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 .
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.
Response to Argument
Applicant's arguments, filed 12/08/25, with respect to claims have been considered but are moot in view of the new ground(s) of rejection.
Claims 8, 21, 37, 50, 66, 79, 95, 108 have been canceled.
Claims 117-124 are added.
Claims 1-7, 9-20, 22-36, 38-49, 51-65, 67-78, 80-94, 96-107 and 109-124 are currently pending.
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 1-5, 15, 30-34, 44, 59-63, 73, 88-92 and 102 are rejected under 35 U.S.C. 103 as being unpatentable over Cole Shrestha et al. (U.S. 20230296722) in view of Prasad (U.S. 20200053591).
For claim 1, Shrestha et al. disclose a method of wireless communication performed by a user equipment (UE), the method comprising:
obtaining, from one or more sensors, sensor data (at least [0092]; claim 1 and 11. A method performed by a wireless communication device configured for use in a wireless communication network, the method comprising: obtaining backscattering measurements for the one or more uplink transmissions; and reporting the backscattering measurements to the wireless communication network. The backscattering measurements for the one or more uplink transmissions are obtained using a different set of at least one antenna element or antenna panel than used for the transmission of the one or more uplink transmissions.)
identifying, based on the sensor data, a first context scenario associated with a surrounding environment of the UE or a user status (at least [0037]; [0048]-[0051]; claim 1; and claim 11. A method performed by a wireless communication device configured for use in a wireless communication network, the method comprising: obtaining backscattering measurements for the one or more uplink transmissions; and reporting the backscattering measurements to the wireless communication network. The backscattering measurements comprise one or more of: a backscattered signal received power; ranging information indicative of a distance between the wireless communication device an object in a vicinity of the wireless communication device; and doppler shift of a backscattered signal. The WCD may for example performs filtering of the backscattered/reflected signals to extract only measurements of interest.);
transmitting, to a network node, an indication of the first context scenario (at least [0048]-[0051]; claim 1; and claim 11. A method performed by a network node in a wireless communication network, the method comprising: receiving backscattering measurements for one or more uplink transmissions, the backscattering measurements having been obtained by a wireless communication device that transmitted the one or more uplink transmissions; and estimating an environment of the wireless communication device based on the backscattering measurements.);
receiving, from the network node in response to the indication, a first configuration for the first context scenario, wherein the first configuration comprises scheduling information and a reference signal resource allocation (at least [0037]-[0051] and claim 16. Scheduling a transmission based on the estimated environment of the wireless communication device; Selecting beamforming based on the estimated environment of the wireless communication device and adapting a positioning reference signal configuration based on the estimated environment of the wireless communication device.); and
communicating with the network node based on the first configuration (at least [0048]-[0051]; claim 1; and claim 11. Scheduling 404 a transmission based on the estimated environment of the WCD. The scheduled transmission may for example be a downlink transmission to the WCD or an uplink transmission from the WCD. Scheduling may for example be adapted to the environment of the WCD in the sense that a frequency resource and/or a time resource and/or a coding and/or a transmission scheme and/or a transmission power of a transmission is adapted based on the environment.) However, Shrestha et al. do not disclose identifying, based on the sensor data, a first context scenario associated with a surrounding environment of the UE by applying a machine learning-based network, with a time sequence predictive capability, to a sequence of the sensor data.
In the same field of endeavor, Prasad discloses identifying, based on the sensor data, a first context scenario associated with a surrounding environment of the UE by applying a machine learning-based network, with a time sequence predictive capability, to a sequence of the sensor data (at least [0068]. The process of FIG. 6 may further include training a second set of machine learning models to predict a channel quality class based on a time series of radio signal quality parameter values and/or application performance parameter values (block 650). For example, modeling manager 320 may instruct channel quality prediction manager 340 to train models 345-A to 345-N using one or more training sets that include a time series of radio signal quality parameter values and/or application performance parameter values, to predict a future channel quality class based on a changing pattern of radio signal quality parameter values and/or application performance parameter values.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Shrestha et al. as taught by Prasad for purpose of carrying select an application bandwidth for the application data stream based on the determined channel quality class, and send application data associated with the application data stream to the UE device based on the selected application bandwidth.
For claim 2, the combination of Shrestha et al. and Prasad disclose the method of claim 1. Shrestha et al. disclose wherein the one or more sensors comprises at least one of a camera, a microphone, a global positioning system (GPS), an accelerometer, a gyroscope, a magnetometer, or a biometric sensor (at least [0092]. The backscattering from these custom signals may for example be measured using onboard sensors like lidars, proximity-sensors, etc., and a measurement report comprising such measurement may be transmitted from the WCD to the LS.)
For claim 3, the combination of Shrestha et al. and Prasad disclose the method of claim 1. Shrestha et al. disclose the identifying comprises identifying the first context scenario from a set of context scenarios (at least [0037]-[0047]. The WCD may perform measurements on such received reflected versions of the one or more uplink transmissions, to obtain measurement values. Measurements performed on such reflected signals or reflected transmissions are referred to herein as backscattering measurements. The WCD may for example performs filtering of the backscattered/reflected signals to extract only measurements of interest. The backscattering measurements may for example comprise a backscattered signal received power, and/or ranging information indicative of a distance between the wireless communication device an object in a vicinity of the wireless communication device, and/or doppler shift of a backscattered signal.)
For claim 4, the combination of Shrestha et al. and Prasad disclose the method of claim 1. Shrestha et al. disclose the set of context scenarios is associated with at least one of a user location, a user activity status, or a user health status (at least [0037]-[0047The backscattering measurements may for example comprise a backscattered signal received power, and/or ranging information indicative of a distance between the wireless communication device an object in a vicinity of the wireless communication device, and/or doppler shift of a backscattered signal.)
For claim 5, the combination of Shrestha et al. and Prasad disclose the method of claim 1. Shrestha et al. disclose the user location comprises at least one of a home, an office, a vehicle, a transit path between a first place and a second place, or a public gathering place (at least [0105]. The WCD performing the method 1300 may for example be arranged at a vehicle, such as a car, a truck, a motorcycle, a bicycle, or a drone.)
For claim 15, the combination of Shrestha et al. and Prasad disclose the method of claim 1. Shrestha et al. disclose the receiving the first configuration comprises: receiving the first configuration indicating at least one of a channel scan operation, an operational mode switch, or an initiation of an application (at least [0053] and [0105]. The method 1300 may optionally comprise transmitting 1307 one or more signals for controlling the vehicle based on the estimated environment of the WCD. In other words, the WCD may at least partially control the vehicle via one or more signals generated/determined based on the estimated environment of the WCD. The one or more signals controlling the vehicle may for example be generated/determined based on the estimated environment of the WCD and the estimated position of the WCD.)
For claims 30-34, the claims have features similar to claims 1-5. Therefore, the claims are also rejected for the same reasons in claims 1-5.
For claim 44, the claim has features similar to claim 15. Therefore, the claim is also rejected for the same reasons in claim 15.
For claims 59-63, the claims have features similar to claims 1-5. Therefore, the claims are also rejected for the same reasons in claims 1-5.
For claim 73, the claim has features similar to claim 15. Therefore, the claim is also rejected for the same reasons in claim 15.
For claims 88-92, the claims have features similar to claims 1-5. Therefore, the claims are also rejected for the same reasons in claims 1-5.
For claim 102, the claim has features similar to claim 15. Therefore, the claim is also rejected for the same reasons in claim 15.
Claims 6-7, 35-36, 64-65 and 93-94 are rejected under 35 U.S.C. 103 as being unpatentable over Shrestha et al. (U.S. 20230296722) in view of Prasad (U.S. 20200053591) and in view of Stave et al. (U.S. 20240370745).
For claim 6, the combination of Shrestha et al. and Prasad do not disclose the method of claim 3, wherein the identifying further comprises: applying a machine learning-based network to the sensor data, wherein the machine learning-based network is trained to identify a context scenario from the set of context scenarios.
In the same field of endeavor, Stave et al. disclose applying a machine learning-based network to the sensor data, wherein the machine learning-based network is trained to identify a context scenario from the set of context scenarios (at least claim 4. One or more machine-learning models is trained to only recognize one of the incident-prediction model features of the set of incident-prediction model features.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Shrestha et al. as taught by Stave et al. for purpose of managing application incidents.
For claim 7, the combination of Shrestha et al., Prasad and Stave et al. disclose the method of claim 6. Stave et al. disclose wherein the identifying further comprises: applying the machine learning-based network including a convolutional network to the sensor data (at least [0009]. In conventional approaches, subsequent to the occurrence of an incident in connection with a given application, a myopic analysis is conducted in which only data that is related to that particular application is assessed. Unlike those conventional approaches, embodiments of the present disclosure take an ecosystem-wide view that encompasses multiple interoperating applications and systems, and leverages the power of machine learning to predict and prevent the occurrence of application incidents.)
For claims 35-36, the claims have features similar to claims 6-7. Therefore, the claims are also rejected for the same reasons in claims 6-7.
For claims 64-65, the claims have features similar to claims 6-7. Therefore, the claims are also rejected for the same reasons in claims 6-7.
For claims 93-94, the claims have features similar to claims 6-7. Therefore, the claims are also rejected for the same reasons in claims 6-7.
Claims 9-11, 14, 38-40, 43, 67-69, 72, 96-98 and 101 are rejected under 35 U.S.C. 103 as being unpatentable over Shrestha et al. (U.S. 20230296722) in view of Prasad (U.S. 20200053591) and further in view of Hong (U.S. 20230232213).
For claim 9, the combination of Shrestha et al. and Prasad do not disclose the method of claim 1, further comprising: transmitting, to the network node, a context scenario recognition capability report.
In the same field of endeavor, Hong discloses transmitting, to the BS, a context scenario recognition capability report (at least [0007]. Reporting artificial intelligence (AI) capability information indicating an AI capability of the UE to a base station.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Shrestha et al. as taught by Hong for purpose of improving information interaction between the UE and the base station and increasing the transparency of the UE capability information.
For claim 10, the combination of Shrestha et al., Prasad and Hong disclose the method of claim 9. Hong discloses the transmitting the context scenario recognition capability report comprises: transmitting the context scenario recognition capability report including a value indicating whether context scenario recognition is supported or not supported (at least [0041], [0050]-[0051] and [0087]. Reporting the AI capability information indicating an identifier of the on-device AI model supported by the UE to the base station.)
For claim 11, the combination of Shrestha et al., Prasad and Hong disclose the method of claim 9. Hong discloses the wherein the transmitting the context scenario recognition capability report comprises: transmitting the context scenario recognition capability report including a context scenario recognition level (at least [0053], [0081] and [0088]. Each on-device AI model has a unique identifier, and the UE may indicate an on-device AI model that it has in a manner of uploading the identifier.)
For claim 14, the combination of the combination of Shrestha et al., Prasad, Hong disclose the method of claim 9. Hong discloses the method of claim 9, further comprising: receiving, from the BS in response to the context scenario recognition capability report, at least one set of context scenarios including the first context scenario (at least [0115]. An AI service corresponding to the AI capability is allocated to the UE based on the AI capability information.)
For claims 38-40, the claims have features similar to claims 9-11. Therefore, the claims are also rejected for the same reasons in claims 9-11.
For claim 43, the claim has features similar to claim 14. Therefore, the claim is also rejected for the same reasons in claim 14.
For claims 67-69, the claims have features similar to claims 9-11. Therefore, the claims are also rejected for the same reasons in claims 9-11.
For claim 72, the claim has features similar to claim 14. Therefore, the claim is also rejected for the same reasons in claim 14.
For claims 96-98, the claims have features similar to claims 9-11. Therefore, the claims are also rejected for the same reasons in claims 9-11.
For claim 101, the claim has features similar to claim 14. Therefore, the claim is also rejected for the same reasons in claim 14.
Claims 12-13, 41-42, 70-71 and 99-100 are rejected under 35 U.S.C. 103 as being unpatentable over Shrestha et al. (U.S. 20230296722) in view of Prasad (U.S. 20200053591) in view of Hong (U.S. 20230232213) and further in view of Ryden et al. (U.S. 20240049003).
For claim 12, the combination of Shrestha et al., Prasad and Hong do not disclose the method of claim 11, determining the context scenario recognition level based on at least one of a sensor capability associated with the one or more sensors or a machine learning-based network capability.
In the same field of endeavor, Ryden et al. disclose determining the context scenario recognition level based on at least one of a sensor capability associated with the one or more sensors or a machine learning-based network capability (at least [0063]. The wireless device may first, in step 402, send to a RAN node of the communication network information about a capability of the wireless device to execute an ML model. The information about a capability of the wireless device to execute an ML model may be included as part of information about an internal run-time environment of the wireless device that may be sent by the wireless device to the RAN node. The information about a capacity of the wireless device to execute an ML model may for example include the maximum available memory that can be consumed by an ML model, floating point support, wireless device computational capabilities, (number of operations per second, type and number of processors, etc.), types of ML model supported, maximum supported computational cost for executing a model or a particular type of model, etc. In some examples, at least a part of the capability information may be implicitly indicated via provision by the wireless device of its make and model to the RAN node.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Shrestha et al. as taught by Ryden et al. for purpose of carrying out a display switching operation to switch between a plurality of application software when they are activated and processed in parallel in a portable information apparatus, and the like.
For claim 13, the combination of Shrestha et al., Prasad, Hong and Ryden et al. disclose the method of claim 12. Ryden et al. disclose the wherein the machine learning-based network capability is associated with at least one of a convolutional layer processing capability, or a computational capability (at least [0063]. The wireless device may first, in step 402, send to a RAN node of the communication network information about a capability of the wireless device to execute an ML model. The information about a capability of the wireless device to execute an ML model may be included as part of information about an internal run-time environment of the wireless device that may be sent by the wireless device to the RAN node. The information about a capacity of the wireless device to execute an ML model may for example include the maximum available memory that can be consumed by an ML model, floating point support, wireless device computational capabilities, (number of operations per second, type and number of processors, etc.), types of ML model supported, maximum supported computational cost for executing a model or a particular type of model, etc. In some examples, at least a part of the capability information may be implicitly indicated via provision by the wireless device of its make and model to the RAN node.)
For claims 41-42, the claims have features similar to claims 12-13. Therefore, the claims are also rejected for the same reasons in claims 12-13.
For claims 70-71, the claims have features similar to claims 12-13. Therefore, the claims are also rejected for the same reasons in claims 12-13.
For claims 99-100, the claims have features similar to claims 12-13. Therefore, the claims are also rejected for the same reasons in claims 12-13.
Claims 16, are rejected under 35 U.S.C. 103 as being unpatentable over Shrestha et al. (U.S. 20230296722) in view of Prasad (U.S. 20200053591) and in view of Teyeb et al. (U.S. 20220264620).
For claim 16, the combination of Shrestha et al. and Prasad do not disclose the method of claim 1, wherein the receiving the first configuration comprises: receiving, in response to the indication of the first context scenario, an indication to switch from a second configuration associated with a second context scenario to the first configuration.
In the same field of endeavor, Teyeb et al. disclose the receiving the first configuration comprises: receiving, in response to the indication of the first context scenario, an indication to switch from a second configuration associated with a second context scenario to the first configuration (at least [0131]-[0132]. The UE transmits the capability response message 822 to the network node 804. UE 802 receives (step s708) a control message 824 (e.g., RRCConnectionRelease) transmitted by network node 804, wherein the control message includes a dedicated measurement configuration for configuring the UE to perform measurements while in an IDLE state. Process 700 may further include step s710 in which UE 802 enters the IDLE state in response to receiving the control message, and, while in the IDLE state, the UE performs a measurement in accordance with the dedicated measurement configuration.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Shrestha et al. as taught by Teyeb et al. for purpose of measuring during IDLE or IDLE with suspended mode or INACTIVE mode, etc.
For claims 45, 74 and 103, the claims have features similar to claim 16. Therefore, the claims are also rejected for the same reason in claim 16.
Claims 117, 119, 121 and 123 are rejected under 35 U.S.C. 103 as being unpatentable over Ryden et al. (U.S. 20240049003) in view of Shrestha et al. (U.S. 20230296722) and in view of Andersson et al. (U.S. 20190140806).
For claim 117, the combination of Ryden et al. and Shrestha et al. do not disclose the method of claim 1, the first configuration indicates a high-density demodulation reference signal (DMRS) resource allocation based at least in part on a speed of the UE.
In the same field of endeavor, Andersson et al. disclose the first configuration indicates a high-density demodulation reference signal (DMRS) resource allocation based at least in part on a speed of the UE (at least [0139]. The RNN 210 requests the wireless device 208 for input regarding the DMRS configuration, the wireless device 208 determines the input which may depend on its speed or requirements on time-critical decoding, e.g. on a service class, and wherein it is further described that the RNN 210 may determine the DMRS configuration based on the received input.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Ryden et al. as taught by Andersson et al. for purpose of improving the performance of the wireless communications network.
For claims 119, 121 and 123, the claims have features similar to claim 117. Therefore, the claims are also rejected for the same reasons in claim 117.
Claims 17-120, 23-27, 46-49, 52-56, 75-78, 81-85, 104-107 and 110-114 are rejected under 35 U.S.C. 103 as being unpatentable over Ryden et al. (U.S. 20240049003) in view of Shrestha et al. (U.S. 20230296722).
For claim 17, Ryden et al. disclose a method of wireless communication performed by a network node, the method comprising:
receiving, from a user equipment (UE), a context scenario recognition capability report associated with a sensor capability at the UE (at least [0063], [0098]-[0105] and [0106]. The wireless device may first, in step 402, send to a RAN node of the communication network information about a capability of the wireless device to execute an ML model. The information about a capability of the wireless device to execute an ML model may be included as part of information about an internal run-time environment of the wireless device that may be sent by the wireless device to the RAN node. The information about a capacity of the wireless device to execute an ML model may for example include the maximum available memory that can be consumed by an ML model, floating point support, wireless device computational capabilities, (number of operations per second, type and number of processors, etc.), types of ML model supported, maximum supported computational cost for executing a model or a particular type of model, etc.);
transmitting, to the UE in response to the context scenario recognition capability report, a first configuration for a first context scenario (at least [0064]-[0072], [0079], [0106], [0110], [0112] and [0130]. In response to the capability of the wireless device, the wireless device receives, from the RAN node, configuration information for an ML model to be executed by the wireless device. The configuration information received at step 410 may include any combination of one, some, all or none of the information illustrated at 410a to 410h); and
communicating with the UE based on the first configuration (at least [0064]-[0072], [0079], [0106], [0110], [0112] and [0130]. The wireless device performs a RAN operation configured on the basis of an output of the executed ML model. In step 440, the wireless device may send, to the RAN node, information based on an output of the executed ML model.) However, Ryden et al. do not disclose wherein the first configuration comprises scheduling information and a reference signal resource allocation.
In the same field of endeavor, Shrestha et al. disclose receiving, from the network node in response to the indication, a first configuration for the first context scenario, wherein the first configuration comprises scheduling information and a reference signal resource allocation (at least [0037]-[0051] and claim 16. Scheduling a transmission based on the estimated environment of the wireless communication device; Selecting beamforming based on the estimated environment of the wireless communication device and adapting a positioning reference signal configuration based on the estimated environment of the wireless communication device.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Ryden et al. as taught by Shrestha et al. for purpose of enhancing the positioning measurements and making the transmission more robust/reliable in a less favorable environment for radio transmissions.
For claim 18, the combination of Ryden et al. and Shrestha et al. disclose the method of claim 17. Ryden et al. disclose selecting the first configuration from among a set of configurations associated with a set of context scenarios including the first context scenario, the first configuration being associated with the first context scenario (at least [0059] and [0098]. Signaling by a UE of its capabilities to the network node may enable the node to select a suitable model based on the UE capability report.)
For claim 19, the combination of Ryden et al. and Shrestha et al. disclose the method of claim 18. Ryden et al. disclose the set of context scenarios is associated with at least one of a user location, a user activity status, or a user health status (at least [0042]. The RAN node may obtain information about an operational environment of the wireless device to be managed. As illustrated at step 202, the information about an operating environment of the wireless device may comprise information about at least one of the internal run-time environment of the wireless device, the external environment within which the wireless device is operating, and/or the communication network environment at the location in which the wireless device is operating. The internal run-time environment of a wireless device may for example include firmware, memory and/or processing capacity or configuration. The external environment within which the wireless device is operating may include geographic location or activities of the wireless device. The communication network environment at the location in which the wireless device is operating may include network congestion or other network KPIs in the cell or group of cells in which the wireless device is located. The information obtained at step 202 may be obtained from one or more different sources. For example, some information may be available locally at the RAN node, while other information may be obtained from a current or previous serving node of the wireless device, or from a core network or other management node. In further examples, information about the operational environment of the wireless device may be obtained in step 202 from the wireless device itself.)
For claim 20, the combination of Ryden et al. and Shrestha et al. disclose the method of claim 19. Ryden et al. disclose wherein the user location comprises at least one of a home, an office, a vehicle, a transit path between a first place and a second place, a transportation or a public gathering place (at least [0042]. The RAN node may obtain information about an operational environment of the wireless device to be managed. As illustrated at step 202, the information about an operating environment of the wireless device may comprise information about at least one of the internal run-time environment of the wireless device, the external environment within which the wireless device is operating, and/or the communication network environment at the location in which the wireless device is operating. The internal run-time environment of a wireless device may for example include firmware, memory and/or processing capacity or configuration. The external environment within which the wireless device is operating may include geographic location or activities of the wireless device. The communication network environment at the location in which the wireless device is operating may include network congestion or other network KPIs in the cell or group of cells in which the wireless device is located. The information obtained at step 202 may be obtained from one or more different sources. For example, some information may be available locally at the RAN node, while other information may be obtained from a current or previous serving node of the wireless device, or from a core network or other management node. In further examples, information about the operational environment of the wireless device may be obtained in step 202 from the wireless device itself.)
For claim 23, the combination of Ryden et al. and Shrestha et al. disclose the method of claim 17. Ryden et al. disclose receiving the context scenario recognition capability report comprises: receiving the context scenario recognition capability report including a context scenario recognition level (at least [0063]. The wireless device may first, in step 402, send to a RAN node of the communication network information about a capability of the wireless device to execute an ML model. The information about a capability of the wireless device to execute an ML model may be included as part of information about an internal run-time environment of the wireless device that may be sent by the wireless device to the RAN node. The information about a capacity of the wireless device to execute an ML model may for example include the maximum available memory that can be consumed by an ML model, floating point support, wireless device computational capabilities, (number of operations per second, type and number of processors, etc.), types of ML model supported, maximum supported computational cost for executing a model or a particular type of model, etc. In some examples, at least a part of the capability information may be implicitly indicated via provision by the wireless device of its make and model to the RAN node.)
For claim 24, the combination of Ryden et al. and Shrestha et al. disclose the method of claim 13. Ryden et al. disclose the context scenario recognition level is further associated with a machine learning-based network capability (at least [0063]. The wireless device may first, in step 402, send to a RAN node of the communication network information about a capability of the wireless device to execute an ML model. The information about a capability of the wireless device to execute an ML model may be included as part of information about an internal run-time environment of the wireless device that may be sent by the wireless device to the RAN node. The information about a capacity of the wireless device to execute an ML model may for example include the maximum available memory that can be consumed by an ML model, floating point support, wireless device computational capabilities, (number of operations per second, type and number of processors, etc.), types of ML model supported, maximum supported computational cost for executing a model or a particular type of model, etc. In some examples, at least a part of the capability information may be implicitly indicated via provision by the wireless device of its make and model to the RAN node.)
For claim 25, the combination of Ryden et al. and Shrestha et al. disclose the method of claim 24. Ryden et al. disclose the machine learning-based network capability is associated with at least one of a convolutional layer processing capability, a time sequence predictive capability, or a computational capability. (at least [0063]. The wireless device may first, in step 402, send to a RAN node of the communication network information about a capability of the wireless device to execute an ML model. The information about a capability of the wireless device to execute an ML model may be included as part of information about an internal run-time environment of the wireless device that may be sent by the wireless device to the RAN node. The information about a capacity of the wireless device to execute an ML model may for example include the maximum available memory that can be consumed by an ML model, floating point support, wireless device computational capabilities, (number of operations per second, type and number of processors, etc.), types of ML model supported, maximum supported computational cost for executing a model or a particular type of model, etc. In some examples, at least a part of the capability information may be implicitly indicated via provision by the wireless device of its make and model to the RAN node.)
For claim 26, the combination of Ryden et al. and Shrestha et al. disclose the method of claim 25. Ryden et al. disclose transmitting, to the UE in response to the context scenario recognition capability report, at least one set of context scenarios including the first context scenario (at least [0098]. This may enable the network node to select the most appropriate model (type, dimension, etc.) for a specific UE based on the UE capabilities.)
For claim 27, the combination of Ryden et al. and Shrestha et al. disclose the method of claim 26. Ryden et al. disclose selecting the at least one set of context scenarios from among a plurality of sets of context scenarios based on the context scenario recognition capability report (at least [0098]. This may enable the network node to select the most appropriate model (type, dimension, etc.) for a specific UE based on the UE capabilities.)
For claims 46-49, the claims have features similar to claims 17-20. Therefore, the claims are also rejected for the same reasons in claims 17-20.
For claims 52-56, the claims have features similar to claims 23-27. Therefore, the claims are also rejected for the same reasons in claims 17-20.
For claims 75-78, the claims have features similar to claims 17-20. Therefore, the claims are also rejected for the same reasons in claims 17-20.
For claims 81-85, the claims have features similar to claims 23-27. Therefore, the claims are also rejected for the same reasons in claims 17-20.
For claims 104-107, the claims have features similar to claims 17-20. Therefore, the claims are also rejected for the same reasons in claims 17-20.
For claims 110-114, the claims have features similar to claims 23-27. Therefore, the claims are also rejected for the same reasons in claims 17-20.
Claims 22, 51, 80 and 109 are rejected under 35 U.S.C. 103 as being unpatentable over Ryden et al. (U.S. 20240049003) in view of Shrestha et al. (U.S. 20230296722) and further in view of Hong (U.S. 20230232213).
For claim 22, the combination of Ryden et al. and Shrestha et al. do not disclose the receiving the context scenario recognition capability report comprises: receiving the context scenario recognition capability report including a value indicating whether context scenario recognition is supported or not supported.
In the same field of endeavor, Hong discloses receiving the context scenario recognition capability report including a value indicating whether context scenario recognition is supported or not supported (at least [0041], [0050]-[0051] and [0087]. Reporting the AI capability information indicating an identifier of the on-device AI model supported by the UE to the base station.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Ryden et al. as taught by Hong for purpose of improving information interaction between the UE and the base station and increasing the transparency of the UE capability information.
For claims 51, 80 and 109, the claims have features similar to claim 22. Therefore, the claims are also rejected for the same reasons in claim 22.
Claims 28-29, 57-58, 86-87 and 115-116 are rejected under 35 U.S.C. 103 as being unpatentable over Ryden et al. (U.S. 20240049003) in view of Shrestha et al. (U.S. 20230296722) and in view of Teyeb et al. (U.S. 20220264620).
For claim 28, the combination of Ryden et al. and Shrestha et al. do not disclose the method of claim 17, transmitting, in response to the indication of the first context scenario, an indication to switch from a second configuration associated with a second context scenario to the first configuration.
In the same field of endeavor, Teyeb et al. disclose transmitting, in response to the indication of the first context scenario, an indication to switch from a second configuration associated with a second context scenario to the first configuration (at least [0131]-[0132]. The UE transmits the capability response message 822 to the network node 804. UE 802 receives (step s708) a control message 824 (e.g., RRCConnectionRelease) transmitted by network node 804, wherein the control message includes a dedicated measurement configuration for configuring the UE to perform measurements while in an IDLE state. Process 700 may further include step s710 in which UE 802 enters the IDLE state in response to receiving the control message, and, while in the IDLE state, the UE performs a measurement in accordance with the dedicated measurement configuration.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Ryden et al. as taught by Teyeb et al. for purpose of measuring during IDLE or IDLE with suspended mode or INACTIVE mode, etc.
For claim 29, the combination of Ryden et al. and Shrestha et al. do not disclose the method of claim 17, wherein the transmitting the first configuration comprises: transmitting the first configuration indicating at least one of a channel scan operation, an operational mode switch, or an initiation of an application.
In the same field of endeavor, Teyeb et al. disclose transmitting the first configuration indicating at least one of a channel scan operation, an operational mode switch, or an initiation of an application (at least [0131]-[0132]. The UE transmits the capability response message 822 to the network node 804. UE 802 receives (step s708) a control message 824 (e.g., RRCConnectionRelease) transmitted by network node 804, wherein the control message includes a dedicated measurement configuration for configuring the UE to perform measurements while in an IDLE state. Process 700 may further include step s710 in which UE 802 enters the IDLE state in response to receiving the control message, and, while in the IDLE state, the UE performs a measurement in accordance with the dedicated measurement configuration.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Ryden et al. as taught by Teyeb et al. for purpose of measuring during IDLE or IDLE with suspended mode or INACTIVE mode, etc.
For claims 57-58, the claims have features similar to claims 28-29. Therefore, the claims are also rejected for the same reasons in claims 28-29.
For claims 86-87, the claims have features similar to claims 28-29. Therefore, the claims are also rejected for the same reasons in claims 28-29.
For claims 115-116, the claims have features similar to claims 28-29. Therefore, the claims are also rejected for the same reasons in claims 28-29.
Claims 118, 120, 122 and 124 are rejected under 35 U.S.C. 103 as being unpatentable over Ryden et al. (U.S. 20240049003) in view of Shrestha et al. (U.S. 20230296722) and in view of Andersson et al. (U.S. 20190140806).
For claim 118, the combination of Ryden et al. and Shrestha et al. do not disclose the method of claim 17, the first configuration indicates a high-density demodulation reference signal (DMRS) resource allocation based at least in part on a speed of the UE.
In the same field of endeavor, Andersson et al. disclose the first configuration indicates a high-density demodulation reference signal (DMRS) resource allocation based at least in part on a speed of the UE (at least [0139]. The RNN 210 requests the wireless device 208 for input regarding the DMRS configuration, the wireless device 208 determines the input which may depend on its speed or requirements on time-critical decoding, e.g. on a service class, and wherein it is further described that the RNN 210 may determine the DMRS configuration based on the received input.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Ryden et al. as taught by Andersson et al. for purpose of improving the performance of the wireless communications network.
For claims 120, 122 and 124, the claims have features similar to claim 118. Therefore, the claims are also rejected for the same reasons in claim 118.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAI PHUONG whose telephone number is 571-272-7896. The examiner can normally be reached on Monday-Friday, 8am-5pm.
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, Kathy Wang-Hurst can be reached on 571-270-5371. The fax phone number for the organization where this application or proceeding is assigned is 571-273-7687.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/DAI PHUONG/ Primary Examiner, Art Unit 2644
Prosecution Insights