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 .
Response to Remarks
This communication is considered fully responsive to the Amendment filed on 2/5/26.
Response to Arguments
Applicant’s 2/5/26 arguments with respect to claims have been considered but are moot in view of new ground(s) of rejection.
Claim Interpretation
Support cited (IFW [0077]) for claim(s) amendment(s) discloses same recitation in amended claims “... plurality of machine learning schemes may include at least one of a machine learning model for generating a prediction associated with a first serving cell based at least in part on at least one reference signal associated with a second serving cell ...” with no further details and, therefore, claim amendment(s) is/are reasonably broadly interpretable.
Per IFW application, claimed ‘first network node’ and ‘second network node’ may be user equipment(s), base station(s), relay device(s), network controller(s), an apparatus, device, computing system, one or more components of any of these and/or processing entit(ies) configured to perform techniques described ... aggregated base station and/or components of a disaggregated base station ... ... consistent with this disclosure, once a specific example is broadened in accordance with this disclosure ... the broader example of the narrower example may be interpreted in reverse but in a broad open-ended way (see IFW [0041 and also 34;109;125;171;204]) and, therefore, claimed network node(s) are very reasonably broadly interpretable and re-interpretable in reverse in a broad open-ended way during examination.
Allowable Subject Matter
Claims 7-9, 12-13, 15-19, 21-23 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.
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 1-4 and 24-30 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2021/0091838 to Bai et al. (“Bai”) in view of U.S. Patent Publication No. 2020/0412417 to Calzolari et al. (“Calzolari”) and further in view of U.S. Patent Publication No. 2021/0289406 to Feki et al. (“Feki”).
As to claim 1, Bai discloses a first network node for wireless communication, comprising: one or more antennas and a processing system that includes one or more processors and one or more memories that store code and are coupled with one or more processors (Bai: fig 1-10, [0031;133]: fig 1 & 10 communication links 120 may user MIMO antenna technology ... processor 1004 coupled to computer-readable medium/ memory 1006, processor responsible for general processing including execution of software stored on computer-readable medium/ memory 1006 ... causes processing system 1014 to perform various functions described), the processing system configured to cause the first network node to:
receive assistance information indicating an occurrence of at least one dynamic network change of a set of dynamic network changes (Bai: fig 1-10, [0005-131]: fig 4 ... when UE 404 detects 422 that UE is operating in high-mobility state, UE may transmit an indication of the high-mobility state 424 to base station and base station receives the indication of the high-mobility state 424 (receive assistance information indicating ... ) and, therefore, base station is aware that UE 404 is operating in a high-mobility state and that channel condition may rapidly change (... an occurrence of at least one dynamic network change of a set of dynamic network changes) [0068] ... information acknowledging use of a predictive model associated with predicting CSI is communicated with a base station based on detection of a high-mobility state [0122]),
wherein the at least one dynamic network change corresponds to a change from a first network state to a second network state (Bai: fig 1-10, [0005-131]: ... detecting high-mobility state may be associated with speed of UE (see with [0068] – corresponds to channel condition rapidly changing – thus - wherein the at least one dynamic network change corresponds to ...) ... and determining whether the speed satisfies e.g. meets or exceeds a threshold (see with [0068] – corresponds to channel condition rapidly changing – thus - wherein the at least one dynamic network change corresponds to a change from a first network state to a second network state) indicating UE is operating in high-mobility state [0067] ... the faster the UE is moving, the faster channel conditions change over time and coherence time of a channel may be due to the high-mobility state of the UE and less than delay experienced during CSI reporting ... consequently, CSI reported may not accurately reflect current channel conditions ... causing degradation in performance [0008] ... to mitigate ... UE and/or base station may determine e.g. estimate, predict, etc a predicted CSI based on past observed CSI [0009] ... determination e.g. estimation, prediction, etc of predicted CSI based on past CSI using at least one parameter reported by UE.[0011]),
wherein a first machine learning scheme, of a plurality of machine learning schemes configured at the first network node, is associated with the first network state (Bai: fig 1-10, [0005-131]: ... base station may select a predictive model from a plurality of potential predictive models (... of a plurality of machine learning schemes configured at the first network node ... ) and communicate e.g. transmit and/or receive information with the UE to determine which predictive model from a plurality of potential predictive models to select ... may negotiate with UE such as by transmitting request to use a predictive model (... of a plurality of machine learning schemes configured at the first network node ... ) and receive acknowledgement or vice versa [0109] ... based on the indication of the high-mobility state 424, the base station and UE 404 communicate to acknowledge use of a predictive model (configured at the first network node) associated with predicting CSI (see with [0109; 067-68] - is associated with the first network state) which may be at least one of a linear model (scheme), a higher-order model (scheme) and/or a neural network (scheme) or machine learning model (scheme) (see with [0109; 067-68] - wherein a first machine learning scheme, of a plurality of machine learning schemes configured ...) [0069]).
Bai did not explicitly disclose perform a wireless communication based at least in part on the second machine learning scheme.
Calzolari discloses perform a wireless communication based at least in part on the second machine learning scheme (Calzolari: fig 1-16, [0004-60]: fig 1-2 ... wireless communication system 200 may use a dynamic antenna switching threshold based on ... or a configured threshold pattern or algorithm (see with [0063] - (scheme(s)) ... UE 115a may be pre-configured to select a lower dynamic switching threshold ... or select a greater dynamic switching threshold ... such a algorithm may or may not be based on machine learning (see with [0063] - (scheme(s)) [0059] ... UE may update a dynamic threshold based on a current communication measurement .. may select a threshold value to use for ASDIV (antenna switching diversity) based on ... algorithm (see with [0059] – machine learning scheme), neural network (other scheme) or some combination of these (plurality of schemes) ... may determine whether to switch an active antenna e.g. from a first antenna to a second antenna based on a comparison on one or more current communication measurements and compare this/these comparison values to an updated value (see with [0059] - perform a wireless communication based at least in part on the second machine learning scheme) ... based on the comparison(s) UE may switch from one operating antenna to another operating antenna and UE may communicate with base station using the active antenna e.g. second antenna (see with [0059] - perform a wireless communication based at least in part on the second machine learning scheme) [0063]).
Bai and Calzolari are analogous art because they are from the same field of endeavor with respect to wireless communications.
Before the effective filing date, for AIA , it would have been obvious to a person of ordinary skill in the art to incorporate the strategies by Calzolari into the node by Bai. The suggestion/motivation would have been to improve thresholds for antenna switching diversity (ASDIV) (Calzolari: [0001]).
Bai did not explicitly disclose wherein the plurality of machine learning schemes include a machine learning model for generating a prediction associated with a first serving cell based at least in part on at least one reference signal associated with a second serving cell.
Feki discloses wherein the plurality of machine learning schemes include a machine learning model for generating a prediction associated with a first serving cell based at least in part on at least one reference signal associated with a second serving cell (Feki: fig 1-16, [0014-93]: fig 3-4 ... reinforcement learning is an area of machine learning (include a machine learning model for generating a prediction ...) to determine what actions a software agent, for example an algorithm, should take in an environment (associated with 1st cell 2nd cell ... nth cell) in order to maximize a reward [0057] ... step 303 historical information of terminal devices is obtained for example from one or more base stations (serving 1st cell 2nd cell ... nth cell) and historical information may comprise a cell ID of a cell that the terminal device has been served by (1st cell 2nd cell ... nth cell IDs), a beam ID of a beam that the terminal device has been served by (serving 1st cell 2nd cell ... nth cell) and/or one or more measured RSRP values of the beam reported during a predefined time period (... based at least in part on at least one reference signal associated with a 1st cell 2nd cell ... nth serving cell(s)) [0058] ... after the training is completed in step 306 the trained second machine learning model may be provided (the plurality of machine learning schemes include a 1st 2nd ... ... nth machine learning model(s)) for example transmitted, to one or more base stations, which in turn may use the trained second machine learning model to predict optimal handover parameter values for example a TTT value and a hysteresis margin value, for one or more terminal devices continuously in real time based on the historical information of the one or more terminal devices (see with [0065; 67; 76-78] below -... generating a prediction associated with a first serving cell based at least in part on at least one reference signal associated with a second serving cell) [0064] ... for example an algorithm, may learn to adapt to a particular environment by performing actions and analyzing the resulting performances and a software agent may be described by a specific state, denoted herein as s, and the software agent may interact with the environment by performing different actions, denoted herein as a, the actions may be characterized by a reward, denoted as r [0065] ... a software agent may move from one state to another by choosing an action ... for example there may be 9 possible actions: ... 9. Stay at the current state (i.e. first cell) (see with [0057-58;64] above -... generating a prediction associated with a first serving cell based at least in part on at least one reference signal associated with a second serving cell)... in other words, it may be possible to move from the current state to a neighbor state (i.e. second cell) as depicted in Table 1, or to stay at the current state (i.e. first cell) (see with [0057-58;64] above -... generating a prediction associated with a first serving cell based at least in part on at least one reference signal associated with a second serving cell) [0076-77] ... during DQN training, a concept called experience replay may be applied to store the experiences in order to help avoid forgetting previous experiences and to reduce correlations between experiences [0078]).
Bai, Calzolari and Feki are analogous art because they are from the same field of endeavor with respect to machine learning.
Before the effective filing date, for AIA , it would have been obvious to a person of ordinary skill in the art to incorporate the strategies by Feki into the node by Bai and Calzolari. The suggestion/motivation would have been to provide an RNN that remembers historical information, and the decisions may be influenced by what is learnt from the past (Feki: [0063]).
As to claim 2, Bai, Calzolari and Feki disclose wherein the prediction comprises at least one of:
a predicted value of at least one channel characteristic associated with the first serving cell (Bai: fig 1-10, [0005-131]: fig 1 ... base stations 102 may wirelessly communicate with UEs 104 to provide coverage for a respective geographic coverage area 110, for example, small cell 102’ has coverage area 110’ (... associated with the first serving cell) [0031] ... referring again to fig 1, user equipment 104 and base station 102/180 configured to use predictive model 198 to determine e.g. estimate, predict etc future channel information (CSI) (a predicted value of at least one channel characteristic ...) [0041] ), or
a beam failure associated with the first serving cell.
As to claim 3, Bai, Calzolari and Feki disclose wherein the at least one channel characteristic comprises at least one of:
a layer 1 reference signal received power,
a layer 1 signal to interference plus noise ratio,
a rank indicator,
a precoding matrix indicator,
a channel quality index, or
a layer indicator (Bai: fig 1-10, [0005-131]: base station may schedule communication with a UE based on the conditions or quality of the channel ... may transmit reference signal(s) and UE measures one or more values indicative of channel conditions (channel characteristics) ... examples include ... reference signal received power (RSRP) (a layer 1 reference signal received power), a channel coefficient at one or more tones, CQI, PMI (precoding matric indicator), and/or RI (rank indicator and/or layer indicator) [0061]).
For motivation, see rejection of claim 1.
As to claim 4, see similar rejection to claim 1 where the node is taught by the node.
As to claim 4, Bai, Calzolari and Feki further disclose wherein the identification of the second machine learning scheme is based at least in part on a specified linkage, of a plurality of specified linkages, that associates the dynamic network change with a specified set of machine learning schemes of the plurality of machine learning schemes, wherein the specified set of machine learning schemes includes the second machine learning scheme (Calzolari: fig 1-16, [0004-60]: ... a machine learning agent unit may be developed prior to deployment at a UE where different decisions e.g. antenna switching decisions, threshold value decisions etc may be rewarded or penalized to construct a neural network (wherein the identification of the second machine learning scheme ...) for updating a threshold (... that associates the dynamic network change ...) ... the neural network may be trained using training data corresponding to many different environments, scenarios and use cases (... is based at least in part on a specified linkage, of a plurality of specified linkages ...) such that the ASDIV may adapt to or handle different conditions in a wireless communication system ... may be trained for a specific environment ... may dynamically adjust to a specific user, model, chipset, network and/or operating band (see with [0059;63] - wherein the identification of the second machine learning scheme is based at least in part on a specified linkage, of a plurality of specified linkages, that associates the dynamic network change with a specified set of machine learning schemes of the plurality of machine learning schemes) [0057] ... UE 115a may be pre-configured to select a lower dynamic switching threshold ... or select a greater dynamic switching threshold ... such an algorithm may or may not be based on machine learning (see with [0057;63] - ... that associates the dynamic network change with a specified set of machine learning schemes of the plurality of machine learning schemes ...) [0059] ... UE may update a dynamic threshold based on a current communication measurement .. may select a threshold value to use for ASDIV (antenna switching diversity) based on ... algorithm (see with [0057;59] – machine learning scheme), neural network (other scheme) or some combination of these (see with [0057;59] - wherein the specified set of machine learning schemes includes the second machine learning scheme) [0063]).
For motivation, see rejection of claim 1.
As to claim 24, Bai, Calzolari and Feki disclose wherein a machine learning parameter associated with the machine learning model indicates at least one of:
a weight associated with at least one of a neuron, a kernel, or a layer (Calzolari: fig 1-16, [0004-81]: ... machine learning model 500 includes a neural network 510 where weights between nodes of the neural network 510 are determined according to machine learning agent unit training 400 in fig 4 [0073]),
a number of neurons in the machine learning model,
a number of kernels in the machine learning model,
a number of hidden layers in the machine learning model,
a dimension of a layer of the machine learning model,
a dimension of an input to the machine learning model,
a dimension of an output of the machine learning model,
an association between an estimated signal and a machine learning model input feature,
an association between a machine learning model output feature and a predicted channel state information, or
an association between a machine learning model output feature and a predicted beam failure instance
For motivation, see rejection of claim 1.
As to claim 25, see similar rejection to claim 1.
As to claim 25, Bai, Calzolari and Feki further disclose a first network node for wireless communication comprising: one or more antennas and a processing system that includes one or more processors and one or more memories that store code and are coupled with one or more processors (Bai: fig 1-10, [0005-131]: fig 1 & 10 communication links 120 may user MIMO antenna technology [0031] ... processor 1004 coupled to computer-readable medium/ memory 1006, processor responsible for general processing including execution of software stored on computer-readable medium/ memory 1006 ... causes processing system 1014 to perform various functions described [0133] ... base station may select a predictive model from a plurality of potential predictive models and communicate e.g. transmit and/or receive information with the UE to determine which predictive model from a plurality of potential predictive models to select ... may negotiate with UE such as by transmitting request to use a predictive model and receive acknowledgement or vice versa (i.e. – either the base station and/or UE may serve as “first node” or “second node” as claimed) [0109]), the processing system configured to cause the first network node to:
transmit, to a second network node, a machine learning configuration associated with a plurality of machine learning schemes, wherein a first machine learning scheme, of the plurality of machine learning schemes, is associated with a first network state (Bai: fig 1-10, [0005-131]: ... base station (see with [0109] – may be first node or vice versa) may select a predictive model (transmit, to a second network node ... wherein a first machine learning scheme...) from a plurality of potential predictive models (... of a plurality of machine learning schemes ... ) and communicate e.g. transmit and/or receive information (a machine learning configuration) with the UE to determine which predictive model from a plurality of potential predictive models to select (transmit, to a second network node, a machine learning configuration associated with a plurality of machine learning schemes ...) ... may negotiate with UE such as by transmitting request to use a predictive model (... wherein a first machine learning scheme, of the plurality of machine learning schemes ... ) and receive acknowledgement or vice versa [0109] ... based on the indication of the high-mobility state 424, the base station and UE 404 communicate to acknowledge use of a predictive model (... wherein a first machine learning scheme, of the plurality of machine learning schemes ...) associated with predicting CSI (see with [0109; 067-68] - wherein a first machine learning scheme, of the plurality of machine learning schemes, is associated with a first network state) which may be at least one of a linear model (scheme), a higher-order model (scheme) and/or a neural network (scheme) or machine learning model (scheme) (see with [0109; 067-68] - wherein a first machine learning scheme, of the plurality of machine learning schemes, is associated with a first network state) [0069])) and
a second machine learning scheme, of the plurality of machine learning schemes, is associated with a second network state (Bai: fig 1-10, [0005-131]: fig 4 ... predictive model may be based on at least two CSI 436 ... CSI(0) may be associated with a reference time (see with [0109; 067-69;122] - identify, based at least in part on the assistance information, a 1st machine learning scheme, from the plurality of machine learning schemes, wherein the first machine learning scheme is associated with the first network state) and CSI(1) may be another CSI 436 associated with a subsequent time after the reference time (see with [0109; 067-69;122] - identify, based at least in part on the assistance information, a 2nd machine learning scheme, from the plurality of machine learning schemes, wherein the second machine learning scheme is associated with the second network state)... to obtain an output indicative of predicted CSI e.g. CSI(t) (see with [0109; 067-69;122] - identify, based at least in part on the assistance information, a 1st 2nd ... n machine learning scheme(s), from the plurality of machine learning schemes, wherein the n machine learning scheme is associated with the n network state(s)) [0085] ... when UE 404 detects 422 that UE is operating in high-mobility state, UE may transmit an indication of the high-mobility state 424 to base station and base station receives the indication of the high-mobility state 424 (identify, based at least in part on the assistance information... ) and, therefore, base station is aware that UE 404 is operating in a high-mobility state and that channel condition may rapidly change (see with [0085;109; 067;69;122] - is associated with the 1st 2nd ... n network state(s)) [0068] ... information acknowledging use of a predictive model associated with predicting CSI is communicated with a base station based on detection of a high-mobility state [0122]),
and wherein the plurality of machine learning schemes include a machine learning model for generating a prediction associated with a first serving cell based at least in part on at least one reference signal associated with a second serving cell (Feki: fig 1-16, [0014-93]: fig 3-4 ... reinforcement learning is an area of machine learning (include a machine learning model for generating a prediction ...) to determine what actions a software agent, for example an algorithm, should take in an environment (associated with 1st cell 2nd cell ... nth cell) in order to maximize a reward [0057] ... step 303 historical information of terminal devices is obtained for example from one or more base stations (serving 1st cell 2nd cell ... nth cell) and historical information may comprise a cell ID of a cell that the terminal device has been served by (1st cell 2nd cell ... nth cell IDs), a beam ID of a beam that the terminal device has been served by (serving 1st cell 2nd cell ... nth cell) and/or one or more measured RSRP values of the beam reported during a predefined time period (... based at least in part on at least one reference signal associated with a 1st cell 2nd cell ... nth serving cell(s)) [0058] ... after the training is completed in step 306 the trained second machine learning model may be provided (the plurality of machine learning schemes include a 1st 2nd ... ... nth machine learning model(s)) for example transmitted, to one or more base stations, which in turn may use the trained second machine learning model to predict optimal handover parameter values for example a TTT value and a hysteresis margin value, for one or more terminal devices continuously in real time based on the historical information of the one or more terminal devices (see with [0065; 67; 76-78] below -... generating a prediction associated with a first serving cell based at least in part on at least one reference signal associated with a second serving cell) [0064] ... for example an algorithm, may learn to adapt to a particular environment by performing actions and analyzing the resulting performances and a software agent may be described by a specific state, denoted herein as s, and the software agent may interact with the environment by performing different actions, denoted herein as a, the actions may be characterized by a reward, denoted as r [0065] ... a software agent may move from one state to another by choosing an action ... for example there may be 9 possible actions: ... 9. Stay at the current state (i.e. first cell) (see with [0057-58;64] above -... generating a prediction associated with a first serving cell based at least in part on at least one reference signal associated with a second serving cell)... in other words, it may be possible to move from the current state to a neighbor state (i.e. second cell) as depicted in Table 1, or to stay at the current state (i.e. first cell) (see with [0057-58;64] above -... generating a prediction associated with a first serving cell based at least in part on at least one reference signal associated with a second serving cell) [0076-77] ... during DQN training, a concept called experience replay may be applied to store the experiences in order to help avoid forgetting previous experiences and to reduce correlations between experiences [0078]); and
transmit, to the second network node, assistance information indicating an occurrence of a dynamic network change of a set of specified dynamic network changes, wherein the dynamic network change corresponds to a change from the first network state to the second network state (Bai: fig 1-10, [0005-131]: ... the issues e.g. delay experienced due to CSI (channel state information) reporting by UE may be exacerbated when UE is in a high-mobility state (assistance information) ... may introduce the Doppler effect ... thereby making CSI reporting time-variant ... in other words, the faster UE is moving, the faster the channel conditions change over time (assistance information indicating an occurrence of a dynamic network change of a set of specified dynamic network changes) [0063] ... the UE 404 may detect that the UE is operating in high-mobility state (assistance information) by, first, determining the speed at which the UE 404 is traveling e.g. in miles or kilometers per hour and, second, determining whether the speed satisfies e.g. meets or exceeds a threshold (see with [0063] - assistance information indicating an occurrence of a dynamic network change of a set of specified dynamic network changes) which may indicate the UE is operating in high-mobility state (see with [0063] - wherein the dynamic network change corresponds to a change from the first network state to the second network state) [0067] ... when UE 404 detects 422 that UE is operating in high-mobility state, UE may transmit an indication of the high-mobility state 424 to base station and base station receives the indication of the high-mobility state 424 (see with [0063;67] - transmit, to the second network node, assistance information indicating an occurrence of a dynamic network change ... ) and, therefore, base station is aware that UE 404 is operating in a high-mobility state and that channel condition may rapidly change (see with [0085;109; 063;67;69;122] - wherein the dynamic network change corresponds to a change from the first network state to the second network state) [0068] ... information acknowledging use of a predictive model associated with predicting CSI is communicated with a base station based on detection of a high-mobility state [0122]).
For motivation, see rejection of claim 1.
As to claim 26, see similar rejection to claim 2 where the node is taught by the node.
As to claims 27-28, see similar rejection to claims 1-2, respectively where the method is taught by the node.
As to claims 29-30, see similar rejection to claims 25-26, respectively, where the method is taught by the node.
Claims 5-6, 10-11 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2021/0091838 to Bai et al. (“Bai”) in view of U.S. Patent Publication No. 2020/0412417 to Calzolari et al. (“Calzolari”), U.S. Patent Publication No. 2021/0289406 to Feki et al. (“Feki”) and further in view of U.S. Patent Publication No. 2019/0103954 to Lee et al. (“Lee”).
As to claim 5, Bai, Calzolari and Feki disclose the node of claim 1.
For motivation, see rejection of claim 1.
Bai did not explicitly disclose wherein the dynamic network change comprises at least one of: a bandwidth part switch associated with the second serving cell, a precoder pattern change associated with the second serving cell, a reference signal pattern change associated with the second serving cell, a serving cell identifier (ID) change associated with the second serving cell, or a change of a combination of serving cell IDs associated with the second serving cell.
Lee discloses wherein the dynamic network change comprises at least one of:
a bandwidth part switch associated with the second serving cell (Lee: fig 1-19, [0006-214]: fig 16 block 1605 establish at UE a connection to base station using primary component carrier (CC) have a plurality of bandwidth parts (BWP) ... and block 1610-1615 receive first DCI (downlink control information) including indication to activate a secondary CC via BWP indication in the secondary CC (a bandwidth part switch associated with the second serving cell) and activate the secondary CC (wherein the dynamic network change comprises at least one of: ...) [0190-195]),
a precoder pattern change associated with the second serving cell,
a reference signal pattern change associated with the second serving cell,
a serving cell identifier (ID) change associated with the second serving cell, or
a change of a combination of serving cell IDs associated with the second serving cell.
Bai, Calzolari, Feki and Lee are analogous art because they are from the same field of endeavor with respect to wireless communications.
Before the effective filing date, for AIA , it would have been obvious to a person of ordinary skill in the art to incorporate the strategies by Lee into the node by Bai, Calzolari and Feki. The suggestion/motivation would have been to provide for bandwidth part activation, deactivation and switching in wireless communications (Lee: [0190]).
As to claim 6, Bai, Calzolari, Feki and Lee disclose wherein the dynamic network change comprises the bandwidth part switch associated with the second serving cell, the bandwidth part switch comprising a change from a first bandwidth part being active to a second bandwidth part being active (Lee: fig 1-19, [0006-214]: ... configuration includes a table with entries that correspond to different combinations of BWPs that may be activated or deactivated (any bandwidth part switch comprising a change from a first bandwidth part being active to a second bandwidth part being active) [0105] ... one or more BWPs may correspond to one or more different CCs (dynamic network change comprises the bandwidth part switch associated with 1st 2nd ... n service cell(s)) and determination to activate BWPs may be determined based on activating one or more CCs (dynamic network change comprises the bandwidth part switch associated with 1st 2nd ... n service cell(s)) [0106] ... activated BWPs may be determined based on an index provided in DCI indicating a table entry with associated combination of activated and deactivated BWPs ... DCI may include bitmap indicating which BWPs are activated and deactivated (see with [0105-106] - wherein the dynamic network change comprises the bandwidth part switch associated with the second serving cell, the bandwidth part switch comprising a change from a first bandwidth part being active to a second bandwidth part being active) [0108]).
For motivation, see rejection of claim 5.
As to claim 10, see similar rejection to claim 1 where the node is taught by the node.
As to claim 10, Bai, Calzolari, Feki and Lee further disclose wherein the dynamic network change comprises the reference signal pattern change associated with the second serving cell, wherein the reference signal pattern change comprises a change from a first reference signal pattern to a second reference signal pattern, wherein the first machine learning scheme is associated with the first reference signal pattern and the second machine learning scheme is associated with the second reference signal pattern (Bai: fig 1-10, [0005-131]: ... a base station whether a small cell 102’ or large cell e.g. macro base station (1st 2nd ... n service cell(s)) [0035] ... may transmit a beamformed signal to UE 104 in one or more transmit directions 182’ (see with [0035] - 1st 2nd ... n reference signal pattern(s) associated with 1st 2nd ... n serving cell(s)) and UE receive the beamformed signal in in one or more receive directions 182’ see with [0035] - 1st 2nd ... n reference signal pattern(s) associated with 1st 2nd ... n serving cell(s)) ... and may perform beam training (1st 2nd ... n machine learning scheme(s)) ... wherein the reference signal pattern change comprises a change from a first reference signal pattern to a second reference signal pattern) to determine the best receive and transmit directions for each of base station 180/UE 104 and the transmit and receive directions for the base station(s) and/or UE(s) may or may not be the same (... wherein the first machine learning scheme is associated with the first reference signal pattern and the second machine learning scheme is associated with the second reference signal pattern) [0036]).
For motivation, see rejection of claim 5.
As to claim 11, see similar rejection to claim 10.
As to claim 11, Bai, Calzolari, Feki and Lee further disclose wherein the reference signal pattern change comprises a change from a first reference signal characteristic of a plurality of reference signal characteristics to a second reference signal characteristic of the plurality of reference signal characteristics, wherein the first machine learning scheme is associated with the first reference signal characteristic and the second machine learning scheme is associated with the second reference signal characteristic (Bai: fig 1-10, [0005-131]: ... a base station whether a small cell 102’ or large cell e.g. macro base station (1st 2nd ... n service cell(s)) [0035] ... may transmit a beamformed signal to UE 104 in one or more transmit directions 182’ (see with [0035] - 1st 2nd ... n reference signal characteristic(s) of a plurality of reference signal characteristic(s) associated with 1st 2nd ... n serving cell(s)) and UE receive the beamformed signal in in one or more receive directions 182’ see with [0035] - 1st 2nd ... n reference signal pattern(s) associated with 1st 2nd ... n serving cell(s)) ... and may perform beam training (1st 2nd ... n machine learning scheme(s)) ... wherein the reference signal pattern change comprises a change from a first reference signal characteristic of a plurality of reference signal characteristics to a second reference signal characteristic of the plurality of reference signal characteristics) to determine the best receive and transmit directions for each of base station 180/UE 104 and the transmit and receive directions for the base station(s) and/or UE(s) may or may not be the same (... wherein the first machine learning scheme is associated with the first reference signal characteristic and the second machine learning scheme is associated with the second reference signal characteristic) [0036]).
For motivation, see rejection of claim 5.
As to claim 14, Bai, Calzolari, Feki and Lee disclose wherein the prediction comprises a predicted value of at least one channel characteristic associated with the first serving cell (Calzolari: fig 1-16, [0004-60]: fig 4 ... pre-deployment training 405-a may train a neural network at each machine learning agent units 410-a, for example, at 425, machine learning agent units 410-a may receive environment status indicating RSRP values (wherein the prediction comprises a predicted value of at least one channel characteristic associated with the first serving cel ...l) for a measurement period for a set of antennas of UEs 115 and based on the current neural network, the machine learning agent unit 410-a determines a dynamic antenna switching threshold and determine whether to switch an active antenna from a fist antenna to a second antenna [0067]),
wherein the dynamic network change comprises a change of a first antenna array structure to a second antenna array structure (Calzolari: fig 1-16, [0004-60]: fig 4 ... following pre-deployment training 405-a trained machine learning agent units 410-a may be deployed to one or more wireless UEs 115 at 445 ... to optimize or improve performance of machine learning agent units 410-a in different environments, environment-specific training updates may be done independently by specific machine learning agent units 410-a (wherein the dynamic network change comprises a change of a first antenna array structure to a second antenna array structure) [0069] ... for example, a first UE 115 may train a neural network based on a first environment 450-a and update machine learning agent units 455-a to obtain machine learning agent units 410-b trained specifically for environment 450-a (wherein the dynamic network change comprises a change of a first antenna array structure to a second antenna array structure) [0070]),
wherein the first machine learning scheme is associated with the first antenna array structure and the second machine learning scheme is associated with the second antenna array structure (Calzolari: fig 1-16, [0004-60]: ... UEs 115 may train a plurality of different neural networks (1st 2nd ... n machine learning scheme(s), each for a different combination of users, environments e.g. geographic locations (associated with a 1st 2nd ... n cells), carrier frequencies (associated with 1st 2nd ... n antenna array structure(s)) or the like (wherein the first machine learning scheme is associated with the first antenna array structure and the second machine learning scheme is associated with the second antenna array structure) [0070]).
For motivation, see rejection of claim 5.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2021/0091838 to Bai et al. (“Bai”) in view of U.S. Patent Publication No. 2020/0412417 to Calzolari et al. (“Calzolari”), U.S. Patent Publication No. 2021/0289406 to Feki et al. (“Feki”) and further in view of U.S. Patent Publication No. 2020/0259575 to Bai et al. (“Bai-575”).
As to claim 20, Bai, Calzolari and Feki disclose the node of claim 1.
For motivation, see rejection of claim 1.
Bai did not explicitly disclose wherein the prediction comprises a predicted beam failure associated with the first serving cell.
Bai-575 discloses wherein the prediction comprises a predicted beam failure associated with the first serving cell (Bai-575: fig 1-11, [0010-95]: fig 4 ... illustrates beam failure detection (BFD) in response to link degradation ... illustrates a channel RSRP (reference signal received power) but any other channel metric or condition may be used ... UE 104 declares BFD by transmitting signal to base station indicating BFD (see with [0069] - wherein the prediction comprises a predicted beam failure associated with the first serving cell) [0054] ... prediction could be performed at the base station 102 or UE 104 ((see with [0069] - wherein the prediction comprises a predicted beam failure associated with the first serving cell)) [0057] ... based on the report or indication of prediction (see with [0054] - BFD) a scheduling decision based on the report ... may include instruction to switch beams or to handover from a current cell to a neighboring cell (see with [0054] - wherein the prediction comprises a predicted beam failure associated with the first serving cell) [0069]).
Bai, Calzolari, Feki and Bai-575 are analogous art because they are from the same field of endeavor with respect to beam failure detection (BFD).
Before the effective filing date, for AIA , it would have been obvious to a person of ordinary skill in the art to incorporate the strategies by Bai-575 into the node by Bai, Calzolari and Feki. The suggestion/motivation would have been to provide for beam failure detection (BFD) reporting (Bai-575: [0054]).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
A) US 20220104033 – Ly
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client device may receive, using at least one lower layer of a wireless communication protocol stack, a machine learning component from a server device. The client device may transmit, to the server device and using the at least one lower layer, an update associated with the machine learning component, wherein transmitting the update comprises transmitting a plurality of transport blocks. Numerous other aspects are described.
B) US 20260040175 – Mahmoud
Intelligent seamless handover in cellular networks (e.g., using a computerized tool), is enabled. For example, a system can comprise at least one processor, and at least one memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations. The operations can comprise, based on serving cell connection data, neighbor cell connection data, and user equipment data, determining, using a time-series machine learning model trained using past serving cell connection data, past neighbor cell connection data, and past user equipment data, a predicted connection status for the user equipment, and based on the predicted connection status, serving cell load data representative of a first load on the serving cell, and neighbor cell load data representative of a second load on the neighbor cell, controlling a handover of the user equipment between the serving cell and the neighbor cell.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/JUNE SISON/Primary Examiner, Art Unit 2455
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