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 § 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes – concepts performed in the human mind and mathematical concepts – mathematical calculations.
Regarding claim 1, the claims recite mental processes. The limitations ‘determining a health state of the hardware resource as a self-calibration according to a deviation of the machine failure incident values from the machine failure reference values; generating a pair of embeddings associated with the one or more feature variables of the hardware resource, wherein respective embeddings of the pair of embeddings depict feature variables with respective values of the feature variable of the hardware resource’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional element ‘retrieving one or more feature variables and feature data of a hardware resource, wherein the one or more feature variables represent machine failure information of the hardware resource’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), in this case data gathering.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element ‘based on the pair of embeddings using a Siamese network, wherein the Siamese network comprises a first neural network and a second neural network, and the Siamese network is previously trained by executing operations comprising: creating a pair of sample embeddings comprising first embedding of sample incident data of the hardware resource and second embeddings of sample reference data of the hardware resource; and training the Siamese network using the pair of sample embeddings and an indication of a ground truth health state of the hardware resource, thereby sharing common weight values between a first layer of the first neural network and a first layer of the second neural network, based on the health state indicating the deviation larger than a predetermined deviation as determined by using the trained Siamese network’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element ‘decommissioning operation of the hardware resource for replacement’ are simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, MPEP 2106.05(d). JP2010152993A - In the related art disclosed in Patent Document 2, the component is changed in a state where the component is removed from the apparatus, for example, the component (part) is removed and replaced with an equivalent component (part). JP2000148531A - Description of the Related Art Conventionally, a failure analysis of a computer system is performed by identifying a failure location of a computer when a failure has occurred, a name of a component that needs to be replaced in order to replace a failed component, and a location of the failed computer. (Location in the computer) is used to indicate to maintenance personnel. JPH0631585 - Description of the Related Art In various information processing apparatuses, when an event such as occurrence of some kind of failure or replacement of an input / output device during normal use occurs, the event is stored as a history of the information processing apparatus on a magnetic disk or the like.
Regarding claim 2, the limitation ‘the one or more feature variables corresponds to at least one of a hardware failure occurrence, a performance degradation, or a power consumption degradation’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), in this case data gathering.
Regarding claim 3, the limitation ‘the Siamese network includes a plurality of neural networks in parallel, wherein one of the plurality of neural networks receives first embeddings representing reference data as input, and wherein another one of the plurality of neural networks receives second embeddings representing incident data as input’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 4, the limitation ‘retrieving hardware operation data, wherein the hardware operation data include machine operation records and warranty log data associated with the hardware resource; retrieving performance data associated with the hardware resource and data associated with client requests received by the hardware resource; and retrieving power telemetry data and processor utilization data’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), in this case data gathering.
Regarding claim 5, the limitation ‘determining a hardware failure rate associated with the hardware resource, wherein the hardware resource includes a server; determining a performance rate associated with the hardware resource; and determining a power consumption data associated with the server’ are directed to mathematical concepts – mathematical calculations.
Regarding claim 6, the limitation ‘a first layer of the first neural network and a first layer of the second neural network are trained by sharing common weight values’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 7, the limitation ‘the determining the hardware failure rate is based on fitting an exponential distribution of hardware failures associated with a set of hardware resources with hardware failures associated with the hardware resource’ is a mathematical concept – mathematical calculations.
Regarding claim 8, the limitation ‘the determining a performance rate includes determining a residual of a linear regression model between hourly machine performance data and a number of client requests received on the server’ is a mathematical concept – mathematical calculations.
Regarding claim 9, the limitation ‘the determining a power consumption degradation further includes determining a residual of a linear regression model indicating an increase of power consumption as a processor utilization increases and a power consumption by the hardware resource when the hardware resource is in an idle state’ is a mathematical concept – mathematical calculations.
Regarding claim 10, with the exception of the recitation of the limitation ‘a processor; a memory storing computer-executable instructions that when executed by the processor cause the system to execute a method’, the claim recites limitations that are mental processes – concepts performed in the human mind. The limitations ‘determining a health state of the server as a self-calibration according to a deviation of the machine failure incident values from the machine failure reference values; generate, based on a combination of the hardware failure rate, the performance rate, and the power consumption rate, a pair of embeddings associated with the server, wherein respective embeddings of the pair of embeddings represent machine failure incident values and machine failure reference values of the hardware resource’ are mental processes – concepts performed in the human mind by observation, evaluation, judgment, and/or opinion.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional element ‘a processor; a memory storing computer-executable instructions that when executed by the processor cause the system to execute a method; based on the pair of embeddings by a Siamese network, wherein the Siamese network comprises a first neural network and a second neural network, the Siamese network is previously trained by executing operations comprising: creating a pair of sample embeddings comprising first embedding of sample incident data of the hardware resource and second embeddings of sample reference data of the hardware resource, thereby sharing common weight values between a first layer of the first neural network and a first layer of the second neural network; and training the Siamese network using the pair of sample embeddings and an indication of a ground truth health state of the hardware resource, based on the health state indicating the deviation larger than a predetermined deviation as determined by using the trained Siamese network’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element ‘retrieving hardware operation data of a server; retrieving performance data of the server; retrieving power telemetry data of the server’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), in this case data gathering.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element ‘decommissioning operation of the hardware resource for replacement’ are simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, MPEP 2106.05(d). JP2010152993A - In the related art disclosed in Patent Document 2, the component is changed in a state where the component is removed from the apparatus, for example, the component (part) is removed and replaced with an equivalent component (part). JP2000148531A - Description of the Related Art Conventionally, a failure analysis of a computer system is performed by identifying a failure location of a computer when a failure has occurred, a name of a component that needs to be replaced in order to replace a failed component, and a location of the failed computer. (Location in the computer) is used to indicate to maintenance personnel. JPH0631585 - Description of the Related Art In various information processing apparatuses, when an event such as occurrence of some kind of failure or replacement of an input / output device during normal use occurs, the event is stored as a history of the information processing apparatus on a magnetic disk or the like.
Regarding claim 11, the limitation ‘training, based at least in part on the pair of embeddings, the Siamese network’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f))..
Regarding claim 12, the limitation ‘the Siamese network includes a plurality of neural networks in parallel, wherein one of the plurality of neural networks receives first embeddings representing reference data as input, and wherein another one of the plurality of neural networks receives second embeddings representing incident data as input’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 13, the limitation ‘the Siamese network includes a first neural network and a second neural network, and wherein a first layer of the first neural network and a first layer of the second neural network are trained by sharing common weight values’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 14, the limitation ‘the determining the hardware failure rate is based on fitting an exponential distribution of hardware failures associated with a set of hardware resources with hardware failures associated with the server’ is mathematical concept – mathematical calculations.
Regarding claim 15, the limitation ‘the determining a performance rate includes determining a residual of a linear regression model between hourly machine performance data and a number of client requests received on the server’ is a mathematical concept – mathematical calculations.
Regarding claim 16, the limitation ‘the determining a power consumption degradation further includes determining a residual of a linear regression model indicating an increase of power consumption as a processor utilization increases and a power consumption by the server when the server is in an idle state’ is a mathematical concept – mathematical calculations.
Regarding claim 17, the claims recite mental processes. The limitation ‘determining degradation of values of the feature variables, wherein the values of the feature variables comprise a machine failure rate of the hardware resource; determining the health state’ is a mental process – concept performed in the human mind by observation, evaluation, judgment, and/or opinion.
Step 2A: Prong two
This judicial exception is not integrated into a practical application because the additional element ‘retrieving data associated with feature variables, wherein the feature variables indicate a health state of a hardware resource, wherein the feature variables represent performance data of the hardware resource’ is directed to adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)).
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element ‘using a linear regression model; based on a plurality of embeddings associated with the feature variables, using the trained Siamese network; training a Siamese network using embeddings representing a healthy state as training data, wherein the Siamese network comprises a first neural network and a second neural network, respective embeddings of the pair of embeddings represent machine failure incident values and machine failure reference values of the hardware resource, and the training further comprises: creating a pair of sample embeddings comprising first embedding of sample incident data of the hardware resource and second embeddings of sample reference data of the hardware resource; and training the Siamese network using the pair of sample embeddings and an indication of a ground truth health state of the hardware resource thereby sharing common weight values between a first layer of the first neural network and a first layer of the second neural network, based on the health state indicating the deviation larger than a predetermined deviation as determined by using the trained Siamese network’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element ‘decommissioning operation of the hardware resource for replacement’ are simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, MPEP 2106.05(d). JP2010152993A - In the related art disclosed in Patent Document 2, the component is changed in a state where the component is removed from the apparatus, for example, the component (part) is removed and replaced with an equivalent component (part). JP2000148531A - Description of the Related Art Conventionally, a failure analysis of a computer system is performed by identifying a failure location of a computer when a failure has occurred, a name of a component that needs to be replaced in order to replace a failed component, and a location of the failed computer. (Location in the computer) is used to indicate to maintenance personnel. JPH0631585 - Description of the Related Art In various information processing apparatuses, when an event such as occurrence of some kind of failure or replacement of an input / output device during normal use occurs, the event is stored as a history of the information processing apparatus on a magnetic disk or the like.
Regarding claim 18, the limitation ‘the Siamese network includes a plurality of neural networks in parallel, wherein one of the plurality of neural networks receives first embeddings representing reference data as input, and wherein another one of the plurality of neural networks receives second embeddings representing incident data as input’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 19, the limitation ‘the Siamese network includes a first neural network and a second neural network, and wherein a first layer of the first neural network and a first layer of the second neural network are trained by sharing common weight values’ is directed to generic computer components recited at a high-level of generality such that they amount to nothing more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(f)).
Regarding claim 20, the limitation ‘the determining degradation of values associated with the feature variables includes determining a residual of a linear regression model between hourly machine performance data and a number of client requests received on the hardware resource’ is a mathematical concept – mathematical calculations.
There is no prior art rejection for claims 1-20 because of the inclusion of the added limitations.
Response to Arguments
Applicant's arguments and amendments filed 09/08/2025 have been fully considered. The newly added limitations have been rejected under 35 USC 101 – abstract idea. Please see the above rejection. Concerning Applicant’s arguments of the 101 abstract idea rejection, the newly added limitations are considered be well-understood routine and conventional.
THIS ACTION IS MADE FINAL. 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.
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/Yolanda L Wilson/Primary Examiner, Art Unit 2113