DETAILED ACTION
Notice of Pre-AIA or AIA Status
This action is responsive to communication filed on 06/22/2026. Claim(s) 2 & 14 have been cancelled. Claim(s) 1, 9, 13, 16, 19 & 20 have been amended. Claim(s) 1, 3-13, 15-20 are currently pending examination. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The Applicant filed amendment(s) to the claim(s) on 06/22/2026 to remedy the rejection(s).
Response to Arguments
Applicant’s arguments, see Page(s) 10-11, filed 06/22/2026, with respect to Claim(s) 1, 3-13, 15-20 have been fully considered and are persuasive. The USC 101 Rejection of Claim(s) 1, 3-13, 15-20 has been withdrawn.
Applicant’s arguments, see Page(s) 11, filed 06/22/2026, with respect to Claim(s) 1, 13 & 20 have been fully considered and are persuasive. The USC 112 Rejection of Claim(s) 1, 13 & 20 has been withdrawn.
In addition, the Applicant argues that claim 1/13 has been amended to include subject matter from claim 2/14, which Applicant characterizes as previously allowable because claim 2/14 was not rejected under prior art. This argument has been considered but it is not persuasive. The prior Office Action did not indicate that claim 2/14 was allowable, did not object to claim 2/14 as containing allowable subject matter and it did not state that claim 2/14 would be allowable it rewritten in dependent form. Rather, claim 2/14 was rejected under 35 U.S.C. 101 and subject to the 112 issue by virtue of its dependency from claim 1/13. The mere absence of a prior art rejection under 36 U.S.C. 102/103 is not an indication of allowable subject matter. A claim must comply with applicable statutory requirements, including 101, 102, 103 and 112. Accordingly, incorporation of the limitations of claim 2/14 into claim 1/13 does not, by itself, place claim 1/13 into condition for allowance.
Applicant’s arguments with respect to claim(s) 1, 3-13, 15-20 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.
Allowable Subject Matter
Claim(s) 10 & 17 is/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 (e.g. Claim 10 into Claim 1 would be allowable if elements from Claim 9, Claim 8).
Claim Rejections - 35 USC § 112
Claim(s) 1, 13 & 20 is/are 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.
“Artificial Intelligent” should be “artificial intelligence”, appropriate correction is required.
“using the selecting AI/ML model” should be “using the selected AI/ML model”. The grammar creates unclear claim scope, appropriate correction is required.
“based on threshold applicability condition being determines as satisfied”, it ambiguous and missing antecedent/relationship to which threshold condition.
“a device configure to” should be tied to structure, appropriate correction is required.
Claim(s) 1, 13 & 20 recites the limitation "the service" in the 3rd limitation. There is insufficient antecedent basis for this limitation in the claim.
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.
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, 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.
Claim(s) 1, 3-9, 13, 15-16 & 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US 2023/0075276 A1) and further in view of ZHU et al. (US 2023/0100253 A1).
Re Claim 1, 13 & 20, Zhu teaches a device configured to: (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090]; Devices.)
evaluate an applicability of an Artificial Intelligent (AI)/ Machine Learning (ML) model in relation to a mobile telecommunication network; (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090]; A applicable scope (evaluate an applicability) of a machine learning model in a mobile telecommunication network.)
generate a report message that includes the evaluated applicability; and (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090]; The system determines that a condition associated with a model ID is met, the ML request messages including model related data indicating if a condition has been satisfied.)
provide the report message to at least one Network Element (NE) of the mobile telecommunication network to select the AI/ML model to be used to provide the service, (Zhu; FIG. 1-15; Background, Summary, ¶ [0139]-[0172]; The sending of a ML request message to the CU-CUP that indicates UE related assistance information.)
from among a plurality of AI/ML models for providing the service under respectively different threshold applicability conditions, (Zhu; FIG. 1-15; Background, Summary, ¶ [0081]-[0107], [0139]-[0172]; The sending of the message and the and the CU-XP or CU-CP selecting a neural network function and ML model that corresponding to the model ID indication in the request. The configuration of the UE with the selected model and parameters that meet the various conditions. In addition, the embodiment(s) detail storing multiple selectable ML models.)
based on threshold applicability condition being determined as satisfied; and (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090]; The UE determines whether the condition is associated with a model ID is satisfied, when the condition is met, the UE send the request and the network selects the ML model corresponding to the model ID in the request.)
receive the service provided using the selected AI/ML model, (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090], [0130]-[0132]; The sending of an activation message and the ML selected model performs a task/service related to the request.)
Zhu does not explicitly disclose wherein the device is configured to autonomously perform the evaluation of the AI/ML model applicability, the generation of the reporting message, and the providing of the report message, based on a triggering condition, and wherein the triggering condition comprises a predefined activation condition associated with a functionality of at least one of: the device and the NE.
However, in analogous art, ZHU teaches
wherein the device is configured to autonomously perform the evaluation of the AI/ML model applicability, the generation of the reporting message, and (ZHU; FIG. 1-15; Background, Summary, ¶ [0034]-[0039], [0118]-[0124]; The embodiment(s) detail the autonomously request network configuration based on model scope and request.)
the providing of the report message, based on a triggering condition, and wherein the triggering condition comprises a predefined activation condition associated with a functionality of at least one of: the device and the NE. (ZHU; FIG. 1-15; Background, Summary, ¶ [0034]-[0039], [0118]-[0124]; The embodiment(s) details whitelist/blacklist/prohibit-timer control for the UE which is trigger via an RRC reconfiguration and it also discloses model activation after the network model is configured.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify Zhu in view of ZHU autonomously selected ML models for the reasons of selecting neural network models based on parameters and functionality. (ZHU Abstract)
Re Claim 3 & 15, Zhu-ZHU discloses the device according to claim 1,
wherein the device is configured to perform the evaluation of the AI/ML model applicability, the generation of the reporting message, and the providing of the report message, in response to receiving a query message from the at least one NE, and (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090], [0139]-[0172]; Performing the eval, generating a report, providing the report all in response to a query/inquiry/request of a network related element.)
wherein the query message comprises information associated with at least one of:
a network configuration and a network condition. (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090], [0139]-[0172]; Configuration and conditions.)
Re Claim 4, Zhu-ZHU discloses the device according to claim 1, wherein the report message comprises at least one of:
a Radio Resource Control (RRC) message, a User Equipment (UE) Assistance Information (UAI) message, a Medium Access Control (MAC) Control Element (CE) message, a Physical (PHY) layer message, and a Non-Access Stratum (NAS) layer message. (Zhu; FIG. 1; Background, Summary; A UE, RRC.)
Re Claim 5, Zhu-ZHU discloses the device according to claim 4, wherein the report message comprises the RRC message, and the RRC message comprises at least one of:
an applicability condition under which the AI/ML model is applicable,
an applicability result that indicates whether a feature of the AI/ML model is applicable,
an applicability reason associated with a reason of the applicability result,
an action suggestion associated with an action the at least one NE should take based on the applicability result,
a parameter associated with a status of a UE, and (Zhu; FIG. 1; Background, Summary, ¶ [0050], [0076], [0082]-[0085]; RRC related capability/parameters and status of a UE.)
a parameter associated with a capability of the UE. (Zhu; FIG. 1; Background, Summary, ¶ [0050], [0076], [0082]-[0085]; RRC related capability/parameters and status of a UE.)
Re Claim 6, Zhu-ZHU discloses the device according to claim 4, wherein the report message comprises the MAC CE message, and the MAC CE message comprises at least one of: (Zhu; FIG. 1; Background, Summary, ¶ [0080]-[0090]; Sending messages, MAC CE related message.)
an identifier (ID) associated with a feature of the AI/ML model, (Zhu; FIG. 1; Background, Summary, ¶ [0090]-[0100]; Model ID, parameter set ID, assistance information.)
an applicability result that indicates whether the feature of the AI/ML model is applicable, and (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090]; A applicable scope (evaluate an applicability) of a machine learning model in a mobile telecommunication network.)
an applicability reason associated with a reason of the applicability result. (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090]; A applicable scope (evaluate an applicability) of a machine learning model in a mobile telecommunication network related to a request.)
Re Claim 7, Zhu-ZHU discloses the device according to claim 4, wherein the report message comprises the UAI message, (ZHU; FIG. 1; Background, Summary, ¶ [0038]-[0039], [0050], [0063], [0120]-[0125]; UAI message.)
wherein the UAI message comprises at least one of:
an identifier (ID) associated with a feature of the AI/ML model, ZHU; FIG. 1; Background, Summary, ¶ [0038]-[0039], [0050], [0063], [0120]-[0125]; The message includes related identifiers and features of models.)
a performance metric associated with the AI/ML model,
a training progress associated with the AI/ML model, and
an inference accuracy associated with the AI/ML model.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify Zhu in view of ZHU autonomously selected ML models for the reasons of selecting neural network models based on parameters and functionality. (ZHU Abstract)
Re Claim 8, Zhu-ZHU discloses the device according to claim 1, wherein the device is configured to evaluate the applicability by:
determining whether the AI/ML model is configured correctly; (Zhu; FIG. 1-15; Background, Summary, ¶ [0081]-[0107], [0139]-[0172]; Determine if the model is configured correctly.)
based on determining that the AI/ML model is configured correctly, determining that the AI/ML model is applicable; and (Zhu; FIG. 1-15; Background, Summary, ¶ [0081]-[0107], [0139]-[0172]; Upon determination and applicability.)
based on determining that the AI/ML model is not configured correctly, determining that the AI/ML model is not applicable. (Zhu; FIG. 1-15; Background, Summary, ¶ [0081]-[0107], [0139]-[0172]; If conditions are not met, it is not correct.)
Re Claim 9, Zhu-ZHU discloses the device according to claim 8, wherein the device is configured to determine whether the AI/ML model is configured correctly by: (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090]; The system determines that a condition associated with a model ID is met, the ML request messages including model related data indicating if a condition has been satisfied.)
comparing an applicability condition with a predefined threshold, wherein the applicability condition comprises at least one of: (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090]; The system determines that a condition associated with a model ID is met, the ML request messages including model related data indicating if a condition has been satisfied.)
a network condition, a mobility of a UE, and a battery level of the UE; (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090]; The system determines that a condition associated with a model ID is met, the ML request messages including model related data indicating if a condition has been satisfied, network related conditions.)
based on determining that the applicability condition fulfills a condition defined by the predefined threshold, determining that the AI/ML model is configured correctly; and (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090]; The system determines that a condition associated with a model ID is met, the ML request messages including model related data indicating if a condition has been satisfied.)
based on determining that the applicability condition does not fulfill the condition defined by the predefined threshold, determining that the AI/ML model is not configured correctly. (Zhu; FIG. 1-15; Background, Summary, ¶ [0081]-[0107], [0139]-[0172]; If conditions are not met, it is not correct.)
Re Claim 16, Zhu-ZHU discloses the method according to claim 13,
wherein the evaluating the applicability comprises:
determining whether the AI/ML model is configured correctly; (Zhu; FIG. 1-15; Background, Summary, ¶ [0081]-[0107], [0139]-[0172]; Determine if the model is configured correctly.)
based on determining that the AI/ML model is configured correctly, determining that the AI/ML model is applicable; and (Zhu; FIG. 1-15; Background, Summary, ¶ [0081]-[0107], [0139]-[0172]; Upon determination and applicability.)
based on determining that the AI/ML model is not configured correctly, determining that the AI/ML model is not applicable. (Zhu; FIG. 1-15; Background, Summary, ¶ [0081]-[0107], [0139]-[0172]; If conditions are not met, it is not correct.)
wherein the determining whether the AI/ML model is configured correctly comprises:
comparing an applicability condition with a predefined threshold, wherein the applicability condition comprises at least one of: (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090]; The system determines that a condition associated with a model ID is met, the ML request messages including model related data indicating if a condition has been satisfied.)
a network condition, a mobility of a UE, and a battery level of the UE; (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090]; The system determines that a condition associated with a model ID is met, the ML request messages including model related data indicating if a condition has been satisfied, network related conditions.)
based on determining that the applicability condition fulfills a condition defined by the predefined threshold, determining that the AI/ML model is configured correctly; and (Zhu; FIG. 1-15; Background, Summary, ¶ [0080]-[0090]; The system determines that a condition associated with a model ID is met, the ML request messages including model related data indicating if a condition has been satisfied.)
based on determining that the applicability condition does not fulfill the condition defined by the predefined threshold, determining that the AI/ML model is not configured correctly. (Zhu; FIG. 1-15; Background, Summary, ¶ [0081]-[0107], [0139]-[0172]; If conditions are not met, it is not correct.)
Claim(s) 11 & 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US 2023/0075276 A1) and further in view of ZHU et al. (US 2023/0100253 A1) and further in view of WENCHEL et al. (US 2021/0174258 A1).
Re Claim 11 & 18, Zhu-ZHU discloses the device according to claim 1, yet does not explicitly suggest wherein the device is further configured to: monitor a performance metric of the AI/ML model; generate a third report message that comprises an ID associated with the monitored performance metric, a metric value associated with the monitored performance metric, and a timestamp associated with a time when the performance metric is monitored; and provide, to the at least one NE, the third report message.
However, in analogous art, WENCHEL teaches wherein the device is further configured to:
monitor a performance metric of the AI/ML model; (WENCHEL; FIG. 1-10; Background, Summary, ¶ [0016]-[0051]; Monitoring performance metrics of a MLM.)
generate a third report message that comprises an ID associated with the monitored performance metric, a metric value associated with the monitored performance metric, and (WENCHEL; FIG. 1-10; Background, Summary, ¶ [0016]-[0051]; Transmitting reports/messages that contain identifiable information of performance metrics, metric value are monitored.)
a timestamp associated with a time when the performance metric is monitored; and (WENCHEL; FIG. 1-10; Background, Summary, ¶ [0016]-[0058]; Timestamp associated with performance metrics which are monitored.)
provide, to the at least one NE, the third report message. (WENCHEL; FIG. 1-10; Background, Summary, ¶ [0016]-[0058]; Transmitting, reports message related data of the performance.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify Zhu-ZHU in view of WENCHEL to transmit performance metrics data for the reasons of generating and transmitting metrics related to a machine learning model. (WENCHEL Abstract)
Claim(s) 12 & 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US 2023/0075276 A1) and further in view of ZHU et al. (US 2023/0100253 A1) and further in view of HUANGFU et al. (US 2023/0179490 A1).
Re Claim 12 & 19, Zhu-ZHU discloses the device according to claim 1, yet does not explicitly suggest wherein the device is further configured to: obtain a real-time data, wherein the real-time data comprises at least one of: a current network condition, a current UE capability, and an environmental factor; select, based on the real-time data, an AI/ML model; generate a fourth report message that comprises information associated with the selected AI/ML model; and provide, to the at least one NE, the fourth report message.
However, in analogous art, HUANGFU teaches wherein the device is further configured to:
obtain a real-time data, wherein the real-time data comprises at least one of: (HUANGFU; FIG. 1-11; Background, Summary, ¶ [0199]-[0200], [0255]-[0257]; Real time data.)
a current network condition, a current UE capability, and an environmental factor; select, based on the real-time data, an AI/ML model; (HUANGFU; FIG. 1-11; Background, Summary, ¶ [0199]-[0200], [0255]-[0257]; Network parameters and capabilities.)
generate a fourth report message that comprises information associated with the selected AI/ML model; and (HUANGFU; FIG. 1-11; Background, Summary, ¶ [0199]-[0200], [0255]-[0257]; Sending messages.)
provide, to the at least one NE, the fourth report message. (HUANGFU; FIG. 1-11; Background, Summary, ¶ [0199]-[0200], [0255]-[0257]; Sending to a network element.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify Zhu-ZHU in view of HUANGFU to transmit real-time data for the reasons of transmitting communication data related to AI models. (HUANGFU Abstract)
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 CHRISTOPHER B ROBINSON whose telephone number is (571)270-0702. The examiner can normally be reached M-F 7:00-3:00 EST.
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, Nicholas R Taylor can be reached at 571-272-3889. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER B ROBINSON/Primary Examiner, Art Unit 2443
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