DETAILED ACTION
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.
This Office Action is in response to the amendment filed on 4/16/2026.
Claims 1, 10 and 19 have been amended.
Claims 1-20 are pending for consideration.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/13/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Regarding to the Double Patenting rejection, the pending claims are still rejectable over claims 1-20 of U.S. Patent No. 12,124,577. Therefore, the rejection has been maintained.
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,124,577. Although the claims at issue are not identical, they are not patentably distinct from each other because both applications disclose the detection and alerting on malicious queries that are directed towards a data store. (See Claims Comparison Table)
Instant Application 18882129
Patent Application 12124577
Claim 1:
A computing system that detects malicious queries directed towards a data store, said computing system comprising: one or more processors; and one or more hardware storage devices that store instructions that are executable by the one or more processors to cause the computing system to: access syntax metrics for a query directed towards a data store, wherein the syntax metrics include a complexity measure of the query;
predict a maliciousness of the query based on the syntax metrics, wherein predicting the maliciousness is based on a code density, the code density determined by a comparison between a number of commands in the query and a number of parameters in the query; and based on the maliciousness, alert a computing entity associated with the data store.
Claim 1:
A computing system that detects malicious queries directed towards a data store, said computing system comprising: one or more processors; and one or more hardware storage devices that store instructions that are executable by the one or more processors to cause the computing system to: access one or more syntax metrics of a query directed towards a data store, wherein the one or more syntax metrics include a complexity measure of the query, the complexity measure defining a complexity of the query as a function of a number of commands and a number of parameters in the query; feed the one or more syntax metrics into a model that is configured to predict maliciousness of the query based on the one or more syntax metrics, wherein predicting the maliciousness based on the one or more syntax metrics includes predicting the maliciousness based on a code density between the number of commands and the number of parameters in the query; access an output of the model, the output representing a predicted maliciousness of the query; and based on the output of the model representing the predicted maliciousness, alert a computing entity associated with the data store.
Claim 10:
A method for detecting malicious queries directed towards a data store, said method comprising: accessing syntax metrics for a query directed towards a data store, wherein the syntax metrics include a complexity measure of the query;
predicting a maliciousness of the query based on the syntax metrics, wherein predicting the maliciousness is based on a code density, the code density determined by a comparison between a number of commands in the query and a number of parameters in the query; and based on the maliciousness, alerting a computing entity associated with the data store.
Claim 9:
A method for detecting and alerting on malicious queries directed towards a data store, the method comprising: accessing one or more syntax metrics of a query directed towards a data store, wherein the one or more syntax metrics include a complexity measure of the query, the complexity measure defining a complexity of the query as a function of a number of commands and a number of parameters in the query; feeding the one or more syntax metrics into a model that is configured to predict maliciousness of the query based on the one or more syntax metrics, wherein predicting the maliciousness based on the one or more syntax metrics includes predicting the maliciousness based on a code density between the number of commands and the number of parameters in the query; accessing an output of the model, the output representing a predicted maliciousness of the query; and based on the output of the model representing the predicted maliciousness, alerting a computing entity associated with the data store.
Claim 19:
One or more hardware storage devices that store instructions that are executable by one or more processors to cause the one or more processors to: access syntax metrics for a query directed towards a data store, wherein the syntax metrics include a complexity measure of the query;
predict a maliciousness of the query based on the syntax metrics, wherein predicting the maliciousness is based on a code density, the code density determined by a comparison between a number of commands in the query and a number of parameters in the query; and based on the maliciousness, alert a computing entity associated with the data store.
Claim 20:
One or more hardware storage devices that store instructions that are executable by one or more processors to cause the one or more processors to: access one or more syntax metrics of a query directed towards a data store, wherein the one or more syntax metrics include a complexity measure of the query, the complexity measure defining a complexity of the query as a function of a number of commands and a number of parameters in the query; feed the one or more syntax metrics into a model that is configured to predict maliciousness of the query based on the one or more syntax metrics, wherein predicting the maliciousness based on the one or more syntax metrics includes predicting the maliciousness based on a code density between the number of commands and the number of parameters in the query; access an output of the model, the output representing a predicted maliciousness of the query; and based on the output of the model representing the predicted maliciousness, alert a computing entity associated with the data store.
The dependent claims of the instant application recite language similar to the dependent claims of the patent application and are covered by the patent application.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Talmat (US 20220156361) (hereinafter Talmat) in view of Ray et al. (US 20210406004) (hereinafter Ray).
Regarding claim 1, Talmat discloses a computing system that detects malicious queries directed towards a data store (Talmat: paragraph 0010-0013, “The detection and prevention of unauthorized commands is disclosed”), said computing system comprising: one or more processors (Talmat: paragraphs 0008-0013, “The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor.”); and one or more hardware storage devices that store instructions that are executable by the one or more processors to cause the computing system to: access syntax metrics for a query directed towards a data store, wherein the syntax metrics include a complexity measure of the query (Talmat: paragraphs 0013 and 0016, “an execution tree can be analyzed to identify one or more metrics of a generated command… identify the number of predicted processes that will be spawned from executing the generated command and/or the number of supplied arguments to the generated command.. the identified metrics are compared to determine whether a security risk is associated with the generated command”, NOTE: the approved numbers of arguments, corresponding to the approved number of processes. As a result, the comparison is based on the number of arguments passed into the generated command and the number of processes); predict a maliciousness of the query based on the syntax metrics, wherein predicting the maliciousness is based on a deviation (Talmat: paragraphs 0013, 0016 and 0040-0046, “an execution tree can be analyzed to identify one or more metrics of a generated command… identify the number of predicted processes that will be spawned from executing the generated command and/or the number of supplied arguments to the generated command.. the identified metrics are compared to determine whether a security risk is associated with the generated command”… “in the event the number of arguments passed into the generated operating system command deviates from the approved number of arguments”, NOTE: the approved numbers of arguments, corresponding to the approved number of processes. As a result, the comparison is based on the number of arguments passed into the generated command and the number of processes); and based on the maliciousness, alert a computing entity associated with the data store (Talmat: paragraphs 0013, 0034 and 0046, “The two extra processes exceed the approved number of processes for the command and a security risk is identified. In some embodiments, upon detecting a security risk, the generated operating system command is disallowed from being executed. For example, an operator is notified, and the generated command may be quarantined until an operator has reviewed the suspicious command.”).
Talmat discloses two metrics, one from the number of processes and another from the number of arguments to a command to computing system, which is a computer code, to compute deviation measure (Talmat: paragraphs 0013, 0016 and 0040-0046) to determine the risk. Talmat does not explicitly mention how the deviation measure is calculated. The deviation measure is corresponded to a code density recited in the claim. On the other hand, Ray discloses a code density (Ray: paragraphs 0026, 0034, 0063 and 0065-0068, “Halstead metrics which identifies the number of distinct operators and operands, total number of operators and operands, program length, volume and difficulty, as well as Raw metrics which identifies the number of Source Lines of Code (SLOC), Logical Lines of Code (LLOC) and Blank Lines (BL).”… The code audit tool may specifically focus on Maintainability Index, which is a calculated using SLOC, Complexity and Halstead Volume as inputs).
Talmat and Ray are analogous art because they are from the same field of endeavor, data protection. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Talmat and Ray before him or her, to modify the system of Talmat to include the code density of Ray. The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results.
Regarding claim 10, the claim 10 discloses a method claim that is substantially equivalent to the system of claim 1. Therefore, the arguments set forth above with respect to claim 1 are equally applicable to claim 10 and rejected for the same reasons.
Regarding claim 19, the claim 19 discloses a storage device claim that is substantially equivalent to the system of claim 1. Therefore, the arguments set forth above with respect to claim 1 are equally applicable to claim 19 and rejected for the same reasons.
Regarding claims 2, 11 and 20, Talmat as modified discloses wherein the complexity measure defines a complexity of the query based on a number of commands in the query (Ray: paragraphs 0026, 0034, 0063 and 0065-0068, “Halstead metrics which identifies the number of distinct operators and operands, total number of operators and operands, program length, volume and difficulty, as well as Raw metrics which identifies the number of Source Lines of Code (SLOC), Logical Lines of Code (LLOC) and Blank Lines (BL).”… The code audit tool may specifically focus on Maintainability Index, which is a calculated using SLOC, Complexity and Halstead Volume as inputs). The same motivation to modify Talmat in view of Ray, as applied in claim 1 above, applies here.
Regarding claims 3 and 12, Talmat as modified discloses wherein the complexity measure defines a complexity of the query based on a number of parameters in the query (Ray: paragraphs 0026, 0034, 0063 and 0065-0068, “Halstead metrics which identifies the number of distinct operators and operands, total number of operators and operands, program length, volume and difficulty, as well as Raw metrics which identifies the number of Source Lines of Code (SLOC), Logical Lines of Code (LLOC) and Blank Lines (BL).”… The code audit tool may specifically focus on Maintainability Index, which is a calculated using SLOC, Complexity and Halstead Volume as inputs). The same motivation to modify Talmat in view of Ray, as applied in claim 1 above, applies here.
Regarding claims 4 and 13, Talmat as modified discloses wherein predicting the maliciousness of the query is performed by a machine learning model (Talmat: paragraphs 0022 and 0029, “Whereas valid behavior definitions can be used to match and identify authorized commands, invalid behavior definitions can be used to match and identify unauthorized commands. In some embodiments, the dataset is a trained machine learning model.”… “based on the security analysis performed as part of applying an automation plan to input data at 207, behavior definitions associated with generated operating system commands can be updated. In some embodiments, the updated behavior definitions are used to keep the dataset of behavior definitions fresh by expiring older definitions. In some embodiments, the updated dataset is used to train (or retrain) a machine learning model to identify valid behaviors associated with generated operating system commands.”).
Regarding claims 5 and 14, Talmat as modified discloses wherein accessing the syntax metrics includes generating a syntax metric without evaluating unmasked content of the query (Talmat: paragraphs 0032 and 0040-0046, “The execution trees can be analyzed to determine whether the predicted behavior of the generated commands match behavior definitions for valid (or invalid) uses of the corresponding commands. An automation script with valid generated commands is validated whereas an automation script that includes invalid generated commands is not validated. In some embodiments, the generated operating system commands are also sanitized to limited additional potential attacks.”).
Regarding claims 6 and 15, Talmat as modified discloses wherein accessing the syntax metrics includes generating a syntax metric without retaining unmasked content of the query (Talmat: paragraphs 0032-0034 and 0040-0046, “In some embodiments, a timeout or throttling is imposed on any access privileges associated with invalid automation script, for example, until an incident team can address the identified security threat. In some embodiments, the invalid generated operating system commands are dropped from the automation script and corresponding alerts are triggered.”…“The execution trees can be analyzed to determine whether the predicted behavior of the generated commands match behavior definitions for valid (or invalid) uses of the corresponding commands. An automation script with valid generated commands is validated whereas an automation script that includes invalid generated commands is not validated. In some embodiments, the generated operating system commands are also sanitized to limited additional potential attacks.”).
Regarding claims 7 and 16, Talmat as modified discloses wherein the syntax metrics include data generated by a compilation of the query (Talmat: paragraph 0016, “For example, identified metrics can include the number of spawned processes predicted by the generated operating system command and/or the number of arguments for the generated operating system command. The identified one or more metrics are automatically evaluated to determine a security risk associated with the generated operating system command. For example, in the event the number of processes the generated operating system command is predicted to spawn deviates from an approved number of processes, the generated operating system command is flagged as a security risk. As another example, in the event the number of arguments passed into the generated operating system command deviates from the approved number of arguments, the generated operating system command is also flagged as a security risk. In various embodiments, determining the security risk includes determining whether to allow execution of the generated operating system command. For example, in the event the generated operating system command is determined to spawn more processes than it is approved for, the generated operating system command is blocked from being executed. In automatically evaluating the identified one or more metrics, the identified one or more metrics can be compared with one or more historical reference metrics. For example, a valid execution behavior dataset is created to track valid metrics. The valid metrics can include history reference metrics, such as run-time behavior characteristics, of previously approved executions of the same or related operating system commands”).
Regarding claims 8 and 17, Talmat as modified discloses wherein the syntax metrics include a string entropy of all or a portion of the query (Talmat: paragraph 0016, “For example, identified metrics can include the number of spawned processes predicted by the generated operating system command and/or the number of arguments for the generated operating system command. The identified one or more metrics are automatically evaluated to determine a security risk associated with the generated operating system command. For example, in the event the number of processes the generated operating system command is predicted to spawn deviates from an approved number of processes, the generated operating system command is flagged as a security risk. As another example, in the event the number of arguments passed into the generated operating system command deviates from the approved number of arguments, the generated operating system command is also flagged as a security risk. In various embodiments, determining the security risk includes determining whether to allow execution of the generated operating system command. For example, in the event the generated operating system command is determined to spawn more processes than it is approved for, the generated operating system command is blocked from being executed. In automatically evaluating the identified one or more metrics, the identified one or more metrics can be compared with one or more historical reference metrics. For example, a valid execution behavior dataset is created to track valid metrics. The valid metrics can include history reference metrics, such as run-time behavior characteristics, of previously approved executions of the same or related operating system commands”).
Regarding claims 9 and 18, Talmat as modified discloses wherein the query is determined to be a suspicious query when the code density indicates that the number of commands is higher than the number of parameters in the query (Talmat: paragraphs 0013, 0016 and 0040, “The two extra processes exceed the approved number of processes for the command and a security risk is identified. In some embodiments, upon detecting a security risk, the generated operating system command is disallowed from being executed. For example, an operator is notified, and the generated command may be quarantined until an operator has reviewed the suspicious command. In some embodiments, a network session associated with the analyzed automation process with a detected security risk is invalidated. Similarly, an account, email address, IP Address, and/or other user/client property associated with the analyzed automation process with a detected security risk can be disabled. In some embodiments, the execution of the automation is blocked or throttled until an incident team can address the identified security threat.”… “For example, a generated operating system command with fewer or more predicted processes or input arguments would not match the corresponding behavior definitions and the automation script is invalidated due to an identified security risk. An invalid generated operating system command corresponds to an identified security risk. For example, a command predicted to spawn five processes when the corresponding behavior definition allows for only three processes is a security risk. The additional two extra processes can indicate a command line injection attack.”).
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.
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/TRANG T DOAN/Primary Examiner, Art Unit 2431
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