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 .
DETAILED ACTION
This office action is in response to the claims received on 11/20/2025.
Claim Interpretation
Plain Meaning (MPEP 2111.01): MPEP 2111.01 states: The plain meaning of a term means the ordinary and customary meaning given to the term by those of ordinary skill in the art at the time of the invention. The ordinary and customary meaning of a term may be evidenced by a variety of sources, including the words of the claims themselves, the specification, drawings, and prior art. However, the best source for determining the meaning of a claim term is the specification. An applicant is entitled to be their own lexicographer and may rebut the presumption that claim terms are to be given their ordinary and customary meaning by clearly setting forth a definition of the term that is different from its ordinary and customary meaning(s) in the specification at the relevant time. See In re Paulsen, 30 F.3d 1475, 1480, 31 USPQ2d 1671, 1674 (Fed. Cir. 1994). In this case:
"Or": The Examiner consulted the specification to verify whether there is a definition for "or", but there is no such definition. MPEP 2143.03 explains that language that suggests or makes a feature or step optional, but does not require that feature or step, does not limit the scope of a claim under the broadest reasonable claim interpretation. When a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009). In the instant case, a claim using the term "or" is interpreted as a claim which requires selection of an element from a list of alternatives, which are separated by the term “or”, as described by MPEP 2117, 2143.03, and 2173.05(h).
"Controller": Independent claims 21, 41 recite a “controller”. The specification mentions "controller" several times, for example in par. 273, without redefining it. Therefore, it has its original meaning. A “controller” is a controller device such as a processor, which has sufficiently definite meaning as the name for the structure that performs the respective functions. Therefore, claimed "controller" is not a generic placeholder as defined in MPEP 2181.
"Radio Intelligence Controller (RIC)": Independent claims 21, 41 recite a “Radio Intelligence Controller (RIC)”. The specification mentions "RIC" several times, for example in par. 5, 230-232, without redefining it. Therefore, it has its original meaning. A “RIC” is interpreted as any controller device such as a processor.
MPEP 2111.04: "Adapted to," "Adapted for," "Wherein," "Whereby" clauses in method claims: MPEP 2111.04 explains that claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. The determination of whether an "adapted to", "adapted for", "wherein", or a "whereby" clause is a limitation in a claim depends on the specific facts of the case. In Hoffer v. Microsoft Corp., 405 F.3d 1326, 1329, 74 USPQ2d 1481, 1483 (Fed. Cir. 2005), the court noted that a "‘whereby clause in a method claim is not given weight when it simply expresses the intended result of a process step positively recited.’" Id. (quoting Minton v. Nat’l Ass’n of Securities Dealers, Inc., 336 F.3d 1373, 1381, 67 USPQ2d 1614, 1620 (Fed. Cir. 2003)). Likewise, in this case, regarding independent claims 21, 41, they recite the following “wherein” clauses: "Wherein the first task configuration information indicates configuration information of one or more artificial intelligence(AI) tasks," "wherein for each of the one or more AI tasks, the configuration information of the respective task indicates one or more of the following content of the respective task: a task identifier (ID), a task type, task content, a task execution body, or a task status, and" "wherein each of the one or more AI tasks is executed by a base station." However, these claims are actually directed to one positively recited step of " receiving first task configuration information", which can take place regardless of the contents of the wherein clauses. Likewise the Hoffer case, these "wherein" clauses are not given weight because they simply express the intended result of a process step positively recited. In order to have patentable weight, it would be necessary to replace the “wherein” clauses with positively recited steps, such as: "Indicating, by the first task configuration information, configuration information of one or more artificial intelligence(AI) tasks," "indicating, for each of the one or more AI tasks, by the configuration information of the respective task, one or more of the following content of the respective task: a task identifier (ID), a task type, task content, a task execution body, or a task status, and" "executing, by a base station, each of the one or more AI tasks."
Regarding independent claims 21, 41, they are interpreted as follows:
21. (Amended) An apparatus, comprising:
a processor, coupled with a non-transitory (the specification is silent with respect to claimed "non-transitory") memory (the claimed processor coupled with a memory is interpreted as a computer, which is not a generic placeholder or a replacement for "means"), wherein the non-transitory memory stores instructions, which when executed by the processor, cause the apparatus perform the following:
receiving first task configuration information from a radio intelligence controller (RIC, interpreted as any controller. The "wherein" clauses from here to the end of the claim don't have patentable weight).
wherein (this "wherein" clause doesn't have patentable weight. See MPEP 2111.04) the first task configuration information indicates configuration information of one or more artificial intelligence (AI) tasks,
wherein (this "wherein" clause doesn't have patentable weight. See MPEP 2111.04) for each of the one or more AI tasks, the configuration information of the respective task indicates one or more of the following content of the respective task (this limitation is met if the prior art teaches only one of the alternatives): a task identifier (ID), a task type, task content, a task execution body, or a task status, and
wherein (this "wherein" clause doesn't have patentable weight. See MPEP 2111.04) each of the one or more AI tasks is executed by a base station.
Arguments Are Moot Because of New Ground of Rejection
Applicant’s arguments with respect to claims 21-24, 26, 28-32, 41-49 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Eligibility under 35 USC 101
Please refer to the Subject Matter Eligibility Test for Products and Processes in MPEP 2106 and in the 2019 Revised Patent Subject Matter Eligibility Guidance, hereinafter, the “2019 PEG”:
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Step 1: See MPEP 2106.03: 35 U.S.C. 101 enumerates four categories of subject matter that Congress deemed to be appropriate subject matter for a patent: processes, machines, manufactures and compositions of matter. Claims 21-24, 26, 28-32, 41-49 include claims directed to a machine, which is a statutory category. Therefore, the answer in step 1 is YES.
Step 2A: MPEP 2106 subclause II. “ELIGIBILITY STEP 2A: WHETHER A CLAIM IS DIRECTED TO A JUDICIAL EXCEPTION” explains that 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. Together, these prongs represent the first part of the Alice/Mayo test, which determines whether a claim is directed to a judicial exception.
Step 2A prong 1: The 2019 PEG explains that Step 2A prong 1 procedure for determining whether a claim “recites” an abstract idea is: identify the specific limitations in the claim under examination that the examiner believes recites an abstract idea; and determine whether the identified limitations fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG, which are: Mathematical Concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations), Mental Processes, concepts performed in the human mind (including an observation, evaluation, judgment, opinion), and Certain Methods Of Organizing Human Activity fundamental economic principles or practices (including hedging, insurance, mitigating risk), commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). In this case, independent claim 21 recites an abstract idea of a concept performed in the human mind, including a judgment of configuring a task for a mobile phone to perform; therefore, the answer to Step 2 prong 1 is YES.
Step 2A prong 2: Yes, the claim does recite additional elements that integrate the exception into a practical application of the exception. Par. 228 of the specification explains that the task description to describe for a mobile device, which data it needs to collect and report to an artificial intelligence (AI) training tool, which can then be optimized based on inputs from a plurality of mobile devices or by a plurality of base stations. Therefore, the claimed invention provides an improvement to the state-of-the-art wireless communications, which is therefore a specific improvement over prior systems, and the claimed invention reflects such practical application. Please refer to MPEP 2106.04(d): "Integration of a Judicial Exception Into A Practical Application" under the header "Relevant considerations for evaluating whether additional elements integrate a judicial exception into a practical application": "Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include: • An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a)". See for example the court decision in 118 USPQ2d 1684 Enfish, LLC v. Microsoft Corp; U.S. Court of Appeals Federal Circuit, page 1689: “much of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes”. Therefore, the answer to prong 2 is YES, and the claims would be eligible in step 2A, except for the double patenting issues presented below.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 21-24, 26, 28-32, 41-49 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant) regards as the invention.
MPEP 2173.04 “Breadth is Not Indefiniteness” explains that a genus claim that covers multiple species is broad, but is not indefinite because of its breadth, which is otherwise clear. But a genus claim that could be interpreted in such a way that it is not clear which species are covered would be indefinite (e.g., because there is more than one reasonable interpretation of what species are included in the claim). In re Miller, 441 F.2d 689, 169 USPQ 597 (CCPA 1971); In re Gardner, 427 F.2d 786, 788, 166 USPQ 138, 140 (CCPA 1970) ("Breadth is not indefiniteness."). In this case, the independent claims were amended by adding the term "AI" preceding "task"; however, in a second embodiment within each independent claim, the claim refrains from preceding "task" by "AI". This ambiguity renders the claims incoherent (see Packard, 751 F.3d at 1311, 110 USPQ2d at 1787 cited in MPEP 2173.02) and they are indefinite. The independent claims recite: “Wherein for each of the one or more AI tasks, the configuration information of the respective task indicates one or more of the following content of the respective task: a task identifier (ID), a task type, task content, a task execution body, or a task status”. This “genus” excerpt from the claim covers at least the following “species”:
1. “Wherein for each of the one or more AI tasks, the configuration information of the respective AI task indicates one or more of the following content of the respective AI task:an AI task identifier (ID),an AI task type,an AI task content,an AI task execution body, oran AI task status”: the specification enables this embodiment in par. 228: "a module for implementing an AI function may be referred to as a radio intelligence control (RIC) module"; par. 301: "FIG. 7B shows that an artificial intelligence control (artificial intelligence control, AIC) layer that is parallel to an RRC layer is added", and Fig. 9A where the UE is equipped with AIC.
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2. “Wherein for each of the one or more AI tasks, the configuration information of a respective task indicates one or more of the following content of a respective task where there is no independent AIC protocol layer: a task identifier (ID), a task type, task content, a task execution body, or a task status”: the disclosure enables this embodiment in Fig. 8A, 8B, where the UE is not equipped with a RIC or AIC as explained in par. 332: "In example 1, there is no independent AIC protocol layer"; par. 335: "When the UE does not support a RIC function (that is, does not have a RIC module)", the base station sends a ready inference result to the UE, since the UE is not equipped with a RIC or AIC. The data that needs to be collected by the UE, i.e., the "task" may be RRC layer data, layer 2 data, or physical layer data. This type of data may be AI-related or not.
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Because there is more than one reasonable interpretation of what species are included in the claim, the genus claim can be interpreted in more than one way, and the above-demonstrated ambiguity makes unclear which species is covered, rendering the claims indefinite. The dependent claims incorporate the limitations of independent claims 21, 41, and are therefore indefinite for the same reasons above. In order to overcome indefiniteness, the Examiner recommends amending the claims as in one of the options above, which would NOT create a new matter issue, because all the alternatives are supported in the specification and drawings. For purposes of examination, the claims are interpreted as alternative 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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.
7.20.02.aia Joint Inventors, Common Ownership Presumed
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 at the time any inventions covered therein were effectively filed 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 at the time a later invention was effectively filed 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) 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.
Claims 21-24, 26, 28-32, 41-49 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (international publication number WO2021/086308), hereinafter Wang, and further in view of Pantelidou et al (international publication number WO2021047781), hereinafter Pantelidou.
Wang teaches (please refer to Wang Fig. 9) a Core Network Server 302 which sends neural network formation configuration to a base station 121 (Wang Fig. 9 step 910); the configuration information can be selected from Neural Network Tables 216, 272, or 316 as explained in Wang par. 34, 35 in reference to Figs. 2, 3: the core network server's memory contains table 316, and par. 35 explains that the server's training module 314 generates neural network table 316 the same elements as neural network table 272 and neural network table 216.
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Table 216 contains the following (Wang par. 20):
parameter configurations that form a neural network;
parameters that specify a fully connected layer neural network architecture, a convolutional layer neural network architecture, a recurrent neural network layer, a number of connected hidden neural network layers, an input layer architecture, an output layer architecture, a number of nodes utilized by the neural network;
coefficients (e.g., weights and biases) utilized by the neural network, kernel parameters, a number of filters utilized by the neural network, strides/pooling configurations utilized by the neural network, an activation function of each neural network layer, interconnections between neural network layers, and neural network layers to skip.
Table 272 contains the following (Wang par. 28):
input characteristics for each NN formation configuration element and/or NN formation configuration, where the input characteristics describe properties about the training data used to generate the NN formation configuration element and/or NN formation configuration;
power information, signal-to-interference-plus-noise ratio (SINR) information, channel quality indicator (CQI) information, channel state information (CSI), Doppler feedback, frequency bands, Block Error Rate (BLER), Quality of Service (QoS), Hybrid Automatic Repeat reQuest (HARQ) information (e.g, first transmission error rate, second transmission error rate, maximum retransmissions), latency, Radio Link Control (RLC), Automatic Repeat reQuest (ARQ) metrics, received signal strength (RSS), uplink SINR, timing measurements, error metrics, UE capabilities, BS capabilities, power mode, Internet Protocol (IP) layer throughput, end2end latency, end2end packet loss ratio, etc.
Layer 1, Layer 2, and/or Layer 3 metrics.
A single index value of the neural network table 272 may map to a single NN formation configuration element (e.g., a 1:1 correspondence).
A single index value of the neural network table 272 may map to a NN formation configuration (e.g., a combination of NN formation configuration elements)
Wang's acronyms are described in par. 4-10:
End-to-end machine-learning (E2E ML);
Neural network (NN);
User equipment (UE), which equates to claimed "terminal device".
Regarding claim 21, Wang teaches an apparatus (please refer to Wang Fig. 9; claimed "apparatus" equates to base station 121), comprising:
a processor (please refer to Wang par. 22-26 in reference to base station 120 of Fig. 2: processor 260), coupled with a non-transitory memory (Wang par. 22-26 in reference to base station 120 of Fig. 2: memory 262; par. 111 also cites a memory), wherein the non-transitory memory stores instructions (Wang par. 111: Some operations of the example methods may be described in the general context of executable instructions stored on computer- readable storage memory that includes software applications and programs), which when executed by the processor, cause the apparatus perform the following:
receiving first task configuration information (Wang par. 100 in reference to Fig. 9: At 910, the core network server 302 communicates the neural network formation configuration to the base station 121) from a controller (RIC, claimed "RIC" equates to Wang's Core Network Server 302 which has a processor 304 of Fig. 3),
wherein the first task configuration information (Wang par. 100 in reference to Fig. 9: At 910, the core network server 302 communicates the neural network formation configuration to the base station 121; message 910 constitutes a task because Wang's base station 121 will execute the task in steps 920, 930, 940) indicates configuration information of one or more artificial intelligence (AI) tasks (Wang's neural network formation configuration sent to the base station 121 contains items from Table 216, 272, or 316 as explained in Wang par. 34, 35 in reference to Figs. 2, 3; take for example from table 216 described in par. 20: a convolutional layer neural network architecture, a recurrent neural network layer, a number of connected hidden neural network layers; par. 10: the network entity determines an end-to-end machine-learning configuration (E2E ML configuration) based on the received capabilities and communicates the configuration to other devices, such as a base station. Wang's machine learning and neural networks equate to claimed "AI"),
wherein for each of the one or more AI tasks, the configuration information of the respective task (Wang's neural network formation configuration sent to the base station 121, which contains items from Table 216, 272, or 316 as explained in Wang par. 34, 35 in reference to Figs. 2, 3) indicates one or more of the following content of the respective task (this limitation is met if the prior art teaches only one of the alternatives): a task identifier (ID, Wang par. 100 in reference to Fig. 9: at 910, the core network server 302 communicates the neural network formation configuration to the base station 121, including an index value which maps to an entry in the neural network table 272 of FIG. 2; despite being meant to identify a table entry, the index value also identifies the task), a task type (even though the previous alternative has been met, for example, Wang par. 105-107 explain that in response to the configuration message in step 910, the base station generates metrics in step 945 which the base station will send to the core network server in step 955, so the task type may be "to generate metrics"), task content (even though the previous alternative has been met, for example, Wang par. 100 in reference to Fig. 9: at 910, the core network server 302 communicates the neural network formation configuration to the base station 121, including the contents from Table 216, 272, or 316 as explained in Wang par. 34, 35 in reference to Figs. 2, 3), a task execution body, or a task status, and
wherein each of the one or more AI tasks is executed by a base station (Wang par. 103 in reference to Fig. 9: At 920, the base station 121 forms a first deep neural network using the neural network formation configuration received from the core network server, i.e., executes the task. Wang par. 105-107 in reference to Fig. 9: At 940, the base station generates base station metrics and sends those metrics to the core network server).
Wang does not explicitly teach claimed "radio intelligence controller (RIC)".
Pantelidou teaches (Please refer to Pantelidou Fig. 4):
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Radio Intelligence Controller (RIC, Pantelidou page 12 line 35 – page 13 line 11: examples of network functions are a network node, e.g. gNB, and a radio intelligent controller (RIC)); and
Artificial Intelligence (AI, Pantelidou page 12 line 35 – page 13 line 11: Machine learning, ML, refers to algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence, AI.) tasks (claimed "tasks" equate to Pantelidou's request 440 for a set of measurements from the data producer 430 explained in page 16 line 25 – page 17 line 16 in reference to Fig. 4: Fig. 4 shows, by way of example, a set of different ML optimization problems 411, 412, 413, 414 within a consumer, e.g. an ML entity 420, and a producer entity 430. ML entity solves a set of k ML optimization problems with needed measurements provided from a UE or another network function that own the needed measurements. The ML entity 420 may request 440 the set of measurements from the data producer 430. The measurement request may be the message "Request Filter Set".)
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify the disclosure of Wang, by adding Pantelidou's artificial intelligence to Wang's messages, by recognizing that ML is a subset of AI, by deploying Pantelidou's RIC, and by requesting measurements from a producer entity, as suggested by Pantelidou, in order to improve the data collection to enable the operators to monitor and optimize their network deployments, and in order to obtain an algorithm for optimizing and improving operation, performance and functions of a radio access network, or RAN, comprising capacity and coverage optimization, load sharing, load balancing, random access channel (RACH) optimization and energy saving (Pantelidou page 1 lines 9-11, page 12 lines 25 - 35).
Regarding claims 22, 42, they recite "wherein the task type of each task is respectively data collection, inference result publishing, model publishing, or model training." However, claimed “task type” doesn’t limit the claim, and isn’t required, because it is alternative to another element already taught by prior art in claim 21.
Regarding claims 23, 43, they recite "wherein: when the task type of a respective task is data collection, the task content of the respective task indicates one or more of the following content: a data measurement type, a measurement condition, or a measurement result report; when the task type of a respective task is inference result publishing, the task content of the respective task indicates an inference result; when the task type of a respective task is model publishing, the task content of the respective task indicates model information; or when the task type of the respective task is model training, the task content of the respective task indicates one or more of the following content: a condition for reporting model parameter information or reporting model parameter gradient information, information about a reference neural network, or a neural network training data set." However, claimed “task type” doesn’t limit the claim, and isn’t required, because it is alternative to another element already taught by prior art in claim 21.
Regarding claims 24, 44, they recite "wherein the task status of each task comprises an activated state or a deactivated state, or the task status of each task comprises an activated state, a deactivated state, or a released state." However, claimed “task status” doesn’t limit the claim, and isn’t required, because it is alternative to another element already taught by prior art in claim 21.
Regarding claims 26, 45, Wang teaches sending a first interface establishment request message to the RIC (Wang Fig. 21 steps 2105, 2110 described in par. 201-203: at 2105, the UE 110 requests to establish an E2E communication. At 2110, the base station 120 relays and/or forwards the request to the core network server. In some cases, the base station 120 generates and sends a new message that encapsulates the request to the core network server 302), wherein (this "wherein" clause doesn't have patentable weight. See MPEP 2111.04) the first interface establishment request message indicates one or more of the following content: a message type (Wang par. 201 provides examples of message types: the UE 110 requests (implicitly or explicitly) to establish the E2E communication for other types of communications, such as IoT messages and/or communications, vehicle-to-everything (V2X) messages and/or communications); an ID of a central unit (CU) of the base station; CU capability information; CU configuration information; or CU status information."
Regarding claims 28, 46, Wang teaches wherein (this "wherein" clause doesn't have patentable weight. See MPEP 2111.04) the one or more AI tasks comprise at least one data collection task (Wang par. 105 explains that in response to the configuration message in step 910, the base station generates metrics which the base station will send to the core network server in step 955; Wang's "metrics" meet claimed "data collection"), and the instructions, when executed by the processor, further cause the apparatus to perform the following: sending collected data to the RIC (Wang par. 105, the base station generates metrics which the base station will send to the core network server in step 955; Wang's "metrics" meet claimed "data collection").
Regarding claims 29, 47, Wang teaches wherein (this "wherein" clause doesn't have patentable weight. See MPEP 2111.04) the data is received (Wang par. 105 explains that in response to the configuration message in step 910, the base station generates metrics which the base station will send to the core network server in step 955; Wang's "metrics" meet claimed "data collection") from a distributed unit (DU, Wang par. 25 in reference to base station 120 represented in Fig. 2: core network interface 276) of the base station.
Regarding claims 30, 48, Wang teaches wherein (this "wherein" clause doesn't have patentable weight. See MPEP 2111.04) the instructions, when executed by the processor, further cause the apparatus to perform the following: receiving an inference result from the RIC (An inference is the same as a determination. Wang teaches in par. 213 in reference to Fig. 22: At 2230, the core network server 302 determines, by way of the E2E ML controller 318, an E2E ML configuration based on the information previously received, for example the UE capabilities. In par. 215, Wang teaches that at 2235, the core network server 302 communicates the E2E ML configuration to the base station).
Regarding claims 31, 49, Wang teaches wherein (this "wherein" clause doesn't have patentable weight. See MPEP 2111.04) the instructions, when executed by the processor, further cause the apparatus to perform the following: sending an inference result to a terminal device (An inference is the same as a determination. Wang teaches in par. 213 in reference to Fig. 22: At 2230, the core network server 302 determines, by way of the E2E ML controller 318, an E2E ML configuration based on the information previously received, for example the UE capabilities. In par. 215, Wang teaches that at 2235, the core network server 302 communicates the E2E ML configuration to the base station. In step 2240, the base station relays the determined configuration to the UE).
Regarding claim 32, Wang teaches wherein (this "wherein" clause doesn't have patentable weight. See MPEP 2111.04) the one or more tasks comprise at least one model training task (Wang par. 128 in reference to Fig. 12: Training data 1202 is an example input to the machine-learning module 400. Training data 1202 includes UE capabilities), and the instructions, when executed by the processor, further cause the apparatus to perform the following: sending model parameter information (Claimed "model parameter information" equates to UE capabilities in Wang par. 128 in reference to Fig. 12: Training data 1202 is an example input to the machine-learning module 400. Training data 1202 includes UE capabilities. Wang par. 33 explains that UE capabilities comprise a parameter) or model parameter gradient information to the RIC (Wang par. 212 in reference to Fig. 22: at 2220, the UE 110 transmits user equipment machine-learning capabilities, i.e., UE ML capabilities, to the base station, where the base station 120 relays the UE ML capabilities to the core network server at 2225), wherein the model parameter information or the model parameter gradient information is from the terminal device (Wang par. 212 in reference to Fig. 22: at 2220, the UE 110 transmits user equipment machine-learning capabilities, i.e., UE ML capabilities, to the base station, which relays these UE ML capabilities to the core network server).
Regarding claim 41, Wang teaches a method (please refer to Wang Fig. 9, which is a signaling diagram representing a method), comprising:
receiving first task configuration information (Wang par. 100 in reference to Fig. 9: At 910, the core network server 302 communicates the neural network formation configuration to the base station 121) from a controller (RIC, claimed "RIC" equates to Wang's Core Network Server 302 which has a processor 304 of Fig. 3),
wherein the first task configuration information (Wang par. 100 in reference to Fig. 9: At 910, the core network server 302 communicates the neural network formation configuration to the base station 121; message 910 constitutes a task because Wang's base station 121 will execute the task in steps 920, 930, 940) indicates configuration information of one or more artificial intelligence (AI) tasks (Wang's neural network formation configuration sent to the base station 121 contains items from Table 216, 272, or 316 as explained in Wang par. 34, 35 in reference to Figs. 2, 3; take for example from table 216 described in par. 20: a convolutional layer neural network architecture, a recurrent neural network layer, a number of connected hidden neural network layers; par. 10: the network entity determines an end-to-end machine-learning configuration (E2E ML configuration) based on the received capabilities and communicates the configuration to other devices, such as a base station. Wang's machine learning and neural networks equate to claimed "AI"),
wherein for each of the one or more AI tasks, the configuration information of the respective task (Wang's neural network formation configuration sent to the base station 121, which contains items from Table 216, 272, or 316 as explained in Wang par. 34, 35 in reference to Figs. 2, 3) indicates one or more of the following content of the respective task (this limitation is met if the prior art teaches only one of the alternatives): a task identifier (ID, Wang par. 100 in reference to Fig. 9: at 910, the core network server 302 communicates the neural network formation configuration to the base station 121, including an index value which maps to an entry in the neural network table 272 of FIG. 2; despite being meant to identify a table entry, the index value also identifies the task), a task type (even though the previous alternative has been met, for example, Wang par. 105-107 explain that in response to the configuration message in step 910, the base station generates metrics in step 945 which the base station will send to the core network server in step 955, so the task type may be "to generate metrics"), task content (even though the previous alternative has been met, for example, Wang par. 100 in reference to Fig. 9: at 910, the core network server 302 communicates the neural network formation configuration to the base station 121, including the contents from Table 216, 272, or 316 as explained in Wang par. 34, 35 in reference to Figs. 2, 3), a task execution body, or a task status, and
wherein each of the one or more AI tasks is executed by a base station (Wang par. 103 in reference to Fig. 9: At 920, the base station 121 forms a first deep neural network using the neural network formation configuration received from the core network server, i.e., executes the task. Wang par. 105-107 in reference to Fig. 9: At 940, the base station generates base station metrics and sends those metrics to the core network server).
Wang does not explicitly teach claimed "radio intelligence controller (RIC)".
Pantelidou teaches (Please refer to Pantelidou Fig. 4):
Radio Intelligence Controller (RIC, Pantelidou page 12 line 35 – page 13 line 11: examples of network functions are a network node, e.g. gNB, and a radio intelligent controller (RIC)); and
Artificial Intelligence (AI, Pantelidou page 12 line 35 – page 13 line 11: Machine learning, ML, refers to algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence, AI.) tasks (claimed "tasks" equate to Pantelidou's request 440 for a set of measurements from the data producer 430 explained in page 16 line 25 – page 17 line 16 in reference to Fig. 4: Fig. 4 shows, by way of example, a set of different ML optimization problems 411, 412, 413, 414 within a consumer, e.g. an ML entity 420, and a producer entity 430. ML entity solves a set of k ML optimization problems with needed measurements provided from a UE or another network function that own the needed measurements. The ML entity 420 may request 440 the set of measurements from the data producer 430. The measurement request may be the message "Request Filter Set".)
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify the disclosure of Wang, by adding Pantelidou's artificial intelligence to Wang's messages, by recognizing that ML is a subset of AI, by deploying Pantelidou's RIC, and by requesting measurements from a producer entity, as suggested by Pantelidou, in order to improve the data collection to enable the operators to monitor and optimize their network deployments, and in order to obtain an algorithm for optimizing and improving operation, performance and functions of a radio access network, or RAN, comprising capacity and coverage optimization, load sharing, load balancing, random access channel (RACH) optimization and energy saving (Pantelidou page 1 lines 9-11, page 12 lines 25 - 35).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RONALD EISNER whose telephone number is (571)270-3334. The examiner can normally be reached on Monday and Tuesday from 9:00 AM to 5:30 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kathy Wang-Hurst, can be reached at telephone number (571) 270-5371. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RONALD EISNER/
Primary Examiner, Art Unit 2644
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