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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/21/2026 has been entered. Claims 1-20 are pending.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-3, 8, 10-11, 15, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bedekar (US 2021/0306874), hereinafter “Bedekar”, in view of Elgabroun et al. (WO 2024/002491), hereinafter “Elgabroun”, and further in view of Li et al. (US 2023/0361908), hereinafter “Li”.
Regarding claims 1, 20, Bedekar teaches:
A network node or a method for recommending a modulation and coding scheme (MCS), comprising:
a processor (see Bedekar, par. [0183]: the various examples shown may be implemented in hardware or in special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device);
at least one network interface controller configured to provide access to a network (see Bedekar, par. [0097]: controller 204 obtains information from RAN 206 on the results of various transmission actions. The transmissions may by uplink (UL) or downlink (DL) transmissions. The transmissions may by NB-10T transmissions. The controller 204 may also obtain certain measurements from RAN 206); and
a memory communicatively coupled to the processor, wherein the memory comprises a modulation and coding scheme (MCS) logic that is configured (see Bedekar, par. [0183]: the various examples shown may be implemented in hardware or in special purpose circuits, software, logic or any combination thereof, and see par. [0188]: any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) to:
identify one or more recommended MCSs for the client device based on at least:
the identified at least one characteristic (see Bedekar, Fig. 6, par. [0144]: at 603 the controller 604 determines an initial MCS for transmissions in the RAN 606. The initial MCS may be determined as a function of outcomes of earlier transmissions (which may be represented, for example, by a ratio of ack/nacks) at various previously used MCS and repetition levels. In some examples, for each MCS and repetition level, controller 604 can transform to (i.e. determine) an equivalent coding rate, R(MCS, repetition), and calculate a probability of error based on the ratio of acks to nacks observed at that MCS and repetition level. This may be represented as P(MCS, repetition). Controller 624 can then select an MCS and repetition level that achieves a desired target error probability (X%), or as the MCS and repetition level that corresponds to the bottom Nth percentile of error probability across MCS and repetition levels; in this case, MCS for transmission is selected based on characteristics of earlier transmissions); and
a machine learning process (see Bedekar, par. [0155]: Using the initialized neural network, controller 604 then can input, into the neural network, the data received from the RAN 606 for a particular cell in the RAN 606 to generate a selection of an optimal MCS and/or repetition rate for transmissions),
wherein the one or more recommended MCSs indicate whether the client device needs to retry a transmission or perform a rate shift (see Bedekar, Fig. 6, par. [0148]: At 609, the RAN 606 can adjust transmission attributes based on the indication from the controller sent at 605. At 611, UE 636 sends an NPRACH transmission to RAN 606. RAN 606 responds at 613 by transmitting an NPRACH response with the adjusted transmission attributes (such as MCS and repetition level) that may be selected, for example, based on MCS and repetition level provided by controller 604 at 605, or based on the RACH signature of the UE's PRACH and the mapping of RACH signatures to MCS and repetition level provided by controller 604at 605; in this case, the transmission attributes indicating MCS level and repetition level correspond to the recommended MCSs indicating information for retrying a transmission or performing a rate shift);
However, Bedekar does not teach:
receive one or more frames from a client device;
identify at least one characteristic associated with the client device based on the received one or more frames;
identify one or more recommended MCSs for the client device based on at least:
a machine learning process configured to distinguish between a temporal interference and a radio frequency condition issue, and
transmit an indication of the one or more recommended MCSs to the client device.
Elgabroun, in the same field of endeavor, teaches:
receive one or more frames from a client device (see Elgabroun, Fig. 4, page 15, lines 9-13: The first value of MCS is determined on the basis of a Signal to Interference and Noise Ratio, SINR, estimated by the network node 400 for the future transmission at transmission time interval k. For example, this first MCS value is the MCS value described above and mapped using look-up tables by the network node 400. The estimation is preferably performed using reference signals transmitted by the UE 401 to the network node 400);
identify at least one characteristic associated with the client device based on the received one or more frames (see Elgabroun, Fig. 4, page 14, lines 25-26: the SINR calculated at the network node 400. As mentioned above, in the UL case, the network node 400 estimates the SINR using the reference signals transmitted by the UE); and
transmit an indication of the one or more recommended MCSs to the client device (see Elgabroun, Fig. 5, page 18, lines 17-18: The Q-Learning process, from the input above detailed, outputs the second MSC value. This value may be sent to the UE as shown in step 503).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the network node or method of Bedekar with the identifying a characteristic and transmitting an indication of MCS of Elgabroun with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of a high link throughput and decreasing block error rate (see Elgabroun, page 17, lines 17-22).
However, the combination of Bedekar in view of Elgabroun does not teach:
identify one or more recommended MCSs for the client device based on at least:
a machine learning process configured to distinguish between a temporal interference and a radio frequency condition issue,
Li, in the same field of endeavor, teaches:
identify one or more recommended MCSs for the client device based on at least:
a machine learning process configured to distinguish between a temporal interference and a radio frequency condition issue (see Li, Fig. 7, par. [0240]: In the scheme 700 provided by the present disclosure, as shown in FIG. 7, in order to determine the MCS of the target UE, on one hand, the short-term SINR offset corresponding to the target UE needs to be determined; on the other hand, the long-term SINR corresponding to the target UE needs to the determined, the real-time SINR of the target UE is then determined based on the short-term SINR offset and the long-term SINR, and an appropriate MCS is finally selected based on the determined real-time SINR. The determination of both the short-term SINR offset and the long-term SINR may be implemented by the AI network; in this case, determining a short term SINR offset and a long-term SINR via an AI network for selecting an MCS of the UE corresponds to identifying recommended MCSs based on a machine learning process that distinguishes between short-term performance (i.e. a temporal interference) and long-term performance (i.e. a radio frequency condition issue)),
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the identifying recommended MCSs of the combination of Bedekar in view of Elgabroun with the determination based on the particular machine learning process of Li with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of determining a more accurate MCS and improving user throughput (see Li, par. [0238]).
Regarding claim 2, the combination of Bedekar in view of Elgabroun, and further in view of Li, teaches the network node. Bedekar further teaches:
wherein the at least one characteristic associated with the client device includes a radio frequency (RF) parameter associated with the client device (see Bedekar, Fig. 6, par. [0136]: Aspect B is illustrated at 621 of FIG. 6. RAN 606 can provide information to controller 604 comprising a set of data/attributes to facilitate determination of optimal adjustments for transmission attributes, and see par. [0139]: For UL transmissions, received signal level and measured interference and/or noise level, or power control commands or transmit power adjustments can also be provided).
Regarding claim 3, the combination of Bedekar in view of Elgabroun, and further in view of Li, teaches the network node. Bedekar further teaches:
wherein the RF parameter includes at least one of a received signal strength indicator (RSSI), a signal-to-noise ratio (SNR), or a signal-to-interference-plus-noise ratio (SINR) (see Bedekar, Fig. 6, par. [0136]: Aspect B is illustrated at 621 of FIG. 6. RAN 606 can provide information to controller 604 comprising a set of data/attributes to facilitate determination of optimal adjustments for transmission attributes, and see par. [0139]: For UL transmissions, received signal level and measured interference and/or noise level, or power control commands or transmit power adjustments can also be provided).
Regarding claim 8, the combination of Bedekar in view of Elgabroun, and further in view of Li, teaches the network node. Bedekar further teaches:
wherein the machine learning process is associated with a machine learning model, and the machine learning model is trained based on one or more of radio frequency (RF) parameter data, MCS data, retry indicator data, channel state information (CSI) data, noise floor data, acknowledgement (ACK) data, client device type data, or client device movement data (see Bedekar, par. [0152]: The inputs to the neural network can be at least one of: the RACH received power; power head room reports; the sequence of MCS/repetition levels; coding rates used by RAN 606 (or for example, by a base station in the RAN); the number of retransmissions; the fraction of ack/nacks at each coding rate; the achieved effective throughput; in this case, the neural network (i.e. machine learning model) is trained at least based on MCS data, number of retransmissions (i.e. retry indicator data), and ack/nacks (i.e. ACK data)).
Regarding claim 10, the combination of Bedekar in view of Elgabroun, and further in view of Li, teaches the network node.
Bedekar does not teach, but Elgabroun teaches:
wherein the MCS logic is further configured to predict that a short-term channel condition between the client device and the network node is unstable, and wherein the indication of the one or more recommended MCSs is transmitted to the client device in response to the prediction that the short-term channel condition between the client device and the network node is unstable (see Elgabroun, page 14, lines 1-5: The network node 400 needs to know about the UE's channel conditions in order to perform scheduling. This may be done using the Channel Quality Indicator (CQI) feedback that the UE 401 calculates based on its downlink Signal to Interference and Noise Ratio (SINR). Once the network node 400 gets the CQI, the network node 400 converts it to a Modulation and Coding Scheme (MCS) and schedule the user on basis of this MCS, and see page 2, lines 4-6: The scheduler chooses the MCS value that corresponds to given measured inputs, mainly Signal to Interference and Noise Ratio (SINR), while satisfying the constraint of keeping the BLER below a certain threshold (10%), and see page 17, lines 6-8: The Q-Learning process adjusts the MCS value (first MCS value) estimated by the network node (for example via the look-up tables) to predict a new and more accurate value suitable for the transmission conditions, the second MCS value, and see page 17, lines 6-8: The Q-Learning process adjusts the MCS value (first MCS value) estimated by the network node (for example via the look-up tables) to predict a new and more accurate value suitable for the transmission conditions, the second MCS value, and see Fig. 5, page 18, lines 17-18: The Q-Learning process, from the input above detailed, outputs the second MSC value. This value may be sent to the UE as shown in step 503; in this case, predicting a second MCS value is more suitable than a first MCS value based on transmission conditions and transmitting the second MCS value corresponds to the indication being transmitted in response to predicting the channel condition is unstable).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the network node or method of Bedekar with the transmitting an indication of MCS based on unstable channel conditions of Elgabroun with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of a high link throughput and decreasing block error rate (see Elgabroun, page 17, lines 17-22).
Regarding claim 11, the combination of Bedekar in view of Elgabroun, and further in view of Li, teaches the network node.
Bedekar does not teach, but Elgabroun teaches:
wherein the indication of the one or more recommended MCSs is transmitted to the client device via an unsolicited action frame or a modified acknowledgement (ACK) message (see Elgabroun, page 15, lines 24-29: The first MCS value obtained in step 501 is one of the inputs of a Q.-learning process, the output of which is a second value of MCS. The second value of MCS is the value that is then preferably transmitted to the UE, for example the allocated second value of MCS is signaled to the UE using DCI over PDCCH channel e.g. DCI l_0, DCI 1_1. Preferably, therefore, the second value of MCS, function of the first value of MCS, is transmitted to the UE so that it can be used for the uplink transmission at transmission time k; in this case, sending the MCS using DCI for use for uplink transmissions corresponds to the indication being transmitted via an unsolicited action frame).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the network node or method of Bedekar with the transmitting an indication of MCS via an unsolicited action frame of Elgabroun with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of a high link throughput and decreasing block error rate (see Elgabroun, page 17, lines 17-22).
Regarding claim 15, the combination of Bedekar in view of Elgabroun, and further in view of Li, teaches the network node.
Bedekar does not teach, but Elgabroun teaches:
wherein the indication of the one or more recommended MCSs is transmitted to the client device in an association process between the client device and the network node (see Elgabroun, Fig. 5, page 18, lines 17-18: The Q-Learning process, from the input above detailed, outputs the second MSC value. This value may be sent to the UE as shown in step 503).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the network node or method of Bedekar with the transmitting an indication of MCS in an association process of Elgabroun with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of a high link throughput and decreasing block error rate (see Elgabroun, page 17, lines 17-22).
Regarding claim 18, Bedekar teaches:
A client device, comprising:
a processor (see Bedekar, par. [0183]: the various examples shown may be implemented in hardware or in special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device);
at least one network interface controller configured to provide access to a network (see Bedekar, par. [0183]: the various examples shown may be implemented in hardware or in special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, and see Fig. 6, par. [0148]: At 611, UE 636 sends an NPRACH transmission to RAN 606. RAN 606 responds at 613 by transmitting an NPRACH response with the adjusted transmission attributes); and
a memory communicatively coupled to the processor, wherein the memory comprises a modulation and coding scheme (MCS) logic that is configured (see Bedekar, par. [0183]: the various examples shown may be implemented in hardware or in special purpose circuits, software, logic or any combination thereof, and see par. [0188]: any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) to:
wherein the one or more recommended MCSs are identified based on a machine learning process (see Bedekar, par. [0155]: Using the initialized neural network, controller 604 then can input, into the neural network, the data received from the RAN 606 for a particular cell in the RAN 606 to generate a selection of an optimal MCS and/or repetition rate for transmissions), wherein the one or more recommended MCSs indicate whether the client device needs to retry a transmission or perform a rate shift (see Bedekar, Fig. 6, par. [0148]: At 609, the RAN 606 can adjust transmission attributes based on the indication from the controller sent at 605. At 611, UE 636 sends an NPRACH transmission to RAN 606. RAN 606 responds at 613 by transmitting an NPRACH response with the adjusted transmission attributes (such as MCS and repetition level) that may be selected, for example, based on MCS and repetition level provided by controller 604 at 605, or based on the RACH signature of the UE's PRACH and the mapping of RACH signatures to MCS and repetition level provided by controller 604at 605; in this case, the transmission attributes indicating MCS level and repetition level correspond to the recommended MCSs indicating information for retrying a transmission or performing a rate shift); and
apply one of the one or more recommended MCSs at the client device (see Bedekar, Fig. 6, par. [0148]: RAN 606 responds at 613 by transmitting an NPRACH response with the adjusted transmission attributes (such as MCS and repetition level) that may be selected, for example, based on MCS and repetition level provided by controller 604 at 605, or based on the RACH signature of the UE's PRACH and the mapping of RACH signatures to MCS and repetition level provided by controller 604at 605. At 615, an RRC connection establishment message is sent from the UE 636 to RAN 606 using the adjusted transmission attributes; in this case, the UE sending a message with the adjusted transmission attributes corresponds to applying a recommended MCS).
However, Bedekar does not teach:
transmit at least one frame to a network node;
receive an indication of one or more recommended MCSs from the network node,
wherein the one or more recommended MCSs are identified based on a machine learning process configured to distinguish between a temporal interference and a radio frequency condition issue,
Elgabroun, in the same field of endeavor, teaches:
transmit at least one frame to a network node (see Elgabroun, Fig. 4, page 15, lines 9-13: The first value of MCS is determined on the basis of a Signal to Interference and Noise Ratio, SINR, estimated by the network node 400 for the future transmission at transmission time interval k. For example, this first MCS value is the MCS value described above and mapped using look-up tables by the network node 400. The estimation is preferably performed using reference signals transmitted by the UE 401 to the network node 400);
receive an indication of one or more recommended MCSs from the network node (see Elgabroun, Fig. 5, page 18, lines 17-18: The Q-Learning process, from the input above detailed, outputs the second MSC value. This value may be sent to the UE as shown in step 503; in this case, the network node sends an MCS value to the UE which receives the value);
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the network node or method of Bedekar with the transmitting and receiving of Elgabroun with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of a high link throughput and decreasing block error rate (see Elgabroun, page 17, lines 17-22).
However, the combination of Bedekar in view of Elgabroun does not teach:
wherein the one or more recommended MCSs are identified based on a machine learning process configured to distinguish between a temporal interference and a radio frequency condition issue,
Li, in the same field of endeavor, teaches:
wherein the one or more recommended MCSs are identified based on a machine learning process configured to distinguish between a temporal interference and a radio frequency condition issue (see Li, Fig. 7, par. [0240]: In the scheme 700 provided by the present disclosure, as shown in FIG. 7, in order to determine the MCS of the target UE, on one hand, the short-term SINR offset corresponding to the target UE needs to be determined; on the other hand, the long-term SINR corresponding to the target UE needs to the determined, the real-time SINR of the target UE is then determined based on the short-term SINR offset and the long-term SINR, and an appropriate MCS is finally selected based on the determined real-time SINR. The determination of both the short-term SINR offset and the long-term SINR may be implemented by the AI network; in this case, determining a short term SINR offset and a long-term SINR via an AI network for selecting an MCS of the UE corresponds to identifying recommended MCSs based on a machine learning process that distinguishes between short-term performance (i.e. a temporal interference) and long-term performance (i.e. a radio frequency condition issue)),
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the identifying recommended MCSs of the combination of Bedekar in view of Elgabroun with the determination based on the particular machine learning process of Li with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of determining a more accurate MCS and improving user throughput (see Li, par. [0238]).
Claims 4-5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Bedekar in view of Elgabroun, and further in view of Li, as applied to claims 1-3, 8, 10-11, 15, 18, and 20 above, and further in view of Su et al. (US 2020/0287639), hereinafter “Su”.
Regarding claim 4, the combination of Bedekar in view of Elgabroun, and further in view of Li, teaches the network node.
However, the combination of Bedekar in view of Elgabroun, and further in view of Li, does not teach:
wherein the at least one characteristic associated with the client device includes a device type of the client device.
Su, in the same field of endeavor, teaches:
wherein the at least one characteristic associated with the client device includes a device type of the client device (see Su, par. [0017]: device type has an impact on transmission throughput for a given transmission parameter. Accordingly, the embodiments use a highly informed learning-based approach to select a transmission parameter. The learning-based approach selects a “best” transmission parameter for a given condition of a communication channel, as defined by a combination of CSI, SNR, and device type, based on training or learning from previously observed transmissions over the communication channel, and see par. [0025]: At 208, AP 102 records an indication of a device type (d) of client device 108(i). In one example, AP 102 may record a media access control (MAC) address of client device 108(i) as a proxy for the device type. In another example, AP 102 records a model number of client device 108(i) as the device type).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the characteristic of the combination of Bedekar in view of Elgabroun, and further in view of Li, with the characteristic including a device type of the client device of Su with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of maintaining a maximum transmission throughput with minimum packet errors over time (see Su, par. [0017]).
Regarding claim 5, the combination of Bedekar in view of Elgabroun, and further in view of Li, teaches the network node.
However, the combination of Bedekar in view of Elgabroun, and further in view of Li, does not teach:
wherein the at least one characteristic associated with the client device includes a movement of the client device relative to the network node.
Su, in the same field of endeavor, teaches:
wherein the at least one characteristic associated with the client device includes a movement of the client device relative to the network node (see Su, Fig. 2, par. [0022]: At 204, AP 102 determines a motion indictor (mt) indicative of motion of client device 108(i). The motion indicator indicates an extent to which client device 108(i) is moving, e.g., whether client device 108(i) is moving or is not moving, how likely it is that client device 108(i) is moving, and/or how fast client device 108(i) is moving, based on the sequence of CSI measurements obtained at 202, and see pars. [0026-0028]: At 210, using a learning-based algorithm, AP 102 selects a PHY or link layer transmission parameter (pt) among multiple candidate PHY or link layer transmission parameters (p1-pN) based on a set of observation parameters including the motion indicator, the measured SNR, and the device identifier for client device 108(i). The PHY and link layer transmission parameters are generally referred to as “MAC layer transmission parameters” because they configure aspects of a MAC layer implemented in AP 102. AP 102 may select the transmission parameter from among the following candidate transmission parameters, for example: a. Lower dimensional modulation and coding (MCS) indexes, e.g., MCS only, including {MCS0-MCS9} for 9 choices/candidates. b. Higher dimensional MCS indexes, e.g., joint selection of spatial stream and MCS, including {MCS0-SS1, MCS1-SS1-MCS9-SS1, and MCS0-SS2-MCS9-SS2} for 18 choices/candidates for a two spatial stream client device).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the characteristic of the combination of Bedekar in view of Elgabroun, and further in view of Li, with the characteristic including a movement of the client device of Su with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of maintaining a maximum transmission throughput with minimum packet errors over time (see Su, par. [0017]).
Regarding claim 14, the combination of Bedekar in view of Elgabroun, and further in view of Li, teaches the network node.
Bedekar does not teach, but Elgabroun teaches:
wherein the MCS logic is further configured to:
identify a most suitable MCS for the network node in response to the prediction that the short-term channel condition between the client device and the network node is unstable (see Elgabroun, page 14, lines 1-5: The network node 400 needs to know about the UE's channel conditions in order to perform scheduling. This may be done using the Channel Quality Indicator (CQI) feedback that the UE 401 calculates based on its downlink Signal to Interference and Noise Ratio (SINR). Once the network node 400 gets the CQI, the network node 400 converts it to a Modulation and Coding Scheme (MCS) and schedule the user on basis of this MCS, and see page 2, lines 4-6: The scheduler chooses the MCS value that corresponds to given measured inputs, mainly Signal to Interference and Noise Ratio (SINR), while satisfying the constraint of keeping the BLER below a certain threshold (10%), and see page 17, lines 6-8: The Q-Learning process adjusts the MCS value (first MCS value) estimated by the network node (for example via the look-up tables) to predict a new and more accurate value suitable for the transmission conditions, the second MCS value; in this case, predicting a second MCS value is more suitable than a first MCS value based on transmission conditions and transmitting the second MCS value corresponds to identifying a most suitable MCS in response to predicting the channel condition is unstable);
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the network node or method of Bedekar with the identifying a most suitable MCS of Elgabroun with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of a high link throughput and decreasing block error rate (see Elgabroun, page 17, lines 17-22).
However, the combination of Bedekar in view of Elgabroun, and further in view of Li, does not teach:
apply the most suitable MCS at the network node.
Su, in the same field of endeavor, teaches:
apply the most suitable MCS at the network node (see Su, par. [0015]: AP 102 transmits packets to (and receives packets from client devices 108) in accordance with physical (i.e., PHY) layer and link layer protocols implemented by the AP. AP 102 also has the flexibility to select PHY layer parameters and link layer parameters (collectively referred to as “transmission parameters”) with which to configure the PHY layer protocol and the link layer protocol for per-packet transmissions to client devices 108. Example transmission parameters that may be selected for per-packet transmission include, but are not limited to, a modulation and coding (MCS) index (which represents a combination of modulation and channel coding, guarding interval, and various other parameters), a number of spatial streams, SU-MIMO operation, and MU-MIMO operation, and see pars. [0026-0028]: AP 102 may select the transmission parameter from among the following candidate transmission parameters, for example: a. Lower dimensional modulation and coding (MCS) indexes, e.g., MCS only, including {MCS0-MCS9} for 9 choices/candidates. b. Higher dimensional MCS indexes, e.g., joint selection of spatial stream and MCS, including {MCS0-SS1, MCS1-SS1-MCS9-SS1, and MCS0-SS2-MCS9-SS2} for 18 choices/candidates for a two spatial stream client device).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the MCS logic of the combination of Bedekar in view of Elgabroun, and further in view of Li, with the applying a suitable MCS at the network node of Su with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of maintaining a maximum transmission throughput with minimum packet errors over time (see Su, par. [0017]).
Claims 6-7, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bedekar in view of Elgabroun, and further in view of Li, as applied to claims 1-3, 8, 10-11, 15, 18, and 20 above, and further in view of Zeng et al. (US 2022/0352955), hereinafter “Zeng”.
Regarding claim 6, the combination of Bedekar in view of Elgabroun, and further in view of Li, teaches the network node.
However, the combination of Bedekar in view of Elgabroun, and further in view of Li, does not teach:
wherein to identify the one or more recommended MCSs for the client device, the MCS logic is further configured to identify a probability of success associated with each of the one or more recommended MCSs.
Zeng, in the same field of endeavor, teaches:
wherein to identify the one or more recommended MCSs for the client device, the MCS logic is further configured to identify a probability of success associated with each of the one or more recommended MCSs (see Zeng, pars. [0103-0104]: Given the inputs, the UE 115-a may use the NN to predict whether a non-granted MCS that is relatively higher than the granted MCS can pass the TB CRC. In some examples, the prediction may be a binary classification (for example, 1 or 0, pass or fail). Specifically, the output layer of the NN may utilize a sigmoid activation function (in other words, a final output between 0 and 1). In some examples, an output from the NN below a threshold (for example, <0.5) may correspond to a prediction indicating that the higher, non-granted MCS has a relatively low likelihood of passing the TB CRC. Similarly, an output from the NN above a threshold (for example, 0.5) may correspond to a prediction indicating that the higher, non-granted MCS has a relatively high likelihood of passing the TB CRC. It should be noted that the NN may be utilized at the UE 115-a, at the base station 105-a, or any combination thereof. For example, the UE 115-a may compute the output of the NN and, in some examples, may transmit an indication of the output to the base station 105-a via the communication link 205-b. In some other examples, the UE 115-a may transmit an indication of the one or more preprocessed decoder parameters for the base station 105-a to use in determining the output of the NN).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the MCS logic of the combination of Bedekar in view of Elgabroun, and further in view of Li, with the identifying a probability of success of MCSs of Zeng with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of increasing throughput over communication links (see Zeng, par. [0045]).
Regarding claim 7, the combination of Bedekar in view of Elgabroun, and further in view of Li, and further in view of Zeng, teaches the network node.
The combination of Bedekar in view of Elgabroun, and further in view of Li, does not teach, but Zeng teaches:
wherein the indication of the one or more recommended MCSs further includes an indication of the probability of success associated with each of the one or more recommended MCSs (see Zeng, pars. [0103-0104]: Given the inputs, the UE 115-a may use the NN to predict whether a non-granted MCS that is relatively higher than the granted MCS can pass the TB CRC. In some examples, the prediction may be a binary classification (for example, 1 or 0, pass or fail). Specifically, the output layer of the NN may utilize a sigmoid activation function (in other words, a final output between 0 and 1). In some examples, an output from the NN below a threshold (for example, <0.5) may correspond to a prediction indicating that the higher, non-granted MCS has a relatively low likelihood of passing the TB CRC. Similarly, an output from the NN above a threshold (for example, 0.5) may correspond to a prediction indicating that the higher, non-granted MCS has a relatively high likelihood of passing the TB CRC. It should be noted that the NN may be utilized at the UE 115-a, at the base station 105-a, or any combination thereof. For example, the UE 115-a may compute the output of the NN and, in some examples, may transmit an indication of the output to the base station 105-a via the communication link 205-b. In some other examples, the UE 115-a may transmit an indication of the one or more preprocessed decoder parameters for the base station 105-a to use in determining the output of the NN).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the indication of the combination of Bedekar in view of Elgabroun, and further in view of Li, with the indication including the probability of success of MCSs of Zeng with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of increasing throughput over communication links (see Zeng, par. [0045]).
Regarding claim 16, the combination of Bedekar in view of Elgabroun, and further in view of Li, teaches the network node.
However, the combination of Bedekar in view of Elgabroun, and further in view of Li, does not teach:
wherein the MCS logic is further configured to generate an identification that a first frame in the one or more frames from the client device is faulty, and wherein the indication of the one or more recommended MCSs is transmitted to the client device in response to the identification that the first frame is faulty.
Zeng, in the same field of endeavor, teaches:
wherein the MCS logic is further configured to generate an identification that a first frame in the one or more frames from the client device is faulty, and wherein the indication of the one or more recommended MCSs is transmitted to the client device in response to the identification that the first frame is faulty (see Zeng, pars. [0103-0104]: Given the inputs, the UE 115-a may use the NN to predict whether a non-granted MCS that is relatively higher than the granted MCS can pass the TB CRC. In some examples, the prediction may be a binary classification (for example, 1 or 0, pass or fail). Specifically, the output layer of the NN may utilize a sigmoid activation function (in other words, a final output between 0 and 1). In some examples, an output from the NN below a threshold (for example, <0.5) may correspond to a prediction indicating that the higher, non-granted MCS has a relatively low likelihood of passing the TB CRC. Similarly, an output from the NN above a threshold (for example, 0.5) may correspond to a prediction indicating that the higher, non-granted MCS has a relatively high likelihood of passing the TB CRC. It should be noted that the NN may be utilized at the UE 115-a, at the base station 105-a, or any combination thereof. For example, the UE 115-a may compute the output of the NN and, in some examples, may transmit an indication of the output to the base station 105-a via the communication link 205-b. In some other examples, the UE 115-a may transmit an indication of the one or more preprocessed decoder parameters for the base station 105-a to use in determining the output of the NN; in this case, the granted MCS having a lower likelihood of passing the CRC corresponds to the first frame being faulty. This is identified as part of the process indicating a different recommended MCS).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the MCS logic of the combination of Bedekar in view of Elgabroun, and further in view of Li, with the transmission of an indication of MCS based on an identification of a faulty frame of Zeng with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of increasing throughput over communication links (see Zeng, par. [0045]).
Regarding claim 19, the combination of Bedekar in view of Elgabroun, and further in view of Li, teaches the client device.
However, the combination of Bedekar in view of Elgabroun, and further in view of Li, does not teach:
wherein the indication of the one or more recommended MCSs further includes an indication of a probability of success associated with each of the one or more recommended MCSs.
Zeng, in the same field of endeavor, teaches:
wherein the indication of the one or more recommended MCSs further includes an indication of a probability of success associated with each of the one or more recommended MCSs (see Zeng, pars. [0103-0104]: Given the inputs, the UE 115-a may use the NN to predict whether a non-granted MCS that is relatively higher than the granted MCS can pass the TB CRC. In some examples, the prediction may be a binary classification (for example, 1 or 0, pass or fail). Specifically, the output layer of the NN may utilize a sigmoid activation function (in other words, a final output between 0 and 1). In some examples, an output from the NN below a threshold (for example, <0.5) may correspond to a prediction indicating that the higher, non-granted MCS has a relatively low likelihood of passing the TB CRC. Similarly, an output from the NN above a threshold (for example, 0.5) may correspond to a prediction indicating that the higher, non-granted MCS has a relatively high likelihood of passing the TB CRC. It should be noted that the NN may be utilized at the UE 115-a, at the base station 105-a, or any combination thereof. For example, the UE 115-a may compute the output of the NN and, in some examples, may transmit an indication of the output to the base station 105-a via the communication link 205-b. In some other examples, the UE 115-a may transmit an indication of the one or more preprocessed decoder parameters for the base station 105-a to use in determining the output of the NN).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the indication of the combination of Bedekar in view of Elgabroun, and further in view of Li, with the indication including the probability of success of MCSs of Zeng with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of increasing throughput over communication links (see Zeng, par. [0045]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Bedekar in view of Elgabroun, and further in view of Li, as applied to claims 1-3, 8, 10-11, 15, 18, and 20 above, and further in view of Soldati et al. (US 2024/0098773), hereinafter “Soldati”.
Regarding claim 9, the combination of Bedekar in view of Elgabroun, and further in view of Li, teaches the network node.
However, the combination of Bedekar in view of Elgabroun, and further in view of Li, does not teach:
wherein the machine learning model includes a linear regression model.
Soldati, in the same field of endeavor, teaches:
wherein the machine learning model includes a linear regression model (see Soldati, pars. [0147-0154]: a machine learning model/function used for determining one or more link adaptation parameter value may be one or more of the following: A feedforward neural network; A recurrent neural network; A convolutional neural network; An ensemble of neural networks, such as feedforward neural networks, recurrent neural networks, convolutional neural networks or a combination thereof; A decision tree; A decision forest; A linear regression model).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the machine learning model of the combination of Bedekar in view of Elgabroun, and further in view of Li, with the model including a linear regression model of Soldati with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of enabling the network node to better optimize link adaptation parameters (see Soldati, par. [0053]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Bedekar in view of Elgabroun, and further in view of Li, as applied to claims 1-3, 8, 10-11, 15, 18, and 20 above, and further in view of Raghavan et al. (US 2023/0319760), hereinafter “Raghavan”.
Regarding claim 12, the combination of Bedekar in view of Elgabroun, and further in view of Li, teaches the network node.
However, the combination of Bedekar in view of Elgabroun, and further in view of Li, does not teach:
wherein the short-term channel condition between the client device and the network node is predicted to be unstable based on a non-linear process that compares a current stochasticity of at least one radio frequency (RF) parameter to an expected range.
Raghavan, in the same field of endeavor, teaches:
wherein the short-term channel condition between the client device and the network node is predicted to be unstable based on a non-linear process that compares a current stochasticity of at least one radio frequency (RF) parameter to an expected range (see Raghavan, Fig. 5, pars. [0113-0116]: The UE 115-b may also communicate a location of the UE 115-b or observed signal strengths (e.g., RSRPs) to the second network entity 105-c (e.g., in the different locations). The second network entity 105-c may then estimate a correlation between the beam ID, the location of the UE 115-b, or the observed signal strengths to determine whether the UE 115-b is in a near field or a far field (e.g., the location of the UE 115-b may be classified as being in a near field or a far field. In some cases, the second network entity 105-c may estimate the correlation based on past or historical data that determines thresholds for signal strengths. For instance, the second network entity 105-c may input the information received from the UE 115-b to a model trained to identify such thresholds to determine whether the UE 115-b is in the near field or the far field. The second network entity 105-c (e.g., AI or ML server) may identify whether the UE 115-b is in the near field or far field (e.g., relative to the first network entity 105-b) and may communicate this information (e.g., identification or determination) back to the UE 115-b, the first network entity 105-b, or both. In some cases, the UE 115-b may provide additional information or additional measurements as input to the second network entity 105-c for the second network entity 105-c to use to determine whether the UE 115-b is in a near field or a far field. This additional information or these additional measurements may be useful for the second network entity 105-c (e.g., an AI or ML model at the second network entity 105-c) to learn a distinction between fading and near-field operation. In some examples, the UE 115-b may determine a quality of a line-of-sight path between the UE 115-b and the first network entity 105-b (e.g., line-of-sight determination and estimation) based on polarization characteristics, and the UE 115-b may provide the quality of the line-of-sight path to the second network entity 105-c as the additional information or additional measurements. In such examples, the first network entity 105-b may transmit reference signals to the UE 115-b with polarizations in two perpendicular directions. Fading randomness may be associated with a lack of a line-of-sight path or a weak or low-quality line-of-sight path. Accordingly, detection and strength estimation of a line-of-sight path may help distinguish fading from near-field operation (e.g., where near-field operation and far-field operation assume the presence of a line-of-sight path). In one example, the first network entity 105-b may transmit a first reference signal with a first polarization and a second reference signal with a second polarization to the UE 115-b. The signals with the two polarizations may be independent but equally strong, and the UE 115-b may provide the strength of the respective signals (e.g., strength with which the respective signals are transmitted and received) to the second network entity 105-c (e.g., input to AI or ML algorithms). If the strengths of the signals transmitted with different polarizations and received at the UE 115-b are equally strong, a line-of-sight path may be dominant. Alternatively, if the UE 115-b observes local signal variation between the signals transmitted with different polarizations, the UE 115-b may be in a near-field. Further, if no line-of-sight path is present, the UE 115-b may be experiencing fading (e.g., in a fading condition); in this case, comparing variation and randomness of quality parameters for determining strength of a line-of-sight path corresponds to comparing current stochasticity to an expected range).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the prediction of the short-term channel condition of the combination of Bedekar in view of Elgabroun, and further in view of Li, with the prediction based on a non-linear process of Raghavan with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of efficiently determining a region in which a UE is located for selecting beams for communication (see Raghavan, par. [0004]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Bedekar in view of Elgabroun, and further in view of Li, and further in view of Raghavan, as applied to claim 12 above, and further in view of Ouyang et al. (US 9,538,401), hereinafter “Ouyang”.
Regarding claim 13, the combination of Bedekar in view of Elgabroun, and further in view of Li, and further in view of Raghavan, teaches the network node.
However, the combination of Bedekar in view of Elgabroun, and further in view of Li, and further in view of Raghavan, does not teach:
wherein the non-linear process is associated with a Lyapunov exponent.
Ouyang, in the same field of endeavor, teaches:
wherein the non-linear process is associated with a Lyapunov exponent (see Ouyang, col. 9, lines 24-35: network analytics system 245 may receive and/or store a set of rules that indicate which network parameter values or calculations from network parameter values are to be extracted and/or processed to cluster cells 215 into multiple groups of clusters. For example, network analytics system 245 may receive and/or store an initial set of network parameter values, and the set of rules may indicate a subset of values to be extracted from the initial set of network parameter values. Additionally, or alternatively, the set of rules may indicate a set of values to be calculated based on the initial set of network parameter values, and see col. 10, lines 23-34: the set of rules may indicate that a value indicative of the Box-Pierce test statistic of independence calculated for the initial set of network parameter values, a value indicative of the Ljung-Box test statistic of independence calculated for the initial set of network parameter values, a value indicative of the Tsay's test statistic of nonlinearity calculated for the initial set of network parameter values, a value indicative of the Hurst exponent calculated for the initial set of network parameter values, a value indicative of the Lyapunov exponent (e.g., the greatest Lyapunov exponent) calculated for the initial set of network parameter values).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the non-linear process of the combination of Bedekar in view of Elgabroun, and further in view of Li, and further in view of Raghavan, with the process being associated with a Lyapunov exponent of Ouyang with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of improving network performance and increasing network capacity (see Ouyang, col. 3, lines 8-26).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Bedekar in view of Elgabroun, and further in view of Li, and further in view of Zeng, as applied to claims 6-7, 16, and 19 above, and further in view of Wang et al. (US 2020/0119859), hereinafter “Wang”.
Regarding claim 17, the combination of Bedekar in view of Elgabroun, and further in view of Li, and further in view of Zeng, teaches the network node.
However, the combination of Bedekar in view of Elgabroun, and further in view of Li, and further in view of Zeng, does not teach:
wherein the faulty first frame fails a cyclic redundancy check (CRC) but includes a decodable header based on which the client device is identifiable as being a source and the network node is identifiable as being a destination.
Wang, in the same field of endeavor, teaches:
wherein the faulty first frame fails a cyclic redundancy check (CRC) but includes a decodable header based on which the client device is identifiable as being a source and the network node is identifiable as being a destination (see Wang, par. [0104]: A receiving STA may determine that a HARQ (or regular) transmission was addressed to itself when, for example, one or more HARQ or regular packets may have been scheduled to be transmitted to a receiving STA by one or more scheduling IEs or frames, but the reception of such packets failed. The failure may be indicated by failed FCS/CRC/LDPC check of the PLOP header, the MAC header, and/or the MAC frame, and see par. [0107]: A receiving STA may determine that a HARQ (or regular) transmission was addressed to itself when for example, a STA may have detected a valid PLOP header and a MAC header, where the MAC header may be considered reliable and addressed to the receiving STA, but the STA may fail to decode the packet correctly. The failure may be indicated, e.g., by failed FCS test or LDPC test, and see par. [0110]: The PAID and/or GrouplD field in the PLOP header may be used to include additional information on the extended PAID or AID, or other form of IDs of a receiving STA. The PLOP header may include the PAID or AID or other form of IDs of the transmitting STA, and see par. [0051]: a source STA (e.g., STA 206) may have traffic intended for a destination STA (e.g., STA 204). STA 206 may send the traffic to AP 202, and, AP 202 may send the traffic to STA 204).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the faulty first frame of the combination of Bedekar in view of Elgabroun, and further in view of Li, and further in view of Zeng, with the faulty first frame failing a CRC but including a decodable header of Wang with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification for the benefit of reducing latency and minimizing signaling overhead (see Wang, par. [0154]).
Response to Arguments
Applicant’s arguments with respect to claims 1, 18, and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Chintalapudi et al. (US 2023/0336228) teaches a method for managing wireless communication including determining and selecting MCS based on channel quality information.
Kim (US 2023/0090593) teaches a method for transmitting data including determining an MCS level based on channel quality information and an offset determined according to a machine learning process.
Liu et al. (US 2023/0239069) teaches a modulation and coding scheme selection process.
Wang et al. (US 2024/0284203) teaches a method for communication including communication mechanism indication information is obtained based on a robustness requirement of a distributed learning system and the first distributed node performs inference based on second data of the first distributed node and the at least one piece of first data by using a distributed learning model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CALEB J BALLOWE whose telephone number is (571)270-0410. The examiner can normally be reached MON-FRI 7:30-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nishant B. Divecha can be reached at (571) 270-3125. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/C.J.B./Examiner, Art Unit 2419
/Nishant Divecha/Supervisory Patent Examiner, Art Unit 2419
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