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 .
DETAILED ACTION
Claim Rejections - 35 USC § 103
2. 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.
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 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.
2a. Claims 1-26 are rejected under 35 U.S.C. 103 as being unpatentable over Lutchoomun (US 20250350383 A1) in view of Ottrsten (US 20210345134 A1).
2b. Summary of the Cited Prior Art
Lutchoomun discloses a method for machine learning channel state reporting.
Ottrsten discloses a method for improving network performance by machine learning.
2c. Claim Analysis
Regarding Claim 1, Lutchoomun discloses:
An apparatus (Fig 1B) for wireless communications, the apparatus comprising:
at least one memory (Fig 1B, Memory 130); and at least one processor (Fig 1B, Processor 118) coupled to the at least one memory, the at least one processor being configured to:
receive a first set of operations (Fig 4, CSI-RS for online Training) supported by one or more machine learning models (Fig 3 Receiver ML) of a network entity (Fig 4. Model Trainer WTRU),
receive a first set of parameters (Fig 4, Data Distribution Statistics 413) associated with the first set of operations (Fig 4, CSI-RS for online Training),
wherein the first set of parameters (Fig 4, Data Distribution Statistics 413) are supported by the one or more machine learning models (Fig 3 Receiver ML; see: [0199] … each ML model may comprise any of: performance metric, threshold T; data distribution statistics (e.g., Doppler, SINR); and model parameters (e.g., size, latency, RS overhead) of the network entity (Fig 4. Model Trainer WTRU),
select a machine learning model (Fig 5, CSI-RS for Model Selection 515; see: [0211] … the WTRU selects a model whose data distribution most closely matches its environmental parameters) for performing a first operation of the first set of operations (Fig 4, CSI-RS for online Training) based on the first set of parameters (Fig 4, Data Distribution Statistics 413);
detect a change in at least one of (see: [0150] … to check for AIML model suitability; configuration, activation and/or deactivation of a cell; change in channel conditions):
the first operation;
or a parameter associated with the first operation (see [0201] … WTRU determines that no available trained model parameters match its requirements); and
transmit an indication (Fig 4, Indicate that no Current Model Fits 417) to change the first operation based on the detected change (see: [0201] … the WTRU may send an indication 417 to the gNB that no current ML model fits its requirements).
Lutchoomun does not elaborate about network “change”.
However, Ottrsten discloses:
detect a change in at least one of (Fig 3, 305 Update the machine learning model; see: [0058] … Refined and reinforcement learning may be used to continuously update the one or more machine learning models based on new inputs. This provides flexibility if something in the network environment changes).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to integrate Lutchoomun’s method for machine learning channel state reporting with Ottrsten’s method for improving network performance by machine learning with the motivation being to improve the performance of a wireless communications network (Ottrsten, [0001]).
Regarding Claim 2, Lutchoomun discloses:
wherein the at least one processor (Fig 1B, Processor 118) is further configured to:
determine a second set of operations (Fig 4, ML Model Transfer Request, 421; Examiner’s Note: ML Model Transfer Request functions as a second operations) supported by one or more machine learning models (Fig 3 Receiver ML) of the apparatus (Fig 1B);
determine a second set of parameters (Fig 4, ML Model Transfer Request DCI, 421; Examiner’s Note: the DCI includes the parameters for the transfer request) associated with the second set of operations (Fig 4, ML Model Transfer Request, 421),
wherein the second set of parameters (Fig 4, ML Model Transfer Request DCI, 421; Examiner’s Note: the DCI includes the parameters for the transfer request) are supported by the one or more machine learning models (Fig 3 Receiver ML) of the apparatus (Fig 1B); and
transmit, to the network entity (Fig 4. Model Trainer WTRU), the second set of operations and the second set of parameters
Regarding Claim 3, Lutchoomun discloses:
wherein the first set of operations (Fig 4, CSI-RS for online Training) and the first set of parameters (Fig 4, Data Distribution Statistics 413) are based on the second set of operations and the second set of parameters (Fig 4, ML Model Transfer Request and DCI, 421; Examiner’s Note: ML Model Transfer Request functions as a second operator and the DCI includes the parameters for the transfer request. Further, the first operations and parameters are for the second operations).
Regarding Claim 4, Lutchoomun discloses:
wherein the first set of operations (Fig 4, CSI-RS for online Training) and the first set of parameters (Fig 4, Data Distribution Statistics 413) are received in a unicast, multicast, or broadcast (see: [0099] In an example, the gNB may send a list of one or more trained models to a WTRU (e.g., through broadcast in a System Information Block (SIB)) from the network entity (Fig 4. Model Trainer WTRU).
Regarding Claim 5, Lutchoomun discloses:
wherein the at least one processor (Fig 1B, Processor 118) is further configured to:
receive an activation message (see: [0150] … a WTRU may be triggered with … configuration, activation and/or deactivation of a cell), wherein the activation message is configured to activate (see: [0074] … WTRU reports related measurements only when the resource is activated) a second operation of the first set of operations (Fig 4, CSI-RS for online Training); and
select a machine learning model (Fig 5, CSI-RS for Model Selection 515; see: [0211] … the WTRU selects a model whose data distribution most closely matches its environmental parameters) to perform the second operation (Fig 4, ML Model Transfer Request, 421; Examiner’s Note: ML Model Transfer Request functions as a second operations).
Regarding Claim 6, Lutchoomun discloses:
wherein the at least one processor (Fig 1B, Processor 118) is further configured to:
receive a deactivation message (see: [0150] … a WTRU may be triggered with … configuration, activation and/or deactivation of a cell), wherein the deactivation message specifies the first operation of the first set of operations (Fig 4, CSI-RS for online Training); and
based on the deactivation message (see: [0150] … a WTRU may be triggered with … configuration, activation and/or deactivation of a cell), stop performing the first operation (see: [0074] … WTRU reports related measurements only when the resource is activated; Examiner’s Note: the disclosures imply that the operation is stop when deactivation message is received or deactivation is configured) using the selected machine learning model (Fig 5, CSI-RS for Model Selection 515; see: [0211] … the WTRU selects a model whose data distribution most closely matches its environmental parameters).
Regarding Claim 7, Lutchoomun discloses:
wherein the at least one processor (Fig 1B, Processor 118) is further configured to transmit (Fig 4, ML Model Parameters Via PUCCH/PUSCH, 423), to the network entity (Fig 4. Model Trainer WTRU), a message based on an output of the selected machine learning model (see: [0206] … In response, at 423, the WTRU may transfer the ML model parameters to the gNB).
Regarding Claim 8, Lutchoomun discloses:
An apparatus (Fig 1B) for wireless communications, the apparatus (Fig 1B) comprising:
at least one memory (Fig 1B, Memory 130); and at least one processor (Fig 1B, Processor 118) coupled to the at least one memory, the at least one processor (Fig 1B, Processor 118) being configured to:
receive an indication of a first set of operations (Fig 4, CSI-RS for online Training) supported by one or more machine learning models (Fig 3 Receiver ML) of a network entity (Fig 4. Model Trainer WTRU);
select a machine learning model (Fig 5, CSI-RS for Model Selection 515; see: [0211] … the WTRU selects a model whose data distribution most closely matches its environmental parameters) for performing a first operation of the first set of operations (Fig 4, CSI-RS for online Training);
detect a change in at least one of (see: [0150] … to check for AIML model suitability; configuration, activation and/or deactivation of a cell; change in channel conditions):
the first operation;
or a parameter associated with the first operation (see [0201] … WTRU determines that no available trained model parameters match its requirements); and
transmit an indication (Fig 4, Indicate that no Current Model Fits 417) to change the first operation based on the detected change (see: [0201] … the WTRU may send an indication 417 to the gNB that no current ML model fits its requirements).
Lutchoomun does not elaborate about network “change”.
However, Ottrsten discloses:
detect a change in at least one of (Fig 3, 305 Update the machine learning model; see: [0058] … Refined and reinforcement learning may be used to continuously update the one or more machine learning models based on new inputs. This provides flexibility if something in the network environment changes).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to integrate Lutchoomun’s method for machine learning channel state reporting with Ottrsten’s method for improving network performance by machine learning with the motivation being to improve the performance of a wireless communications network (Ottrsten, [0001]).
Regarding Claim 9, Lutchoomun discloses:
wherein the indication of the first set of operations (Fig 4, CSI-RS for online Training) comprises a set of identifiers, the set of identifiers comprising a respective identifier for each machine learning model (see: [0099] … The models may have model IDs that uniquely identify them, assigned either by the WTRU that trained them or by the network) of the one or more machine learning models (Fig 3 Receiver ML) of the network entity (Fig 4. Model Trainer WTRU).
Regarding Claim 10, Lutchoomun discloses:
wherein the at least one processor (Fig 1B, Processor 118) is further configured to determine a first set of parameters (Fig 4, Data Distribution Statistics 413) associated with the one or more machine learning models (Fig 3 Receiver ML) identified by the set of identifiers (see: [0099] … The models may have model IDs that uniquely identify them, assigned either by the WTRU that trained them or by the network), the first set of parameters (Fig 4, Data Distribution Statistics 413) indicating parameters (see [0109] … The first part may carry a WTRU-specific identifier and a second part may be a model-specific identifier) supported by the one or more machine learning models (Fig 3 Receiver ML) of the network entity (Fig 4. Model Trainer WTRU).
Regarding Claim 11, Lutchoomun discloses:
wherein the set of identifiers (see: [0209] … a list of (one of more) models WTRU is configured with (i.e., model IDs) indicates an operation applicability (Fig 5A, Assistance info, e.g. Capabilities, 511) for operations of the first set of operations (Fig 4, CSI-RS for online Training; Fig 5A, CSI-RS for Model Selection) and parameters associated with operations of the first set of operations (Fig 4, CSI-RS for online Training).
Regarding Claim 12, Lutchoomun discloses:
wherein the set of identifiers (see: [0209] … a list of (one of more) models WTRU is configured with (i.e., model IDs) indicates configurations for using operations of the first set of operations (Fig 4, CSI-RS for online Training).
Regarding Claim 13, Lutchoomun discloses:
wherein the at least one processor (Fig 1B, Processor 118) is configured to receive the indication of the first set of operations (Fig 4, CSI-RS for online Training) in a unicast, multicast, or broadcast (see: [0099] In an example, the gNB may send a list of one or more trained models to a WTRU (e.g., through broadcast in a System Information Block (SIB)) from the network entity (Fig 4. Model Trainer WTRU).
Regarding Claim 14, Lutchoomun discloses:
wherein the at least one processor (Fig 1B, Processor 118) is further configured to:
receive an assistance message (Fig 4, ML Model Transfer Request in DCI, 421; Examiner’s Note: DCI may function as an assistance message); and
select a second operation (Fig 4, ML Model Transfer Request, 421; Examiner’s Note: ML Model Transfer Request functions as a second operations) from the first set of operations (Fig 4, CSI-RS for online Training) based on the assistance message (Fig 4, ML Model Transfer Request in DCI, 421; Examiner’s Note: DCI may function as an assistance message).
Regarding Claim 15, Lutchoomun discloses:
wherein the at least one processor (Fig 1B, Processor 118) is further configured to transmit (Fig 4, Indication of Selection as Basis, 425), to the network entity (Fig 4. Model Trainer WTRU), a message based on an output of the selected machine learning model (see: [0207] Finally, the WTRU may receive, at 425, an indication from the gNB that it has been selected as the basis for transfer learning (designated anchor WTRU)).
Regarding Claims 16-21, the apparatus claims disclose similar features as of Claims 1, 1, 2, 5-6 and 1, and are rejected accordingly. Further, Claims 16-21 disclose the same operations of Claims 1-7, but are performed by a transmitter.
Regarding Claims 22-26, the apparatus claims disclose similar features as of Claims 1, 4, 2, and 5-6, and are rejected accordingly. Further, Claims 16-21 disclose the same operations of Claims 1-7, but are performed by a transmitter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jung-Jen Liu whose telephone number is 571-270-7643. The examiner can normally be reached on Monday to Friday, 9:00 AM to 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kwang B. Yao can be reached on 571-272-3182. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JUNG LIU/Primary Examiner, Art Unit 2473