DETAILED ACTION
1.This communication is in response to the amendment filed on 11/26/2025. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
1a. Status of the claims:
Claims 8 and 19 are amended.
Claims 1- 20 are pending.
Response to Argument
2. Applicant's arguments filed 11/26/2025 have been fully considered but are moot in view of the new grounds of rejection. A new non-final is made because of the Japanese reference translation discrepancy as discussed in November 25, 2025 interview.
Claim Rejections - 35 USC § 103
3. 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 of this title, 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.
4. Claims 1-6 and 8-20 is rejected under 35 U.S.C. 103 as being unpatentable Bugdayci et al. (hereinafter “Bugdayci”) (US 2022/0237102 A1), in view of JP document (JP 4736192 B2), in view of ZHANG et al. (hereinafter “ZHANG”) (CN 113128694 A), and further in view of ZhangH et al. (hereinafter “ZhangH”) (CN 112491572 A).
Claim 1, Bugdayci discloses a method of training a machine learning model to predict device connectivity, comprising:
receiving a first plurality of historical connectivity records indicating transmissions from a plurality of devices (data collected is log items that measure the access of a system over a period of time where the log data items collected are collected by data type that is determined by data type that were previously accessed said user devices ( Bugdayci, [0025]; [0040]));
training a first machine learning model, of the first machine learning model architecture, based on the first subset of historical connectivity records, wherein the first machine learning model learns to generate forecasted connectivity records for the defined set of connectivity characteristics based on the training ( expected value of access being forecast using Model trainer that used historical training data, where the model trainer for a scenario is equated to a first machine learning model where the access system ( including a server of the system) is disclosed being a multi-tenant architecture (and by using the historical type of data in a machine learning to forecast expected values of a access of a cloud resources the records of access resources has also being forecasted), ( Bugdayci, [0084]-[0085]; [0113])).
Bugdayci does not disclose selecting a first subset of historical connectivity records, from the first plurality of historical connectivity records, wherein each respective historical connectivity record of the first subset of historical connectivity records corresponds to a defined set of connectivity characteristics.
JP document discloses selecting a first subset of historical connectivity records ( a first connection history information selected for a user’s channel, ( the first connection history information is hold indicating a record of connection to a webserver ( the holding of the connection historical information for a webserver is equated to selection a first historical connectivity records), JP document [0017];[0027]), from the first plurality of historical connectivity records ( a first connection history information hold indicating a record of connection to a webserver where a second connection history information for a controller is also part of a plurality of connection history information, JP document [0017]), wherein each respective historical connectivity record of the first subset of historical connectivity records corresponds to a defined set of connectivity characteristics (volume of connection history information is set using various setting values ( by setting the volume of a connection history information the size of the connection history information is determined also), JP document [0025]).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate JP document’s teachings with Bugdayci’s teachings. One skilled in the art would be motivated to combine them in order to select effectively a type of connection history information by selecting a set of parameters corresponding to a webserver.
Bugdayci in view of JP document do not disclose selecting a first machine learning model architecture, of a plurality of machine learning model architectures.
ZHANG discloses selecting a first machine learning model architecture ( selecting of a training data in the machine learning selection based on compression rate, ZHANG, page 12, first full paragraph lines 10-16 starting with In order to improve the communication efficiently… ), of a plurality of machine learning model architectures ( training data in the machine learning based on compression rates such as high data compression and low data compression are two different models of machine learning models ( machine learning based on compression rate is equated to first machine learning model), ZHANG, page 12, first full paragraph lines 10-16 starting with In order to improve the communication efficiently… ).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate ZHANG’s teachings with Bugdayci’s teachings in view of JP document’s teachings. One skilled in the art would be motivated to combine them in order to select effectively a type of connection history information by selecting a set of parameters corresponding to the compression rate of the channel connection.
Bugdayci in view of JP document in view of ZHANG do not disclose selecting a first machine learning model comprising: determining a size of the first subset of historical connectivity records; and selecting the first machine learning model architecture by evaluating the size using one or more criteria.
ZhangH discloses selecting a first machine learning model architecture, comprising: determining a size of the first subset of historical connectivity records ( determining the size of weight value between an output node and a receiving node is done by determining the history connection state information of the plurality of terminal pair to predict the model machine learning algorithm training , the analysis of weigh value that is a criteria for determining the size of the historical connection records is based on the specification that discloses in [0041] that the size is a factor of numbers of connection records ( the size of the weight value is the value of the number of connections between a terminal pair, for example 64), ZhangH, page 15, 2nd full paragraph starting in this embodiment, multi-layer sensing…; page 12,3rd paragraph starting by The analysis device determines a connection state corresponding..; based on the specification that discloses in[0041] that the size is a factor of numbers of connection records; the selection of machine learning model is made by the history of the connection of a terminal pair and the number of connections of the terminal pair ); and selecting the first machine learning model architecture by evaluating the size using one or more criteria (historical connection state information of a plurality terminal pairs is being predicted through a machine learning algorithm training by determining the history connection state information of the plurality of terminal pair to predict the model machine learning algorithm training , the analysis of weigh value that is a criteria for determining the size of the historical connection records is based on then specification that discloses in [0041] that the size is a factor of numbers of connection records, ZhangH, page 10, the paragraph before the last starting with based on the test terminal..; ZhangH, page 15, 2nd full paragraph starting in this embodiment, multi-layer sensing…; the weight is equated to the criteria; page 12,3rd paragraph starting by The analysis device determines a connection state corresponding..) ).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate ZhangH’s teachings with Bugdayci’s teachings in view of JP document’s teachings and in view of ZHANG’s teachings. One skilled in the art would be motivated to combine them in order to determine the selection of machine learning algorithm training by calculating the strength of connection between an output node and a receiving node.
Regarding claim 2, Bugdayci, JP document, ZHANG and ZhangH disclose the method of Claim 1, wherein: all of the first plurality of historical connectivity records were received on defined workdays (shopping in line in a period of week or month is disclosed (where access data collected for a shopping in line in workdays is equated historical connectivity records were received on defined workdays) ( Bugdayci, [0076];[0025])), and a second machine learning model is trained for a second plurality of historical connectivity records that were received on defined non-workdays (train model for days other than work day are disclosed, where access data collected for a shopping in line in non- workdays is equated historical connectivity records were received on defined non- workdays) ( Bugdayci, [0081];[0025])).
Regarding claim 3, Bugdayci, JP document, ZHANG and ZhangH disclose the method of Claim 1, wherein the plurality of machine learning model architectures comprises at least one of: an autoregressive integrated moving average (ARIMA) model architecture, a Gaussian unbiased parameter estimator model architecture, a linear regression model architecture, or a median estimator model architecture(a linear regression training model disclosed ( Bugdayci, [0080])).
Regarding claim 4, Bugdayci, JP document, ZHANG and ZhangH disclose the method of Claim 1, wherein each of the first plurality of historical connectivity records corresponds to a dial-in from a corresponding device and indicates at least one of: a type of the corresponding device, a network technology used for the dial-in, a geographical region of the corresponding device, or a telecom provider of the corresponding device (a smartphone having a user interface to communicate at a high level network ( Bugdayci, [0120])).
Regarding claim 5, Bugdayci, JP document, ZHANG and ZhangH disclose the method of Claim 1, wherein selecting the first machine learning model architecture comprises determining a number of records, in the first plurality of historical connectivity records, that were received per day (a linear regression training model disclosed where the model trainer for a scenario is equated to a first machine learning model where the access system is disclosed being a multi-tenant architecture ( Bugdayci, [0080]; [0113])).
Regarding claim 6, Bugdayci, JP document, ZHANG and ZhangH disclose the method of Claim 1, further comprising re-training the first machine learning model daily, based on historical connectivity records associated with a defined number of previous days ( train model has a repeat operation for forecasting tomorrow operation or access ( next day) that is based on historical training data ( Bugdayci, [0085])).
Regarding claim 8, Bugdayci discloses a method of predicting device connectivity using machine learning, comprising:
receiving a plurality of current connectivity records indicating transmissions from a plurality of devices (data collected is log items that measure the access of a system over a period of time where the log data items collected are for access by user devices that are determined by data type ( Bugdayci, [0025]; [0040])); and generating forecasted connectivity records by processing the first subset of current connectivity records using the first machine learning model ( expected value of access being forecast using Model trainer that used historical training data where the model trainer for a scenario is equated to a first machine learning model (and by using the historical type of data in a machine learning to forecast expected values of a access of a cloud resources the records of access resources has also being forecasted), ( Bugdayci, [0084]-[0085])).
Bugdayci does not disclose selecting a first subset of current connectivity records, from the plurality of current connectivity records, wherein each respective current connectivity record of the first subset of current connectivity records corresponds to a defined set of connectivity characteristics.
JP document discloses selecting a first subset of current connectivity records ( a first connection history information selected for a user’s channel, ( the first connection history information is hold indicating a record of connection to a webserver ( the holding of the connection historical information for a webserver is equated to selection a first historical connectivity records), JP document [0017];[0027]), from the plurality of current connectivity records ( a first connection history information hold indicating a record of connection to a webserver where a second connection history information for a controller is also part of a plurality of connection history information, JP document [0017]), wherein each respective current connectivity record of the first subset of current connectivity records corresponds to a defined set of connectivity characteristics (corresponding to the first connection history information a set of parameters are based on the first connection history information that is defined for Webserver, JP document [0017]).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate JP document’s teachings with Bugdayci’s teachings. One skilled in the art would be motivated to combine them in order to select effectively a type of connection history information by selecting a set of parameters corresponding to a webserver.
Bugdayci in view of JP document do not disclose identifying a first machine learning model, of a plurality of machine learning models, based at least in part on determining that the first machine learning model was trained on a subset of historical connectivity records corresponding to the defined set of connectivity characteristics.
ZHANG discloses identifying a first machine learning model ( selecting of a training data in the machine learning selection based on compression rate, ZHANG, page 12, first full paragraph lines 10-16 starting with In order to improve the communication efficiently… ), of a plurality of machine learning models ( training data in the machine learning based on compression rates such as high data compression and low data compression are two different models of machine learning models ( machine learning based on compression rate is equated to first machine learning model), ZHANG, page 12, first full paragraph lines 10-16 starting with In order to improve the communication efficiently… ), based at least in part on determining that the first machine learning model was trained on a subset of historical connectivity records corresponding to the defined set of connectivity characteristics ( selecting of a training data in the machine learning selection based on compression rate where the data in the compression rate is evaluated base on the size of the information, ZHANG, page 12, first full paragraph lines 10-16 starting with In order to improve the communication efficiently… ).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate ZHANG’s teachings with Bugdayci’s teachings in view of JP document’s teachings. One skilled in the art would be motivated to combine them in order to select effectively a type of connection history information by selecting a set of parameters corresponding to the compression rate of the channel connection.
Bugdayci in view of JP document and in view of ZHANG do not disclose wherein the first machine learning model comprises a first machine learning model architecture selected based on a size of the subset of historical connectivity records.
ZhangH discloses wherein the first machine learning model comprises a first machine learning model architecture selected based on a size of the subset of historical connectivity records ( determining the adjust the size of weight value between an output node and a receiving node is done by calculating the activity function between an output node and a receiving node , ZhangH, page 15, 2nd full paragraph starting in this embodiment, multi-layer sensing…; based on the specification [0061] the size is related to the connectivity).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate ZhangH’s teachings with Bugdayci’s teachings in view of JP document’s teachings and in view of ZHANG’s teachings. One skilled in the art would be motivated to combine them in order to determine the selection of machine learning algorithm training by calculating the strength of connection between an output node and a receiving node.
Regarding claim 9, Bugdayci, JP document, and ZHANG disclose the method of Claim 8, further comprising: determining an allowable range for the forecasted connectivity records; and upon determining that the forecasted connectivity records are outside of the allowable range, generating an alert indicating potential connectivity problems (when a value is outside of an anticipated range of required resources for access of an online device, an alert is generated ( Bugdayci, [0089])).
Regarding claim 10, Bugdayci, JP document, and ZHANG disclose the method of Claim 9, wherein the alert indicates at least one of: a type of device associated with the potential connectivity problems, a network technology associated with the potential connectivity problems, a geographical region associated with the potential connectivity problems, or a telecom provider associated with the potential connectivity problems (a smartphone having a user interface to communicate at a high level network ( Bugdayci, [0120])).
Regarding claim 11, Bugdayci, JP document, and ZHANG disclose the method of Claim 9, further comprising: identifying one or more entities that receive the plurality of current connectivity records (the alert being identified as catastrophic alerts ( Bugdayci, [0023)), and transmitting the alert to the one or more entities (the alert being sent to computer servers ( Bugdayci, [0023]; [0024])).
Regarding claim 12, Bugdayci, JP document, and ZHANG disclose the method of Claim 8, wherein: the plurality of current connectivity records were received on a defined workday, the first machine learning model was trained using a first plurality of historical connectivity records that were received on defined workdays (shopping in line in a period of week or month is disclosed (where access data collected for a shopping in line in workdays is equated historical connectivity records were received on defined workdays) ( Bugdayci, [0076];[0025])), and the plurality of machine learning models comprises at least a second machine learning model that was trained using a second plurality of historical connectivity records that were received on defined non-workdays (train model for days other than work day are disclosed, where access data collected for a shopping in line in non- workdays is equated historical connectivity records were received on defined non- workdays) ( Bugdayci, [0081];[0025]).
Regarding claim 13, Bugdayci, JP document, ZHANG, and ZhangH disclose the method of Claim 8, wherein the plurality of machine learning models comprises at least one of: an autoregressive integrated moving average (ARIMA) model, a Gaussian unbiased parameter estimator model, a linear regression model, or a median estimator model (a smartphone having a user interface to communicate at a high level network ( Bugdayci, [0120])).
Regarding claim 14, Bugdayci, JP document, ZHANG, and ZhangH disclose the method of Claim 8, wherein each of the plurality of current connectivity records corresponds to a dial-in from a corresponding device and indicates connection characteristics comprising at least one of: a type of the corresponding device, a network technology used for the dial-in, a geographical region of the corresponding device, or a telecom provider of the corresponding device (a smartphone having a user interface to communicate at a high level network ( Bugdayci, [0120])).
Regarding claim 15, Bugdayci, JP document, ZHANG, and ZhangH the method of Claim 14, wherein the first machine learning model is identified based on the connection characteristics (device characteristics being used as metrics of the first the machine learning ( Bugdayci, [0028])).
Regarding claim 16, Bugdayci, JP document, ZHANG, and ZhangH a system, comprising: a memory comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the system to perform an operation (executable instructions stored in a non-transitory media being executed by a processor ( Bugdayci, [0108]); In addition, claim 16 is substantially similar to claim 1, thus the same rationale applies.
Regarding claim 17, claim 17 is substantially similar to claim 5, thus the same rationale applies.
Regarding claim 18, claim 18 is substantially similar to claim 6, thus the same rationale applies.
Regarding claim 19, Bugdayci, JP document, ZHANG, and ZhangH a system, comprising: a memory comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the system to perform an operation ( Bugdayci, [0108]); in addition, claim 19 is substantially similar to claim 8, thus the same rationale applies.
Regarding claim 20, claim 20 is substantially similar to claim 9, thus the same rationale applies.
5. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable Bugdayci in view of JP document , in view of ZHANG, in view of ZhangH , and further in view of Joliveau (US 11,532,040 B2).
Regarding claim 7, Bugdayci, JP document, ZHANG, and ZhangH disclose the method of Claim 1, further comprising: adding data associated with a second day to the first plurality of historical connectivity records prior to training the first machine learning model ( processor tomorrow metric being added to the metric values in order to forecast the model forecast value ( Bugdayci, [0084])).
Bugdayci in view of JP document in view of ZHANG and in view of ZhangH do not disclose determining that data, from the first plurality of historical connectivity records, that is associated with a first day is outlier data; removing the outlier data from the first plurality of historical connectivity records.
Joliveau discloses determining that data, from the first plurality of historical connectivity records, that is associated with a first day is outlier data ( dataset associated with an outlier is disclosed, Joliveau, column 9, lines 45-52 )); removing the outlier data from the first plurality of historical connectivity records( remove dataset associated with an outlier is disclosed (Joliveau, column 9, lines 45-52)).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate Joliveau’s teachings with Bugdayci’s teachings, in view of JP document’s teachings, in view of in view of ZHANG’s teachings, and in view of ZhangH’s teachings. One skilled in the art would be motivated to combine them in order to forecast better to activities in a link by using machine language that remove data that is not in the range of the expected data.
Conclusion
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIEGEORGES A HENRY whose telephone number is (571)270-3226. The examiner can normally be reached on 11:00am -8:00pm East M-F.
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/MARIEGEORGES A HENRY/Examiner, Art Unit 2455
/ZI YE/Primary Examiner, Art Unit 2455