DETAILED ACTION
1. It is hereby acknowledged that 18/815149 the following papers have been received and placed of record in the file: Remark date 08/26/24
Specification
2. The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Objections
3. Claims 1-15 are objected to because of the following informalities: The claims 1 and 15 recite “… control data… The use of the term control data is vague. This leaves one questioning what is being controlled, the data or something else. It also leaves one questioning or wondering how is it related to model learning.
The specification discloses, In step S1, the UE 100 transmits or receives control data related to the model learning to or from the gNB 200. The control data may be an RRC message that is RRC layer (i.e., layer-3) signaling. The control data may be a MAC Control Element (CE) that is MAC layer (i.e., layer-2) signaling. The control data may be downlink control information (DCI) that is PHY layer (i.e., layer-1) signaling. The downlink signaling may be UE-specific signaling. The downlink signaling may be broadcast signaling. The control data may be a control message in a control layer (e.g., AI/ML layer) dedicated to artificial intelligence or machine learning. (see paragraph [0064])
Claim 3 recites”….performing model inference through which inference result data is inferred from inference data comprising…” It is unclear the meaning of this limitation. Inferred from inference data makes one question what exactly is being deduced, predicted or estimated. It also is considered vague with all of the inferencing being done in the claim limitation.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
4. 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
5. Claim(s) 1-15 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by FARMANBAR(US 2021/0067297A1),
Regarding claim 1, Farmanbar teaches a communication control method performed by a first communication apparatus configured to perform wireless communication with a second communication apparatus in a mobile communication system using a machine learning technology, the communication control method comprising:
learning by performing model learning through which a learned model is derived by using learning data comprising a reception signal from the second communication apparatus; (see Farmanbar Fig. 2, 3, 4, 10-15 Farmanbar paragraphs [0060]-[0063] [0087] explains….a UE trains one ML module for each of multiple sparse CSI-RS configurations…..)
and controlling transmission and/or reception of control data related to the model learning to and from the second communication apparatus. (see Farmanbar [0051],[0055],[0063],[0165] explains …During a training phase in which the UE 206 is determining appropriate sparse reference signaling that can be used to reduce overhead without significantly impacting performance in respect of determining channel coefficients or parameters, at 210 the BS transmits CSI-RS signaling…UE configuration may be performed even for a UE that determined a sparse signaling configuration at 316. For example, transmitting CSI-RS signaling may involve CSI-RS configuration of a UE, by Radio Resource Control (RRC) signaling for example, and sending CSI-RS signaling……..the processor-executable instructions, when executed by the processor, further cause the processor to receive, from the UE, an indication that the network equipment is to transition from the sparse reference signaling to dense reference signaling for the UE, and to transmit, to the UE, the dense reference signaling…Also see [0071]-[0072],[0075]-[0079],[0101],[0125])
Regarding Claim 2, Farmanbar taught the communication control method according to claim 1, as described above. Fermanbar further teaches wherein the reception signal comprises a reference signal received by the first communication apparatus from the second communication apparatus. (see Farmanbar Fig.2, 3, 4, 10-15 paragraphs [0060]-[0063] [0087] explains….a UE trains one ML module for each of multiple sparse CSI-RS configurations…..)
Regarding Claim 3, Farmanbar taught the communication control method according to claim 1, as described above. Fermanbar further teaches as described above. Farmanbar further teaches comprising: inferring by performing model inference through which inference result data is inferred from inference data comprising the reception signal from the second communication apparatus, by using the learned model. (see Farmanbar Fig.2, 3, 4, 10-15 paragraphs [0060]-[0066] ,[0087]-[0089] explains….UE2 performs CSI estimation and prediction, and CSI feedback is transmitted to the BS by UE2 at 326. The CSI feedback is labeled as sparse CSI feedback in FIG. 3, to indicate that the CSI feedback is based on sparse reference signaling….a UE trains one ML module for each of multiple sparse CSI-RS configurations….. .)
Regarding Claim 4, Farmanbar taught the communication control method according to claim 3, as described above. Fermanbar further teaches wherein the learning comprises receiving a first reference signal from the second communication apparatus by using a first resource, and
deriving the learned model for inferring channel state information from a reference signal by using the learning data comprising the first reference signal, and the inferring comprises receiving a second reference signal from the second communication apparatus by using a second resource less than the first resource, and inferring the channel state information as the inference result data from the inference data comprising the second reference signal, by using the learned model. (see Farmanbar Fig.2, 3, 4, 10-15 paragraphs [0060]-[0066] ,[0087]-[0089] explains….UE2 performs CSI estimation and prediction, and CSI feedback is transmitted to the BS by UE2 at 326. The CSI feedback is labeled as sparse CSI feedback in FIG. 3, to indicate that the CSI feedback is based on sparse reference signaling….a UE trains one ML module for each of multiple sparse CSI-RS configurations….. .)
Regarding Claim 5, Farmanbar taught the communication control method according to claim 4, as described above. Fermanbar further teaches wherein the first communication apparatus is a user equipment, and the second communication apparatus is a base station. (see Farmanbar Fig.2, 3, 4, 10-15 paragraphs [0060]-[0069] ,[0087]-[0089] explains….UE2 performs CSI estimation and prediction, and CSI feedback is transmitted to the BS by UE2 at 326. The CSI feedback is labeled as sparse CSI feedback in FIG. 3, to indicate that the CSI feedback is based on sparse reference signaling….a UE trains one ML module for each of multiple sparse CSI-RS configurations….. .)
Regarding Claim 6, Farmanbar taught the communication control method according to claim 5, as described above. Fermanbar further teaches wherein the controlling comprises receiving, by the user equipment from the base station, a switching notification as the control data, the switching notification providing notification of mode switching between a mode for performing the model learning and a mode for performing the model inference. (see Farmanbar Fig.2, 3, 4, 10-15 paragraphs [0060]-[0069] ,[0078], The number of data samples collected and used to determine sparse reference signaling patterns may be statically or dynamically configured at a UE. Another option for ML embodiments involves monitoring an ML module for convergence, and transitioning from a training phase to an operations phase, or transitioning from relatively more dense reference signaling to sparse reference signaling, when a target degree of convergence is reached. Any of various types of cost functions, and/or other convergence testing techniques, may be applied to determine convergence. ….. .)
Regarding Claim 7, Farmanbar taught the communication control method according to claim 5, as described above. Fermanbar further teaches wherein the controlling comprises transmitting, by the user equipment to the base station, a completion notification as the control data when the model learning is completed, the completion notification indicating that the model learning is completed. (see Farmanbar Fig.2, 3, 4, 10-15 paragraphs [0060]-[0069] ,[0078], The number of data samples collected and used to determine sparse reference signaling patterns may be statically or dynamically configured at a UE. Another option for ML embodiments involves monitoring an ML module for convergence, and transitioning from a training phase to an operations phase, or transitioning from relatively more dense reference signaling to sparse reference signaling, when a target degree of convergence is reached. Any of various types of cost functions, and/or other convergence testing techniques, may be applied to determine convergence. ….. .)
Regarding Claim 8, Farmanbar taught the communication control method according to claim 5, as described above. Fermanbar further teaches wherein the controlling comprises receiving, by the user equipment from the base station, a completion condition notification as the control data, the completion condition notification indicating a completion condition of the model learning. (see Farmanbar Fig.2, 3, 4, 10-15 paragraphs [0060]-[0069] ,[0078], The number of data samples collected and used to determine sparse reference signaling patterns may be statically or dynamically configured at a UE. Another option for ML embodiments involves monitoring an ML module for convergence, and transitioning from a training phase to an operations phase, or transitioning from relatively more dense reference signaling to sparse reference signaling, when a target degree of convergence is reached. Any of various types of cost functions, and/or other convergence testing techniques, may be applied to determine convergence. ….. .)
Regarding Claim 9, Farmanbar taught the communication control method according to claim 5, as described above. Fermanbar further teaches wherein the controlling comprises receiving, by the user equipment from the base station, data type information as the control data, the data type information designating at least a type of data used as the learning data. (see Farmanbar Fig.2, 3, 4, 10-15 paragraphs [0060]-[0069] ,[0078], The number of data samples collected and used to determine sparse reference signaling patterns may be statically or dynamically configured at a UE. Another option for ML embodiments involves monitoring an ML module for convergence, and transitioning from a training phase to an operations phase, or transitioning from relatively more dense reference signaling to sparse reference signaling, when a target degree of convergence is reached. Any of various types of cost functions, and/or other convergence testing techniques, may be applied to determine convergence. ….. .)
Regarding Claim 10, Farmanbar taught the communication control method according to claim 5, wherein the controlling comprises transmitting, by the user equipment to the base station, preference information as the control data, the preference information indicating a preference of the first communication apparatus for a transmission pattern of the second reference signal. (see Farmanbar Fig.2, 3, 4, 10-15 paragraphs [0060]-[0069] ,[0078], The number of data samples collected and used to determine sparse reference signaling patterns may be statically or dynamically configured at a UE. Another option for ML embodiments involves monitoring an ML module for convergence, and transitioning from a training phase to an operations phase, or transitioning from relatively more dense reference signaling to sparse reference signaling, when a target degree of convergence is reached. Any of various types of cost functions, and/or other convergence testing techniques, may be applied to determine convergence. ….. .)
Regarding Claim 11, Farmanbar taught the communication control method according to claim 4, as described above. Fermanbar further teaches wherein the first communication apparatus is a base station, and
the second communication apparatus is a user equipment. (see Farmanbar Fig.2, 3, 4, 10-15 paragraphs [0060]-[0069] ,[0078], The number of data samples collected and used to determine sparse reference signaling patterns may be statically or dynamically configured at a UE. Another option for ML embodiments involves monitoring an ML module for convergence, and transitioning from a training phase to an operations phase, or transitioning from relatively more dense reference signaling to sparse reference signaling, when a target degree of convergence is reached. Any of various types of cost functions, and/or other convergence testing techniques, may be applied to determine convergence. ….. .)
Regarding Claim 12, Farmanbar taught the communication control method according to claim 5, as described above. Fermanbar further teaches wherein the controlling comprises transmitting, by the base station to the user equipment, reference signal type information as the control data, the reference signal type information indicating a type of a reference signal, out of the first reference signal and the second reference signal, to be transmitted by the user equipment. (see Farmanbar Fig.2, 3, 4, 10-15 paragraphs [0060]-[0063] [0087] explains….a UE trains one ML module for each of multiple sparse CSI-RS configurations…..,[0078], The number of data samples collected and used to determine sparse reference signaling patterns may be statically or dynamically configured at a UE. Another option for ML embodiments involves monitoring an ML module for convergence, and transitioning from a training phase to an operations phase, or transitioning from relatively more dense reference signaling to sparse reference signaling, when a target degree of convergence is reached. Any of various types of cost functions, and/or other convergence testing techniques, may be applied to determine convergence. ….. .))
Regarding Claim 13, Farmanbar taught the communication control method according to claim 1, as described above. Fermanbar further teaches wherein the controlling comprises transmitting, as the control data, a variable parameter comprised in the learned model to the second communication apparatus, the first communication apparatus is a user equipment, and the second communication apparatus is a base station. (see paragraph [0075]-[0078] explains…A signaling configuration or pattern may be associated, by pattern index for example, with corresponding CSI measurement results or estimates and/or with antenna port, by antenna port index for example. Such associations may be implicit or explicit, and may be useful in identifying or otherwise obtaining an appropriate predictor for partial channel prediction…… Any of various types of cost functions, and/or other convergence testing techniques, may be applied to determine convergence….)
Regarding Claim 14, Farmanbar taught the communication control method according to claim 13, wherein the controlling further comprises receiving, by the user equipment from the base station, trigger configuration information as the control data, the trigger configuration information being for configuring a transmission trigger condition for the user equipment to transmit the variable parameter. (see paragraph [0075]-[0078] explains…A signaling configuration or pattern may be associated, by pattern index for example, with corresponding CSI measurement results or estimates and/or with antenna port, by antenna port index for example. Such associations may be implicit or explicit, and may be useful in identifying or otherwise obtaining an appropriate predictor for partial channel prediction…… Any of various types of cost functions, and/or other convergence testing techniques, may be applied to determine convergence….)
Regarding Claim 15, Farmanbar teaches a communication apparatus for communicating with another communication apparatus in a mobile communication system using a machine learning technology, the communication apparatus comprising:
a controller configured to perform
processing of performing model learning through which a learned model is derived by using learning data comprising a reception signal from the other communication apparatus, (see Farmanbar Fig. 2, 3, 4, 10-15 Farmanbar paragraphs [0060]-[0063] [0087] ,[0165] explains….a UE trains one ML module for each of multiple sparse CSI-RS configurations…..) and
processing of transmitting and/or receiving control data related to the model learning to and from the other communication apparatus. (see Farmanbar [0051],[0055][0165] explains …During a training phase in which the UE 206 is determining appropriate sparse reference signaling that can be used to reduce overhead without significantly impacting performance in respect of determining channel coefficients or parameters, at 210 the BS transmits CSI-RS signaling…UE configuration may be performed even for a UE that determined a sparse signaling configuration at 316. For example, transmitting CSI-RS signaling may involve CSI-RS configuration of a UE, by Radio Resource Control (RRC) signaling for example, and sending CSI-RS signaling…..the processor-executable instructions, when executed by the processor, further cause the processor to receive, from the UE, an indication that the network equipment is to transition from the sparse reference signaling to dense reference signaling for the UE, and to transmit, to the UE, the dense reference signaling… Also see [0071]-[0072],[0075]-[0079],[0101]) )
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Gerald Smarth whose telephone number is (571) 270-1923. The examiner can normally be reached on Monday-Thursday 6am-4:30pm ET. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Joseph Avellino can be reached on 571-272-3905. 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.
/GERALD A SMARTH/Primary Examiner, Art Unit 2478
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