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 .
Information Disclosure Statements
The information disclosure statements submitted on November 29, 2023, September 17, 2024, and August 27, 2025 have been considered by the Examiner and made of record in the application file.
Drawings
The drawings are objected to because the lines, numbers, and letters are not durable, clean, black, sufficiently dense and dark, and uniformly thick and well-defined in Figures 4 and 7-12. Figures 4 and 7-12 are all in grayscale which cause the lines, numbers, and letters to not be durable, clean, black, sufficiently dense and dark, and uniformly thick and well-defined. Additionally, drawings must be black and white (monochrome) except when another form (grayscale or color) is the only practicable medium for illustrating the claimed invention. For Figures 1 and 2, black and white drawings are sufficient to illustrate the claimed invention. Black and white drawings should be created and filed in monochrome, black only, no gray.
Specification
The disclosure is objected to because of the following informalities:
Paragraph 0043 of the specification refers to the lines indicating approximate extents of the coverage areas 120 and 125 in Figure 1 as “dotted”. In the drawings, the referenced lines appear solid.
Paragraph 0069 of the specification uses the phrasing “Given predictions of the traffic and the network capacity, the can UE estimate the QoS”. The phrase should read “Given predictions of the traffic and the network capacity, the UE can estimate the QoS”.
Appropriate correction is required.
Claim Rejections – 35 USC 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 9, and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wu (US 20230143942 A1).
Consider claim 1, Wu discloses a method comprising:
determining a preferred configuration of a user equipment (UE) based on side information about the UE (“the UE 102 can transmit the first preferred configuration if the UE 102 experiences an overheating situation (e.g., due to heavy application processing)” (see paragraph 0088). Information regarding heavy application processing is equivalent to side information about the UE, read in accordance with paragraph 0074 of Applicant’s specification);
sending the preferred configuration of the UE to a base station via a UE assistance information (UAI) framework (“the UE 102 transmits 532A a UE assistance information
message including a first preferred configuration to the MN 104A” (see paragraph 0178));
receiving a new configuration of the UE from the base station after the base station determines the new configuration based on the preferred configuration (“In response to receiving the UE assistance information message, the SN 106A then generates 536A a second DU configuration in response to receiving the first preferred configuration…after generating the second DU configuration, the SN 106A sends 538A an RRC reconfiguration message including the second DU configuration to the MN 104A, which in turn transmits 540A the RRC reconfiguration message to the UE 102” (see Figure 5A, paragraphs 0180 and 0181)); and
configuring the UE according to the new configuration. (“In response, the UE 102 can transmit 542A an RRC reconfiguration complete message to the MN 104A” (see paragraph 0181)).
Consider claim 9, Wu a user equipment (UE) comprising:
a processor configured to determine a preferred configuration of the UE based on side information about the UE (“UE 102 is equipped with processing hardware 150 that can include one or more general-purpose processors such as CPUs and non-transitory computer-readable memory storing machine-readable instructions” (see paragraph 0057), and “the UE 102 can transmit the first preferred configuration if the UE 102 experiences an overheating situation (e.g., due to heavy application processing)” (see paragraph 0088). Information regarding heavy application processing is equivalent to side information about the UE, read in accordance with paragraph 0074 of Applicant’s specification); and
a transceiver operably connected to the processor; the transceiver configured to:
send the preferred configuration of the UE to a base station via a UE assistance
information (UAI) framework (referring to Figure 1A, the “UE 102 includes processing hardware 150 in an example implementation includes a UE RRC controller 152 configured to manage RRC configurations, such as UE preferred configurations” (see paragraph 0057). It furthermore “transmits 532A a UE assistance information message including a first preferred configuration to the MN 104A” (see paragraph 0178) implying the use of a transceiver in the UE), and
receive a new configuration of the UE from the base station after the base station determines the new configuration based on the preferred configuration (“In response to receiving the UE assistance information message, the SN 106A then generates 536A a second DU configuration in response to receiving the first preferred configuration…after generating the second DU configuration, the SN 106A sends 538A an RRC reconfiguration message including the second DU configuration to the MN 104A, which in turn transmits 540A the RRC reconfiguration message to the UE 102” (see paragraphs 0180 and 0181) implying the use of a transceiver in the UE),
wherein the processor is further configured to configure the UE according to the new configuration (“In response, the UE 102 can transmit 542A an RRC reconfiguration complete message to the MN 104A” (see paragraph 0181)).
Consider claim 17, Wu discloses a non-transitory computer readable medium for managing preferred UE configurations comprising program code (“UE 102 is equipped with processing hardware 150 that can include one or more general-purpose processors such as CPUs and non-transitory computer-readable memory storing machine-readable instructions” (see paragraph 0057),
that, when executed by a processor of a device, causes the device to:
determine a preferred configuration of a user equipment (UE) based on side information about the UE (“the UE 102 can transmit the first preferred configuration if the UE 102 experiences an overheating situation (e.g., due to heavy application processing)” (see paragraph 0088). Information regarding heavy application processing is equivalent to side information about the UE, read in accordance with paragraph 0074 of Applicant’s specification);
send the preferred configuration of the UE to a base station via a UE assistance information (UAI) framework (“the UE 102 transmits 532A a UE assistance information message including a first preferred configuration to the MN 104A” (see paragraph 0178));
receive a new configuration of the UE from the base station after the base station determines the new configuration based on the preferred configuration (“In response to receiving the UE assistance information message, the SN 106A then generates 536A a second DU configuration in response to receiving the first preferred configuration…after generating the second DU configuration, the SN 106A sends 538A an RRC reconfiguration message including the second DU configuration to the MN 104A, which in turn transmits 540A the RRC reconfiguration message to the UE 102” (see paragraphs 0180 and 0181));
and configure the UE according to the new configuration (“In response, the UE 102 can transmit 542A an RRC reconfiguration complete message to the MN 104A” (see paragraph 0181)).
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.
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 2, 8, 10, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wu (US 20230143942 A1) in view of Ryden et al. (WO 2022229233 A1).
Consider claim 2, and as applied to claim 1 above, Wu fails to disclose wherein the preferred configuration of the UE is selected from multiple candidate configurations in order to minimize power consumption of the UE while maintaining a satisfactory quality of service (QoS).
In the same field of endeavor, Ryden et al. disclose wherein the preferred configuration of the UE is selected from multiple candidate configurations in order to minimize power consumption of the UE while maintaining a satisfactory quality of service (QoS) (“As such, embodiments facilitate improved network discovery of configurations that can reduce UE energy consumption for a particular QoS requirement” (see Page 6, lines 26-28)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Wu et al. to incorporate the configuration selection approach disclosed in Ryden et al. in order to accurately select the optimal configuration with respect to energy efficiency and QoS.
Consider claim 8, and as applied to claim 1 above, Wu fails to disclose wherein the preferred configuration of the UE comprises at least one of: a preferred connected mode discontinuous reception (CDRX) configuration; a preferred maximum aggregated bandwidth; a preferred maximum number of component carriers; a preferred maximum number of multiple-input multiple-output (MIMO) layers; and a preferred scheduling offset for cross-slot scheduling.
In the same field of endeavor, Ryden et al. disclose wherein the preferred configuration of the UE comprises at least one of: a preferred connected mode discontinuous reception (CDRX) configuration; a preferred maximum aggregated bandwidth; a preferred maximum number of component carriers; a preferred maximum number of multiple-input multiple-output (MIMO) layers; and a preferred scheduling offset for cross-slot scheduling (“Network configurations for reducing UE energy consumption can include any of the following:… Provide guarantees for maximum required receiver performance to handle scheduled data formats, such as indication of maximum number of MIMO layers that will be scheduled” (see Page 15, lines 15-30) .
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Wu to incorporate the technique disclosed in Ryden et al. in order to include pertinent information when sending the preferred configuration to the BS.
Consider claim 10, and as applied to claim 9 above, Wu fails to disclose wherein the processor is configured to select the preferred configuration of the UE from multiple candidate configurations in order to minimize power consumption of the UE while maintaining a quality of service (QoS).
In the same field of endeavor, Ryden et al. discloses wherein the processor is configured to select the preferred configuration of the UE from multiple candidate configurations in order to minimize power consumption of the UE while maintaining a quality of service (QoS) (“As such, embodiments facilitate improved network discovery of configurations that can reduce UE energy consumption for a particular QoS requirement” (see Page 6, lines 26-28)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the device disclosed by Wu et al. to incorporate the configuration selection approach disclosed in Ryden et al. in order to accurately select the optimal configuration with respect to energy efficiency and QoS.
Consider claim 16, and as applied to claim 9 above, Wu fails to disclose wherein the preferred configuration of the UE comprises at least one of: a preferred connected mode discontinuous reception (CDRX) configuration; a preferred maximum aggregated bandwidth; a preferred maximum number of component carriers; a preferred maximum number of multiple-
input multiple-output (MIMO) layers; and a preferred scheduling offset for cross-slot scheduling.
In the same field of endeavor, Ryden et al. disclose wherein the preferred configuration of the UE comprises at least one of: a preferred connected mode discontinuous reception (CDRX) configuration; a preferred maximum aggregated bandwidth; a preferred maximum number of component carriers; a preferred maximum number of multiple-input multiple-output (MIMO) layers; and a preferred scheduling offset for cross-slot scheduling (“Network configurations for reducing UE energy consumption can include any of the following:… Provide guarantees for maximum required receiver performance to handle scheduled data formats, such as indication of maximum number of MIMO layers that will be scheduled” (see Page 15, lines 15-30) .
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the device disclosed by Wu to incorporate the technique disclosed in Ryden et al. in order to include pertinent information when sending the preferred configuration to the BS.
Consider claim 18, and as applied to claim 17 above, Wu fails to disclose a non-transitory computer readable medium wherein the preferred configuration of the UE is selected from multiple candidate configurations in order to minimize power consumption of the UE while maintaining a quality of service (QoS).
In the same field of endeavor, Ryden et al. discloses wherein the non-transitory computer readable medium is configured to select the preferred configuration of the UE from multiple candidate configurations in order to minimize power consumption of the UE while maintaining a quality of service (QoS) (“As such, embodiments facilitate improved network discovery of configurations that can reduce UE energy consumption for a particular QoS requirement”
(see Page 6, lines 26-28)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the non-transitory computer readable medium disclosed by Wu et al. to incorporate the configuration selection approach disclosed in Ryden et al. in order to accurately select the optimal configuration with respect to energy efficiency and QoS.
Claims 3, 5-7, 11, 13-15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wu (US 20230143942 A1) in view of Ryden et al. (WO 2022229233 A1), further in view of Arora et al. (US 20200136975 A1), and further in view of Vankayala et al. (US 20220278728 A1).
Consider claim 3, and as applied to claim 1 above, Wu fails to disclose wherein the side information about the UE comprises at least one of: an identification of applications executing on the UE; a network traffic forecast determined using artificial intelligence; and a predicted channel condition determined using artificial intelligence.
In the same field of endeavor, Ryden et al. disclose an identification of applications executing on the UE (“the determined information or one or more indications thereof sent to the network node can include an indication of a UE-preferred configuration…In some embodiments, the determined information or one or more indications thereof are sent to the network node responsive to one or more of the following…UE activation or deactivation of applications” (see Page 4, lines 16-25)); and
Therefore, it would have been obvious to a person of ordinary skill in the art before the
effective filing date of the claimed invention to modify the method disclosed by Wu by incorporating the technique disclosed by Ryden et al. in order to account for the effect of applications on power usage.
However, Wu, as modified by Ryden et al., fails to disclose a network traffic forecast determined using artificial intelligence.
In the same field of endeavor, Arora et al. disclose a network traffic forecast determined using artificial intelligence (“The system applies two machine learning techniques that are used together. The first technique uses a traffic forecasting model, which has been trained using time series data indicating historical traffic data to predict expected levels of traffic at future times” (see paragraph 0003)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Wu, as modified by Ryden et al., by incorporating the technique disclosed by Arora et al. in order to better inform the generated UE configuration.
However, Wu, as modified by Ryden et al. and Arora et al. fail to disclose a predicted channel condition determined using artificial intelligence.
In the same field of endeavor, Vankayala et al. disclose a predicted channel condition determined using artificial intelligence (“a method and a system for intelligently predicting a channel quality indicator (CQI), a pre-coding matrix index (PMI), and a rank index (RI) in a wireless communication network using machine learning” (see paragraph 0007)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the
effective filing date of the claimed invention to modify the method disclosed by Wu, as modified
by Ryden et al. and Arora et al. by incorporating the technique disclosed by Vankayala et al. in order to better inform the generated UE configuration.
Consider claim 5, and as applied to claim 3 above, Wu, as modified by Ryden et al. and Arora et al., fails to disclose determining the predicted channel condition using a trained encoder-decoder based channel condition prediction network that receives time series channel quality history as an input.
In the same field of endeavor, Vankayala et al. disclose determining the predicted channel condition using a trained encoder-decoder based channel condition prediction network that receives time series channel quality history as an input (Figure 2 depicts a channel condition prediction network wherein “the UE may be controlled by the BS to periodically or aperiodically for measuring or monitoring the CQS information (such as the CQI, the PMI and the RI)” (see paragraph 0066). Subsequently, “one or more CQI reports, one or more PMI reports and one or more RI reports associated with at least one first frequency band are fed as input to the input layer (202) of the neural network (NN)” (see Figure 2, paragraph 0075). “The encoder/decoder (910) is configured to decode downlink information received from the BS (504) and send encoded information, for example the predicted CQI, PMI and RI of at least the second frequency band, to the BS (504)” (see Figures 5 and 9, paragraph 0126)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Wu. as modified by Ryden et al. and Arora et al. by incorporating the technique disclosed by Vankayala et al. in order to better inform the generated UE configuration by anticipating future UE operating
conditions.
Consider claim 6, and as applied to claim 3 above, Wu in combination with Ryden et el., Arora et al., and Vankayala et al. discloses a method comprising determining a quality of service (QoS) metric of the UE based on the network traffic forecast and the predicted channel condition (in Ryden et al., Figure 9 depicts process block 930 wherein “the UE can determine one or more of the following information associated with at least the received configurations and with UE data traffic: UE energy efficiency (EE), and quality-of-service (QoS). The exemplary method can also include the operations of block 940, where the UE can send, to the network node, the determined information or one or more indications thereof.” (see Page 25, line 8-11). Arora et al. and Vankayala et al. disclose a network traffic forecast and predicted channel condition, respectively).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to utilize predicted network traffic and channel condition to forecast the QoS metric in the near future to better inform the BS when determining the new configuration.
Consider claim 7, and as applied to claim 3 above, Wu in combination with Ryden et el., Arora et al., and Vankayala et al. discloses a method comprising estimating a power consumption of the UE based on the network traffic forecast and the predicted channel condition (in Ryden et al., Figure 9 depicts process block 930 wherein “the UE can determine one or more of the following information associated with at least the received configurations and with UE data traffic: UE energy efficiency (EE), and quality-of-service (QoS)” (see Page 25, line 8-9). In some embodiments, said configuration includes “an estimated UE energy consumption based on durations spent by the UE in each of a plurality of operating states during the observation period, and on a model for UE energy consumption in each of the plurality of operating states” (see Page 25, line 27-30). Arora et al. and Vankayala et al. disclose a network traffic forecast and predicted channel condition, respectively).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to utilize predicted network traffic and channel condition to forecast the UE power consumption in the near future to better inform the BS when determining the new configuration.
Consider claim 11, and as applied to claim 9 above, Wu fails to disclose wherein the side information about the UE comprises at least one of: an identification of applications executing on the UE; a network traffic forecast determined using artificial intelligence; and a predicted channel condition determined using artificial intelligence.
In the same field of endeavor, Ryden et al. disclose an identification of applications executing on the UE (“the determined information or one or more indications thereof sent to the network node can include an indication of a UE-preferred configuration…In some embodiments, the determined information or one or more indications thereof are sent to the network node responsive to one or more of the following…UE activation or deactivation of applications” (see Page 4, lines 16-25)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the device disclosed by Wu by incorporating the technique disclosed by Ryden et al. in order to account for the effect of applications on power usage.
However, Wu as modified by Ryden et al. fail to disclose a network traffic forecast
determined using artificial intelligence.
In the same field of endeavor, Arora et al. disclose a network traffic forecast determined using artificial intelligence (“The system applies two machine learning techniques that are used together. The first technique uses a traffic forecasting model, which has been trained using time series data indicating historical traffic data to predict expected levels of traffic at future times” (see paragraph 0003)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the device disclosed by Wu as modified by Ryden et al. by incorporating the technique disclosed by Arora et al. in order to use an AI-predicted network forecast to better inform the generated UE configuration.
However, Wu as modified by Ryden et al. and Arora et al. fail to disclose a predicted channel condition determined using artificial intelligence.
In the same field of endeavor, Vankayala et al. disclose a predicted channel condition determined using artificial intelligence (“a method and a system for intelligently predicting a channel quality indicator (CQI), a pre-coding matrix index (PMI), and a rank index (RI) in a wireless communication network using machine learning” (see paragraph 0007)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the device disclosed by Wu and modified by Ryden et al. and Arora et al. by incorporating the technique disclosed by Vankayala et al. in order to use an AI-predicted channel condition to better inform the generated UE configuration.
Consider claim 13, and as applied to claim 11 above, Wu as modified by Ryden et al. and Arora fails to disclose wherein the processor is further configured to determine the predicted channel condition using a trained encoder-decoder based channel condition prediction network
that receives time series channel quality history as an input.
In the same field of endeavor, Vankayala et al. disclose determining the predicted channel condition using a trained encoder-decoder based channel condition prediction network that receives time series channel quality history as an input (Figure 2 depicts a channel condition prediction network wherein “the UE may be controlled by the BS to periodically or aperiodically for measuring or monitoring the CQS information (such as the CQI, the PMI and the RI)” (see paragraph 0066). Subsequently, “one or more CQI reports, one or more PMI reports and one or more RI reports associated with at least one first frequency band are fed as input to the input layer (202) of the neural network (NN)” (see Figure 2, paragraph 0075). “The encoder/decoder (910) is configured to decode downlink information received from the BS (504) and send encoded information, for example the predicted CQI, PMI and RI of at least the second frequency band, to the BS (504)” (see Figures 5 and 9, paragraph 0126)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the device disclosed by Wu as modified by Ryden et al. and Arora by incorporating the technique disclosed by Vankayala et al. in order to better inform the generated UE configuration by anticipating future UE operating conditions.
Consider claim 14, and as applied to claim 11 above, Ryden et al. in combination with Wu, Arora et al., and Vankayala et al. disclose wherein the processor is further configured to determine a quality of service (QoS) metric of the UE based on the network traffic forecast and the predicted channel condition (in Ryden et al., Figure 9 depicts process block 930 wherein “the UE can determine one or more of the following information associated with at least the received configurations and with UE data traffic: UE energy efficiency (EE), and quality-of-service (QoS). The exemplary method can also include the operations of block 940, where the UE can send, to the network node, the determined information or one or more indications thereof.” (see Page 25, line 8-11). Arora et al. and Vankayala et al. disclose a network traffic forecast and predicted channel condition, respectively).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to utilize predicted network traffic and channel condition to forecast the QoS metric in the near future to better inform the BS when determining the new configuration.
Consider claim 15, and as applied to claim 11 above, Ryden et al. in combination with Wu, Arora et al., and Vankayala et al. disclose wherein the processor is further configured to estimate a power consumption of the UE based on the network traffic forecast and the predicted channel condition (in Ryden et al., Figure 9 depicts process block 930 wherein “the UE can determine one or more of the following information associated with at least the received configurations and with UE data traffic: UE energy efficiency (EE), and quality-of-service (QoS)” (see Page 25, line 8-9). In some embodiments, said configuration includes “an estimated UE energy consumption based on durations spent by the UE in each of a plurality of operating states during the observation period, and on a model for UE energy consumption in each of the plurality of operating states” (see Page 25, line 27-30). Arora et al. and Vankayala et al. disclose a network traffic forecast and predicted channel condition, respectively).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to utilize predicted network traffic and channel condition to forecast the UE power consumption in the near future to better inform the BS when
determining the new configuration.
Consider claim 19, and as applied to claim 17 above, Wu fails to disclose a non-transitory computer readable medium wherein the side information about the UE comprises at least one of: an identification of applications executing on the UE; a network traffic forecast determined using artificial intelligence; and a predicted channel condition determined using artificial intelligence.
In the same field of endeavor, Ryden et al. disclose an identification of applications executing on the UE (“the determined information or one or more indications thereof sent to the network node can include an indication of a UE-preferred configuration…In some embodiments, the determined information or one or more indications thereof are sent to the network node responsive to one or more of the following…UE activation or deactivation of applications” (see Page 4, lines 16-25)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the non-transitory computer readable medium disclosed by Wu by incorporating the technique disclosed by Ryden et al. in order to account for the effect of applications on power usage.
However, Wu as modified by Ryden et al. fail to disclose a network traffic forecast determined using artificial intelligence.
In the same field of endeavor, Arora et al. disclose a network traffic forecast determined using artificial intelligence (“The system applies two machine learning techniques that are used together. The first technique uses a traffic forecasting model, which has been trained using time series data indicating historical traffic data to predict expected levels of traffic
at future times” (see paragraph 0003)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the non-transitory computer readable medium disclosed by Wu and modified by Ryden et al. by incorporating the technique disclosed by Arora et al. in order to use an AI-predicted network forecast to better inform the generated UE configuration.
However, Wu as modified by Ryden et al. and Arora et al. fail to disclose a predicted channel condition determined using artificial intelligence.
In the same field of endeavor, Vankayala et al. disclose a predicted channel condition determined using artificial intelligence (“a method and a system for intelligently predicting a channel quality indicator (CQI), a pre-coding matrix index (PMI), and a rank index (RI) in a wireless communication network using machine learning” (see paragraph 0007)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the non-transitory computer readable medium disclosed by Wu and modified by Ryden et al. and Arora et al. by incorporating the technique disclosed by Vankayala et al. in order to use an AI-predicted channel condition to better inform the generated UE configuration.
Claims 4, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu (US 20230143942 A1) in view of Ryden et al. (WO 2022229233 A1), further in view of Arora et al. (US 20200136975 A1), further in view of Vankayala et al. (US 20220278728 A1) and further in view of Gupta Hyde (US 20230188233 A1).
Consider claim 4, and as applied to claim 3 above, Wu as modified by Ryden et al., Arora et al., and Vankayala et al. partially disclose a method further comprising determining the network traffic forecast using a trained encoder-decoder based traffic forecast network that receives time series traffic history as an input (Arora et al. teaches a technique that “uses a traffic forecasting model, which has been trained using time series data indicating historical traffic data to predict expected levels of traffic at future times” (see paragraph 0003)).
However, Wu as modified by Ryden et al., Arora et al., and Vankayala et al. fails to disclose wherein the trained model is based on an encoder-decoder format.
In the same field of endeavor, Gupta Hyde et al. disclose a device to improve wireless network energy efficiency wherein a machine learning model is based on an encoder-decoder format (“The neural network may be any kind of neural network, such as a convolutional neural network, an auto-encoder network” (see paragraph 0043)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Ryden et al. and modified by Wu, Arora et al., and Vankayala et al. by incorporating the encoder-decoder format disclosed by Gupta Hyde et al. in order to use a neural network format best suited to produce an accurate traffic forecast using the input data.
Consider claim 12, and as applied to claim 11 above, Wu as modified by Ryden et al., Arora et al., and Vankayala et al. partially disclose a device further comprising determining the network traffic forecast using a trained encoder-decoder based traffic forecast network that receives time series traffic history as an input (Arora et al. teaches a technique that “uses a traffic forecasting model, which has been trained using time series data indicating historical traffic data to predict expected levels of traffic at future times” (see paragraph 0003)).
However, Wu as modified by Ryden et al., Arora et al., and Vankayala et al. fails to disclose wherein the trained model is based on an encoder-decoder format.
In the same field of endeavor, Gupta Hyde et al. disclose a device to improve wireless network energy efficiency wherein a machine learning model is based on an encoder-decoder format (“The neural network may be any kind of neural network, such as a convolutional neural network, an auto-encoder network” (see paragraph 0043)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the device disclosed by Wu and modified by Ryden et al., Arora et al., and Vankayala et al. by incorporating the encoder-decoder format disclosed by Gupta Hyde et al. in order to use a neural network format best suited to produce an accurate traffic forecast using the input data.
Consider claim 20, and as applied to claim 19 above, Wu as modified by Ryden et al., Arora et al., and Vankayala et al. partially disclose a non-transitory computer readable medium further comprising determining the network traffic forecast using a trained encoder-decoder based traffic forecast network that receives time series traffic history as an input (Arora et al. teaches a technique that “uses a traffic forecasting model, which has been trained using time series data indicating historical traffic data to predict expected levels of traffic at future times” (see paragraph 0003)).
However, Wu as modified by Ryden et al., Arora et al., and Vankayala et al. fails to disclose wherein the trained model is based on an encoder-decoder format.
In the same field of endeavor, Gupta Hyde et al. disclose a device to improve wireless
network energy efficiency wherein a machine learning model is based on an encoder-decoder format (“The neural network may be any kind of neural network, such as a convolutional neural network, an auto-encoder network” (see paragraph 0043)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the non-transitory computer readable medium disclosed by Wu and modified by Ryden et al., Arora et al., and Vankayala et al. by incorporating the encoder-decoder format disclosed by Gupta Hyde et al. in order to use a neural network format best suited to produce an accurate traffic forecast using the input data.
Conclusion
Any inquiry concerning this communication from the examiner should be directed to ALEXANDER WU whose telephone number is (571)272-3360. The examiner can normally be reached Monday - Friday, 8:30 am - 5:00 pm.
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 Application/Control Number: 18/508,364 Page 11 Art Unit: 2642 http:/www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, RAFAEL PEREZ-GUTIERREZ can be reached at (571)272-7915. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDER WU/Examiner, Art Unit 2642
/Rafael Pérez-Gutiérrez/Supervisory Patent Examiner, Art Unit 2642