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 .
Response to Arguments
Applicant’s arguments with respect to claims 1, 2, 4, 5 and 7-14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5, 7-10, 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Alabbasi et al(US 2023/0010095 A1) in view of Suryavanshi(US 2016/0150455).
Regarding claims 1, Alabbasi ‘095 teaches, an information processing apparatus([0050], [0052] and Figs. 2, 7, client computing device(UE)), comprising; an arithmetic processing unit configured to: learn a model to predict a wireless environment ([0054]-0056] and Figs. 2, 7, 12, client computing device(UE) 205 comprising client Machin learning model for learning a model to predict wireless network conditions); upload a result of the learning to a server([0052], [0054]-0056], [0112] and Figs. 2, 7, 12, client computing device(UE) 205 uploading client machine learning models 207 to gNB/server); predict, by use of a shared model, the wireless environment, wherein the shared model is based on an integration of one or more learning results in the server([0054]-0056], [0112] and Figs. 2, 7, 12, client computing device(UE) 205 downloading aggregated UEs' model 203 from the server and predicting wireless network condition based on the aggregation), and the one or more learning results include the uploaded result([0054]-0056], [0112] and Figs. 2, 7, 12, client computing device(UE) 205 predicting wireless network condition based on the aggregation of uploaded result from the UE and other UEs); and switch a communication path to be connected from a first communication path to a second communication path([0055], [0056] and Fig. 2, the UEs 205 predicting the quality of network connection and determining to switch to a secondary carrier by performing handover), wherein a plurality of communication paths of the wireless environment includes the first communication path and the second communication path([0055], [0056] and Fig. 2, first carrier(first path) or second carrier(second path) being selected for connection based on predicted performance), the communication path to be connected is switched based on the predicted wireless environment([0055], [0056] and Fig. 2, the UEs 205 predicting the quality of network connection and determining to switch to a secondary carrier by performing handover).
Alabbasi ‘095 does not explicitly teach, the communication path to be connected is switched at a timing at which a user does not use the information processing apparatus.
Suryavanshi ‘455 teaches, the communication path to be connected is switched at a timing at which a user does not use the information processing apparatus ([0098]- [0100] and Figs. 10-11, handover to change a communication path from source eNB to target eNB being performed when a user is not using the UE (during silent period)).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Alabbasi ‘095 by incorporating the teaching of Suryavanshi ‘455, since such modification would provide handover optimization in a wireless communication system, as suggested by Suryavanshi ‘455([0006]).
Regarding claim 5, the combination of Alabbasi ‘095 and Suryavanshi ‘455 teaches all of the claim limitations, Alabbasi ‘095 further teaches, wherein the first communication path and the second communication path use a same communication method ([0055], [0056] [0059], and Fig. 2, the same wireless communication method via first and second carriers).
Regarding claim 7, the combination of Alabbasi ‘095 and Suryavanshi ‘455 teaches all of the claim limitations Suryavanshi ‘455 further teaches, wherein the switch of the communication path to be connected is at least one of a timing at which a communication traffic volume of an application becomes equal to or smaller than a threshold, or a timing based on an attribute of the user( [0098]-[0100] switching to second path based on attribute/characteristics of the parent user(e.g. talking and silent pattern)) .
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Alabbasi ‘095 by incorporating the teaching of Suryavanshi ‘455, since such modification would provide handover optimization in a wireless communication system, as suggested by Suryavanshi ‘455([0006]).
Regarding claim 8, the combination of Alabbasi ‘095 and Suryavanshi ‘455 teaches all of the claim limitations, Alabbasi ‘095 further teaches, wherein the arithmetic processing unit is further configured to predict information about a relationship between a time a position of the information processing apparatus, and the wireless environment ([0105], [0160] and fig. 2, client/UE predicting based on time and location information).
Regarding claim 9, the combination of Alabbasi ‘095 and Suryavanshi ‘455 teaches all of the claim limitations, Alabbasi ‘095 further teaches, wherein the shared model is classified for a cluster based on an attribute of the user ([0042], [0043] shared model that is modified depending on the UE’s privacy setting).
Regarding claim 10, the combination of Alabbasi ‘095 and Suryavanshi ‘455 teaches all of the claim limitations, Alabbasi ‘095 further teaches, wherein the arithmetic processing unit is further configured to predict the wireless environment based on a composite model, and the composite model is based on combination of the result of the learning and the shared model([0055], [0056], [0112] and Figs. 2, the UEs 205 predicting network condition based on aggregated model that is received form the server and a learning model in the UE)
Regarding claim 13, Alabbasi ‘095 teaches, an information processing system( [0050], [0052] and Figs. 2, communication network 200), comprising: one or more information processing apparatuses wherein each of the one or more information processing apparatuses comprises: a first arithmetic processing unit configured to: learn a model to predict a wireless environment([0054]-0056] and Figs. 2, 7, 12, client computing device(UE) 205 comprising client Machin learning model for learning a model to predict wireless network conditions),
upload a result of the learning to a server ([0052], [0054]-0056], [0112] and Figs. 2, 7, 12, client computing device (UE) 205 uploading client machine learning models 207 to gNB/server), predict, by use of a shared model, the wireless environment ([0054]-0056], [0112] and Figs. 2, 7, 12, client computing device (UE) 205 downloading aggregated UEs' model 203 from the server and predicting wireless network condition based on the aggregation), wherein the shared model is based on integration of one or more learning results in the server([0054]-0056], [0112] and Figs. 2, 7, 12, client computing device(UE) 205 downloading aggregated UEs' model 203 from the server and predicting wireless network condition based on the aggregation), and the one or more learning results include the uploaded result([0054]-0056], [0112] and Figs. 2, 7, 12, client computing device(UE) 205 predicting wireless network condition based on the aggregation of uploaded result from the UE and other UEs); and switch a communication path to be connected from a first communication path to a second communication path ([0055], [0056] and Fig. 2, the UEs 205 predicting the quality of network connection and determining to switch to a secondary carrier by performing handover), wherein a plurality of communication paths of the wireless environment includes the first communication path and the second communication path([0055], [0056] and Fig. 2, first carrier(first path) or second carrier(second path) being selected for connection based on predicted performance), the communication path to be connected is switched based on the predicted wireless environment([0055], [0056] and Fig. 2, the UEs 205 predicting the quality of network connection and determining to switch to a secondary carrier by performing handover), and the server, including a second arithmetic processing unit configured to(0059], [0112] and Figs. 2, 8, 9, 12, the server includes aggregating a plurality of client machine learning models (e.g., 207)):integrate one or more learning results of the one or more information processing apparatuses; generate the shared model ([0059], [0112] and Figs. 2, 8, 9, 12, the server includes aggregating a plurality of client machine learning models (e.g., 207) received from a plurality of client computing devices (e.g., 205, 700) in the telecommunications network to obtain an aggregated machine learning model (e.g., 203) and predicting wireless network condition based on the model); and distribute the shared model to the one or more information processing apparatuses ([0059], [0112] and Figs. 2, 8, 9, 12, the server sending to the plurality of client computing devices (e.g., 205) the aggregated machine learning model (e.g., 203) and the output parameter (e.g., 307) of the network machine learning model).
Alabbasi ‘095 does not explicitly teach, the communication path to be connected is switched at a timing at which a user does not use the information processing apparatus.
Suryavanshi ‘455 teaches, the communication path to be connected is switched at a timing at which a user does not use the information processing apparatus ([0098]- [0100] and Figs. 10-11, handover to change a communication path from source eNB to target eNB being performed when a user is not using the UE (during silent period)).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Alabbasi ‘095 by incorporating the teaching of Suryavanshi ‘455, since such modification would provide handover optimization in a wireless communication system, as suggested by Suryavanshi ‘455([0006]).
Regarding claim 14, Alabbasi ‘095 teaches, an information processing method ([0050], [0052] and Figs. 2,12 communication network 200), comprising: learning, in one or more information processing apparatuses, a model for predicting a wireless environment; uploading a result of the learning to a server ([0052], [0054]-0056], [0112] and Figs. 2, 7, 12, client computing device (UE) 205 uploading client machine learning models 207 to gNB/server);
integrating, in the server, one or more learning results uploaded from the one or more information processing apparatuses; generating a shared model([0059], [0112] and Figs. 2, 8, 9, 12, the server includes aggregating a plurality of client machine learning models (e.g., 207) received from a plurality of client computing devices (e.g., 205, 700) in the telecommunications network to obtain an aggregated machine learning model (e.g., 203) and predicting wireless network condition based on the model); distributing the shared model to the one or more information processing apparatuses ([0059], [0112] and Figs. 2, 8, 9, 12, the server sending to the plurality of client computing devices (e.g., 205) the aggregated machine learning model (e.g., 203) and the output parameter (e.g., 307) of the network machine learning model); predicting, in the one or more information processing apparatuses, the wireless environment by using the shared model([0054]-0056], [0112] and Figs. 2, 7, 12, client computing device (UE) 205 downloading aggregated UEs' model 203 from the server and predicting wireless network condition based on the aggregation).wherein the shared model is based on the integration of the one or more learning results in the server([0054]-0056], [0112] and Figs. 2, 7, 12, client computing device(UE) 205 downloading aggregated UEs' model 203 from the server and predicting wireless network condition based on the aggregation); and
switching a communication path to be connected from a first communication path to a second communication path([0055], [0056] and Fig. 2, the UEs 205 predicting the quality of network connection and determining to switch to a secondary carrier by performing handover), wherein a plurality of communication paths of the wireless environment includes the first communication path and the second communication path([0055], [0056] and Fig. 2, first carrier(first path) or second carrier(second path) being selected for connection based on predicted performance), the communication path to be connected is switched based on the predicted wireless environment([0055], [0056] and Fig. 2, the UEs 205 predicting the quality of network connection and determining to switch to a secondary carrier by performing handover).
Alabbasi ‘095 does not explicitly teach, the communication path to be connected is switched at a timing at which a user does not use the information processing apparatus.
Suryavanshi ‘455 teaches, the communication path to be connected is switched at a timing at which a user does not use the information processing apparatus ([0098]- [0100] and Figs. 10-11, handover to change a communication path from source eNB to target eNB being performed when a user is not using the UE (during silent period)).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Alabbasi ‘095 by incorporating the teaching of Suryavanshi ‘455, since such modification would provide handover optimization in a wireless communication system, as suggested by Suryavanshi ‘455([0006]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Alabbasi et al(US 2023/0010095 A1) in view of Vodencarevic et al (US 2021/0097439 A1) and further in view of Prakash et al(US 2019/0138934 A1).
Regarding claim 11, Alabbasi ‘095 teaches, a server ([0050], [0059] and Figs. 2, 8, 9, 12 server/eNB), comprising; an arithmetic processing unit configured to: integrate integrates one or more learning results of a model to predict for predicting a wireless environment in one or more information processing apparatuses([0059], [0112] and Figs. 2, 8, 9, 12, the server includes aggregating a plurality of client machine learning models (e.g., 207) received from a plurality of client computing devices (e.g., 205, 700) in the telecommunications network to obtain an aggregated machine learning model (e.g., 203) and predicting wireless network condition based on the model), wherein the one or more learning results are based on the learning operation; ([0059], [0112] and Figs. 2, 8, 9, 12, the server includes aggregating a plurality of client machine learning models (e.g., 207) received from a plurality of client computing devices (e.g., 205, 700)) generate a shared model based on the integrated one or more learning results([0059], [0112] and Figs. 2, 8, 9, 12, the server generating the aggregated machine learning model); and distribute distributes the shared model to the one or more information processing apparatuses([0059], [0112] and Figs. 2, 8, 9, 12, the server sending to the plurality of client computing devices (e.g., 205) the aggregated machine learning model (e.g., 203) and the output parameter (e.g., 307) of the network machine learning model).
Alabbasi ‘095 does not explicitly teach, receive learning joining permission notification from an information processing apparatus, wherein the learning joining permission notification is associated with a learning operation of the information processing apparatus; determine to join in the learning operation of the information processing apparatus.
Vodencarevu ‘439 teaches, receive learning joining permission notification from an information processing apparatus, wherein the learning joining permission notification is associated with a learning operation of the information processing apparatus; determine to join in the learning operation of the information processing apparatus ([0243], [0244] server receiving a request for enrollment in the federated learning from client device),
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Alabbasi ‘095 by incorporating the teaching of Vodencarevu ‘439, since such modification would improve a shared machine learned model while their local and potentially private data is not distributed outside of the client units, as suggested by Vodencarevu ‘439([0005]).
The combination of Alabbasi ‘095 and Vodencarevu ‘439 does not explicitly teach, wherein the determination is based on a charge status of the information processing apparatus, and the charge status is equal to or greater than a specific threshold.
Prakash ‘934 teaches, wherein the determination is based on a charge status of the information processing apparatus, and the charge status is equal to or greater than a specific threshold ([0040], [0048], [0286], and Figs. 1-4, edge node being selected to participate on the federated learning based on its battery level/budget).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Alabbasi ‘095 by incorporating the teaching of Prakash ‘934, since such modification would provide efficient distributed machine learning (ML) in distributed heterogeneous computing environments, as suggested by Prakash ‘934([0021]).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Alabbasi ‘095 and Suryavanshi ‘455 as applied to claims above, and further in view of Akdeniz et al(US 2023/0068386 A1).
Regarding claim 2, the combination of Alabbasi ‘095 and Suryavanshi ‘455 teaches all of the claim limitations, Alabbasi ‘095 further teaches, the wireless environment is at least one of that a communication status of the first communication path is deteriorated, wherein the first communication path is a communication path to which a prediction target of the wireless environment is connected ([0055], [0056] and Fig. 2, the UEs 205 predicting the quality of network connection on the first carrier is deteriorating and making the determination to handover to a secondary carrier, notice the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations) or that characteristics of the third communication path are not good, wherein the third communication path is a communication path to which the prediction target of the wireless environment is not connected([0055], [0056] and Fig. 2, the UEs 205 predicting the quality of network connection on the first carrier is deteriorating and making the determination to handover to a secondary carrier, notice the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations).
The combination of Alabbasi ‘095 and Suryavanshi ‘455 does not explicitly teach, the plurality of communication paths of the wireless environment includes a third communication path.
Akdeniz ‘386 teaches, the plurality of communication paths of the wireless environment includes a third communication path ([0107]- [0108] and Fig. 9, client compute node 902 communicating via three different paths 912).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Alabbasi ‘095 by incorporating the teaching of Akdeniz ‘386, since such modification would provide accurate learning models over wireless edge networks with distributed data, and online, distributed algorithms deployed in real-time, as suggested by Akdeniz ‘386([0005]).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Alabbasi ‘095 and Suryavanshi ‘455 as applied to claims above, and further in view of Prakash et al (US 2019/0138934 A1).
Regarding claim 4, the combination Alabbasi ‘095 and Suryavanshi ‘455 teaches all of the claim limitations except, wherein the first communication path and the second communication path use different communication methods.
Prakash ‘934 teaches, wherein the first communication path and the second communication path use different communication methods ([0045], [0059] and Fig. 1, communication paths that use different communication types/method (e.g. WLAN and cellular networks)).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Alabbasi ‘095 by incorporating the teaching of Prakash ‘934, since such modification would provide efficient distributed machine learning (ML) in distributed heterogeneous computing environments, as suggested by Prakash ‘934([0021]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Alabbasi ‘095 and Vodencarevu ‘439 and Prakash ‘934 as applied to claims above, and further in view of Tsunoda et al(US 2020/0279158 A1).
Regarding claim 12, the combination Alabbasi ‘095, Vodencarevu ‘439 and Prakash ‘934 teaches all of the claim limitations except, wherein the server comprises a plurality of server apparatuses, a first server apparatus of the plurality of server apparatuses has a class relationship with a second server apparatus of the plurality of server apparatuses, and the first server apparatus, at an upper-level class. of the plurality of server apparatuses is configured to; integrate shared models generated by server apparatuses, of the plurality of server apparatuses, in a lower-level class, wherein the shared models include the shared model; and generate a specific shared model for the upper-level class.
Tsunoda ‘158 teaches, wherein the server comprises a plurality of server apparatuses, a first server apparatus of the plurality of server apparatuses has a class relationship with a second server apparatus of the plurality of server apparatuses ([0103], [0121] and Fig. 8, server having hierarchy relationship), the first server apparatus, at an upper-level class. of the plurality of server apparatuses is configured to; integrate shared models generated by server apparatuses, of the plurality of server apparatuses, in a lower-level class, wherein the shared models include the shared model; and generate a specific shared model for the upper-level class ([0103], [0121] and Fig. 8, a cloud server 8 at a higher level configured to aggregate shared models from lower level servers).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Alabbasi ‘095 by incorporating the teaching of Tsunoda ‘158, since such modification would provide a machining condition adjustment system that adjust at least either of machining conditions and machining parameters in consideration of particulars demanded in accordance with a machining type, as suggested by Tsunoda ‘158([0006]).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AWET A HAILE whose telephone number is (571)270-3114. The examiner can normally be reached Monday through Friday 8:30 AM - 4:30 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Thier can be reached at (571)272-2832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AWET HAILE/ Primary Examiner, Art Unit 2474