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 .
Claims 1-17 are pending.
Claim Objections
Claims 3, and 5 are objected to because of the following informalities:
-- an inference task -- should be -- the inference task -- in claim 3 line 4.
-- a status report -- should be -- the status report -- in claim 5 line 1.
-- the state report -- should be -- the status report -- in claim 5 line 2.
-- one occurs among -- should be -- one case of the plurality cases [among] including -- claim 5 line 3.
Appropriate correction is required.
Specification
The disclosure is objected to because of the following informalities:
-- ms -- is abbreviated without reciting the full form in [0122 ].
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 1-17 are rejected under 35 U.S.C. 112 (b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or joint inventor regards as the invention.
The following claim language is not clearly understood:
Claim 1 recites “communication node”, “network node” without clearly reciting how these nodes are connected and the environment these nodes are operating; while claim 10 recites server that manages models, or a cloud that manages models without clearly reciting the relationship between the communication node and the server and /or the cloud.
Claim 1 recites “each artificial neural network models”. It is unclear which artificial neural network models are being referred to and if these models are available at only communication node or only network node or both communication and network nodes or neither at communication node nor at network nodes.
Claim 1 recites “status report” without clearly reciting the possible values of status i.e. what information related to the status of models is included in the status report.
Claim 5 recites “handover procedure” without clearly reciting “handover” of what.
Claim 9 recites “receiving …in advance” without clearly reciting in advance of what.
Claim 11 recite elements of claim 1 and have similar deficiency as claim 1. Therefore, they are rejected for the same rational. Remaining dependent claims 2-10 and 12-17 are also rejected due similar deficiency inherited from the rejected independent claims.
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 6-7, 11-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more or integrating into practical application.
Claims 6-7, 11-17 are determined to be directed to an abstract idea. Examples of abstract ideas include at least Mathematical concepts, Mental process and Certain Methods of organizing human activity. Dependent claim 6 recites “ignoring the activation or deactivation of neural network model not included in the status report” at high level of generality. Dependent claim 7 recites “activating the one or more neural network models based on the activation indication”. Independent claim 11 recites “determining whether to allow each of artificial neural network models based on the received status report of the first model and a load of network node”, at a high level of generality”.
Step 1
As described in MPEP § 2106, subsection III, Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 6-7 and 11 recites a method, which falls within the “process” category of 35 U.S.C. § 101. Thus, the analysis determines whether the claims recite a judicial exception and fail to integrate the exception into practical application. If both elements are satisfied, the claims are directed to a judicial exception under the first step of the Alice/Mayo test.
Step 2A Prong One
As described in MPEP § 2106, subsection III, Step 2A of the Office’s eligibility analysis is the first part of the Alice/Mayo test, i.e., the Supreme Court’s "framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts." Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217-18, 110 USPQ2d 1976, 1981 (2014) (citing Mayo, 566 U.S. at 77-78, 101 USPQ2d at 1967-68).
Step 2A is a two-prong inquiry, in which examiners determine in Prong One whether a claim recites a judicial exception, and if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
claims
i
1. A method of a communication node, comprising:
common computing
ii
transmitting required network configurations for applying each artificial neural network models to a network node; and
information transmission-common computing
iii
transmitting a status report of the first model including a model identifier field and a model information field for each of the artificial neural network models to the network node to activate at least one artificial neural network model among the artificial neural network models,
information transmission-common computing
iv
wherein each of the required network configurations includes a configuration identifier and network configuration information.
describing information being transmitted
i
6. The method according to claim 1, further comprising:
common computing
ii
receiving, from the network node, indication information on activation or deactivation of an artificial neural network model corresponding to an artificial neural network model not included in the status report of the first model; and
information gathering
iii
ignoring the activation or deactivation of the artificial neural network model according to the indication information.
mental process abstract idea
i
7. The method according to claim 1, further comprising:
common computing
ii
receiving, from the network node, an activation indication on one or more artificial neural network models in response to the status report of the first model;
information gathering - insignificant extra solution activity
iii
activating the one or more artificial neural network models based on the activation indication;
mental process abstract idea
iv
when an artificial neural network model activated in the communication node is deactivated, generating a status report of the second model including deactivation information of the deactivated artificial neural network model; and
mental process abstract idea; generating report - common computing method
v
transmitting the status report of the second model to the network node.
information transmission-common computing
i
11. A method of a network node, comprising:
common computing
ii
receiving required network configurations for applying each of artificial neural network models from a communication node;
information gathering
iii
receiving at least one status report of the first model including a model identifier field and a model information field for each of the artificial neural network models;
information gathering
iv
determining whether to allow each of the artificial neural network models based on the received status report of the first model and a load of the network node; and
mental process abstract idea
v
transmitting information indicating whether or not to allow each of the artificial neural network models to the communication node,
information transmission-common computing
vi
wherein each of the required network configurations includes a configuration identifier and network configuration information.
describing the network configuration
Claim 6 in step [iii] recites “ignoring the activation or deactivation of the artificial neural network model according to the indication information”, which is a combination of observation, evaluation, judgement and opinion, and can be performed by human mind with or without the help of pen and paper. Claim 7 in step [iii] recites “activating the one or more artificial neural network models based on the activation indication”, which is also a combination of observation, evaluation, judgement and opinion, and can be performed by human mind with or without the help of pen and paper. Claim 11 in step [iv] recites “determining whether to allow each of the artificial neural network models based on the received status report of the first model and a load of the network node”, which is a combination of observation, evaluation, judgement and opinion, and can be performed by human mind with or without the help of pen and paper. Therefore, these limitations are resembles the idea of performing observation, evaluation, judgement and opinion according to the broadest reasonable interpretations of the claim elements and can be performed by human mind alone or with the aid of pen and paper. The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
Thus, claims 6-7 and 11 recite a judicial exception.
Step 2A, Prong Two
As described in MPEP § 2106, subsection III, Step 2A of the Office’s eligibility analysis is the first part of the Alice/Mayo test, i.e., the Supreme Court’s "framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts." Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217-18, 110 USPQ2d 1976, 1981 (2014) (citing Mayo, 566 U.S. at 77-78, 101 USPQ2d at 1967-68).
Step 2A is a two-prong inquiry, in which examiners determine in Prong One whether a claim recites a judicial exception, and if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Because claims 6-7 and 11 recite a judicial exception, Analysis determines if the claims recites additional elements that integrate the judicial exception into practical application.
In addition to limitations of claim 6 discussed above under Prong One, claim 6 also recites additional claim elements. For example, claim 6 step [i] recites “a method of a communication node”, which is directed to a common computing method and associated with a common communication node. These are generic computing components/method and do not provide an improvement in the functioning of a computer or technology or technical field. Claim 6 step [ii] recites “receiving, from the network node, indication information on activation or deactivation of an artificial neural network model corresponding to an artificial neural network model not included in the status report of the first model”, which is directed to receiving information at high level and is resembles the idea of information gathering and is considered insignificant extra solution activity. Therefore, it doesn’t integrate the abstract idea into practical application. Claim 6 is dependent on claim 1, which recites “transmitting network configuration and status report to the network node” at high level of generality and is directed to transmitting information. Transmitting information is common computing methods and doesn’t integrate the abstract idea into practical application. The Specification doesn’t provide additional details that would distinguish these additional limitations recited in claim 1 and claim 6 from a generic implementation of the abstract idea. Based on similar analysis and rationales, claims 7 and 11 also recites judicial exception without integrating into practical applications.
Step 2B
As described in MPEP § 2106, subsection III, Step 2B of the Office’s eligibility analysis is the second part of the Alice/Mayo test, i.e., the Supreme Court’s "framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts." Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, 110 USPQ2d 1976, 1981 (2014) (citing Mayo, 566 U.S. 66, 101 USPQ2d 1961 (2012)).
Step 2B asks: Does the claim recite additional elements that amount to significantly more than the judicial exception.
Because claims 6-7 and 11 are directed to judicial exception, analysis must determine, according to Alice, whether these claims recite an element, or combination of elements that is enough to ensure that the claim is directed to significantly more than a judicial exception.
The Memorandum, Section III (B) (footnote 36) states:
In accordance with existing guidance, an Examiner’s conclusion that an additional element (or combination of elements) is well understood, routine, conventional activity must be supported with a factual determination. For more information concerning evaluation of well-understood, routine, convention activity, see MPEP 2106.05(d), as modified by the USPTO Berkheimer Memorandum.
The Berkheimer Memorandum, Section III(A)(1) states:
A Specification demonstrates the well-understood, routine, conventional nature of additional elements when it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, on in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 §U.S.C. 112(a). A finding that an element is well-understood, routine, or conventional cannot be based only on the fact that the specification is silent with respect to describing such element. Claim 6, 7 and 11 recites in step [i], method of node and is of generic category and therefore, do not amount to significantly more. Claim 1 in steps [ii]-[iii], claim 7 step [v] and claim 11 step [v] are directed to transmission of information, which is also commonly performed in the field of computing as recognized by one of ordinary skills in the art and is not considered amount to significantly more. Claim 6 in step [ii], claim 7 step [ii], claim 11 steps [ii]-[iii] are directed to receiving information and resembles the idea of information gathering and is considered insignificant extra solution activity. Claim 1 step [iv], claim 6 step [vi], claim 7 step [v], and claim 11 step [vi] are directed to describe information being transmitted or gathered and do not further impose limitation on the abstract idea in such as manner to make the abstract idea patent eligible. These limitations either alone or in combination simply append well-understood, routine, conventional activities previously known to the industry as recognized by one of ordinary skills in the art, specified at a high level of generality, to the judicial exception. Further, the Specification doesn’t provide additional details that would distinguish the additional limitations as recited in the claim from a generic implementation of the abstract idea.
As such, it has been recognized by court that receiving, processing, and storing data as well as receiving or transmitting data over a network are a well-understood, routine and conventional activities. Mortg. Grader, Inc. v. First choice Loan Servs. Inc., 811 F.3d 1314 (Fed. Cir. 2016) (generic computer components, such as interface, “network”, and “database,” fail to satisfy the inventive concept requirement); see also TLI Commc’ns, 823 F.3d 607; Elec. Power, 830 F.3d at 1350. There is no indication that the recited claim elements override the conventional use of known features or involve an unconventional arrangement or combination of elements such that the particular combination of generic technology results in anything beyond well-understood, routine, and conventional data gathering and output. Alice, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention.”) See also Customedia Techs. LLC v. Dish Network Corp., 951 F.3d 1359, 1366(Fed. Cir. 2020) (“[T]he invocation of ‘already-available computers that are not themselves plausibly asserted to be an advance…amounts to a recitation of what is well-understood, routine, and conventional.”)(quoting SAP Am., Inc. v. InvestPic, LLC, 898F3.d 1161, 1170 (Fed. Cir. 2018)); and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355(Fed. Cir 2014)(“That a computer receives and sends the information over a network -- with no further specification -- is not even arguably inventive.”).
Thus, Claims 6-7 and 11 are directed to judicial exception without integrating into practical application and do not amount to significantly more.
Based on similar analysis as above, dependent claims 12-17 recite claim elements that are either abstract idea and/or additional claim elements, that individually or in combination, are either generic computing methods/components or insignificant pre/post solution activity, as recognized by one of ordinary skills in the art and neither integrate into practical application nor amount to significantly more.
Therefore, the claim(s) 6-7 and 11-17 are rejected under 35 U.S.C. 101 as being directed to judicial exception without integrating into practical application or significantly more.
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 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US 2022/0400373 A1, hereafter Zhu) in view of Wentink et al. (US 2006/0153156 A1, hereafter Wentink), and further in view of Ren et al. (US 2024/0147267 A1, hereafter Ren).
As per claim 1, Zhu teaches the invention substantially as claimed including a method of a communication node (fig. 1 wireless communication network 100 User equipment104 ), comprising:
transmitting required network configurations for applying each artificial neural network models to a network node ([0066] UE 504 transmitting to the BS 502, UE capability information, radio capability of the UE, ML capability of the UE [0067] radio capability of the UE, perform one or more wireless communication management procedures, ML-based [0068] one or more capabilities, UE, performing ML [0071] context setup request message, include ML capability of the UE, UE context information, data radio bearer/antenna configurations [0112] ML capability of the UE, indication of one or more locally cached ML models); and
transmitting a status report of the first model including a model identifier field and a model information field for each of the artificial neural network models ([0061] ML model, perform, neural network function NNF, different NNF identified by an NNF identifier [0090] ML configuration, NNF identifier, ML model ID [0058] different types of artificial neural networks) to the network node to activate at least one artificial neural network model among the artificial neural network models ([0085] BS 502, activate, use of ML model at the BS 502 [0031] neural network function, corresponding machine learning models [0058] different types of artificial neural networks, implement machine learning),
wherein each of the required network configurations includes a configuration identifier and network configuration information ([0111] radio capability, UE, cell reselecion, ML-based idle/inactive mode, ML-based radio resource management measurement, ML-based radio-link monitoring, ML-based channel state information (CSI) measurement, perform an ML-based precoding matrix indicator (PMI), rank indicator (RI), and channel quality indicator (CQI) feedback, perform an ML-based radio link failure (RLF) and beam failure recovery (BFR), or perform an ML-based RRM relaxation procedure).
Zhu doesn’t specifically teach transmitting the status report of the model to the network node; network configuration includes a configuration identifier.
Wentink, however, teaches transmitting network configuration includes a configuration identifier ([0044] master node constructs a remote configuration frame, configuration information to be transmitted to the slave, used for establishing a connection [0053] configuration frame, identifier [0047] master node [0016] network configuration parameters [0067] access point is slave node, any other wireless node may be master node).
It would have been obvious to one of ordinary skills in the art before the effective filing date of the invention was made to combine the teachings of Zhu with the teachings of Wentink of transmitting identifier in the remote configuration frame from a wireless node to access point to improve efficiency and allow transmitting network configuration including a configuration identifier from the communication device to the network node to the method of Zhu as in the instant invention.
The combination would have been obvious because applying the known method of transmitting the configuration parameter including the identifier in the configuration frame to the access point as taught by Wentink to the method of Zhu to yield expected result and improved efficiency and flexibility.
Zhu and Wentink, in combination, do not specifically teach transmitting the status report of the model to the network node to activate at least one of the artificial neural network model.
Ren, however, teaches transmitting the status report of the model to the network node ([0008] UE, monitor, status, machine learning model, report status of the machine learning model ).
It would have been obvious to one of ordinary skills in the art before the effective filing date of the invention was made to combine the teachings of Zhu and Wentink with the teachings of Ren of UE reporting machine learning model status to the network/access point to improve efficiency and allow transmitting the status report of the model to the network node to the method of Zhu and Wentink as in the instant invention.
The combination would have been obvious because applying the known method of transmitting the status of machine learning model to the network node as taught by Ren to the method of Zhu and Wentink to yield expected result and improved efficiency and flexibility.
As per claim 2, Zhu teaches wherein the network configuration information includes one or more Radio Resource Configuration (RRC) information elements (IEs) corresponding to a required network configuration corresponding to each of the artificial neural network models ([0067] radio capability of the UE, perform one or more wireless communication management procedures, ML-based [0068] one or more capabilities, UE, performing ML [0071] context setup request message, include ML capability of the UE, UE context information, data radio bearer/antenna configurations [0112] ML capability of the UE, indication of one or more locally cached ML models [0004] radio capability of the UE, ML configuration information, NNF, ML model).
As per claim 3, Zhu teaches wherein the model information field includes at least one of required network configuration information for an inference task corresponding to each of the artificial neural network models, auxiliary network configuration information for an inference task corresponding to each of the artificial neural network models, model performance indicator for each of the artificial neural network models, preference for each of the artificial neural network models, or preference priority information for each of the artificial neural network models ([0004] ML capability, ML configuration, NNF, ML model, [0032] ML model, form inferences about input data, executed NNF, wireless communication management, flexibility to choose NNF and corresponding model, dynamic configuration [0068] ML capability, one or more supported ML model formats / libraries).
Ren teaches remaining claim elements of or an inference task corresponding to each of the artificial neural network models, model performance indicator for each of the artificial neural network models ([0037] satisfactory model performance i.e. level where the model is performing satisfactorily has to be known in advance [0093] validate the performance of the model), preference for each of the artificial neural network models, or preference priority information for each of the artificial neural network models ([0109] higher priority models [0128] machine learning models, different priorities).
As per claim 4, Zhu teaches wherein the status report of the first model includes only a model status report corresponding to a currently supportable artificial neural network model ([0068] ML capability of UE, one or more supported ML model formats, indication of one or more locally cached ML models).
Ren teaches remaining claim elements of status report ([0007] UE, machine learning model, status report).
As per claim 5, Zhu teaches the transmitting a status report of the second model to the network node ([0066] UE 504 transmitting to the BS 502, UE capability information, radio capability of the UE, ML capability of the UE [0112] ML capability of the UE, indication of one or more locally cached ML models [0062] different ML models, employed).
Ren teaches remaining claim elements wherein the second model state report is transmitted to the network node, when at least one occurs among a case when model status information of the communication node is changed ([0099] UE triggering model status failure), a case when the network node indicates the communication node to transmit the status report of the second model ([0108] model status report, periodic, any model indicated by the network 850), a case when a retransmission prohibit timer for the status report of the first model expires ([0107] periodic model status reporting, UE determine which/ when/ whether model status report is transmitted) and there is an artificial neural network model currently supported by the communication node ([0108] model status report, periodic, any model indicated by the network 850 ), a case when a periodic transmission timer for the status report of the first model expires ([0107] periodic model status reporting, UE determine which/ when/ whether model status report is transmitted) and there is an artificial neural network model currently supported by the communication node ([0108] model status report, periodic, any model indicated by the network 850), or a case when a handover procedure occurs.
As per claim 6, Zhu teaches receiving, from the network node, indication information on activation or deactivation of an artificial neural network model corresponding to an artificial neural network model not included in the status report of the first model ([0078] BS transmits signal to UE, activating use of ML model [0085] BS, also activate, use of the second ML based model at the BS, [0072] NNF indicated in the context setup request message e.g. accepted NNF list i.e. can also include one not in accepted list); and
ignoring the activation or deactivation of the artificial neural network model according to the indication information ([0072] NNF indicated in the context setup request message e.g. accepted NNF list i.e. can also include one not in accepted list, i.e. if not accepted list, it will be ignored).
As per claim 7, Zhu teaches receiving, from the network node, an activation indication on one or more artificial neural network models in response to the status report of the first model ([0078] BS transmits signal to UE, activating use of ML model [0085] BS, also activate, use of the second ML based model at the BS);
activating the one or more artificial neural network models based on the activation indication (fig. 5 activate ML model 575).
Ren teaches remaining claim elements of when an artificial neural network model activated in the communication node is deactivated, generating a status report of the second model including deactivation information of the deactivated artificial neural network model ([0099] UE triggering model status failure reporting; fig. 12 model failure indication 12 model failure report 14); and
transmitting the status report of the second model to the network node (fig. 12 model failure report 14 fig. 13 model status reporting).
As per claim 8, Zhu teaches wherein when there is a first artificial neural network model on which the communication node and the network node need to jointly perform an inference task among the artificial neural network models ([0014] selecting at least one ML model for the wireless communication management associated with the UE [0032] ML model, form inference about input data, dynamic configuration may provide the base station with flexibility to selectively choose, at any given time and for a particular scenario, which NNF(s) and/or corresponding model(s) to use for performing one or more ML-based wireless communications management), the model information field includes at least one of whether or not a network node-sided artificial neural network model exists in the network node, an identifier of the network node-sided artificial neural network model of the network node ([0062] ML model ID), input and output of the network node-sided artificial neural network model of the network node ([0079] input variables, NL model, obtaining output from the ML model), execution environment information of the network node-sided artificial neural network model of the network node (fig. 4 node 420 model 424; fig. 5 UE 504- BS 502), or an inference latency required for an inference operation of the network node-sided artificial neural network model of the network node.
As per claim 9, Zhu teaches receiving, from the network node and in advance, information of a first artificial neural network model on which the communication node and the network node need to jointly perform an inference task ([0005] transmit to the UE ML configuration information, NNF, ML model [0014] selecting at least one ML model for the wireless communication management associated with the UE [0032] ML model, form inference about input data, dynamic configuration may provide the base station with flexibility to selectively choose, at any given time and for a particular scenario, which NNF(s) and/or corresponding model(s) to use for performing one or more ML-based wireless communications management).
As per claim 10, Zhu teaches wherein the network node is one of a base station connected to the communication node (fig. 5 BS 502), a server that manages the artificial neural network models, or a cloud that manages the artificial neural network models.
Claims 11-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Wentink, and further in view of Ren, as applied to above claims, and further in view of Zhu et al. (US 2023/0344899 A1, hereafter Zhu1).
As per claim 11, Zhu teaches the invention substantially as claimed including a method of a network node ([0004] base station BS or other network entity), comprising:
receiving required network configurations for applying each of artificial neural network models from a communication node ([0004] receiving from a user equipment, UE capability information, radio capability information, ML configuration information, indicating neural network function, ML model);
receiving at least one status report of the first model including a model identifier field and a model information field for each of the artificial neural network models ([0004] receiving, from UE, ML configuration information, at least one neural network function and at least one ML model corresponding to the at least one NNF [0090] NNF identifier, ML model ID);
determining whether to allow each of the artificial neural network models based on the received status report of the first model and a load of the network node ([0069]BS 502 determines whether to use ML functionality to perform one or more wireless communication management, select an ML-based management procedure, determine NNF for performing selected ML-based management [0071] select, ML model, based, ML capability of the UE); and
transmitting information indicating whether or not to allow each of the artificial neural network models to the communication node ([0073] BS 502 transmits to the UE, ML configuration information, indication of NNF accepted list, ML model corresponding to the NNF, NNF ID, ML model ID), wherein each of the required network configurations includes a configuration identifier and network configuration information ([0111] radio capability, UE, cell reselecion, ML-based idle/inactive mode, ML-based radio resource management measurement, ML-based radio-link monitoring, ML-based channel state information (CSI) measurement, perform an ML-based precoding matrix indicator (PMI), rank indicator (RI), and channel quality indicator (CQI) feedback, perform an ML-based radio link failure (RLF) and beam failure recovery (BFR), or perform an ML-based RRM relaxation procedure).
Zhu doesn’t specifically teach receiving at least one status report of the model; determine based on the status and a load on the network node; network configuration include a configuration identifier.
Wentink, however, teaches transmitting network configuration includes a configuration identifier ([0044] master node constructs a remote configuration frame, configuration information to be transmitted to the slave, used for establishing a connection [0053] configuration frame, identifier [0047] master node [0016] network configuration parameters [0067] access point is slave node, any other wireless node may be master node).
It would have been obvious to one of ordinary skills in the art before the effective filing date of the invention was made to combine the teachings of Zhu with the teachings of Wentink of transmitting identifier in the remote configuration frame from a wireless node to access point to improve efficiency and allow transmitting network configuration including a configuration identifier from the communication device to the network node to the method of Zhu as in the instant invention.
The combination would have been obvious because applying the known method of transmitting the configuration parameter including the identifier in the configuration frame to the access point as taught by Wentink to the method of Zhu to yield expected result and improved efficiency and flexibility.
Zhu and Wentink, in combination, do not specifically teach transmitting the status report of the model to the network node to activate at least one of the artificial neural network model.
Ren, however, teaches receiving at least one status report of the model to the network node ([0007] receiving a status report of the machine learning model [0008] UE, monitor, status, machine learning model, report status of the machine learning model);
It would have been obvious to one of ordinary skills in the art before the effective filing date of the invention was made to combine the teachings of Zhu and Wentink with the teachings of Ren of UE reporting machine learning model status to the network/access point to improve efficiency and allow receiving the status report of the model by the network node to the method of Zhu and Wentink as in the instant invention.
The combination would have been obvious because applying the known method of receiving the status of machine learning model to the network node as taught by Ren to the method of Zhu and Wentink to yield expected result and improved efficiency and flexibility.
Zhu, Wentink and Ren, in combination, do not specifically teach determine based a load on the network node.
Zhu1, however, teaches determine based a load on the network node ([0102] implementing a reinforcement learning model based on usage data for the network node).
It would have been obvious to one of ordinary skills in the art before the effective filing date of the invention was made to combine the teachings of Zhu, Wentink and Ren with the teachings of Zhu1 of implementing a reinforcement learning model based on usage data for the network to improve efficiency and allow determine AI model based a load on the network node to the method of Zhu, Wentink and Ren as in the instant invention.
The combination would have been obvious because applying the known method of implementing reinforcement learning model based on the usage data for the network node as taught by Zhu1 to the method of Zhu, Wentink and Ren to yield expected result and improved efficiency and flexibility.
Claim 12 recites elements similar to claim 2. Therefore, it is rejected for the same rationale.
Claim 13 recites elements similar to claim 3. Therefore, it is rejected for the same rationale.
As per claim 14, Zhu teaches when deactivation of an activated artificial neural network model is required based on the model performance indicator of each of the artificial neural network models, transmitting information indicating deactivation of the activated artificial neural network model to the communication node (fig. 5 activate ML model - can also send deactivate command [0078] transmit a signal to the UE, activating use of ML model).
Ren teaches remaining claim elements of when deactivation of an activated artificial neural network model is required based on the model performance indicator of each of the artificial neural network models ([0037] reconfiguration of the model, occur, provide satisfactory model performance i.e. can also deactivate the mode [0036] performance degradation of the deployed machine learning model).
Claim 15 recites the elements similar to part of claim 6. Therefore, it is rejected for the same rationale.
As per claim 16, Zhu teaches starting a procedure for deactivating an activated artificial neural network model (fig. 5 activate ML model - can also send deactivate command [0078] transmit a signal to the UE, activating use of ML model).
Ren teaches remaining claim elements of receiving a status report of the second model from the communication node ([0007] receiving a status report of the machine learning model [0008] UE, monitor, status, machine learning model, report status of the machine learning model); and
based on the received status report of the second model ([0007] receiving a status report of the machine learning model), when the status report of the second model indicates deactivation of the activated artificial neural network model (fig. 12 model failure indication 12 model failure report 14).
As per claim 17, Zhu teaches providing, to the communication node, information of a first artificial neural network model on which the communication node and the network node need to jointly perform an inference task ([0073] BS 502 transmits to the UE 504 ML configuration information, indication of at least one NNF and at least one ML model [0060] ML model, used to form inference about input data [0050] machine learning, producing predictive ML model, e.g. artificial neural network).
Examiners Note
Applicant is further reminded of that the cited paragraphs and in the references as applied to the claims above for the convenience of the applicant(s) and although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider all of the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bedekar et al. (US 2021/0385682 A1) teaches configuration of a neural network for a radio access network (RAN) node of a wireless network
Jeon et al. (US 2022/0287104 A1) teaches method for support of machine learning or artificial intelligence technique in communication system.
Wang et al. (US 2021/0342687 A1) teaches base station user equipment messaging regarding deep neural networks
Wang et al. (US 2026/0012829 A1) teaches method of monitoring artificial intelligence model in Radio Access Network
Zhu et al. (US 2023/0100253 A1) teaches network-based artificial intelligence model configuration
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/ABU ZAR GHAFFARI/Primary Examiner, Art Unit 2195
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