DETAILED ACTION
This Office Action is in response to Applicant's Communication received on 10/21/2022.
Claims 1-62 are canceled in preliminary amendment. Claims 63-83 are presented for examination. Claims 63 and 75 are independent claims.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant’s claim for the benefit of a prior-filed PCT Application No. PCT/EP2020/061543 filed on 04/24/2020 is acknowledged by the examiner.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/22/2022 has been considered by the examiner.
Claim Objections
Claim 66 is objected to because of the following informalities: Claim 66 recites “the input layer” and “the at least part of the hidden layer,” which have no antecedent basis. Appropriate correction is required.
Claim 68 is objected to because of the following informalities: in Claim 68, “the signalling” should be “the signalling information” to be consistent. Appropriate correction is required.
Claim 69 is objected to because of the following informalities: in Claim 69, “the signalling” should be “the signalling information” to be consistent; “the operating mode” has no antecedent basis. Appropriate correction is required.
Claim 75 is objected to because of the following informalities: in Claim 69, “second apparatus configured artificial configured artificial comprising an input (401)” should be “second apparatus comprising an input”; “an neural network” should be “a neural network”; “one user the equipment” should be “one user equipment”; and “the artificial neural network” has no antecedent basis. Appropriate correction is required.
Claims 80 and 81 are objected to because of the following informalities: these claims recite “the receiver,” which has no antecedent basis. Appropriate correction is required.
Claim 82 is objected to because of the following informalities: Claim 82 recites “the signalling information,” which has no antecedent basis. Appropriate correction is required.
Claim 83 is objected to because of the following informalities: in Claim 83, “the signalling” should be “the signalling information” to be consistent. 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.
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.
Claim 72 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 72 recites the following: "the processor is configured to determine the output of the part of the artificial neural network depending on input for the part of the artificial neural network determined depending on at least a part of the first input data and at least a part of the second input data”. This limitation is indefinite because it’s unclear whether the phrase "depending on at least a part of the first input data and at least a part of the second input data" is referring to ‘the output of the part of the artificial neural network’ OR ‘input for the part of the artificial neural network. For the purposes of examination, the Examiner will interpret the limitation "the processor is configured to determine the output of the part of the artificial neural network depending on input for the part of the artificial neural network determined depending on at least a part of the first input data and at least a part of the second input data” as: "the screen is configured to display a prediction result of an operation state of a prediction target device based on an extracted second history of an operation state of one or more target device and an operation state filter condition, the extracted second history satisfying an attribute information filter condition in which at least one attribute information included in attribute information of the prediction target device is specified, the operation state filter condition in which at least one operation state included in a first history of the operation state of the prediction target device is specified, the first history being received from the prediction target device via a predetermined network” and will interpret the limitation “the prediction result is a future operation state output from a learning model that is generated using the third history of the operation state as learning data and that receives the first history of the operation state of the prediction target device” as: “the processor is configured to determine the output of the part of the artificial neural network depending on input for the part of the artificial neural network, wherein the input for the part of the artificial neural network is determined depending on at least a part of the first input data and at least a part of the second input data”.
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 63-67 and 69-82 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 63-67 and 69-82 are directed to an apparatus. Thus, the claims fall within one of the statutory categories (machine) and are eligible under Step 1.
Step 2A Prong 1
Independent Claims
Claim 63 recites:
determine an input to at least a part of at least one input layer of an artificial neural network depending on the input data and to determine an output of a part of the artificial neural network - these limitations encompass a user to perform these steps mentally or by using pen and paper, such as using judgment to determine input to one of the layers in a network and calculating output of the network, which is observing, evaluating and judging that is practically capable of being performed in the human mind or by a human using a pen and paper.
Claim 75 recites:
determine an output of the part of neural network for at least one user the equipment depending on the input - these limitations encompass a user to perform these steps mentally or by using pen and paper, such as determining/ calculating output of a network depending on the input, which is observing, evaluating and judging that is practically capable of being performed in the human mind or by a human using a pen and paper.
Thus, the claims recite an abstract idea that falls under the “Mental Processes” grouping.
Step 2A Prong 2
Independent Claims
Additional elements
Claim 63 recites:
a first apparatus comprising at least a receiver configured to receive input data of at least one user equipment; a processor configured to; a transmitter configured to transmit the output of the part of the artificial neural network - these limitations amount to insignificant extra-solution activity of mere data gathering and outputting (see MPEP § 2106.05(g)). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer and/ or neural networks (see MPEP § 2106.05(h)).
Claim 75 recites:
a second apparatus configured artificial configured artificial comprising an input (401) to receive an input for a part of an neural network; a processor to; and an output configured to output the output - these limitations amount to insignificant extra-solution activity of mere data gathering and outputting (see MPEP § 2106.05(g)). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer and/ or neural networks (see MPEP § 2106.05(h)).
the part of the artificial neural network comprising at least a part of at least one hidden layer or at least a part of an output layer of the artificial neural network or at least a part of at least one hidden layer and at least a part of an output layer of the artificial neural network - these limitations are recited at a high-level of generality such that it amount to no more than generally linking the use of the judicial exception to the technological environment of neural networks (see MPEP § 2106.05(h)).
Accordingly, these additional elements do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to the abstract idea.
Step 2B
Independent Claims
Additional elements
Claim 63 recites:
a first apparatus comprising at least a receiver configured to receive input data of at least one user equipment; a processor configured to; a transmitter configured to transmit the output of the part of the artificial neural network - these limitations amount to insignificant extra-solution activity of mere data gathering and outputting, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer and/ or neural networks (see MPEP § 2106.05(h)).
Claim 75 recites:
a second apparatus configured artificial configured artificial comprising an input (401) to receive an input for a part of an neural network; a processor to; and an output configured to output the output - these limitations amount to insignificant extra-solution activity of mere data gathering and outputting, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”, “presenting offers”). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer and/ or neural networks (see MPEP § 2106.05(h)).
the part of the artificial neural network comprising at least a part of at least one hidden layer or at least a part of an output layer of the artificial neural network or at least a part of at least one hidden layer and at least a part of an output layer of the artificial neural network - these limitations are recited at a high-level of generality such that it amount to no more than generally linking the use of the judicial exception to the technological environment of neural networks (see MPEP § 2106.05(h)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are patent ineligible.
Step 2A Prong 1
Dependent Claims
Claim 65:
select the input for at least the part of the at least one input layer from the input data depending on the first configuration command for the first apparatus - these limitations encompass mental process, such as a user using judgement to select an input based on a rule/ command.
Claim 66:
select the input layer for the input from a plurality of input layers or to select the at least part of the hidden layer depending on a second configuration command for the first apparatus - these limitations encompass mental process, such as a user using judgement to select a layer in the network for the input based on a rule/ command.
Claim 67:
configure at least one parameter of the artificial neural network depending on the configuration or training information - these limitations encompass mental process, such as a user designing a parameter based on some information.
Claim 70:
determine an output of the first apparatus depending on the activations - these limitations encompass mental process, such as a user calculating output using activation values.
Claim 71:
determine an output of the first apparatus depending on output features of an output layer of the artificial neural network - these limitations encompass mental process, such as a user calculating output based on the features of the network.
Claim 72:
determine the output of the part of the artificial neural network depending on input for the part of the artificial neural network determined depending on at least a part of the first input data and at least a part of the second input data - these limitations encompass mental process, such as a user calculating output based on certain given data.
Claim 73:
determine from the input data preprocessed input data, and determine the input for the input layer of the artificial neural network depending on the pre-processed input data - these limitations encompass mental process, such as a user using judgement to decide the input data based on certain data.
Claim 74:
determine an input to a hidden layer of the artificial neural network depending on an output of the at least one input layer, and to determine the output of the part of the artificial neural network depending on the output of the hidden layer - these limitations encompass mental process, such as a user using judgement to calculate input and output of layers depending on the calculated outputs of previous layers.
Claim 76:
determine a first configuration command for selecting an input for an input layer from input data - these limitations encompass mental process, such as a user using judgement to come up with a rule/ command for selecting input.
Claim 77:
determine a second configuration command for selecting an input layer for an input from a plurality of input layers or for selecting at least part of the hidden layer depending on the second configuration command - these limitations encompass mental process, such as a user using judgement to come up with a rule/ command for selecting input layer.
Claim 78:
determine the output of the second apparatus depending on activations output by a hidden layer of the part of the artificial neural network - these limitations encompass mental process, such as a user as a user calculating output based on the activation values.
Claim 79:
determine the output of the second apparatus depending on output features of an output layer of the artificial neural network - these limitations encompass mental process, such as a user calculating output based on the features of the network.
Claim 80:
determine a first output of the second apparatus attributed to the at least one user equipment - these limitations encompass mental process, such as a user calculating output based on certain data.
Claim 81:
determine at least a first output of the second apparatus attributed to the first user equipment, a second output of the second apparatus attributed to the second user equipment, a third output of the second apparatus attributed to the third user equipment and a fourth output of the second apparatus attributed to the fourth user equipment - these limitations encompass mental process, such as a user calculating outputs based on certain data.
Claim 82:
determine signalling to instruct a first apparatus to operate in an operating mode for training the artificial neural network or in an operating mode for inference with the artificial neural network - these limitations encompass mental process, such as a user using judgement to come up with a rule/ signalling for different modes of operation.
Thus, the claims recite the abstract idea.
Step 2A Prong 2
Dependent Claims
Additional elements
Claim 64:
an interface configured to receive configuration or training information for the part of the artificial neural network - these limitations amount to insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer and/ or neural networks (see MPEP § 2106.05(h)).
Claim 65:
the interface is configured to receive a first configuration command; the processor is configured to - these limitations amount to insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer (see MPEP § 2106.05(h)).
Claim 66:
the interface is configured to receive a second configuration command; the processor is configured to - these limitations amount to insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer (see MPEP § 2106.05(h)).
Claim 67:
the interface is configured to receive configuration or training information; the processor is configured to - these limitations amount to insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer (see MPEP § 2106.05(h)).
Claim 69:
the interface is configured to send signalling information indicating the operating mode for training the artificial neural network or the operating mode for inference with the artificial neural network selected depending on the signalling - these limitations amount to insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer and/ or neural networks (see MPEP § 2106.05(h)).
Claim 70:
the interface is configured to receive activations; the processor is configured to - these limitations amount to insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer (see MPEP § 2106.05(h)).
Claim 71:
the processor is configured to - These limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of computer (see MPEP § 2106.05(h)).
Claim 72:
the receiver is configured to receive first input data from a first user equipment and second input data from a second user equipment and the processor is configured to - these limitations amount to insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer (see MPEP § 2106.05(h)).
Claim 73:
a pre-processor configured to; the processor is configured to - These limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of computer (see MPEP § 2106.05(h)).
Claim 74:
the processor is configured to - These limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of computer (see MPEP § 2106.05(h)).
Claims 76, 77, 78, 79:
the processor is configured to - These limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of computer (see MPEP § 2106.05(h)).
Claim 80:
the receiver is configured to receive input for at least a part of an output layer of the artificial neural network or the at least part of the hidden layer attributed to at least one user equipment and the processor is configured to - these limitations amount to insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer and/ or neural networks (see MPEP § 2106.05(h)).
Claim 81:
the receiver is configured to receive a first input for at least a part of the output layer or the at least part of the hidden layer attributed at least to a first user equipment and to a second user equipment and to receive a second input for at least a part of the output layer or the at least part of the hidden layer attributed at least to a third user equipment and a fourth user equipment and the processor is configured to - these limitations amount to insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer and/ or neural networks (see MPEP § 2106.05(h)).
Claim 82:
the processor is configured to; the second apparatus may comprise an interface to send the signalling information addressed to the first apparatus - these limitations amount to insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer (see MPEP § 2106.05(h)).
Accordingly, these additional elements do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to the abstract idea.
Step 2B
Dependent Claims
Additional elements
Claim 64:
an interface configured to receive configuration or training information for the part of the artificial neural network - these limitations amount to insignificant extra-solution activity of mere data gathering, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer and/ or neural networks (see MPEP § 2106.05(h)).
Claim 65:
the interface is configured to receive a first configuration command; the processor is configured to - these limitations amount to insignificant extra-solution activity of mere data gathering, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer (see MPEP § 2106.05(h)).
Claim 66:
the interface is configured to receive a second configuration command; the processor is configured to - these limitations amount to insignificant extra-solution activity of mere data gathering, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer (see MPEP § 2106.05(h)).
Claim 67:
the interface is configured to receive configuration or training information; the processor is configured to - these limitations amount to insignificant extra-solution activity of mere data gathering, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer (see MPEP § 2106.05(h)).
Claim 69:
the interface is configured to send signalling information indicating the operating mode for training the artificial neural network or the operating mode for inference with the artificial neural network selected depending on the signalling - these limitations amount to insignificant extra-solution activity of mere data gathering, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer and/ or neural networks (see MPEP § 2106.05(h)).
Claim 70:
the interface is configured to receive activations; the processor is configured to - these limitations amount to insignificant extra-solution activity of mere data, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer (see MPEP § 2106.05(h)).
Claim 71:
the processor is configured to - These limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of computer (see MPEP § 2106.05(h)).
Claim 72:
the receiver is configured to receive first input data from a first user equipment and second input data from a second user equipment and the processor is configured to - these limitations amount to insignificant extra-solution activity of mere data gathering, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer (see MPEP § 2106.05(h)).
Claim 73:
a pre-processor configured to; the processor is configured to - These limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of computer (see MPEP § 2106.05(h)).
Claim 74:
the processor is configured to - These limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of computer (see MPEP § 2106.05(h)).
Claims 76, 77, 78, 79:
the processor is configured to - These limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of computer (see MPEP § 2106.05(h)).
Claim 80:
the receiver is configured to receive input for at least a part of an output layer of the artificial neural network or the at least part of the hidden layer attributed to at least one user equipment and the processor is configured to - these limitations amount to insignificant extra-solution activity of mere data gathering, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer and/ or neural networks (see MPEP § 2106.05(h)).
Claim 81:
the receiver is configured to receive a first input for at least a part of the output layer or the at least part of the hidden layer attributed at least to a first user equipment and to a second user equipment and to receive a second input for at least a part of the output layer or the at least part of the hidden layer attributed at least to a third user equipment and a fourth user equipment and the processor is configured to - these limitations amount to insignificant extra-solution activity of mere data gathering, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer and/ or neural networks (see MPEP § 2106.05(h)).
Claim 82:
the processor is configured to; the second apparatus may comprise an interface to send the signalling information addressed to the first apparatus - these limitations amount to insignificant extra-solution activity of mere data gathering, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”). These are also recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)) and generally linking the use of the judicial exception to the technological environment of a computer (see MPEP § 2106.05(h)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, these claims are patent ineligible.
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 63-67 and 70-74 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 2021/0158151 A1 hereinafter Wang) in view of Wang et al. (US 2020/0366537 A1 hereinafter Wang’537).
Regarding Claim 63, Wang teaches a first apparatus comprising at least a receiver configured to receive input data of at least one user equipment ([0031] FIG. 1 illustrates an example environment 100 which includes a user equipment 110 (UE 110) that can communicate with base stations 120 (i.e., first apparatus) through one or more wireless communication links 130; [0032] the base stations 120 communicate with the user equipment 110 using the wireless links 131 and 132, which may be implemented as any suitable type of wireless link; the wireless links 131 and 132 include control and data communication, such as downlink of data and control information communicated from the base stations 120 to the user equipment 110, uplink of other data and control information communicated from the user equipment 110 to the base stations 120 (i.e., input data of user equipment), or both; [0040] the device diagram for the base station 120 shown in FIG. 2; the base station 120 include one or more wireless transceivers - thus, comprising receiver));
a processor configured to determine an input to at least a part of an artificial neural network depending on the input data and to determine an output of a part of the artificial neural network ([0041] the base station 120 include processor(s) 260 and computer-readable storage media 262; the data in CRM 262 are executable by processor(s) 260 to enable communication with the user equipment 110; [0043] CRM 262 includes a base station neural network manager 268; the BS neural network manager 268 selects the NN formation configurations utilized by the base station 120 and/or UE 110 to configure deep neural networks for processing wireless communications (i.e., input to artificial neural network), such as by selecting a combination of NN formation configuration elements; [0168] signaling and control transactions of using machine-learning architectures for broadcast and multicast communications in wireless communications is illustrated by the signaling and control transaction diagram 1300 of FIG. 13; at 1305, the base station 120 receives metrics and/or UE capabilities from the UE(s) (i.e., input data); [0170] the base station 120 determines a configuration of a DNN for processing the communications at 1315; the configuration of the DNN can include a (partitioned) E2E ML configuration (i.e., part of artificial neural network); [0146] the E2E ML controller partitions the E2E ML configuration based on devices participating in the corresponding E2E communication; [0149] determine an E2E ML configuration that corresponds to a distributed DNN in which multiple devices form portions of the DNN; [0174] at 1330, the base station 120 forms a DNN based on the configuration of the DNN determined at 1315; the DNN formed by the base station performs at least some processing for transmitting broadcast or multicast communications over a wireless communication system; [0175] at 1340, the base station 120 and the UE 110 process broadcast or multicast communications using the DNNs, such as that described with reference to FIGS. 11 and 12 - thus, processing communications using the portion of the DNN formed (i.e., determining input for the part of the neural network) depending the UE capabilities/ input data and transmitting communications processed using the portion of DNN (i.e., determining output of the part of the neural network )); and
a transmitter configured to transmit the output of the part of the artificial neural network ([0040] the device diagram for the base station 120 shown in FIG. 2; the base station 120 include one or more wireless transceivers - thus, comprising transmitter; [0170] the base station 120 determines a configuration of a DNN for processing the communications at 1315; the configuration of the DNN can include a (partitioned) E2E ML configuration (i.e., part of artificial neural network); [0146] the E2E ML controller partitions the E2E ML configuration based on devices participating in the corresponding E2E communication; [0149] determine an E2E ML configuration that corresponds to a distributed DNN in which multiple devices form portions of the DNN; [0174] at 1330, the base station 120 forms a DNN based on the configuration of the DNN determined at 1315; the DNN formed by the base station performs at least some processing for transmitting broadcast or multicast communications over a wireless communication system; [0175] at 1340, the base station 120 and the UE 110 process broadcast or multicast communications using the DNNs, such as that described with reference to FIGS. 11 and 12 - thus, transmitting communications processed using the portion of DNN (i.e., determining output of the part of the neural network)).
However, Wang fails to expressly teach wherein determine input to at least one input layer.
In the same field of endeavor, Wang'537 teaches wherein determine input to at least one input layer ([0083] feedback information processed by the terminal; [0084] the base station determines an input of a specific layer in the neural network of the base station according to the feedback information, so that the specific layer processes the feedback information; the base station input the feedback information to a specific layer of the neural network of the base station, such as an input layer or an intermediate layer of the neural network of the base station).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein determine input to at least one input layer, as taught by Wang'537 into Wang. Doing so would be desirable because it would provide users with wireless communication services more intelligently (Wang'537 [0003]).
As to dependent Claim 64, Wang and Wang'537 teach all the limitations of Claim 63. Wang further teaches wherein an interface configured to receive configuration or training information for the part of the artificial neural network ([0043] CRM 262 includes a base station neural network manager 268; the BS neural network manager 268 selects the NN formation configurations utilized by the base station 120 and/or UE 110 to configure deep neural networks for processing wireless communications, such as by selecting a combination of NN formation configuration elements; [0170] the base station 120 determines a configuration of a DNN for processing the communications at 1315; the configuration of the DNN can include a (partitioned) E2E ML configuration; [0146] the E2E ML controller partitions the E2E ML configuration based on devices participating in the corresponding E2E communication; [0149] determine an E2E ML configuration that corresponds to a distributed DNN in which multiple devices form portions of the DNN; the E2E ML controller 318 identifies a second NN formation configuration that corresponds to a second partition of the E2E ML configuration and communicates the second NN formation configuration to the base station 120 - thus, comprising interface to receive partitioned E2E ML configuration for the DNN).
As to dependent Claim 65, Wang and Wang'537 teach all the limitations of Claim 64. Wang further teaches wherein the interface is configured to receive a first configuration command, wherein the processor is configured to select the input for at least the part from the input data depending on the first configuration command for the first apparatus ([0043] CRM 262 includes a base station neural network manager 268; the BS neural network manager 268 selects the NN formation configurations utilized by the base station 120 and/or UE 110 to configure deep neural networks for processing wireless communications (i.e., input), such as by selecting a combination of NN formation configuration elements; [0170] the base station 120 determines a configuration of a DNN for processing the communications at 1315; the configuration of the DNN can include a (partitioned) E2E ML configuration; [0146] the E2E ML controller partitions the E2E ML configuration based on devices participating in the corresponding E2E communication; [0149] determine an E2E ML configuration that corresponds to a distributed DNN in which multiple devices form portions of the DNN; the E2E ML controller 318 identifies a second NN formation configuration that corresponds to a second partition of the E2E ML configuration and communicates the second NN formation configuration to the base station 120 - thus, the E2E controller communicates first configuration command/ partitioned E2E ML configuration for the DNN to the base station (i.e., interface receiving first configuration command) and the wireless communication/ input is processed accordingly (i.e., selecting input for the portion of the DNN)). Wang'537 further teaches wherein select the input to at least the part of the at least one input layer ([0083] feedback information processed by the terminal; [0084] the base station determines an input of a specific layer in the neural network of the base station according to the feedback information, so that the specific layer processes the feedback information; the base station input the feedback information to a specific layer of the neural network of the base station, such as an input layer or an intermediate layer of the neural network of the base station).
As to dependent Claim 66, Wang and Wang'537 teach all the limitations of Claim 64. Wang further teaches wherein the interface is configured to receive a second configuration command [0170] the base station 120 determines a configuration of a DNN for processing the communications at 1315; the configuration of the DNN can include a (partitioned) E2E ML configuration; [0146] the E2E ML controller partitions the E2E ML configuration based on devices participating in the corresponding E2E communication; determining the partitions can be based on any combination of the capabilities, wireless network resource partitioning, the operating parameters, the current operating environment; [0149] determine an E2E ML configuration that corresponds to a distributed DNN in which multiple devices form portions of the DNN; the E2E ML controller 318 identifies a second NN formation configuration that corresponds to a second partition of the E2E ML configuration and communicates the second NN formation configuration to the base station 120; [0152] the E2E ML controller 318 determines modifications (e.g., parameter changes) to an existing DNN to better accommodate the performance requirements of devices, applications, and/or transmissions in a wireless network - thus, based on the current operating environment, the E2E ML controller communicates different configurations to the base stations (i.e., interface receiving second configuration command). Wang'537 further teaches wherein the processor is configured to select the input layer for the input from a plurality of input layers or to select the at least part of the hidden layer depending on a second configuration command for the first apparatus ([0083] feedback information processed by the terminal; [0084] the base station determines an input of a specific layer in the neural network of the base station according to the feedback information, so that the specific layer processes the feedback information; the base station input the feedback information to a specific layer of the neural network of the base station, such as an input layer or an intermediate layer of the neural network of the base station).
As to dependent Claim 67, Wang and Wang'537 teach all the limitations of Claim 64. Wang further teaches wherein the interface is configured to receive configuration or training information ([0043] CRM 262 includes a base station neural network manager 268; the BS neural network manager 268 selects the NN formation configurations utilized by the base station 120 and/or UE 110 to configure deep neural networks for processing wireless communications, such as by selecting a combination of NN formation configuration elements; [0170] the base station 120 determines a configuration of a DNN for processing the communications at 1315; the configuration of the DNN can include a (partitioned) E2E ML configuration; [0146] the E2E ML controller partitions the E2E ML configuration based on devices participating in the corresponding E2E communication; [0149] determine an E2E ML configuration that corresponds to a distributed DNN in which multiple devices form portions of the DNN; the E2E ML controller 318 identifies a second NN formation configuration that corresponds to a second partition of the E2E ML configuration and communicates the second NN formation configuration to the base station 120 - thus, comprising interface to receive partitioned E2E ML configuration for the DNN); and the processor is configured to configure at least one parameter of the artificial neural network depending on the configuration or training information ([0149] determine an E2E ML configuration that corresponds to a distributed DNN in which multiple devices form portions of the DNN; the E2E ML controller 318 identifies a second NN formation configuration that corresponds to a second partition of the E2E ML configuration and communicates the second NN formation configuration to the base station 120; [0152] the E2E ML controller 318 determines modifications (e.g., parameter changes) to an existing DNN to better accommodate the performance requirements of devices, applications, and/or transmissions in a wireless network; [0170] the base station 120 determines a configuration of a DNN for processing the communications at 1315; the configuration of the DNN can include a (partitioned) E2E ML configuration - thus, the parameter of portion of the DNN based on the configuration information from the E2E ML controller).
As to dependent Claim 70, Wang and Wang'537 teach all the limitations of Claim 64. Wang further teaches wherein the interface is configured to receive activations, and the processor is configured to determine an output of the first apparatus depending on the activations ([0038] the computer-readable storage media includes a neural network table that stores various architecture and/or parameter configurations that form a neural network, such as, parameters that specify a fully-connected layer neural network architecture, an input layer architecture, an output layer architecture, coefficients (e.g., weights and biases) utilized by the neural network, an activation function (i.e., activations) of each neural network layer, etc.; [0103] the base station analyzes multiple neural network formation configurations and/or multiple neural network formation configuration elements included in a neural network table, and determines the neural network formation configuration by selects and/or creates a neural network formation configuration that aligns with current channel condition; [0066] the DNN includes an input layer, an output layer, and one or more hidden layer(s) that are positioned between the input layer and the output layer; each layer has an arbitrary number of nodes, where the number of nodes between layers can be the same or different; [0067] a node receives input data, and processes the input data using algorithm(s) to produce output data; the process repeat throughout multiple layers until the DNN generates an output using the nodes of output layer - thus, the output of the apparatus/ base station depending on the activations received based on neural network table).
As to dependent Claim 71, Wang and Wang'537 teach all the limitations of Claim 63. Wang further teaches wherein the processor is configured to determine an output of the first apparatus depending on output features of an output layer of the artificial neural network ([0103] the base station analyzes multiple neural network formation configurations and/or multiple neural network formation