DETAILED ACTION
This Office Action is in response to the Application Ser. No. 19/173,927 filed on May 7, 2025. Claims 1-20 are pending and are examined.
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
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.
Priority
Acknowledgment is made of applicant’s claim for domestic priority as a continuation of International Application Number PCT/CN2023/123317, filed on October 8, 2023.
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119 (a)-(d) based on Chinese application Ser. No. 202211250071.8 filed on October 12, 2022. Receipt of the certified copy of the Chinese application on May 7, 2025, is hereby acknowledged.
Claim Rejections - 35 USC § 112(b)
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.
Claims 2, 9 and 14 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.
Claim 2 recites the limitation “wherein task identifiers of the plurality of data analytics tasks are the same” in lines 1-2. There is insufficient antecedent basis for the term “the plurality of data analytics tasks” in the claims.
For examination purposes, the term “the plurality of data analytics tasks” is interpreted as “the one or more data analytics tasks”.
Claim 9 recites the limitation “wherein the related information of the model comprises at least one of the following” in lines 1-2. There is insufficient antecedent basis for the term “the related information of the model” in the claims.
For examination purposes, the term “the related information of the model” is interpreted as “related information of a model”.
Insofar as it recites similar claim elements, Claim 14 is rejected for substantially the same reasons presented above with respect to Claim 2.
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, 3-5, 9-13 and 15-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by 3rd Generation Partnership Project (3GPP) Technical Specification 23.288 V17.6.0, titled “Architecture enhancements for 5G System (5GS) to support network data analytics services”, hereby “3GPP”.
Regarding Claim 1, 3GPP discloses “A model request method (3GPP fig. 6.2A.1-1 and § “6.2A.1 ML Model Subscribe/Unsubscribe”: a procedure used by an NWDAF service consumer to request a trained ML Model), comprising:
sending, by a first communication device, a first request, wherein the first request is used to request a plurality of models for one or more data analytics tasks (3GPP fig. 6.2A.1-1, § “6.2A.1 ML Model Subscribe/Unsubscribe” and “6.2A.2 Contents of ML Model Provisioning”: an NWDAF service consumer, e.g., an NWDAF containing AnLF, sends a request, i.e., Nnwdaf_MLModelProvision_Subscribe message, to subscribe to a set of trained ML Models associated with a set of Analytics IDs, i.e., one or more data analytics tasks – while not explicitly stated, implementation of the NWDAF functionality by one or more hardware devices is inferred); and
receiving, by the first communication device, a first response, wherein the first response comprises related information of a plurality of models meeting the first request (3GPP fig. 6.2A.1-1, § “6.2A.1 ML Model Subscribe/Unsubscribe” and “6.2A.2 Contents of ML Model Provisioning”: the NWDAF service consumer, e.g., the NWDAF containing AnLF, receives a response, i.e., a Nnwdaf_MLModelProvision_Notify message, comprising trained ML Model Information for the set of trained ML Models – while not explicitly stated, implementation of the NWDAF functionality by one or more hardware devices is inferred).”
Regarding Claim 3, 3GPP discloses all of the limitations of Claim 1.
Additionally, 3GPP discloses “wherein the first request comprises at least one of the following:
task identification information (3GPP “6.2A.2 Contents of ML Model Provisioning”: the Nnwdaf_MLModelProvision_Subscribe message comprises a list of Analytics IDs identifying the analytics for which the ML models are used);
condition restriction information of the task;
analytics object information of the task (3GPP “6.2A.2 Contents of ML Model Provisioning”: the Nnwdaf_MLModelProvision_Subscribe message may comprise Target of ML Model Reporting, indicating the object(s) for which ML model is requested, e.g., specific UEs, a group of UEs, or all UEs);
first indication information, wherein the first indication information is used to indicate that a plurality of models are requested at a time;
distinguishing information of the plurality of models (3GPP “6.2A.2 Contents of ML Model Provisioning”: the Nnwdaf_MLModelProvision_Subscribe message may comprise ML Target Period indicating a time interval for which the ML model is requested); and
model restriction information, used to restrict a scope of use of a model (3GPP “6.2A.2 Contents of ML Model Provisioning”: the Nnwdaf_MLModelProvision_Subscribe message may comprise ML Model Filter information enabling selection of particular ML model).”
Regarding Claim 4, 3GPP discloses all of the limitations of Claim 3.
Additionally, 3GPP discloses “wherein the distinguishing information comprises at least one of the following:
accuracy information on a plurality of levels corresponding to the plurality of models; and
information on a plurality of time periods corresponding to the plurality of models (3GPP “6.2A.2 Contents of ML Model Provisioning”: for each Analytics ID in the set of Analytics IDs, the ML Target Period indicates the time interval for which the ML Model is requested).”
Regarding Claim 5, 3GPP discloses all of the limitations of Claim 3.
Additionally, 3GPP discloses “wherein the model restriction information comprises at least one of the following:
location information corresponding to the model (3GPP “6.2A.2 Contents of ML Model Provisioning”: ML Model Filter information may comprise an Area of Interest, i.e., location information); and
slice information corresponding to the model (3GPP “6.2A.2 Contents of ML Model Provisioning”: ML Model Filter information may comprise S-NSSAI, i.e., slice information).”
Regarding Claim 9, 3GPP discloses all of the limitations of Claim 1.
Additionally, 3GPP discloses “wherein the related information of the model comprises at least one of the following:
model information (3GPP “6.2A.2 Contents of ML Model Provisioning”: the Nnwdaf_MLModelProvision_Notify message includes ML Model Information);
model identification information;
model description information (3GPP “6.2A.2 Contents of ML Model Provisioning”: the Nnwdaf_MLModelProvision_Notify message includes Validity period and Spatial validity information);
task identification information;
condition restriction information of the task; and
analytics object information of the task.”
Regarding Claim 10, 3GPP discloses all of the limitations of Claim 9.
Additionally, 3GPP discloses “wherein the model description information comprises at least one of the following:
accuracy information corresponding to the model;
time period information corresponding to the model (3GPP “6.2A.2 Contents of ML Model Provisioning”: the Validity period indicates time period when the provided ML Model Information applies);
location information corresponding to the model (3GPP “6.2A.2 Contents of ML Model Provisioning”: the Spatial validity indicates Area where the provided ML Model Information applies); and
slice information corresponding to the model.
Regarding Claim 11, 3GPP discloses all of the limitations of Claim 1.
Additionally, 3GPP discloses “requesting, by the first communication device, required related data based on a received task request (3GPP fig. 6.4.4-1 and § “6.4.4 Procedures to request Service Experience for an Application”: NWDAF subscribes to service data and network data in response to receiving a request from an NF consumer, i.e., a Nnwdaf_AnalyticsSubscription_Subscribe message – while not explicitly stated, implementation of the NWDAF functionality by one or more hardware devices is inferred); and
analyzing, by the first communication device, the related data by using the plurality of models, to obtain a data analytics result (3GPP fig. 6.4.4-1 and § “5.1 General” and “6.4.4 Procedures to request Service Experience for an Application”: the NWDAF derives the requested analytics using the service data and the network data).
Regarding Claim 12, 3GPP discloses all of the limitations of Claim 11.
Additionally, 3GPP discloses “wherein after the obtaining a data analytics result, the method further comprises:
sending, by the first communication device, the data analytics result to a third communication device (3GPP fig. 6.4.4-1 and § "6.4.4 Procedures to request Service Experience for an Application": NWDAF provides a response comprising the data analytics, i.e., an Nnwdaf_AnalyticsSubscription_Notify message, to the consumer – while not explicitly stated, implementation of the NWDAF functionality by one or more hardware devices is inferred).”
Regarding Claim 13, 3GPP discloses “A model request method (3GPP fig. 6.2A.1-1, § “6.2A.1 ML Model Subscribe/Unsubscribe”: a procedure used by an NWDAF service consumer to request a trained ML Model), comprising:
receiving, by a second communication device, a first request, wherein the first request is used to request a plurality of models for one or more data analytics tasks (3GPP fig. 6.2A.1-1, § “6.2A.1 ML Model Subscribe/Unsubscribe” and “6.2A.2 Contents of ML Model Provisioning”: an NWDAF containing MLTF receives a request, i.e., a Nnwdaf_MLModelProvision_Subscribe message, subscribing to a set of trained ML Models associated with a set of Analytics IDs, i.e., one or more data analytics tasks – while not explicitly stated, implementation of the NWDAF functionality by one or more hardware devices is inferred);
obtaining, by the second communication device, a plurality of models meeting the first request (3GPP fig. 6.2A.1-1, § “6.2A.1 ML Model Subscribe/Unsubscribe” and “6.2A.2 Contents of ML Model Provisioning”: the NWDAF containing MLTF determines whether existing trained ML Models can be used for the subscription or whether training for existing trained ML models is needed, i.e., identifies ML models meeting the subscription request – while not explicitly stated, implementation of the NWDAF functionality by one or more hardware devices is inferred); and
sending, by the second communication device, a first response, wherein the first response comprises related information of the plurality of models (3GPP fig. 6.2A.1-1, § “6.2A.1 ML Model Subscribe/Unsubscribe” and “6.2A.2 Contents of ML Model Provisioning”: the NWDAF containing MLTF sends a response, i.e., a Nnwdaf_MLModelProvision_Notify message, comprising trained ML Model Information for the set of trained ML Models to the NWDAF service consumer, e.g., the NWDAF containing AnLF – while not explicitly stated, implementation of the NWDAF functionality by one or more hardware devices is inferred).”
Insofar as it recites similar claim elements, Claim 15 is rejected for substantially the same reasons presented above with respect to Claim 3.
Insofar as it recites similar claim elements, Claim 16 is rejected for substantially the same reasons presented above with respect to Claim 4.
Regarding Claim 17, 3GPP discloses all of the limitations of Claim 13.
Additionally, 3GPP discloses “wherein the obtaining, by the second communication device, a plurality of models meeting the first request comprises at least one of the following:
obtaining, by the second communication device, stored models meeting the first request (3GPP fig. 6.2A.1-1 and § “6.2A.1 ML Model Subscribe/Unsubscribe”: NWDAF containing MTLF determines that an existing trained ML Model can be used for the subscription); and
collecting, by the second communication device, training data based on the first request, and performing model training by using the training data, to obtain trained models (3GPP fig. 6.2A.1-1 and § “6.2A.1 ML Model Subscribe/Unsubscribe”: NWDAF containing MTLF determines further training of an existing trained ML model is needed for the subscription and initiates data collection from NFs, UE Application, or OAM to generate the trained ML Model).”
Regarding Claim 18, 3GPP discloses all of the limitations of Claim 17.
Additionally, 3GPP discloses “wherein the obtaining, by the second communication device, stored models meeting the first request comprises:
obtaining, by the second communication device based on at least one of the following in the first request, the stored models meeting the first request:
distinguishing information of the plurality of models (3GPP fig. 6.2A.1-1, § “6.2A.1 ML Model Subscribe/Unsubscribe” and “6.2A.2 Contents of ML Model Provisioning”: MTLF determines trained ML Models for the subscription based on ML Model Filter information provided in the Nnwdaf_MLModelProvision_Subscribe message);
model restriction information, used to restrict a scope of use of a model;
task identification information (3GPP fig. 6.2A.1-1, § “6.2A.1 ML Model Subscribe/Unsubscribe” and “6.2A.2 Contents of ML Model Provisioning”: MTLF determines trained ML Models for the subscription based on the Analytics IDs provided in the Nnwdaf_MLModelProvision_Subscribe message);
condition restriction information of the task; and
analytics object information of the task (3GPP fig. 6.2A.1-1, § “6.2A.1 ML Model Subscribe/Unsubscribe” and “6.2A.2 Contents of ML Model Provisioning”: MTLF determines trained ML Models for the subscription based on the Target of ML Reporting provided in the Nnwdaf_MLModelProvision_Subscribe message).”
Regarding Claim 19, 3GPP discloses all of the limitations of Claim 17.
Additionally, 3GPP discloses “wherein the collecting, by the second communication device, training data based on the first request comprises:
collecting, by the second communication device, the training data based on at least one of the following in the first request:
distinguishing information of the plurality of models (3GPP fig. 6.4.4-1, § “6.2A.1 ML Model Subscribe/Unsubscribe”, “6.2 Procedures for Data Collection” and “6.4.4 Procedures to request Service Experience for an Application”: NWDAF performs data collection from various sources based on ML Model Filter Information included in the Nnwdaf_MLModelProvision_Subscribe message);
model restriction information, used to restrict a scope of use of a model;
task identification information (3GPP fig. 6.4.4-1, § “6.2A.1 ML Model Subscribe/Unsubscribe”, “6.2 Procedures for Data Collection” and “6.4.4 Procedures to request Service Experience for an Application”: NWDAF performs data collection from various sources based on the set of Analytics IDs included in the Nnwdaf_MLModelProvision_Subscribe message);
condition restriction information of the task; and
analytics object information of the task (3GPP fig. 6.4.4-1, § “6.2A.1 ML Model Subscribe/Unsubscribe”, “6.2 Procedures for Data Collection” and “6.4.4 Procedures to request Service Experience for an Application”: NWDAF performs data collection from various sources based on Target of ML Model Reporting included in the Nnwdaf_MLModelProvision_Subscribe message).”
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.
Claims 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over 3GPP in view of Lee et al., Pub. No. US 2021/0144076 A1, hereby “Lee”.
Regarding Claim 2, 3GPP discloses all of the limitations of Claim 1.
However, while 3GPP discloses that the Nnwdaf_MLModelProvision_Subscribe message may be used to subscribe to a set of trained ML Models associated with a set of Analytics IDs, i.e., a plurality of data analytics tasks (3GPP § “6.2A.1 ML Model Subscribe/Unsubscribe” and “6.2A.2 Contents of ML Model Provisioning”), 3GPP does not explicitly disclose “wherein task identifiers of the plurality of data analytics tasks are the same.”
In the same field of endeavor, Lee discloses “wherein task identifiers of the plurality of data analytics tasks are the same (Lee paragraphs 73-74: the same analytics ID may be associated with a plurality of analytics models, i.e., a plurality of data analytics tasks).”
It would have been obvious to one of ordinary skill in the art at the time of the effective filing to modify the method of 3GPP to subscribe to a plurality of ML Models associated with the same Analytics ID as taught by Lee. One or ordinary skill in the art would have been motivated to combine subscribing to a plurality of ML Models associated with the same Analytics ID to obtain ML models that are optimized for different use cases of the analytics type (Lee paragraph 75).
Insofar as it recites similar claim elements, Claim 14 is rejected for substantially the same reasons presented above with respect to Claim 2.
Claims 6-8 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over 3GPP in view of Papageorgiou et al., Pub. No. US 2023/0060071 A1, hereby “Papageorgiou”.
Regarding Claim 6, 3GPP discloses all of the limitations of Claim 1.
However, while 3GPP discloses receiving an analytics subscription request, i.e., a task request, from a consumer NF (3GPP figs. 6.1.1.1-1 and 6.4.4-1 and § “6.1.1.1 Analytics subscribe/unsubscribe by NWDAF service consumer” and “6.4.4 Procedures to request Service Experience for an Application”: NWDAF receives Nnwdaf_AnalyticsSubscription_Subscribe message from consumer NF), 3GPP does not explicitly disclose “wherein before the sending, by a first communication device, a first request, the method further comprises at least one of the following:
after receiving a task request, generating, by the first communication device, the first request for the task request (emphasis added); and
after receiving a plurality of task requests within a preset time range, generating, by the first communication device, the first request for the plurality of task requests.”
In the same field of endeavor, Papageorgiou discloses “wherein before the sending, by a first communication device, a first request, the method further comprises at least one of the following:
after receiving a task request, generating, by the first communication device, the first request for the task request (Papageorgiou figs. 3 and 4 and paragraphs 105 and 123-125: the NWDAF service consumer, e.g., the NWDAF containing AnLF, sends the model provisioning request, e.g., a Nnwdaf_MLModelProvision_Subscribe message, in response to receiving a request for analytics data from an analytics consumer, e.g., an Nnwdaf_AnalyticsSubscription_Subscribe message); and
after receiving a plurality of task requests within a preset time range, generating, by the first communication device, the first request for the plurality of task requests.”
It would have been obvious to one of ordinary skill in the art at the time of the effective filing to modify the method of 3GPP to transmit, by the NWDAF containing AnLF, the Nnwdaf_MLModelProvision_Subscribe message in response to receiving the Nnwdaf_AnalyticsSubscription_Subscribe message from the NF consumer as taught by Papageorgiou. One of ordinary skill in the art would have been motivated to combine transmitting the Nnwdaf_MLModelProvision_Subscribe message in response to receiving the Nnwdaf_AnalyticsSubscription_Subscribe message from the NF consumer to obtain ML Model(s) that can be used to support the analytics task requested by the NF consumer (Papageorgiou paragraph 125).
Regarding Claim 7, the combination of 3GPP and Papageorgiou discloses all of the limitations of Claim 6.
Additionally, Papageorgiou discloses “wherein task identifiers of a plurality of data analytics tasks corresponding to the plurality of task requests are the same (Papageorgiou paragraphs 119 and 122-124: the same analytics ID may be associated with different ML Models for different use cases, i.e., a plurality of data analytics tasks).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing to modify the method of 3GPP to transmit, by the NWDAF containing AnLF, the Nnwdaf_MLModelProvision_Subscribe message in response to receiving the Nnwdaf_AnalyticsSubscription_Subscribe message from the NF consumer as taught by Papageorgiou for the reasons set forth in the rejection of Claim 6.
Regarding Claim 8, the combination of 3GPP and Papageorgiou discloses all of the limitations of Claim 6.
Additionally, 3GPP discloses “wherein the task request comprises at least one of the following:
task identification information (3GPP § “6.1.3 Contents of Analytics Exposure”: Nnwdaf_AnalyticsSubscription_Subscribe message comprises a list of Analytics ID(s), i.e., task identification information);
condition restriction information of the task; and
analytics object information of the task (3GPP § “6.1.3 Contents of Analytics Exposure”: Nnwdaf_AnalyticsSubscription_Subscribe message comprises Target of Analytics Reporting indicating the object(s) for which Analytics information is requested, e.g., specific UEs, group of UEs, or all UEs).”
Regarding Claim 20, 3GPP discloses “send a first request, wherein the first request is used to request a plurality of models for one or more data analytics tasks (3GPP fig. 6.2A.1-1, § “6.2A.1 ML Model Subscribe/Unsubscribe” and “6.2A.2 Contents of ML Model Provisioning”: an NWDAF service consumer, e.g., an NWDAF containing AnLF, sends a request, i.e., Nnwdaf_MLModelProvision_Subscribe message, to subscribe to a set of trained ML Models associated with a set of Analytics IDs, i.e., one or more data analytics tasks – while not explicitly stated, implementation of the NWDAF functionality by one or more hardware devices is inferred); and
receive a first response, wherein the first response comprises related information of a plurality of models meeting the first request (3GPP fig. 6.2A.1-1, § “6.2A.1 ML Model Subscribe/Unsubscribe” and “6.2A.2 Contents of ML Model Provisioning”: the NWDAF service consumer, e.g., the NWDAF containing AnLF, receives a response, i.e., a Nnwdaf_MLModelProvision_Notify message, comprising trained ML Model Information for the set of trained ML Models – while not explicitly stated, implementation of the NWDAF functionality by one or more hardware devices is inferred).
However, while implementation of the NWDAF functionality disclosed in 3GPP by one or more hardware devices is inferred (3GPP figs. 6.1.1.1-1 and 6.4.4-1 and § “5.1 General”, “6.1.1.1 Analytics subscribe/unsubscribe by NWDAF service consumer” and “6.4.4 Procedures to request Service Experience for an Application”), 3GPP does not explicitly disclose “A model request apparatus, comprising:
at least one hardware processor and a memory, wherein the memory stores a program or instructions capable of running on the at least one hardware processor, and when the program or instructions are executed by the at least one hardware processor...”
In the same field of endeavor, Papageorgiou discloses “A model request apparatus (Papageorgiou figs. 3, 4 and 6 and paragraphs 102, 122 and 163-164: an apparatus 600 implementing an NWDAF service consumer, e.g., an NWDAF AnLF), comprising:
at least one hardware processor and a memory, wherein the memory stores a program or instructions capable of running on the at least one hardware processor, and when the program or instructions are executed by the at least one hardware processor... (Papageorgiou fig. 6 and paragraphs 154-157 and 163-164: at least one processor 610 executing program instructions stored in at least one memory 620)”.
It would have been obvious to one of ordinary skill in the art at the time of the effective filing to implement the NWDAF functionality using an apparatus comprising a processor executing program instructions stored in memory as taught by Papageorgiou because doing so constitutes applying a known technique (implementing NWDAF functionality using an apparatus comprising a processor and memory) to known devices and/or methods (an NWDAF service consumer) ready for improvement to yield predictable and desirable results (implementation of NWDAF service consumer using a generic computing device). See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Fu et al., Pub. No. US 2025/0111223 A1, discloses a method for requesting, from a consumer NWDAF, a trained machine learning model from a producer NWDAF, wherein the consumer NWDAF may include a reliability requirement in the model provisioning request; and
Shariat et al., Pub. No. US 2024/0244466 A1, discloses methods for providing information based on UE locations in 3GPP 5G network using NWDAF data analytics wherein a certain analytics ID identifies a distinct analytics type for a family of use cases.
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