distance range; receiving an auxiliary positioning message returned by the auxiliary positioning device with respect to the auxiliary positioning request, where the auxiliary positioning message carries location information of the auxiliary positioning device; and determining a current location of a current vehicle according to the location information of the auxiliary positioning device and distance information between the current vehicle and the auxiliary positioning device. The method of the present disclosure can implement vehicle positioning without a satellite positioning signal. The vehicle positioning can be performed when the vehicle is blocked by an obstacle such as a tunnel, a building and the satellite positioning signal cannot be received, improving the accuracy of the vehicle positioning.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William J. Kelleher can be reached on (571) 272-7753. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KENNETH W GOOD/
Examiner, Art Unit 3648