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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/30/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because:
Independent claim 20 states “A computer readable medium for storing instruction code”.
Paragraph 0200, of the instant specification, states “The readable storage medium includes a computer-readable storage medium such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk, or an optical disc”.
These cited examples for the “readable storage medium” are only examples (“such as”). These examples do not restrict the “readable storage medium” to “non-transitory” mediums. This paragraph, and the instant specification as a whole, does not provide a clear description of “readable storage medium”, which would exclude transitory storage (communication medium) from the claimed readable storage medium. Therefore, the scope of the recited "readable storage medium” encompasses transitory media such as signals or carrier waves, where, as here the specification does not limit the readable storage medium to non-transitory forms. See MPEP §2106.03(II).
Amending independent claim 20 to include “non-transitory computer readable storage medium” would overcome this 101 rejection.
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)(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.
Claims 1-6 and 8-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yuan et al. (“Yuan”) [PGPUB 2022/0197953].
Regarding claim 1, the Yuan reference discloses a model acquisition method, comprising: receiving, by a first network element, a model storage request message sent by a second network element [ie. model repository (“first network element”) receives store model request (“model storage request message”) sent by sandbox through model engine (“second network element”); Yuan; figure 6; paragraph 0042, 0059, and 0091],
wherein the model storage request message comprises model information for at least one model, the model information comprising at least one of a model file or model file storage information [Yuan; para 0029 and 0042-0043]; and
storing, by the first network element, the model information for the at least one model [Yuan; fig 6; paragraph 0042, 0059, and 0091].
Regarding claim 2, the Yuan reference further discloses the model information further comprises model description information, the model description information comprising at least one of the following: model storage space information; or model identification information [ie. model name; Yuan; para 0042].
Regarding claim 3, the Yuan reference further discloses receiving, by the first network element, a first model request message sent by a third network element, the first model request message comprising model requirement information; determining, by the first network element, a target model based on the model requirement information, the target model matching the model requirement information; and sending, by the first network element, model information for the target model to the third network element [ie. model matching request from orchestrator (“third network element”) and receives model matching is complete (“model information”); Yuan; para 0044-0046 and 0080-0083].
Regarding claim 4, the Yuan reference further discloses the model requirement information comprises at least one of the following: model identification information; framework information on which a model is based; description method information on which a model is based; optimization algorithm information on which a model is based; information about precision achievable by a model; model storage space information; model computing power requirement information; or model version requirement information [ie. model name; Yuan; para 0042].
Regarding claim 5, the Yuan reference further discloses the determining, by the first network element, a target model based on the model requirement information comprises: determining, by the first network element, a model matching the model requirement information as the target model in a case that the first network element comprises the model matching the model requirement information [Yuan; para 0036-0037 and 0080-0086].
Regarding claim 6, the Yuan reference further discloses the determining, by the first network element, a target model based on the model requirement information comprises: sending, by the first network element, a second model request message to a fourth network element in a case that the first network element comprises no models matching the model requirement information, the second model request information comprising the model requirement information; receiving, by the first network element, a model response message sent by the fourth network element, the model response message comprising model information for a model matching the model requirement information; and determining, by the first network element, a model matching the model requirement information as the target model [ie. if no model match within model repository (“first network element”), then request model from joint model repository (“fourth network element”); Yuan; fig 7; para 0047 and 0094-0096].
Regarding claim 8, the Yuan reference further discloses the first network element comprises a data repository function network element, and the second network element comprises a network data analysis function (NWDAF) network element [Yuan; para 0080-0081 and 0086].
Regarding claim 9, the Yuan reference discloses an information sending method, comprising: sending, by a second network element, a model storage request message to a first network element [ie. sandbox through model engine (“second network element”) sends store model request (“model storage request message”) to model repository (“first network element”); Yuan; figure 6; paragraph 0042, 0059, and 0091],
wherein the model storage request message comprises model information for at least one model, the model information comprising at least one of a model file or model file storage information [Yuan; para 0029 and 0042-0043].
Regarding claim 10, the Yuan reference further discloses the model information further comprises model description information, the model description information comprising at least one of the following: model storage space information; or model identification information [ie. model name; Yuan; para 0042].
Regarding claim 11, the Yuan reference further discloses before the sending, by a second network element, a model storage request message to a first network element, the method further comprises: training, by the second network element, the at least one model to obtain model information for the at least one model; or, receiving, by the second network element, model information for the at least one model [Yuan; para 0088-0092].
Regarding claim 12, the Yuan reference further discloses before the sending, by a second network element, a model storage request message to a first network element, the method further comprises: receiving, by the second network element, a second model request message sent by the first network element, wherein the second model request message comprises model requirement information, and the at least one model is a model matching the model requirement information [Yuan; para 0044-0046 and 0080-0083].
Regarding claim 13, the Yuan reference further discloses the model requirement information comprises at least one of the following: model identification information; framework information on which a model is based; description method information on which a model is based; optimization algorithm information on which a model is based; information about precision achievable by a model; model storage space information; model computing power requirement information; or model version requirement information [ie. model name; Yuan; para 0042-0043].
Regarding claim 14, the Yuan reference further discloses the framework information is used to indicate a training framework on which a model is based; and/or the description method information is used to indicate model format information or model language information; and/or the optimization algorithm information is used to indicate a model convergence algorithm; and/or the computing power requirement information is used to indicate computing power required by a model in performing an inference task [ie. model name; Yuan; para 0042-0043 and 0049].
Regarding claim 15, the Yuan reference discloses a model acquisition method, comprising: sending, by a third network element, a first model request message to a first network element, the first model request message comprising model requirement information [ie. orchestrator (“third network element”) sends request to model repository (“first network element”), through model engine (“second network element”); Yuan; fig 6]; and
receiving, by the third network element, model information for a target model sent by the first network element, wherein the target model matches the model requirement information, and the model information comprises at least one of a model file or model file storage information [Yuan; para 0036-0037, 0042, and 0080-0086].
Regarding claim 16, the Yuan reference further discloses the model information further comprises model description information, the model description information comprising at least one of the following: model storage space information; or model identification information [ie. model name; Yuan; para 0042].
Regarding claim 17, the Yuan reference further discloses the model requirement information comprises at least one of the following: model identification information; framework information on which a model is based; description method information on which a model is based; optimization algorithm information on which a model is based; information about precision achievable by a model; model storage space information; model computing power requirement information; or model version requirement information [ie. model name; Yuan; para 0042-0043].
Regarding claim 18, the Yuan reference further discloses the framework information is used to indicate a training framework on which a model is based; and/or the description method information is used to indicate model format information or model language information; and/or the optimization algorithm information is used to indicate a model convergence algorithm; and/or the computing power requirement information is used to indicate computing power required by a model in performing an inference task [“is used to indicate model format information” is just intended use of the description method information; the model name and version would be description method information, since the “indicate model format information or model language information” is not positively described in the claim language (only intended use); Yuan; para 0042-0043].
Regarding claim 19, the Yuan reference discloses a network element, comprising a processor and a memory, wherein the memory stores a program or instruction capable of running on the processor, and when the program or instruction is executed by the processor, the steps of the method according to claim 1 are implemented [Yuan; para 0080 and 0105; claim 14].
Regarding claim 20, the Yuan reference discloses readable storage medium, wherein the readable storage medium has a program or instruction stored thereon, and when the program or instruction is executed by a processor, the steps of the method according to claim 1 are implemented [Yuan; para 0104-0105; claim 13].
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 7 is rejected under 35 U.S.C. 103 as being unpatentable over Yuan in view of Lee et al. (“Lee”) [PGPUB 2022/0108214] (cited on IDS, filed 1/30/2026).
Regarding claim 7, the Yuan reference discloses fourth network element being capable of providing model information for a model matching the model requirement information [ie. if no model match within model repository (“first network element”), then request model from joint model repository (“fourth network element”); Yuan; fig 7; para 0047 and 0094-0096].
The Yuan reference does not specifically disclose “sending, by the first network element, a network element query request message to a fifth network element, wherein the network element query request message comprises network element capability requirement information, the network element capability requirement information being determined by the first network element based on the first model requirement information; receiving, by the first network element, a network element query response message sent by the fifth network element, the network element query response message comprising network element information matching the network element capability requirement information; and determining, by the first network element, the fourth network element based on the network element information”.
However, in the same field of endeavor, the Lee reference discloses sending, by the first network element, a network element query request message to a fifth network element, wherein the network element query request message comprises network element capability requirement information, the network element capability requirement information being determined by the first network element based on the first model requirement information [ie. NWDAF device (“first network element) requests information from NRF device (“fifth network device”); Lee; fig 4, 8, and 9; para 0092, 0117-0119, 0127-0128 and 0238]; receiving, by the first network element, a network element query response message sent by the fifth network element, the network element query response message comprising network element information matching the network element capability requirement information [Lee; para 0127-0128 and 0131-0133]; and determining, by the first network element, the fourth network element based on the network element information [ie. NWDAF determines another NWDAF based on capabilities; Lee; fig 8; para 0117-0119, 0232 and 0238-0240]. The Yuan and Lee references are analogous art, since they have similar problem solving area of managing modeling repository. It would have been obvious to a person of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the teaching of a fifth network element, taught by Lee, into the system, taught by Yuan. The motivation for doing so would have been to correctly select a NWDAF (with a target ML model) [Lee; para 0009-0011 and 0120].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al. [USPAT 11,301,762] discloses optimizing machine learning models.
Hall et al. [PGPUB 2022/0344049] describes AI training on a distributed dataset.
Palmer et al. [USPAT 11,169,798] describes creating new models with training and then deployment.
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/JASON D CARDONE/Primary Examiner, Art Unit 2458
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