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 § 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 9-10 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Calzolari et al. (US Publication2020/0412417 A1).
In regard to claim 9, Calzolari teaches, A communication method, comprising: sending, by a first network device, first Artificial Intelligence (AI)model information (see paragraph 57; a machine learning agent unit at UE 115-a may modify the dynamic antenna switching threshold value. The machine learning agent unit may be developed prior to deployment in the UE 115-a, where different decisions (e.g., antenna switching decisions, threshold value decisions, etc.) may be rewarded or penalized to construct a neural network for updating the threshold. For example, operating on the “optimal” antenna 215 (e.g., the antenna 215 with the highest RSRP value) may be rewarded, while operating on a “non-optimal” antenna 215 (e.g., an antenna 215 without the highest RSRP value) or switching too often (e.g., more frequently than an ASDIV using a static threshold would switch) may be penalized. The neural network may be trained using training data corresponding to many different environments, scenarios, and use cases (e.g., 4G network data, 5G network data, carrier aggregation data, MIMO data, etc.), such that the ASDIV may adapt to or handle different conditions in a wireless communications system 200. In some cases, the machine learning agent unit may additionally or alternatively be trained following deployment in UE 115-a or trained for a specific environment, such that the machine learning agent unit may dynamically adjust to a specific user, model, chipset, network, and/or operating band. Implementing the dynamic antenna switching threshold based on the machine learning agent unit, as opposed to implementing a static threshold, may increase the use of the antenna with the highest RSRP value, improving the ASDIV feature performance. Furthermore, the machine learning agent unit may accelerate threshold tuning and decrease testing costs (e.g., field-based testing costs) by implementing a dynamic threshold), wherein the first AI model information is used for indicating an activation condition of an AI model trained using neural networks, wherein the activation condition of the AI model comprises: a wireless network failure message indicated by a physical layer is received, a radio link failure occurs, Reference Signal Received Power (RSRP) of a reference signal of the first network device is less than or equal to a first threshold, or RSRP of a reference signal of a second network device is greater than or equal to a second threshold (see paragraph 84; the UE 115 determining whether the UE 115 operated on the “optimal” antenna during the preceding measurement interval. For example, the UE 115 may measure RSRP values for a set of antennas during the measurement interval and may determine an “optimal” antenna based on the RSRP measurements. In a first example, the UE 115 may average the RSRP values for each antenna over the preceding measurement interval and may identify the antenna with the greatest average RSRP value as the “optimal” antenna. In a second example, the UE 115 may identify the antenna of the set of antennas with the highest RSRP value for the greatest amount of time during the preceding measurement interval as the “optimal” antenna. In a third example, the UE 115 may calculate RSRPΔ values for the set of antennas throughout the measurement interval and may identify the antenna with the greatest average RSRPΔ value as the “optimal” antenna. If the UE 115 operated on the identified “optimal” antenna for the preceding measurement interval, the UE 115 may not perform an antenna switching test (e.g., including updating a dynamic threshold and determining whether to switch the operating antenna based on the dynamic threshold). Instead, the UE 115 may perform the test again at 605 (e.g., following another measurement interval). Alternatively, if the UE 115 did not operate on the identified “optimal” antenna for the preceding measurement interval, the UE 115 may activate a machine learning agent unit at 615; see paragraph 85; the machine learning agent unit may use a trained neural network to determine an updated dynamic threshold for antenna switching based on the input RSRPΔ value(s)).
In regards to claim 10, Calzolari teaches wherein the first AL model information is used for indicating AI model parameter information (see Calzolari teachings with respect to RSRP stated above).
In regards to claim 19, Calzolari teaches, wherein the network device is a first network device, comprising: a memory storing a computer program; and a processor coupled to the memory and configured to execute the computer program to perform the communication method according to claim 9 (see the base station in figure 8).
Response to Arguments
Applicant’s arguments with respect to claim(s) 9 filed on 7/31/2026 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
With respect to claim 9, it does not include the limitation of handing over, redirection or reestablishing as claimed in claim 1.
In view of the applicant’s amendments filed on 7/31/2026, the rejection under 35 USC 101 and 112 are withdrawn.
Allowable Subject Matter
Claims 1-8 and 18 are allowed.
The following is an examiner’s statement of reasons for allowance: In regards to the claims, the cited prior art fails to teach, the handing over, redirecting or reestablishing a connection to the neighboring cell, wherein the Al model is determined based on first A model information, wherein the first Al model information is used for indicating an activation condition of an Al model trained using neural networks, wherein the activation condition of the Al model comprises: a wireless network failure message indicated by a physical layer is received, a radio link failure occurs, Reference Signal Received Power (RSRP) of a reference signal of the first network device is less than or equal to a first threshold, or RSRP of a reference signal of a second network device is greater than or equal to a second threshold.
Prior art Calzolari et al. (US Publication 2020/0412417 A1) teaches, with respect to figure 6, the UE 115 determining whether the UE 115 operated on the “optimal” antenna during the preceding measurement interval. For example, the UE 115 may measure RSRP values for a set of antennas during the measurement interval and may determine an “optimal” antenna based on the RSRP measurements. In a first example, the UE 115 may average the RSRP values for each antenna over the preceding measurement interval and may identify the antenna with the greatest average RSRP value as the “optimal” antenna. In a second example, the UE 115 may identify the antenna of the set of antennas with the highest RSRP value for the greatest amount of time during the preceding measurement interval as the “optimal” antenna. In a third example, the UE 115 may calculate RSRPΔ values for the set of antennas throughout the measurement interval and may identify the antenna with the greatest average RSRPΔ value as the “optimal” antenna. If the UE 115 operated on the identified “optimal” antenna for the preceding measurement interval, the UE 115 may not perform an antenna switching test (e.g., including updating a dynamic threshold and determining whether to switch the operating antenna based on the dynamic threshold). Instead, the UE 115 may perform the test again at 605 (e.g., following another measurement interval). Alternatively, if the UE 115 did not operate on the identified “optimal” antenna for the preceding measurement interval, the UE 115 may activate a machine learning agent unit at 615 (see paragraph 84).
Claims 11-13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: In regard to the claims, the cited prior art fails to teach the load information to the terminal.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY P PATEL whose telephone number is (571)272-3086. The examiner can normally be reached M-F 9:30-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Faruk Hamza can be reached at 571-272-8786. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAY P PATEL/ Primary Examiner, Art Unit 2466
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