DETAILED ACTION
This action is responsive to claims filed on 27 March 2024.
Claims 1-20 are pending for examination.
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 Objections
Claim 6 and analogous claim 20 are objected to because of the following informalities: “the input format” in line 8 should be “an input format”. Appropriate correction is required.
Claim 6 and analogous claim 20 are objected to because of the following informalities: “the output format” in line 8 should be “an output format”. Appropriate correction is required.
Claim 13 is objected to because of the following informalities: “the input format” in line 8 should be “an input format”. Appropriate correction is required.
Claim 13 is objected to because of the following informalities: “the output format” in line 8 should be “an output format”. Appropriate correction is required.
Claim 15 is objected to because of the following informalities: “the memory” in line 2 should be “a memory”. Appropriate correction is required.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, abstract idea, without significantly more.
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory
category. MPEP 2106.03:
According to the first part of the Alice analysis, in the instant case, the claims were determined
to be directed to one of the four statutory categories: an article of manufacture, a method/process (Claims 1-14), a machine/system/product (Claims 15-20), and a composition of matter. Based on the claims being determined to be within of the four categories (i.e., process, machine, manufacture, or composition of matter), (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim(s) recites a
judicial exception.
Regarding independent claims 1, 8, 15, the claims recite a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG) without significantly more (Step-2A: Prong One). The applicant's claim limitations under broadest reasonable interpretation covers activities classified under mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection Ill) and the 2019 PEG. As evaluated below:
Claims 1, 15:
“wherein the first information is usable to determine N pieces of training data, and N is an integer” (mental process of judgement)
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is
reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below:
“receiving first information from a first device”
“to obtain a first artificial intelligence (Al) model”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“performing model training based on the N pieces of training data”
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. MPEP 2106.05.
First, the additional elements considered as part of the preamble and the additional elements
directed to the use of computer technology are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because they generally link the judicial exception to
the technology environment, see MPEP 2106.05(h).
Second, the additional elements directed to mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Third, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g).
Lastly, the claims directed to data gathering activity as noted above, are deemed directed to an insignificant extra-solution activity. The courts have found these types of limitations insufficient to
qualify as "significantly more", see MPEP 2106.05(g).
Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.05(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering):
The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d).
The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claims 1, 15 do not recite what the courts have identified as "significantly more".
Claim 8:
“determining first information” (mental process of judgement)
“wherein the first information is usable to determine N pieces of training data and N is an integer” (mental process of judgement)
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is
reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below:
“sending the first information to a second device”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“that are usable to train a first artificial intelligence (Al) model”
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. MPEP 2106.05.
First, the additional elements considered as part of the preamble and the additional elements
directed to the use of computer technology are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because they generally link the judicial exception to
the technology environment, see MPEP 2106.05(h).
Second, the additional elements mere application of the abstract idea or mere instructions to
implement an abstract idea on a computer are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because the limitations generally apply the use of a
generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Lastly, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g).
Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.05(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering):
The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d).
The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claim 8 does not recite what the courts have identified as "significantly more".
Furthermore, regarding dependent claims 2-7, which depend from claim 1, claims 9-14, which depend from claim 8, claims 16-20, which depend from claim 15, the claims are directed to a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under the Step2A and 2B:
Claims 2, 16:
Incorporates the rejections of claims 1, 15, respectively.
“wherein the receiving the first information includes”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“first information usable to indicate at least one of the following at least one training set, wherein each training set includes at least one piece of training data; a second Al model, wherein the first Al model is obtained through training based on the second Al model; an input format and/or an output format of the first Al model; or performance requirement information of the first Al model”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to instructions for mere data gathering or data output or directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 3, 17:
Incorporates the rejections of claims 2, 16, respectively.
“receiving second information from the first device”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“wherein the second information is usable to indicate at least one of the following: training data included in the N pieces of training data in a first training set in the at least one training set; a value of N; or a ratio of training data obtained from different training sets in the at least one training set”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to instructions for mere data gathering or data output or directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 4, 18:
Incorporates the rejections of claims 2, 16, respectively.
“wherein in response to there being a plurality of training sets and a plurality of first Al models, the receiving the first information further includes receiving first information”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“usable to indicate a correspondence between the plurality of training sets and the plurality of first Al models”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to instructions for mere data gathering or data output or directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 5, 19:
Incorporates the rejections of claims 1, 15, respectively.
“wherein the receiving the first information includes receiving is a reference signal”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“the method further comprises: determining the N pieces of training data based on the reference signal” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to instructions for mere data gathering or data output or directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 6, 20:
Incorporates the rejections of claims 1, 15, respectively.
“sending request information to the first device, wherein the request information requests the first information, or requests to perform model training”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“the request information is usable to indicate at least one of the following: an application scenario of the first Al model; a function of the first Al model; a type of the training data; the input format and/or the output format of the first Al model; a computing capability of a terminal; or a storage capability of the terminal”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to instructions for mere data gathering or data output or directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 7:
Incorporates the rejection of claim 1.
“sending third information to the first device after the training of the first Al model is completed”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“wherein the third information is usable to indicate at least one of the following: an identifier of the first Al model; or performance of the first Al model”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to instructions for mere data gathering or data output or directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 9:
Incorporates the rejection of claim 8.
“wherein the determining the first information includes determining the first information is usable to indicate” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“at least one of the following: at least one training set, wherein each training set includes at least one piece of training data; a second AI model, wherein the second AI model is usable for training to obtain the first AI model; an input format and/or an output format of the first AI model; or performance requirement information of the first AI model”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception or directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 10:
Incorporates the rejection of claim 9.
“sending second information to the second device”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“wherein the second information is usable to indicate at least one of the following: training data included in the N pieces of training data in a first training set in the at least one training set; a value of N; or a ratio of training data obtained from different training sets in the at least one training set”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to instructions for mere data gathering or data output or directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 11:
Incorporates the rejection of claim 9.
“wherein in response to there being a plurality of training sets and a plurality of first AI models, the determining the first information further includes determining” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“the first information is usable to indicate a correspondence between the plurality of training sets and the plurality of first AI models”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception or directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 12:
Incorporates the rejection of claim 8.
“wherein the determining the first information includes determining the first information is a reference signal that is usable to determine the training data” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 13:
Incorporates the rejection of claim 8.
“receiving request information from the second device, wherein the request information requests the first information, or requests to perform model training”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“the request information is usable to indicate at least one of the following: an application scenario of the first AI model; a function of the first AI model; a type of the training data; the input format and/or the output format of the first AI model; a computing capability of the second device; or a storage capability of the second device”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to instructions for mere data gathering or data output or directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 14:
Incorporates the rejection of claim 8.
“receiving third information from the second device”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“wherein the third information is usable to indicate at least one of the following: an identifier of the first AI model; or performance of the first AI model”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to instructions for mere data gathering or data output or directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The dependent claims as analyzed above, do not recite limitations that integrated the judicial exception into a practical application. In addition, the claim limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step-2B). Therefore, the claims do not recite any limitations, when considered individually or as a whole, that recite what have the courts have identified as "significantly more", see MPEP 2106.05; and therefore, as a whole the claims are not patent eligible. As shown above, the dependent claims do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified. Therefore, as a whole, the dependent claims do not recite what have the courts have identified as "significantly more" than the recited judicial exception. Therefore, claims 2-7, 9-14, 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as "significantly more" than the recited judicial exception.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mu et al. (U.S. Pre-Grant Publication No. 20240265307, hereinafter 'Mu'), in view of Mu.
Regarding claim 1 and analogous claim 15, Mu teaches A communication method, comprising: receiving first information from a first device, wherein the first information is usable to determine N pieces of training data, and N is an integer; and performing model training based on the N pieces of training data, to obtain a first artificial intelligence (Al) model ([0155] FIG. 10 is a schematic diagram of a process for collecting and identifying model training data for a training model structure according to an embodiment. As shown in FIG. 10 , by taking the wireless access network device is a gNB-CU as an example, it includes: [0156] step S421, the receiving first information from a first device OAM sending a request for collecting data to each gNB-CU; [0157] step S422, each gNB-CU collecting terminal data and sending the same to the OAM; [0158] step S423, the OAM aggregating the data sent by each gNB-CU wherein the first information is usable to determine training data to form model training data; [0159] step S424, the OAM performing data processing such as data denoising and normalization on the model training data; and [0160] step S425, the OAM performing data identification on the model training data, to identify the gNB-CU information to which each piece of data record belongs and corresponding ID information.; [0165] The performing model training based on the N pieces of training data model training data provided by the present disclosure can enable a plurality of model training tasks to be performed synergistically, which will increase noise with each other, thereby to obtain a first artificial intelligence (Al) model improving the generalization of the model.; [0166] FIG. 11 is a flowchart of a model training method according to an embodiment. As shown in FIG. 11 , the model training method is used in OAM and includes the following steps.; [0131] FIG. 6 is a flowchart of a model training method according to an embodiment. As shown in FIG. 6 , the model training method is used in OAM and includes the following steps.).
Mu teaches wherein the first information is usable to determine N pieces of training data, and N is an integer ([0134] In an embodiment of the present disclosure, the model training tasks of the model subscription requests are different, and the number of layers and the number of nodes of the to-be-trained models corresponding to the requests are different. The model training task characteristic of each model subscription wherein the first information is usable to determine request may be determined based on the model training task. The and N is an integer number of nodes of the input layer is set to M, which N pieces of training data represents the volume of training data that is input into the input model at one time. The number of nodes of the output layer is set to N, and N depends on the number of gNB-CUs and the training task characteristics.); and
Mu and Mu are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Mu, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Mu to Mu before the effective filing date of the claimed invention in order to reduce the amount of uploaded data, balance allocation of resources, and reduce data security risk (cf. Mu, [0130] The model training method provided by the embodiments of the present disclosure may transfer a portion of the model training work to the wireless access network device, which may reduce the amount of uploaded data, be conducive to the balanced allocation of resources, and reduce data security risk.).
Regarding claim 2 and analogous claim 16, Mu, as modified by Mu, teaches The method of claim 1, The apparatus of claim 15, respectively.
Mu teaches wherein the receiving the first information includes first information usable to indicate at least one of the following: at least one training set, wherein each training set includes at least one piece of training data; a second Al model, wherein the first Al model is obtained through training based on the second Al model; an input format and/or an output format of the first Al model; or performance requirement information of the first Al model ([0098] The data collection/preparation unit collects receiving the first information includes first information usable to indicate at least one of the following: data related to AI model training, updating, and inference, and pre-processes the data according to the performance requirement information of the first Al model requirements of AI model training, updating, and inference on data content, size, format, and period, and provides the processed data to the model training unit and the model prediction unit according to requirements. In addition, the data collection/preparation unit may also determine the effectiveness of the current AI model based on the collected data and provide model performance feedback to the model training unit.).
Mu and Mu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 3 and analogous claim 17, Mu, as modified by Mu, teaches The method of claim 2, The apparatus of claim 16, respectively.
Mu teaches further comprising: receiving second information from the first device, wherein the second information is usable to indicate at least one of the following: training data included in the N pieces of training data in a first training set in the at least one training set; a value of N; or a ratio of training data obtained from different training sets in the at least one training set ([0221] In an embodiment, the wireless access network device determines the output results of the shared output layer belong to the device itself, serially inputs the same into the unique model layer, and obtains output results of the unique model layer, therefore each piece of model training data i corresponds to a set of output results when the number of nodes in the output layer is N.; [0278] 3: The receiving second information from the first device OAM needs to perform data identification on the model training data. The wherein the second information is usable to indicate at least one of the following: identification information includes the gNB-CU to which training data included in the N pieces of training data in a first training set in the at least one training set the training data belongs and the data ID, and the like. The gNB-CU information is identified so that each gNB-CU can filter the training data belonging to the gNB-CU itself accordingly, and thus use a ratio of training data obtained from different training sets in the at least one training set this portion of the data for updating the unique model layer thereof.).
Mu and Mu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 4 and analogous claim 18, Mu, as modified by Mu, teaches The method of claim 2, The apparatus of claim 16, respectively.
Mu teaches wherein in response to there being a plurality of training sets and a plurality of first Al models, the receiving the first information further includes receiving first information usable to indicate a correspondence between the plurality of training sets and the plurality of first Al models ([0131] FIG. 6 is a flowchart of a model training method according to an embodiment. As shown in FIG. 6, the model training method is used in OAM and includes the following steps.; [0132] In step S21, a first number of model subscription requests sent by the first number of wireless access network devices are determined, and model training task characteristics of the first number of model subscription requests are determined.; [0133] The model training task characteristic is configured to indicate a number of layers and a number of nodes of a model.; [0134] In an embodiment of the present disclosure, the wherein in response to there being a plurality of training sets and a plurality of first Al models model training tasks of the model subscription requests are different, and the number of layers and the number of nodes of the to-be-trained models corresponding to the requests are different. The the receiving the first information further includes receiving first information usable model training task characteristic of each model subscription request may be determined based on the model training task. The number of nodes of the input layer is set to M, which represents the volume of training data that is input into the input model at one time. The number of nodes of the output layer is set to N, and N depends on the number of gNB-CUs and the training task characteristics. For example, each gNB-CU corresponds to one node in the prediction task (regression task), and each gNB-CU corresponds to a plurality of nodes in the decision task. The number of hidden layers is set to S, and the number of nodes per hidden layer is set to L. The number of hidden layers needs to take into account factors such as model size and model generalization ability. Therefore, the model training task characteristics are determined.; [0135] In step S22, the to indicate a correspondence between the plurality of training sets and the plurality of first Al models first number of model training structures are determined according to the number of layers and the number of nodes of the model indicated by the model training task characteristics.).
Mu and Mu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 5 and analogous claim 19, Mu, as modified by Mu, teaches The method of claim 1, The apparatus of claim 15, respectively.
Mu teaches wherein the receiving the first information includes receiving a reference signal, and the method further comprises: determining the N pieces of training data based on the reference signal ([0273] FIG. 29 is a schematic diagram of a protocol and interface for model inference data collection of a model training method according to an embodiment. As shown in FIG. 29 , it mainly relates to a terminal, a radio access network device (e.g., a gNB-DU) accessed by the terminal, a radio access network device (e.g., a gNB-CU) accessed by the terminal, and an OAM provided by an embodiment of the present disclosure.; 1 a. the current gNB-CU sends model inference data request signaling to the gNB-DU connected thereto; 1 b. each gNB-DU sends the model inference data includes receiving a reference signal request signaling to the terminal connected thereto; 2. the terminal wherein the receiving the first information receives the model inference data request and determining the N pieces of training data based on the reference signal prepares terminal inference data; 3. the terminal sends the inference data to the gNB-DU connected thereto; 4. each gNB-DU receives the terminal inference data and collects data of the current gNB-DU to form the gNB-DU inference data; 5. each gNB-DU sends the inference data to the gNB-CU connected thereto; 6. the current gNB-CU receives the gNB-DU inference data and collects data of the current gNB-CU to form the model inference data.).
Mu and Mu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 6 and analogous claim 20, Mu, as modified by Mu, teaches The method of claim 1, The apparatus of claim 15, respectively.
Mu teaches further comprising: sending request information to the first device, wherein the request information requests the first information, or requests to perform model training, and the request information is usable to indicate at least one of the following: an application scenario of the first Al model; a function of the first Al model; a type of the training data; the input format and/or the output format of the first Al model; a computing capability of a terminal; or a storage capability of the terminal ([0124] In an embodiment of the present disclosure, the sending request information to the first device OAM receives a plurality of model subscription requests sent by a plurality of wireless access network devices, and wherein the request information requests the first information, or requests to perform model training, and the request information is usable to indicate at least one of the following: determines information such as a terminal identity, a function of the first Al model a model request type, an access location included in each model subscription request. The terminal identity is a Globally Unique Temporary UE Identity (GUTI). The model request type is represented by an analysis ID, such as load prediction analysis service. The access location information mainly includes information of gNB-CU and gNB-DU to which the terminal is currently accessing.).
Mu and Mu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 7, Mu, as modified by Mu, teaches The method of claim 1.
Mu teaches further comprising: sending third information to the first device after the training of the first Al model is completed, wherein the third information is usable to indicate at least one of the following: an identifier of the first Al model; or performance of the first Al model ([0239] FIG. 23 is a flowchart of a model training method according to an embodiment. As shown in FIG. 23 , the model training method is used in a wireless access network device and includes: step S141, receiving a structural parameter of a shared model layer sent by the OAM; and step S142, determining a structural parameter of a subscription model according to the structural parameter of the shared model layer and the structural parameter of the unique model layer after the training of the first Al model is completed after a Tth update.; [0242] T is a predetermined number of times to update the shared model layer and the unique model layer.; [0243] In an embodiment of the present disclosure, the OAM sends the model parameters of the shared model layers to each wireless access network device in the wireless access network device group. After the wireless access network device receives the structural parameters of the shared model layers, based on the connection manner between the shared model layer and the unique model layer stored thereon, the wireless access network device may stitch the structural parameters of the two models together according to a specific connection to integrate them into a complete model and thus obtain the structural parameters of the model, which may be used for model inference.; [0249] (3) The terminal executes the network optimization policy wherein the third information is usable to indicate at least one of the following: performance of the first Al model
based on the model inference result, collects network performance data and sending third information to the first device feeds the same back to the OAM for model training.).
Mu and Mu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 8, Mu teaches A communication method, comprising: determining first information; and sending the first information to a second device, wherein the first information is usable to determine N pieces of training data that are usable to train a first artificial intelligence (Al) model, and N is an integer ([0155] FIG. 10 is a schematic diagram of a process for collecting and identifying model training data for a training model structure according to an embodiment. As shown in FIG. 10 , by taking the wireless access network device is a gNB-CU as an example, it includes: [0156] step S421, the OAM and sending the first information to a second device sending a request for collecting data to each gNB-CU; [0157] step S422, each gNB-CU collecting terminal data and sending the same to the OAM; [0158] step S423, the OAM aggregating the data sent by each gNB-CU wherein the first information is usable to determine training data to form model training data; [0159] step S424, the OAM performing data processing such as data denoising and normalization on the model training data; and [0160] step S425, the OAM performing data identification on the model training data, to identify the gNB-CU information to which each piece of data record belongs and corresponding ID information.; [0165] The that are usable to train a first artificial intelligence (Al) model model training data provided by the present disclosure can enable a plurality of model training tasks to be performed synergistically, which will increase noise with each other, thereby improving the generalization of the model.; [0166] FIG. 11 is a flowchart of a model training method according to an embodiment. As shown in FIG. 11 , the model training method is used in OAM and includes the following steps.).
Mu teaches determining first information; and sending the first information to a second device, wherein the first information is usable to determine N pieces of training data that are usable to train a first artificial intelligence (Al) model, and N is an integer ([0131] FIG. 6 is a flowchart of a model training method according to an embodiment. As shown in FIG. 6, the model training method is used in OAM and includes the following steps.; [0134] In an embodiment of the present disclosure, the model training tasks of the model subscription requests are different, and the number of layers and the number of nodes of the to-be-trained models corresponding to the requests are different. The model training task characteristic of each model subscription determining first information request may be determined based on the model training task. The and N is an integer number of nodes of the input layer is set to M, which N pieces of training data represents the volume of training data that is input into the input model at one time. The number of nodes of the output layer is set to N, and N depends on the number of gNB-CUs and the training task characteristics.).
Mu and Mu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 9, Mu, as modified by Mu, teaches The method of claim 8.
Mu teaches wherein the determining the first information includes determining the first information is usable to indicate at least one of the following: at least one training set, wherein each training set includes at least one piece of training data; a second AI model, wherein the second AI model is usable for training to obtain the first AI model; an input format and/or an output format of the first AI model; or performance requirement information of the first AI model ([0098] The data collection/preparation unit wherein the determining the first information includes determining the first information is usable to indicate at least one of the following: collects data related to AI model training, updating, and inference, and pre-processes the data according to the performance requirement information of the first AI model requirements of AI model training, updating, and inference on data content, size, format, and period, and provides the processed data to the model training unit and the model prediction unit according to requirements. In addition, the data collection/preparation unit may also determine the effectiveness of the current AI model based on the collected data and provide model performance feedback to the model training unit.).
Mu and Mu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 10, Mu, as modified by Mu, teaches The method of claim 9.
Mu teaches further comprising: sending second information to the second device, wherein the second information is usable to indicate at least one of the following: training data included in the N pieces of training data in a first training set in the at least one training set; a value of N; or a ratio of training data obtained from different training sets in the at least one training set ([0221] In an embodiment, the wireless access network device determines the output results of the shared output layer belong to the device itself, serially inputs the same into the unique model layer, and obtains output results of the unique model layer, therefore each piece of model training data i corresponds to a set of output results when the number of nodes in the output layer is N.; [0277] 2: The OAM collects model training data. The OAM first sending second information to the second device, wherein the second information is usable to indicate at least one of the following: sends a model training data request to each gNB-CU in the gNB-CU group, then the gNB-CU will send the model training data request to the gNB-DU connected thereto, then the gNB-DU will send the model training data request to the terminal, and after the terminal sends the data to the gNB-DU, the gNB-DU will send the data from the terminal and local data collected by the gNB-DU together to the gNB-CU, and similarly, the gNB-CU will send the data from the gNB-DU and local data collected by the gNB-CU together to the OAM. [0278] 3: The OAM needs to perform data identification on the model training data. The identification information includes the gNB-CU to which training data included in the N pieces of training data in a first training set in the at least one training set the training data belongs and the data ID, and the like. The gNB-CU information is identified so that each gNB-CU can filter the training data belonging to the gNB-CU itself accordingly, and thus use a ratio of training data obtained from different training sets in the at least one training set this portion of the data for updating the unique model layer thereof.).
Mu and Mu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 11, Mu, as modified by Mu, teaches The method of claim 9.
Mu teaches wherein in response to there being a plurality of training sets and a plurality of first AI models, the determining the first information further includes determining the first information is usable to indicate a correspondence between the plurality of training sets and the plurality of first AI models ([0131] FIG. 6 is a flowchart of a model training method according to an embodiment. As shown in FIG. 6, the model training method is used in OAM and includes the following steps.; [0132] In step S21, a first number of model subscription requests sent by the first number of wireless access network devices are determined, and model training task characteristics of the first number of model subscription requests are determined.; [0133] The model training task characteristic is configured to indicate a number of layers and a number of nodes of a model.; [0134] In an embodiment of the present disclosure, the wherein in response to there being a plurality of training sets and a plurality of first AI models model training tasks of the model subscription requests are different, and the number of layers and the number of nodes of the to-be-trained models corresponding to the requests are different. The the determining the first information further includes determining the first information is usable model training task characteristic of each model subscription request may be determined based on the model training task. The number of nodes of the input layer is set to M, which represents the volume of training data that is input into the input model at one time. The number of nodes of the output layer is set to N, and N depends on the number of gNB-CUs and the training task characteristics. For example, each gNB-CU corresponds to one node in the prediction task (regression task), and each gNB-CU corresponds to a plurality of nodes in the decision task. The number of hidden layers is set to S, and the number of nodes per hidden layer is set to L. The number of hidden layers needs to take into account factors such as model size and model generalization ability. Therefore, the model training task characteristics are determined.; [0135] In step S22, the to indicate a correspondence between the plurality of training sets and the plurality of first AI models first number of model training structures are determined according to the number of layers and the number of nodes of the model indicated by the model training task characteristics.).
Mu and Mu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 12, Mu, as modified by Mu, teaches The method of claim 8.
Mu teaches wherein the determining the first information includes determining the first information is a reference signal that is usable to determine the training data ([0273] FIG. 29 is a schematic diagram of a protocol and interface for model inference data collection of a model training method according to an embodiment. As shown in FIG. 29 , it mainly relates to a terminal, a radio access network device (e.g., a gNB-DU) accessed by the terminal, a radio access network device (e.g., a gNB-CU) accessed by the terminal, and an OAM provided by an embodiment of the present disclosure.; 1 a. the current gNB-CU sends model inference data request signaling to the gNB-DU connected thereto; 1 b. wherein the determining the first information includes determining the first information is a reference signal each gNB-DU sends the model inference data request signaling to the terminal connected thereto; 2. the terminal receives the model inference data request and that is usable to determine the training data prepares terminal inference data; 3. the terminal sends the inference data to the gNB-DU connected thereto; 4. each gNB-DU receives the terminal inference data and collects data of the current gNB-DU to form the gNB-DU inference data; 5. each gNB-DU sends the inference data to the gNB-CU connected thereto; 6. the current gNB-CU receives the gNB-DU inference data and collects data of the current gNB-CU to form the model inference data.).
Mu and Mu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 13, Mu, as modified by Mu, teaches The method of claim 8.
Mu teaches further comprising: receiving request information from the second device, wherein the request information requests the first information, or requests to perform model training, and the request information is usable to indicate at least one of the following: an application scenario of the first AI model; a function of the first AI model; a type of the training data; the input format and/or the output format of the first AI model; a computing capability of the second device; or a storage capability of the second device ([0124] In an embodiment of the present disclosure, the OAM receives a plurality of model subscription requests receiving request information from the second device sent by a plurality of wireless access network devices, and wherein the request information requests the first information, or requests to perform model training, and the request information is usable to indicate at least one of the following: determines information such as a terminal identity, a function of the first AI model a model request type, an access location included in each model subscription request. The terminal identity is a Globally Unique Temporary UE Identity (GUTI). The model request type is represented by an analysis ID, such as load prediction analysis service. The access location information mainly includes information of gNB-CU and gNB-DU to which the terminal is currently accessing.).
Mu and Mu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 14, Mu, as modified by Mu, teaches The method of claim 8.
Mu teaches further comprising: receiving third information from the second device, wherein the third information is usable to indicate at least one of the following: an identifier of the first AI model; or performance of the first AI model ([0239] FIG. 23 is a flowchart of a model training method according to an embodiment. As shown in FIG. 23 , the model training method is used in a wireless access network device and includes: step S141, receiving a structural parameter of a shared model layer sent by the OAM; and step S142, determining a structural parameter of a subscription model according to the structural parameter of the shared model layer and the structural parameter of the unique model layer after a Tth update.; [0242] T is a predetermined number of times to update the shared model layer and the unique model layer.; [0243] In an embodiment of the present disclosure, the OAM sends the model parameters of the shared model layers to each wireless access network device in the wireless access network device group. After the wireless access network device receives the structural parameters of the shared model layers, based on the connection manner between the shared model layer and the unique model layer stored thereon, the wireless access network device may stitch the structural parameters of the two models together according to a specific connection to integrate them into a complete model and thus obtain the structural parameters of the model, which may be used for model inference.; [0249] (3) The terminal executes the network optimization policy wherein the third information is usable to indicate at least one of the following: performance of the first AI model based on the model inference result, collects network performance data and feeds the same receiving third information from the second device back to the OAM for model training.).
Mu and Mu are combinable for the same rationale as set forth above with respect to claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mu et al. (U.S. Pre-Grant Publication No. 20240276247) teaches a model data management method applied to a radio access network device, the method includes: determining a model task completion state of a terminal in response to the terminal handing over a radio access network device; and according to the model task completion state, determining a first radio access network device which transmits model data.
Yang et al. (U.S. Pre-Grant Publication No. 20200027019) teaches a method for training a model for creating POI data on a terminal through federated learning, the terminal associated with artificial intelligence modules, drones (unmanned aerial vehicles (UAVs)), robots, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G service.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAGGIE MAIDO whose telephone number is (703) 756-1953. The examiner can normally be reached M-Th: 6am - 4pm.
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/MM/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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