DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112: Indefiniteness
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 4 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.
Regarding claim 4, the term “according to a degree of match” is a relative term which renders the claim indefinite. The term “according to a degree of match” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 11-16, and 26-32 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites An information sending method. The claim recites a method. A method is one of the four statutory categories of invention.
In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components:
comprising: generating a parameter of a neural network; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like determining a parameter for a model, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea.
In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
and sending first information, wherein the first information comprises the parameter of the neural network. (i.e., the broadest reasonable interpretation of sending a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
applied by a first communication node, (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea.
In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation(s) (II), under the broadest reasonable interpretation, recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)).
Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Further, limitation (III), under the broadest reasonable interpretation, merely recite steps that apply generic computer components as a tool to perform judicial exceptions, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 2 recites further comprising at least one of: training the neural network using the parameter of the neural network; or testing the neural network using the parameter of the neural network. Under the broadest reasonable interpretation, the limitations recite training a model using a parameter which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 2 does not solve the deficiencies of claim 1.
Regarding claim 3, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 3 recites wherein the parameter of the neural network is used for a second communication node to perform at least one of the following operations: training the neural network; testing the neural network; or applying the neural network. Under the broadest reasonable interpretation, the limitations recite training a model using a parameter which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 3 does not solve the deficiencies of claim 1.
Regarding claim 4, it is dependent upon claim 3 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 4 recites wherein training the neural network comprises: inputting sample data into the neural network, and adjusting the parameter of the neural network to enable output of the neural network to match with label data corresponding to the sample data;. Under the broadest reasonable interpretation, the limitations recite training a model based on matching which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. Claim 4 also recites testing the neural network comprises: inputting test data into the neural network, and determining a performance index of the neural network according to a degree of match between the output of the neural network and label data corresponding to the test data;. Under the broadest reasonable interpretation, the limitations recite training a model based on determining performances which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Claim 4 also recites and applying the neural network comprises: inputting actual input data into the neural network to obtain actual output data of the neural network. Under the broadest reasonable interpretation, the limitations recite steps of mere data outputting, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Therefore, claim 4 does not solve the deficiencies of claim 3.
Regarding claim 5, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 5 recites wherein the parameter of the neural network comprises weights and biases corresponding to neurons in the neural network; or, wherein the parameter of the neural network comprises a type of the neural network; and the type of the neural network comprises at least one of: a fully connected neural network, a convolutional neural network (CNN), a recurrent neural network (RNN), or a long short-term memory (LSTM) neural network; or, wherein the parameter of the neural network comprises a depth of the neural network; or, wherein the parameter of the neural network comprises a number of neurons comprised in each of one or more layers of the neural network; or, wherein the parameter of the neural network comprises at least one of: a type of input data of the neural network or a type of output data of the neural network; or, wherein the parameter of the neural network comprises a function of the neural network; and the function of the neural network comprises at least one of: positioning, beam management, channel state information (CSI) prediction, mobility management, time domain resource prediction, frequency domain resource prediction, channel estimation, or line-of-sight/non-line-of-sight (LOS/NLOS) channel identification. Under the broadest reasonable interpretation, the limitations recite generating model parameters by determining types of data which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 5 does not solve the deficiencies of claim 1.
Regarding claim 11, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 11 recites before generating the parameter of the neural network, further comprising: receiving second information reported by a second communication node, wherein the neural network is trained according to the second information. Under the broadest reasonable interpretation, the limitations recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Therefore, claim 11 does not solve the deficiencies of claim 1.
Regarding claim 12, it is dependent upon claim 11 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 12 recites further comprising: selecting at least one of a training set, a test set, or a verification set according to the second information. Under the broadest reasonable interpretation, the limitations recite selecting data which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 12 does not solve the deficiencies of claim 11.
Regarding claim 13, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites An information sending method. The claim recites a method. A method is one of the four statutory categories of invention.
In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components:
comprising: generating second information, wherein the second information is used for a first communication node to perform neural network computing; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like determining a parameter for a model, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea.
In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
and sending the second information. (i.e., the broadest reasonable interpretation of sending a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
applied by a second communication node, (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea.
In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation(s) (II), under the broadest reasonable interpretation, recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)).
Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Further, limitation (III), under the broadest reasonable interpretation, merely recite steps that apply generic computer components as a tool to perform judicial exceptions, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 14, it is dependent upon claim 13 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 14 recites further comprising: receiving first information, wherein the first information comprises a parameter of a neural network;. Under the broadest reasonable interpretation, the limitations recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Claim 14 also recites and performing a corresponding operation on the neural network according to the first information. Under the broadest reasonable interpretation, the limitations recites performing an operation based on information which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 14 does not solve the deficiencies of claim 13.
Regarding claim 15, it is dependent upon claim 13 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 15 recites wherein performing the neural network computing comprises at least one of: training a neural network according to the second information; testing a neural network according to the second information; or applying a neural network according to the second information. Under the broadest reasonable interpretation, the limitations recite training a model using a parameter which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 15 does not solve the deficiencies of claim 13.
Regarding claim 16, it is dependent upon claim 13 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 16 recites wherein the second information comprises a type of a neural network supported by the second communication node; and the type of the neural network comprises at least one of: a fully connected neural network, a convolutional neural network (CNN), a recurrent neural network (RNN), or a long short-term memory (LSTM) neural network; or, wherein the second information comprises at least one of: a maximum depth of a neural network supported by the second communication node; a maximum number of neurons of a neural network supported by the second communication node; or, a maximum number of neurons comprised in each of one or more layers of a neural network supported by the second communication node; or, wherein the second information comprises a function of a neural network; and the function of the neural network comprises at least one of: positioning, beam management, channel state information (CSI) prediction, mobility management, time domain resource prediction, frequency domain resource prediction, channel estimation, or line-of- sight/non-line-of-sight (LOS/NLOS) channel identification; or, wherein the second information comprises at least one of: a type of input data of a neural network or a type of output data of a neural network; or, wherein the second information comprises a virtualization model of transceiver units (TXRUs) of the second communication node; and the virtualization model of the TXRUs comprises at least one of a subarray partition model or a fully connected model; or, wherein the second information comprises at least one of: a mapping relationship between TXRUs of the second communication node and antenna elements of the second communication node; spatial distribution information of antenna panels of the second communication node; or, a polarization manner of antenna elements of the second communication node; or, wherein the second information comprises at least one of: a polarization angle of an antenna element of the second communication node; or, a height of an antenna of the second communication node; or, wherein the second information comprises a mapping relationship between TXRUs of the second communication node and antenna elements of the second communication node; and the mapping relationship comprises at least one of: a number of rows of antenna elements to which one TXRU is mapped; a number of columns of antenna elements to which one TXRU is mapped; a row spacing of antenna elements to which one TXRU is mapped; or, a column spacing of antenna elements to which one TXRU is mapped; or, wherein the second information comprises spatial distribution information of antenna panels of the second communication node; and the spatial distribution information of the antenna panels comprises at least one of: a number of rows of the antenna panels, a number of columns of the antenna panels, a row spacing of the antenna panels, or a column spacing of the antenna panels; or, wherein the second information comprises a polarization manner of antenna elements of the second communication node; and wherein the polarization manner comprises at least one of co-polarized antenna elements or cross-polarized antenna elements. Under the broadest reasonable interpretation, the limitations recite generating model parameters by determining types of data which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 16 does not solve the deficiencies of claim 13.
Regarding claim 26, it is dependent upon claim 13 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 26 recites wherein the second information comprises at least one of: a gain of an antenna element in a maximum gain direction; an angle corresponding to a set antenna attenuation in a horizontal direction; an angle corresponding to a set antenna attenuation in a vertical direction; a radiation pattern of an antenna element; or an angle of a normal direction of an antenna panel. Under the broadest reasonable interpretation, the limitations recite calculating a gain of an antenna which is interpreted as using a mathematical calculation. A mathematical calculation is interpreted as a mathematical concept. Therefore, claim 26 does not solve the deficiencies of claim 13.
Regarding claim 27, it is dependent upon claim 26 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 27 recites wherein the radiation pattern comprises at least one of a horizontal radiation pattern, a vertical radiation pattern, a spatial three-dimensional radiation pattern, or an omnidirectional antenna. Under the broadest reasonable interpretation, the limitations recite calculating a radiation pattern of an antenna which is interpreted as using a mathematical calculation. A mathematical calculation is interpreted as a mathematical concept. Therefore, claim 27 does not solve the deficiencies of claim 26.
Regarding claim 28, it is dependent upon claim 26 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 28 recites wherein the set antenna attenuation comprises an attenuation relative to an antenna gain in a maximum antenna gain direction. Under the broadest reasonable interpretation, the limitations recite calculating a gain direction of an antenna which is interpreted as using a mathematical calculation. A mathematical calculation is interpreted as a mathematical concept. Therefore, claim 28 does not solve the deficiencies of claim 26.
Regarding claim 29, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A communication node, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor executes the computer program to perform. The claim recites a communication node which is interpreted as a machine. A machine is one of the four statutory categories of invention. For the Step 2A/2B analyses, since claim 29 is similar to claim 1 it is rejected under the same rationales as claim 1.
The additional limitation below fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception.
A communication node, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor executes the computer program to perform… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 30, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A non-transitory computer-readable storage medium. The claim recites a non-transitory computer readable medium which is interpreted as an article of manufacture. An article of manufacture is one of the four statutory categories of invention. For the Step 2A/2B analyses, since claim 30 is similar to claim 1 it is rejected under the same rationales as claim 1.
The additional limitation below fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception.
A non-transitory computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 31, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A communication node, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor executes the computer program to perform. The claim recites a communication node which is interpreted as a machine. A machine is one of the four statutory categories of invention. For the Step 2A/2B analyses, since claim 31 is similar to claim 13 it is rejected under the same rationales as claim 13.
The additional limitation below fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception.
A communication node, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor executes the computer program to perform… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 32, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A non-transitory computer-readable storage medium. The claim recites a non-transitory computer readable medium which is interpreted as an article of manufacture. An article of manufacture is one of the four statutory categories of invention. For the Step 2A/2B analyses, since claim 32 is similar to claim 13 it is rejected under the same rationales as claim 13.
The additional limitation below fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception.
A non-transitory computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 11-16, and 29-32 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wang, et al., US Pre-Grant Publication US20210342687A1 (“Wang”).
Regarding claim 1, Wang discloses:
An information sending method, applied by a first communication node, comprising: generating a parameter of a neural network; (Wang, abstract, “Techniques and apparatuses are described for enabling base station-user equipment messaging regarding deep neural networks. A network entity (base station 121, core network server 320) [applied by a first communication node,] determines a neural network formation configuration (architecture and/or parameter configurations 1208) [comprising: generating a parameter of a neural network;] for a deep neural network (deep neural network(s) 604, 608, 612, 616) for processing communications transmitted over the wireless communication system [An information sending method, applied by a first communication node,].”).
and sending first information, wherein the first information comprises the parameter of the neural network. (Wang, abstract, “A network entity (base station 121, core network server 320) determines a neural network formation configuration (architecture and/or parameter configurations 1208) for a deep neural network (deep neural network(s) 604, 608, 612, 616) for processing communications transmitted over the wireless communication system [and sending first information, wherein the first information comprises the parameter of the neural network.].”).
Regarding claim 2, Wang discloses the information sending method according to claim 1. Wang further discloses further comprising at least one of: training the neural network using the parameter of the neural network; or testing the neural network using the parameter of the neural network. (Wang, ⁋51, “In implementations, the training module 270 extracts learned parameter configurations from the DNN to identify the NN formation configuration elements and/or NN formation configuration, and then adds and/or updates the NN formation configuration elements and/or NN formation configuration in the neural network table 272 [further comprising at least one of: training the neural network using the parameter of the neural network;].”).
Regarding claim 3, Wang discloses the information sending method according to claim 1. The examiner notes that the claim is written in an alternative embodiment format and selects the applying a neural network embodiment. Wang further discloses wherein the parameter of the neural network is used for a second communication node to perform at least one of the following operations: training the neural network; testing the neural network; or applying the neural network. (Wang, abstract, “The network entity (base station 121, core network server 302) communicates the neural network formation configuration to a user equipment (UE 110) [wherein the parameter of the neural network is used for a second communication node]. The user equipment (UE 110) configures a first neural network (deep neural network(s) 608, 612) based on the neural network formation configuration. In implementations, the user equipment (UE 110) recovers information communicated over the wireless network using the first neural network (deep neural network(s) 608, 612) [to perform at least one of the following operations:…or applying the neural network.].”).
Regarding claim 4, Wang discloses the information sending method according to claim 3. Wang further discloses wherein training the neural network comprises: inputting sample data into the neural network, and adjusting the parameter of the neural network to enable output of the neural network to match with label data corresponding to the sample data; testing the neural network comprises: inputting test data into the neural network, and determining a performance index of the neural network according to a degree of match between the output of the neural network and label data corresponding to the test data; and applying the neural network comprises: inputting actual input data into the neural network to obtain actual output data of the neural network. (Wang, ⁋5, “In some aspects, a user equipment associated with a wireless communication system receives a message that indicates a NN formation configuration for a DNN for processing communications transmitted over the wireless communication system. In turn, the user equipment forms the DNN using the NN formation configuration indicated in the message. The user equipment receives the communications from a base station, and processes the communications using the DNN to extract information transmitted in the communications [and applying the neural network comprises: inputting actual input data into the neural network to obtain actual output data of the neural network.].”).
Regarding claim 5, Wang discloses the information sending method according to claim 1. Wang further discloses wherein the parameter of the neural network comprises weights and biases corresponding to neurons in the neural network; or, wherein the parameter of the neural network comprises a type of the neural network; and the type of the neural network comprises at least one of: a fully connected neural network, a convolutional neural network (CNN), a recurrent neural network (RNN), or a long short-term memory (LSTM) neural network; or, wherein the parameter of the neural network comprises a depth of the neural network; or, wherein the parameter of the neural network comprises a number of neurons comprised in each of one or more layers of the neural network; or, wherein the parameter of the neural network comprises at least one of: a type of input data of the neural network or a type of output data of the neural network; or, wherein the parameter of the neural network comprises a function of the neural network; and the function of the neural network comprises at least one of: positioning, beam management, channel state information (CSI) prediction, mobility management, time domain resource prediction, frequency domain resource prediction, channel estimation, or line-of-sight/non-line-of-sight (LOS/NLOS) channel identification. (Wang, ⁋44, “parameter configurations that form a neural network, such as, by way of example and not of limitation, 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) [wherein the parameter of the neural network comprises weights and biases corresponding to neurons in the neural network;]”).
Regarding claim 11, Wang discloses the information sending method according to claim 1. Wang further discloses before generating the parameter of the neural network, further comprising: receiving second information reported by a second communication node, wherein the neural network is trained according to the second information. (Wang, ⁋57, “a core network neural network manager 312 that manages NN formation configurations used to process communications exchanged between UE 110 [further comprising: receiving second information reported by a second communication node,] and the base stations 120. In some implementations, the core network neural network manager 312 analyzes various parameters… The core network neural network manager selects, based on these parameters, a NN formation configuration that improves the accuracy of a DNN processing the communications [before generating the parameter of the neural network,]. Improving the accuracy signifies an improved accuracy in the output, such as lower bit errors, generated by the neural network relative to a neural network configured with another NN formation configuration.”, and Wang, ⁋59, “the training module 314 of the core network neural network manager 312 generates complementary NN formation configurations and/or NN formation configuration elements to those stored in the neural network table 216 at the UE 110 [wherein the neural network is trained according to the second information.]”).
Regarding claim 12, Wang discloses the information sending method according to claim 11. Wang further discloses further comprising: selecting at least one of a training set, a test set, or a verification set according to the second information. (Wang, ⁋59, “the training module 314 of the core network neural network manager 312 generates complementary NN formation configurations and/or NN formation configuration elements to those stored in the neural network table 216 at the UE 110 and/or the neural network table 272 at the base station 121. As one example, the training module 314 generates neural network table 316; the NN manager takes in the UE information, second information, to update and train the NN using the training module and the training module then updates the NN table (i.e. according to the second information.)” and Wang, ⁋61, “neural network table 316 stores multiple different NN formation configuration elements generated using the training module 314. In some implementations, the neural network table includes 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 [further comprising: selecting at least one of a training set,…according to the second information.].”).
Regarding claim 13, Wang discloses An information sending method, applied by a second communication node, comprising: generating second information, wherein the second information is used for a first communication node to perform neural network computing; and sending the second information. (Wang, ⁋57, “a core network neural network manager 312 that manages NN formation configurations used to process communications exchanged between UE 110 [An information sending method, applied by a second communication node, comprising: generating second information,…and sending the second information.] and the base stations 120. In some implementations, the core network neural network manager 312 analyzes various parameters… The core network neural network manager selects, based on these parameters, a NN formation configuration that improves the accuracy of a DNN processing the communications the NN manager is located on a network server or a first communication node (i.e. wherein the second information is used for a first communication node to perform neural network computing;).”).
Regarding claim 14, Wang discloses the information sending method according to claim 13. Wang further discloses:
further comprising: receiving first information, wherein the first information comprises a parameter of a neural network; (Wang, abstract, “The network entity (base station 121, core network server 302) communicates the neural network formation configuration to a user equipment (UE 110). The user equipment (UE 110) configures a first neural network (deep neural network(s) 608, 612) based on the neural network formation configuration [further comprising: receiving first information, wherein the first information comprises a parameter of a neural network;].”).
and performing a corresponding operation on the neural network according to the first information. (Wang, ⁋35, “In some aspects, a user equipment in a wireless communication system receives a neural network table that includes a plurality of NN formation configuration elements that provide the user equipment with an ability to configure a DNN for processing communications transmitted over the wireless communication system [and performing a corresponding operation on the neural network according to the first information.].”).
Regarding claim 15, Wang discloses the information sending method according to claim 13. Wang further discloses wherein performing the neural network computing comprises at least one of: training a neural network according to the second information; testing a neural network according to the second information; or applying a neural network according to the second information. (Wang, ⁋57, “a core network neural network manager 312 that manages NN formation configurations used to process communications exchanged between UE 110 [according to the second information.] and the base stations 120. In some implementations, the core network neural network manager 312 analyzes various parameters”, and Wang, ⁋59, “the training module 314 of the core network neural network manager 312 generates complementary NN formation configurations and/or NN formation configuration elements to those stored in the neural network table 216 at the UE 110 [wherein performing the neural network computing comprises at least one of:…or applying a neural network]”).
Regarding claim 16, Wang discloses the information sending method according to claim 13. Wang further discloses wherein the second information comprises a type of a neural network supported by the second communication node; and the type of the neural network comprises at least one of: a fully connected neural network, a convolutional neural network (CNN), a recurrent neural network (RNN), or a long short-term memory (LSTM) neural network; or, wherein the second information comprises at least one of: a maximum depth of a neural network supported by the second communication node; a maximum number of neurons of a neural network supported by the second communication node; or, a maximum number of neurons comprised in each of one or more layers of a neural network supported by the second communication node; or, wherein the second information comprises a function of a neural network; and the function of the neural network comprises at least one of: positioning, beam management, channel state information (CSI) prediction, mobility management, time domain resource prediction, frequency domain resource prediction, channel estimation, or line-of- sight/non-line-of-sight (LOS/NLOS) channel identification; or, wherein the second information comprises at least one of: a type of input data of a neural network or a type of output data of a neural network; or, wherein the second information comprises a virtualization model of transceiver units (TXRUs) of the second communication node; and the virtualization model of the TXRUs comprises at least one of a subarray partition model or a fully connected model; or, wherein the second information comprises at least one of: a mapping relationship between TXRUs of the second communication node and antenna elements of the second communication node; spatial distribution information of antenna panels of the second communication node; or, a polarization manner of antenna elements of the second communication node; or, wherein the second information comprises at least one of: a polarization angle of an antenna element of the second communication node; or, a height of an antenna of the second communication node; or, wherein the second information comprises a mapping relationship between TXRUs of the second communication node and antenna elements of the second communication node; and the mapping relationship comprises at least one of: a number of rows of antenna elements to which one TXRU is mapped; a number of columns of antenna elements to which one TXRU is mapped; a row spacing of antenna elements to which one TXRU is mapped; or, a column spacing of antenna elements to which one TXRU is mapped; or, wherein the second information comprises spatial distribution information of antenna panels of the second communication node; and the spatial distribution information of the antenna panels comprises at least one of: a number of rows of the antenna panels, a number of columns of the antenna panels, a row spacing of the antenna panels, or a column spacing of the antenna panels; or, wherein the second information comprises a polarization manner of antenna elements of the second communication node; and wherein the polarization manner comprises at least one of co-polarized antenna elements or cross-polarized antenna elements. (Wang, ⁋122, “the core network server 302 determines a neural network formation configuration based on a variety of metrics and/or parameters, such as metrics from the UE 110, metrics from the base station 121, UE capabilities, etc. For example, the core network server receives any combination of metrics and/or parameters from the base station 121 and/or the UE 110, such as power information, SINR information, CQI, CSI, Doppler feedback, QoS, latency, UE capabilities, a base station type (e.g., eNB, gNB or ng-eNB), protocol versions, error metrics, UE capabilities, BS capabilities, power mode, and so forth [or, wherein the second information comprises at least one of: a type of input data of a neural network or a type of output data of a neural network;].”).
Regarding claim 29, the claim is similar to claim 1 and is rejected under the same rationale. Wang further discloses the additional limitations A communication node, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor executes the computer program to perform: (Wang, ⁋55, “The core network server 302 is illustrated as being embodied on a single server that includes processor(s) 304 and computer-readable storage media 306 (CRM 306). The processor 304 may be a single core processor or a multiple core processor composed of a variety of materials, such as silicon, polysilicon, high-K dielectric, copper, and so on. CRM 306 may include any suitable memory or storage device such as random-access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), non-volatile RAM (NVRAM), read-only memory (ROM), hard disk drives, or Flash memory useful to store device data 308 of the core network server 302 [A communication node, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor executes the computer program to perform:].”).
Regarding claim 30, the claim is similar to claim 1 and is rejected under the same rationale. Wang further discloses the additional limitations (Wang, claim 19, “A network entity comprising: a wireless transceiver; a processor; and computer-readable storage media comprising instructions to implement a deep neural network manager module that, responsive to execution by the processor, directs the network entity to perform operations comprising: [A non-transitory computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform]”).
Regarding claim 31, the claim is similar to claim 13 and is rejected under the same rationale. Wang further discloses the additional limitations A communication node, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor executes the computer program to perform: (Wang, 43, “The user equipment 110 also includes processor(s) 210 and computer-readable storage media 212 (CRM 212). The processor 210 may be a single core processor or a multiple core processor composed of a variety of materials, such as silicon, polysilicon, high-K dielectric, copper, and so on. The computer-readable storage media described herein excludes propagating signals. CRM 212 may include any suitable memory or storage device such as random-access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), non-volatile RAM (NVRAM), read-only memory (ROM), or Flash memory useable to store device data 214 of the user equipment 110 [A communication node, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor executes the computer program to perform:].”).
Regarding claim 32, the claim is similar to claim 13 and is rejected under the same rationale. Wang further discloses the additional limitations (Wang, see element 110 in Figure 2, Figure 2 shows that the user equipment has a processor and a CRM with computer programs or operations (i.e. A non-transitory computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform)”).
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 26-28 are rejected under 35 U.S.C. 103 as being unpatentable over Wang, et al., US Pre-Grant Publication US20210342687A1 (“Wang”) in view of Tran, US Pre-Grant Publication US20240136710A1 (“Tran”).
Regarding claim 26, Wang discloses the information sending method of claim 13. While Wang teaches a system of sending neural network parameters between nodes in a communication network, Wang does not explicitly teach wherein the second information comprises at least one of: a gain of an antenna element in a maximum gain direction; an angle corresponding to a set antenna attenuation in a horizontal direction; an angle corresponding to a set antenna attenuation in a vertical direction; a radiation pattern of an antenna element; or an angle of a normal direction of an antenna panel.
The examiner notes that the claim is written in an alternative embodiment format and selects the maximum gain direction embodiment. Tran teaches wherein the second information comprises at least one of: a gain of an antenna element in a maximum gain direction; an angle corresponding to a set antenna attenuation in a horizontal direction; an angle corresponding to a set antenna attenuation in a vertical direction; a radiation pattern of an antenna element; or an angle of a normal direction of an antenna panel. (Tran, ⁋174, “The system tracks the location of interfering signals and readjusts the digital beam forming coefficients to create nulls in the antenna pattern directed towards that interfering signal. The digital beam forming coefficients are adjusted to improve or maximize the signal quality of communication signals received from the UEs. The UE provides the cell tower BS with quality indicators which indicate the quality of the signals received by the UE. In response to received link quality indicators, the digital beam former in the BS dynamically adjusts its antenna directionality and the antenna beam pattern to help optimize the signal transmitted to the UE. The digital beam forming coefficients are readjusted to continually help maintain and help improve or maximize the signal quality of the received signals as the UE and/or the cell tower change their relative positions [wherein the second information comprises at least one of: a gain of an antenna element in a maximum gain direction;].”).
Wang and Tran are both in the same field of endeavor (i.e. communication networks). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Wang and Tran to teach the above limitation(s). The motivation for doing so is that considering the antenna angle of nodes improves the communication between nodes in a communication network (cf. Tran, ⁋174, “The system tracks the location of interfering signals and readjusts the digital beam forming coefficients to create nulls in the antenna pattern directed towards that interfering signal. The digital beam forming coefficients are adjusted to improve or maximize the signal quality of communication signals received from the UEs.”).
Regarding claim 27, the claim is directed to a non-selected alternative embodiment and therefore is not considered. The claim is rejected for at least its dependence on claim 26.
Regarding claim 28, the claim is directed to a non-selected alternative embodiment and therefore is not considered. The claim is rejected for at least its dependence on claim 26.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bai, et al., US20210326701A1 discloses a system that incorporates artificial intelligence into a wireless communications network.
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/N.S.W./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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