DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 35 U.S.C. 112 (pre-AIA ), 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.
Claims 9 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 recites the limitation " the number of repeated transmissions of beams or the number of repeated transmissions of channel state information reference signals" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. The claims are read as best understood by the examiner for purpose of examination.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1-4, 8, 12-14, 18-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by KUMAR et al (US 2025/0219919)
Regarding claim 1, KUMAR et al (US 2025/0219919) discloses an AI monitoring device, comprising: a receiver configured to receive a signal or information transmitted by a terminal equipment (KUMAR: ¶111-112, Fig. 7, the receiver of a gNB which is a monitoring device for an AI/ML model being run at least at a UE, receiving at least an input data and input data to the data collection function including measurement information); and processor circuitry configured to perform monitoring or performance evaluation on an AI/ML model in a network device and/or the terminal equipment according to the signal or information (KUMAR: Fig. 7, Fig. 10, ¶126, the network performance monitoring is performed at the network device based on the reported signals from the UE including the collected measurements and KPI information).
Regarding claim 2, KUMAR discloses device according to claim 1, wherein the network device performs monitoring or performance evaluation on the AI/ML model in the terminal equipment (KUMAR: Fig. 7, Fig. 8, ¶126, the network device or terminal device monitors performance of the AI/ML model according to the signals provided by the UE/terminal), and the device further comprises:
a transmitter configured to, in a case where the performance of the AI/ML model satisfies a predetermined condition, transmit instruction information for stopping the AI/ML model to the terminal equipment (KUMAR: Fig. 7, Fig. 8, ¶126, ¶129, ¶134, ¶172-173, the AI/ML model is stopped and the gNB replaces it with a legacy algorithm, equivalent to stopping the AI/ML model used previously when the performance is poor).
Regarding claim 3, KUMAR discloses device according to claim 2, wherein the signal or information transmitted by the terminal equipment is a signal or information related to the AI/ML model in the terminal equipment for a signal processing function (KUMAR: ¶129, ¶134, ¶140, Fig. 11, a performance report for the AI/ML model running at the UE is transmitted by the UE for a signal throughput/latency, packet drop rate for the signal processing at the UE and gNB).
Regarding claim 4, KUMAR discloses according to claim 2, wherein the instruction information further includes identification information of the AI/ML model, and/or, the instruction information further instructs the terminal equipment to switch to non-AI/ML processing corresponding to a signal processing function, the identification information of the AI/ML model including at least one of the following: a signal processing function identifier to which the AI/ML model corresponds, an identifier of the AI/ML model, a model group identifier of the AI/ML model, or an intra-group identifier of the AI/ML model (KUMARL ¶136, feedback configuration parametewrs are configured by the eNB which include model IDs).
Regarding claim 8, KUMAR discloses device according to claim 2, wherein the AI/ML model operates in the terminal equipment and is used for beam management or beam prediction, and the network device monitors an uplink signal from the terminal equipment, and performs performance evaluation on the AI/ML model according to the uplink signal (KUMAR: ¶153, ¶88, beam management is performed for communication between the network device/gNB and the UE; ¶153, ¶119,the AI/ML model is used for evaluation the communication between the UE and the network device/gNB which also evaluates based on the beam management).
Regarding claim 12, KUMAR discloses device according to claim 1, wherein the network device performs monitoring or performance evaluation on the AI/ML model in the network device, and in a case where the performance of the AI/ML model satisfies the predetermined condition, the network device stops the AI/ML model (KUMAR: Fig. 7, Fig. 8, ¶126, ¶129, ¶134, ¶172-173; the AI/ML model is stopped and the gNB replaces it with a legacy algorithm, equivalent to stopping the AI/ML model used previously when the performance is poor).
Regarding claim 13, KUMAR discloses device according to claim 12, wherein the signal or information transmitted by the terminal equipment includes at least one of the following: a sounding reference signal, reference signal received power, hybrid automatic retransmission request feedback information, beam failure request information, or beam failure recovery information (KUMAR: ¶67, ¶86, ¶108, sounding reference signals (SRS), RSRP received from the UE).
Regarding claim 14, KUMAR discloses device according to claim 1, wherein the network device performs monitoring or performance evaluation on an AI/ML model in a cell, and in a case where the number of terminal equipments with the performance of the AI/ML model satisfying the predetermined condition reaches a threshold, the network device determines to stop the AI/ML model (KUMAR: Fig. 7, Fig. 8, ¶126, ¶129, ¶134, ¶172-173; the AI/ML model is stopped and the gNB replaces it with a legacy algorithm, equivalent to stopping the AI/ML model used previously when the performance is poor; ¶105, Fig. 1, this is for a serving cell and neighboring cells).
Regarding claim 18, KUMAR discloses AI monitoring device, comprising: a receiver configured to receive a signal or information transmitted by a network device (KUMAR: ¶136, Fig. 11, the UE monitors/evaluates performance of an AI/ML model; UE receives configuration for monitoring from the gNB ); and processor circuitry configured to perform monitoring or performance evaluation on an AI/ML model in the network device and/or a terminal equipment according to the signal or information (KUMAR: ¶136, Fig. 11, the UE monitors/evaluates performance according to the receive configuration AI/ML model).
Regarding claim 19, KUMAR discloses device according to claim 18, wherein the terminal equipment performs monitoring or performance evaluation on the AI/ML model in the network device, and in a case where the performance of the AI/ML model satisfies a predetermined condition, the terminal equipment transmits request information for stopping the AI/ML model to the network device ; and/or, the terminal equipment performs monitoring or performance evaluation on the AI/ML model in the terminal equipment, and in a case where the performance of the AI/ML model satisfies the predetermined condition, the terminal equipment transmits request information for stopping the AI/ML model to the network device (KUMAR: Fig. 7, Fig. 11, ¶124, the network performance monitoring is performed at the network device based on the reported signals from the UE including the collected measurements and KPI information).
Regarding claim 20, KUMAR discloses communication system, comprising: a terminal equipment configured to receive a signal or information transmitted by a network device, and perform monitoring or performance evaluation on an AI/ML model in the network device and/or the terminal equipment according to the signal or information; and/or the network device configured to receive a signal or information transmitted by the terminal equipment, and perform monitoring or performance evaluation on the AI/ML model in the network device and/or the terminal equipment according to the signal or information (KUMAR: Fig. 7, Fig. 11, ¶124, 126, the network performance monitoring is performed at the network device based on the reported signals from the UE including the collected measurements and KPI information).
Claim(s) 5-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over KUMAR et al (US 2025/0219919) in view of KANAMARLAPUDI et al (US 2023/0246750)
Regarding claim 5, KUMAR discloses according to claim 2, wherein the AI/ML model operates in the terminal equipment and is used for channel state information estimation or prediction (KUMAR: ¶142-143, the performance reports include channel state information which is derived by using AI/ML model), and the network device monitors KPI feedback information from the terminal equipment, and performs performance evaluation on the AI/ML model according to the KPI feedback information (KUMAR: ¶143-144, the network device/gNB monitors KPI feedback information from the terminal equipment; and performs evaluation of the AI/ML based on the KPI feedback information).
KUMAR remains silent regarding KPI is based on hybrid automatic retransmission request feedback.
However, KANAMARLAPUDI et al (US 2023/0246750) discloses KPI is based on hybrid automatic retransmission request feedback (KANAMARLAPUDI: ¶95, KPIs include HARQ feedback).
A person of ordinary skill in the art working with the invention of KUMAR would have been motivated to use the teachings of KANAMARLAPUDI as it provides standard KPIs used for a connection between UE and base station. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify invention of KUMAR with teachings of KANAMARLAPUDI in order to improve compatibility of the technique with various performance evaluation algorithms used in the field of art.
Regarding claim 10, KUMAR discloses device according to claim 2, wherein the AI/ML model operates in the terminal equipment and is used for beam management or beam prediction (KUMAR: ¶153, ¶119,the AI/ML model is used for evaluation the communication between the UE and the network device/gNB which also evaluates based on the beam management), and the network device monitors KPI feedback information from the terminal equipment, and performs performance evaluation on the AI/ML model according to the KPI feedback information (KUMAR: ¶143-144, the network device/gNB monitors KPI feedback information from the terminal equipment; and performs evaluation of the AI/ML based on the KPI feedback information).
KUMAR remains silent regarding KPI is based on hybrid automatic retransmission request feedback.
However, KANAMARLAPUDI et al (US 2023/0246750) discloses KPI is based on hybrid automatic retransmission request feedback (KANAMARLAPUDI: ¶95, KPIs include HARQ feedback).
A person of ordinary skill in the art working with the invention of KUMAR would have been motivated to use the teachings of KANAMARLAPUDI as it provides standard KPIs used for a connection between UE and base station. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify invention of KUMAR with teachings of KANAMARLAPUDI in order to improve compatibility of the technique with various performance evaluation algorithms used in the field of art.
Regarding claim 6, KUMAR modified by KANAMARLAPUDI discloses device according to claim 5, wherein the network device configures and instructs the terminal equipment to perform channel state information estimation and reporting, the terminal equipment performing channel state information estimation or prediction by using the AI/ML model (KUMAR: ¶149, ¶142, ¶119, ¶81, the network device configures the UE with CSI estimation and compare the predicted CSI estimation), the network device receives channel state information reported by the terminal equipment, transmits downlink information according to the reported channel state information (KUMAR: ¶142, ¶104, channel state information reported by the UE is received at the gNB and model update and communication parameter adjustment is performed for at least downlink based on the channel state information feedback), and receives the hybrid automatic retransmission request feedback information transmitted by the terminal equipment, wherein the hybrid automatic retransmission request feedback information is generated by the terminal equipment based on the downlink information (KUMAR: ¶143-144, the network device/gNB monitors KPI feedback information from the terminal equipment; and performs evaluation of the AI/ML based on the KPI feedback information based on downlink information; KANAMARLAPUDI: ¶95, KPIs include HARQ feedback which is based on downlink information).
Regarding claim 7, KUMAR modified by KANAMARLAPUDI discloses device according to claim 6, wherein the network device receives channel change instruction information transmitted by the terminal equipment, the channel change instruction information including information on a change of a channel between the network device and the terminal equipment in a time domain and/or a frequency domain and/or a space domain, (KUMAR: ¶94-101, a beam (space domain channel) is changed by the gNB based on an indication from the UE) the network device generates measurement resource configuration information and/or report resource configuration information according to the channel change instruction information, and transmits the measurement resource configuration information and/or the report resource configuration information to the terminal equipment (KUMAR: ¶94-101, a beam (space domain channel) measurement information/configuration is provided by the gNB to the UE so the UE can measure these beams and report the measurements to the gNB).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over KUMAR et al (US 2025/0219919) in view of KWAK et al (US 2025/0350341)
Regarding claim 9, KUMAR discloses device according to claim 8, wherein the network device transmits reference signals corresponding to the number of repeated transmissions of beams or the number of repeated transmissions of channel state information reference signals (KUMAR: ¶106-108, the UE transmits reference signals), the terminal equipment performing beam management by using the AI/ML model (KUMAR: ¶106-108, ¶119, beam beamforming is based on AI/ML model), the network device receives the beam management information reported by the terminal equipment, and receives uplink signals transmitted by the terminal equipment, wherein the uplink signals are generated by the terminal equipment based on the beam management information (KUMAR: ¶106-108, ¶119, ¶153, the KPIs are based on the beamforming which is based on the AI/ML model being used at the UE and the gNB).
KUMAR remains silent regarding beam management including beam estimation or prediction and the beam management is to obtain beam estimation information.
However, KWAK et al (US 2025/0350341) discloses beam management including beam estimation or prediction and the beam management is to obtain beam estimation information (KWAK: ¶174, the beam estimation is performed based on the AI/ML model; and beam management includes beam estimation/prediction).
A person of ordinary skill in the art working with the invention of KUMAR would have been motivated to use the teachings of KWAK as optimizing reference signal (RS) measurement overheads and latency for measurement and reporting are crucial for wireless systems in higher frequencies. Accordingly, more efficient beam measurement and reporting methods are desirable (¶2). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify invention of KUMAR with teachings of KWAK in order to improve efficiency of beam measurement and reporting.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over KUMAR modified by KANAMARLAPUDI as applied to claim 10 above, further in view of KWAK et al (US 2025/0350341)
Regarding claim 11, KUMAR modified by KANAMARLAPUDI discloses device according to claim 10, wherein the network device configures and instructs the terminal equipment to perform beam management, the terminal equipment performing beam management by using the AI/ML model, the network device receives the beam management information reported by the terminal equipment (KUMAR: ¶153, ¶119, the AI/ML model is used for evaluation the communication between the UE and the network device/gNB which also evaluates based on the beam management including beam prediction), transmits downlink information according to the reported beam management information (KUMARL ¶141, ¶88, based on the KPIs, which are based on the beam management, which is in turn based on the AI/ML, the beam management, the downlink transmission between the UE and the gNB is performed over the communication link), and receives the hybrid automatic retransmission request feedback information transmitted by the terminal equipment, wherein the hybrid automatic retransmission request feedback information is generated by the terminal equipment based on the downlink information (KUMAR: ¶143-144, the network device/gNB monitors KPI feedback information from the terminal equipment; and performs evaluation of the AI/ML based on the KPI feedback information; KANAMARLAPUDI: ¶95, KPIs include HARQ feedback).
KUMAR remains silent regarding beam management including beam prediction.
However, KWAK et al (US 2025/0350341) discloses beam management including beam prediction (KWAK: ¶174, the beam estimation is performed based on the AI/ML model; and beam management includes beam estimation/prediction).
A person of ordinary skill in the art working with the invention of KUMAR modified by KANAMARLAPUDI would have been motivated to use the teachings of KWAK as optimizing reference signal (RS) measurement overheads and latency for measurement and reporting are crucial for wireless systems in higher frequencies. Accordingly, more efficient beam measurement and reporting methods are desirable (¶2). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify invention of KUMAR modified by KANAMARLAPUDI with teachings of KWAK in order to improve efficiency of beam measurement and reporting.
Claim(s) 15-17, is/are rejected under 35 U.S.C. 103 as being unpatentable over KUMAR et al (US 2025/0219919) in view of PEZESHKI et al (US 2022/0294548)
Regarding claim 15, KUMAR discloses device according to claim 14, wherein the network device transmits information of an AI/ML model in the cell with performance lower than the threshold via system information in the cell, and/or, broadcasts identification information of an AI/ML model in the cell with performance higher than the threshold via system information in the cell (KUMAR: ¶138, ¶132, there are multiple AI/ML models and some have higher than threshold degradation and some have lower than threshold degradation; a model configuration is transmitted).
KUMAR remains silent transmitting regarding information of an AI/ML model involves broadcasts identification
However, PEZESHKI et al (US 2022/0294548) transmitting regarding information of an AI/ML model involves broadcasts identification (PEZESHKI: ¶86, ¶92 the AI/ML model IDs are broadcasted to the UEs)
A person of ordinary skill in the art working with the invention of KUMAR would have been motivated to use the teachings of PEZESHKI due to its explicit identification along with a tabulated parameters, the UEs would have better understanding of the ML models and applicable zones they can be applied to. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify invention of KUMAR with teachings of PEZESHKI in order to improve accuracy of AI/ML model implementation.
Regarding claim 16, KUMAR modified by PEZESHKI discloses device according to claim 15, wherein the network device configures cell-specific configuration information for one or more terminal equipments, so that the one or more terminal equipments feed(s) back a signal or information for monitoring an AI/ML model according to the configuration information (PEZESHKI: ¶126, ¶86, zone specific AI/ML model configuration for the UE; KUMAR: ¶36, the geographical area is the cellular coverage/cell; ¶112, ¶115, the feedback is based on the configuration and measuring according to the AI/ML model implemented).
Regarding claim 17, KUMAR modified by PEZESHKI discloses device according to claim 15, wherein the network device transmits identification information of an AI/ML model in the cell with performance lower than the threshold to another cell or a core network device, and/or transmits identification information of an AI/ML model in the cell with performance higher than the threshold to another cell or a core network device (KUMAR: ¶138, ¶132, there are multiple AI/ML models and some have higher than threshold degradation and some have lower than threshold degradation; a model configuration is transmitted).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
See PTO-892
Document U discloses that Downlink Multi-User MIMO Channel State Information Reference Signal (CSI-RS) transmission scheme is proposed. It is based on an OFDM system and deployment of a large number of antennas at LTE Evolved NodeB transmitter (eNB). The developed scheme efficiently utilizes the transmitted power distribution by the adaptive elimination of the coverage zones where no users are to be served. Hence, more directed CSI-RS beamforming and higher received Signal To Noise and Interference Ratios (SINRs) are achieved for both uniform and clustered user distributions. In addition, Zero Forcing (ZF) beamforming is employed on top of the developed scheme for MU-MIMO data transmissions. Proposed scheme shows significant performance improvement than the conventional standard LTE-A dual codebook while drastically reducing the needed Downlink feedback overhead regardless of the number of physical antennas at eNB.
Document V discloses that beamforming and precoding/combining are techniques aimed at processing multiantenna signals at the transmitter and/or at the receiver of a wireless communication system. While they have been routinely used to improve performance in current and previous generations of mobile communications systems, they are expected to play a more fundamental role in 5th Generation (5G) New Radio (NR) cellular systems, whose functionalities have been defined in the first phase of 3GPP 5G standardization process. Besides operating in traditional cellular sub-6 GHz frequency band, 5G NR has been natively designed also to work in the higher millimeter-wave (MMW) band. At lower frequencies, multiantenna techniques for 5G NR are mainly refinements of those originally designed for 4G Long Term Evolution (LTE). On the contrary, to cope with the peculiarities of MMW scenarios, such as the larger number of antenna elements, the more directional transmission, and the higher path loss values, new dynamic, user-specific, and computationally-efficient multiantenna solutions and procedures have been incorporated in 5G NR specifications. In particular, since multiantenna techniques for 5G NR generally need detailed channel state information (CSI), a complete redesign of the set of reference signals and procedures used for CSI acquisition and reporting was carried out. 5G NR is continuously evolving and new features will be added, while the existing ones will be enhanced in the second phase of 5G standardization, with emphasis on reduction of CSI overhead, robustness against spatial correlation among channels, unconventional transmission methods, and software-based reconfigurable antennas.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OMER S MIAN whose telephone number is (571)270-7524. The examiner can normally be reached M,T,W,Th: 10a-7p, Fri, 9a-12p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Huy D Vu can be reached at 571-272-3155. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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OMER S. MIAN
Primary Examiner
Art Unit 2461
/OMER S MIAN/Primary Examiner, Art Unit 2461
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