DETAILED ACTION
This action is in response to an amendment filed on April 8, 2026 for the application of Balaka for a “System and method for database system anomaly detection and incident management” filed on May 9, 2024. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending in the application.
Claims 1, 11, and 18 have been amended.
Claims 1-20 are rejected under 35 USC § 101.
Claims 1-20 are rejected under 35 USC § 103.
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 human mind in addition to mathematical relationships/calculations.
As per claim 1, with the exception of the recitation of the limitations “a processor”, the limitations “receiving a plurality of input metric values via a communication interface, the plurality of input metric values characterizing one or more operating conditions of a database system; determining via a processor a plurality of output metric values corresponding to the input metric values by applying a machine learning model to the plurality of input metric values, the machine learning model being pre-trained to project the input metric values into a latent space having a level of dimensionality lower than that of the input metric values, the machine learning model being pre-trained to project the latent space into the output metric values, based on a decoding of the latent space and an expansion of the level of dimensionality to an original level of dimensionality of the input metric values, the output metric values predicting the input metric values; comparing the output metric values to the corresponding input metric values to identify a plurality of corresponding discrepancy values indicating one or more discrepancies and one or more representations of variances between the output metric values generated based on the projection of the latent space and the corresponding input metric values received via the communication interface; based on the corresponding discrepancy values, determining that a database incident implicating operating conditions corresponding with a portion of the database system has occurred” can be performed by a human mind or with the aid of pen and paper (MPEP 2106.04(a)(2)) in addition to mathematical relationships/calculations (MPEP 2106.04(a)(2) I. A. and C.).
Step 2A. This judicial exception is not integrated into a practical application because the additional element(s) “a processor” is/are 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 limitations of “receiving a plurality of input metric values via a communication interface, the plurality of input metric values characterizing one or more operating conditions of a database system” and “transmitting an instruction to the database system via the communication interface to implement a policy to address the database incident” is/are mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. These limitations amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity (MPEP 2106.05(d)).
The limitation of “comparing the output metric values to the corresponding input metric values to identify a plurality of corresponding discrepancy values indicating one or more discrepancies and one or more representations of variances between the output metric values generated based on the projection of the latent space and the corresponding input metric values received via the communication interface; based on the corresponding discrepancy values, determining that a database incident implicating operating conditions corresponding with a portion of the database system has occurred” is directed to mathematical relationships/calculations (MPEP 2106.04(a)(2) I. A. and C.).
Step 2B. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element(s) “a processor” does/do not provide significantly more than the recited judicial exception because the additional elements are mere instructions to implement an abstract idea or other exception on a computer and in this case generic computer components (MPEP 2106.05(f)).
The limitation(s) “determining via a processor a plurality of output metric values corresponding to the input metric values by applying a machine learning model to the plurality of input metric values, the machine learning model being pre-trained to project the input metric values into a latent space having a level of dimensionality lower than that of the input metric values, the machine learning model being pre-trained to project the latent space into the output metric values based on a decoding of the latent space and an expansion of the level of dimensionality to an original level of dimensionality of the input metric values, the output metric values predicting the input metric values”, under the broadest reasonable interpretation, recite(s) steps that merely apply a machine learning model to obtain a prediction, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)).
As for the limitations recited in claims 2-10, when considering each of the claims as a whole these additional elements do not integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit. The additional elements do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field. The additional elements do not implement a judicial exception with, or use a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim. The additional element do not apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
As per claim 11, with the exception of the recitation of the limitations “A system comprising: a communication interface” and “a processor”, the limitations “a communication interface configured to receive a plurality of input metric values characterizing one or more operating conditions of a database system; determine a plurality of output metric values corresponding to the input metric values by applying a machine learning model to the plurality of input metric values, the machine learning model being pre-trained to project the input metric values into a latent space having a level of dimensionality lower than that of the input metric values, the machine learning model being pre-trained to project the latent space into the output metric values based on a decoding of the latent space and an expansion of the level of dimensionality to an original level of dimensionality of the input metric values, the output metric values predicting the input metric values, and compare the output metric values to the corresponding input metric values to identify a plurality of corresponding discrepancy values indicating one or more discrepancies and one or more representations of variances between the output metric values generated based on the projection of the latent space and the corresponding input metric values received via the communication interface; and a policy engine configured to determine that a database incident implicating operating conditions corresponding with a portion of the database system has occurred based on the corresponding discrepancy value” can be performed by a human mind or with the aid of pen and paper (MPEP 2106.04(a)(2)) in addition to mathematical relationships/calculations (MPEP 2106.04(a)(2) I. A. and C.).
Step 2A. This judicial exception is not integrated into a practical application because the additional element(s) “A system comprising: a communication interface” and “a processor” is/are 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 limitations of “receive a plurality of input metric values characterizing one or more operating conditions of a database system” and “transmit an instruction to the database system via the communication interface to implement a policy to address the database incident” is/are mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. These limitations amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity (MPEP 2106.05(d)).
The limitation of “compare the output metric values to the corresponding input metric values to identify a plurality of corresponding discrepancy values indicating one or more discrepancies and one or more representations of variances between the output metric values generated based on the projection of the latent space and the corresponding input metric values” is directed to mathematical relationships/calculations (MPEP 2106.04(a)(2) I. A. and C.).
Step 2B. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element(s) “A system comprising: a communication interface” and “a processor” does/do not provide significantly more than the recited judicial exception because the additional elements are mere instructions to implement an abstract idea or other exception on a computer and in this case generic computer components (MPEP 2106.05(f)).
The limitation(s) “determine a plurality of output metric values corresponding to the input metric values by applying a machine learning model to the plurality of input metric values, the machine learning model being pre-trained to project the input metric values into a latent space having a level of dimensionality lower than that of the input metric values, the machine learning model being pre-trained to project the latent space into the output metric values based on a decoding of the latent space and an expansion of the level of dimensionality to an original level of dimensionality of the input metric values, the output metric values predicting the input metric values”, under the broadest reasonable interpretation, recite(s) steps that merely apply a machine learning model to obtain a prediction, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)).
As per claims 12-16, please refer to analysis section for claims 2-10.
As per claim 18, with the exception of the recitation of the limitations “One or more non-transitory computer readable media having instructions stored thereon for performing a method” and “a processor”, the limitations “the plurality of input metric values characterizing one or more operating conditions of a database system; determining a plurality of output metric values corresponding to the input metric values by applying a machine learning model to the plurality of input metric values, the machine learning model being pre-trained to project the input metric values into a latent space having a level of dimensionality lower than that of the input metric values based on a decoding of the latent space and an expansion of the level of dimensionality to an original level of dimensionality of the input metric values, the machine learning model being pre-trained to project the latent space into the output metric values, the output metric values predicting the input metric values; comparing the output metric values to the corresponding input metric values to identify a plurality of corresponding discrepancy values indicating one or more discrepancies and one or more representations of variances between the output metric values generated based on the projection of the latent space and the corresponding input metric values; based on the corresponding discrepancy values, determining that a database incident implicating operating conditions corresponding with a portion of the database system has occurred” can be performed by a human mind or with the aid of pen and paper (MPEP 2106.04(a)(2)) in addition to mathematical relationships/calculations (MPEP 2106.04(a)(2) I. A. and C.).
Step 2A. This judicial exception is not integrated into a practical application because the additional element(s) “One or more non-transitory computer readable media having instructions stored thereon for performing a method” and “a processor” is/are 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 limitations of “receiving a plurality of input metric values via a communication interface, the plurality of input metric values characterizing one or more operating conditions of a database system” and “transmitting an instruction to the database system via the communication interface to implement a policy to address the database incident” is/are mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. These limitations amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity (MPEP 2106.05(d)).
The limitation of “comparing the output metric values to the corresponding input metric values to identify a plurality of corresponding discrepancy values indicating one or more discrepancies and one or more representations of variances between the output metric values generated based on the projection of the latent space and the corresponding input metric values; based on the corresponding discrepancy values, determining that a database incident implicating operating conditions corresponding with a portion of the database system has occurred” is directed to mathematical relationships/calculations (MPEP 2106.04(a)(2) I. A. and C.).
Step 2B. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element(s) “One or more non-transitory computer readable media having instructions stored thereon for performing a method” and “a processor” does/do not provide significantly more than the recited judicial exception because the additional elements are mere instructions to implement an abstract idea or other exception on a computer and in this case generic computer components (MPEP 2106.05(f)).
The limitation(s) “determining a plurality of output metric values corresponding to the input metric values by applying a machine learning model to the plurality of input metric values, the machine learning model being pre-trained to project the input metric values into a latent space having a level of dimensionality lower than that of the input metric values based on a decoding of the latent space and an expansion of the level of dimensionality to an original level of dimensionality of the input metric values, the machine learning model being pre-trained to project the latent space into the output metric values, the output metric values predicting the input metric values”, under the broadest reasonable interpretation, recite(s) steps that merely apply a machine learning model to obtain a prediction, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)).
As per claims 19-20, please refer to analysis section for claims 2-10.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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.
Claims 1, 3-4, 6-11, 13-14, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (U.S. PGPUB 20200160211) in view of Yoon et al. (U.S. PGPUB 20200234143).
As per claims 1, 11, and 18, Kumar discloses a method/a system/One or more non-transitory computer readable media having instructions stored thereon for performing a method ([0013])/ comprising:
receiving a plurality of input metric values ([0028], “the series of one or more performance metrics may include, for example, at least one performance metric received and/or collected from the database system at successive time intervals”) via a communication interface ([0029]), the plurality of input metric values characterizing one or more operating conditions of a database system ([0028], “The series of one or more performance metrics may include any performance metric that may be indicative of an operational state of the database system”);
determining via a processor a plurality of output metric values corresponding to the input metric values by applying a machine learning model to the plurality of input metric values ([0045], “In some example embodiments, the long short-term memory neural network 300 may receive, at an input 302, series of performance metrics and provide, at an output 304, a corresponding classification of the series of performance metrics, for example, as being indicative of normal and/or anomalous.”), the machine learning model being pre-trained to project the input metric values into a latent space having a level of dimensionality lower than that of the input metric values, the machine learning model being pre-trained to project the latent space into the output metric values ([0042], “a dimensionality reduction model”), the output metric values predicting the input metric values ([0070], “the anomaly prediction engine 110 may process, with the trained machine-learning model 115, the series of performance metrics in order to predict the occurrence of an anomaly at the database system 140”);
comparing the output metric values to the corresponding input metric values to identify a plurality of corresponding discrepancy values indicating one or more discrepancies between the output metric values and the corresponding input metric values ([0045], “In some example embodiments, the long short-term memory neural network 300 may receive, at an input 302, series of performance metrics and provide, at an output 304, a corresponding classification of the series of performance metrics, for example, as being indicative of normal and/or anomalous.”);
based on the corresponding discrepancy values, determining that a database incident implicating operating conditions corresponding with a portion of the database system has occurred ([0003], “processing, with the trained machine learning model, the series of performance metrics to predict the occurrence of the anomaly at the database system”); and
transmitting an instruction to the database system via the communication interface to implement a policy to address the database incident (Abstract, “In response to detecting the presence of the anomaly at the database system, one or more remedial actions may be determined for correcting and/or preventing the anomaly at the database system. The one or more remedial actions may further be sent to a database management system associated with the database system.”).
Kumar fails to explicitly disclose decoding of the latent space and an expansion of the level of dimensionality.
Yoon of analogous art teaches the machine learning model being pre-trained to project the latent space into the output metric values based on a decoding of the latent space and an expansion of the level of dimensionality to an original level of dimensionality ([0129], “the neural network model 50 may include an encoding layer 51 performing dimensionality reduction on the input data, a decoding layer 55 performing dimensionality restoration on the input data and generating output data in which the input data is restored, and an intermediate layer 53 connected with an encoder and a decoder.”) of the input metric values, the output metric values predicting the input metric values ([0129]-[0136]);
comparing the output metric values to the corresponding input metric values to identify a plurality of corresponding discrepancy values indicating one or more discrepancies and one or more representations of variances ([0112]) between the output metric values generated based on the projection of the latent space and the corresponding input metric values ([0125], “training data that is the input is compared with an output of the neural network, so that an error may be calculated.”).
All of the claimed elements were known in Kumar and Yoon and could have been combined by known methods with no change in their respective functions. It therefore would have been obvious to a person of ordinary skill in the art before the time of effective filing language to combine their methods. One would be motivated to make this combination since Yoon’s machine learning model is a mere example of Kumar’s neural network model.
As per claims 3 and 13, Kumar discloses the database system is a multitenant database system storing information for a plurality of tenants that access the database system ([0031], “It should be appreciated that the database system 140 may be deployed across multiple hosts. As such, the system monitor system 170 may be further configured to separate raw performance metrics collected from different hosts”) via the Internet (Fig. 1) and ([0029]).
As per claims 4 and 14, Kumar discloses a subset of the plurality of input metric values are specific to a designated tenant of the plurality of tenants ([0031], “It should be appreciated that the database system 140 may be deployed across multiple hosts. As such, the system monitor system 170 may be further configured to separate raw performance metrics collected from different hosts”).
As per claims 6 and 16, Kumar discloses the database incident is specific to the designated tenant ([0031], “It should be appreciated that the database system 140 may be deployed across multiple hosts. As such, the system monitor system 170 may be further configured to separate raw performance metrics collected from different hosts”), and wherein the policy is specific to the designated tenant (Abstract, “In response to detecting the presence of the anomaly at the database system, one or more remedial actions may be determined for correcting and/or preventing the anomaly at the database system. The one or more remedial actions may further be sent to a database management system associated with the database system.”).
As pe claims 7 and 17, Kumar discloses the database system is an element of a computing services environment that provides computing services to a plurality of entities via the Internet (Fig. 1) and ([0029]).
As per claim 8, Yoon discloses the machine learning model is a variational autoencoder ([0026]).
As per claim 9, Yoon discloses the machine learning model is a generative adversarial network ([0112]).
As per claim 10, Kumar discloses one or more of the input metric values are specific to a designated time period ([0028], “at least one performance metric received and/or collected from the database system at successive time intervals including, for example, a first time interval, a second time interval, a third time interval, and/or the like”), and wherein the input metric values include a value selected from the group consisting of: a CPU usage value, a memory usage value, a network bandwidth value, and a number of requests ([0005]).
Claims 2, 5, 12, 15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (U.S. PGPUB 20200160211) in view of Yoon et al. (U.S. PGPUB 20200234143) and in further view of Baldassarre et al. (U.S. PGPUB 20210303381).
As per claims 2, 12, and 19, Kumar discloses determining that the database incident has occurred comprises identifying a subset of the plurality of corresponding discrepancy values ([0045]). Kumar fails to explicitly disclose corresponding discrepancy values exceed a respective designated threshold.
Baldassarre of analogous art teaches identifying a subset of the plurality of corresponding discrepancy values that each exceed a respective designated threshold ([0046], “The predicted metrics may be compared to predefined thresholds”).
All of the claimed elements were known in Kumar and Baldassarre and could have been combined by known methods with no change in their respective functions. It therefore would have been obvious to a person of ordinary skill in the art before the time of effective filing language to combine their methods. One would be motivated to make this combination since Baldassarre’s threshold comparison is a mere example of Kumar’s data comparison.
As per claims 5 and 15, Kumar discloses determining that the database incident has occurred comprises identifying a designated discrepancy value corresponding with a designated input metric value of the subset of the plurality of input metric values ([0045]). Baldassarre discloses identifying a designated discrepancy value corresponding with a designated input metric value of the subset of the plurality of input metric values that exceeds a designated threshold ([0046]).
As per claim 20, Kumar discloses the database system is a multitenant database system storing information for a plurality of tenants that access the database system ([0031], “It should be appreciated that the database system 140 may be deployed across multiple hosts. As such, the system monitor system 170 may be further configured to separate raw performance metrics collected from different hosts”) via the Internet (Fig. 1) and ([0029]), wherein a subset of the plurality of input metric values are specific to a designated tenant of the plurality of tenants ([0031]),
wherein determining that the database incident has occurred comprises identifying a designated discrepancy value corresponding with a designated input metric value of the subset of the plurality of input metric values ([0045], “In some example embodiments, the long short-term memory neural network 300 may receive, at an input 302, series of performance metrics and provide, at an output 304, a corresponding classification of the series of performance metrics, for example, as being indicative of normal and/or anomalous.”), and wherein the database incident is specific to the designated tenant, and wherein the policy is specific to the designated tenant (Abstract, “In response to detecting the presence of the anomaly at the database system, one or more remedial actions may be determined for correcting and/or preventing the anomaly at the database system. The one or more remedial actions may further be sent to a database management system associated with the database system.”).
Baldassarre discloses identifying a designated discrepancy value corresponding with a designated input metric value of the subset of the plurality of input metric values that exceeds a designated threshold ([0046]).
Response to Arguments
Applicant’s amendments filed on April 8, 2026 necessitated a new ground(s) of rejection in this Office action. Accordingly, Applicant’s arguments have been fully considered but are moot in view of the new ground(s) of 35 U.S.C. 103 rejection, as set forth in this office action.
Regarding the U.S.C. 101 rejections, applicant's arguments have been fully considered but they are not persuasive.
As per claims 1-20 applicant argues that claims do not recite a mental process.
The Examiner would like to point out that claims were rejected because they recite(s) mental processes-concepts performed in human mind in addition to mathematical relationships/calculations. More specifically, claims are directed to making a decision by using the explicitly recited mathematical relationships/calculations.
Applicants argue that “Moreover, Applicant submits that the claimed features integrate any alleged exception into a practical application because the discrepancy-based incident determination is used to implement policy in a database system. More specifically, the claims recite transmitting an instruction to implement a policy to address the database incident. In this way, the claims improve the operation of such database systems and do not just merely report a result. As additionally described in Applicant's Specification, such database incident determination and policy implementation may be used for real-time/near-real-time incident detection as well as remedial actions such as throttling, isolating, or transferring a tenant to protect database operation. Accordingly, Applicant submits that the claims are integrated in a practical application and improve the operation of database systems.”
The examiner respectfully disagrees and would like to point out that regarding “transmitting an instruction to implement a policy to address the database incident”, per MPEP 2106.05(d)(II), the courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC V. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. V. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The examiner would like to further point out that it is noted that the features upon which applicant relies (i.e., as remedial actions such as throttling, isolating, or transferring a tenant to protect database operation) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
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 Elmira Mehrmanesh whose telephone number is (571)272-5531. The examiner can normally be reached on M-F from 10-6.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bryce Bonzo, can be reached at telephone number (571) 272-3655. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Elmira Mehrmanesh/
Primary Examiner, Art Unit 2113
Prosecution Insights