DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112: Indefiniteness
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 6 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 6, the claim is indefinite as it is not clear what the term “lagitude” means in the claimed equation. For the purposes of examination, the term is being interpreted as a typo and the term is supposed to mean latitude given that the subsequent equation has to do with longitude.
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 1-11 and 13-21 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A method for selecting participants in federated learning,. The claim recites a method. A method is one of the four statutory categories of invention.
In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components:
determining a data quality factor, a service factor and a stability factor of each participant to be selected respectively; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like determining factors of a participant, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
and determining a selected participant based on the data quality factor, service factor and stability factor of the plurality of participants to be selected. (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like determining the best participant by comparing factors, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea.
In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
comprising, acquiring a plurality of participants to be selected; (i.e., the broadest reasonable interpretation of receiving a plurality of participants is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea.
In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (III), under the broadest reasonable interpretation, recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)).
Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 2 recites wherein, determining the data quality factor, the service factor and the stability factor of each participant to be selected respectively comprises, determining the data quality factor, the service factor and the stability factor of the respective participant to be selected based on a type of the federated learning. Under the broadest reasonable interpretation, the limitations recite selecting factors based on a type of learning which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 2 does not solve the deficiencies of claim 1.
Regarding claim 3, it is dependent upon claim 2 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 3 recites wherein, the type of the federated learning comprises at least one of, Key Performance Indicator (KPI) degradation detection, cell weight optimization, or optical module fault prediction. Under the broadest reasonable interpretation, merely recite steps that amount to indicating a field of use or technological environment in which to apply a judicial exception (MPEP 2106.05(h)). Therefore, claim 3 does not solve the deficiencies of claim 2.
Regarding claim 4, it is dependent upon claim 3 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 4 recites wherein, in response to the type of the federated learning being cell weight optimization, determining the data quality factor, the service factor and the stability factor of the respective participant to be selected based on the type of the federated learning comprises, determining the data quality factor based on a quality of service (QoS) alarm parameter of the respective participant to be selected; determining the service factor based on a latitude and a longitude of the respective participant to be selected; and determining the stability factor based on an in-service duration of the respective participant to be selected. Under the broadest reasonable interpretation, the limitations recite determining factors associated with cell weight optimization which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 4 does not solve the deficiencies of claim 3.
Regarding claim 5, it is dependent upon claim 4 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 5 recites wherein, determining the selected participant based on the data quality factor, service factor and stability factor of the plurality of participants to be selected, comprises, determining the participant to be selected as the selected participant, in response to the participant to be selected having the data quality factor, the service factor, and the stability factor that meet a value of an optimal function. Under the broadest reasonable interpretation, the limitations recite selecting a participant that meets thresholds for the factors which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 5 does not solve the deficiencies of claim 4.
Regarding claim 6, it is dependent upon claim 5 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 6 recites an equation for calculating an optimal function. Under the broadest reasonable interpretation, the limitations recite an equation for calculating an optimal function which is interpreted as using a mathematical equation. A mathematical equation is interpreted as a mathematical concept. Therefore, claim 6 does not solve the deficiencies of claim 5.
Regarding claim 7, it is dependent upon claim 3 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 7 recites wherein, in response to the type of the federated learning being KPI deterioration or optical module fault prediction, determining the selected participant based on the data quality factor, service factor and stability factor of the plurality of participants to be selected, comprises, determining a selection parameter for each participant to be selected based on the data quality factor, service factor and stability factor of the respective participant, and performing a descending sorting with the selection parameter of each participant to be selected, and determining top L participants to be selected in the sorting as selected participants. Under the broadest reasonable interpretation, the limitations recite sorting participants by applying values to each factor and then selecting the top participants which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 7 does not solve the deficiencies of claim 3.
Regarding claim 8, it is dependent upon claim 7 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 8 recites in response to the type of the federated learning being KPI deterioration, determining the data quality factor, service factor and stability factor of the respective participant to be selected based on the type of the federated learning comprises, determining the data quality factor based on an integrity parameter of performance data of the respective participant to be selected; determining the service factor based on a quality of service (QoS) alarm parameter of the respective participant to be selected; and determining the stability factor based on an in-service duration of the respective participant to be selected. Under the broadest reasonable interpretation, the limitations recite determining factors associated with KPI deterioration which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 8 does not solve the deficiencies of claim 7.
Regarding claim 9, it is dependent upon claim 8 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 9 recites wherein, determining the selection parameter of the respective participant to be selected based on the data quality factor, service factor and stability factor of the respective of the plurality of participants to be selected, comprises, normalizing the data quality factor, service factor and stability factor respectively by means of a linear normalization method for each participant to be selected;. Under the broadest reasonable interpretation, the limitations recite normalizing values using a linear normalization function which is interpreted as using a mathematical calculation. A mathematical calculation is interpreted as a mathematical concept. Claim 9 also recites and performing a first weighting processing to the normalized data quality factor, service factor and stability factor to obtain the selection parameter of the respective participant to be selected. Under the broadest reasonable interpretation, the limitations recite weighting factors by importance which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 9 does not solve the deficiencies of claim 8.
Regarding claim 10, it is dependent upon claim 7 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 10 recites wherein, in response to the type of the federated learning being optical module fault prediction, determining the data quality factor, service factor and stability factor of the respective participant to be selected based on the type of the federated learning comprises, determining the data quality factor based on an integrity parameter of performance data of the respective participant to be selected; determining the service factor based on a quality of link of optical module of the respective participant to be selected; and determining the stability factor based on an in-service duration of the respective participant to be selected. Under the broadest reasonable interpretation, the limitations recite determining factors associated with optical module fault prediction which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 10 does not solve the deficiencies of claim 7.
Regarding claim 11, it is dependent upon claim 10 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 11 recites wherein, determining the selection parameter of the respective participant to be selected based on the data quality factor, service factor and stability factor of the respective of the plurality of participants to be selected, comprises, for each participant to be selected, performing a second weighting processing to the data quality factor, service factor and stability factor to obtain the selection parameter of the respective participant to be selected. Under the broadest reasonable interpretation, the limitations recite weighting factors by importance which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 11 does not solve the deficiencies of claim 10.
Regarding claim 13, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites An apparatus for selecting participants in federated learning,. The claim recites an apparatus which is interpreted as a machine. A machine is one of the four statutory categories of invention. For the Step 2A/2B analyses, since claim 13 is similar to claim 1 it is rejected under the same rationales as claim 1.
The additional limitation below fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception.
An apparatus for selecting participants in federated learning, comprising, at least one processor; and a memory for storing at least one program which, when executed by the at least one processor, causes the at least one processor to carry out (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 14, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A non-transitory computer-readable storage medium. The claim recites a computer readable storage medium which is interpreted as an article of manufacture. An article of manufacture is one of the four statutory categories of invention. For the Step 2A/2B analyses, since claim 14 is similar to claim 1 it is rejected under the same rationales as claim 1.
The additional limitation below fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception.
A non-transitory computer-readable storage medium storing at least one computer program which, when executed by a processor, causes the processor to carry out (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claims 15-17, the claims are similar to claims 2-4 and are rejected under the same rationales.
Regarding claim 18, the claim is similar to claim 2 and rejected under the same rationales.
Regarding claim 19, the claim is similar to claim 3 and rejected under the same rationales.
Regarding claims 20-21, the claims are similar to claims 4-5 and rejected under the same rationales.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 7-11, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huang, et al., Foreign Patent Publication CN111866954A (“Huang”), please see the provided translated version for art mapping purposes.
Regarding claim 1, Huang discloses:
A method for selecting participants in federated learning, comprising, acquiring a plurality of participants to be selected; (Huang, pg. 4, “Under the condition, the invention provides a user selection and resource allocation method based on federal learning. The method jointly models user selection, QoS analysis, FL calculation overhead optimization and transmission resource allocation into a problem of minimizing FL task total overhead. First, IDs are selected based on reputation value, reputation value threshold is set, IDs higher than the threshold are added into FL [A method for selecting participants in federated learning, comprising, acquiring a plurality of participants to be selected;].”).
determining a data quality factor, a service factor and a stability factor of each participant to be selected respectively; (Huang, pg. 7, “Under the influence of a plurality of factors, the traditional subjective logic develops to multi-weight subjective logic, and the reputation evaluation is calculated by considering the following factors [of each participant to be selected respectively;]. (1) Interaction reliability [determining a data quality factor,]: and (4) performing quality evaluation on the local model update, wherein positive and negative interaction results of historical interaction exist, and the credit value of IDs can be improved through the positive interaction…(2) freshness of interaction [and a stability factor]: the reliability of IDs may change over time, with recent interaction events having more freshness having more weight than past events during interactions of FNs with IDs.”, and Huang, pg. 6, “The probability of success of a data packet transmission representing ID u, i.e. the quality of the communication, affects the uncertainty of the reputation evaluation [a service factor]”).
and determining a selected participant based on the data quality factor, service factor and stability factor of the plurality of participants to be selected. (Huang, pg. 7, “Under the influence of a plurality of factors, the traditional subjective logic develops to multi-weight subjective logic, and the reputation evaluation is calculated by considering the following factors [and determining a selected participant based on…of the plurality of participants to be selected.]. (1) Interaction reliability [the data quality factor,]: and (4) performing quality evaluation on the local model update, wherein positive and negative interaction results of historical interaction exist, and the credit value of IDs can be improved through the positive interaction…(2) freshness of interaction [and a stability factor]: the reliability of IDs may change over time, with recent interaction events having more freshness having more weight than past events during interactions of FNs with IDs.”, and Huang, pg. 6, “The probability of success of a data packet transmission representing ID u, i.e. the quality of the communication, affects the uncertainty of the reputation evaluation [service factor]”).
Regarding claim 2, Huang discloses the method according to claim 1. Huang further discloses wherein, determining the data quality factor, the service factor and the stability factor of each participant to be selected respectively comprises, determining the data quality factor, the service factor and the stability factor of the respective participant to be selected based on a type of the federated learning. (Huang, abstract, “The invention relates to a method for user selection and resource allocation based on federated learning, and belongs to the technical field of mobile communication [based on a type of the federated learning.]. First, users who participate in FL are screened. Considering the interaction reliability and interaction freshness of IDs, the reputation value of IDs is generated; as shown in claim 1, participants are selected based on reputation values of each participant which are derived from data quality, service, and stability factors (i.e. wherein, determining the data quality factor, the service factor and the stability factor of each participant to be selected respectively comprises, determining the data quality factor, the service factor and the stability factor of the respective participant to be selected).”).
Regarding claim 3, Huang discloses the method according to claim 2. The examiner notes that claim 3 is written in an alternative embodiment format and selects the cell weight optimization embodiment. Huang further discloses wherein, the type of the federated learning comprises at least one of, Key Performance Indicator (KPI) degradation detection, cell weight optimization, or optical module fault prediction. (Huang, abstract, “The invention relates to a method for user selection and resource allocation based on federated learning, and belongs to the technical field of mobile communication [wherein, the type of the federated learning comprises at least one of]. First, users who participate in FL are screened. Considering the interaction reliability and interaction freshness of IDs, the reputation value of IDs is generated…Finally, the problem of minimizing the total cost of the FL task is decomposed into two sub-optimization problems, namely, the computational cost and the allocation of communication resources. In the FL task calculation optimization stage, considering the different CPU frequencies of IDs, the calculation time and computing energy consumption need to be weighed [cell weight optimization]”).
Regarding claims 7-11, the claims are directed to the non-selected embodiments and therefore are not considered. The claims are rejected for at least their dependence to claim 3.
Regarding claim 19, the claim is similar to claim 3 and rejected under the same rationales.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 4-5, 13-18, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Huang, et al., Foreign Patent Publication CN111866954A (“Huang”), please see the provided translated version for art mapping purpose, in view of Bonawitz, et al., US Pre-Grant Publication US20190171978A1 (“Bonawitz”).
Regarding claim 4, Huang teaches the method according to claim 3. Huang further teaches:
wherein, in response to the type of the federated learning being cell weight optimization, (Huang, abstract, “The invention relates to a method for user selection and resource allocation based on federated learning, and belongs to the technical field of mobile communication. First, users who participate in FL are screened. Considering the interaction reliability and interaction freshness of IDs, the reputation value of IDs is generated…Finally, the problem of minimizing the total cost of the FL task is decomposed into two sub-optimization problems, namely, the computational cost and the allocation of communication resources. In the FL task calculation optimization stage, considering the different CPU frequencies of IDs, the calculation time and computing energy consumption need to be weighed [wherein, in response to the type of the federated learning being cell weight optimization,]”).
determining the data quality factor, the service factor and the stability factor of the respective participant to be selected based on the type of the federated learning comprises, (Huang, abstract, “First, users who participate in FL are screened. Considering the interaction reliability and interaction freshness of IDs, the reputation value of IDs is generated; as shown in claim 1, participants are selected based on reputation values of each participant which are derived from data quality, service, and stability factors (i.e. determining the data quality factor, the service factor and the stability factor of the respective participant to be selected based on the type of the federated learning comprises,).”).
determining the data quality factor based on a quality of service (QoS) alarm parameter of the respective participant to be selected; determining the service factor…and determining the stability factor based on an in-service duration of the respective participant to be selected. (Huang, pg. 7, “Under the influence of a plurality of factors, the traditional subjective logic develops to multi-weight subjective logic, and the reputation evaluation is calculated by considering the following factors. (1) Interaction reliability [determining a data quality factor]: and (4) performing quality evaluation on the local model update, wherein positive and negative interaction results of historical interaction exist, and the credit value of IDs can be improved through the positive interaction [based on a quality of service (QoS) alarm parameter of the respective participant to be selected;]…(2) freshness of interaction [and determining the stability factor]: the reliability of IDs may change over time, with recent interaction events having more freshness having more weight than past events during interactions of FNs with IDs [based on an in-service duration of the respective participant to be selected.].”, and Huang, pg. 6, “The probability of success of a data packet transmission representing ID u, i.e. the quality of the communication, affects the uncertainty of the reputation evaluation [determining the service factor…]”).).
While Huang teaches a method for selecting users for federated learning by using multiple factors, Huang does not explicitly teach:
…based on a latitude and a longitude of the respective participant to be selected;
Bonawitz teaches …based on a latitude and a longitude of the respective participant to be selected; (Bonawitz, ⁋19, “For example, the world can be subdivided such that each region is large enough to ensure that enough devices (e.g., thousands of devices) can be consistently found online for a period of at least several hours each day and each region is small enough and is determined such that a typical diurnal cycle for users in the region does not vary much within the region [of the respective participant to be selected;]. In some implementations, for example, the world can be subdivided into a plurality of regions based on latitude and/or longitude […based on a latitude and a longitude]”).
Huang and Bonawitz are both in the same field of endeavor (i.e. federated learning). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Huang and Bonawitz to teach the above limitation(s). The motivation for doing so is that considering geographic locations of devices improves training as data availability times for physically close devices are similar (cf. Bonawitz, ⁋16, “disclosure can subdivide the world into regions (e.g., spatial regions and/or the like) and leverage these regions to improve data availability for training of machine learning models. According to an aspect of the present disclosure, the regions can include regions in which the respective devices located in each region exhibit similar temporal availability patterns. By using these regions, the data held by a subset of users selected in a region for an iteration of training a machine learning model can be a more consistently representative sample of all users in the region, thereby improving the training of a machine learning model.”).
Regarding claim 5, Huang in view of Bonawitz teaches the method according to claim 4. The combination also teaches wherein, determining the selected participant based on the data quality factor, service factor and stability factor of the plurality of participants to be selected as seen in claim 4. Huang further teaches comprises, determining the participant to be selected as the selected participant, in response to the participant to be selected having the data quality factor, the service factor, and the stability factor that meet a value of an optimal function. (Huang, pg. 7, “Under the influence of a plurality of factors, the traditional subjective logic develops to multi-weight subjective logic, and the reputation evaluation is calculated by considering the following factors [that meet a value of an optimal function.]. (1) Interaction reliability [in response to the participant to be selected having the data quality factor,]: and (4) performing quality evaluation on the local model update, wherein positive and negative interaction results of historical interaction exist, and the credit value of IDs can be improved through the positive interaction [based on a quality of service (QoS) alarm parameter of the respective participant to be selected;]…(2) freshness of interaction [and the stability factor]: the reliability of IDs may change over time, with recent interaction events having more freshness having more weight than past events during interactions of FNs with IDs.”, and Huang, pg. 6, “The probability of success of a data packet transmission representing ID u, i.e. the quality of the communication, affects the uncertainty of the reputation evaluation [the service factor,]”).).
Regarding claim 13, Huang discloses:
a method for selecting participants in federated learning, comprising, acquiring a plurality of participants to be selected; (Huang, pg. 4, “Under the condition, the invention provides a user selection and resource allocation method based on federal learning. The method jointly models user selection, QoS analysis, FL calculation overhead optimization and transmission resource allocation into a problem of minimizing FL task total overhead. First, IDs are selected based on reputation value, reputation value threshold is set, IDs higher than the threshold are added into FL [A method for selecting participants in federated learning, comprising, acquiring a plurality of participants to be selected;].”).
determining a data quality factor, a service factor and a stability factor of each participant to be selected respectively; (Huang, pg. 7, “Under the influence of a plurality of factors, the traditional subjective logic develops to multi-weight subjective logic, and the reputation evaluation is calculated by considering the following factors [of each participant to be selected respectively;]. (1) Interaction reliability [determining a data quality factor,]: and (4) performing quality evaluation on the local model update, wherein positive and negative interaction results of historical interaction exist, and the credit value of IDs can be improved through the positive interaction…(2) freshness of interaction [and a stability factor]: the reliability of IDs may change over time, with recent interaction events having more freshness having more weight than past events during interactions of FNs with IDs.”, and Huang, pg. 6, “The probability of success of a data packet transmission representing ID u, i.e. the quality of the communication, affects the uncertainty of the reputation evaluation [a service factor]”).
and determining a selected participant based on the data quality factor, service factor and stability factor of the plurality of participants to be selected. (Huang, pg. 7, “Under the influence of a plurality of factors, the traditional subjective logic develops to multi-weight subjective logic, and the reputation evaluation is calculated by considering the following factors [and determining a selected participant based on…of the plurality of participants to be selected.]. (1) Interaction reliability [the data quality factor,]: and (4) performing quality evaluation on the local model update, wherein positive and negative interaction results of historical interaction exist, and the credit value of IDs can be improved through the positive interaction…(2) freshness of interaction [and a stability factor]: the reliability of IDs may change over time, with recent interaction events having more freshness having more weight than past events during interactions of FNs with IDs.”, and Huang, pg. 6, “The probability of success of a data packet transmission representing ID u, i.e. the quality of the communication, affects the uncertainty of the reputation evaluation [service factor]”).
While Huang teaches a method for selecting users for federated learning by using multiple factors, Huang does not explicitly teach:
An apparatus for selecting participants in federated learning, comprising, at least one processor; and a memory for storing at least one program which, when executed by the at least one processor, causes the at least one processor to carry out
Bonawitz teaches An apparatus for selecting participants in federated learning, comprising, at least one processor; and a memory for storing at least one program which, when executed by the at least one processor, causes the at least one processor to carry out (Bonawitz, ⁋6, “The computing device includes one or more processors; and one or more non-transitory computer-readable media that store instructions. The instructions, when executed by the one or more processors [An apparatus for selecting participants in federated learning, comprising, at least one processor; and a memory for storing at least one program which, when executed by the at least one processor, causes the at least one processor to carry out]”).
Huang and Bonawitz are both in the same field of endeavor (i.e. federated learning). Huang teaches a base method for selecting users for federated learning. Bonawitz teaches a known technique of using a computer to perform federated learning functions. It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Huang and Bonawitz to teach the above limitation(s). The motivation for doing so is that applying Bonawitz’s known technique of using a computer to perform federated learning to Huang’s base system of selecting users for federated learning would yield predictable results.
Regarding claim 14, Huang discloses:
a method for selecting participants in federated learning, comprising, acquiring a plurality of participants to be selected; (Huang, pg. 4, “Under the condition, the invention provides a user selection and resource allocation method based on federal learning. The method jointly models user selection, QoS analysis, FL calculation overhead optimization and transmission resource allocation into a problem of minimizing FL task total overhead. First, IDs are selected based on reputation value, reputation value threshold is set, IDs higher than the threshold are added into FL [A method for selecting participants in federated learning, comprising, acquiring a plurality of participants to be selected;].”).
determining a data quality factor, a service factor and a stability factor of each participant to be selected respectively; (Huang, pg. 7, “Under the influence of a plurality of factors, the traditional subjective logic develops to multi-weight subjective logic, and the reputation evaluation is calculated by considering the following factors [of each participant to be selected respectively;]. (1) Interaction reliability [determining a data quality factor,]: and (4) performing quality evaluation on the local model update, wherein positive and negative interaction results of historical interaction exist, and the credit value of IDs can be improved through the positive interaction…(2) freshness of interaction [and a stability factor]: the reliability of IDs may change over time, with recent interaction events having more freshness having more weight than past events during interactions of FNs with IDs.”, and Huang, pg. 6, “The probability of success of a data packet transmission representing ID u, i.e. the quality of the communication, affects the uncertainty of the reputation evaluation [a service factor]”).
and determining a selected participant based on the data quality factor, service factor and stability factor of the plurality of participants to be selected. (Huang, pg. 7, “Under the influence of a plurality of factors, the traditional subjective logic develops to multi-weight subjective logic, and the reputation evaluation is calculated by considering the following factors [and determining a selected participant based on…of the plurality of participants to be selected.]. (1) Interaction reliability [the data quality factor,]: and (4) performing quality evaluation on the local model update, wherein positive and negative interaction results of historical interaction exist, and the credit value of IDs can be improved through the positive interaction…(2) freshness of interaction [and a stability factor]: the reliability of IDs may change over time, with recent interaction events having more freshness having more weight than past events during interactions of FNs with IDs.”, and Huang, pg. 6, “The probability of success of a data packet transmission representing ID u, i.e. the quality of the communication, affects the uncertainty of the reputation evaluation [service factor]”).
While Huang teaches a method for selecting users for federated learning by using multiple factors, Huang does not explicitly teach:
A non-transitory computer-readable storage medium storing at least one computer program which, when executed by a processor, causes the processor to carry out
Bonawitz teaches A non-transitory computer-readable storage medium storing at least one computer program which, when executed by a processor, causes the processor to carry out (Bonawitz, ⁋6, “The computing device includes one or more processors; and one or more non-transitory computer-readable media that store instructions. The instructions, when executed by the one or more processors [A non-transitory computer-readable storage medium storing at least one computer program which, when executed by a processor, causes the processor to carry out]”).
Huang and Bonawitz are both in the same field of endeavor (i.e. federated learning). Huang teaches a base method for selecting users for federated learning. Bonawitz teaches a known technique of using a computer to perform federated learning functions. It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Huang and Bonawitz to teach the above limitation(s). The motivation for doing so is that applying Bonawitz’s known technique of using a computer to perform federated learning to Huang’s base system of selecting users for federated learning would yield predictable results.
Regarding claim 15, Huang in view of Bonawitz teaches the apparatus according to claim 13. Huang further teaches wherein, determining the data quality factor, the service factor and the stability factor of each participant to be selected respectively comprises, determining the data quality factor, the service factor and the stability factor of the respective participant to be selected based on a type of the federated learning. (Huang, abstract, “The invention relates to a method for user selection and resource allocation based on federated learning, and belongs to the technical field of mobile communication [based on a type of the federated learning.]. First, users who participate in FL are screened. Considering the interaction reliability and interaction freshness of IDs, the reputation value of IDs is generated; as shown in claim 1, participants are selected based on reputation values of each participant which are derived from data quality, service, and stability factors (i.e. wherein, determining the data quality factor, the service factor and the stability factor of each participant to be selected respectively comprises, determining the data quality factor, the service factor and the stability factor of the respective participant to be selected).”).
Regarding claim 16, Huang in view of Bonawitz teaches the apparatus according to claim 15. The examiner notes that claim 16 is written in an alternative embodiment format and selects the cell weight optimization embodiment. Huang further discloses wherein, the type of the federated learning comprises at least one of, Key Performance Indicator (KPI) degradation detection, cell weight optimization, or optical module fault prediction. (Huang, abstract, “The invention relates to a method for user selection and resource allocation based on federated learning, and belongs to the technical field of mobile communication [wherein, the type of the federated learning comprises at least one of]. First, users who participate in FL are screened. Considering the interaction reliability and interaction freshness of IDs, the reputation value of IDs is generated…Finally, the problem of minimizing the total cost of the FL task is decomposed into two sub-optimization problems, namely, the computational cost and the allocation of communication resources. In the FL task calculation optimization stage, considering the different CPU frequencies of IDs, the calculation time and computing energy consumption need to be weighed [cell weight optimization]”).
Regarding claim 17, Huang in view of Bonawitz teaches the non-transitory computer-readable storage medium according to claim 16. Huang further teaches:
wherein, in response to the type of the federated learning being cell weight optimization, (Huang, abstract, “The invention relates to a method for user selection and resource allocation based on federated learning, and belongs to the technical field of mobile communication. First, users who participate in FL are screened. Considering the interaction reliability and interaction freshness of IDs, the reputation value of IDs is generated…Finally, the problem of minimizing the total cost of the FL task is decomposed into two sub-optimization problems, namely, the computational cost and the allocation of communication resources. In the FL task calculation optimization stage, considering the different CPU frequencies of IDs, the calculation time and computing energy consumption need to be weighed [wherein, in response to the type of the federated learning being cell weight optimization,]”).
determining the data quality factor, the service factor and the stability factor of the respective participant to be selected based on the type of the federated learning comprises, (Huang, abstract, “First, users who participate in FL are screened. Considering the interaction reliability and interaction freshness of IDs, the reputation value of IDs is generated; as shown in claim 1, participants are selected based on reputation values of each participant which are derived from data quality, service, and stability factors (i.e. determining the data quality factor, the service factor and the stability factor of the respective participant to be selected based on the type of the federated learning comprises,).”).
determining the data quality factor based on a quality of service (QoS) alarm parameter of the respective participant to be selected; determining the service factor…and determining the stability factor based on an in-service duration of the respective participant to be selected. (Huang, pg. 7, “Under the influence of a plurality of factors, the traditional subjective logic develops to multi-weight subjective logic, and the reputation evaluation is calculated by considering the following factors. (1) Interaction reliability [determining a data quality factor]: and (4) performing quality evaluation on the local model update, wherein positive and negative interaction results of historical interaction exist, and the credit value of IDs can be improved through the positive interaction [based on a quality of service (QoS) alarm parameter of the respective participant to be selected;]…(2) freshness of interaction [and determining the stability factor]: the reliability of IDs may change over time, with recent interaction events having more freshness having more weight than past events during interactions of FNs with IDs [based on an in-service duration of the respective participant to be selected.].”, and Huang, pg. 6, “The probability of success of a data packet transmission representing ID u, i.e. the quality of the communication, affects the uncertainty of the reputation evaluation [determining the service factor…]”).).
Bonawitz further teaches …based on a latitude and a longitude of the respective participant to be selected; (Bonawitz, ⁋19, “For example, the world can be subdivided such that each region is large enough to ensure that enough devices (e.g., thousands of devices) can be consistently found online for a period of at least several hours each day and each region is small enough and is determined such that a typical diurnal cycle for users in the region does not vary much within the region [of the respective participant to be selected;]. In some implementations, for example, the world can be subdivided into a plurality of regions based on latitude and/or longitude […based on a latitude and a longitude]”).
Huang and Bonawitz are both in the same field of endeavor (i.e. federated learning). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Huang and Bonawitz to teach the above limitation(s). The motivation for doing so is that considering geographic locations of devices improves training as data availability times for physically close devices are similar (cf. Bonawitz, ⁋16, “disclosure can subdivide the world into regions (e.g., spatial regions and/or the like) and leverage these regions to improve data availability for training of machine learning models. According to an aspect of the present disclosure, the regions can include regions in which the respective devices located in each region exhibit similar temporal availability patterns. By using these regions, the data held by a subset of users selected in a region for an iteration of training a machine learning model can be a more consistently representative sample of all users in the region, thereby improving the training of a machine learning model.”).
Regarding claim 18, Huang in view of Bonawitz teaches the non-transitory computer-readable storage medium according to claim 14. Huang further teaches wherein, determining the data quality factor, the service factor and the stability factor of each participant to be selected respectively comprises, determining the data quality factor, the service factor and the stability factor of the respective participant to be selected based on a type of the federated learning. (Huang, abstract, “The invention relates to a method for user selection and resource allocation based on federated learning, and belongs to the technical field of mobile communication [based on a type of the federated learning.]. First, users who participate in FL are screened. Considering the interaction reliability and interaction freshness of IDs, the reputation value of IDs is generated; as shown in claim 1, participants are selected based on reputation values of each participant which are derived from data quality, service, and stability factors (i.e. wherein, determining the data quality factor, the service factor and the stability factor of each participant to be selected respectively comprises, determining the data quality factor, the service factor and the stability factor of the respective participant to be selected).”).
Regarding claim 20, the claim is similar to claim 17 and rejected under the same rationales.
Regarding claim 21, Huang in view of Bonawitz teaches the non-transitory computer-readable storage medium according to claim 20. The combination also teaches wherein, determining the selected participant based on the data quality factor, service factor and stability factor of the plurality of participants to be selected as seen in claim 20. Huang further teaches comprises, determining the participant to be selected as the selected participant, in response to the participant to be selected having the data quality factor, the service factor, and the stability factor that meet a value of an optimal function. (Huang, pg. 7, “Under the influence of a plurality of factors, the traditional subjective logic develops to multi-weight subjective logic, and the reputation evaluation is calculated by considering the following factors [that meet a value of an optimal function.]. (1) Interaction reliability [in response to the participant to be selected having the data quality factor,]: and (4) performing quality evaluation on the local model update, wherein positive and negative interaction results of historical interaction exist, and the credit value of IDs can be improved through the positive interaction [based on a quality of service (QoS) alarm parameter of the respective participant to be selected;]…(2) freshness of interaction [and the stability factor]: the reliability of IDs may change over time, with recent interaction events having more freshness having more weight than past events during interactions of FNs with IDs.”, and Huang, pg. 6, “The probability of success of a data packet transmission representing ID u, i.e. the quality of the communication, affects the uncertainty of the reputation evaluation [the service factor,]”).).
Allowable Subject Matter
Claim 6 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, and 35 U.S.C. 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 6, Below are the closest cited references, each of which disclose various aspects of claim 6:
Huang, et al., CN111866954A discloses a system that selects clients for a federated learning system by considering a reputation value attached to each client. The reputation value being derived from multiple factors. However, even though Huang teaches using reputation values derived from weighted factors, Huang does not explicitly teach a reputation function that has the same equations as required by the optimal function in claim 6.
Bonawitz, et al., US20190171978A1 discloses a system that groups clients together based on geographical location for federated learning. The clients are grouped to different regions that are determined based on geographical boundaries. However, even though Bonawitz considers geographical location when grouping clients, Bonawitz does not explicitly teach an optimal function for selecting clients let alone the required latitude or longitude equations required within the optimal function in claim 6.
Nishio, et al., “Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge” discloses a system that selects clients for a federated learning system by considering client resource constraints during client selection. However, even though Nishio considers client resource constraints during client selection, Nishio does not explicitly teach an optimal function that combines multiple factors let alone an optimal function that has to deal with geographical locations of clients.
While the above prior arts disclose the aforementioned concepts, however, none of the prior arts, individually or in reasonable combination, discloses all the limitations in the manner recited in claim 6. Specifically, the claim requires the data quality factor, the service factor, and the stability factors to be considered in specific equations in an optimal function. While the references cited above mention aspects of the data quality factor, the service factor, and the stability factor they do not recite the equations of the optimal function as claimed in claim 6. Therefore, claim 6 is allowable over the prior art.
Conclusion
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/N.S.W./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148