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 Statement
The information disclosure statements (IDS), submitted on 8/27/2024 and 1/27/2026, are being considered by the examiner.
Objections
Claims 3, 11, 18 and 26 are objected. The claim limitation “an ID of the ML model” should be read “an identification of the ML model”. Appropriate corrections are required.
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, “non-ML based precoder matrix indicator (PMI) codebooks”, “CSI reference signal”, “an ML-based CSI report”, “an ID of the ML model”, must be shown or the feature(s) must be canceled from the claims 1-30. No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejection – 35 U.S.C. § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of pre-AIA 35 U.S.C. 112, second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter, which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Claim 3 recites the limitation "the same CSI reference signal ". There is insufficient antecedent basis for this limitation in the claim. Therefore, the claim(s) is/are indefinite and is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
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.
Claims 1-30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Echigo (US Patent Application Publication US 2025/0227498 A1), (“Echigo”).
Regarding claim 1, Echigo meets the claim limitations, as follows:
A method of wireless communications (the present disclosure is to provide a terminal, a radio communication method, and a base station capable of achieving preferable overhead reduction/channel estimation/resource use) [Echigo: para. 0007] by a user equipment (UE) (It is preferable to appropriately control AI model training/model inference in a terminal (user terminal, User Equipment (UE))) [Echigo: para. 0006], comprising:obtaining (The UE may update, in the training, parameters to be updated (fine-tuned) which are obtained from the reference AI model information) [Echigo: para. 0162], a performance report ((From a viewpoint of the communication overhead reduction, the UE preferably applies a report of FIG. 13B in a case that the performance is sufficient in FIG. 13B, a report of FIG. 14A in a case that the performance is sufficient in FIG. 14A, and a report of FIG. 14B in a case that the
performance is insufficient in FIG. 14A) [Echigo: para. 0195; Figs. 13A-14B]; (In the present disclosure, a measured/reported RS may mean an RS measured/reported for CSI reporting) [Echigo: para. 0079]; (In the present disclosure, UCI, CSI reporting, CSI feedback, feedback information, feedback bit, and the like may be interchangeably interpreted. In the present disclosure, a bit, a bit string, a bit sequence, a sequence, a value, information, a value obtained from a bit, information obtained from a bit, and the like may be interchangeably interpreted) [Echigo: para. 0088]; Figs. 1, 4) for a machine learning (ML) model running on at least one of the UE ((It is preferable to appropriately control AI model training/model inference in a terminal (user terminal, User Equipment (UE))) [Echigo: para. 0006]; (In one embodiment of the present disclosure, a terminal (user terminal, User Equipment (UE))/base station (BS) performs training of an ML mode in a training mode, and performs an ML model in inference mode (also referred to as inference mode or the like). In the inference mode, validation of the accuracy of the ML model trained in the training mode (trained ML model) may be performed. In the present disclosure, the UE/BS may input channel state information, a reference signal measurement value, and the like to the ML model and output highly accurate channel state information/measured value/beam selection/location, future channel state information/radio link quality, and the like) [Echigo: para. 0049-0050]) or a network entity (In the present disclosure, an autoencoder, an encoder, a decoder, or the like may be interpreted as at least one of a model, an ML model, a neural network model, an AI model, an AI algorithm, and the like. The autoencoder may be interchangeably interpreted as an arbitrary autoencoder such as a stacked autoencoder and a convolutional autoencoder. The encoder/decoder in the present disclosure may adopt a model of Residual Network (ResNet), DenseNet, RefineNet, or the like) [Echigo: para. 0084]; andparticipating in a change to the ML model based on the performance report ((A data collection stage corresponds to a stage to collect data for generating/updating the AI model. The data collection stage may include data arrangement (for example, determining which data is forwarded for model training/model inference), data forwarding (for example, forwarding data to an entity (for example, UE, gNB) performing model training/model inference), and the like) [Echigo: para. 0035; Figs. 1, 4]; (FIG. 4 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0013; Fig. 4]; (FIG. 7 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0016; Fig. 7]; (FIG. 9 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0018; Fig. 9] ; (FIG. 10 is a diagram to show an example of update of the AI model according to the second embodiment) [Echigo: para. 0019; Fig. 10]; (FIGS. 16A to 16C are diagrams to show an example of updated AI model information) [Echigo: para. 0025; Figs. 16A, 16C]).
Regarding claims 2 and 25, Echigo meets the claim limitations as set forth in claims 1 and 24. Echigo further meets the claim limitations as follow.
further comprising transmitting, to the network entity ((In the present disclosure, the UE/BS may input channel state information, a reference signal measurement value, and the like to the ML model) [Echigo: para. 0050]; (The UE may transmit AI model information related to the UE -trained AI model (hereinafter, also referred to as UE -trained AI model information) to an NW by using physical layer signaling (for example, VCI), higher layer signaling (for example, RRC signaling, MAC CE), a specific signal/channel, or a combination of these. The UE-trained AI model information may include, instead of in addition to the AI model information already
described, information of any or a combination of the following: (an index of) the reference AI model/NW-trained AI model that is the base of the UE -trained AI model, and information of a difference of the UE-trained AI model from the reference AI model/NW-trained AI model.) [Echigo: para. 0183-0186], assistance information (report information) [Echigo: para. 0290] to assist the network entity in generating (In the present disclosure, generation, computation, derivation, and the like may be interchangeably interpreted) [Echigo: para. 0064] the performance report ((For example, the UE may (generate by using the encoder, for example, and) report information related to a location ( or positioning)/information related to location estimation in the location management function (LMF) to the network in accordance with at least one of the above described embodiments. The information may be information of a channel impulse response (CIR) per subband/per antenna port) [Echigo: para. 0290; Figs. 13A-14B]; (In the present disclosure, a measured/reported RS may mean an RS measured/reported for CSI reporting) [Echigo: para. 0079]; (In the present disclosure, UCI, CSI reporting, CSI feedback, feedback information, feedback bit, and the like may be interchangeably interpreted. In the present disclosure, a bit, a bit string, a bit sequence, a sequence, a value, information, a value obtained from a bit, information obtained from a bit, and the like may be interchangeably interpreted) [Echigo: para. 0088]; Figs. 1, 4)).
Regarding claims 3 and 26, Echigo meets the claim limitations as set forth in claims 2 and 25. Echigo further meets the claim limitations as follow.
wherein the assistance information (report information) [Echigo: para. 0290] comprises at least one of: ground truth information collected by the UE, an ID of the ML model ((A data collection stage corresponds to a stage to collect data for generating/updating the AI model. The data collection stage may include data arrangement (for example, determining which data is forwarded for model training/model inference), data forwarding (for example, forwarding data to an entity (for example, UE, gNB) performing model training/model inference), and the like) [Echigo: para. 0035; Figs. 1, 4], a corresponding channel station information (CSI) report configuration (Uplink control information (UCI) including at least one of channel state information (CSI), transmission confirmation information (for example, which may be referred to as Hybrid Automatic Repeat reQuest ACKnowledgement (HARQ-ACK), ACK/NACK, and so on), and scheduling request (SR) may be communicated by means of the PUCCH. By means of the PRACH, random access preambles for establishing connections with cells may be communicated) [Echigo: para. 0337], information regarding the CSI report configuration triggering occasion (An actor stage may include an action trigger (for example, determining whether to trigger action to another entity), feedback (for example, feeding back information required for training data/inference data/performance feedback), and the like) [Echigo: para. 0038] or reporting occasion, or a baseline CSI report using non-ML based precoder matrix indicator (PMI) codebooks (What is compressed by a UE/base station in the present disclosure by using the encoder is not limited to the CSI (or a channel/precoding matrix) but may be arbitrary information) [Echigo: para. 0275], wherein the baseline CSI report is based on the same CSI reference signal (CSI-RS) resource for channel measurement as an ML-based CSI report ((In the present disclosure, a measured/reported RS may mean an RS measured/reported for CSI reporting) [Echigo: para. 0079]; (In the present disclosure, UCI, CSI reporting, CSI feedback, feedback information, feedback bit, and the like may be interchangeably interpreted. In the present disclosure, a bit, a bit string, a bit sequence, a sequence, a value, information, a value obtained from a bit, information obtained from a bit, and the like may be interchangeably interpreted) [Echigo: para. 0088]; Figs. 1, 4).
Regarding claims 4, 13, 21 and 28, Echigo meets the claim limitations as set forth in claims 1, 8, 16 and 24. Echigo further meets the claim limitations as follow.
wherein participating in a change to the ML model based on the performance report ((A data collection stage corresponds to a stage to collect data for generating/updating the AI model. The data collection stage may include data arrangement (for example, determining which data is forwarded for model training/model inference), data forwarding (for example, forwarding data to an entity (for example, UE, gNB) performing model training/model inference), and the like) [Echigo: para. 0035; Figs. 1, 4]; (FIG. 4 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0013; Fig. 4]; (FIG. 7 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0016; Fig. 7]; (FIG. 9 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0018; Fig. 9] ; (FIG. 10 is a diagram to show an example of update of the AI model according to the second embodiment) [Echigo: para. 0019; Fig. 10]; (FIGS. 16A to 16C are diagrams to show an example of updated AI model information) [Echigo: para. 0025; Figs. 16A, 16C]) comprises participating in retraining the ML model or switching to a different ML model ((The UE may transmit information related to a student model to re-train (hereinafter, also referred to as re-trained student model information) to the NW by using physical layer signaling (for example, UCI), higher layer signaling (for example, RRC signaling, MAC CE), a specific signal/channel, or a combination of these. The re-trained student model information may include, instead of or in addition to the AI model information already described, any or combinations of the following: (an index of) the student model that is the base of the re-trained student model, and information related to knowledge of the retrained student model (for example, a soft target, an output from a layer), information related to a data set used for retraining, intermediate information of the re-trained student model (for example, a gradient, a feature, and the like), and information related to a hard target loss for the re-trained student model) [Echigo: para. 0262-0266; Figs. 16A, 16C]; (In the present disclosure, training, learning, update, retraining, and the like may be interchangeably interpreted) [Echigo: para. 0064]; (The communication apparatus 1004 may be configured to include a high frequency switch, a duplexer, a filter, a frequency synthesizer, and so on in order to realize, for example, at least one of frequency division duplex (FDD) and time division duplex (TDD). The input apparatus 1005 is an input device that receives input from the outside (for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, and so on)) [Echigo: para. 0401-0402]).
Regarding claims 5, 14, 22, and 29, Echigo meets the claim limitations as set forth in claims 4, 13, 21 and 28. Echigo further meets the claim limitations as follow.
receiving (The UE may update, in the training, parameters to be updated (fine-tuned) which are obtained from the reference AI model information) [Echigo: para. 0162] an indication (In the present disclosure, UCI, CSI reporting, CSI feedback, feedback information, feedback bit, and the like may be interchangeably interpreted. In the present disclosure, a bit, a bit string, a bit sequence, a sequence, a value, information, a value obtained from a bit, information obtained from a bit, and the like may be interchangeably interpreted) [Echigo: para. 0088]; Figs. 1, 4), from the network entity (In the present disclosure, an autoencoder, an encoder, a decoder, or the like may be interpreted as at least one of a model, an ML model, a neural network model, an AI model, an AI algorithm, and the like. The autoencoder may be interchangeably interpreted as an arbitrary autoencoder such as a stacked autoencoder and a convolutional autoencoder. The encoder/decoder in the present disclosure may adopt a model of Residual Network (ResNet), DenseNet, RefineNet, or the like) [Echigo: para. 0084] to retrain the ML model or switching to the different ML model ((The UE may transmit information related to a student model to re-train (hereinafter, also referred to as re-trained student model information) to the NW by using physical layer signaling (for example, UCI), higher layer signaling (for example, RRC signaling, MAC CE), a specific signal/channel, or a combination of these. The re-trained student model information may include, instead of or in addition to the AI model information already described, any or combinations of the following: (an index of) the student model that is the base of the re-trained student model, and information related to knowledge of the retrained student model (for example, a soft target, an output from a layer), information related to a data set used for retraining, intermediate information of the re-trained student model (for example, a gradient, a feature, and the like), and information related to a hard target loss for the re-trained student model) [Echigo: para. 0262-0266; Figs. 16A, 16C]; (In the present disclosure, training, learning, update, retraining, and the like may be interchangeably interpreted) [Echigo: para. 0064]; (The communication apparatus 1004 may be configured to include a high frequency switch, a duplexer, a filter, a frequency synthesizer, and so on in order to realize, for example, at least one of frequency division duplex (FDD) and time division duplex (TDD). The input apparatus 1005 is an input device that receives input from the outside (for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, and so on)) [Echigo: para. 0401-0402]).
Regarding claims 6, 15, 22, 30, Echigo meets the claim limitations as set forth in claims 1, 8, 16, and 24. Echigo further meets the claim limitations as follow.
wherein the indication (In the present disclosure, UCI, CSI reporting, CSI feedback, feedback information, feedback bit, and the like may be interchangeably interpreted. In the present disclosure, a bit, a bit string, a bit sequence, a sequence, a value, information, a value obtained from a bit, information obtained from a bit, and the like may be interchangeably interpreted) [Echigo: para. 0088]; Figs. 1, 4) comprises a deactivation ((An actor stage may include an action trigger (for example, determining whether to trigger action to another entity), feedback (for example, feeding back information required for training data/inference data/performance feedback), and the like) [Echigo: para. 0038]; (In the present disclosure, "activate," "deactivate," "indicate," "select,!" "configure,!" "update,!" "determine,!" and the like may be interchangeably interpreted. In the present disclosure, "support," "control," "controllable," "operate," "operable," and the like may be interchangeably interpreted.) [Echigo: para. 0071]) of the ML model (In the present disclosure, the UE/BS may input channel state information, a reference signal measurement value, and the like to the ML model) [Echigo: para. 0050].
Regarding claims 7 and 23, Echigo meets the claim limitations as set forth in claims 5 and 22. Echigo further meets the claim limitations as follow.
comprising receiving an acknowledgment of the UE receiving the indication (Uplink control information (UCI) including at least one of channel state information (CSI), transmission confirmation information (for example, which may be referred to as Hybrid Automatic Repeat reQuest ACKnowledgement (HARQ-ACK), ACK/NACK, and so on)) [Echigo: para. 0337]; Figs. 1, 4).
Regarding claim 8, Echigo meets the claim limitations, as follows:
A method of wireless communications (the present disclosure is to provide a terminal, a radio communication method, and a base station capable of achieving preferable overhead reduction/channel estimation/resource use) [Echigo: para. 0007] by a user equipment (UE) (It is preferable to appropriately control AI model training/model inference in a terminal (user terminal, User Equipment (UE))) [Echigo: para. 0006], comprising:generating (In the present disclosure, generation, computation, derivation, and the like may be interchangeably interpreted) [Echigo: para. 0064], a performance report ((For example, the UE may (generate by using the encoder, for example, and) report information related to a location (or positioning)/information related to location estimation in the location management function (LMF) to the network in accordance with at least one of the above described embodiments. The information may be information of a channel impulse response (CIR) per subband/per antenna port) [Echigo: para. 0290; Figs. 13A-14B]; (In the present disclosure, a measured/reported RS may mean an RS measured/reported for CSI reporting) [Echigo: para. 0079]; (In the present disclosure, UCI, CSI reporting, CSI feedback, feedback information, feedback bit, and the like may be interchangeably interpreted. In the present disclosure, a bit, a bit string, a bit sequence, a sequence, a value, information, a value obtained from a bit, information obtained from a bit, and the like may be interchangeably interpreted) [Echigo: para. 0088]; Figs. 1, 4) for a machine learning (ML) model running on at least one of the UE ((It is preferable to appropriately control AI model training/model inference in a terminal (user terminal, User Equipment (UE))) [Echigo: para. 0006]; (In one embodiment of the present disclosure, a terminal (user terminal, User Equipment (UE))/base station (BS) performs training of an ML mode in a training mode, and performs an ML model in inference mode (also referred to as inference mode or the like). In the inference mode, validation of the accuracy of the ML model trained in the training mode (trained ML model) may be performed. In the present disclosure, the UE/BS may input channel state information, a reference signal measurement value, and the like to the ML model and output highly accurate channel state information / measured value/beam selection/location, future channel state information/radio link quality, and the like) [Echigo: para. 0049-0050]) or a network entity (In the present disclosure, an autoencoder, an encoder, a decoder, or the like may be interpreted as at least one of a model, an ML model, a neural network model, an AI model, an AI algorithm, and the like. The autoencoder may be interchangeably interpreted as an arbitrary autoencoder such as a stacked autoencoder and a convolutional autoencoder. The encoder/decoder in the present disclosure may adopt a model of Residual Network (ResNet), DenseNet, RefineNet, or the like) [Echigo: para. 0084]; andparticipating in a change to the ML model based on the performance report ((A data collection stage corresponds to a stage to collect data for generating/updating the AI model. The data collection stage may include data arrangement (for example, determining which data is forwarded for model training/model inference), data forwarding (for example, forwarding data to an entity (for example, UE, gNB) performing model training/model inference), and the like) [Echigo: para. 0035; Figs. 1, 4]; (FIG. 4 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0013; Fig. 4]; (FIG. 7 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0016; Fig. 7]; (FIG. 9 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0018; Fig. 9] ; (FIG. 10 is a diagram to show an example of update of the AI model according to the second embodiment) [Echigo: para. 0019; Fig. 10]; (FIGS. 16A to 16C are diagrams to show an example of updated AI model information) [Echigo: para. 0025; Figs. 16A, 16C]).
Regarding claim 9, Echigo meets the claim limitations as set forth in claim 8. Echigo further meets the claim limitations as follow.
wherein the performance report is generated (The UE preferably applies a report of FIG. 13B in a case that the performance is sufficient in FIG. 13B, a report of FIG. 14A in a case that the performance is sufficient in FIG. 14A, and a report of FIG. 14B in a case that the performance is insufficient in FIG. 14A. In this manner, the UE may dynamically control the communication overhead involved by the notification of the AI model) [Echigo: para. 0192-0195; Figs. 13A-14B] based on at least one of:statistics associated with the ML model (FIG. 13B corresponds to a case where the encoder only is updated. The UE need not include decoder information in the UE-trained AI model information, but may transmit the UE-trained AI model information including, for example, information related to estimated performance of the updated model. The case of FIG. 13B may be used in a case where change in statistical nature of input can be sufficiently dealt with only by modification of the encoder. FIG. 14A corresponds to a case where the encoder and part of the decoder are updated. The UE may transmit the UE-trained AI model information including the decoder information (information of the updated part), for example,
information related to estimated performance of the updated model. The case of FIG. 14A may be used in a case where change in statistical nature of input can be sufficiently dealt with only by relatively minor modification of the decoder. FIG. 14B corresponds to a case where the encoder and entire of the decoder are updated. The UE may transmit the UE-trained AI model information including the decoder information, for example, information related to estimated performance of the updated model, a reference model report, and the like. The case of FIG. 14B may be used in a case where relatively major modification of the decoder is
required for change in statistical nature of input. From a viewpoint of the communication overhead reduction, the UE preferably applies a report of FIG. 13B in a case that the performance is sufficient in FIG. 13B, a report of FIG. 14A in a case that the performance is sufficient in FIG. 14A, and a report of FIG. 14B in a case that the performance is insufficient in FIG. 14A. In this manner, the UE may dynamically control the communication overhead involved by the notification of the AI model) [Echigo: para. 0192-0195; Figs. 13A-14B]; or
results obtained from a separate ML model ((It is preferable to appropriately control AI model training/model inference in a terminal (user terminal, User Equipment (UE))) [Echigo: para. 0006]; (In one embodiment of the present disclosure, a terminal (user terminal, User Equipment (UE))/base station (BS) performs training of an ML mode in a training mode, and performs an ML model in inference mode (also referred to as inference mode or the like). In the inference mode, validation of the accuracy of the ML model trained in the training mode (trained ML model) may be performed. In the present disclosure, the UE/BS may input channel state information, a reference signal measurement value, and the like to the ML model and output highly accurate channel state information/measured value/beam selection/location, future channel state information/radio link quality, and the like) [Echigo: para. 0049-0050]).
Regarding claims 10 and 17, Echigo meets the claim limitations as set forth in claims 8 and 16. Echigo further meets the claim limitations as follow.
receiving assistance information generated by the UE (In one embodiment of the present disclosure, a terminal (user terminal, User Equipment (UE))/base station (BS) performs training of an ML mode in a training mode, and performs an ML model in inference mode (also referred to as inference mode or the like). In the inference mode, validation of the accuracy of the ML model trained in the training mode (trained ML model) may be performed. In the present disclosure, the UE/BS may input channel state information, a reference signal measurement value, and the like to the ML model and output highly accurate channel state information / measured value/beam selection/location, future channel state information/radio link quality, and the like) [Echigo: para. 0049-0050]; andusing the assistance information when generating ((In the present disclosure, the UE/BS may input channel state information, a reference signal measurement value, and the like to the ML model and output highly accurate channel state information / measured value/beam selection/location, future channel state information/radio link quality, and the like) [Echigo: para. 0050]; (In the present disclosure, generation, computation, derivation, and the like may be interchangeably interpreted) [Echigo: para. 0064]) the performance report ((For example, the UE may (generate by using the encoder, for example, and) report information related to a location ( or positioning)/information related to location estimation in the location management function (LMF) to the network in accordance with at least one of the above described embodiments. The information may be information of a channel impulse response (CIR) per subband/per antenna port) [Echigo: para. 0290; Figs. 13A-14B]; (In the present disclosure, a measured/reported RS may mean an RS measured/reported for CSI reporting) [Echigo: para. 0079]; (In the present disclosure, UCI, CSI reporting, CSI feedback, feedback information, feedback bit, and the like may be interchangeably interpreted. In the present disclosure, a bit, a bit string, a bit sequence, a sequence, a value, information, a value obtained from a bit, information obtained from a bit, and the like may be interchangeably interpreted) [Echigo: para. 0088]; Figs. 1, 4)).
Regarding claims 11 and 18, Echigo meets the claim limitations as set forth in claims 10 and 17. Echigo further meets the claim limitations as follow.
wherein the assistance information comprises at least one of: ground truth information collected by the UE, an ID of the ML model ((A data collection stage corresponds to a stage to collect data for generating/updating the AI model. The data collection stage may include data arrangement (for example, determining which data is forwarded for model training/model inference), data forwarding (for example, forwarding data to an entity (for example, UE, gNB) performing model training/model inference), and the like) [Echigo: para. 0035; Figs. 1, 4], a corresponding channel station information (CSI) report configuration (Uplink control information (UCI) including at least one of channel state information (CSI), transmission confirmation information (for example, which may be referred to as Hybrid Automatic Repeat reQuest ACKnowledgement (HARQ-ACK), ACK/NACK, and so on), and scheduling request (SR) may be communicated by means of the PUCCH. By means of the PRACH, random access preambles for establishing connections with cells may be communicated) [Echigo: para. 0337], information regarding the CSI report configuration triggering occasion (An actor stage may include an action trigger (for example, determining whether to trigger action to another entity), feedback (for example, feeding back information required for training data/inference data/performance feedback), and the like) [Echigo: para. 0038] or reporting occasion, or a baseline CSI report using non-ML based precoder matrix indicator (PMI) codebooks (What is compressed by a UE/base station in the present disclosure by using the encoder is not limited to the CSI (or a channel/precoding matrix) but may be arbitrary information) [Echigo: para. 0275], wherein the baseline CSI report is based on the same CSI reference signal (CSI-RS) resource for channel measurement as an ML-based CSI report ((In the present disclosure, a measured/reported RS may mean an RS measured/reported for CSI reporting) [Echigo: para. 0079]; (In the present disclosure, UCI, CSI reporting, CSI feedback, feedback information, feedback bit, and the like may be interchangeably interpreted. In the present disclosure, a bit, a bit string, a bit sequence, a sequence, a value, information, a value obtained from a bit, information obtained from a bit, and the like may be interchangeably interpreted) [Echigo: para. 0088]; Figs. 1, 4).
Regarding claims 12 and 23, Echigo meets the claim limitations as set forth in claim2 8 and 16. Echigo further meets the claim limitations as follow.
wherein the performance report (The UE preferably applies a report of FIG. 13B in a case that the performance is sufficient in FIG. 13B, a report of FIG. 14A in a case that the performance is sufficient in FIG. 14A, and a report of FIG. 14B in a case that the performance is insufficient in FIG. 14A. In this manner, the UE may dynamically control the communication overhead involved by the notification of the AI model.) [Echigo: para. 0192-0195; Figs. 13A-14B] indicates at least one of:an indication of a difference in predicted and actual performance (checking whether a trained model meets a performance threshold) [Echigo: para. 0036], general system performance (model performance feedback (feeding back model performance
to an entity performing model training)) [Echigo: para. 0037], statistics per individual inference by the ML model ((In one embodiment of the present disclosure, a terminal (user terminal, User Equipment (UE))/base station (BS) performs training of an ML mode in a training mode, and performs an ML model in inference mode (also referred to as inference mode or the like). In the inference mode, validation of the accuracy of the ML model trained in the training mode (trained ML model) may be performed. In the present disclosure, the UE/BS may input channel state information, a reference signal measurement value, and the like to the ML model and output highly accurate channel state information / measured value/beam selection/location, future channel state information/radio link quality, and the like) [Echigo: para. 0049-0050]), or
statistics per multiple inferences by the ML model (FIG. 13B corresponds to a case where the encoder only is updated. The UE need not include decoder information in the UE -trained AI model information, but may transmit the UE -trained AI model information including, for example, information related to estimated performance of the updated model. The case of FIG. 13B may be used in a case where change in statistical nature of input can be sufficiently dealt with only by modification of the encoder. FIG. 14A corresponds to a case where the encoder and part of the decoder are updated. The UE may transmit the UE-trained AI model information including the decoder information (information of the updated part), for example, information related to estimated performance of the updated model. The case of FIG. 14A may be used in a case where change in statistical nature of input can be sufficiently dealt with only by relatively minor modification of the decoder. FIG. 14B corresponds to a case where the encoder and entire of the decoder are updated. The UE may transmit the UE-trained AI model information including the decoder information, for example, information related to estimated performance of the updated model, a reference model report, and the like. The case of FIG. 14B may be used in a case where relatively major modification of the decoder is required for change in statistical nature of input. From a viewpoint of the communication overhead reduction, the UE preferably applies a report of FIG. 13B in a case that the performance is sufficient in FIG. 13B, a report of FIG. 14A in a case that the performance is sufficient in FIG. 14A, and a report of FIG. 14B in a case that the performance is insufficient in FIG. 14A. In this manner, the UE may dynamically control the communication overhead involved by the notification of the AI model.) [Echigo: para. 0192-0195; Figs. 13A-14B].
Regarding claim 16, Echigo meets the claim limitations, as follows:
A method of wireless communications (the present disclosure is to provide a terminal, a radio communication method, and a base station capable of achieving preferable overhead reduction/channel estimation/resource use) [Echigo: para. 0007] by a user equipment (UE) (It is preferable to appropriately control AI model training/model inference in a terminal (user terminal, User Equipment (UE))) [Echigo: para. 0006], comprising:transmiting (The control section 110 may generate data, control information, a sequence and so on to transmit as a signal, and forward the generated items to the transmitting/receiving section 120) [Echigo: para. 0345], a performance report ((From a viewpoint of the communication overhead reduction, the UE preferably applies a report of FIG. 13B in a case that the performance is sufficient in FIG. 13B, a report of FIG. 14A in a case that the performance is sufficient in FIG. 14A, and a report of FIG. 14B in a case that the
performance is insufficient in FIG. 14A) [Echigo: para. 0195; Figs. 13A-14B]; (In the present disclosure, a measured/reported RS may mean an RS measured/reported for CSI reporting) [Echigo: para. 0079]; (In the present disclosure, UCI, CSI reporting, CSI feedback, feedback information, feedback bit, and the like may be interchangeably interpreted. In the present disclosure, a bit, a bit string, a bit sequence, a sequence, a value, information, a value obtained from a bit, information obtained from a bit, and the like may be interchangeably interpreted) [Echigo: para. 0088]; Figs. 1, 4) for a machine learning (ML) model running on at least one of the UE ((It is preferable to appropriately control AI model training/model inference in a terminal (user terminal, User Equipment (UE))) [Echigo: para. 0006]; (In one embodiment of the present disclosure, a terminal (user terminal, User Equipment (UE))/base station (BS) performs training of an ML mode in a training mode, and performs an ML model in inference mode (also referred to as inference mode or the like). In the inference mode, validation of the accuracy of the ML model trained in the training mode (trained ML model) may be performed. In the present disclosure, the UE/BS may input channel state information, a reference signal measurement value, and the like to the ML model and output highly accurate channel state information/measured value/beam selection/location, future channel state information/radio link quality, and the like) [Echigo: para. 0049-0050]) or a network entity (In the present disclosure, an autoencoder, an encoder, a decoder, or the like may be interpreted as at least one of a model, an ML model, a neural network model, an AI model, an AI algorithm, and the like. The autoencoder may be interchangeably interpreted as an arbitrary autoencoder such as a stacked autoencoder and a convolutional autoencoder. The encoder/decoder in the present disclosure may adopt a model of Residual Network (ResNet), DenseNet, RefineNet, or the like) [Echigo: para. 0084]; andparticipating in a change to the ML model based on the performance report ((A data collection stage corresponds to a stage to collect data for generating/updating the AI model. The data collection stage may include data arrangement (for example, determining which data is forwarded for model training/model inference), data forwarding (for example, forwarding data to an entity (for example, UE, gNB) performing model training/model inference), and the like) [Echigo: para. 0035; Figs. 1, 4]; (FIG. 4 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0013; Fig. 4]; (FIG. 7 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0016; Fig. 7]; (FIG. 9 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0018; Fig. 9] ; (FIG. 10 is a diagram to show an example of update of the AI model according to the second embodiment) [Echigo: para. 0019; Fig. 10]; (FIGS. 16A to 16C are diagrams to show an example of updated AI model information) [Echigo: para. 0025; Figs. 16A, 16C]).
Regarding claim 19, Echigo meets the claim limitations as set forth in claim 17. Echigo further meets the claim limitations as follow.
wherein receiving the assistance information (report information) [Echigo: para. 0290] comprises receiving a baseline CSI report (In the present disclosure, a measured / reported RS may mean an RS measured/reported for CSI reporting) [Echigo: para. 0079] using a non-ML based precoder matrix indicator (PMI) codebook (a precoding matrix indicator (PMI)) [Echigo: para. 0086], wherein the baseline CSI report is based on same CSI reference signal (CSI-RS_ resource for channel measurement as a ML-based CSI report (a CSI-RS resource indicator (CRI)) [Echigo: para. 0087].
Regarding claim 20, Echigo meets the claim limitations as set forth in claim 19. Echigo further meets the claim limitations as follow.
further comprising transmitting (The control section 110 may generate data, control information, a sequence and so on to transmit as a signal, and forward the generated items to the transmitting/receiving section 120) [Echigo: para. 0345] a request (Uplink control information (UCI) including at least one of channel state information (CSI), transmission confirmation information (for example, which may be referred to as Hybrid Automatic Repeat reQuest ACKnowledgement (HARQ-ACK), ACK/NACK, and so on), and scheduling request (SR) may be communicated by means of the PUCCH. By means of the PRACH, random access preambles for establishing connections with cells may be communicated) [Echigo: para. 0337] or configuration of a baseline CSI report (In the present disclosure, a measured / reported RS may mean an RS measured/reported for CSI reporting) [Echigo: para. 0079] using non-ML based PMI codebook (a precoding matrix indicator (PMI)) [Echigo: para. 0086], wherein the configuration or request comprises configuring same CSI-RS resource (a CSI-RS resource indicator (CRI)) [Echigo: para. 0087] for channel measurement for the ML-based CSI report (If the model training of the autoencoder is performed in the gNB/OAM, the UE needs to transmit the complete downlink CSI information (or channel information), but a communication overhead is large. On the other hand, if the model training of the autoencoder is performed
in the UE, the UE needs to maintain the complete downlink CSI information ( or channel information), but a storage/computation resource of the UE is scarcer than that of the gNB/OAM.) [Echigo: para. 0046] and the baseline CSI report (For CSI feedback/channel estimation using an autoencoder, the OAM/gNB/UE may perform the model training, and the gNB/UE may perform the model inference (jointly)) [Echigo: para. 0043].
Regarding claim 24, Echigo meets the claim limitations, as follows:
A method of wireless communications (the present disclosure is to provide a terminal, a radio communication method, and a base station capable of achieving preferable overhead reduction/channel estimation/resource use) [Echigo: para. 0007] by a user equipment (UE) (It is preferable to appropriately control AI model training/model inference in a terminal (user terminal, User Equipment (UE))) [Echigo: para. 0006], comprising:receiving (The UE may update, in the training, parameters to be updated (fine-tuned) which are obtained from the reference AI model information) [Echigo: para. 0162], a performance report ((From a viewpoint of the communication overhead reduction, the UE preferably applies a report of FIG. 13B in a case that the performance is sufficient in FIG. 13B, a report of FIG. 14A in a case that the performance is sufficient in FIG. 14A, and a report of FIG. 14B in a case that the
performance is insufficient in FIG. 14A) [Echigo: para. 0195; Figs. 13A-14B]; (In the present disclosure, a measured/reported RS may mean an RS measured/reported for CSI reporting) [Echigo: para. 0079]; (In the present disclosure, UCI, CSI reporting, CSI feedback, feedback information, feedback bit, and the like may be interchangeably interpreted. In the present disclosure, a bit, a bit string, a bit sequence, a sequence, a value, information, a value obtained from a bit, information obtained from a bit, and the like may be interchangeably interpreted) [Echigo: para. 0088]; Figs. 1, 4) for a machine learning (ML) model running on at least one of the UE ((It is preferable to appropriately control AI model training/model inference in a terminal (user terminal, User Equipment (UE))) [Echigo: para. 0006]; (In one embodiment of the present disclosure, a terminal (user terminal, User Equipment (UE))/base station (BS) performs training of an ML mode in a training mode, and performs an ML model in inference mode (also referred to as inference mode or the like). In the inference mode, validation of the accuracy of the ML model trained in the training mode (trained ML model) may be performed. In the present disclosure, the UE/BS may input channel state information, a reference signal measurement value, and the like to the ML model and output highly accurate channel state information/measured value/beam selection/location, future channel state information/radio link quality, and the like) [Echigo: para. 0049-0050]) or a network entity (In the present disclosure, an autoencoder, an encoder, a decoder, or the like may be interpreted as at least one of a model, an ML model, a neural network model, an AI model, an AI algorithm, and the like. The autoencoder may be interchangeably interpreted as an arbitrary autoencoder such as a stacked autoencoder and a convolutional autoencoder. The encoder/decoder in the present disclosure may adopt a model of Residual Network (ResNet), DenseNet, RefineNet, or the like) [Echigo: para. 0084]; andparticipating in a change to the ML model based on the performance report ((A data collection stage corresponds to a stage to collect data for generating/updating the AI model. The data collection stage may include data arrangement (for example, determining which data is forwarded for model training/model inference), data forwarding (for example, forwarding data to an entity (for example, UE, gNB) performing model training/model inference), and the like) [Echigo: para. 0035; Figs. 1, 4]; (FIG. 4 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0013; Fig. 4]; (FIG. 7 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0016; Fig. 7]; (FIG. 9 is a diagram to show an example of update of the AI model according to the first embodiment) [Echigo: para. 0018; Fig. 9] ; (FIG. 10 is a diagram to show an example of update of the AI model according to the second embodiment) [Echigo: para. 0019; Fig. 10]; (FIGS. 16A to 16C are diagrams to show an example of updated AI model information) [Echigo: para. 0025; Figs. 16A, 16C]).
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) 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 under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 6, 15, 22, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Echigo (US Patent Application Publication US 2025/0227498 A1), (“Echigo”), in view of Cella et al. (US Patent Application Publication 2022/0366494 A1), (“Cella”).
Regarding claims 6, 15, 22, 30, Echigo meets the claim limitations as set forth in claims 1, 8, 16, and 24. Echigo further meets the claim limitations as follow.
wherein the indication (In the present disclosure, UCI, CSI reporting, CSI feedback, feedback information, feedback bit, and the like may be interchangeably interpreted. In the present disclosure, a bit, a bit string, a bit sequence, a sequence, a value, information, a value obtained from a bit, information obtained from a bit, and the like may be interchangeably interpreted) [Echigo: para. 0088]; Figs. 1, 4) comprises a deactivation (An actor stage may include an action trigger (for example, determining whether to trigger action to another entity), feedback (for example, feeding back information required for training data/inference data/performance feedback), and the like) [Echigo: para. 0038] of the ML model (In the present disclosure, the UE/BS may input channel state information, a reference signal measurement value, and the like to the ML model) [Echigo: para. 0050].
Echigo does not explicitly disclose the following claim limitations (Emphasis added).
wherein the indication comprises a deactivation of the ML model.
However, in the same field of endeavor Cella further discloses the deficient claim limitations as follows:
wherein the indication comprises a deactivation (the conditioning of an activation / deactivation of a system) [Echigo: para. 0275].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Echigo with Cella to program the system to implement of Cella’s method.
Therefore, the combination of Echigo with Cella will enable the system to determine data resources and their configuration in a model, which help to launch a new service [Cella: Abstract].
Reference Notice
Additional prior arts, included in the Notice of Reference Cited, made of record and not relied upon is considered pertinent to applicant's disclosure.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip Dang whose telephone number is (408) 918-7529. The examiner can normally be reached on Monday-Thursday between 8:30 am - 5:00 pm (PST).
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 http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sath Perungavoor can be reached on 571-272-7455. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000./Philip P. Dang/Primary Examiner, Art Unit 2488
