DETAILED ACTION
This communication is in response to the amendment filed on 4/28/2026. 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 1, 8, 16 and 19 are amended.
Claims 1- 20 are pending.
Response to Argument
Applicant’s arguments with respect to claim(s) 1, 8, 16 and 19 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 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.
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 Kneuper et al. (US 2019/0057611).
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 determining that the size exceeds a defined threshold.
Kneuper discloses selecting a first machine learning model architecture, comprising: determining a size of the first subset of historical records and selecting the first machine learning model architecture by determining that the size exceeds a defined threshold (selecting a particular machine-learning technique based on a size of the data portion or in response to determining the size of the data portion is greater than to the size threshold, section [0024], the data portion is a portion of historical data that satisfies filter criterion, sections [0026], [0027], and [0029]).
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 Kneuper’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 train the machine-learning data model efficiently based on the data complexity.
Regarding claim 2, Bugdayci, JP document, ZHANG and Kneuper 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 Kneuper 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 Kneuper 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 Kneuper 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 Kneuper 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 by determining that a size of the subset of historical connectivity records exceeds a defined threshold.
Kneuper discloses wherein the first machine learning model comprises a first machine learning model architecture selected by determining that a size of the subset of historical records exceeds a defined threshold (selecting a particular machine-learning technique based on a size of the data portion or in response to determining the size of the data portion is greater than to the size threshold, section [0024], the data portion is a portion of historical data that satisfies filter criterion, sections [0026], [0027], and [0029]).
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 Kneuper’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 train the machine-learning data model efficiently based on the data complexity.
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 Kneuper 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 Kneuper 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 Kneuper disclose 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 Kneuper disclose 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 Kneuper disclose 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.
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 Kneuper, and further in view of Joliveau (US 11,532,040 B2).
Regarding claim 7, Bugdayci, JP document, ZHANG, and Kneuper 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 Kneuper 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 Kneuper’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
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 JOON H HWANG whose telephone number is (571)272-4036. The examiner can normally be reached on Monday-Friday, 9:00 am - 5:00 pm.
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/JOON H HWANG/Supervisory Patent Examiner, Art Unit 2447
9/28/2026
