DETAILED ACTION
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
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. The claims fall within at least one of the four categories of patent eligible subject matter. However, the claimed invention is directed to performing a mental process and mathematical calculations without significantly more.
The following is an analysis of the claims regarding subject matter eligibility in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG):
Subject Matter Eligibility Analysis
Step 1: Do the Claims Specify a Statutory Category?
Claims 1-13 describe a system, claims 14-19 describe a method, and claim 20 describes a non-transitory computer-readable medium, therefore satisfying Step 1 of the analysis.
Step 2 Analysis for Claims 1-11
Step 2A – Prong 1: Is a Judicial Exception Recited?
Claim 1 recites receiving a selection to view data on a GUI of a plurality of entity accounts, executing an anomaly function, identify parameters based on the function, determine ranges of values for the parameters, detect an anomaly corresponding to a parameter out of range using an ML model, select an action to address the anomaly and perform an operation responsive to said action. The examiner interprets these limitations as merely a mental process using a computer or computing components as a tool. That is, nothing in the claim elements preclude the steps from practically being performed in the mind. The limitations involve using collected data to make determinations of an anomaly using said data and selecting an action to address the anomaly. The examiner interprets the network operation being performed to address the anomaly as merely presenting an action with any network communication, such as an email. Again, this could be interpreted as a user performing a mental process with a computer as a tool or in a computer environment as sending an email is likened to a pen and paper with mailing.
If a claim limitation, under its broadest reasonable interpretation, covers the practical performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See the 2019 Revised Patent Subject Matter Eligibility Guidance. Accordingly, the claim recites an abstract idea.
Claims 2-3 recites using a timer in correlation to displaying the collected data as determined by a user selection. The examiner interprets these limitations as merely a user performing a mental process on a computer.
Claims 4-5, 9, 11 recite more user interaction and resultant displays.
Claims 6-8, 10, 12-13 recite more user determinations and basic mathematical concepts. If a claim limitation, under its broadest reasonable interpretation, describes the performance of mathematical calculations (even if a formula is not recited in the claim), then it falls within the “Mathematical Concepts” grouping of abstract ideas. See the 2019 Revised Patent Subject Matter Eligibility Guidance. Accordingly, claims 2-11 each recite an abstract idea.
Step 2A – Prong 2: Is the Judicial Exception Integrated into a Practical Application?
Claim 1 recites a processor, a memory and a GUI. Even if the described methods are implemented on a computer, there is no indication that the combination of elements in the claim solves any particular technological problem other than merely taking advantage of the inherent advantages of using existing computer technology in its ordinary, off-the-shelf capacity to apply the identified judicial exceptions. Simply implementing the abstract idea(s) on a general purpose processor or other generic computer component is not a practical application of the abstract idea(s). The processor cited in the claim is described at a high level of generality such that it represents no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)).
Claim 1 further recites analyzing collected data for determination of an anomaly, and performing an action to address the anomaly. These limitations describe insignificant extra-solution activity pertaining to mere data gathering, display of results, and generically applying a resolution to an unidentified problem, respectively, without providing any details regarding a specific problem being solved or specific remedial actions being taken. As such, these limitations do not integrate the abstract idea(s) into a practical application.
Claim 1-13 recite the use of machine learning model. The limitations in the claims merely describe the use of machine learning without any specification of details pertaining to how the associated machine learning model is trained other than suing inputted data to produce output data. Such details would include description of specific algorithms used in training the machine learning model. As currently written, the limitations in the amended claims describe certain types of data and mathematical calculations and evaluations performed on the data. The mathematical calculations and evaluations describe mathematical concepts that can be performed by a human (i.e., as a mental process and/or by using pen/paper) and are therefore directed to the identified judicial exception. See MPEP 2106.05(f). There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing artificial intelligence technology (i.e., machine learning) in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply implementing the abstract idea(s) on a general purpose processor or other generic computer component is not a practical application of the abstract idea(s).
Claims 2-13 further recite data analysis, display and mathematical concepts. These claims contain no additional elements which would integrate the abstract idea(s) into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the identified abstract idea(s).
Step 2B: Do the Claims Provide an Inventive Concept?
When evaluating whether the claims provide an inventive concept, the presence of any additional elements in the claims need to be considered to determine whether they add “significantly more” than the judicial exception.
In the instant case, as detailed in the analysis for Step 2A-Prong 2, claim 1 contains additional elements which require evaluation as to whether they provide an inventive concept to the identified abstract idea. The processor, memory and GUI devices recited in the claim describe a generic computer processor and/or computer components at a high level and do not represent “significantly more” than the judicial exception.
The limitations pertaining to gathering of object information, display of data and of calculation results, and generically applying a resolution to an unidentified problem describe insignificant extra-solution activity and are written at a high level in a generic manner without providing any details regarding a specific problem being solved or specific remedial actions being taken. Therefore, these limitations recite no additional elements that would amount to significantly more than the abstract ideas defined in the claim.
Claims 1-13 recite limitations regarding the use of machine learning and the training of a machine learning model. As discussed above in the Step 2A - Prong 2 analysis regarding integration of the abstract idea into a practical application, the limitations, as currently written, describe mathematical calculations and evaluations describe mathematical concepts that can be performed by a human (i.e., as a mental process and/or by using pen/paper) and are therefore directed to the identified judicial exception. See MPEP 2106.05(f). There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing artificial intelligence technology (i.e., machine learning) in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply implementing the abstract idea(s) on a general purpose processor or other generic computer component, or utilizing generic artificial intelligence technology to apply the identified judicial exception, does not describe an inventive concept.
Step 2 Analysis for Claims 14-19
Claims 14-19 contain limitations for a system which are similar to the limitations for the methods specified in claims 1-13, respectively. As such, the analysis under Step 2A – Prong 1, Step 2A – Prong 2, and Step 2B for claims 14-19 is similar to that presented above for claims 1-13.
In light of the above, the limitations in claims 14-19 recite and are directed to an abstract idea and recite no additional elements that would amount to significantly more than the identified abstract ideas(s). Claims 14-19 are therefore not patent eligible.
Step 2 Analysis for Claim 20
Claim 20 contains limitations for a non-transitory computer-readable medium which are similar to the limitations for the methods specified in claim 1, respectively. As such, the analysis under Step 2A – Prong 1 and Step 2A – Prong 2 for claim 20 is similar to that presented above for claim 1.
Step 2B: Do the Claims Provide an Inventive Concept?
When evaluating whether the claims provide an inventive concept, the presence of any additional elements in the claims need to be considered to determine whether they add “significantly more” than the judicial exception.
Claim 20 contains additional elements which require evaluation as to whether they provide an inventive concept to the identified abstract idea.
Claim 20 recites the additional elements of a “non-transitory computer-readable medium.” The computer-readable medium and processors cited in the claim describe generic computer components at a high level and do not represent “significantly more” than the identified judicial exception. The enabling of the processors to troubleshoot a performance problem recites intended use of the claimed limitations and does not represent “significantly more” than the identified judicial exception.
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 (i.e., changing from AIA to pre-AIA ) 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, 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, 4-6, 9-11, 14, 16-18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wakui et al. U.S. Patent Application Publication US2023/0418701A1 in view of Xu et al. U.S. Patent Application Publication US2021/0200616.
As per claim 1, Wakui teaches a system, comprising: one or more processors, coupled with memory, to: receive, via a graphical user interface (GUI) of a device, a selection of an element of the GUI to view data of a plurality of entity accounts generated using network operations (¶ 0066-0068, wherein multiple VMs are shown and are selectable); execute, responsive to the selection, and prior to performance of actions responsive to the selection, an anomaly detection function (¶ 0068); identify, based on the execution of the anomaly detection function, from the data of the plurality of entity accounts, a plurality of parameters associated with execution of a plurality of network operations (¶ 0069-0070, wherein data/parameters from multiple VMs on the network are collected and listed); determine, based on the data identified based on the execution of the anomaly detection function, a plurality of ranges of values for the plurality of parameters, each range of values of the plurality of ranges corresponding to a respective parameter of the plurality of parameters (¶ 0041, wherein a parameter range is presented for a VM for multiple VMs, ¶ 0070); and detect, based on the plurality of parameters and the plurality of ranges of values input into one or more machine learning (ML) models, an anomaly corresponding to a parameter of the plurality of parameters that is out of a range of values of the plurality of ranges of values corresponding to the parameter, the one or more ML models are trained using parameters and ranges of values for network operations of entity accounts (¶ 0042-0043). While Wakui teaches finding a corrected range for use by the VM, he does not explicitly teach to select, responsive to the detection, an action to address the anomaly; and perform, responsive to the selected action, and subsequent to the execution of the anomaly detection function, a network operation of the plurality of network operations to address the anomaly. Xu does teach to select, responsive to the detection, an action to address the anomaly; and perform, responsive to the selected action, and subsequent to the execution of the anomaly detection function, a network operation of the plurality of network operations to address the anomaly (¶ 0015, wherein a VM is remedied according to the collected data and analysis of the collected data, ¶ 0027). It would have been obvious to one of ordinary skill in the art to use the process of Xu in the process of Wakui. One of ordinary skill in the art would have been motivated to use the process of Xu in the process of Wakui because using the process of Xu would yield the predictable result of using collected network data to predict future operation of a VM network, an explicit desire of Wakui.
As per claim 4, Wakui teaches the system of claim 1, wherein the one or more processors further: provide, for display via the GUI of the device, the element providing access to a graphical representation of the data of the plurality of entity accounts (¶ 0068); receive, from the device, the selection of the element in response to an interaction with the element via the GUI (¶ 0068); and provide, for display via the GUI responsive to the selection, the graphical representation of the data (¶ 0066, 0068).
As per claim 5, Wakui teaches the system of claim 1, wherein the one or more processors further provide, for display on the GUI responsive to the detection, an indication of at least one of the anomaly corresponding to the parameter or the action to address the anomaly (¶ 0069).
As per claim 6, Wakui teaches the system of claim 1, wherein the one or more processors further: determine that the anomaly is responsive to a value of the parameter for an amount withheld from a first electronic transaction implemented via the network operation falling out of the range of values for the amount withheld; adjust the value for the parameter according to the range of values for the amount withheld; and perform the network operation for a second electronic transaction using the adjusted value for the parameter (¶ 0052, wherein the parameter ranges are checked and, if out of range, the range is adjusted and is checked again with adjusted, corrected range).
As per claim 9, Wakui teaches the system of claim 1, wherein the one or more processors further: receive, via the GUI, a selection of a second element to generate a report summarizing one or more detected anomalies including the anomaly and corresponding one or more actions for the detected anomalies including the action; and provide, for display on the GUI, the generated report (figure 6, wherein the user can generate multiple report summaries based on selected drop down parameter settings).
As per claim 10, Wakui teaches the system of claim 1, wherein the one or more processors further: determine, based on historical data of the plurality of entity accounts, a threshold value for the range of values for the parameter; and generate the ranges of values of the parameter based on the threshold value (¶ 0004, 0056).
As per claim 11, Wakui teaches the system of claim 1, wherein the one or more processors further: receive, via the GUI, a selection of a second element to initiate a manual review of the detected anomaly; and provide, for display on the GUI, an interface for an input of a result for the manual review (Figure 6, see claim 9).
As per claim 14, Wakui teaches a computer-implemented method, comprising: receiving, by one or more processors coupled with memory, via a graphical user interface (GUI) of a device, a selection of an element of the GUI to view data of a plurality of entity accounts; identifying, by the one or more processors responsive to execution of an anomaly detection function, from the data of the plurality of entity accounts, a plurality of parameters associated with execution of a plurality of network operations; determining, by the one or more processors based on the data, a plurality of ranges of values for the plurality of parameters, each range of values of the plurality of ranges corresponding to a respective parameter of the plurality of parameters; detecting, by the one or more processors based on the plurality of parameters and the plurality of ranges of values input into one or more machine learning (ML) models, an anomaly corresponding to a parameter of the plurality of parameters that is out of a range of values of the plurality of ranges of values corresponding to the parameter, the one or more ML models are trained using parameters and ranges of values for network operations of entity accounts; (¶ 0066-0070, 0041-0043, see claim 1). While Wakui teaches finding a corrected range for use by the VM, he does not explicitly teach to selecting, responsive to the detection, an action to address the anomaly; and performing, responsive to the selected action, and subsequent to the execution of the anomaly detection function, a network operation of the plurality of network operations to address the anomaly. Xu does teach to selecting, responsive to the detection, an action to address the anomaly; and performing, responsive to the selected action, and subsequent to the execution of the anomaly detection function, a network operation of the plurality of network operations to address the anomaly (¶ 0015, wherein a VM is remedied according to the collected data and analysis of the collected data, ¶ 0027). It would have been obvious to one of ordinary skill in the art to use the process of Xu in the process of Wakui. One of ordinary skill in the art would have been motivated to use the process of Xu in the process of Wakui because using the process of Xu would yield the predictable result of using collected network data to predict future operation of a VM network, an explicit desire of Wakui.
As per claim 16, Wakui teaches the method of claim 14, comprising: providing, by the one or more processors for display via the GUI of the device, the element providing access to a graphical representation of the data of the plurality of entity accounts; receiving, by the one or more processors from the device, the selection of the element in response to an interaction with the element via the GUI; and providing, by the one or more processors for display via the GUI responsive to the selection, the graphical representation of the data (¶ 0066, 0068, see claim 4). As per claim 17, Wakui teaches the method of claim 14, comprising: providing, by the one or more processors for display on the GUI responsive to the detection, an indication of at least one of the anomaly corresponding to the parameter or the action to address the anomaly (¶ 0069).
As per claim 18, Wakui teaches the method of claim 14, comprising: determining, by the one or more processors that the anomaly is responsive to a value of the parameter for an amount withheld from a first electronic transaction implemented via the network operation falling out of the range of values for the amount withheld; adjusting, by the one or more processors, the value for the parameter according to the range of values for the amount withheld; and performing, by the one or more processors, the network operation for a second electronic transaction using the adjusted value for the parameter (¶ 0052, see claim 6).
As per claim 20, Wakui teaches a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors coupled with memory, cause the one or more processors to: receive, via a graphical user interface (GUI) of a device, a selection of an element of the GUI to view data of a plurality of entity accounts generated using network operations; execute, responsive to the selection, and prior to performance of actions responsive to the selection, an anomaly detection function; identify, based on the execution of the anomaly detection function, from the data of the plurality of entity accounts, a plurality of parameters associated with execution of a plurality of network operations; determine, based on the data identified based on the execution of the anomaly detection function, a plurality of ranges of values for the plurality of parameters, each range of values of the plurality of ranges corresponding to a respective parameter of the plurality of parameters; and detect, based on the plurality of parameters and the plurality of ranges of values input into one or more machine learning (ML) models, an anomaly corresponding to a parameter of the plurality of parameters that is out of a range of values of the plurality of ranges of values corresponding to the parameter, the one or more ML models are trained using parameters and ranges of values for network operations of entity accounts (¶ 0042-0043). While Wakui teaches finding a corrected range for use by the VM, he does not explicitly teach to select, responsive to the detection, an action to address the anomaly; and perform, responsive to the selected action, and subsequent to the execution of the anomaly detection function, a network operation of the plurality of network operations to address the anomaly. Xu does teach to select, responsive to the detection, an action to address the anomaly; and perform, responsive to the selected action, and subsequent to the execution of the anomaly detection function, a network operation of the plurality of network operations to address the anomaly (¶ 0015, wherein a VM is remedied according to the collected data and analysis of the collected data, ¶ 0027). It would have been obvious to one of ordinary skill in the art to use the process of Xu in the process of Wakui. One of ordinary skill in the art would have been motivated to use the process of Xu in the process of Wakui because using the process of Xu would yield the predictable result of using collected network data to predict future operation of a VM network, an explicit desire of Wakui.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2023/0214672A1 to Lee: Network reconstruction according to parameters.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER S MCCARTHY whose telephone number is (571)272-3651. The examiner can normally be reached Monday-Friday 8:30-5:00.
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/CHRISTOPHER S MCCARTHY/Primary Examiner, Art Unit 2113
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