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
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nammi (US 20190052441 A1) in view of Zhang (CN 117425211 A).
For claim 1, Nammi discloses base station equipment (FIG. 10, network device/BS 102), comprising: at least one processor; and at least one memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations (the processor(s) and memory of BS 102, such as processor 210 and memory 208 within BS 102 in FIG. 2), comprising:
receiving, from a user equipment of a group of user equipment, channel quality indicator data representative of a channel quality corresponding to a first defined sub-band of a group of sub-bands that is to be used in scheduling a subsequent transmission of data (FIGs. 1-14 and the associated text, such as FIG. 2 shows BS102 communicates with a group of UEs 104, and FIG. 10 shows BS 102 receives a CSI 108 in view of “[0039] … the mobile device 104 can compute the channel estimates … channel state information (CSI) reporting, as indicated at 108. The CSI report can comprise, for example, the CQI, a precoding matrix index (PMI), rank information (RI), the best subband indices, best beam indices, and so on, or any number of other types of information. … [0040] … he network device 102 can send the scheduling parameters to the mobile device 104 in a downlink control channel … After the scheduling parameter information is transmitted, the actual data transfer can take place from the network device 102 to the mobile device 104 over the data traffic channel 116.”);
in response to determining that the channel quality corresponding to the first defined sub-band is changing at a rate of change faster than a defined transmission time interval value associated with the channel quality indicator data, selecting a modulation and coding scheme value from a pool of modulation and coding scheme values (FIGs. 1-14 and the associated text, such as “[0082] … For the rate of change of uplink channel estimates, the network device can estimate the uplink channel The rate of change of uplink channel can provide a measure of Doppler metric. [0083] … For the mobile device capability, with reference again to FIGS. 3 and 4, thresholds for choosing 16 QAM and 64 QAM are different. For example for the two transmit antenna case (FIG. 3), at a geometry of 10 dB the majority of modulation can be dominated by CQI indices corresponding to QPSK. For the four transmit antenna case (FIG. 4), the modulation can be dominated by 16-QAM. Therefore, based on the mobile device capability of receiving either two transmit antennas or four transmit antennas, the specific bits in the CQI subset restriction can be set.” and “[0043] … That specific reference signal is then only transmitted within the resource blocks assigned for data traffic channel transmission to that terminal (e.g., mobile device).”; note that “16 QAM” and “64 QAM” are MCS values and “resource blocks” specifies time interval values); and
transmitting, to the group of user equipment, the modulation and coding scheme value to be used by the group of user equipment for the subsequent transmission of data (FIGs. 1-14 and the associated text, such as [0040] … After the scheduling parameter information is transmitted, the actual data transfer can take place from the network device 102 to the mobile device 104 over the data traffic channel 116.”).
Nammi teaches neural networks being employed to process network parameters (p15, last para “[0145] … Various classification schemes and/or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, and data fusion engines) can be employed in connection with performing automatic and/or inferred action in connection with the disclosed subject matter.”), but does not specifically disclose using an output from a neural networks to select a modulation and coding scheme (MCS) value. In the same field of endeavor of wireless communication, Zhang disclose using an output from a neural networks to select a MCS value (p15, last para “In order to assist eMBB users in transmission to select the optimal MCS scheme, the present application uses the feedforward neural network to output a modulation coding scheme capable of maximizing eMBB throughput based on input data through deep learning.”). OOSA would have been motivated to apply the teach of Zhang above to the MCS selection by Nammi to yield a predictable result of maximizing throughput.
Therefore, it would have been obvious to OOSA before the effective filing date of the application to combine Nammi and Zhang for the benefit of maximizing throughput. (p15, last para of Zhang).
For claim 9, Nammi discloses a method, comprising:
receiving, by network equipment comprising at least one processor from a user equipment of a group of user equipment, channel quality indicator data representative of a channel quality corresponding to a first defined sub-band of a group of sub-bands that is to be used in scheduling a subsequent transmission of data (FIGs. 1-14 and the associated text, such as FIG. 2 shows BS102 communicates with a group of UEs 104, and FIG. 10 shows BS 102 receives a CSI 108 in view of “[0039] … the mobile device 104 can compute the channel estimates … channel state information (CSI) reporting, as indicated at 108. The CSI report can comprise, for example, the CQI, a precoding matrix index (PMI), rank information (RI), the best subband indices, best beam indices, and so on, or any number of other types of information. … [0040] … he network device 102 can send the scheduling parameters to the mobile device 104 in a downlink control channel … After the scheduling parameter information is transmitted, the actual data transfer can take place from the network device 102 to the mobile device 104 over the data traffic channel 116.”);
in response to determining that the channel quality corresponding to the first defined sub-band satisfies a change criterion defined based on a defined transmission time interval value associated with the channel quality indicator data, using, by the network equipment, an output from a neural network process to select a modulation and coding scheme value from a pool of modulation and coding scheme values (FIGs. 1-14 and the associated text, such as “[0083] … For the mobile device capability, with reference again to FIGS. 3 and 4, thresholds for choosing 16 QAM and 64 QAM are different. For example for the two transmit antenna case (FIG. 3), at a geometry of 10 dB the majority of modulation can be dominated by CQI indices corresponding to QPSK. For the four transmit antenna case (FIG. 4), the modulation can be dominated by 16-QAM. Therefore, based on the mobile device capability of receiving either two transmit antennas or four transmit antennas, the specific bits in the CQI subset restriction can be set.” and “[0043] … That specific reference signal is then only transmitted within the resource blocks assigned for data traffic channel transmission to that terminal (e.g., mobile device).”; note that “16 QAM” and “64 QAM” are MCS values and “resource blocks” specifies time interval values); and
transmitting, by the network equipment, to the group of user equipment, the modulation and coding scheme value to be used by the group of user equipment for the subsequent transmission of data (FIGs. 1-14 and the associated text, such as [0040] … After the scheduling parameter information is transmitted, the actual data transfer can take place from the network device 102 to the mobile device 104 over the data traffic channel 116.”).
Nammi teaches neural networks being employed to process network parameters (p15, last para “[0145] … Various classification schemes and/or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, and data fusion engines) can be employed in connection with performing automatic and/or inferred action in connection with the disclosed subject matter.”), but does not specifically disclose using an output from a neural networks to select a modulation and coding scheme (MCS) value. In the same field of endeavor of wireless communication, Zhang disclose using an output from a neural networks to select a MCS value (p15, last para “In order to assist eMBB users in transmission to select the optimal MCS scheme, the present application uses the feedforward neural network to output a modulation coding scheme capable of maximizing eMBB throughput based on input data through deep learning.”). OOSA would have been motivated to apply the teach of Zhang above to the MCS selection by Nammi to yield a predictable result of maximizing throughput.
Therefore, it would have been obvious to OOSA before the effective filing date of the application to combine Nammi and Zhang for the benefit of maximizing throughput. (p15, last para of Zhang). Independent claim 17 is rejected because it is a non-transitory machine-readable medium storing code for performing the method of 9 and has the same subject matter of claim 9.
As to claims 2, 10 and 18, Nammi in view of Zhang discloses claims 1, 9 and 17, Zhang further discloses: wherein the neural network process determines the modulation and coding scheme value from the pool of modulation and coding scheme values by maximizing an instantaneous data throughput rate of the user equipment and an energy value associated with using the modulation and coding scheme value, by the base station equipment, for the defined transmission time interval value of a group of defined transmission time intervals (p15, last para “In order to assist eMBB users in transmission to select the optimal MCS scheme, the present application uses the feedforward neural network to output a modulation coding scheme capable of maximizing eMBB throughput based on input data through deep learning.”).
The motivation of combining Nammi and Zhang is the same as stated in the parent claims.
As to claims 3, 11 and 19, Nammi in view of Zhang discloses claims 2, 10 and 18, Nammi further discloses: wherein the modulation and coding scheme value is selected based on a table of modulation and coding scheme values representative of the pool of modulation and coding scheme values ([0068], Table 1).
As to claims 4, 12 and 20, Nammi in view of Zhang discloses claims 1, 9 and 17, Zhang further discloses: wherein the modulation and coding scheme value is a first modulation and coding scheme value, and wherein the neural network process, based on an implementation of an adaptive greedy ε process, compares a first probability value associated with the first modulation and coding scheme value with a second probability value associated with a second modulation and coding scheme value to reduce switching between the first modulation and coding scheme value and the second coding scheme value, wherein the first probability value represents a first probability value that the first modulation and coding scheme value will reduce energy consumption by the base station equipment in a subsequent transmission time interval and the second probability value represents a second probability value that the second modulation and coding scheme will increase energy consumption by the base station equipment in the subsequent transmission time interval (p17, 2nd para from the bottom “… the application estimates the information amount and the probability by using the sample observation value through the actual sampling, and continuously updates the value of sigma through the greedy algorithm.”; p19, 2nd para from the bottom “A given MCS selection scheme for deep learning is based on output results wherein the prediction error rate is used. …, the MCS selection result is more reliable”).
The motivation of combining Nammi and Zhang is the same as stated in the parent claims.
As to claims 5 and 13, Nammi in view of Zhang discloses claims 1 and 9, Zhang further discloses: wherein the neural network process determines the modulation and coding scheme value based on a determined data throughput improvement measure value representative of a data type being sent to the user equipment (p15, last para “In order to assist eMBB users in transmission to select the optimal MCS scheme, the present application uses the feedforward neural network to output a modulation coding scheme capable of maximizing eMBB throughput based on input data through deep learning.”).
The motivation of combining Nammi and Zhang is the same as stated in the parent claims.
As to claims 6 and 14, Nammi in view of Zhang discloses claims 5 and 13, Zhang further discloses: wherein the data throughput improvement measure value is determined based on a difference between a future modulation and coding scheme value to be used by the base station to communicate with the user equipment in a future transmission time interval and a current modulation and coding scheme that is being used by the user equipment in a current transmission time interval (p15, last para “In order to assist eMBB users in transmission to select the optimal MCS scheme, the present application uses the feedforward neural network to output a modulation coding scheme capable of maximizing eMBB throughput based on input data through deep learning.”).
The motivation of combining Nammi and Zhang is the same as stated in the parent claims.
As to claims 7 and 15, Nammi in view of Zhang discloses claims 6 and 14, Zhang further discloses: wherein the difference between the future modulation and coding scheme value to be used by the base station to communicate with the user equipment in a future transmission time interval and the current modulation and coding scheme that is being used by the user equipment in the current transmission time interval is determined as a first function of a defined time duration and as a second function of a value associated with a total data demand requested by the user equipment to produce the data throughput improvement measure value (p15, last para “In order to assist eMBB users in transmission to select the optimal MCS scheme, the present application uses the feedforward neural network to output a modulation coding scheme capable of maximizing eMBB throughput based on input data through deep learning.”).
The motivation of combining Nammi and Zhang is the same as stated in the parent claims.
As to claims 8 and 16, Nammi in view of Zhang discloses claims 7 and 15, Zhang further discloses: wherein the data throughput improvement measure value is compared to a specified requested increase in data throughput threshold value, and wherein, in response to the data throughput improvement measure value being determined to be less than the specified requested increase t in the data throughput threshold value, the modulation and coding scheme value is selected (p15, last para “In order to assist eMBB users in transmission to select the optimal MCS scheme, the present application uses the feedforward neural network to output a modulation coding scheme capable of maximizing eMBB throughput based on input data through deep learning.”).
The motivation of combining Nammi and Zhang is the same as stated in the parent claims
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANYE WU whose telephone number is (571)270-1665. The examiner can normally be reached M-TH 8am-6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Yemane Mesfin can be reached at (571) 272-3927. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JIANYE WU/Primary Examiner, Art Unit 2462
