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 .
Drawings
The drawings filed on 12/18/2023 are accepted.
Specification
The specification filed on 12/18/2023 is accepted.
Information Disclosure Statement
The examiner has considered the information disclosure statements (IDS) submitted on 01/04/2024 and 02/04/2025.
Claim Objections
Claims 1, 4 and 7 are objected to because of the following informalities:
In claim 1, line 7, limitation “the training uses using” should read “the training using”.
In claim 4, line 5, limitation “paus criteria” should read “pause criteria”.
In claim 4, line 6, limitation “other, second wireless devices” should read “other wireless devices”.
In claim 7, line 1, limitation “herein” should read “wherein”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 13 and 15 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.
Claim 13 recites the limitation “the instructing information” in lines 2-3. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation “the instructing information” in lines 1-2. There is insufficient antecedent basis for this limitation in the claim.
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 1-10, 12-15, 18-21, 34 and 49 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “predict certain output values”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “predict” in the context of this claim encompasses the user performs a method to determine an outcome based on some input data.
The limitation of “determining, based on the received reporting information, regarding application of the trained ML model, the ready criteria relating provision of the predicted output values to provision of actual output values provided by the first wireless device according to a first functionality”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determining” in the context of this claim encompasses the user determines a similarity by comparing the two outcome values.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “one or more network nodes”, “a first wireless device” and “a machine learning, ML, model”. The additional elements are recited at a high-level of generality (i.e., as a generic device performing the generic computer functions) such that they amount no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The additional element of “sending, to the first wireless device, configuration information for configuring the first wireless device to, during operation with the wireless communication network” amounts to insignificant extra-solution activities of data transmitting, which does not amount to significantly more than the abstract idea (MPEP 2106.05(g)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim recites the additional element of “train a machine learning, ML, model to predict certain output values, the training uses using certain input training data that at least partly are determined by operative conditions of the first wireless device and using certain desired output values with the input training data, the training ending when the ML model being trained fulfills certain one or more ready criteria”. This additional element is recited at a high-level of generality (i.e., as a generic device performing the generic computer functions of predicting) such that it amounts to no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The additional element of “receiving, from the first wireless device, reporting information at least indicating that the first wireless device has trained the ML model and fulfilled the certain one or more ready criteria” amounts to insignificant extra-solution activities of data outputting (MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “one or more network nodes”, “a first wireless device” and “a machine learning, ML, model” to perform the “predicting” step amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The additional element of “sending, to the first wireless device, configuration information for configuring the first wireless device to, during operation with the wireless communication network” is recited at a high level of generality and amounts to extra-solution activity of data gathering and/or transmitting (MPEP 2106.05(g)). The courts have found limitations directed to receiving and transmitting information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”).
The additional element of “train a machine learning, ML, model to predict certain output values, the training uses using certain input training data that at least partly are determined by operative conditions of the first wireless device and using certain desired output values with the input training data, the training ending when the ML model being trained fulfills certain one or more ready criteria” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim recites the additional element of “receiving, from the first wireless device, reporting information at least indicating that the first wireless device has trained the ML model and fulfilled the certain one or more ready criteria”. The courts have similarly found limitations directed to [displaying, presenting, outputting] a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.")
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites an additional element of “sending, to the first wireless device in response to the determination regarding application of the trained ML model, instructing information instructing the first wireless device regarding application of the trained ML model”. This limitation amounts to insignificant extra-solution activities of data transmitting, which does not amount to significantly more than the abstract idea (MPEP 2106.05(g)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “sending, to the first wireless device in response to the determination regarding application of the trained ML model, instructing information instructing the first wireless device regarding application of the trained ML model” is recited at a high level of generality and amounts to extra-solution activity of data gathering and/or transmitting (MPEP 2106.05(g)). The courts have found limitations directed to receiving and transmitting information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”).
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The additional element of “receiving, from the first wireless device in response to the sent instructing information, prediction based information being information based on predicted output values resulting from application of the trained ML model by the first wireless device” amounts to insignificant extra-solution activities of data outputting (MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional element of “receiving, from the first wireless device in response to the sent instructing information, prediction based information being information based on predicted output values resulting from application of the trained ML model by the first wireless device”. The courts have similarly found limitations directed to [displaying, presenting, outputting] a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.")
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites an additional element of “the configuration information identifies one or more of the following: the ML model to be trained, at least part of the input training data to be used during the training, one or more of the desired output values to be used during the training, one or more of the ready criteria, one or more training stop or paus criteria, one or more parameters of other trained ML models that have been trained by one or more other, second wireless devices and which other trained ML models are compatible with and based on the same type of parameters as the ML model subject for the training by the fist wireless device”. This limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not integrate into a practical application (see MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “the configuration information identifies one or more of the following: the ML model to be trained, at least part of the input training data to be used during the training, one or more of the desired output values to be used during the training, one or more of the ready criteria, one or more training stop or paus criteria, one or more parameters of other trained ML models that have been trained by one or more other, second wireless devices and which other trained ML models are compatible with and based on the same type of parameters as the ML model subject for the training by the fist wireless device” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The additional element of “at least part of the desired output values are provided during the training by the first wireless device according to a first functionality for providing output values” amounts to insignificant extra-solution activities of data outputting (MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional element of “at least part of the desired output values are provided during the training by the first wireless device according to a first functionality for providing output values”. The courts have similarly found limitations directed to [displaying, presenting, outputting] a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.")
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites an additional element of “the output values are values for forming information that the first wireless device is configured to transmit to the wireless communication network when operating with the wireless communication network”. This limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not integrate into a practical application (see MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “the output values are values for forming information that the first wireless device is configured to transmit to the wireless communication network when operating with the wireless communication network” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites an additional element of “the output values are values for use by the wireless communication network for serving the first wireless device”. This limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not integrate into a practical application (see MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “the output values are values for use by the wireless communication network for serving the first wireless device” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites an additional element of “the output values relate to one or more of the following: allocation of resources for transmission in one or both of the uplink and the downlink, scheduling of resources in the one of the uplink and the downlink, handover regarding the first wireless device”. This limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not integrate into a practical application (see MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “the output values relate to one or more of the following: allocation of resources for transmission in one or both of the uplink and the downlink, scheduling of resources in the one of the uplink and the downlink, handover regarding the first wireless device” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites an additional element of “the ready criteria relate to accuracy of predicted output values”. This limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not integrate into a practical application (see MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “the ready criteria relate to accuracy of predicted output values” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites an additional element of “the ready criteria relate to timely provision of predicted output values”. This limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not integrate into a practical application (see MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “the ready criteria relate to timely provision of predicted output values” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The additional element of “the reporting information identifies one or more of the following: accuracy obtained by the trained ML model, one or more model parameters of the trained ML model, the trained ML model” amounts to insignificant extra-solution activities of data outputting or displaying (MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional element of “the reporting information identifies one or more of the following: accuracy obtained by the trained ML model, one or more model parameters of the trained ML model, the trained ML model”. The courts have similarly found limitations directed to [displaying, presenting, outputting] a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.")
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites the method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “the determination regarding application of the trained ML model and the instructing information relate to one or more of the following: instructing the first wireless device to apply the ML model and thereby provide predicted output values accordingly, instructing the first wireless device regarding when to apply the trained ML model, instructing the first wireless device to provide output values additionally or alternatively according to another functionality, instructing the first wireless device to retrain the ML model”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “the determination” in the context of this claim encompasses the user selecting a certain model/method to determine/calculate the outcome.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Indeed, the claim does not recite any additional element beside the limitation that can be performed in a human mind. The claim is not patent eligible.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claim does not recite any additional element beside the limitation that can be performed in a human mind. The claim is not patent eligible.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites the method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “the determination regarding application of the trained ML model is further based on other reporting information”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “the determination” in the context of this claim encompasses the user selecting a model/method to determine/calculate the outcome based on some factors.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites an additional element of “other reporting information that the one or more network nodes have received from one or more second wireless devices relating to one or more other trained ML models that have been trained by the second wireless devices, respectively”. This limitation amounts to insignificant extra-solution activities of data gathering, which does not amount to significantly more than the abstract idea (MPEP 2106.05(g)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “other reporting information that the one or more network nodes have received from one or more second wireless devices relating to one or more other trained ML models that have been trained by the second wireless devices, respectively” is recited at a high level of generality and amounts to extra-solution activity of data gathering and/or transmitting (MPEP 2106.05(g)). The courts have found limitations directed to receiving and transmitting information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”).
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites an additional element of “the instructing information identifies one or more of the following: one or more model parameters of another second trained ML model, comprised in the other trained ML models, for replacing corresponding one or more model parameters of the ML model trained by the first wireless device; and another second trained ML model, comprised in the other trained ML models, for replacing the ML model trained by the first wireless device”. This limitation amounts to insignificant extra-solution activities of data gathering, which does not amount to significantly more than the abstract idea (MPEP 2106.05(g)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “the instructing information identifies one or more of the following: one or more model parameters of another second trained ML model, comprised in the other trained ML models, for replacing corresponding one or more model parameters of the ML model trained by the first wireless device; and another second trained ML model, comprised in the other trained ML models, for replacing the ML model trained by the first wireless device” is recited at a high level of generality and amounts to extra-solution activity of data gathering and/or transmitting (MPEP 2106.05(g)). The courts have found limitations directed to receiving and transmitting information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”).
Claim 18 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “predict certain output values”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “predict” in the context of this claim encompasses the user performs a method to determine an outcome based on some input data.
The limitation of “the ready criteria relating provision of the predicted output values to provision of actual output values provided by the first wireless device according to a first functionality”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “relating” in the context of this claim encompasses the user determines a similarity by comparing the two outcome values.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “a first wireless device”, “one or more network nodes” and “a machine learning, ML, model”. The additional elements are recited at a high-level of generality (i.e., as a generic device performing the generic computer functions) such that they amount no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The additional element of “receiving, from one or more network nodes of the wireless communication network, configuration information for configuring the first wireless device to, during operation with the wireless communication network” amounts to insignificant extra-solution activities of data gathering, which does not amount to significantly more than the abstract idea (MPEP 2106.05(g)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim recites the additional element of “train a machine learning, ML, model to predict certain output values, the training using certain input training data that at least partly are determined by operative conditions of the first wireless device and using certain desired output values with the input training data, the training ending when the ML model being trained fulfills certain one or more ready criteria”. This additional element is recited at a high-level of generality (i.e., as a generic device performing the generic computer functions of predicting) such that it amounts to no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The additional element of “sending, to one or more network nodes of the wireless communication network, reporting information at least indicating that the first wireless device has trained the ML model and fulfilled the certain one or more ready criteria” amounts to insignificant extra-solution activities of data outputting (MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “a first wireless device”, “one or more network nodes” and “a machine learning, ML, model” to perform the “predicting” step amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The additional element of “receiving, from one or more network nodes of the wireless communication network, configuration information for configuring the first wireless device to, during operation with the wireless communication network” is recited at a high level of generality and amounts to extra-solution activity of data gathering and/or transmitting (MPEP 2106.05(g)). The courts have found limitations directed to receiving and transmitting information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”).
The additional element of “train a machine learning, ML, model to predict certain output values, the training using certain input training data that at least partly are determined by operative conditions of the first wireless device and using certain desired output values with the input training data, the training ending when the ML model being trained fulfills certain one or more ready criteria” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim recites the additional element of “sending, to one or more network nodes of the wireless communication network, reporting information at least indicating that the first wireless device has trained the ML model and fulfilled the certain one or more ready criteria”. The courts have similarly found limitations directed to [displaying, presenting, outputting] a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.")
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites an additional element of “receiving, from one or more network nodes of the wireless communication network in response to the sent reporting information, instructing information instructing the first wireless device regarding application of the trained ML model”. This limitation amounts to insignificant extra-solution activities of data gathering and/or transmitting, which does not amount to significantly more than the abstract idea (MPEP 2106.05(g)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “receiving, from one or more network nodes of the wireless communication network in response to the sent reporting information, instructing information instructing the first wireless device regarding application of the trained ML model” is recited at a high level of generality and amounts to extra-solution activity of data gathering and/or transmitting (MPEP 2106.05(g)). The courts have found limitations directed to receiving and transmitting information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”).
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites the additional element of “applying, in response to the received instructing information, the trained ML model, thereby providing predicted output values”. This additional element is recited at a high-level of generality (i.e., as a generic device performing the generic computer functions of predicting) such that it amounts to no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The additional element of “sending, to one or more network nodes of the wireless communication network, prediction based information being information based on the predicted output values resulting from application of the trained ML model” amounts to insignificant extra-solution activities of data outputting (MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “applying, in response to the received instructing information, the trained ML model, thereby providing predicted output values” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim recites the additional element of “sending, to one or more network nodes of the wireless communication network, prediction based information being information based on the predicted output values resulting from application of the trained ML model”. The courts have similarly found limitations directed to [displaying, presenting, outputting] a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.")
Claim 21 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites an additional element of “the configuration information identifies one or more of the following: the ML model to be trained, at least part of the input training data to be used during the training, one or more of the desired output values to be used during the training, one or more of the ready criteria, one or more training stop or paus criteria, one or more parameters of other trained ML models that have been trained by one or more other, second wireless devices and which other trained ML models are compatible with and based on the same type of parameters as the ML model subject for the training by the fist wireless device”. This limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not integrate into a practical application (see MPEP 2106.05(h)).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “the configuration information identifies one or more of the following: the ML model to be trained, at least part of the input training data to be used during the training, one or more of the desired output values to be used during the training, one or more of the ready criteria, one or more training stop or paus criteria, one or more parameters of other trained ML models that have been trained by one or more other, second wireless devices and which other trained ML models are compatible with and based on the same type of parameters as the ML model subject for the training by the fist wireless device” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
Claim 34 is substantially similar to claim 1 and thus rejected for similar reasons as claim 1.
Claim 49 is substantially similar to claim 18 and thus rejected for similar reasons as claim 18.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-9, 12-13, 18-19, 21, 34 and 49 is rejected under 35 U.S.C. 103 as being unpatentable over Tomala et al. (US Pub. 2022/0279341) in view of Mu et al. (US Pub. 2024/0023082).
As per claim 1, Tomala teaches a method, performed by one or more network nodes, for supporting value prediction by a first wireless device being served by a wireless communication network [Fig. 1, abstract, “the method may include receiving, by a user equipment (UE), machine learning (ML) configuration from a network node; collecting, by the user equipment (UE), machine learning (ML) data based at least on the machine learning (ML) configuration received from the network node”], the method comprising:
sending, to the first wireless device, configuration information for configuring the first wireless device to, during operation with the wireless communication network, train a machine learning, ML, model to predict certain output values [Fig. 1, abstract, “the method may include receiving, by a user equipment (UE), machine learning (ML) configuration from a network node; collecting, by the user equipment (UE), machine learning (ML) data based at least on the machine learning (ML) configuration received from the network node”; examiner interprets a process of collecting ML data by the user equipment as a process of predicting output values using ML configuration from a network node];
receiving, from the first wireless device, reporting information [abstract, “transmitting, by the user equipment (UE), the collected machine learning (ML) data to the network node”];
Tomala does not explicitly teach
the training uses using certain input training data that at least partly are determined by operative conditions of the first wireless device and using certain desired output values with the input training data, the training ending when the ML model being trained fulfills certain one or more ready criteria;
receiving, from the first wireless device, reporting information at least indicating that the first wireless device has trained the ML model and fulfilled the certain one or more ready criteria; and
determining, based on the received reporting information, regarding application of the trained ML model, the ready criteria relating provision of the predicted output values to provision of actual output values provided by the first wireless device according to a first functionality.
Mu teaches
the training uses using certain input training data that at least partly are determined by operative conditions of the first wireless device and using certain desired output values with the input training data [Fig. 8, paragraph 0059, “the UE perceives and collects data to generate the local dataset, and processes the local dataset to generate a local training set; the UE randomly initializes a local model parameter, performs local learning model training by using a local training set … the above process is repeated until the model accuracy satisfies the requirements”; It can be seen that the training data includes certain desired output so that the predicted output from the model is compared to the desired output to determine whether the model accuracy is satisfied], the training ending when the ML model being trained fulfills certain one or more ready criteria [Fig. 8, paragraph 0059, “the UE perceives and collects data to generate the local dataset, and processes the local dataset to generate a local training set; the UE randomly initializes a local model parameter, performs local learning model training by using a local training set … the above process is repeated until the model accuracy satisfies the requirements. When the model training is completed, each base station reports the model training result and the statistical characteristic of the training data to the network”; paragraph 0100, “requirement of the OAM includes a specific requirement for model accuracy … when the global learning model satisfies the subscription requirement of the OAM, it means that the current global learning model has reached sufficient accuracy, and as a result, the federated learning may be terminated, and the global learning model that can be used is obtained”; examiner interprets the one or more ready criteria as the model accuracy requirement];
receiving, from the first wireless device, reporting information at least indicating that the first wireless device has trained the ML model and fulfilled the certain one or more ready criteria [paragraph 0045, “the base station may establish communication connections with a plurality of UE and schedule at least a portion of the UE for performing the federated learning. The process of the federated learning requires the local data of the UE to satisfy certain requirements”; Fig. 8, paragraph 0059, “the UE perceives and collects data to generate the local dataset, and processes the local dataset to generate a local training set; the UE randomly initializes a local model parameter, performs local learning model training by using a local training set, and uploads the training result to the core network or data center; the base station requests the local training result of the UE from the core network or data center, and obtains an updated result of the global learning model by performing the federated average learning using the local learning result of each UE; the base station feeds back the updated result to the UE via the network, and the UE fine tunes the local model based on the result of the feedback; the above process is repeated until the model accuracy satisfies the requirements. When the model training is completed, each base station reports the model training result and the statistical characteristic of the training data to the network”]; and
determining, based on the received reporting information, regarding application of the trained ML model, the ready criteria relating provision of the predicted output values to provision of actual output values provided by the first wireless device according to a first functionality [paragraph 0045, “the base station may establish communication connections with a plurality of UE and schedule at least a portion of the UE for performing the federated learning. The process of the federated learning requires the local data of the UE to satisfy certain requirements”; Fig. 8, paragraph 0059, “When the model training is completed, each base station reports the model training result and the statistical characteristic of the training data to the network, and the network selects the appropriate model according to the characteristic of the task data”; paragraph 0100, “requirement of the OAM includes a specific requirement for model accuracy … when the global learning model satisfies the subscription requirement of the OAM, it means that the current global learning model has reached sufficient accuracy, and as a result, the federated learning may be terminated, and the global learning model that can be used is obtained”; paragraph 0185, “The base station monitors the training effectiveness of the global learning model. When a certain round of the updated model satisfies the subscription requirement of the OAM, the base station sends a signal to each UE to terminate the training, and the federated learning ends. When the model training is completed, each base station reports the result of the model training and the statistical characteristic of the training data to the OAM, the OAM selects the appropriate model according to the characteristic of the task data”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for collecting data at a UE for machine learning of Tomala to include the training uses using certain input training data and desired output values with the input training data, the training ending when the ML model being trained fulfills certain one or more ready criteria, receiving reporting information at least indicating that the first wireless device has trained the ML model and fulfilled the certain one or more ready criteria, and determining the ready criteria relating provision of the predicted output values to provision of actual output values provided by the first wireless device according to a first functionality of Mu. Doing so would help selecting one or more suitable base station models based on the statistical information and the model accuracy for performing a task (Mu, 0242).
As per claim 2, Tomala and Mu teach the method as claimed in claim 1.
Mu further teaches
sending, to the first wireless device in response to the determination regarding application of the trained ML model, instructing information instructing the first wireless device regarding application of the trained ML model [paragraph 0059, “the UE perceives and collects data to generate the local dataset, and processes the local dataset to generate a local training set; the UE randomly initializes a local model parameter, performs local learning model training by using a local training set, and uploads the training result to the core network or data center; the base station requests the local training result of the UE from the core network or data center, and obtains an updated result of the global learning model by performing the federated average learning using the local learning result of each UE; the base station feeds back the updated result to the UE via the network, and the UE fine tunes the local model based on the result of the feedback; the above process is repeated until the model accuracy satisfies the requirements. When the model training is completed, each base station reports the model training result and the statistical characteristic of the training data to the network”; paragraphs 0169-0170, “when the global learning model satisfies the subscription requirement of the OAM, it means that the current global learning model has reached sufficient accuracy, and as a result, the federated learning may be terminated, and the global learning model that can be used is obtained … the UE may also obtain the final global learning model for local use”; paragraph 0178, “the base station determines to schedule the UE for performing the federated learning based on the distribution characteristic of the local dataset reported by the UE, the UE may be determined to participate in the federated learning based on the received instructions from the base station, such as the scheduling information, and perform the model training based on the data in the local dataset”; It can be seen that the final trained model is sent to the user equipment (EU) for performing task or model training].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for collecting data at a UE for machine learning of Tomala to include sending, to the first wireless device in response to the determination regarding application of the trained ML model, instructing information instructing the first wireless device regarding application of the trained ML model of Mu. Doing so would help performing certain task using the final trained model (Mu, 0170).
As per claim 4, Tomala and Mu teach the method as claimed in claim 1.
Tomala further teaches
the configuration information identifies one or more of the following: the ML model to be trained, at least part of the input training data to be used during the training, one or more of the desired output values to be used during the training, one or more of the ready criteria, one or more training stop or paus criteria, one or more parameters of other trained ML models that have been trained by one or more other, second wireless devices and which other trained ML models are compatible with and based on the same type of parameters as the ML model subject for the training by the fist wireless device [paragraph 0038, “gNB 204 may transmit the generated ML configuration to UE 202. In some implementations, for example, the ML configuration may include a ML command which indicates the ML data to be collected at the UE. In an example implementation, the ML command may indicate to the UE to collect an error metric at one or more layers of the UE”].
As per claim 5, Tomala and Mu teach the method as claimed in claim 1.
Mu further teaches
at least part of the desired output values are provided during the training by the first wireless device according to a first functionality for providing output values [paragraph 0059, “the UE perceives and collects data to generate the local dataset, and processes the local dataset to generate a local training set; the UE randomly initializes a local model parameter, performs local learning model training by using a local training set, and uploads the training result to the core network or data center; the base station requests the local training result of the UE from the core network or data center, and obtains an updated result of the global learning model by performing the federated average learning using the local learning result of each UE; the base station feeds back the updated result to the UE via the network, and the UE fine tunes the local model based on the result of the feedback; the above process is repeated until the model accuracy satisfies the requirements; It can be seen that the training data includes certain desired output so that the predicted output from the model is compared to the desired output to determine whether the model accuracy is satisfied].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for collecting data at a UE for machine learning of Tomala to include at least part of the desired output values are provided during the training by the first wireless device according to a first functionality for providing output values of Mu. Doing so would help determining whether the model accuracy satisfy the requirements (Mu, 0059).
As per claim 6, Tomala and Mu teach the method as claimed in claim 1.
Mu further teaches
the output values are values for forming information that the first wireless device is configured to transmit to the wireless communication network when operating with the wireless communication network [paragraph 0059, “the UE perceives and collects data to generate the local dataset, and processes the local dataset to generate a local training set; the UE randomly initializes a local model parameter, performs local learning model training by using a local training set, and uploads the training result to the core network or data center; the base station requests the local training result of the UE from the core network or data center, and obtains an updated result of the global learning model by performing the federated average learning using the local learning result of each UE; the base station feeds back the updated result to the UE via the network, and the UE fine tunes the local model based on the result of the feedback].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for collecting data at a UE for machine learning of Tomala to include the output values are values for forming information that the first wireless device is configured to transmit to the wireless communication network of Mu. Doing so would help obtaining an updated result of the global learning model using the local learning results of the UEs (Mu, 0079).
As per claim 7, Tomala and Mu teach the method as claimed in claim 1.
Tomala further teaches
the output values are values for use by the wireless communication network for serving the first wireless device [abstract, “the method may include receiving, by a user equipment (UE), machine learning (ML) configuration from a network node; collecting, by the user equipment (UE), machine learning (ML) data based at least on the machine learning (ML) configuration received from the network node … transmitting, by the user equipment (UE), the collected machine learning (ML) data to the network node; examiner interprets a process of collecting ML data by the user equipment as a process of predicting output values using ML configuration from a network node; paragraph 0047, “gNB 204 may perform actions based at least on the ML data received from the UE. In an example implementation, the action may include changing values of one or parameters at the UE for network optimization”];
As per claim 8, Tomala and Mu teach the method as claimed in claim 1.
Mu further teaches
wherein the output values relate to one or more of the following: allocation of resources for transmission in one or both of the uplink and the downlink, scheduling of resources in the one of the uplink and the downlink, handover regarding the first wireless device [paragraph 0184, “The UE and the base station start the federated learning … After completing the training of the local learning model, the result is transmitted to the base station via a wireless channel. The base station updates the weight coefficient of the UE in the federated averaging based on whether a handover occurs to the UE … When a handover occurs to a UE during the training, the UE may directly exit the process of the federated learning, and the base station updates the weight coefficient of the exited UE to zero. The base station performs the federated average learning to obtain an updated result of the global learning model. The base station feeds back the updated result of the global learning model to the UE via the wireless channel, and the UE updates the local learning model based on this result”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for collecting data at a UE for machine learning of Tomala to include the output values relate to handover regarding the first wireless device of Mu. Doing so would help updating the weight value of the UE and stopping the UE to continue the federated learning (Mu, 0184).
As per claim 9, Tomala and Mu teach the method as claimed in claim 1.
Mu further teaches
the ready criteria relate to accuracy of predicted output values [Fig. 8, paragraph 0059, “the UE perceives and collects data to generate the local dataset, and processes the local dataset to generate a local training set; the UE randomly initializes a local model parameter, performs local learning model training by using a local training set … the above process is repeated until the model accuracy satisfies the requirements. When the model training is completed, each base station reports the model training result and the statistical characteristic of the training data to the network”; paragraph 0100, “requirement of the OAM includes a specific requirement for model accuracy … when the global learning model satisfies the subscription requirement of the OAM, it means that the current global learning model has reached sufficient accuracy, and as a result, the federated learning may be terminated, and the global learning model that can be used is obtained”; examiner interprets the one or more ready criteria as the model accuracy requirement].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for collecting data at a UE for machine learning of Tomala to include the ready criteria relate to accuracy of predicted output values of Mu. Doing so would help determining whether the model output is within a desired range (Mu, 0059).
As per claim 12, Tomala and Mu teach the method as claimed in claim 1.
Mu further teaches
the reporting information identifies one or more of the following: accuracy obtained by the trained ML model, one or more model parameters of the trained ML model, the trained ML model [Fig. 8, paragraph 0059, “the UE perceives and collects data to generate the local dataset, and processes the local dataset to generate a local training set; the UE randomly initializes a local model parameter, performs local learning model training by using a local training set, and uploads the training result to the core network or data center; the base station requests the local training result of the UE from the core network or data center, and obtains an updated result of the global learning model by performing the federated average learning using the local learning result of each UE; the base station feeds back the updated result to the UE via the network, and the UE fine tunes the local model based on the result of the feedback; the above process is repeated until the model accuracy satisfies the requirements. When the model training is completed, each base station reports the model training result and the statistical characteristic of the training data to the network”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for collecting data at a UE for machine learning of Tomala to include the reporting information identifies one or more of the following: accuracy obtained by the trained ML model, one or more model parameters of the trained ML model, the trained ML model of Mu. Doing so would help updating the parameter of the model or ending the training based on the statistical information and the model accuracy (Mu, 0242).
As per claim 13, Tomala and Mu teach the method as claimed in claim 1.
Mu further teaches
the determination regarding application of the trained ML model and the instructing information relate to one or more of the following: instructing the first wireless device to apply the ML model and thereby provide predicted output values accordingly, instructing the first wireless device regarding when to apply the trained ML model, instructing the first wireless device to provide output values additionally or alternatively according to another functionality, instructing the first wireless device to retrain the ML model [paragraph 0059, “the UE perceives and collects data to generate the local dataset, and processes the local dataset to generate a local training set; the UE randomly initializes a local model parameter, performs local learning model training by using a local training set, and uploads the training result to the core network or data center; the base station requests the local training result of the UE from the core network or data center, and obtains an updated result of the global learning model by performing the federated average learning using the local learning result of each UE; the base station feeds back the updated result to the UE via the network, and the UE fine tunes the local model based on the result of the feedback; the above process is repeated until the model accuracy satisfies the requirements. When the model training is completed, each base station reports the model training result and the statistical characteristic of the training data to the network”; paragraphs 0169-0170, “when the global learning model satisfies the subscription requirement of the OAM, it means that the current global learning model has reached sufficient accuracy, and as a result, the federated learning may be terminated, and the global learning model that can be used is obtained … the UE may also obtain the final global learning model for local use”; paragraph 0178, “the base station determines to schedule the UE for performing the federated learning based on the distribution characteristic of the local dataset reported by the UE, the UE may be determined to participate in the federated learning based on the received instructions from the base station, such as the scheduling information, and perform the model training based on the data in the local dataset”; It can be seen that the final trained model is sent to the user equipment (EU) for performing task or model training].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for collecting data at a UE for machine learning of Tomala to include the determination regarding application of the trained ML model and the instructing information relate to one or more of the following: instructing the first wireless device to apply the ML model and thereby provide predicted output values accordingly, instructing the first wireless device regarding when to apply the trained ML model, instructing the first wireless device to provide output values additionally or alternatively according to another functionality, instructing the first wireless device to retrain the ML model of Mu. Doing so would help performing certain task using the final trained model (Mu, 0170).
As per claim 18, Tomala teaches a method, performed by a first wireless device, for supporting value prediction by the first wireless device, the first wireless device being served by a wireless communication network [Fig. 1, abstract, “the method may include receiving, by a user equipment (UE), machine learning (ML) configuration from a network node; collecting, by the user equipment (UE), machine learning (ML) data based at least on the machine learning (ML) configuration received from the network node”], wherein the method comprises:
receiving, from one or more network nodes of the wireless communication network, configuration information for configuring the first wireless device to, during operation with the wireless communication network [Fig. 1, abstract, “the method may include receiving, by a user equipment (UE), machine learning (ML) configuration from a network node],
train a machine learning, ML, model to predict certain output values [Fig. 1, abstract, “the method may include receiving, by a user equipment (UE), machine learning (ML) configuration from a network node; collecting, by the user equipment (UE), machine learning (ML) data based at least on the machine learning (ML) configuration received from the network node”; examiner interprets a process of collecting ML data by the user equipment as a process of predicting output values using ML configuration from a network node];
sending, to one or more network nodes of the wireless communication network, reporting information [abstract, “transmitting, by the user equipment (UE), the collected machine learning (ML) data to the network node”];
Tomala does not explicitly teach
the training using certain input training data that at least partly are determined by operative conditions of the first wireless device and using certain desired output values with the input training data, the training ending when the ML model being trained fulfills certain one or more ready criteria;
training the ML model based on the received configuration information until the certain one or more ready criteria are fulfilled; and
sending, to one or more network nodes of the wireless communication network, reporting information at least indicating that the first wireless device has trained the ML model and fulfilled the certain one or more ready criteria,
the ready criteria relating provision of the predicted output values to provision of actual output values provided by the first wireless device according to a first functionality.
Mu teaches
the training using certain input training data that at least partly are determined by operative conditions of the first wireless device and using certain desired output values with the input training data [Fig. 8, paragraph 0059, “the UE perceives and collects data to generate the local dataset, and processes the local dataset to generate a local training set; the UE randomly initializes a local model parameter, performs local learning model training by using a local training set … the above process is repeated until the model accuracy satisfies the requirements”; It can be seen that the training data includes certain desired output so that the predicted output from the model is compared to the desired output to determine whether the model accuracy is satisfied], the training ending when the ML model being trained fulfills certain one or more ready criteria [Fig. 8, paragraph 0059, “the UE perceives and collects data to generate the local dataset, and processes the local dataset to generate a local training set; the UE randomly initializes a local model parameter, performs local learning model training by using a local training set … the above process is repeated until the model accuracy satisfies the requirements. When the model training is completed, each base station reports the model training result and the statistical characteristic of the training data to the network”; paragraph 0100, “requirement of the OAM includes a specific requirement for model accuracy … when the global learning model satisfies the subscription requirement of the OAM, it means that the current global learning model has reached sufficient accuracy, and as a result, the federated learning may be terminated, and the global learning model that can be used is obtained”; examiner interprets the one or more ready criteria as the model accuracy requirement];
training the ML model based on the received configuration information until the certain one or more ready criteria are fulfilled [Fig. 8, paragraph 0059, “the UE perceives and collects data to generate the local dataset, and processes the local dataset to generate a local training set; the UE randomly initializes a local model parameter, performs local learning model training by using a local training set … the above process is repeated until the model accuracy satisfies the requirements. When the model training is completed, each base station reports the model training result and the statistical characteristic of the training data to the network”; paragraph 0100, “requirement of the OAM includes a specific requirement for model accuracy … when the global learning model satisfies the subscription requirement of the OAM, it means that the current global learning model has reached sufficient accuracy, and as a result, the federated learning may be terminated, and the global learning model that can be used is obtained”; examiner interprets the one or more ready criteria as the model accuracy requirement]; and
sending, to one or more network nodes of the wireless communication network, reporting information at least indicating that the first wireless device has trained the ML model and fulfilled the certain one or more ready criteria [paragraph 0045, “the base station may establish communication connections with a plurality of UE and schedule at least a portion of the UE for performing the federated learning. The process of the federated learning requires the local data of the UE to satisfy certain requirements”; Fig. 8, paragraph 0059, “the UE perceives and collects data to generate the local dataset, and processes the local dataset to generate a local training set; the UE randomly initializes a local model parameter, performs local learning model training by using a local training set, and uploads the training result to the core network or data center; the base station requests the local training result of the UE from the core network or data center, and obtains an updated result of the global learning model by performing the federated average learning using the local learning result of each UE; the base station feeds back the updated result to the UE via the network, and the UE fine tunes the local model based on the result of the feedback; the above process is repeated until the model accuracy satisfies the requirements. When the model training is completed, each base station reports the model training result and the statistical characteristic of the training data to the network”], the ready criteria relating provision of the predicted output values to provision of actual output values provided by the first wireless device according to a first functionality paragraph 0045, “the base station may establish communication connections with a plurality of UE and schedule at least a portion of the UE for performing the federated learning. The process of the federated learning requires the local data of the UE to satisfy certain requirements”; Fig. 8, paragraph 0059, “When the model training is completed, each base station reports the model training result and the statistical characteristic of the training data to the network, and the network selects the appropriate model according to the characteristic of the task data”; paragraph 0100, “requirement of the OAM includes a specific requirement for model accuracy … when the global learning model satisfies the subscription requirement of the OAM, it means that the current global learning model has reached sufficient accuracy, and as a result, the federated learning may be terminated, and the global learning model that can be used is obtained”; paragraph 0185, “The base station monitors the training effectiveness of the global learning model. When a certain round of the updated model satisfies the subscription requirement of the OAM, the base station sends a signal to each UE to terminate the training, and the federated learning ends. When the model training is completed, each base station reports the result of the model training and the statistical characteristic of the training data to the OAM, the OAM selects the appropriate model according to the characteristic of the task data”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for collecting data at a UE for machine learning of Tomala to include the training uses using certain input training data and desired output values with the input training data, the training ending when the ML model being trained fulfills certain one or more ready criteria, receiving reporting information at least indicating that the first wireless device has trained the ML model and fulfilled the certain one or more ready criteria, and determining the ready criteria relating provision of the predicted output values to provision of actual output values provided by the first wireless device according to a first functionality of Mu. Doing so would help selecting one or more suitable base station models based on the statistical information and the model accuracy for performing a task (Mu, 0242).
Claim 19 is substantially similar to claim 2 and thus rejected for similar reasons as claim 2.
Claim 21 is substantially similar to claim 4 and thus rejected for similar reasons as claim 4.
Claim 34 is substantially similar to claim 1 and thus rejected for similar reasons as claim 1.
Claim 49 is substantially similar to claim 18 and thus rejected for similar reasons as claim 18.
Claims 3 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tomala et al. in view of Mu et al. and further in view of Morris, II et al. (US Pub. 2016/0350671).
As per claim 3, Tomala and Mu teach the method as claimed in claim 2.
Mu teaches
the instructing information instructs the first wireless device to apply the trained ML model [paragraphs 0169-0170, “when the global learning model satisfies the subscription requirement of the OAM, it means that the current global learning model has reached sufficient accuracy, and as a result, the federated learning may be terminated, and the global learning model that can be used is obtained … the UE may also obtain the final global learning model for local use”];
Tomala and Mu do not explicitly teach
receiving, from the first wireless device in response to the sent instructing information, prediction based information being information based on predicted output values resulting from application of the trained ML model by the first wireless device;
Morris, II teaches
receiving, from the first wireless device in response to the sent instructing information, prediction based information being information based on predicted output values resulting from application of the trained ML model by the first wireless device [paragraphs 0074-0075, “if the validation indicates that the performance of the newly trained predictive model 194 is better for predicting the operational outcome of interest than the predictive model 194 currently in use, this validation can be used as a basis for deploying the newly trained predictive model 194 to replace the predictive model 194 that is currently being used. The predictive models 194 can be stored (e.g., in a central data store of the predictive system 160 and/or the historical source database 170) and used to determine the predicted outputs … The predicted outputs 196 of the trained models 194 can be sent to users and/or operators of the operating system”; Since Tomala (as modified) teaches the trained model is sent to the EU (Mu, paragraphs 0169-0170), while Morris, II teaches the trained model is deployed and used to predict an output which is sent to user of the operating system, therefore, the combination of Tomala (as modified) and Morris, II teaches the above claim limitation];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for collecting data at a UE for machine learning of Tomala to include receiving prediction based information being information based on predicted output values resulting from application of the trained ML model of Morris, II. Doing so would help taking appropriate action based on the model outputs (Morris, II 0075).
Claim 20 is substantially similar to claim 3 and thus rejected for similar reasons as claim 3.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Tomala et al. in view of Mu et al. and further in view of Bhalgat et al. (US Pub. 2022/0261648).
As per claim 10, Tomala and Mu teach the method as claimed in claim 1.
Tomala and Mu do not explicitly teach
the ready criteria relate to timely provision of predicted output values.
Bhalgat teaches
the ready criteria relate to timely provision of predicted output values [paragraph 0075, “the processing system determines whether predefined termination criteria are satisfied. The termination criteria generally relate to any conditions or events indicating that the training should stop. This may include, by way of example and not limitation, a maximum number of epochs, a maximum amount of time, a minimum accuracy of the model, and the like”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for collecting data at a UE for machine learning of Tomala to include the ready criteria relate to timely provision of predicted output values of Bhalgat. Doing so would help determining when the training should stop (Bhalgat, 0075).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Tomala et al. in view of Mu et al. and further in view of Visentini Scarzanella et al. (US Pub. 2020/0218937).
As per claim 14, Tomala and Mu teach the method as claimed in claim 1.
Tomala and Mu do not teach
the determination regarding application of the trained ML model is further based on other reporting information that the one or more network nodes have received from one or more second wireless devices relating to one or more other trained ML models that have been trained by the second wireless devices, respectively;
Visentini Scarzanella teaches
the determination regarding application of the trained ML model is further based on other reporting information that the one or more network nodes have received from one or more second wireless devices relating to one or more other trained ML models that have been trained by the second wireless devices, respectively [abstract, “method is presented for constructing a trained model for a plurality of edge classifiers in a network having a federated classifier, a generator, and a discriminator. The method includes obtaining edge trained models from the plurality of edge devices, each edge trained model being trained independently with data from private data of each edge, training the generator model and discriminator model by employing the edge trained models and an unlabeled set of data by employing a generative adversarial training procedure, generating data samples by the trained generator model, training the federated classifier with the data samples from the generator model, and deploying the trained model back to the plurality of edge devices”; It can be seen that the trained model which applies to a certain node is based on the trained models that is trained from one or more second nodes];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for collecting data at a UE for machine learning of Tomala to include the determination regarding application of the trained ML model is further based on other reporting information that the one or more network nodes have received from one or more second wireless devices relating to one or more other trained ML models that have been trained by the second wireless devices of Visentini Scarzanella. Doing so would help maximizing network performance by training a model which incorporates all the information from different edge nodes (Visentini Scarzanella, 0002).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Tomala et al. in view of Mu et al. in view of Visentini Scarzanella et al. and further in view of Morris, II et al. (US Pub. 2016/0350671).
As per claim 15, Tomala, Mu and Visentini Scarzanella teach the method as claimed in claim 14.
Tomala, Mu and Visentini Scarzanella do not each
the instructing information identifies one or more of the following:
one or more model parameters of another second trained ML model, comprised in the other trained ML models, for replacing corresponding one or more model parameters of the ML model trained by the first wireless device; and
another second trained ML model, comprised in the other trained ML models, for replacing the ML model trained by the first wireless device.
Morris, II teaches
the instructing information identifies one or more of the following:
one or more model parameters of another second trained ML model, comprised in the other trained ML models, for replacing corresponding one or more model parameters of the ML model trained by the first wireless device; and
another second trained ML model, comprised in the other trained ML models, for replacing the ML model trained by the first wireless device.
[paragraphs 0074-0075, “if the validation indicates that the performance of the newly trained predictive model 194 is better for predicting the operational outcome of interest than the predictive model 194 currently in use, this validation can be used as a basis for deploying the newly trained predictive model 194 to replace the predictive model 194 that is currently being used. The predictive models 194 can be stored (e.g., in a central data store of the predictive system 160 and/or the historical source database 170) and used to determine the predicted outputs];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for collecting data at a UE for machine learning of Tomala to include the instructing information identifies one or more of the following: one or more model parameters of another second trained ML model, comprised in the other trained ML models, for replacing corresponding one or more model parameters of the ML model trained by the first wireless device; and another second trained ML model, comprised in the other trained ML models, for replacing the ML model trained by the first wireless device of Morris, II. Doing so would help providing better predicted outcome using the replaced model (Morris, II paragraphs 0074-0075).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Choi et al. (US Pub. 2022/0245527) describes methods and devices for wireless communications.
Ren et al. (US Pub. 2023/0379692) describes methods and apparatus for wireless communication between a user equipment (UE) and a base station.
Conclusion
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/TRI T NGUYEN/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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