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 .
Examiner Comments
The examiner notes that claims 6 and 17 recite contingent limitations. The contingent limitations are: determining, if the degree of contribution of the transmission condition is greater than the reference degree of contribution, that the degree of contribution of the transmission condition is the higher degree of contribution, and reducing a data volume of training data under the transmission condition; or determining, if the degree of contribution of the transmission condition is not greater than the reference degree of contribution, that the degree of contribution of the transmission condition is the lower degree of contribution, and increasing the data volume of the training data under the transmission condition; in claim 6 and skipping, if a proportion of real-time data under any transmission condition of the transmission conditions is greater than a proportion of training data under the any transmission condition, inputting data that is in the real-time data under the any transmission condition and whose proportion is the proportion of the real-time data minus the proportion of the training data under the any transmission condition into the trained neural network model in a process of adjusting the trained neural network model online in claim 17. Contingent limitations are not required to be met as they are conditional to a prior condition (MPEP 2111.04). The examiner recommends the applicant amends the claims to fix the contingency. For the purposes of compact prosecution, the contingent limitation is interpreted as having its prior condition met.
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.
Claims 2, 4-7, 9, and 11-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 2, the claim recites wherein the determining, based on degrees of contribution of transmission conditions to an optimization objective of a neural network, data volumes of training data under the transmission conditions comprises:. The claim is indefinite as it is unclear whether the optimization objective and neural network recited are the same optimization objective and neural network recited in claim 1 or a different optimization objective and neural network. For the purposes of examination, the optimization objective and neural network are interpreted as being the same optimization objective and neural network recited in claim 1.
Regarding claim 4, the claim recites wherein the obtaining the training data under the transmission conditions, to form a training data set for training the neural network comprises:. The claim is indefinite as it is unclear whether the training data set is the same training data set recited in claim 1 or a different training data set. For the purposes of examination, the training data set is interpreted as being the same training data set recited in claim 1.
Regarding claim 7, the claim recites wherein the determining, based on degrees of contribution of transmission conditions to an optimization objective of a neural network, data volumes of training data under the transmission conditions comprises:. The claim is indefinite as it is unclear whether the optimization objective and neural network recited are the same optimization objective and neural network recited in claim 1 or a different optimization objective and neural network. For the purposes of examination, the optimization objective and neural network are interpreted as being the same optimization objective and neural network recited in claim 1.
Regarding claim 9, the claim recites wherein the sending the training data set to a target device comprises:. The claim is indefinite as it is unclear whether the target device is the same target device recited in claim 8 or a different target device. For the purposes of examination, the target device is interpreted as being the same target device recited in claim 8.
Regarding claim 11, the claim recites wherein before the performing an operation of wireless transmission based on a neural network model, the wireless transmission method further comprises:. The claim is indefinite as it is unclear whether the operation and neural network recited are the same operation and neural network recited in claim 10 or a different operation and neural network. For the purposes of examination, the operation and neural network are interpreted as being the same operation and neural network recited in claim 10.
Regarding claim 17, the claim recites wherein the obtaining real-time data under the transmission conditions and adjusting a trained neural network model online based on the real-time data comprises:. The claim is indefinite as it is unclear whether the trained neural network model is the same trained neural network model recited in claim 16 or a different trained neural network model. For the purposes of examination, the trained neural network model is interpreted as being the same trained neural network model recited in claim 16.
Regarding claims 5 and 6, the claims are rejected for at least their dependence on claim 2.
Regarding claims 12-18, they are rejected for at least their dependence on claim 11.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A training data set obtaining 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:
determining, based on degrees of contribution of transmission conditions to an optimization objective of a neural network, data volumes of training data under the transmission conditions; (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 how much training data to select based on transmission conditions, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
to form a training data set for training the 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 which samples of training data to use, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
wherein the degrees of contribution of the transmission conditions to the optimization objective of the neural network represent degrees of impact of the transmission conditions on a value of the optimization objective of the 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 which transmission conditions are more important based on their impact on model loss, 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 obtaining, based on the data volumes of the training data under the transmission conditions, the training data under the transmission conditions, (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
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) (IV), 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).
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 sorting the degrees of contribution of the transmission conditions; and executing, based on equal-proportion mixing, at least one of reducing a data volume of training data under a transmission condition corresponding to a higher degree of contribution in the sorting or increasing a data volume of training data under a transmission condition corresponding to a lower degree of contribution in the sorting. Under the broadest reasonable interpretation, the limitations recite sorting transmission conditions and then determining whether to increase of decrease the amount of training data based on the transmission condition’s contribution 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 types of the transmission conditions comprise at least one of: a signal to noise ratio or a signal to interference plus noise ratio; a reference signal received power; a signal strength; an interference strength; a terminal moving speed; a channel parameter; a distance from a terminal to a base station; a cell size; a carrier frequency; a modulation order or a modulation and coding scheme; a cell type; an inter-site distance; weather and environment factors; antenna configuration information of a transmit end or receive end; a terminal capability or type; or a base station capability or type. Under the broadest reasonable interpretation, merely recite steps that amount to indicating a field of use or technological environment in which to apply a judicial exception (MPEP 2106.05(h)). Therefore, claim 3 does not solve the deficiencies of claim 1.
Regarding claim 4, 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 4 recites collecting, based on the data volumes of the training data under the transmission conditions, data under the transmission conditions and calibrating the data, to form the training data set under the transmission conditions; or collecting specified volumes of data under the transmission conditions, and selecting, based on the data volumes of the training data under the transmission conditions, partial data from the specified volumes of data and calibrating the partial data, or replenishing the specified volumes of data and calibrating replenished data, to form the training data set under the transmission conditions. Under the broadest reasonable interpretation, the limitations recite different ways to change training 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 4 does not solve the deficiencies of claim 1.
Regarding claim 5, it is dependent upon claim 2 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 executing, according to a rule, at least one of reducing a data volume of training data under a transmission condition corresponding to a higher degree of contribution in the sorting or increasing a data volume of training data under a transmission condition corresponding to a lower degree of contribution in the sorting, wherein the rule comprises: that a larger value of the higher degree of contribution indicates a larger amplitude of the reducing; and that a smaller value of the lower degree of contribution indicates a larger amplitude of the increasing;. Under the broadest reasonable interpretation, the limitations recite that a higher impact determines a greater reduction and a smaller impact determines a greater increase which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. Claim 5 also recites wherein in a case that a result of the sorting is an ascending order, the data volumes of the training data under the transmission conditions decrease in a direction of the sorting; and in a case that a result of the sorting is a descending order, the data volumes of the training data under the transmission conditions increase in the direction of the sorting. Under the broadest reasonable interpretation, the limitations recite that more training data is allocated to transmission conditions that contribute less 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 2.
Regarding claim 6, it is dependent upon claim 2 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 6 recites determining a reference degree of contribution according to the sorting, and comparing a degree of contribution of a transmission condition with the reference degree of contribution; and executing at least one of operations according to a comparison result, wherein the operations comprise: determining, if the degree of contribution of the transmission condition is greater than the reference degree of contribution, that the degree of contribution of the transmission condition is the higher degree of contribution, and reducing a data volume of training data under the transmission condition;. Under the broadest reasonable interpretation, the limitations recite reducing training data of a transmission condition when the condition has a higher impact in view of a reference value which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. Claim 6 also recites or determining, if the degree of contribution of the transmission condition is not greater than the reference degree of contribution, that the degree of contribution of the transmission condition is the lower degree of contribution, and increasing the data volume of the training data under the transmission condition;. Under the broadest reasonable interpretation, the limitations recite increasing training data of a transmission condition when the condition has a lower impact in view of a reference value which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. Claim 6 also recites wherein the reference degree of contribution is a median of the sorting, or a degree of contribution at a specified position in the sorting, or an average of the degrees of contribution in the sorting, or a degree of contribution in the sorting closest to the average. Under the broadest reasonable interpretation, the limitations recite determining how to set the reference contribution 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 6 does not solve the deficiencies of claim 2.
Regarding claim 7, 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 7 recites determining, based on probability densities of the transmission conditions in an actual application, weighting coefficients corresponding to the transmission conditions; and determining, based on the degrees of contribution of the transmission conditions to the optimization objective and with reference to the weighting coefficients, the data volumes of the training data under the transmission conditions. Under the broadest reasonable interpretation, the limitations recite determining training data based on determining which transmission conditions occur the most in a real world application 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 7 does not solve the deficiencies of claim 1.
Regarding claim 8, 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 8 recites sending the training data set to a target device,. 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 8 also recites wherein the target device is configured to train the neural network based on the training data set. Under the broadest reasonable interpretation, the limitations merely recite steps that apply generic training of a neural network, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 8 does not solve the deficiencies of claim 1.
Regarding claim 9, it is dependent upon claim 8 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 9 recites directly sending the training data set to the target device, or sending the training data set on which specified conversion is performed to the target device, wherein the specified conversion comprises at least one of specific quantization, specific compression, or processing that is performed according to a neural network agreed or configured in advance. Under the broadest reasonable interpretation, the limitations recite steps of selecting a particular data type to be manipulated, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to selecting a particular data type to be manipulated 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 9 does not solve the deficiencies of claim 8.
Regarding claim 10, 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 10 recites performing an operation of wireless transmission based on a neural network model, to implement the wireless transmission,. 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)). Claim 10 also recites performing an operation of wireless transmission based on a neural network model, to implement the wireless transmission, wherein the neural network model is obtained by performing training by using a training data set in advance. Under the broadest reasonable interpretation, the limitations merely recite steps that apply a generic neural network as a tool, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 10 does not solve the deficiencies of claim 1.
Regarding claim 11, it is dependent upon claim 10 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 the wireless transmission method further comprises: performing, based on the training data set in any one of training manners, training to obtain the neural network model, wherein the training manners comprise: centralized training of a single terminal; centralized training of a single network-side device; joint distributed training of a plurality of terminals; joint distributed training of a plurality of network-side devices; joint distributed training of a single network-side device and a plurality of terminals; joint distributed training of a plurality of network-side devices and a plurality of terminals; and joint distributed training of a plurality of network-side devices and a single terminal. Under the broadest reasonable interpretation, merely recite steps that amount to indicating a field of use or technological environment in which to apply a judicial exception (MPEP 2106.05(h)). Therefore, claim 11 does not solve the deficiencies of claim 10.
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 sharing, in a process of a distributed training, proportions of the training data under the transmission conditions between bodies executing the distributed training. Under the broadest reasonable interpretation, the limitations recite sharing training data between elements performing training 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, it is dependent upon claim 12 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 13 recites wherein in a case that the distributed training is joint distributed training of a plurality of network-side devices, any network-side device of the plurality of network-side devices performs calculation to determine the proportions of the training data under the transmission conditions,. Under the broadest reasonable interpretation, the limitations recite determining different portions of training data based on transmission conditions 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 13 also recites and sends the proportions to other network-side devices different from the any network-side device of the plurality of network-side devices through a first specified type of interface signaling; wherein the first specified type of interface signaling comprises Xn interface signaling, N1 interface signaling, N2 interface signaling, N3 interface signaling, N4 interface signaling, N5 interface signaling, N6 interface signaling, N7 interface signaling, N8 interface signaling, N9 interface signaling, N10 interface signaling, N11 interface signaling, N12 interface signaling, N13 interface signaling, N14 interface signaling, N15 interface signaling, or N22 interface signaling. 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 13 does not solve the deficiencies of claim 12.
Regarding claim 14, it is dependent upon claim 12 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 wherein in a case that the distributed training is joint distributed training of a plurality of terminals, any terminal of the plurality of terminals performs calculation to determine the proportions of the training data under the transmission conditions,. Under the broadest reasonable interpretation, the limitations recite determining different portions of training data based on transmission conditions 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 13 also recites and sends the proportions to other terminals different from the any terminal of the plurality of terminals through a second specified type of interface signaling; wherein the second specified type of interface signaling comprises PC5 interface signaling or sidelink interface signaling. 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 13 does not solve the deficiencies of claim 12.
Regarding claim 15, it is dependent upon claim 12 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 13 recites wherein in a case that the distributed training is joint distributed training of network-side device(s) and terminal(s), any network-side device or any terminal of the network-side device(s) and the terminal(s) performs calculation to determine the proportions of the training data under the transmission conditions,. Under the broadest reasonable interpretation, the limitations recite determining different portions of training data based on transmission conditions 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 13 also recites and sends the proportions to other network-side devices or terminals different from the any network-side device or the any terminal of the network-side device(s) and the terminal(s) through a third specified type of signaling; wherein the third specified type of signaling comprises radio resource control (RRC), physical downlink control channel (PDCCH) layer-1 signaling, physical downlink shared channel (PDSCH), medium access control control element (MAC CE), system information block (SIB), Xn interface signaling, N1 interface signaling, N2 interface signaling, N3 interface signaling, N4 interface signaling, N5 interface signaling, N6 interface signaling, N7 interface signaling, N8 interface signaling, N9 interface signaling, N10 interface signaling, N11 interface signaling, N12 interface signaling, N13 interface signaling, N14 interface signaling, N15 interface signaling, N22 interface signaling, physical uplink control channel (PUCCH) layer-1 signaling, physical uplink shared channel (PUSCH), physical random access channel's messagel (PRACH's MSG1), PRACH's MSG3, PRACH's MSG A, PC5 interface signaling, or sidelink interface signaling. 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 15 does not solve the deficiencies of claim 12.
Regarding claim 16, it is dependent upon claim 12 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 further comprising: obtaining real-time data under the transmission conditions. 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 16 also recites and adjusting a trained neural network model online based on the real-time data. Under the broadest reasonable interpretation, the limitations merely recite steps that apply generic training of a neural network, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 16 does not solve the deficiencies of claim 12.
Regarding claim 17, it is dependent upon claim 16 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 17 recites obtaining the real-time data under the transmission conditions based on proportions of the training data under the transmission conditions;. 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 17 also recites and skipping, if a proportion of real-time data under any transmission condition of the transmission conditions is greater than a proportion of training data under the any transmission condition, inputting data that is in the real-time data under the any transmission condition and whose proportion is the proportion of the real-time data minus the proportion of the training data under the any transmission condition into the trained neural network model in a process of adjusting the trained neural network model online. Under the broadest reasonable interpretation, the limitations recite determining whether to skip adding new training data if obtained real time data does not satisfy a proportion condition 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 17 does not solve the deficiencies of claim 16.
Regarding claim 18, it is dependent upon claim 17 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 18 recites wherein the obtaining the real-time data under the transmission conditions comprises: collecting data of at least one of a network-side device or a terminal under the transmission conditions online as the real-time data under the transmission conditions;. 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 18 also recites and the adjusting the trained neural network model online comprises: adjusting, based on the data of the at least one of the network-side device or the terminal under the transmission conditions, the trained neural network model online through the network-side device or the terminal. Under the broadest reasonable interpretation, the limitations merely recite steps that apply a generic computing components to perform generic training, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 18 does not solve the deficiencies of claim 17.
Regarding claim 19, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A communications device, comprising a processor, a memory. The claim recites device with hardware components 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 19 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 communications device, comprising a processor, a memory, and a program or an instruction stored in the memory and executable on the processor, wherein the program or the instruction, when executed by the processor, causes the communications device 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 20, 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 20 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, wherein the non-transitory computer-readable storage medium stores a program or an instruction, and the program or the instruction, 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, 3-4, 7-12, and 16-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu, et al., Non-Patent Literature “Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission” (“Liu”).
Regarding claim 1, Liu discloses:
A training data set obtaining method, comprising: determining, based on degrees of contribution of transmission conditions to an optimization objective of a neural network, data volumes of training data under the transmission conditions; (Liu, pg. 407 col. 2, “This work concerns wireless data acquisition in edge learning. In this work, we propose a new retransmission protocol called data-importance aware ARQ, or importance ARQ for short [A training data set obtaining method, comprising: determining, based on degrees of contribution], which adapts retransmission decisions to both data importance and reliability (or equivalently the channel state) [of transmission conditions].”, and Liu, abstract, “Unlike the classic ARQ focusing merely on reliability, importance ARQ selectively retransmits a data sample based on its uncertainty which helps learning and can be measured using the model under training [to an optimization objective of a neural network, data volumes of training data under the transmission conditions;].”).
and obtaining, based on the data volumes of the training data under the transmission conditions, the training data under the transmission conditions, to form a training data set for training the neural network; (Liu, pg. 407 col. 2, “The protocol selectively retransmits a data sample based on its underlying importance for training an SVM model which is estimated using the real-time model under training [and obtaining, based on the data volumes of the training data under the transmission conditions, the training data under the transmission conditions, to form a training data set]… Subsequently, a case study on designing importance ARQ for the modern convolutional neural networks (CNN) classifier is presented [for training the neural network;].”).
wherein the degrees of contribution of the transmission conditions to the optimization objective of the neural network represent degrees of impact of the transmission conditions on a value of the optimization objective of the neural network. (Liu, abstract, “Unlike the classic ARQ focusing merely on reliability, importance ARQ selectively retransmits a data sample based on its uncertainty which helps learning and can be measured using the model under training [wherein the degrees of contribution of the transmission conditions to the optimization objective of the neural network represent degrees of impact of the transmission conditions on a value of the optimization objective of the neural network.].”).
Regarding claim 3, Liu discloses the training data set obtaining method according to claim 1. Liu further discloses wherein types of the transmission conditions comprise at least one of: a signal to noise ratio or a signal to interference plus noise ratio; a reference signal received power; a signal strength; an interference strength; a terminal moving speed; a channel parameter; a distance from a terminal to a base station; a cell size; a carrier frequency; a modulation order or a modulation and coding scheme; a cell type; an inter-site distance; weather and environment factors; antenna configuration information of a transmit end or receive end; a terminal capability or type; or a base station capability or type. (Liu, abstract, “Underpinning the proposed protocol is a derived elegant communication-learning relation between two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data uncertainty. This relation facilitates the design of a simple threshold based policy for importance ARQ [wherein types of the transmission conditions comprise at least one of: a signal to noise ratio or a signal to interference plus noise ratio;].”).
Regarding claim 4, Liu discloses the training data set obtaining method according to claim 1. Liu further discloses wherein the obtaining the training data under the transmission conditions, to form a training data set for training the neural network comprises: collecting, based on the data volumes of the training data under the transmission conditions, data under the transmission conditions and calibrating the data, to form the training data set under the transmission conditions; or collecting specified volumes of data under the transmission conditions, and selecting, based on the data volumes of the training data under the transmission conditions, partial data from the specified volumes of data and calibrating the partial data, or replenishing the specified volumes of data and calibrating replenished data, to form the training data set under the transmission conditions. (Liu, pg. 407 col. 2, “The protocol selectively retransmits a data sample based on its underlying importance for training an SVM model which is estimated using the real-time model under training [collecting, based on the data volumes of the training data under the transmission conditions, data under the transmission conditions and calibrating the data, to form the training data set under the transmission conditions;]”).
Regarding claim 7, Liu discloses the training data set obtaining method according to claim 1. Liu further discloses wherein the determining, based on degrees of contribution of transmission conditions to an optimization objective of a neural network, data volumes of training data under the transmission conditions comprises: determining, based on probability densities of the transmission conditions in an actual application, weighting coefficients corresponding to the transmission conditions; and determining, based on the degrees of contribution of the transmission conditions to the optimization objective and with reference to the weighting coefficients, the data volumes of the training data under the transmission conditions. (Liu, abstract, “edge learning can leverage the enormous real-time data [in an actual application,] generated by billions of mobile devices to train”, and Liu, pg. 410 col. 2, “The importance of a data sample for learning is usually measured by its uncertainty, as viewed by the model under training [6]. Two uncertainty measures targeting SVM and CNN respectively are introduced as follows… Uncertainty Measure for CNN: For CNN, a suitable measure is entropy, an information theoretic notion, defined as follows [22]: equation (17) where c denotes a class label and θ the set of model parameters to be learnt [and determining, based on the degrees of contribution of the transmission conditions to the optimization objective and with reference to the weighting coefficients,]. To be precise, the model parameters are the weights and biases of the neurons in CNN [weighting coefficients corresponding to the transmission conditions;].”, and Liu, pg. 414 col. 1, “for CNN, the softmax output, which gives the posterior probability for each predicted class [determining, based on probability densities of the transmission conditions], makes the entropy in (17) a more natural choice for measuring uncertainty.”, and Liu, abstract, “Liu, abstract, “Unlike the classic ARQ focusing merely on reliability, importance ARQ selectively retransmits a data sample based on its uncertainty which helps learning and can be measured using the model under training [the data volumes of the training data under the transmission conditions.]”).
Regarding claim 8, Liu discloses the training data set obtaining method according to claim 1. Liu further discloses wherein the method further comprises: sending the training data set to a target device, wherein the target device is configured to train the neural network based on the training data set. (Liu, pg. 408 col. 1 and see Figure 1, “We consider an edge learning system as shown in Fig. 1 comprising an edge server and multiple edge devices, each equipped with a single antenna. A classifier is trained at the server using a labelled dataset distributed over devices [sending the training data set to a target device, wherein the target device is configured to train the neural network based on the training data set.].”).
Regarding claim 9, Liu discloses the training data set obtaining method according to claim 8. Liu further discloses wherein the sending the training data set to a target device comprises: directly sending the training data set to the target device, or sending the training data set on which specified conversion is performed to the target device, wherein the specified conversion comprises at least one of specific quantization, specific compression, or processing that is performed according to a neural network agreed or configured in advance. (Liu, pg. 408 col. 1 and see Figure 1, “We consider an edge learning system as shown in Fig. 1 comprising an edge server and multiple edge devices, each equipped with a single antenna. A classifier is trained at the server using a labelled dataset distributed over devices [directly sending the training data set to the target device,].”).
Regarding claim 10, Liu discloses the training data set obtaining method according to claim 1. Liu further discloses A wireless transmission method, comprising: performing an operation of wireless transmission based on a neural network model, to implement the wireless transmission, wherein the neural network model is obtained by performing training by using a training data set in advance, and the training data set is obtained based on the training data set obtaining method according to claim 1. (Liu, pg. 406 col. 2, “This vision motivates Internet companies and telecommunication operators to develop technologies for deploying machine learning on the (network) edge to support intelligent mobile applications [A wireless transmission method, comprising: performing an operation of wireless transmission based on a neural network model, to implement the wireless transmission,], named as edge learning [1]–[4]. This trend aims at leveraging enormous real-time data generated by billions of edge devices to train AI models. In return, downloading the learnt intelligence onto the devices will enable them to respond to real-time events with human-like capabilities. Edge learning crosses two disciplines, wireless communication and machine learning, which cannot be decoupled as their performances are interwound under a common goal of fast learning [wherein the neural network model is obtained by performing training by using a training data set in advance, and the training data set is obtained based on the training data set obtaining method according to claim 1.].”).
Regarding claim 11, Liu discloses the wireless transmission method according to claim 10. Liu further discloses wherein before the performing an operation of wireless transmission based on a neural network model, the wireless transmission method further comprises: performing, based on the training data set in any one of training manners, training to obtain the neural network model, wherein the training manners comprise: centralized training of a single terminal; centralized training of a single network-side device; joint distributed training of a plurality of terminals; joint distributed training of a plurality of network-side devices; joint distributed training of a single network-side device and a plurality of terminals; joint distributed training of a plurality of network-side devices and a plurality of terminals; and joint distributed training of a plurality of network-side devices and a single terminal. (Liu, pg. 408 col. 1, “We consider an edge learning system as shown in Fig. 1 comprising an edge server and multiple edge devices, each equipped with a single antenna. A classifier is trained at the server using a labelled dataset distributed over devices [performing, based on the training data set in any one of training manners, training to obtain the neural network model, wherein the training manners comprise:…joint distributed training of a single network-side device and a plurality of terminals;].”).
Regarding claim 12, Liu discloses the wireless transmission method according to claim 11. Liu further discloses wherein the wireless transmission method further comprises: sharing, in a process of a distributed training, proportions of the training data under the transmission conditions between bodies executing the distributed training. (Liu, pg. 408 col. 1, “We consider an edge learning system as shown in Fig. 1 comprising an edge server and multiple edge devices, each equipped with a single antenna. A classifier is trained at the server using a labelled dataset distributed over devices [sharing, in a process of a distributed training, proportions of the training data under the transmission conditions between bodies executing the distributed training.].”).
Regarding claim 16, Liu discloses the wireless transmission method according to claim 12. Liu further discloses further comprising: obtaining real-time data under the transmission conditions and adjusting a trained neural network model online based on the real-time data. (Liu, pg. 407 col. 2, “The importance ARQ is designed to improve the quality-vs-quantity tradeoff. The protocol selectively retransmits a data sample based on its underlying importance for training an SVM model which is estimated using the real-time model under training [further comprising: obtaining real-time data under the transmission conditions and adjusting a trained neural network model online based on the real-time data.].”).
Regarding claim 17, Liu discloses the wireless transmission method according to claim 16. Liu further discloses:
obtaining the real-time data under the transmission conditions based on proportions of the training data under the transmission conditions; (Liu, pg. 407 col. 2, “The importance ARQ is designed to improve the quality-vs-quantity tradeoff. The protocol selectively retransmits a data sample based on its underlying importance for training an SVM model which is estimated using the real-time model under training [obtaining the real-time data under the transmission conditions].”, and Liu, pg. 408 col. 1, “We consider an edge learning system as shown in Fig. 1 comprising an edge server and multiple edge devices, each equipped with a single antenna. A classifier is trained at the server using a labelled dataset distributed over devices [based on proportions of the training data under the transmission conditions;].”).
and skipping, if a proportion of real-time data under any transmission condition of the transmission conditions is greater than a proportion of training data under the any transmission condition, inputting data that is in the real-time data under the any transmission condition and whose proportion is the proportion of the real-time data minus the proportion of the training data under the any transmission condition into the trained neural network model in a process of adjusting the trained neural network model online. (Liu, pg. 413 col. 2, “Protocol 1 Importance ARQ for Binary SVM Classification: Consider the acquisition of a data sample x from a scheduled edge device. The edge server repeatedly requests the device to retransmit x until the effective receive SNR satisfies SNR(T )>min(θ0 Ud (ˆx(T )) , θSNR), (29) where θSNR is a given maximum SNR; the limitation requires that if a proportion of real-time data is more than proportion of training data, the inputting of the real-time training data is skipped and a model is not updated. Liu satisfies this limitation as the claim only requires that a skipping criteria exists and Liu’s requirement that a SNR threshold must be satisfied before a retransmission can be performed is interpreted as a skipping criterion. Therefore, the skipping criteria condition is met because the claimed skipping criteria: if a proportion of real-time data is more than proportion of training data, is conditional and not required by the claim. (i.e. and skipping, if a proportion of real-time data under any transmission condition of the transmission conditions is greater than a proportion of training data under the any transmission condition, inputting data that is in the real-time data under the any transmission condition and whose proportion is the proportion of the real-time data minus the proportion of the training data under the any transmission condition into the trained neural network model in a process of adjusting the trained neural network model online.).”).
Regarding claim 18, Liu discloses the wireless transmission method according to claim 17. Liu further discloses:
wherein the obtaining the real-time data under the transmission conditions comprises: collecting data of at least one of a network-side device or a terminal under the transmission conditions online as the real-time data under the transmission conditions; (Liu, abstract, “edge learning can leverage the enormous real-time data generated by billions of mobile devices to train AI models [collecting data of at least one of a network-side device or a terminal under the transmission conditions online as the real-time data]”, and Liu, pg. 407 col. 2, “This work concerns wireless data acquisition in edge learning. In this work, we propose a new retransmission protocol called data-importance aware ARQ, or importance ARQ for short, which adapts retransmission decisions to both data importance and reliability (or equivalently the channel state) [under the transmission conditions;].”,).
and the adjusting the trained neural network model online comprises: adjusting, based on the data of the at least one of the network-side device or the terminal under the transmission conditions, the trained neural network model online through the network-side device or the terminal. (Liu, pg. 407 col. 2, “First, consider the classic classifier model of support vector machine (SVM). The importance ARQ is designed to improve the quality-vs-quantity tradeoff. The protocol selectively retransmits a data sample based on its underlying importance for training an SVM model which is estimated using the real-time model under training [and the adjusting the trained neural network model online comprises: adjusting, based on the data of the at least one of the network-side device or the terminal under the transmission conditions, the trained neural network model online through the network-side device or the terminal.].”).
Regarding claim 19, the claim is similar to claim 1 and rejected under the same rationales. Liu further discloses the additional limitations of A communications device, comprising a processor, a memory, and a program or an instruction stored in the memory and executable on the processor, wherein the program or the instruction, when executed by the processor, causes the communications device to perform: (Liu, pg. 409 see Figure 1, Figure 1 shows that the edge server is a desktop computer which is interpreted as a communications device that has a processor, a memory, and program instructions (i.e. A communications device, comprising a processor, a memory, and a program or an instruction stored in the memory and executable on the processor, wherein the program or the instruction, when executed by the processor, causes the communications device to perform:)).
Regarding claim 20, the claim is similar to claim 1 and rejected under the same rationales. Liu further discloses the additional limitations of A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores a program or an instruction, and the program or the instruction, when executed by a processor, causes the processor to perform: (Liu, pg. 409 see Figure 1, Figure 1 shows that the edge server is a desktop computer which is interpreted as a communications device that has a processor, a non-transitory computer-readable storage medium, and program instructions (i.e. A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores a program or an instruction, and the program or the instruction, 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 2 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Liu, et al., Non-Patent Literature “Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission” (“Liu”) in view of Barua, et al., “MWMOTE—Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning” (“Barua”).
Regarding claim 2, Liu discloses the training data set obtaining method of claim 1. Liu teaches a transmission condition as seen in claim 1. While Liu teaches a method for collecting training data based on sample contribution of transmission conditions, Liu does not explicitly teach:
wherein the determining, based on degrees of contribution of transmission conditions to an optimization objective of a neural network, data volumes of training data under the transmission conditions comprises: sorting the degrees of contribution of the transmission conditions;
and executing, based on equal-proportion mixing, at least one of reducing a data volume of training data under a transmission condition corresponding to a higher degree of contribution in the sorting or increasing a data volume of training data under a transmission condition corresponding to a lower degree of contribution in the sorting.
Barua teaches:
wherein the determining, based on degrees of contribution of transmission conditions to an optimization objective of a neural network, data volumes of training data under the transmission conditions comprises: sorting the degrees of contribution of the transmission conditions; (Barua, pg. 408 col. 2, “In the first phase, MWMOTE identifies the most important and hard-to-learn minority class samples from the original minority set, Smin and construct a set, Simin, by the identified samples. In the second phase, each member of Simin is given a selection weight, Sw, according to its importance in the data [sorting the degrees of contribution of the transmission conditions;].”).
and executing, based on equal-proportion mixing, at least one of reducing a data volume of training data under a transmission condition corresponding to a higher degree of contribution in the sorting or increasing a data volume of training data under a transmission condition corresponding to a lower degree of contribution in the sorting. (Barua, pg. 408 col. 2, “In the third phase, MWMOTE generates the synthetic samples from Simin using Sws and produces the output set, Somin, by adding the synthetic samples to Smin [or increasing a data volume of training data under a transmission condition corresponding to a lower degree of contribution in the sorting.].”, and Barua, abstract, “Imbalanced learning problems contain an unequal distribution of data samples among different classes and pose a challenge to any classifier as it becomes hard to learn the minority class samples. Synthetic oversampling methods address this problem by generating the synthetic minority class samples to balance the distribution between the samples of the majority and minority classes [and executing, based on equal-proportion mixing,].”).
Liu and Barua are both in the same field of endeavor (i.e. dataset balancing). 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 Liu and Barua to teach the above limitation(s). The motivation for doing so is that increasing the amount of minority class samples reduces model bias and improves accuracy (cf. Barua, pg. 405 col. 2, “The primary goal of any classifier is to reduce its classification error, i.e., to maximize its overall accuracy. However, imbalance learning problems pose a great challenge to the classifier as it becomes very hard to learn the minority class samples”).
Regarding claim 6, Liu in view of Barua teaches the training data set obtaining method of claim 2. Liu teaches a transmission condition as seen in claim 1. Barua further teaches:
wherein the executing at least one of reducing a data volume of training data under a transmission condition corresponding to a higher degree of contribution in the sorting or increasing a data volume of training data under a transmission condition corresponding to a lower degree of contribution in the sorting comprises: determining a reference degree of contribution according to the sorting, and comparing a degree of contribution of a transmission condition with the reference degree of contribution; (Barua, pg. 410 col. 2, “Observation 2: The minority class samples in a sparse cluster are more important than those in a dense cluster. From the perspective of the synthetic sample generation, the members of a sparse cluster are more important than those of a dense cluster. This is due to the fact that the dense cluster contains more information than the sparse cluster; the density of samples is a degree of contribution (i.e. determining a reference degree of contribution according to the sorting, and comparing a degree of contribution of a transmission condition with the reference degree of contribution;).”).
and executing at least one of operations according to a comparison result, wherein the operations comprise: determining, if the degree of contribution of the transmission condition is greater than the reference degree of contribution, that the degree of contribution of the transmission condition is the higher degree of contribution, and reducing a data volume of training data under the transmission condition; or determining, if the degree of contribution of the transmission condition is not greater than the reference degree of contribution, that the degree of contribution of the transmission condition is the lower degree of contribution, and increasing the data volume of the training data under the transmission condition; (Barua, pg. 410 col. 2, “Observation 2: The minority class samples in a sparse cluster are more important than those in a dense cluster. From the perspective of the synthetic sample generation, the members of a sparse cluster are more important than those of a dense cluster. This is due to the fact that the dense cluster contains more information than the sparse cluster [and executing at least one of operations according to a comparison result, wherein the operations comprise:…or determining, if the degree of contribution of the transmission condition is not greater than the reference degree of contribution, that the degree of contribution of the transmission condition is the lower degree of contribution,]. Thus, the sparse cluster requires more synthetic samples to increase its size for reducing within-class imbalance [that the degree of contribution of the transmission condition is the lower degree of contribution, and increasing the data volume of the training data under the transmission condition;].”).
wherein the reference degree of contribution is a median of the sorting, or a degree of contribution at a specified position in the sorting, or an average of the degrees of contribution in the sorting, or a degree of contribution in the sorting closest to the average. (Barua, pg. 410 col. 2, “Observation 2: The minority class samples in a sparse cluster are more important than those in a dense cluster. From the perspective of the synthetic sample generation, the members of a sparse cluster are more important than those of a dense cluster. This is due to the fact that the dense cluster contains more information than the sparse cluster; the density of samples is a degree of contribution based on positioning (i.e. wherein the reference degree of contribution is…a degree of contribution at a specified position in the sorting).”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Barua with the teachings of Liu for the same reasons disclosed in claim 2.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Liu, et al., Non-Patent Literature “Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission” (“Liu”) in view of Barua, et al., “MWMOTE—Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning” (“Barua”) and further in view of Dalli, et al., US Pre-Grant Publication US20220012591A1 (“Dalli”).
Regarding claim 5, Liu in view of Barua teaches the training data set obtaining method according to claim 2. Liu teaches a transmission condition in claim 1. Barua further teaches:
wherein the executing at least one of reducing a data volume of training data under a transmission condition corresponding to a higher degree of contribution in the sorting or increasing a data volume of training data under a transmission condition corresponding to a lower degree of contribution in the sorting comprises: executing, according to a rule, at least one of reducing a data volume of training data under a transmission condition corresponding to a higher degree of contribution in the sorting or increasing a data volume of training data under a transmission condition corresponding to a lower degree of contribution in the sorting, (Barua, pg. 408 col. 2, “In the first phase, MWMOTE identifies the most important and hard-to-learn minority class samples from the original minority set, Smin and construct a set, Simin, by the identified samples. In the second phase, each member of Simin is given a selection weight, Sw, according to its importance in the data. In the third phase, MWMOTE generates the synthetic samples from Simin using Sws and produces the output set, Somin, by adding the synthetic samples to Smin [executing, according to a rule, at least one of…or increasing a data volume of training data under a transmission condition corresponding to a lower degree of contribution in the sorting,].”).
wherein the rule comprises: that a larger value of the higher degree of contribution indicates a larger amplitude of the reducing; and that a smaller value of the lower degree of contribution indicates a larger amplitude of the increasing; (Barua, pg. 410 col. 1, “Hence, it is necessary for assigning weights to the samples according to their importance. A large weight implies that the sample requires many synthetic samples to be generated from and nearby it. This is due to the insufficiency of information in its minority concept [and that a smaller value of the lower degree of contribution indicates a larger amplitude of the increasing;].”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Barua with the teachings of Liu for the same reasons disclosed in claim 2.
While Liu in view of Barua teaches a method of increasing samples for minority classes to boost contribution, the combination does not explicitly teach:
wherein in a case that a result of the sorting is an ascending order, the data volumes of the training data under the transmission conditions decrease in a direction of the sorting; and in a case that a result of the sorting is a descending order, the data volumes of the training data under the transmission conditions increase in the direction of the sorting.
Dalli teaches wherein in a case that a result of the sorting is an ascending order, the data volumes of the training data under the transmission conditions decrease in a direction of the sorting; and in a case that a result of the sorting is a descending order, the data volumes of the training data under the transmission conditions increase in the direction of the sorting. (Dalli, ⁋63, “A feature importance vector I may represent the feature importance in a global manner such that I={β1, β2+β10, β3+β5, β7, β8}, corresponding to the features {x, y, xy, x2, y2}. The vector I may be sorted in descending order such that the most prominent feature is placed in the beginning of the vector; sorting in descending order is interpreted as also sorting in ascending order as a descending order is a flipped ascending order (i.e. wherein in a case that a result of the sorting is an ascending order, the data volumes of the training data under the transmission conditions decrease in a direction of the sorting; and in a case that a result of the sorting is a descending order, the data volumes of the training data under the transmission conditions increase in the direction of the sorting.).”).
Liu, in view of Barua, and Dalli are both in the same field of endeavor (i.e. dataset bias). 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 Liu, in view of Barua, and Dalli to teach the above limitation(s). The motivation for doing so is that sorting samples by importance improves model explainability by clearly identifying sample bias (cf. Dalli, ⁋3-4).
Claims 13 is rejected under 35 U.S.C. 103 as being unpatentable over Liu, et al., Non-Patent Literature “Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission” (“Liu”) in view of Lyu, et al., Non-Patent Literature “Optimal Online Data Partitioning for Geo-Distributed Machine Learning in Edge of Wireless Networks” (“Lyu”) and further in view of ShareTechnote, Non-Patent literature “NR Network Architecture/Network Interface - N1/N1-Mode” (“ST”).
Regarding claim 13, Liu discloses the wireless transmission method according to claim 12. Liu teaches a transmission condition as seen in claim 1. While Liu teaches a method for collecting training data based on sample contribution of transmission conditions for a wireless transmission operation, Liu does not explicitly teach:
wherein in a case that the distributed training is joint distributed training of a plurality of network-side devices, any network-side device of the plurality of network-side devices performs calculation to determine the proportions of the training data under the transmission conditions, and sends the proportions to other network-side devices different from the any network-side device of the plurality of network-side devices through a first specified type of interface signaling;
wherein the first specified type of interface signaling comprises Xn interface signaling, N1 interface signaling, N2 interface signaling, N3 interface signaling, N4 interface signaling, N5 interface signaling, N6 interface signaling, N7 interface signaling, N8 interface signaling, N9 interface signaling, N10 interface signaling, N11 interface signaling, N12 interface signaling, N13 interface signaling, N14 interface signaling, N15 interface signaling, or N22 interface signaling.
Lyu teaches:
wherein in a case that the distributed training is joint distributed training of a plurality of network-side devices, any network-side device of the plurality of network-side devices performs calculation to determine the proportions of the training data under the transmission conditions, (Lyu, pg. 2393-2394, “As shown in Fig. 1, geo-distributed machine learning operates at multiple network edge servers (also referred to as “workers”), such as gateways, routers, switches, and base stations [wherein in a case that the distributed training is joint distributed training of a plurality of network-side devices,]. Data needs to be partitioned at the data sources for the different workers. The data partitioning must be carried out online, adapting to the changing network conditions and the different availability of computing resources at the workers [any network-side device of the plurality of network-side devices performs calculation to determine the proportions of the training data under the transmission conditions,].”).
and sends the proportions to other network-side devices different from the any network-side device of the plurality of network-side devices through a first specified type of interface signaling; (Lyu, pg. 2394 col. 1, “As a result, the representativeness of the partitioned data can be preserved, the local deep learning models can be kept from deviating from the global model, and the workers can avoid frequent synchronizations and the associated cost of excessive networking and signaling overhead. However, the costs of sending the same data to different workers [and sends the proportions to other network-side devices different from the any network-side device of the plurality of network-side devices] can differ more than ten times [4]; sending data between each worker is interpreted as using a type of signaling (i.e. through a first specified type of interface signaling;).”).
Liu and Lyu are both in the same field of endeavor (i.e. data unbalance in distributed environments). 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 Liu and Lyu to teach the above limitation(s). The motivation for doing so is that syncing data between different sources of data in a distributed network improves model’s robustness (cf. Lyu, pg. 2395 col. 1, “Synchronization among workers is critical to the convergence of distributed machine learning based on the parameter server framework [18]–[20]. Existing approaches [8]–[10] randomly shuffled and partitioned the data offline for different workers after each epoch of training, and would incur prohibitive communication cost in the geo-distributed machine learning of interest.”).
While Liu in view of Lyu teaches a method for balancing data between entities in a wireless network, the combination does not explicitly teach:
wherein the first specified type of interface signaling comprises Xn interface signaling, N1 interface signaling, N2 interface signaling, N3 interface signaling, N4 interface signaling, N5 interface signaling, N6 interface signaling, N7 interface signaling, N8 interface signaling, N9 interface signaling, N10 interface signaling, N11 interface signaling, N12 interface signaling, N13 interface signaling, N14 interface signaling, N15 interface signaling, or N22 interface signaling.
ST teaches wherein the first specified type of interface signaling comprises Xn interface signaling, N1 interface signaling, N2 interface signaling, N3 interface signaling, N4 interface signaling, N5 interface signaling, N6 interface signaling, N7 interface signaling, N8 interface signaling, N9 interface signaling, N10 interface signaling, N11 interface signaling, N12 interface signaling, N13 interface signaling, N14 interface signaling, N15 interface signaling, or N22 interface signaling. (ST, pg. 1, “N1 is the interface between UE and AMF. N1 represents the combined path UE <--> Access Network and Access Network <--> AMF. The Access Network can be a 3GPP based (e.g, gNB) or non 3GPP based. Most of NAS signaling is going through N1 [wherein the third specified type of signaling comprises…N1 interface signaling,].”).
Liu, in view of Lyu, and ST are both in the same field of endeavor (i.e. wireless communications). 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 Liu, in view of Lyu, and ST to teach the above limitation(s). The motivation for doing so is that using N1 interface signaling allows for Non-Access Stratum (NAS) signaling between entities in a wireless network (cf. ST, pg. 1, “Most of NAS signaling is going through N1.”).
Claims 14 is rejected under 35 U.S.C. 103 as being unpatentable over Liu, et al., Non-Patent Literature “Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission” (“Liu”) in view of Cai, et al., Non-Patent Literature “D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless Network Edge” (“Cai”) and further in view of Feron, Non-Patent Literature “lte-sidelink” (“Feron”).
Regarding claim 14, Liu discloses the wireless transmission method according to claim 12. Liu teaches a transmission condition as seen in claim 1. While Liu teaches a method for collecting training data based on sample contribution of transmission conditions for a wireless transmission operation, Liu does not explicitly teach:
wherein in a case that the distributed training is joint distributed training of a plurality of terminals, any terminal of the plurality of terminals performs calculation to determine the proportions of the training data under the transmission conditions, and sends the proportions to other terminals different from the any terminal of the plurality of terminals through a second specified type of interface signaling;
wherein the second specified type of interface signaling comprises PC5 interface signaling or sidelink interface signaling.
Cai teaches wherein in a case that the distributed training is joint distributed training of a plurality of terminals, any terminal of the plurality of terminals performs calculation to determine the proportions of the training data under the transmission conditions, and sends the proportions to other terminals different from the any terminal of the plurality of terminals through a second specified type of interface signaling; (Cai, pg. 1457 col. 2, “Motivated by this, we propose a new D2D-enabled data sharing design for mobile edge learning [wherein in a case that the distributed training is joint distributed training of a plurality of terminals,], which allows edge devices to share their data samples over D2D communication links. By properly controlling the amounts of data samples exchanged [and sends the proportions to other terminals different from the any terminal of the plurality of terminals through a second specified type of interface signaling;], this design can not only adjust the computation loads at devices for enhancing the training speed, but also reshape the data distribution (if data samples at edge devices are non-IID) for enhancing the training accuracy [any terminal of the plurality of terminals performs calculation to determine the proportions of the training data under the transmission conditions,].”).
Liu and Cai are both in the same field of endeavor (i.e. edge learning). 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 Liu and Cai to teach the above limitation(s). The motivation for doing so is that sharing data between devices using D2D communications in an edge learning system improves learning efficiency (cf. Cai, pg. 1457 col. 2, “Motivated by this, we propose a new D2D-enabled data sharing design for mobile edge learning, which allows edge devices to share their data samples over D2D communication links. By properly controlling the amounts of data samples exchanged, this design can not only adjust the computation loads at devices for enhancing the training speed, but also reshape the data distribution (if data samples at edge devices are non-IID) for enhancing the training accuracy.”).
While Liu in view of Cai teaches edge learning that shares training data between mobile devices, the combination does not explicitly teach:
wherein the second specified type of interface signaling comprises PC5 interface signaling or sidelink interface signaling.
Feron teaches wherein the second specified type of interface signaling comprises PC5 interface signaling or sidelink interface signaling. (Feron, pg. 3, “Sidelink is a new LTE feature introduced in 3GPP Release 12 aiming at enabling device-to-device (D2D) communications within legacy cellular-based LTE radio access networks [wherein the second specified type of interface signaling comprises PC5 interface signaling or sidelink interface signaling.].”).
Liu, in view of Cai, and Feron are both in the same field of endeavor (i.e. wireless communication). 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 Liu, in view of Cai, and Feron to teach the above limitation(s). The motivation for doing so is that sidelink interface signaling enables the use of D2D communications (cf. Feron, pg. 3, “Sidelink is a new LTE feature introduced in 3GPP Release 12 aiming at enabling device-to-device (D2D) communications”).
Claims 15 is rejected under 35 U.S.C. 103 as being unpatentable over Liu, et al., Non-Patent Literature “Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission” (“Liu”) in view of ShareTechnote, Non-Patent literature “NR Network Architecture/Network Interface - N1/N1-Mode” (“ST”).
Regarding claim 15, Liu discloses the wireless transmission method according to claim 12. Liu further discloses:
wherein in a case that the distributed training is joint distributed training of network-side device(s) and terminal(s), any network-side device or any terminal of the network-side device(s) and the terminal(s) performs calculation to determine the proportions of the training data under the transmission conditions, (Liu, pg. 408 col. 1, “We consider an edge learning system as shown in Fig. 1 comprising an edge server and multiple edge devices, each equipped with a single antenna. A classifier is trained at the server using a labelled dataset distributed over devices [wherein in a case that the distributed training is joint distributed training of network-side device(s) and terminal(s),].”, and Liu, pg. 408 col. 2, “Considering latency constrained data acquisition, a transmission budget is defined as a given transmission duration of N channel uses, over which a dataset is acquired for training the classifier model. The budget represents the total radio resource utilization for the learning task [any network-side device or any terminal of the network-side device(s) and the terminal(s) performs calculation to determine the proportions of the training data under the transmission conditions,].”).
and sends the proportions to other network-side devices or terminals different from the any network-side device or the any terminal of the network-side device(s) and the terminal(s) through a third specified type of signaling; (Liu, pg. 408 col. 1, “We consider an edge learning system as shown in Fig. 1 comprising an edge server and multiple edge devices, each equipped with a single antenna. A classifier is trained at the server using a labelled dataset distributed over devices; the edge devices sending their data to the edge server is interpreted as using a type of signaling (i.e. and sends the proportions to other network-side devices or terminals different from the any network-side device or the any terminal of the network-side device(s) and the terminal(s) through a third specified type of signaling;).”
While Liu teaches a distributed learning system of network devices and terminals, Liu does not explicitly teach:
wherein the third specified type of signaling comprises radio resource control (RRC), physical downlink control channel (PDCCH) layer-1 signaling, physical downlink shared channel (PDSCH), medium access control control element (MAC CE), system information block (SIB), Xn interface signaling, N1 interface signaling, N2 interface signaling, N3 interface signaling, N4 interface signaling, N5 interface signaling, N6 interface signaling, N7 interface signaling, N8 interface signaling, N9 interface signaling, N10 interface signaling, N11 interface signaling, N12 interface signaling, N13 interface signaling, N14 interface signaling, N15 interface signaling, N22 interface signaling, physical uplink control channel (PUCCH) layer-1 signaling, physical uplink shared channel (PUSCH), physical random access channel's messagel (PRACH's MSG1), PRACH's MSG3, PRACH's MSG A, PC5 interface signaling, or sidelink interface signaling.
ST teaches wherein the third specified type of signaling comprises radio resource control (RRC), physical downlink control channel (PDCCH) layer-1 signaling, physical downlink shared channel (PDSCH), medium access control control element (MAC CE), system information block (SIB), Xn interface signaling, N1 interface signaling, N2 interface signaling, N3 interface signaling, N4 interface signaling, N5 interface signaling, N6 interface signaling, N7 interface signaling, N8 interface signaling, N9 interface signaling, N10 interface signaling, N11 interface signaling, N12 interface signaling, N13 interface signaling, N14 interface signaling, N15 interface signaling, N22 interface signaling, physical uplink control channel (PUCCH) layer-1 signaling, physical uplink shared channel (PUSCH), physical random access channel's messagel (PRACH's MSG1), PRACH's MSG3, PRACH's MSG A, PC5 interface signaling, or sidelink interface signaling. (ST, pg. 1, “N1 is the interface between UE and AMF. N1 represents the combined path UE <--> Access Network and Access Network <--> AMF. The Access Network can be a 3GPP based (e.g, gNB) or non 3GPP based. Most of NAS signaling is going through N1 [wherein the third specified type of signaling comprises…N1 interface signaling,].”).
Liu and ST are both in the same field of endeavor (i.e. wireless communications). 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 Liu and ST to teach the above limitation(s). The motivation for doing so is that using N1 interface signaling allows for Non-Access Stratum (NAS) signaling between entities in a wireless network (cf. ST, pg. 1, “Most of NAS signaling is going through N1.”).
Conclusion
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/N.S.W./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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