DETAILED ACTION
Claims 1-19 have been examined and are pending.
Claims 1-19 are rejected (Non-Final Rejection).
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of the certified copy of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 05/26/2023 & 05/22/2024, respectively, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDSs have been considered by the examiner
Claim Objections
Claims 1 and 4-12 are objected to because of the following informalities:
Claim 1 recites: “… method for predicting risk of under-voltage failure for a Remote Radio Unit (RRU), comprising, …”. This claim language appears to be based on translation of the subject matter from a foreign language; Applicant may respond by amending claim 1 the same (or similar to) as suggested below and/or by traversing the claim objection. Examiner suggests amending the limitation to recite: “… method for predicting risk of under-voltage failure for a Remote Radio Unit (RRU), the method comprising: the device comprising: …” and “… the system comprising: …”, respectively.
Claim 4 also recites: “… the device parameter of the target RRU comprises any one of, overall power consumption, …”. Examiner suggests amending claim 4 to recite: “… the device parameter of the target RRU comprises at least one of:
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “acquisition module”, “input module”, and “prediction module” in claim 7.
The “acquisition module”, “input module”, and “prediction module” in claim 7 are considered “generic placeholders” under Prong A. Prong B is satisfied because each of these claim elements are modified by functional language including reciting “configured to ...”.
For claim 7 and Prong C, the only acts provided in the claim for performing: (i) the “acquisition module” are: “acquire a device parameter of a target RRU,” (ii) the “input module” are: “input the device parameter of the target RRU into a prediction model”, and (iii) the “prediction module” are: “perform a prediction of the risk of under-voltage failure for the target RRU by means of the prediction model”. These acts of claim 7 do not provide “sufficient structure, material, or acts to entirely perform the recited function.”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 U.S.C. § 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.
Claim 7 is rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Regarding claim 7, the claim limitations of “acquisition module”, “input module”, and “prediction module” in claim 7 each invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. While the claimed limitations are present in the specification, the specification does not disclose a corresponding algorithm associated with a computer or microprocessor. See MPEP 2181 II(B). Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 U.S.C. § 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.
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
1. Determining if the claim falls within a statutory category;
2A. Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and
2B. If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception.
(See MPEP 2106).
Claims 1-19 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite mathematical calculation(s). See MPEP § 2106.04(a)(2)(III).
The following is an analysis based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category:
Yes: Claims 1-6 and 11-19 are directed to the statutory category of a process. See MPEP § 2106.03.
Yes: Claims 7-9 are directed to the statutory category of a machine. See MPEP § 2106.03.
Yes: Claims 10 is directed to the statutory category of a manufacture. See MPEP § 2106.03.
Step 2A:
Step 2A is a two-prong inquiry. See MPEP 2106.04(II)(A). Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2106.04(a)(2). The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Claim 1 Step 2A prong 1: Does the Claim Recite a Judicial Exception?
For the sake of identifying the abstract ideas, a copy of the claim is provided below. The limitations of the claims that describe abstract ideas are bolded.
1. A method for predicting risk of under-voltage failure for a Remote Radio Unit (RRU), comprising,
acquiring a device parameter of a target RRU;
inputting the device parameter of the target RRU into a prediction model, wherein the prediction model is acquired by federated learning through a common node and at least one local node, and the common node connects with each of the at least one local node, and each of the at least one local node comprises at least one RRU; and
performing a prediction of the risk of under-voltage failure for the target RRU by means of the prediction model.
The limitations of “predicting risk of under-voltage failure for a Remote Radio Unit (RRU)” and “performing a prediction of the risk of under-voltage failure for the target RRU by means of the prediction model” are abstract ideas because they are directed to directed to mathematical calculations. The bolded portion, as drafted and under broadest reasonable interpretation, “can be performed using mathematical equations”. See MPEP 2106.04(a)(2)(I).
Claim 1 Step 2A prong 2: Does the claim recite additional elements that integrate the judicial exception/Abstract idea into practical application?
Under Step 2A prong two, this judicial exception is not integrated into a practical application because the additional claim limitations outside of the abstract idea only present mere instructions to apply an exception, generally link the use of the judicial exception to the technological environment, or insignificant extra-solution activity. In particular, the claim recites the additional limitations of:
• “acquiring a device parameter of a target RRU” and “inputting the device parameter of the target RRU into a prediction model, wherein the prediction model is acquired by federated learning through a common node and at least one local node, and the common node connects with each of the at least one local node, and each of the at least one local node comprises at least one RRU” (insignificant extra-solution activity – mere data inputting/gathering – See MPEP 2106.04(d) referencing MPEP 2106.05(g); this limitation can be viewed as nothing more than mere data outputting in conjunction with the abstract idea (see MPEP 2106.05(g)).
Claim 1 Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
The Examiner must consider whether each claim limitation individually or as an ordered combination amount to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, there is one type of additional elements.
The first type of additional element is the acquiring and inputting of the device parameter, which as explained previously is insignificant extra-solution activity (mere data gathering/outputting). Recitation of acquiring and inputting a device parameter of a target RRU is mere data gathering that is recited at a high level of generality, and, is also Well-Understood, Routine and Conventional (WURC). This limitation therefore remains insignificant extra-solution activity even upon reconsideration and does not amount to significantly more.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and/or insignificant extra-solution activity, which do not provide an inventive concept. The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(f).
Considering the claim limitations as an ordered combination, claim 1 does not include significantly more than the abstract idea. The claim 1 is not patent subject matter eligible. Dependent claims 2-6 and 9-19 are further addressed below after addressing each independent claim.
Claim 7 Step 2A prong 1: Does the Claim Recite a Judicial Exception?
For the sake of identifying the abstract ideas, a copy of the claim is provided below. The limitations of the claims that describe abstract ideas are bolded.
7. A device for predicting risk of under-voltage failure for a Remote Radio Unit (RRU), comprising, an acquisition module, configured to acquire a device parameter of a target RRU; an input module, configured to input the device parameter of the target RRU into a prediction model, wherein, the prediction model is acquired by federated learning through a common node, and at least one local node, and each of the at least one local node comprises at least one RRU; and a prediction module, configured to perform a prediction of the risk of under-voltage failure for the target RRU by means of the prediction model.
The limitations of “predicting risk of under-voltage failure for a Remote Radio Unit (RRU)” and “perform a prediction of the risk of under-voltage failure for the target RRU by means of the prediction model” are abstract ideas because they are directed to directed to mathematical calculations. The bolded portion, as drafted and under broadest reasonable interpretation, “can be performed using mathematical equations”. See MPEP 2106.04(a)(2)(I).
Claim 7 Step 2A prong 2: Does the claim recite additional elements that integrate the judicial exception/Abstract idea into practical application?
Under Step 2A prong two, this judicial exception is not integrated into a practical application because the additional claim limitations outside of the abstract idea only present mere instructions to apply an exception, generally link the use of the judicial exception to the technological environment, or insignificant extra-solution activity. In particular, the claim recites the additional limitations of:
• “device … comprising … an acquisition module … an input module …and a prediction module” configured to perform the operations (mere instructions to apply an exception to a computer – see MPEP 2106.04(d) referencing MPEP 2106.05(f); these limitations can be viewed as nothing more than high level recitations of generic computer components or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a generic computer).
• “acquire a device parameter of a target RRU” and “input the device parameter of the target RRU into a prediction model, wherein the prediction model is acquired by federated learning through a common node and at least one local node, and the common node connects with each of the at least one local node, and each of the at least one local node comprises at least one RRU” (insignificant extra-solution activity – mere data inputting/gathering – See MPEP 2106.04(d) referencing MPEP 2106.05(g); this limitation can be viewed as nothing more than mere data outputting in conjunction with the abstract idea (see MPEP 2106.05(g)).
Claim 7 Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
The Examiner must consider whether each claim limitation individually or as an ordered combination amount to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, there are two types of additional elements.
The first type of additional element are the generic computer components, which are for performing limitations (1) through (3) and which are high level recitations of generic computer component(s) or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a computer, see MPEP 2106.05(f). Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. See MPEP 2106.05(f).
The second type of additional element is the acquiring and inputting of the device parameter, which as explained previously is insignificant extra-solution activity (mere data gathering/outputting). Recitation of acquiring and inputting a device parameter of a target RRU is mere data gathering that is recited at a high level of generality, and, is also Well-Understood, Routine and Conventional (WURC). This limitation therefore remains insignificant extra-solution activity even upon reconsideration and does not amount to significantly more.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and/or insignificant extra-solution activity, which do not provide an inventive concept. The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(f).
Considering the claim limitations as an ordered combination, claim 7 does not include significantly more than the abstract idea. The claim 7 is not patent subject matter eligible.
Claim 8 Step 2A prong 1: Does the Claim Recite a Judicial Exception?
For the sake of identifying the abstract ideas, a copy of the claim is provided below. The limitations of the claims that describe abstract ideas are bolded.
8. A system for predicting risk of under-voltage failure for a Remote Radio Unit (RRU), comprising a common node and at least one local node, wherein initial models of the common node and each of the at least one local node are the same, and each of the at least one local node comprises at least one RRU; and
the common node is configured to: before a loss function of a model of each of the at least one local node converges, receive each model parameter reported by each local node, integrate each reported model parameter and update the model parameter of the model of the common node, and send the updated model parameter to each local node; and
each of the at least one local node is configured to: before the loss function of the respective local node converges, calculate the model parameter of the model of the respective local node according to sample data for under-voltage failure of RRU in the respective local node, report the calculated model parameter to the common node, receive the model parameter sent by the common node, and update the model of the respective local node according to the sent model parameter; and each of the at least one local node is further configured to: after the loss function of the respective local node converges, utilize the model of the respective local node as the prediction model to acquire a device parameter of the target RRU, input the device parameter of the target RRU into the prediction model, and perform a prediction of the risk of the under-voltage failure of the target RRU by means of the prediction model.
The limitations of “predicting risk of under-voltage failure for a Remote Radio Unit (RRU)”, “integrate each reported model parameter and update the model parameter of the model of the common node”, “before the loss function of the respective local node converges, calculate the model parameter of the model of the respective local node according to sample data for under-voltage failure of RRU in the respective local node”, “update the model of the respective local node according to the sent model parameter”, “ and “perform a prediction of the risk of under-voltage failure for the target RRU by means of the prediction model” are abstract ideas because they are directed to directed to mathematical calculations. The bolded portion, as drafted and under broadest reasonable interpretation, “can be performed using mathematical equations”. See MPEP 2106.04(a)(2)(I).
Claim 8 Step 2A prong 2: Does the claim recite additional elements that integrate the judicial exception/Abstract idea into practical application?
Under Step 2A prong two, this judicial exception is not integrated into a practical application because the additional claim limitations outside of the abstract idea only present mere instructions to apply an exception, generally link the use of the judicial exception to the technological environment, or insignificant extra-solution activity. In particular, the claim recites the additional limitations of:
• “a common node and at least one local node, wherein initial models of the common node and each of the at least one local node are the same, and each of the at least one local node comprises at least one RRU” configured to perform the operations (mere instructions to apply an exception to a computer – see MPEP 2106.04(d) referencing MPEP 2106.05(f); these limitations can be viewed as nothing more than high level recitations of generic computer components or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a generic computer).
• “before a loss function of a model of each of the at least one local node converges, receive each model parameter reported by each local node”, “send the updated model parameter to each local node”, “report the calculated model parameter to the common node, receive the model parameter sent by the common node” and “after the loss function of the respective local node converges, utilize the model of the respective local node as the prediction model to acquire a device parameter of the target RRU, input the device parameter of the target RRU into the prediction model” (insignificant extra-solution activity – mere data inputting/gathering – See MPEP 2106.04(d) referencing MPEP 2106.05(g); this limitation can be viewed as nothing more than mere data outputting in conjunction with the abstract idea (see MPEP 2106.05(g)).
Claim 8 Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
The Examiner must consider whether each claim limitation individually or as an ordered combination amount to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, there are two types of additional elements.
The first type of additional element are the generic computer components, which are for performing various operations, and which are high level recitations of generic computer component(s) or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a computer, see MPEP 2106.05(f). Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. See MPEP 2106.05(f).
The second type of additional element is the data gathering (e.g., receiving and sending the model parameter), which as explained previously is insignificant extra-solution activity (mere data gathering/outputting). Recitation of receiving and sending a model parameter is mere data gathering that is recited at a high level of generality, and, is also Well-Understood, Routine and Conventional (WURC). This limitation therefore remains insignificant extra-solution activity even upon reconsideration and does not amount to significantly more.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and/or insignificant extra-solution activity, which do not provide an inventive concept. The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(f).
Considering the claim limitations as an ordered combination, claim 8 does not include significantly more than the abstract idea. The claim 8 is not patent subject matter eligible.
Dependent Claims 2-6 and 9-19
Regarding claim 2, the claim depends from claim 1 and further recites: “wherein initial models of the local node and the common node are the same, and the prediction model is acquired through operations of the federated learning comprising, calculating, by each of the at least one local node, a model parameter of a model of the respective local node according to sample data of under-voltage failure of RRU in the respective local node, and reports the calculated model parameter to the common node; integrating, by the common node, each reported model parameter to update a model parameter of a model of the common node, and sends the updated model parameter to each local node; updating, by each of the at least one local node, the model of the respective local node according to the sent model parameter; repeating the above operations until a loss function of the model of the respective local node converges; and acquiring the prediction model according to the model of the common node or the model of the respective local node.” This feature has been considered in combination with the features required by the claim(s) from which this claim depends. The bolded portion of the additional feature are considered to further clarify the details of the mathematical calculations. In addition, the “acquiring”, “reporting”, “integrating”, “sending”, “updating” are insignificant extra-solution activity (e.g., data gathering/inputting and/or outputting), are recited at a high level of generality, and, are also Well-Understood, Routine and Conventional (WURC). See MPEP 2106.05(d)(II)(iii): II. ELEMENTS THAT THE COURTS HAVE RECOGNIZED AS WELL-UNDERSTOOD, ROUTINE, CONVENTIONAL ACTIVITY IN PARTICULAR FIELDS, iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log). Therefore, these feature(s) are considered to be drawn to the abstract idea without adding significantly more, and hence claim 2 is considered to be ineligible under 35 U.S.C. § 101.
Regarding claims 3-6, 9 and 10, claim 3 claim depends from claim 2 and further recites: “wherein the initial model is established based on a linear regression algorithm”, claim 4 depends from claim 1 and further recites: “wherein the device parameter of the target RRU comprises any one of, overall power consumption, overall input voltage, overall transmission power, RRU model, maximum configurable power of carrier, actual transmission power of carrier, rectifier module capacity, cable length, cable diameter, or a combination thereof”, claim 5 depends from claim 1 and further recites: “wherein, the local node is a local node where a proportion of under-voltage failure alarms of RRUs reaches a preset threshold, and the proportion of under-voltage failure alarms is the proportion of a quantity of RRUs with under-voltage failure alarms to a total quantity of RRUs in the local node”, claim 6 depends from claim 1 and further recites: “wherein, the common node is a Network Data Analytics Function (NWDAF) server”, claim 9 depends from claim 1 and further recites: “[a]n electronic apparatus, comprising, at least one processor; and, a memory in communication with the at least one processor; wherein, the memory stores an instruction executable by the at least one processor which, when executed by the at least one processor, causes the at least one processor to carry out the method” and claim 10 depends from claim 1 and further recites: “[a] non-transitory computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to carry out the method”. These features have been considered in combination with the features required by the claim(s) from which this claim depends. The bolded portions of the additional feature(s) are considered to further clarify the details of the mathematical calculations. The additional features of “the common node is a Network Data Analytics Function (NWDAF) server”, “electronic apparatus, comprising, at least one processor; and, a memory …” and “a non-transitory computer-readable storage medium …” are mere instructions to apply an exception to a generic computer, which cannot provide an inventive concept. See MPEP 2106.05(f). Therefore, these features are considered to be drawn to the abstract idea without adding significantly more, and hence claims 3-6, 9 and 10 are considered to be ineligible under 35 U.S.C. § 101.
Claims 11 and 12 have substantially similar limitations as recited in claim 4; therefore, they are rejected under 35 U.S.C. § 101 for the same reasons.
Claims 13-15 have substantially similar limitations as recited in claim 5; therefore, they are rejected under 35 U.S.C. § 101 for the same reasons.
Claims 16-19 have substantially similar limitations as recited in claim 6; therefore, they are rejected under 35 U.S.C. § 101 for the same reasons.
For the foregoing reasons, claims 1-19 are rejected under 35 U.S.C. § 101 as being directed to patent ineligible subject matter.
Claim Rejections - 35 U.S.C. § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4 and 7-12 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by PRAKASH et al. (U.S. Patent Application Publication No. 2019/0220703 A1).
Regarding claim 1, PRAKASH discloses a method for predicting risk of under-voltage failure (power management integrated circuitry (PMIC) 1026 may be included in the platform 1000 to track the state of charge (SoCh) of the battery 1024, and to control charging of the platform 1000 … the PMIC 1026 may be used to monitor other parameters of the battery 1024 to provide failure predictions, such as the state of health (SoH) and the state of function (SoF) of the battery 1024 … the PMIC 1026 may include power alarm detection circuitry, which may detect one or more of brown out (under-voltage) and surge (over-voltage) conditions, Para. [0213] of PRAKASH; See also detect brown out (under-voltage), Para. [0178] of PRAKASH; [failure prediction such as state of health of battery that includes under-voltage detection is interpreted as corresponding to predicting risk of under-voltage failure]) for a Remote Radio Unit (RRU) (as shown in FIG. 10 of PRAKASH, the platform 1000 that the PMIC 1026 and battery 1024 are tied to/power, include transceivers 1011, 1012 including, for example, radios compliant with standards issued by the ITU, Para. [0205] of PRAKASH; [the transceivers that have compliant radios are interpreted as corresponding to remote radio units and the under-voltage detection (via PMIC 1026) is for the same platform device 1000 that has those transceivers 1011, 1012, FIG. 10 of PRAKASH]; See also radio head, Paras. [0057] & [0166] of PRAKASH; [the under-voltage detection of FIG. 9 (Para. [0178] of PRAKASH) is for a radio head, i.e., remote radio unit]), comprising, acquiring a device parameter of a target RRU (number of data points included in each training data partition x1, x2, x3, xm-1, and xm is based on one or more operational parameters of the corresponding edge compute nodes 101, 201, which is discussed infra with further details, Para. [0083] of PRAKASH; See also operational parameters of the edge compute nodes 101, 201 includes compute node capabilities, including network capabilities, e.g., configured and/or maximum transmit power and operational constraints or contexts, Para. [0046] of PRAKASH; See also operational contexts and/or constraints include conditions of individual hardware components including current or predicted available power and energy consumption measurements, Para. [0047] of PRAKASH [current or predicted available power is interpreted as corresponding to actual transmission power of carrier, and energy consumption is interpreted as corresponding to overall power consumption]); inputting the device parameter of the target RRU into a prediction model (an ML model may be any object or data structure created after an ML algorithm is trained with one or more training datasets … after training, an ML model may be used to make predictions on new datasets … although the term “ML algorithm” refers to different concepts than the term “ML model,” these terms as discussed herein may be used interchangeably for the purposes of the present disclosure, Para. [0021] of PRAKASH; [the training of the model is interpreted as including inputting parameters into the model, and the model, as shown above, is used for predictions]), wherein the prediction model is acquired by federated learning through a common node and at least one local node, and the common node connects with each of the at least one local node (federated learning has been proposed for distributed GD (gradient descent) computation, where learning takes place by a federation of client compute [local] nodes that are coordinated by a central server [common node], Para. [0034] of PRAKASH), and each of the at least one local node comprises at least one RRU (radio head, Paras. [0057] & [0166] of PRAKASH; [a radio head is a remote radio unit]; See also as shown in FIG. 10 of PRAKASH, the platform 1000 that the PMIC 1026 and battery 1024 are tied to/power, include transceivers 1011, 1012 including, for example, radios compliant with standards issued by the ITU, Para. [0205] of PRAKASH; [the transceivers that have compliant radios are interpreted as corresponding to remote radio units in this mobile context); and performing a prediction of the risk of under-voltage failure for the target RRU by means of the prediction model (power management integrated circuitry (PMIC) 1026 may be included in the platform 1000 to track the state of charge (SoCh) of the battery 1024, and to control charging of the platform 1000 … the PMIC 1026 may be used to monitor other parameters of the battery 1024 to provide failure predictions, such as the state of health (SoH) and the state of function (SoF) of the battery 1024 … the PMIC 1026 may include power alarm detection circuitry, which may detect one or more of brown out (under-voltage) and surge (over-voltage) conditions, Para. [0213] of PRAKASH; See also detect brown out (under-voltage), Para. [0178] of PRAKASH; [failure prediction such as state of health of battery that includes under-voltage detection is interpreted as corresponding to predicting risk of under-voltage failure]).
Regarding claim 2, PRAKASH discloses the method according to claim 1, wherein initial models of the local node and the common node are the same (in the federated learning Para. [0034], PRAKASH teaches: each client compute node fetches a global model, Para. [0034] of PRAKASH; [the global model fetched from the central server is interpreted as meaning the local (client compute) node has an initial (global) model that is the same as the global model of the common (central server) node]), and the prediction model is acquired through operations of the federated learning (federated learning, Para. [0034] of PRAKASH) comprising, calculating, by each of the at least one local node, a model parameter of a model of the respective local node (in the federated learning Para. [0034], PRAKASH teaches: updating the global model using its local data, Para. [0034] of PRAKASH) according to sample data of under-voltage failure of RRU in the respective local node (training dataset was generated using the standard method … sampled from a standard normal distribution, Para. [0127] of PRAKASH; See also power management integrated circuitry (PMIC) 1026 may be included in the platform 1000 to track the state of charge (SoCh) of the battery 1024, and to control charging of the platform 1000 … the PMIC 1026 may be used to monitor other parameters of the battery 1024 to provide failure predictions, such as the state of health (SoH) and the state of function (SoF) of the battery 1024 … the PMIC 1026 may include power alarm detection circuitry, which may detect one or more of brown out (under-voltage) and surge (over-voltage) conditions, Para. [0213] of PRAKASH; See also detect brown out (under-voltage), Para. [0178] of PRAKASH; [failure prediction such as state of health of battery that includes under-voltage detection is interpreted as corresponding to predicting risk of under-voltage failure]), and reports (communicates) the calculated model parameter to the common node (central server) (in the federated learning Para. [0034], PRAKASH teaches: each client compute node fetches a global model, updates the global model using its local data, and communicates the updated model to the central server, Para. [0034] of PRAKASH); integrating, by the common node, each reported model parameter to update a model parameter of a model of the common node (the central server selects a random set of client compute nodes in each epoch to provide additional updates and waits for the selected client compute nodes to return their updated models … the central server averages the received models to obtain the final global model, Para. [0034] of PRAKASH), and sends the updated model parameter to each local node (in the federated learning Para. [0034], PRAKASH teaches: each client compute node fetches a global model, Para. [0034] of PRAKASH; [because the client compute node fetches the global model, it is interpreted that the final global model is also fetched (sent to) each local/client compute node]); updating, by each of the at least one local node, the model of the respective local node according to the sent model parameter (in the federated learning Para. [0034], PRAKASH teaches: each client compute node fetches a global model, updates the global model using its local data, and communicates the updated model to the central server, Para. [0034] of PRAKASH; See also a load balancing policy is used to partition the computation load across the plurality of edge compute nodes … in embodiments, the master node, for gradient descent computations, obtains partial gradients computed by the plurality of edge compute nodes, and aggregates the partial gradients to obtain a full gradient with little to no decoding complexity … the computed full gradient is then sent back to the edge compute nodes to further refine, e.g., to construct an ML model until the ML model converges, Para. [0030] of PRAKASH); repeating the above operations until a loss function of the model of the respective local node converges (Operations 221-233 repeat until the underlying model sufficiently converges, Para. [0089] of PRAKASH; See also a load balancing policy is used to partition the computation load across the plurality of edge compute nodes … in embodiments, the master node, for gradient descent computations, obtains partial gradients computed by the plurality of edge compute nodes, and aggregates the partial gradients to obtain a full gradient with little to no decoding complexity … the computed full gradient is then sent back to the edge compute nodes to further refine, e.g., to construct an ML model until the ML model converges, Para. [0030] of PRAKASH; See also cost/loss function: cost function indicates how accurate the model β is at making predictions for a given set of parameters … the cost function has a corresponding curve and corresponding gradients, where the slope of the cost function curve indicates how the parameters should be changed to make the model β more accurate … in other words, the model β is used to make predictions, and the cost function is used to update the parameters for the model β, Para. [0093] of PRAKASH); and acquiring the prediction model according to the model of the common node or the model of the respective local node (after training, an ML model may be used to make predictions on new datasets … although the term “ML algorithm” refers to different concepts than the term “ML model,” these terms as discussed herein may be used interchangeably for the purposes of the present disclosure, Para. [0021] of PRAKASH).
Regarding claim 3, PRAKASH discloses the method according to claim 2, wherein the initial model is established based on a linear regression algorithm (gradient descent (GD) algorithms are often used in linear regression, Para. [0003] of PRAKASH; See also although the embodiments herein are discussed in terms of GD algorithms for linear regression, the distributed training embodiments discussed herein are applicable to more complex ML algorithms such as deep neural networks and the like, Para. [0022] of PRAKASH).
Regarding claim 4, PRAKASH discloses the method of any one of claim 1, wherein the device parameter of the target RRU comprises any one of, overall power consumption, overall input voltage, overall transmission power, RRU model, maximum configurable power of carrier, actual transmission power of carrier, rectifier module capacity, cable length, cable diameter, or a combination thereof (operational parameters of the edge compute nodes 101, 201 includes compute node capabilities, including network capabilities, e.g., configured and/or maximum transmit power and operational constraints or contexts, Para. [0046] of PRAKASH; See also operational contexts and/or constraints include conditions of individual hardware components including current or predicted available power and energy consumption measurements, Para. [0047] of PRAKASH [current or predicted available power is interpreted as corresponding to actual transmission power of carrier, and energy consumption is interpreted as corresponding to overall power consumption]).
Regarding claim 7, PRAKASH discloses a device for predicting risk of under-voltage failure (power management integrated circuitry (PMIC) 1026 may be included in the platform 1000 to track the state of charge (SoCh) of the battery 1024, and to control charging of the platform 1000 … the PMIC 1026 may be used to monitor other parameters of the battery 1024 to provide failure predictions, such as the state of health (SoH) and the state of function (SoF) of the battery 1024 … the PMIC 1026 may include power alarm detection circuitry, which may detect one or more of brown out (under-voltage) and surge (over-voltage) conditions, Para. [0213] of PRAKASH; See also detect brown out (under-voltage), Para. [0178] of PRAKASH; [failure prediction such as state of health of battery that includes under-voltage detection is interpreted as corresponding to predicting risk of under-voltage failure]) for a Remote Radio Unit (RRU) (as shown in FIG. 10 of PRAKASH, the platform 1000 that the PMIC 1026 and battery 1024 are tied to/power, include transceivers 1011, 1012 including, for example, radios compliant with standards issued by the ITU, Para. [0205] of PRAKASH; [the transceivers that have compliant radios are interpreted as corresponding to remote radio units and the under-voltage detection (via PMIC 1026) is for the same platform device 1000 that has those transceivers 1011, 1012, FIG. 10 of PRAKASH]; See also radio head, Paras. [0057] & [0166] of PRAKASH; [the under-voltage detection of FIG. 9 (Para. [0178] of PRAKASH) is for a radio head, i.e., remote radio unit]), comprising, an acquisition module, configured to acquire a device parameter of a target RRU (number of data points included in each training data partition x1, x2, x3, xm-1, and xm is based on one or more operational parameters of the corresponding edge compute nodes 101, 201, which is discussed infra with further details, Para. [0083] of PRAKASH; See also operational parameters of the edge compute nodes 101, 201 includes compute node capabilities, including network capabilities, e.g., configured and/or maximum transmit power and operational constraints or contexts, Para. [0046] of PRAKASH; See also operational contexts and/or constraints include conditions of individual hardware components including current or predicted available power and energy consumption measurements, Para. [0047] of PRAKASH [current or predicted available power is interpreted as corresponding to actual transmission power of carrier, and energy consumption is interpreted as corresponding to overall power consumption]); an input module, configured to input the device parameter of the target RRU into a prediction model (an ML model may be any object or data structure created after an ML algorithm is trained with one or more training datasets … after training, an ML model may be used to make predictions on new datasets … although the term “ML algorithm” refers to different concepts than the term “ML model,” these terms as discussed herein may be used interchangeably for the purposes of the present disclosure, Para. [0021] of PRAKASH; [the training of the model is interpreted as including inputting parameters into the model, and the model, as shown above, is used for predictions]), wherein, the prediction model is acquired by federated learning through a common node, and at least one local node (federated learning has been proposed for distributed GD (gradient descent) computation, where learning takes place by a federation of client compute [local] nodes that are coordinated by a central server [common node], Para. [0034] of PRAKASH), and each of the at least one local node comprises at least one RRU (radio head, Paras. [0057] & [0166] of PRAKASH; [a radio head is a remote radio unit]; See also as shown in FIG. 10 of PRAKASH, the platform 1000 that the PMIC 1026 and battery 1024 are tied to/power, include transceivers 1011, 1012 including, for example, radios compliant with standards issued by the ITU, Para. [0205] of PRAKASH; [the transceivers that have compliant radios are interpreted as corresponding to remote radio units in this mobile context); and a prediction module, configured to perform a prediction of the risk of under-voltage failure for the target RRU by means of the prediction model (power management integrated circuitry (PMIC) 1026 may be included in the platform 1000 to track the state of charge (SoCh) of the battery 1024, and to control charging of the platform 1000 … the PMIC 1026 may be used to monitor other parameters of the battery 1024 to provide failure predictions, such as the state of health (SoH) and the state of function (SoF) of the battery 1024 … the PMIC 1026 may include power alarm detection circuitry, which may detect one or more of brown out (under-voltage) and surge (over-voltage) conditions, Para. [0213] of PRAKASH; See also detect brown out (under-voltage), Para. [0178] of PRAKASH; [failure prediction such as state of health of battery that includes under-voltage detection is interpreted as corresponding to predicting risk of under-voltage failure]).
Regarding claim 8, PRAKASH discloses a system for predicting risk of under-voltage failure (power management integrated circuitry (PMIC) 1026 may be included in the platform 1000 to track the state of charge (SoCh) of the battery 1024, and to control charging of the platform 1000 … the PMIC 1026 may be used to monitor other parameters of the battery 1024 to provide failure predictions, such as the state of health (SoH) and the state of function (SoF) of the battery 1024 … the PMIC 1026 may include power alarm detection circuitry, which may detect one or more of brown out (under-voltage) and surge (over-voltage) conditions, Para. [0213] of PRAKASH; See also detect brown out (under-voltage), Para. [0178] of PRAKASH; [failure prediction such as state of health of battery that includes under-voltage detection is interpreted as corresponding to predicting risk of under-voltage failure]) for a Remote Radio Unit (RRU) (as shown in FIG. 10 of PRAKASH, the platform 1000 that the PMIC 1026 and battery 1024 are tied to/power, include transceivers 1011, 1012 including, for example, radios compliant with standards issued by the ITU, Para. [0205] of PRAKASH; [the transceivers that have compliant radios are interpreted as corresponding to remote radio units and the under-voltage detection (via PMIC 1026) is for the same platform device 1000 that has those transceivers 1011, 1012, FIG. 10 of PRAKASH]; See also radio head, Paras. [0057] & [0166] of PRAKASH; [the under-voltage detection of FIG. 9 (Para. [0178] of PRAKASH) is for a radio head, i.e., remote radio unit]), comprising a common node and at least one local node (federated learning has been proposed for distributed GD (gradient descent) computation, where learning takes place by a federation of client compute [local] nodes that are coordinated by a central server [common node], Para. [0034] of PRAKASH), wherein initial models of the common node and each of the at least one local node are the same (in the federated learning Para. [0034], PRAKASH teaches: each client compute node fetches a global model, Para. [0034] of PRAKASH; [the global model fetched from the central server is interpreted as meaning the local (client compute) node has an initial (global) model that is the same as the global model of the common (central server) node]), and each of the at least one local node comprises at least one RRU (radio head, Paras. [0057] & [0166] of PRAKASH; [a radio head is a remote radio unit]; See also as shown in FIG. 10 of PRAKASH, the platform 1000 that the PMIC 1026 and battery 1024 are tied to/power, include transceivers 1011, 1012 including, for example, radios compliant with standards issued by the ITU, Para. [0205] of PRAKASH; [the transceivers that have compliant radios are interpreted as corresponding to remote radio units in this mobile context); and the common node is configured to: before a loss function of a model of each of the at least one local node converges (Operations 221-233 repeat until the underlying model sufficiently converges, Para. [0089] of PRAKASH; See also a load balancing policy is used to partition the computation load across the plurality of edge compute nodes … in embodiments, the master node, for gradient descent computations, obtains partial gradients computed by the plurality of edge compute nodes, and aggregates the partial gradients to obtain a full gradient with little to no decoding complexity … the computed full gradient is then sent back to the edge compute nodes to further refine, e.g., to construct an ML model until the ML model converges, Para. [0030] of PRAKASH; See also cost/loss function: cost function indicates how accurate the model β is at making predictions for a given set of parameters … the cost function has a corresponding curve and corresponding gradients, where the slope of the cost function curve indicates how the parameters should be changed to make the model β more accurate … in other words, the model β is used to make predictions, and the cost function is used to update the parameters for the model β, Para. [0093] of PRAKASH), receive each model parameter reported by each local node, integrate each reported model parameter and update the model parameter of the model of the common node (the central server selects a random set of client compute nodes in each epoch to provide additional updates and waits for the selected client compute nodes to return their updated models … the central server averages the received models to obtain the final global model, Para. [0034] of PRAKASH), and send the updated model parameter to each local node (in the federated learning Para. [0034], PRAKASH teaches: each client compute node fetches a global model, Para. [0034] of PRAKASH; [because the client compute node fetches the global model, it is interpreted that the final global model is also fetched (sent to) each local/client compute node]); and each of the at least one local node is configured to: before the loss function of the respective local node converges (Operations 221-233 repeat until the underlying model sufficiently converges, Para. [0089] of PRAKASH; See also a load balancing policy is used to partition the computation load across the plurality of edge compute nodes … in embodiments, the master node, for gradient descent computations, obtains partial gradients computed by the plurality of edge compute nodes, and aggregates the partial gradients to obtain a full gradient with little to no decoding complexity … the computed full gradient is then sent back to the edge compute nodes to further refine, e.g., to construct an ML model until the ML model converges, Para. [0030] of PRAKASH; See also cost/loss function: cost function indicates how accurate the model β is at making predictions for a given set of parameters … the cost function has a corresponding curve and corresponding gradients, where the slope of the cost function curve indicates how the parameters should be changed to make the model β more accurate … in other words, the model β is used to make predictions, and the cost function is used to update the parameters for the model β, Para. [0093] of PRAKASH), calculate the model parameter of the model of the respective local node (in the federated learning Para. [0034], PRAKASH teaches: updating the global model using its local data, Para. [0034] of PRAKASH) according to sample data for under-voltage failure of RRU in the respective local node (training dataset was generated using the standard method … sampled from a standard normal distribution, Para. [0127] of PRAKASH; See also power management integrated circuitry (PMIC) 1026 may be included in the platform 1000 to track the state of charge (SoCh) of the battery 1024, and to control charging of the platform 1000 … the PMIC 1026 may be used to monitor other parameters of the battery 1024 to provide failure predictions, such as the state of health (SoH) and the state of function (SoF) of the battery 1024 … the PMIC 1026 may include power alarm detection circuitry, which may detect one or more of brown out (under-voltage) and surge (over-voltage) conditions, Para. [0213] of PRAKASH; See also detect brown out (under-voltage), Para. [0178] of PRAKASH; [failure prediction such as state of health of battery that includes under-voltage detection is interpreted as corresponding to predicting risk of under-voltage failure]), report the calculated model parameter to the common node (central server) (in the federated learning Para. [0034], PRAKASH teaches: each client compute node fetches a global model, updates the global model using its local data, and communicates the updated model to the central server, Para. [0034] of PRAKASH), receive the model parameter sent by the common node (in the federated learning Para. [0034], PRAKASH teaches: each client compute node fetches a global model, Para. [0034] of PRAKASH; [because the client compute node fetches the global model, it is interpreted that the final global model is also fetched (sent to) each local/client compute node]), and update the model of the respective local node according to the sent model parameter (in the federated learning Para. [0034], PRAKASH teaches: each client compute node fetches a global model, updates the global model using its local data, and communicates the updated model to the central server, Para. [0034] of PRAKASH; See also a load balancing policy is used to partition the computation load across the plurality of edge compute nodes … in embodiments, the master node, for gradient descent computations, obtains partial gradients computed by the plurality of edge compute nodes, and aggregates the partial gradients to obtain a full gradient with little to no decoding complexity … the computed full gradient is then sent back to the edge compute nodes to further refine, e.g., to construct an ML model until the ML model converges, Para. [0030] of PRAKASH); and each of the at least one local node is further configured to: after the loss function of the respective local node converges (Operations 221-233 repeat until the underlying model sufficiently converges, Para. [0089] of PRAKASH; See also a load balancing policy is used to partition the computation load across the plurality of edge compute nodes … in embodiments, the master node, for gradient descent computations, obtains partial gradients computed by the plurality of edge compute nodes, and aggregates the partial gradients to obtain a full gradient with little to no decoding complexity … the computed full gradient is then sent back to the edge compute nodes to further refine, e.g., to construct an ML model until the ML model converges, Para. [0030] of PRAKASH; See also cost/loss function: cost function indicates how accurate the model β is at making predictions for a given set of parameters … the cost function has a corresponding curve and corresponding gradients, where the slope of the cost function curve indicates how the parameters should be changed to make the model β more accurate … in other words, the model β is used to make predictions, and the cost function is used to update the parameters for the model β, Para. [0093] of PRAKASH), utilize the model of the respective local node as the prediction model to acquire a device parameter of the target RRU (number of data points included in each training data partition x1, x2, x3, xm-1, and xm is based on one or more operational parameters of the corresponding edge compute nodes 101, 201, which is discussed infra with further details, Para. [0083] of PRAKASH; See also operational parameters of the edge compute nodes 101, 201 includes compute node capabilities, including network capabilities, e.g., configured and/or maximum transmit power and operational constraints or contexts, Para. [0046] of PRAKASH; See also operational contexts and/or constraints include conditions of individual hardware components including current or predicted available power and energy consumption measurements, Para. [0047] of PRAKASH [current or predicted available power is interpreted as corresponding to actual transmission power of carrier, and energy consumption is interpreted as corresponding to overall power consumption]), input the device parameter of the target RRU into the prediction model (an ML model may be any object or data structure created after an ML algorithm is trained with one or more training datasets … after training, an ML model may be used to make predictions on new datasets … although the term “ML algorithm” refers to different concepts than the term “ML model,” these terms as discussed herein may be used interchangeably for the purposes of the present disclosure, Para. [0021] of PRAKASH; [the training of the model is interpreted as including inputting parameters into the model, and the model, as shown above, is used for predictions]), and perform a prediction of the risk of the under-voltage failure of the target RRU by means of the prediction model (power management integrated circuitry (PMIC) 1026 may be included in the platform 1000 to track the state of charge (SoCh) of the battery 1024, and to control charging of the platform 1000 … the PMIC 1026 may be used to monitor other parameters of the battery 1024 to provide failure predictions, such as the state of health (SoH) and the state of function (SoF) of the battery 1024 … the PMIC 1026 may include power alarm detection circuitry, which may detect one or more of brown out (under-voltage) and surge (over-voltage) conditions, Para. [0213] of PRAKASH; See also detect brown out (under-voltage), Para. [0178] of PRAKASH; [failure prediction such as state of health of battery that includes under-voltage detection is interpreted as corresponding to predicting risk of under-voltage failure]).
Regarding claim 9, PRAKASH discloses an electronic apparatus, comprising, at least one processor; and, a memory in communication with the at least one processor; wherein, the memory stores an instruction executable by the at least one processor which, when executed by the at least one processor, causes the at least one processor (processors (or cores) of the processor circuitry 1002 may be coupled with or may include memory/storage and may be configured to execute instructions stored in the memory/storage to enable various applications or operating systems to run on the platform 1000, Para. [0184] of PRAKASH; See also Para. [0168] of PRAKASH) to carry out the method of any one of claim 1 (see mapping of claim 1 above).
Regarding claim 10, PRAKASH discloses a non-transitory computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor (components of the CN 120 may be implemented in one physical node or separate physical nodes including components to read and execute instructions from a machine-readable or computer-readable medium (e.g., a non-transitory machine-readable storage medium), Para. [0072] of PRAKASH; See also processors (or cores) of the processor circuitry 1002 may be coupled with or may include memory/storage and may be configured to execute instructions stored in the memory/storage to enable various applications or operating systems to run on the platform 1000, Para. [0184] of PRAKASH; See also Para. [0168] of PRAKASH) to carry out the method of any one of claim 1 (see mapping of claim 1 above).
Claims 11 and 12 have substantially similar limitations as recited in claim 4; therefore, they are rejected under 35 U.S.C. § 102 for the same reasons.
Claim Rejections - 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 5 and 13-15 are rejected under 35 U.S.C. § 103 as being unpatentable over PRAKASH et al. (U.S. Patent Application Publication No. 2019/0220703 A1) in view of BROBSTON (U.S. Patent Application Publication No. 2023/0397105 A1).
Regarding claim 5, PRAKASH discloses the method of any one of claim 1 (as shown above), but appears to fail to explicitly disclose wherein, the local node is a local node where a proportion of under-voltage failure alarms of RRUs reaches a preset threshold, and the proportion of under-voltage failure alarms is the proportion of a quantity of RRUs with under-voltage failure alarms to a total quantity of RRUs in the local node.
BROBSTON, however, is in the field of managing remote radio units (Paras. [0002] & [0003] of BROBSTON) and teaches wherein, the local node is a local node where a proportion of under-voltage failure alarms of RRUs reaches a preset threshold, and the proportion of under-voltage failure alarms is the proportion of a quantity of RRUs with under-voltage failure alarms to a total quantity of RRUs in the local node (in response to one or more data metrics falling below a threshold, one or more power reductions steps may be activated in one or more radio units 108, e.g., power reduction step(s) is/are triggered in response to downlink throughput for a base station 100 falling below a throughput threshold determined based on the quantity of radio resources needed to support that throughput threshold … additionally or alternatively, power reduction step(s) may be triggered based on the current capacity (of particular base station(s) 100 or the entire system 101) being utilized, e.g., power reduction step(s) may be triggered in response to the network operating at or below 10% (or any other threshold percentage) of its downlink and/or uplink throughput capacity, Para. [0062] of BROBSTON).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the under-voltage modeling method of PRAKASH to use using a operation percentage/proportion threshold, as in BROBSTON, for the purpose of maintaining cost effective operations by reducing radio unit power consumption (Para. [0002] of BROBSTON).
Claims 13-15 have substantially similar limitations as recited in claim 5; therefore, they are rejected under 35 U.S.C. § 103 for the same reasons.
Claims 6 and 16-19 are rejected under 35 U.S.C. § 103 as being unpatentable over PRAKASH et al. (U.S. Patent Application Publication No. 2019/0220703 A1) in view of WANG et al. (U.S. Patent Application Publication No. 2023/0037031 A1).
Regarding claim 6, PRAKASH discloses the method of any one of claim 1 (as shown above) but appears to fail to explicitly disclose wherein, the common node is a Network Data Analytics Function (NWDAF) server.
WANG, however, is in the field of remote radio heads/units (Paras. [0042] & [0050] of WANG) and teaches the common node is a Network Data Analytics Function (NWDAF) server (receive automatic notifications about context changes about edge data networks/servers from NWDAF, Para. [0341]). It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the under-voltage modeling method of PRAKASH to use an NWDAF server for the purpose of providing automatic updates (Para. [0341] of WANG).
Claims 16-19 have substantially similar limitations as recited in claim 6; therefore, they are rejected under 35 U.S.C. § 103 for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: FISCHER (U.S. Patent Application Publication No. 2019/0289541 A1) teaches, at Para. [0036], if the electrical current sample data 236 indicates that the voltage at the RRU 106 is below a voltage threshold value, and the number of dropped data packets exceeds a first drop threshold value, the adaptive voltage boost controller 302 may generate a first value for the voltage boost. However, if the voltage at the RRU 106 is determined to be below the voltage threshold value, and the number of dropped data packets exceeds a second drop threshold value that is higher or lower than the first drop threshold value, the adaptive voltage boost controller 302 may generate a second value for the voltage boost.
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JOHN P. HOCKER
Examiner
Art Unit 2189
/JOHN P HOCKER/Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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