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 § 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 person shall be entitled to a patent unless –
(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.
Claim(s) 1-11 and 13-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al. (US Publication 2021/0126737 A1; Corresponds to the CN 112714486 A; provided by the applicant in the IDS filed on 7/2/2026).
In regards to claims 1, 19 and 21, Zhang et al. (US Publication 2021/0126737 A1) teaches, a parameter selection method, comprising: determining, by a terminal, a first condition that the terminal meets (see terminal 120 in figure 1 and paragraph 104; the terminal 120 may obtain a cell identification. to which the terminal is attached); and using, by the terminal, an artificial intelligence (AI) model parameter corresponding to the first condition (see paragraph 104; The terminal 120 may identify the aggregation level prediction model that corresponds to the cell identification); wherein the AI model parameter comprises at least one of the following: structure information of an AI model (see paragraph 104; The terminal 120 may identify the aggregation level prediction model that corresponds to the cell identification); or a parameter of each neuron in the AI model.
In regards to claims 1, 19 and 21, Zhang teaches, wherein the first condition comprises at least one of the following: initial access; multi-cells; cell switching; a condition determined based on a cell identifier (see terminal 120 in figure 1; see figure 6 and paragraph 130; when the terminal 120 predicts the AL related information by using the AL prediction model, terminal 120 may find the AL prediction model corresponding to a cell to which the terminal 120 is attached according to a cell identification of the cell to which the terminal 120 is attached and a correspondence relationship between the AL prediction model and the cell identification and predict the AL related information by using the found AL prediction model based on the obtained input data); a condition determined based on a location area; a condition determined based on at least one of the following: a signal-to-noise ratio (SNR), a reference signal received power (RSRP), a signal-to-interference-plus-noise ratio (SINR), a reference signal received quality (RSRQ), a layer 1 SNR, a layer 1 RSRP, a layer 1 SINR, or a layer 1 RSRQ (see paragraph 107; In exemplary embodiments, the terminal 120 may obtain an input data for predicting the aggregation level related information. For example, the input data may correspond to input data 310 of AL prediction model 320. The input data may include at least one of RSRP, a CQI, an SNR, a downlink control information (DCI) payload, or a PDCCH slot index); a condition determined based on a bandwidth part (BWP); a condition determined based on a tracking area (TA) and/or a radio access network notification area (RNA); a condition determined based on an operating frequency; a condition determined based on a public land mobile network (PLMN); a condition determined based on a terminal state; a condition determined based on a quality of service flow (QoS flow); a condition determined based on a radio link failure (RLF) event; a condition determined based on a radio resource management (RRM) event; a condition determined based on a beam failure (BF) event and/or a beam failure recovery (BFR) event; a condition determined based on a timing measurement result and/or a timing advance measurement result; a condition determined based on a round-trip time (RTT) measurement result; or a condition determined based on an observed time difference of arrival (OTDOA) measurement result.
In regards to claim 2, Zhang teaches, wherein the method further comprises: receiving, by the terminal, first configuration information from a network side device, wherein the first configuration information is for configuring AI model parameters in different conditions for the terminal; and the using an AI model parameter corresponding to the first condition comprises: using, by the terminal based on the first configuration information, the AI model parameter corresponding to the first condition (see paragraph 263; Assuming that in the PDCCH configuration, the probabilities of occurrence of five kinds of ALs (AL=1, AL=2, AL=4, AL=8, AL=16) are uniformly distributed, and at different AL levels, the complexity required for one candidate PDCCH detection is proportional to the PDCCH length, for example the AL of the PDCCH, the complexity of PDCCH blind detection may be about 67.6 units. Assuming that the PDCCH AI model may accurately find the AL of the PDCCH, the complexity of the PDCCH detection may be about 11.6 units, and the detection complexity of the PDCCH may be reduced by about 83%.).
In regards to claim 3, Zhang teaches, wherein the first configuration information is for indicating, configuring, or activating an AI model parameter corresponding to each condition .
In regards to claim 4, Zhang teaches, wherein the first configuration information comprises at least one of the following: a correspondence between the AI model parameter and the condition (see paragraph 211; when the AI model is not suitable for a current network, for example, when it is found that the base station 110 is no longer suitable for a certain AI model, the NCGI corresponding to the AI model can be deleted from the NCGI list. For example, the case where the AI model is not suitable for the current network may include the prediction result of the AI model conflicts with the newly collected data, for example the predicted AL or the probability of the AL is different from the AL obtained by the actual detection or the probability of the AL. The conflict may mean that a new base station is found or connected to, or the configuration of the base station changes); a correspondence between the AI model parameter and an event; or a correspondence between the AI model parameter and a cell.
In regards to claim 5, Zhang teaches, wherein the first configuration information is for indicating, configuring, or activating an AI model parameter set corresponding to each condition (see paragraph 294; the prediction unit may be configured to find the AL prediction model corresponding to the cell to which the terminal is attached according to a cell identification of the cell to which the terminal is attached and a correspondence relationship between the AL prediction model and the cell identification; and predict the AL related information by using the found AL prediction model based on the obtained input data. As an example, the cell identification may include the NCGI, the NCGI including at least one of the MCC, the MNC, and the gNB identification).
In regards to claim 6, Zhang teaches, wherein the using, based on the first configuration information, the AI model parameter corresponding to the first condition comprises: receiving, by the terminal, first indication information from the network side device, and using, based on the first configuration information and the first indication information, the AI model parameter corresponding to the first condition, wherein the first indication information is for indicating the AI model parameter, in the AI model parameter set, that corresponds to the first condition; or the first indication information is for indicating the terminal to use at least one of the following when the condition is met: an AI model parameter used by default, an initially activated AI model parameter (see paragraph 133; when the terminal 120 may find the AL prediction model corresponding to a cell to which the terminal 120 is attached, the terminal 120 may find the AL prediction model corresponding to the cell to which the terminal 120 is attached from the terminal 120 locally or the server; reads on the default and the initially activated), or a preferentially used AI model parameter.
In regards to claim 7, Zhang teaches, wherein the using, based on the first configuration information, the AI model parameter corresponding to the first condition comprises: using, by the terminal based on the first configuration information and a protocol agreement, the AI model parameter corresponding to the first condition, wherein the protocol agreement is that the terminal uses at least one of the following when the condition is met: an AI model parameter used by default, an initially activated AI model parameter (see paragraph 133; when the terminal 120 may find the AL prediction model corresponding to a cell to which the terminal 120 is attached, the terminal 120 may find the AL prediction model corresponding to the cell to which the terminal 120 is attached from the terminal 120 locally or the server; reads on the default and the initially activated), or a preferentially used AI model parameter.
In regards to claim 8, Zhang teaches, wherein any one of the AI model parameter used by default, the initially activated AI model parameter, and the preferentially used AI model parameter comprises at least one of the following: an AI model parameter with a minimum identifier; an AI model parameter with a maximum identifier; an AI model parameter with a maximum data amount; an AI model parameter with a minimum data amount; an AI model parameter with a most complex model structure; an AI model parameter with a simplest model structure; an AI model parameter with a largest quantity of model layers; an AI model parameter with a smallest quantity of model layers; an AI model parameter with a highest quantization level; an AI model parameter with a lowest quantization level; an AI model parameter with a fully-connected neural network structure (see paragraph 143; The AI model may be, but is not limited to, a deep learning model, a deep neural network model); or an AI model parameter with a convolutional neural network structure (see paragraph 143; The AI model may be, but is not limited to, a deep learning model, a deep neural network model).
In regards to claim 9, Zhang teaches, the using an AI model parameter corresponding to the first condition comprises: using, by the terminal according to a first preset rule, the AI model parameter corresponding to the first condition, wherein the first preset rule comprises at least one of the following: the AI model parameter of the terminal is used by default, initially activated, or preferentially used in each condition; the terminal uses any AI model parameter; or a common AI model parameter is used by default, initially activated, or preferentially used in each condition(see paragraph 133; when the terminal 120 may find the AL prediction model corresponding to a cell to which the terminal 120 is attached, the terminal 120 may find the AL prediction model corresponding to the cell to which the terminal 120 is attached from the terminal 120 locally or the server; reads on the default and the initially activated); or, wherein the method further comprises: skipping using, by the terminal according to a second preset rule, the AI model parameter corresponding to the first condition, wherein the second preset rule comprises: a non-AI model parameter is used by default, initially activated, or preferentially used in each condition.
In regards to claim 10, Zhang teaches, wherein the receiving first configuration information from a network side device comprises: receiving, by the terminal, the first configuration information from the network side device by using at least one of the following: radio resource control (RRC) signaling, a medium access control unit (MAC CE), or downlink control information (DCI) (see paragraph 107; the terminal 120 may obtain an input data for predicting the aggregation level related information. For example, the input data may correspond to input data 310 of AL prediction model 320. The input data may include at least one of RSRP, a CQI, an SNR, a downlink control information (DCI) payload).
In regards to claim 11, Zhang teaches, wherein the method further comprises: receiving, by the terminal, second configuration information from a network side device, wherein the second configuration information comprises an updated AI model parameter (see paragraph 109; the terminal 120 may obtain the aggregation level prediction model from the server. Also, the terminal 120 may update the aggregation level prediction model based on the reporting data which is transmitted to the base station 110 for deciding an aggregation level of a CCE).
In regards to claim 13, Zhang teaches, wherein the AI model parameter comprises at least one of the following: structure information of an AI model; or a parameter of each neuron in the AI model wherein an AI model corresponding to the AI model parameter is used for at least one of the following: signal processing; signal transmission; signal demodulation; obtaining of channel state information; beam management; channel prediction; interference suppression; positioning; prediction of a higher layer service and a higher layer parameter; management of the higher layer service and the higher layer parameter; or parsing of control signaling (see paragraph 190; The AI detection 1000 may be performed by using the AI model 1022. The input data 1020 of the AI model 1022 may be collected by the terminal 120, and the input data 1020 may be related parameters of the PDCCH AL adaptive algorithm of the gNB (e.g., base station 110). The input data 1020 may include at least one of the RSRP, the received signal strength indication (RSSI), the CQI, the SNR, the DCI bit size, or the PDCCH slot index. The output data 1024 of the AI model 1022 may include the probability of each AL of the uplink (UL) and the downlink (DL), for example, the probability of each AL of the DL and/or the probability of each AL of the UL; this reads on the signal processing, signal transmission, signal demodulation and interference suppression).
In regards to claims 14 and 20, Wang teaches, a parameter configuration method, comprising: sending, by a network side device, first configuration information to a terminal (see paragraph 177; the NCGI herein may be suitable for a 5G cell and may be replaced with a cell identification suitable for a 3G or 4G cell. In this way, the finding the AI model may include: finding the AI model corresponding to the cell according to the correspondence relationship between the AI model and the cell identification of the cell to which the terminal is attached, and judging whether the prediction accuracy of the AI model satisfies a requirement, and determining that the AI model is valid when the prediction accuracy meets the requirement, wherein the predicting the AL related information may include: predicting the AL related information based on the valid AI model), wherein the first configuration information is for configuring AI model parameters in different conditions for the terminal (see terminal 120 in figure 1 and paragraph 104; the terminal 120 may obtain a cell identification. to which the terminal is attached).
wherein the AI model parameter comprises at least one of the following: structure information of an AI model (see paragraph 104; The terminal 120 may identify the aggregation level prediction model that corresponds to the cell identification); or a parameter of each neuron in the AI model.
In regards to claims 14 and 20, Zhang teaches, wherein the first condition comprises at least one of the following: initial access; multi-cells; cell switching; a condition determined based on a cell identifier (see terminal 120 in figure 1; see figure 6 and paragraph 130; when the terminal 120 predicts the AL related information by using the AL prediction model, terminal 120 may find the AL prediction model corresponding to a cell to which the terminal 120 is attached according to a cell identification of the cell to which the terminal 120 is attached and a correspondence relationship between the AL prediction model and the cell identification and predict the AL related information by using the found AL prediction model based on the obtained input data); a condition determined based on a location area; a condition determined based on at least one of the following: a signal-to-noise ratio (SNR), a reference signal received power (RSRP), a signal-to-interference-plus-noise ratio (SINR), a reference signal received quality (RSRQ), a layer 1 SNR, a layer 1 RSRP, a layer 1 SINR, or a layer 1 RSRQ (see paragraph 107; In exemplary embodiments, the terminal 120 may obtain an input data for predicting the aggregation level related information. For example, the input data may correspond to input data 310 of AL prediction model 320. The input data may include at least one of RSRP, a CQI, an SNR, a downlink control information (DCI) payload, or a PDCCH slot index); a condition determined based on a bandwidth part (BWP); a condition determined based on a tracking area (TA) and/or a radio access network notification area (RNA); a condition determined based on an operating frequency; a condition determined based on a public land mobile network (PLMN); a condition determined based on a terminal state; a condition determined based on a quality of service flow (QoS flow); a condition determined based on a radio link failure (RLF) event; a condition determined based on a radio resource management (RRM) event; a condition determined based on a beam failure (BF) event and/or a beam failure recovery (BFR) event; a condition determined based on a timing measurement result and/or a timing advance measurement result; a condition determined based on a round-trip time (RTT) measurement result; or a condition determined based on an observed time difference of arrival (OTDOA) measurement result.
In regards to claim 15, Zhang teaches, wherein the first configuration information is for indicating, configuring, or activating an AI model parameter corresponding to each condition (see paragraph 177; the NCGI herein may be suitable for a 5G cell and may be replaced with a cell identification suitable for a 3G or 4G cell. In this way, the finding the AI model may include: finding the AI model corresponding to the cell according to the correspondence relationship between the AI model and the cell identification of the cell to which the terminal is attached, and judging whether the prediction accuracy of the AI model satisfies a requirement, and determining that the AI model is valid when the prediction accuracy meets the requirement, wherein the predicting the AL related information may include: predicting the AL related information based on the valid AI model); or the first configuration information is for indicating, configuring, or activating an AI model parameter set corresponding to each condition.
In regards to claim 16, Zhang teaches, wherein the sending first configuration information to a terminal comprises: sending, by the network side device, the first configuration information to the terminal by using at least one of the following: RRC signaling, a MAC CE, or DCI (see paragraph 107; the terminal 120 may obtain an input data for predicting the aggregation level related information. For example, the input data may correspond to input data 310 of AL prediction model 320. The input data may include at least one of RSRP, a CQI, an SNR, a downlink control information (DCI) payload).
In regards to claim 17, Zhang teaches, wherein when the first configuration information is for indicating, configuring, or activating the AI model parameter set corresponding to each condition, the method further comprises: sending, by the network side device, first indication information to the terminal, wherein the first indication information is for indicating an AI model parameter, in the AI model parameter set, that corresponds to a current condition of the terminal; or the first indication information is for indicating the terminal to use at least one of the following when the condition is met: an AI model parameter used by default, an initially activated AI model parameter (see paragraph 133; when the terminal 120 may find the AL prediction model corresponding to a cell to which the terminal 120 is attached, the terminal 120 may find the AL prediction model corresponding to the cell to which the terminal 120 is attached from the terminal 120 locally or the server; reads on the default and the initially activated), or a preferentially used AI model parameter.
In regards to claim 18, Zhang teaches, wherein the method further comprises: sending, by the network side device, second configuration information to the terminal, wherein the second configuration information comprises an updated AI model parameter (see paragraph 109; the terminal 120 may obtain the aggregation level prediction model from the server. Also, the terminal 120 may update the aggregation level prediction model based on the reporting data which is transmitted to the base station 110 for deciding an aggregation level of a CCE).
Response to Arguments
Applicant’s arguments with respect to the claims and the Wang reference filed on 7/2/2026 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
Applicant's submission of an information disclosure statement under 37 CFR 1.97(c) with the timing fee set forth in 37 CFR 1.17(p) on 7/2/2026 prompted the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 609.04(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JAY P PATEL/Primary Examiner, Art Unit 2466
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