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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7 recites the limitation "the communication based prediction model" in lines 1-2. There is insufficient antecedent basis for this limitation in the claim. It appears that the limitation "the communication based prediction model" should be amended to "the communication device based prediction model".
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.
Claims 1, 6, 9, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (CN 104822162 A)(hereinafter “Wei”)(cited in the IDS dated 04/27/2023) in view of Eleftheriadis et al. (US 2019/0182766 A1)(hereinafter “Eleftheriadis”).
Regarding claim 1, Wei discloses a method performed by a network node for a telecommunications network (Fig. 4, [0119]: Fig. 4 is a detailed distribution process of a green base station in a hybrid energy network.), the method comprising:
comparing, for each of a plurality of time periods, (1) a predicted energy consumption of each network node in a set of network nodes for an uplink or downlink communication with a communication device ([0114]: in order to ensure the accuracy of the determined number of the first UEs, when determining the number of the first UEs, the energy consumption of the green base station may also be considered. The energy consumption of the green base station mainly includes: the self consumption of the green base station when there is no user in one time slot, and the energy consumption of the green base station serving the accessed UE in the slot.), wherein the predicted energy consumption is from a communication device based prediction model (The energy consumption of the green base station is determined in part based on the energy consumption used to serve the accessing UEs, and it thus a communication device based prediction model.), with (2) a predicted amount of energy available from each network node in the set of network nodes, wherein the predicted amount of energy available is from a network node based prediction model (Fig. 4, [0149]: S401 – For each green base station, a green energy forecasting model is used for each time slot in the off-loaded time period to predict the energy collected by the green base station. Fig. 5, [0156]-[0158]: the first determining module 51 is configured to use the green energy prediction model for each base station in each time slot in the split time period to predict the green energy collected by the green base station in the time slot. The second determining module 53 is configured to determine the first flow split of the green base station in the time slot according the green energy collected by the green base station in the time slot, the current remaining energy, and the number of UEs currently accessing the green base station.);
determining, for each of the plurality of time periods, whether the predicted amount of energy available from each network node in the set of network nodes is sufficient to provide at least a portion of the predicted energy consumption ([0065]: according to the green energy model, the green energy collected by the green base station in the time slot is determined, and according to the current remaining energy of the green base station, the load quantity that the green base station can still carry is determined. While reducing energy consumption, it can effectively guarantee the quality of service provided to users.); and
identifying one or more time periods from the plurality of time periods, a length of each of the one or more time periods, and a corresponding network node from the set of network nodes for each of the one or more time periods… when the predicted amount of energy available is sufficient to provide at least a portion of the predicted energy consumption ([0040]: for each green base station, a green energy prediction model is used to predict the green base station in each time slot in the offloading time period. The green energy collected in this time slot and the current remaining energy of the green base station are acquired, so as to determine the number of first UEs to be split by the green base station in the time slot. According to the number of first UEs, the traditional energy in the hybrid energy network is determined. The base station performs offloading. Since in the embodiment of the present invention, the green energy collected by the green base station in the time slot is determined according to the green energy model, and according to the current residual energy of the green base station, the load amount that the green base station can also carry is determined. While reducing energy consumption, it can effectively guarantee the quality of services provided to users. [0164]-[0165]: the time slot determining module 56 is configured to determine the duration of each time slot according to the type of user service and the duration of each service. The first determining module 51 is specifically configured to determine whether the time duration of the time slot is greater than a set duration threshold; if yes, a medium-term energy prediction model is used to predict the green energy collected by the green base station in the time slot; otherwise the short-term energy prediction model is used to predict the green energy collected by the green base station during this time slot.).
Wei does not explicitly disclose that the identified one or more time periods are for when the predicted amount of energy available is sufficient to provide at least a portion of the predicted energy consumption. However, Eleftheriadis discloses identifying time periods when the predicted amount of energy available is sufficient to provide at least a portion of the predicted energy consumption ([0059]: further, since scheduling is known some time in advance, by determining electricity consumption from scheduler data it will be possible to control the available energy sources continuously (for example to switch on or off PSUs to save on idling power consumption) to match the load without unnecessary excess capacity and thus improve energy efficiency.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify one or more time periods, a length of each of the one or more time periods, and a corresponding network node from the set of network nodes for each of the one or more time periods, as taught by Wei, when the predicted amount of energy available is sufficient to provide at least a portion of the predicted energy consumption, as taught by Eleftheriadis. Doing so provides for improved energy efficiency in the communication system (See Eleftheriadis [0059]).
Regarding claim 6, Wei in view of Eleftheriadis discloses all features of claim 1 as outlined above.
Wei also discloses wherein the predicted energy consumption is an output from the communication device based prediction model ([0114]: in order to ensure the accuracy of the determined number of the first UEs, when determining the number of the first UEs, the energy consumption of the green base station may also be considered. The energy consumption of the green base station mainly includes: the self consumption of the green base station when there is no user in one time slot, and the energy consumption of the green base station serving the accessed UE in the slot. The energy consumption of the green base station is determined in part based on the energy consumption used to serve the accessing UEs, and it thus a communication device based prediction model.) and wherein the predicted amount of energy available is an output from the network node based prediction model (Fig. 4, [0149]: S401 – For each green base station, a green energy forecasting model is used for each time slot in the off-loaded time period to predict the energy collected by the green base station. Fig. 5, [0156]-[0158]: the first determining module 51 is configured to use the green energy prediction model for each base station in each time slot in the split time period to predict the green energy collected by the green base station in the time slot.).
Regarding claim 9, Wei discloses a network node for a telecommunications network (Fig. 4, [0119]: Fig. 4 is a detailed distribution process of a green base station in a hybrid energy network.), the network node comprising:
processing circuitry (Fig. 5: first determination module 51, acquisition module 52, second determination module 53, offloading module 54, third determination module 55); and
memory coupled with the processing circuitry, wherein the memory includes instructions that when executed by the processing circuitry causes the network node to perform operations ([0176]:persons of ordinary skill in the art can understand that all or part of the steps in the foregoing method can be accomplished through a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. Storage media, such as: ROM/RAM, disk, CD, etc.), the operations comprising:
compare, for each of a plurality of time periods, (1) a predicted energy consumption of each network node in a set of network nodes for an uplink or downlink communication with a communication device ([0114]: in order to ensure the accuracy of the determined number of the first UEs, when determining the number of the first UEs, the energy consumption of the green base station may also be considered. The energy consumption of the green base station mainly includes: the self consumption of the green base station when there is no user in one time slot, and the energy consumption of the green base station serving the accessed UE in the slot.), wherein the predicted energy consumption is from a communication device based prediction model (The energy consumption of the green base station is determined in part based on the energy consumption used to serve the accessing UEs, and it thus a communication device based prediction model.), with (2) a predicted amount of energy available from each network node in the set of network nodes, wherein the predicted amount of energy available is from a network node based prediction model (Fig. 4, [0149]: S401 – For each green base station, a green energy forecasting model is used for each time slot in the off-loaded time period to predict the energy collected by the green base station. Fig. 5, [0156]-[0158]: the first determining module 51 is configured to use the green energy prediction model for each base station in each time slot in the split time period to predict the green energy collected by the green base station in the time slot. The second determining module 53 is configured to determine the first flow split of the green base station in the time slot according the green energy collected by the green base station in the time slot, the current remaining energy, and the number of UEs currently accessing the green base station.);
determine, for each of the plurality of time periods, whether the predicted amount of energy available from each network node in the set of network nodes is sufficient to provide at least a portion of the predicted energy consumption ([0065]: according to the green energy model, the green energy collected by the green base station in the time slot is determined, and according to the current remaining energy of the green base station, the load quantity that the green base station can still carry is determined. While reducing energy consumption, it can effectively guarantee the quality of service provided to users.); and
identify one or more time periods from the plurality of time periods, a length of each of the one or more time periods, and a corresponding network node from the set of network nodes for each of the one or more time periods… when the predicted amount of energy available is sufficient to provide at least a portion of the predicted energy consumption ([0040]: for each green base station, a green energy prediction model is used to predict the green base station in each time slot in the offloading time period,. The green energy collected in this time slot and the current remaining energy of the green base station are acquired, so as to determine the number of first UEs to be split by the green base station in the time slot. According to the number of first UEs, the traditional energy in the hybrid energy network is determined. The base station performs offloading. Since in the embodiment of the present invention, the green energy collected by the green base station in the time slot is determined according to the green energy model, and according to the current residual energy of the green base station, the load amount that the green base station can also carry is determined. While reducing energy consumption, it can effectively guarantee the quality of services provided to users. [0164]-[0165]: the time slot determining module 56 is configured to determine the duration of each time slot according to the type of user service and the duration of each service. The first determining module 51 is specifically configured to determine whether the time duration of the time slot is greater than a set duration threshold; if yes, a medium-term energy prediction model is used to predict the green energy collected by the green base station in the time slot; otherwise the short-term energy prediction model is used to predict the green energy collected by the green base station during this time slot.).
Wei does not explicitly disclose that the identified one or more time periods are for when the predicted amount of energy available is sufficient to provide at least a portion of the predicted energy consumption. However, Eleftheriadis discloses identifying time periods when the predicted amount of energy available is sufficient to provide at least a portion of the predicted energy consumption ([0059]: further, since scheduling is known some time in advance, by determining electricity consumption from scheduler data it will be possible to control the available energy sources continuously (for example to switch on or off PSUs to save on idling power consumption) to match the load without unnecessary excess capacity and thus improve energy efficiency.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify one or more time periods, a length of each of the one or more time periods, and a corresponding network node from the set of network nodes for each of the one or more time periods, as taught by Wei, when the predicted amount of energy available is sufficient to provide at least a portion of the predicted energy consumption, as taught by Eleftheriadis. Doing so provides for improved energy efficiency in the communication system (See Eleftheriadis [0059]).
Regarding claim 30, Wei in view of Eleftheriadis discloses all features of claim 9 as outlined above.
Wei also discloses wherein the predicted energy consumption is an output from the communication device based prediction model ([0114]: in order to ensure the accuracy of the determined number of the first UEs, when determining the number of the first UEs, the energy consumption of the green base station may also be considered. The energy consumption of the green base station mainly includes: the self consumption of the green base station when there is no user in one time slot, and the energy consumption of the green base station serving the accessed UE in the slot. The energy consumption of the green base station is determined in part based on the energy consumption used to serve the accessing UEs, and it thus a communication device based prediction model.) and wherein the predicted amount of energy available is an output from the network node based prediction model (Fig. 4, [0149]: S401 – For each green base station, a green energy forecasting model is used for each time slot in the off-loaded time period to predict the energy collected by the green base station. Fig. 5, [0156]-[0158]: the first determining module 51 is configured to use the green energy prediction model for each base station in each time slot in the split time period to predict the green energy collected by the green base station in the time slot.).
Claims 17 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Ang et al. (US 2015/0334653 A1)(hereinafter “Ang”) in view of Wei.
Regarding claim 17, Ang discloses a method performed by a communication device in a telecommunication network (Fig. 5, [0117]: referring to FIG. 5, a flow diagram illustrating an exemplary method for managing a power consumption of a mobile device, in accordance with various aspects of the present disclosure is shown as a method 500. In an aspect, the base station may be the base station 410 of FIG. 4 and the mobile device may be the mobile device 440 of FIG. 4.), the method comprising:
transmitting to a network node a predicted energy consumption of each network node in a set of network nodes for an uplink or downlink communication with the communication device… ([0095]: the mobile device 440 may provide information identifying the energy metric to the base station 410, and the base station 410 may access the energy metrics information 420 to determine a configuration of a transmission between the base station 410 and the mobile device based on the information identifying the energy metric. For example, the mobile device 440 may determine a transmission configuration that provides a desired energy metric or power consumption for processing a transmission, and may transmit the desired energy metric to the base station 410.); and
responsive to the transmitting, receiving a response from the network node comprising at least one of (1) a plurality of appointments between the communication device and another network node from the set of network nodes, wherein the plurality of appointments comprise an appointment for the uplink or downlink communication for the communication device with another network node from the set of network nodes (an alternative limitation not given mapping in the claims), and (2) a message to continue communications with the network node ([0119]: at 520, the method 500 includes configuring a transmission between the base station and the mobile device based at least in part on the energy metric. Configuring the transmission may include determining a first energy consumption for the transmission between the base station and the mobile device based at least in part on the energy metric, and determining a second energy consumption for a transmission between the base station and the mobile device independent of the energy metric [0120]: in an aspect, in response to configuring the transmission based on the energy metric (or independently of the energy metric), the method 500 may include, at 529, scheduling the transmission to the mobile device.).
Ang does not disclose wherein the predicted energy consumption is from a communication device based prediction model. However, Wei discloses wherein the predicted energy consumption is from a communication device based prediction model ([0114]: in order to ensure the accuracy of the determined number of the first UEs, when determining the number of the first UEs, the energy consumption of the green base station may also be considered. The energy consumption of the green base station mainly includes: the self consumption of the green base station when there is no user in one time slot, and the energy consumption of the green base station serving the accessed UE in the slot. The energy consumption of the green base station is determined in part based on the energy consumption used to serve the accessing UEs, and it thus a communication device based prediction 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 transmit to a network node a predicted energy consumption of each network node in a set of network nodes for an uplink or downlink communication with the communication device, as taught by Ang, wherein the predicted energy consumption is from a communication device based prediction model as taught by Wei. Doing so provides for a more accurate determination of the predicted energy consumption of the network node (See Wei [0114].).
Regarding claim 22, Ang discloses a communication device in a telecommunications network (FIG. 4: mobile device 440), the communication device comprising:
processing circuitry; and memory coupled with the processing circuitry, wherein the memory includes instructions that when executed by the processing circuitry causes the communication device to perform operations ([0012]: the mobile device may include a processor and a memory that is accessible to the processor. The memory may store instructions that, when executed by the processor, cause the processor to perform various operations.), the operations comprising:
transmit to a network node a predicted energy consumption of each network node in a set of network nodes for an uplink or downlink communication with the communication device, …([0095]: the mobile device 440 may provide information identifying the energy metric to the base station 410, and the base station 410 may access the energy metrics information 420 to determine a configuration of a transmission between the base station 410 and the mobile device based on the information identifying the energy metric. For example, the mobile device 440 may determine a transmission configuration that provides a desired energy metric or power consumption for processing a transmission, and may transmit the desired energy metric to the base station 410.); and
receive a response from the network node comprising at least one of (1) a plurality of appointments between the communication device and another network node from the set of network nodes, wherein the plurality of appointments comprise an appointment for the uplink or downlink communication for the communication device with another network node from the set of network nodes (an alternative limitation not given mapping in the claims), and (2) a message to continue communications with the network node ([0119]: at 520, the method 500 includes configuring a transmission between the base station and the mobile device based at least in part on the energy metric. Configuring the transmission may include determining a first energy consumption for the transmission between the base station and the mobile device based at least in part on the energy metric, and determining a second energy consumption for a transmission between the base station and the mobile device independent of the energy metric [0120]: in an aspect, in response to configuring the transmission based on the energy metric (or independently of the energy metric), the method 500 may include, at 529, scheduling the transmission to the mobile device.).
Ang does not disclose wherein the predicted energy consumption is from a communication device based prediction model. However, Wei discloses wherein the predicted energy consumption is from a communication device based prediction model ([0114]: in order to ensure the accuracy of the determined number of the first UEs, when determining the number of the first UEs, the energy consumption of the green base station may also be considered. The energy consumption of the green base station mainly includes: the self consumption of the green base station when there is no user in one time slot, and the energy consumption of the green base station serving the accessed UE in the slot. The energy consumption of the green base station is determined in part based on the energy consumption used to serve the accessing UEs, and it thus a communication device based prediction 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 transmit to a network node a predicted energy consumption of each network node in a set of network nodes for an uplink or downlink communication with the communication device, as taught by Ang, wherein the predicted energy consumption is from a communication device based prediction model as taught by Wei. Doing so provides for a more accurate determination of the predicted energy consumption of the network node (See Wei [0114].).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wei in view of Eleftheriadis and further in view of Rangarajan et al. (US 2016/0150072 A1)(hereinafter “Rangarajan”).
Regarding claim 7, Wei in view of Eleftheriadis discloses all features of claim 1 as outlined above.
Wei in view of Eleftheriadis does not disclose wherein the communication device based prediction model comprises at least one of a machine learning model or a federated machine learning model and wherein the network node based prediction model comprises at least one of a machine learning model or a federated machine learning model. However, Rangarajan discloses wherein the communication [device] based prediction model comprises at least one of a machine learning model or a federated machine learning model and wherein the network node based prediction model comprises at least one of a machine learning model or a federated machine learning model (Abstract: techniques for predictive power management of a mobile device that includes [0105]: in some embodiments, the power analytics modeling component 114-2 may be configured to generate analytics context information 212-3 utilizing well-known machine learning techniques.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the models used to predict energy consumption and the network node based prediction model, as taught by Wei, using machine learning models, as taught by Rangarajan. Doing so provides for a more accurate determination of the predicted energy consumption and the of the network node (See Rangarajan [0129].).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Ang in view of Wei and further in view of Rangarajan.
Regarding claim 21, Ang in view of Wei discloses all features of claim 17 as outlined above.
Ang in view of Wei does not disclose wherein the communication device based prediction model comprises at least one of a machine learning model or a federated machine learning model and wherein the network node based prediction model comprises at least one of a machine learning model or a federated machine learning model. However, Rangarajan discloses wherein the communication device based prediction model comprises at least one of a machine learning model or a federated machine learning model (Abstract: techniques for predictive power management of a mobile device that includes [0105]: in some embodiments, the power analytics modeling component 114-2 may be configured to generate analytics context information 212-3 utilizing well-known machine learning techniques.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the model used to predict energy consumption of each network node in a set of network nodes for an uplink or downlink communication with the communication device, as taught by the Ang-Wei combination, using a machine learning model, as taught by Rangarajan. Doing so provides for a more accurate determination of the predicted energy consumption of the network node (See Rangarajan [0129].).
Allowable Subject Matter
Claims 2-5, 8, 10, 18-20, 23, 29, and 31 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hallberg et al. (US 2012/0289224 A1) – Method and A Radio Base Station For Handling Of Data Traffic – discloses that a radio base station may maintain a model for the amount of power being consumed for different configurations, and for different data traffic cases.
Lindoff et al. (US 2019/0230594 A1) A Wireless Device and a Method Therein For Performing One Or More Operations Based On Available Energy – discloses wireless device that determines an amount of energy that is available for performing one or more operations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W MADDOX whose telephone number is (571)272-5834. The examiner can normally be reached M-Th 7:30am-5:00pm, 1st F 7:30am-4:00pm, 2nd F off.
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/MICHAEL WAYNE MADDOX/Examiner, Art Unit 2463
/CHI TANG P CHENG/Primary Examiner, Art Unit 2463
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