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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
1. Claims 1, 6, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ren et al (2023/0276208) in view of Furukawa et al (20050130692).
Regarding claim 1. Ren teaches an operating method of a user equipment (UE), the operating method comprising:
generating communication environment information performing wireless communication with a base station (BS), the communication environment information including position information corresponding to the UE and at least one of sensor information corresponding to the UE, information related to a reception operation of the UE or (0034 – UE equipped with a wide variety of sensors, such as GPS, microphone, camera, and accelerometer, a gyroscope, magnetometer, a biometric sensor, etc., 0064 – UE preforms signal measurements from the BS … RSSI, CSI, channel quality indicator (CQI), RSRP, RSRQ, SNR, SINR and/or any suitable type of signal measurements, 0076 – sensor data obtained from various sensors may serve as inputs to a machine learning-based network);
determining whether a current communication environment corresponds to one of a plurality of main communication environments based on the communication environment information (0072 – techniques for the UE to identify a context scenario (e.g, a main communication environment) from sensor data obtained from various sensors, for example, using sensor fusion techniques and/or Machine Learning-based techniques, and indicate the context scenario to a BS or network in real-time. In response, the BS or network can determine an optimized configuration for the UE based on the context scenario, 0077 – based on sensor data, the UE is configured to determine a context scenario, such as indoor and outdoor, 0078 – the UE may indicate the identified context scenario to the BS, and in response, the BS may determine a most suitable or efficient configuration for the UE to operate under the identified context scenario, 0081 – it may be desirable to identify a UE context scenario based on past sensor information in addition to current sensor information, for example using a time sequence prediction model as shown in figure 3B, 0100-0102 – multiple sets of context scenarios, such as a first set = high-speed, car, bus and a second set = on a bed, on a desk, in a mall, from outdoor to indoor, 0103 – UE obtains sensor data, identifies a current context scenario from the acquired sensor data, indicate the current context scenario to the BS and receive configuration (e.g,. optimized configuration) for operating in the context scenario and switch to a different configuration that is most suitable (optimized) for the new context scenario);
setting values of communication parameters to match a first main communication environment in response to determining that the current communication environment corresponds to the first main communication environment, the first main communication environment being among the plurality of main communication environments (0072 – techniques for the UE to identify a context scenario (e.g, a main communication environment) from sensor data obtained from various sensors, for example, using sensor fusion techniques and/or Machine Learning-based techniques, and indicate the context scenario to a BS or network in real-time. In response, the BS or network can determine an optimized configuration for the UE based on the context scenario, 0077 – based on sensor data, the UE is configured to determine a context scenario, such as indoor and outdoor, 0078 – the UE may indicate the identified context scenario to the BS, and in response, the BS may determine a most suitable or efficient configuration for the UE to operate under the identified context scenario, 0081 – it may be desirable to identify a UE context scenario based on past sensor information in addition to current sensor information, for example using a time sequence prediction model as shown in figure 3B, 0100-0102 – multiple sets of context scenarios, such as a first set = high-speed, car, bus and a second set = on a bed, on a desk, in a mall, from outdoor to indoor, 0103 – UE obtains sensor data, identifies a current context scenario from the acquired sensor data, indicate the current context scenario to the BS and receive configuration (e.g,. optimized configuration) for operating in the context scenario and switch to a different configuration that is most suitable (optimized) for the new context scenario); and
performing wireless communication with the BS based on the values of the communication parameters (0072 – techniques for the UE to identify a context scenario (e.g, a main communication environment) from sensor data obtained from various sensors, for example, using sensor fusion techniques and/or Machine Learning-based techniques, and indicate the context scenario to a BS or network in real-time. In response, the BS or network can determine an optimized configuration for the UE based on the context scenario, 0077 – based on sensor data, the UE is configured to determine a context scenario, such as indoor and outdoor, 0078 – the UE may indicate the identified context scenario to the BS, and in response, the BS may determine a most suitable or efficient configuration for the UE to operate under the identified context scenario, 0081 – it may be desirable to identify a UE context scenario based on past sensor information in addition to current sensor information, for example using a time sequence prediction model as shown in figure 3B, 0094 – The UE may operate(e.g., in an airplane mode or navigation mode) according to the configuration, 0100-0102 – multiple sets of context scenarios, such as a first set = high-speed, car, bus and a second set = on a bed, on a desk, in a mall, from outdoor to indoor, 0103 – UE obtains sensor data, identifies a current context scenario from the acquired sensor data, indicate the current context scenario to the BS and receive configuration (e.g,. optimized configuration) for operating in the context scenario and switch to a different configuration that is most suitable (optimized) for the new context scenario).
Ren does not explicitly teach information related to a transmission operation of the UE.
Furukawa teaches UE adjust communication parameters based on UL/DL transmission operation of the UE (0008 – propagation environment in the DL is bad … change the coding rate to a smaller one or change the modulation method into one applying larger transmission energy pre bit, 0044 – UE measures SIR of the downlink, 0059 and 0061 – UE can increase the frequency of transmitting the transmission parameter value when the moving speed of the UE is high and the propagation environment in the DL changes for a large amount during a short period of time, 0066 and 0071 – SIR values used to change UE coding rate and/or transmission power, 0072 – SIR and UE moving speed used to change transmission parameters, 0090 – UE lowers the Rate-Matching ratio when the propagation environment of the DL radio channel is bad, while it raises the Rate-Matching ratio when the propagation environment of the DL radio channel is good, 0091 – the present invention can also be applied for a case when transmission parameters of the UL radio channel are changed) which enables the UE to rapidly respond to a fluctuation of a propagation environment of the channel (0022).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ren to consider information related to a transmission/reception operation of the UE as taught by Furukawa enabling the UE to rapidly respond to a fluctuation of a propagation environment of the UL/DL channel.
Regarding claim 18. Ren teaches a user equipment (UE) for wireless communication, the UE comprising: processing circuitry (0110, figure 9 – UE comprises processor, memory, sensors, transceiver, etc.) configured to generate communication environment information, the communication environment information including position information about the UE, sensor information about the UE, information related to a reception operation of the UE, and (0034 – UE equipped with a wide variety of sensors, such as GPS, microphone, camera, and accelerometer, a gyroscope, magnetometer, a biometric sensor, etc., 0064 – UE preforms signal measurements from the BS … RSSI, CSI, channel quality indicator (CQI), RSRP, RSRQ, SNR, SINR and/or any suitable type of signal measurements, 0076 – sensor data obtained from various sensors may serve as inputs to a machine learning-based network),
determine whether a current communication environment corresponds to one of a plurality of main communication environments based on the communication environment information (0072 – techniques for the UE to identify a context scenario (e.g, a main communication environment) from sensor data obtained from various sensors, for example, using sensor fusion techniques and/or Machine Learning-based techniques, and indicate the context scenario to a BS or network in real-time. In response, the BS or network can determine an optimized configuration for the UE based on the context scenario, 0077 – based on sensor data, the UE is configured to determine a context scenario, such as indoor and outdoor, 0078 – the UE may indicate the identified context scenario to the BS, and in response, the BS may determine a most suitable or efficient configuration for the UE to operate under the identified context scenario, 0081 – it may be desirable to identify a UE context scenario based on past sensor information in addition to current sensor information, for example using a time sequence prediction model as shown in figure 3B, 0100-0102 – multiple sets of context scenarios, such as a first set = high-speed, car, bus and a second set = on a bed, on a desk, in a mall, from outdoor to indoor, 0103 – UE obtains sensor data, identifies a current context scenario from the acquired sensor data, indicate the current context scenario to the BS and receive configuration (e.g,. optimized configuration) for operating in the context scenario and switch to a different configuration that is most suitable (optimized) for the new context scenario), and
set values of communication parameters such that the current communication environment matches a first main communication environment, the first main communication environment being among the plurality of main communication environments (0072 – techniques for the UE to identify a context scenario (e.g, a main communication environment) from sensor data obtained from various sensors, for example, using sensor fusion techniques and/or Machine Learning-based techniques, and indicate the context scenario to a BS or network in real-time. In response, the BS or network can determine an optimized configuration for the UE based on the context scenario, 0077 – based on sensor data, the UE is configured to determine a context scenario, such as indoor and outdoor, 0078 – the UE may indicate the identified context scenario to the BS, and in response, the BS may determine a most suitable or efficient configuration for the UE to operate under the identified context scenario, 0081 – it may be desirable to identify a UE context scenario based on past sensor information in addition to current sensor information, for example using a time sequence prediction model as shown in figure 3B, 0100-0102 – multiple sets of context scenarios, such as a first set = high-speed, car, bus and a second set = on a bed, on a desk, in a mall, from outdoor to indoor, 0103 – UE obtains sensor data, identifies a current context scenario from the acquired sensor data, indicate the current context scenario to the BS and receive configuration (e.g,. optimized configuration) for operating in the context scenario and switch to a different configuration that is most suitable (optimized) for the new context scenario).
Ren does not explicitly teach information related to a transmission operation of the UE.
Furukawa teaches UE adjust communication parameters based on UL/DL transmission operation of the UE (0008 – propagation environment in the DL is bad … change the coding rate to a smaller one or change the modulation method into one applying larger transmission energy pre bit, 0044 – UE measures SIR of the downlink, 0059 and 0061 – UE can increase the frequency of transmitting the transmission parameter value when the moving speed of the UE is high and the propagation environment in the DL changes for a large amount during a short period of time, 0066 and 0071 – SIR values used to change UE coding rate and/or transmission power, 0072 – SIR and UE moving speed used to change transmission parameters, 0090 – UE lowers the Rate-Matching ratio when the propagation environment of the DL radio channel is bad, while it raises the Rate-Matching ratio when the propagation environment of the DL radio channel is good, 0091 – the present invention can also be applied for a case when transmission parameters of the UL radio channel are changed) which enables the UE to rapidly respond to a fluctuation of a propagation environment of the channel (0022).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ren to consider information related to a transmission/reception operation of the UE as taught by Furukawa enabling the UE to rapidly respond to a fluctuation of a propagation environment of the UL/DL channel.
Regarding claim 6. Ren teaches comparing the communication environment information with reference information corresponding to the plurality of main communication environments to obtain a comparison result; and
detecting whether the current communication environment corresponds to one of the plurality of main communication environments based on the comparison result (0077 – using ML to compare/determine if the UE is indoor or outdoor scenario).
Regarding clam 19. Ren teaches a neural processing unit (NPU) configured to generate a trained machine learning model based on the communication environment information, wherein the processing circuitry is configured to determine whether the current communication environment corresponds to one of the plurality of main communication environments by using the trained machine learning model (0037 – UE may use machine learning, 0076 – machine learning).
2. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Ren in view of Furukawa further in view of Zu et al (2019/0162542).
Regarding claim 2. Regarding claim 2. Ren teaches wherein the determining of whether the current communication environment corresponds to one of the plurality of main communication environments is performed using a machine learning model (0037 – UE may use machine learning, 0076 – machine learning).
Ren in view of Furukawa do not teach using a statistical characteristic, the statistical characteristic including variance, mean, or correlation.
Zu teaches the user can input the name of at least one of the plurality of main communication environments (0054, 0070-0071) so that a database of feature sets (0054 – cell ID fingerprinting) associated with certain positions can be built up (0062). Zu teaches a statistical feature by contrast statistically describes characteristics of an indoor environment, e.g., in terms of average, mean, median, standard deviation, minimum, maximum, percentile, a histogram, or other statistical information including positioning information, trajectory data, imaging data, etc. (0043, 0049, 0050).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Ren in view of Furukawa to further train the ML model to include statistical information as taught by Zu thereby providing for more accurate indoor environment feature mapping.
3. Claims 3, 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ren in view of Furukawa further in view of Winters (2010/0323715).
Regarding claims 3 and 20. Ren in view of Furukawa do not teach wherein a second main communication environment among the plurality of main communication environments is set to correspond to a specific communication environment indicated by an input received on a user interface of the UE.
Winters teaches using UE location information to adjust UE communication parameters (abstract, 0013, 0027-0028). Winters further teaches the user may input specific environment information (0019 – automatic via UE and/or provide user interface that allows the user to input information, 0034 – user may specify his/her current location through a user interface on the UE, 0035 – user manually specifies, 0045 – user inputs current location, 0067 – user manual entry).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Ren in view of Furukawa to include the user interface as taught by Winters in order to enable the user to confirm and/or specify his/her current communication environment.
Regarding claim 12. Ren teaches an operating method of a user equipment (UE), the operating method comprising:
periodically (0086 – UE may configure the one or more sensors to capture sensor data at certain time instances (e.g. periodic capture) and/or based on certain events or triggers, 0131 – UE may configure one or more sensors to acquire the sensor data periodically) generating communication environment information by performing wireless communication with a base station (BS), the communication environment information including position information corresponding to the UE and at least one of sensor information corresponding to the UE, information related to a reception operation of the UE, or (0034 – UE equipped with a wide variety of sensors, such as GPS, microphone, camera, and accelerometer, a gyroscope, magnetometer, a biometric sensor, etc., 0064 – UE preforms signal measurements from the BS … RSSI, CSI, channel quality indicator (CQI), RSRP, RSRQ, SNR, SINR and/or any suitable type of signal measurements, 0076 – sensor data obtained from various sensors may serve as inputs to a machine learning-based network);
determining whether a current communication environment corresponds to one of a plurality of main communication environments based on the communication environment information (0072 – techniques for the UE to identify a context scenario (e.g, a main communication environment) from sensor data obtained from various sensors, for example, using sensor fusion techniques and/or Machine Learning-based techniques, and indicate the context scenario to a BS or network in real-time. In response, the BS or network can determine an optimized configuration for the UE based on the context scenario, 0077 – based on sensor data, the UE is configured to determine a context scenario, such as indoor and outdoor, 0078 – the UE may indicate the identified context scenario to the BS, and in response, the BS may determine a most suitable or efficient configuration for the UE to operate under the identified context scenario, 0081 – it may be desirable to identify a UE context scenario based on past sensor information in addition to current sensor information, for example using a time sequence prediction model as shown in figure 3B, 0100-0102 – multiple sets of context scenarios, such as a first set = high-speed, car, bus and a second set = on a bed, on a desk, in a mall, from outdoor to indoor, 0103 – UE obtains sensor data, identifies a current context scenario from the acquired sensor data, indicate the current context scenario to the BS and receive configuration (e.g,. optimized configuration) for operating in the context scenario and switch to a different configuration that is most suitable (optimized) for the new context scenario);
setting values of communication parameters to match a first main communication environment in response to determining that the current communication environment corresponds to the first main communication environment, the first main communication environment being among the plurality of main communication environments (0072 – techniques for the UE to identify a context scenario (e.g, a main communication environment) from sensor data obtained from various sensors, for example, using sensor fusion techniques and/or Machine Learning-based techniques, and indicate the context scenario to a BS or network in real-time. In response, the BS or network can determine an optimized configuration for the UE based on the context scenario, 0077 – based on sensor data, the UE is configured to determine a context scenario, such as indoor and outdoor, 0078 – the UE may indicate the identified context scenario to the BS, and in response, the BS may determine a most suitable or efficient configuration for the UE to operate under the identified context scenario, 0081 – it may be desirable to identify a UE context scenario based on past sensor information in addition to current sensor information, for example using a time sequence prediction model as shown in figure 3B, 0100-0102 – multiple sets of context scenarios, such as a first set = high-speed, car, bus and a second set = on a bed, on a desk, in a mall, from outdoor to indoor, 0103 – UE obtains sensor data, identifies a current context scenario from the acquired sensor data, indicate the current context scenario to the BS and receive configuration (e.g,. optimized configuration) for operating in the context scenario and switch to a different configuration that is most suitable (optimized) for the new context scenario), and
a second main communication environment among the plurality of main communication environments being set to correspond to a specific communication environment indicated by an input received from a user interface of the UE; and
performing wireless communication with the BS based on the values of the communication parameters (0072 – techniques for the UE to identify a context scenario (e.g, a main communication environment) from sensor data obtained from various sensors, for example, using sensor fusion techniques and/or Machine Learning-based techniques, and indicate the context scenario to a BS or network in real-time. In response, the BS or network can determine an optimized configuration for the UE based on the context scenario, 0077 – based on sensor data, the UE is configured to determine a context scenario, such as indoor and outdoor, 0078 – the UE may indicate the identified context scenario to the BS, and in response, the BS may determine a most suitable or efficient configuration for the UE to operate under the identified context scenario, 0081 – it may be desirable to identify a UE context scenario based on past sensor information in addition to current sensor information, for example using a time sequence prediction model as shown in figure 3B, 0094 – The UE may operate(e.g., in an airplane mode or navigation mode) according to the configuration, 0100-0102 – multiple sets of context scenarios, such as a first set = high-speed, car, bus and a second set = on a bed, on a desk, in a mall, from outdoor to indoor, 0103 – UE obtains sensor data, identifies a current context scenario from the acquired sensor data, indicate the current context scenario to the BS and receive configuration (e.g,. optimized configuration) for operating in the context scenario and switch to a different configuration that is most suitable (optimized) for the new context scenario).
Ren does not explicitly teach information related to a transmission operation of the UE.
Furukawa teaches UE adjust communication parameters based on UL/DL transmission operation of the UE (0008 – propagation environment in the DL is bad … change the coding rate to a smaller one or change the modulation method into one applying larger transmission energy pre bit, 0044 – UE measures SIR of the downlink, 0059 and 0061 – UE can increase the frequency of transmitting the transmission parameter value when the moving speed of the UE is high and the propagation environment in the DL changes for a large amount during a short period of time, 0066 and 0071 – SIR values used to change UE coding rate and/or transmission power, 0072 – SIR and UE moving speed used to change transmission parameters, 0090 – UE lowers the Rate-Matching ratio when the propagation environment of the DL radio channel is bad, while it raises the Rate-Matching ratio when the propagation environment of the DL radio channel is good, 0091 – the present invention can also be applied for a case when transmission parameters of the UL radio channel are changed) which enables the UE to rapidly respond to a fluctuation of a propagation environment of the channel (0022).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ren to consider information related to a transmission/reception operation of the UE as taught by Furukawa enabling the UE to rapidly respond to a fluctuation of a propagation environment of the UL/DL channel.
Ren in view of Furukawa do not teach a second main communication environment among the plurality of main communication environments being set to correspond to a specific communication environment indicated by an input received from a user interface of the UE.
Winters teaches using UE location information to adjust UE communication parameters (abstract, 0013, 0027-0028). Winters further teaches the user may input specific environment information (0019 – automatic via UE and/or provide user interface that allows the user to input information, 0034 – user may specify his/her current location through a user interface on the UE, 0035 – user manually specifies, 0045 – user inputs current location, 0067 – user manual entry).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Ren in view of Furukawa to include the user interface as taught by Winters in order to enable the user to confirm and/or specify his/her current communication environment.
4. Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Ren in view of Furukawa and Winters further in view of Shimizu et al (2007/0037602).
Regarding claim 4. Ren teaches wherein a third main communication environment among the plurality of main communication environments is set to correspond to a past communication environment, the past communication environment having an occurrence frequency
Ren in view of Furukawa and Winters do not teach using a threshold frequency.
Shimizu teaches switching communication modes based on changes in wireless communication environment (0004-005, 0021-0022, 0027-0028). Shimizu teaches using different thresholds for determinations (0035-0037, 0046-0047) and the user is able to change the intervals and thresholds (0038). Shimizu teaches the UE may delete (e.g. unprotected) from the measurement history information that has been sent to the BS. Furthermore, a plurality of the measurement histories may be included in the storage unit so that measurement history can be registered therein even while the measurement history information transmission unit is transmitting the measurement history to the BS (e.g., protected)(0070).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Ren in view of Furukawa and Winters to use thresholds as taught by Shimizu thereby making it possible to perform the appropriate communication adapted to changes in the communication environment (Shimizu at 0004 and 0047).
Regarding claim 5. Ren in view of Furukawa and Winters do not teach wherein the
the
Shimizu teaches switching communication modes based on changes in wireless communication environment (0004-005, 0021-0022, 0027-0028). Shimizu teaches using different thresholds for determinations (0035-0037, 0046-0047) and the user is able to change the intervals and thresholds (0038). Shimizu teaches the UE may delete (e.g. unprotected) from the measurement history information that has been sent to the BS. Furthermore, a plurality of the measurement histories may be included in the storage unit so that measurement history can be registered therein even while the measurement history information transmission unit is transmitting the measurement history to the BS (e.g., protected)(0070).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Ren in view of Furukawa and Winters to use the storage unit as taught by Shimizu thereby protecting some measurement histories while deleting other measurement histories thereby saving on UE memory space.
5. Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Ren in view of Furukawa further in view of Jagannath et al (2021/0345132).
Regarding claim 7. Ren in view of Furukawa do not teach wherein the comparing of the communication environment information with the reference information comprises sequentially comparing the communication environment information with the reference information corresponding to each respective main communication environment among the plurality of main communication environments according to an order based on priorities of the plurality of main communication environments.
Jagannath teaches using ML to prioritize certain objectives based on configurations such as high data rate (0049). Prioritize certain communication routes and/or types, maintaining stability in certain areas, etc. Machine learning model may remain flexible enough to adapt other policies or priorities, e.g., where noise is added to the generated communication settings and transceivers modify their activity to account for such noise (0054). Jagannath teaches evaluating a state of the RF network using ML, generating a set of communication parameters based on the evaluation, causing the network transceiver to communicate using the set of communication parameters, and modifying the ML model (abstract, 0005-0007, 0027, 0033).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Ren in view of Furukawa to use machine learning as taught by Jagannath in order to prioritize certain communication routes and/or types thereby maintaining stability in certain areas.
Regarding claim 8. Ren in view of Furukawa do not teach wherein the priorities correspond to respective occurrence frequencies of the plurality of main communication environments.
Jagannath teaches using ML to prioritize certain objectives based on configurations such as high data rate (0049). Prioritize certain communication routes and/or types, maintaining stability in certain areas, etc. Machine learning model may remain flexible enough to adapt other policies or priorities, e.g., where noise is added to the generated communication settings and transceivers modify their activity to account for such noise (0054). Jagannath teaches evaluating a state of the RF network using ML, generating a set of communication parameters based on the evaluation, causing the network transceiver to communicate using the set of communication parameters, and modifying the ML model (abstract, 0005-0007, 0027, 0033).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Ren in view of Furukawa to use machine learning as taught by Jagannath in order to prioritize certain communication routes and/or types thereby maintaining stability in certain areas.
6. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Ren in view of Furukawa and Zu further in view of Bastani et al (2023/0246698).
Regarding claim 11. Ren in view of Furukawa and Zu do not teach wherein the machine learning model is configured to classify binary classes based on the communication environment information.
Bastani teaches movement of the UE (e.g., change in geographic location and/or orientation of the UE) can affect the cellular parameters of the UE, such as for example the best beam to use for wireless communication and/or the best antenna to use (0013, 0018 – wireless communication may be optimized based on the detected movement and the context of the UE). Therefore, use detected movement of the UE, together with a context of the UE, to configure a wireless communication link between the UE and a Base Station (0015) wherein detecting movement of the UE can be done for example based on sensor data (0016). In particular examples of the step 208 for classifying UE movement, a machine learning algorithm such as binary classification can be used to predict the type of UE movement (0031).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Ren in view of Furukawa and Zu to classify UE movement using ML such as binary classification as taught by Bastani thereby enabling for more accurate UE movement classification.
7. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Ren in view of Furukawa and Winters further in view of Zu et al (2019/0162542).
Regarding claim 13. Ren in view of Furukawa and Winters do not teach, wherein the determining of whether the current communication environment corresponds to one of the plurality of main communication environments comprises determining whether the current communication environment corresponds to one of the plurality of main communication environments by using a statistical characteristic, the statistical characteristic including variance, mean, or correlation, or using a machine learning model.
Zu teaches the user can input the name of at least one of the plurality of main communication environments (0054, 0070-0071) so that a database of feature sets (0054 – cell ID fingerprinting) associated with certain positions can be built up (0062). Zu teaches a statistical feature by contrast statistically describes characteristics of an indoor environment, e.g., in terms of average, mean, median, standard deviation, minimum, maximum, percentile, a histogram, or other statistical information including positioning information, trajectory data, imaging data, etc. (0043, 0049, 0050).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Ren in view of Furukawa and Winters to further train the ML model to include statistical information as taught by Zu thereby providing for more accurate indoor environment feature mapping.
8. Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Ren in view of Furukawa and Winters further in view of Jagannath et al (2021/0345132).
Regarding claim 16. Ren in view of Furukawa and Winters do not teach wherein the determining of whether the current communication environment corresponds to one of the plurality of main communication environments comprises:
sequentially comparing the communication environment information with reference information corresponding to each respective main communication environment among the plurality of main communication environments according to an order based on priorities of the plurality of main communication environments to obtain a comparison result, the priorities corresponding to respective occurrence frequencies of the plurality of main communication environments; and
detecting the first main communication environment as corresponding to the current communication environment based on the comparison result.
Jagannath teaches using ML to prioritize certain objectives based on configurations such as high data rate (0049). Prioritize certain communication routes and/or types, maintaining stability in certain areas, etc. Machine learning model may remain flexible enough to adapt other policies or priorities, e.g., where noise is added to the generated communication settings and transceivers modify their activity to account for such noise (0054). Jagannath teaches evaluating a state of the RF network using ML, generating a set of communication parameters based on the evaluation, causing the network transceiver to communicate using the set of communication parameters, and modifying the ML model (abstract, 0005-0007, 0027, 0033).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Ren in view of Furukawa and Winters to use machine learning as taught by Jagannath in order to prioritize certain communication routes and/or types thereby maintaining stability in certain areas.
9. Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Ren in view of Furukawa and Winters further in view of Shimizu et al (2007/0037602).
Regarding claim 17. Ren teaches a third main communication environment among plurality of the main communication environments is set to correspond to a past communication environment, the past communication environment having an occurrence frequency
Ren in view of Furukawa and Winters do not teach using a threshold frequency.
Shimizu teaches switching communication modes based on changes in wireless communication environment (0004-005, 0021-0022, 0027-0028). Shimizu teaches using different thresholds for determinations (0035-0037, 0046-0047) and the user is able to change the intervals and thresholds (0038). Shimizu teaches the UE may delete (e.g. unprotected) from the measurement history information that has been sent to the BS. Furthermore, a plurality of the measurement histories may be included in the storage unit so that measurement history can be registered therein even while the measurement history information transmission unit is transmitting the measurement history to the BS (e.g., protected)(0070).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Ren in view of Furukawa and Winters to use thresholds as taught by Shimizu thereby making it possible to perform the appropriate communication adapted to changes in the communication environment (Shimizu at 0004 and 0047).
Ren in view of Furukawa and Winters do not teach the first main communication environment is protected from deletion when updating the plurality of main communication environments; and
the second main communication environment is unprotected from deletion when updating the plurality of main communication environments
Shimizu teaches switching communication modes based on changes in wireless communication environment (0004-005, 0021-0022, 0027-0028). Shimizu teaches using different thresholds for determinations (0035-0037, 0046-0047) and the user is able to change the intervals and thresholds (0038). Shimizu teaches the UE may delete (e.g,. unprotected) from the measurement history information that has been sent to the BS. Furthermore, a plurality of the measurement histories may be included in the storage unit so that measurement history can be registered therein even while the measurement history information transmission unit is transmitting the measurement history to the BS (e.g., protected)(0070).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Ren in view of Furukawa and Winters to use the storage unit as taught by Shimizu thereby protecting some measurement histories while deleting other measurement histories thereby saving on UE memory space.
10. Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Ren in view of Furukawa further in view of Bansal et al (2023/0171632).
Regarding claim 21. Ren in view of Furukawa do not teach wherein the communication environment information includes a cell identifier.
Bansal teaches UE determines if a serving Cell ID information element (IE) of MDT measurement collected at a current logging instance is the same as a serving ID information element of MDT measurement collected at a logging instance immediately prior to the current logging instance . If the serving cell IDs are the same, the UE does not include the serving cell ID information element in the MDT log for the current logging instance. For example, when a UE is indoors or stationary, the serving cell ID, neighbor cell ID and/or other information elements may not change across a number of logging instances. If the serving cell IDs information elements are different, the UE includes the serving cell ID information in the MDT log for the current logging instance (0072-0073).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Ren in view of Furukawa to use cell identifier(s) in the communication environment information as taught by Bansal and only report cell ID(s) when the serving cell IDs are different thereby saving on network signaling.
Response to Arguments
11. Applicant's arguments filed 6/15/2026 have been fully considered but they are not persuasive.
a) Applicant argues Ren does not teach setting values of communication parameters to match a first main communication environment in response to determining that the current communication environment corresponds to the first main communication environment (page 12 – page 13).
The Examiner disagrees. Ren teaches the UE obtains sensor data, identifies a current context scenario from the acquired sensor data, indicate the current context scenario to the BS and receive configuration (e.g., optimized configuration) for operating in the context scenario and switch to a different configuration that is most suitable (optimized) for the new context scenario (0100-0103) which reads on setting values of communication parameters to match a first main communication environment in response to determining that the current communication environment corresponds to the first main communication environment.
b) Next, Applicant skips the Furukawa reference (page 14).
The Examiner notes Furukawa teaches UE adjust communication parameters based on UL/DL transmission operation of the UE (0008 – propagation environment in the DL is bad … change the coding rate to a smaller one or change the modulation method into one applying larger transmission energy pre bit, 0044 – UE measures SIR of the downlink, 0059 and 0061 – UE can increase the frequency of transmitting the transmission parameter value when the moving speed of the UE is high and the propagation environment in the DL changes for a large amount during a short period of time, 0066 and 0071 – SIR values used to change UE coding rate and/or transmission power, 0072 – SIR and UE moving speed used to change transmission parameters, 0090 – UE lowers the Rate-Matching ratio when the propagation environment of the DL radio channel is bad, while it raises the Rate-Matching ratio when the propagation environment of the DL radio channel is good, 0091 – the present invention can also be applied for a case when transmission parameters of the UL radio channel are changed) which enables the UE to rapidly respond to a fluctuation of a propagation environment of the channel (0022).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ren to consider information related to a transmission/reception operation of the UE as taught by Furukawa enabling the UE to rapidly respond to a fluctuation of a propagation environment of the UL/DL channel.
c) Applicant also skips the Winters reference (page 15).
The Examiner notes Winters teaches using UE location information to adjust UE communication parameters (abstract, 0013, 0027-0028 – certain service parameters such as HO and HO timing may be set by the UE based upon the UEs location).
Allowable Subject Matter
12. Claims 9 and 14-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
---(2024/0251387) Elshafie et al teaches the UE may have more information than the network regarding information related to a transmission operation of the UE AND information related to a reception operation of the UE (0078, 0082, 0112) and teaches machine learning (0073-0074).
---(2013/0079012) Kubota et al teaches the UE reads broadcast information regarding cell IDs and frequencies it learned from the System Information and automatically construct a neighbor list and when the UE needs to perform some neighbor cell measurement, cell reselection, or other mobility related process, it will only use only those channels on the neighbor cell list that it constructed by filtering using the UE’s own capabilities (0035-0036).
14. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
15. A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BARRY W TAYLOR whose telephone number is (571)272-7509. The examiner can normally be reached Monday-Thursday: 7-5.
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/BARRY W TAYLOR/Primary Examiner, Art Unit 2646
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