DETAILED ACTION
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 for examination. Claims 1, 12, and 19 are independent claims. This Office Action is Non-Final.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/10/2024 is in compliance with the provisions of 37 CFR 1.97, 37 CFR 1.98, and MPEP § 609. The Information Disclosure Statement has been placed in the application file and the information referred to therein has been considered as to the merits.
Claim Objections
Claims 16-18 are objected to because the Examiner is unclear how to interpret this claim as the preamble recites “the electronic apparatus” but they each depend on a method claim. For the remainder of this office action, the Examiner will interpret claim 16 as dependent on claim 12, claim 17 dependent on claim 16, and claim 18 dependent on claim 15. Appropriate correction is required.
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(s) without significantly more.
Claims 1, 12, and 19 recite:
obtaining feature data corresponding to a plurality of running applications, wherein the feature data comprises current system state data and running state data corresponding to the at least one application;
identifying a target abnormal application based on the feature data corresponding to the plurality of running applications;
obtaining interactive data corresponding to a plurality of call actions of the target abnormal application within a preset duration;
identifying a target abnormal call action based on the interactive data corresponding to the plurality of the call actions; and
performing call restriction on the target abnormal call action based on monitoring the target abnormal call action.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes:
Claim 1 is a method.
Claim 12 is an apparatus.
Claim 19 is an article of manufacture.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The limitations of ‘identifying a target abnormal application’ in # 2 and ‘identifying a target abnormal call action” in #4 above, as claimed and under broadest reasonable interpretation (BRI), are mental processes that covers performance of the limitation in the mind. For example, both of these limitations, in the context of this claim, encompass a person making a judgement about data.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
Limitation #1 ‘obtaining feature data’ and limitation #3 ‘obtaining interactive data’ above, as claimed and under BRI, are additional elements that are insignificant extra-solution activity. For example, “obtaining” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
The ‘performing call restriction’ limitation in # 5 above, as claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, “performing call restriction” in the context of this claim encompasses applying generic computer instructions based on monitoring the target abnormal call action to an abstract idea (the claimed identifying steps). See MPEP 2106.05(f).
Additionally, one or more of the claims recite the following additional elements:
an electronic apparatus (Claims 1, 12, 19),
memory (Claim 12),
processors (Claims 12, 19),
storage media (Claims 19, 20).
These additional elements are recited at a high level of generality (i.e. as generic computer components) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s). See MPEP 2106.05(f).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components comprising mere instructions to apply the exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Additionally, with regards to # 1 and 3 above, per MPEP 2106.05(d)(Il), 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:
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Claims 2, 13, and 20 recite:
6. identifying target applications from among the plurality of running applications based on the running state data corresponding to the plurality of running applications; and
7. identifying the target abnormal application from among the target applications based on feature data corresponding to the target applications.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes:
Claim 2 is a method.
Claim 13 is an apparatus.
Claim 20 is an article of manufacture.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘identifying’ limitations in # 6 and # 7 above, as claimed and under broadest reasonable interpretation (BRI), are mental processes that covers performance of the limitation in the mind. For example, both of these limitations, in the context of this claim, encompass a person making a judgement about data.
Claims 3 and 14 recite:
8. wherein, the running state data corresponding to the at least one application comprises a central processing unit (CPU) occupancy rate when the application is running; and
9. selecting top N applications of highest CPU occupancy rate from among the plurality of running applications, wherein the N is a preset positive integer; and
10. identifying the top N applications of highest CPU occupancy rate as the target applications.
Claim 3, limitation #8 merely further describes the claimed state data of Claim 1.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes:
Claim 3 is a method.
Claim 14 is an apparatus.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘selecting’ limitation in # 9 and the ‘identifying’ limitation in # 10 above, as claimed and under broadest reasonable interpretation (BRI), are mental processes that covers performance of the limitation in the mind or mathematical calculations. For example, both of these limitations, in the context of this claim, encompass a person making a judgement about data.
Claims 4 and 15 recite:
11. obtaining a corresponding decision result of each target application through a decision tree model based on the feature data corresponding to each target application; and
12. identifying the target abnormal application from among the target applications based on the decision results.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes:
Claim 4 is a method.
Claim 15 is an apparatus.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘obtaining’ limitation in # 11 and the ‘identifying’ limitation in # 12 above, as claimed and under broadest reasonable interpretation (BRI), are mental processes that covers performance of the limitation in the mind or mathematical calculations. For example, both of these limitations, in the context of this claim, encompass a person making a judgement about data.
Claims 5 and 16 merely further describes insignificant extra-solution activity of Claim 1 under Step 2A, Prong 2 and Step 2B, see analysis above. For example, “displaying”, “obtaining” in the context of this claim encompasses mere data output and gathering.
Claims 6 and 17 merely further describes insignificant extra-solution activity of Claim 5 under Step 2A, Prong 2 and Step 2B, see analysis above. For example, “displaying”, “stopping” in the context of this claim encompasses mere data input, output and gathering.
Claims 7 and 18 recite:
13. obtaining a label indicating whether the decision result is correct; and
14. updating the decision tree model based on the label and the feature data corresponding to the target abnormal application.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes:
Claim 7 is a method.
Claim 18 is an apparatus.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘obtaining’ limitation in # 13 and the ‘updating’ limitation in # 14 above, as claimed and under broadest reasonable interpretation (BRI), are mental processes that covers performance of the limitation in the mind or mathematical calculations. For example, both of these limitations, in the context of this claim, encompass a person making a judgement about data.
Claim 8 merely further describes the claimed call action and selection of call action and the interactive data of Claim 1.
Claim 9 recites:
15. obtaining an abnormal probability of the target call action through a Gaussian mixture clustering model based on the number of calls of the target call action within the preset duration; and
16. identifying the target call action as the target abnormal call action based on the abnormal probability being higher than a preset threshold.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes:
Claim 9 is a method.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘obtaining’ limitation in # 15 and the ‘identifying’ limitation in # 16 above, as claimed and under broadest reasonable interpretation (BRI), are mental processes that covers performance of the limitation in the mind or mathematical calculations. For example, both of these limitations, in the context of this claim, encompass a person making a judgement about data.
Claim 10 ‘suspending execution’ and ‘releasing the suspension’, as claimed and under BRI, are additional elements that are mere instructions to apply an exception under Step 2A, Prong 2 and Step 2B, see analysis above. For example, “suspending” and ‘releasing in the context of this claim encompasses applying generic computer instructions based on monitoring the target abnormal call action to an abstract idea (the claimed identifying steps).
Claim 11 merely further describes the claimed suspension time of Claim 10.
For at least the reasoning provided above, Claims 1-20 are patent ineligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7, 10, 12-20 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Huang et al. (U.S. Patent Publn No. 2020/0371892 A1), hereinafter Huang.
Regarding claim 1, Huang teaches:
A method of controlling an electronic apparatus for running at least one application (Huang, Abstract “detecting anomaly of one or a plurality of applications …abnormality detection model is built … at the remote device based on data collected from multiple devices” Fig. 2 is a flowchart for anomaly detection in an application that allows as shown in paragraph 0053 in Table 1 freeze or restart of the application based on level of abnormality), the method comprising:
obtaining feature data corresponding to a plurality of running applications (Huang, Fig. 1, paragraph 0035 current state of the system “application in an abnormal state may keep running …in a state without stopping or changing to a different state.” Paragraph 0034 and Fig. 1 “computing device may also include one or more applications 106. … “data (e.g., usage data) about one or more resources of the computing device … that are accessed or used by each application executed on the computing device, and associate usage of each of the resources to the corresponding application.” [i.e. feature data for multiple running applications]. Running system state data is taught in paragraph 0035 “This may also be the case when the application keeps attempting to transit from operating mode or state to another operating mode or state but fails.”), wherein the feature data comprises current system state data and running state data corresponding to the at least one application (Huang, Fig. 1, paragraph 0035 current state of the system “application in an abnormal state may keep running …in a state without stopping or changing to a different state.” Running system state data is taught in paragraph 0035 “This may also be the case when the application keeps attempting to transit from operating mode or state to another operating mode or state but fails.”);
identifying a target abnormal application based on the feature data corresponding to the plurality of running applications (Huang, Fig. 2 paragraph 0038 application anomaly detection. Paragraph 0047 “At step 206, the method 200 detects anomaly of the application using the abnormality detection model based on the obtained resource usage data….whether the application has anomaly in resource usage of resources such as CPU BG, CPU FG …”);
obtaining interactive data corresponding to a plurality of call actions of the target abnormal application within a preset duration (Examiner under BRI interprets “plurality of call actions” as calls to procedures or functions within application to use resources. Huang teaches in paragraph 0041 calls to procedures or functions for resources “resource usage may refer to usage … of a resource that is used … by the application … a frequency that the resource is used or accessed (for executing an application), a duration that the resource is used or accessed …Examples of resource usages may include usage … of CPU in background (BG), CPU in foreground (FG) …”);
identifying a target abnormal call action based on the interactive data corresponding to the plurality of the call actions (Huang, paragraph 0047 “the computing device may monitor the resources used by the application, collect the resource usage data of the resources according to requirements of the abnormality detection model, and provide the collected resource usage data to the abnormality detection model. In another example, the abnormality detection model may be configured to monitor and collect the resource usage data of the resources used by the application. … The device may monitor and collect resource usage data corresponding to each and every … process used or accessed within a device …The abnormality detection model may be configured to detect anomaly of multiple applications in resource usages simultaneously. The abnormality detection model may predict or detect abnormality in multiple resource usages or abnormality parameters simultaneously. Multiple levels or classes of abnormality may be detected or identified by the abnormality detection model simultaneously.”); and
performing call restriction on the target abnormal call action based on monitoring the target abnormal call action (Huang, Fig. 2, block 208 paragraph 0052 “Take an action in response to detection of the anomaly …the computing device is configured to perform an operation in response. The operation …involves …limiting resource usage or limiting access to one or more resources [i.e. call restriction] by an application that is detected to be behaving abnormally. …an action may be taken to limit some resources and or services available to the application.”).
Regarding claim 2, the rejection of claim 1 is incorporated as given above. Huang teaches wherein, the identifying of the target abnormal application based on the feature data corresponding to the plurality of running applications comprises:
identifying target applications from among the plurality of running applications based on the running state data corresponding to the plurality of running applications (Huang, Fig. 2 paragraph 0038 application anomaly detection. Paragraph 0047 “At step 206, the method 200 detects anomaly of the application using the abnormality detection model based on the obtained resource usage data….whether the application has anomaly in resource usage of resources such as CPU BG, CPU FG …”.” Running system state data is taught in paragraph 0035 “This may also be the case when the application keeps attempting to transit from operating mode or state to another operating mode or state but fails.”); and
identifying the target abnormal application from among the target applications based on feature data corresponding to the target applications (Huang, Fig. 2 paragraph 0038 application anomaly detection. Paragraph 0047 “At step 206, the method 200 detects anomaly of the application using the abnormality detection model based on the obtained resource usage data….whether the application has anomaly in resource usage of resources such as CPU BG, CPU FG …”).
Regarding claim 3, the rejection of claim 2 is incorporated as given above. Huang teaches wherein, the running state data corresponding to the at least one application comprises a central processing unit (CPU) occupancy rate when the application is running (Huang teaches in paragraph 0041 calls to procedures or functions for resources “resource usage may refer to usage … of a resource that is used … by the application … a frequency that the resource is used or accessed (for executing an application), a duration that the resource is used or accessed …Examples of resource usages may include usage … of CPU in background (BG), CPU in foreground (FG) …”); and
wherein the identifying of the target applications from among the plurality of running applications based on the running state data corresponding to the plurality of running applications comprises:
selecting top N applications of highest CPU occupancy rate from among the plurality of running applications, wherein the N is a preset positive integer (Huang, paragraph 0037 “A single abnormality detection model may support detecting abnormality in several applications used or accessed by the device during a period of time.” Paragraph 0048, 0049 teaches use of mathematical mapping of several resource usage parameters or device information such as CPU usage. Paragraph 0050 the mapping may include levels of abnormality for applications such as no abnormality, abnormality, high abnormality or excessive abnormality and selecting applications with high or excessive abnormality “the abnormality detection model 300 may detect that the application has high abnormality with respect to CPU BG usage”); and
identifying the top N applications of highest CPU occupancy rate as the target applications (Huang, Paragraph 0050 the mapping may include levels of abnormality for applications such as no abnormality, abnormality, high abnormality or excessive abnormality and selecting applications with high or excessive abnormality “the abnormality detection model 300 may detect that the application has high abnormality with respect to CPU BG usage” Paragraph 0052 “an action taken in response for each application may depend on the detected abnormality level and/or the importance of the resource usage.”).
Regarding claim 4, the rejection of claim 2 is incorporated as given above. Huang teaches wherein, the identifying of the target abnormal application from among the target applications based on the feature data corresponding to the target applications comprises:
obtaining a corresponding decision result of each target application through a decision tree model based on the feature data corresponding to each target application (Huang, Fig. 2, block 202 “Receive an abnormality detection model” paragraph 0038 “the abnormality detection model may be a … decision network, an ensemble of decision trees...” Block 206 “Detect anomaly of the application using the abnormality detection model based on the obtained resource usage data”); and
identifying the target abnormal application from among the target applications based on the decision results (Huang, Fig. 2 blocks 206, 208 “takes an action in response to detecting that the application has anomaly”, 210 paragraph 0054 “may record some portion or all of the data at an interval for a period of time, e.g., recording abnormality detection results for every 5 minutes for 2 months.”).
Regarding claim 5, the rejection of claim 1 is incorporated as given above. Huang teaches wherein, after the identifying of the target abnormal application based on the feature data corresponding to the plurality of running applications, the method further comprises:
displaying a first notification information, wherein the first notification information indicates existence of the target abnormal application and prompts a user an optimization function for the target abnormal application is able to be opened (Huang, paragraph 0052 “a notification, e.g., an abnormality notification (e.g. a user interface pop up message, sound alert, instant messaging, or other applicable mechanism to inform a user or system), may be given to a user or controller of the computing device. The notification may indicate the occurrence of the abnormality, so that the user may take an action that he or she deems appropriate in response. The notification may indicate an abnormality detection result for the application. The notification may also indicate a list of actions [i.e. optimization function] that the user can choose in response to the occurrence of the abnormality. In some cases, the user may take no action regardless of the abnormality notification, based on his/her preference, experience or expertise.”); and
executing a first operation based on receiving an opening instruction for the optimization function (Huang, paragraph 0052 “a notification, e.g., an abnormality notification (e.g. a user interface pop up message, sound alert, instant messaging, or other applicable mechanism to inform a user or system), may be given to a user or controller of the computing device. The notification may indicate the occurrence of the abnormality, so that the user may take an action that he or she deems appropriate in response. The notification may indicate an abnormality detection result for the application. The notification may also indicate a list of actions that the user can choose in response to the occurrence of the abnormality. In some cases, the user may take no action regardless of the abnormality notification, based on his/her preference, experience or expertise.”); and
wherein the first operation comprises: obtaining the interactive data corresponding to the plurality of call actions of the target abnormal application within the preset duration, identifying the target abnormal call action based on the interactive data corresponding to the plurality of call actions (Huang, paragraph 0052 “a notification, e.g., an abnormality notification (e.g. a user interface pop up message, sound alert, instant messaging, or other applicable mechanism to inform a user or system), may be given to a user or controller of the computing device. The notification may indicate the occurrence of the abnormality, so that the user may take an action that he or she deems appropriate in response. The notification may indicate an abnormality detection result for the application. The notification may also indicate a list of actions that the user can choose in response to the occurrence of the abnormality. In some cases, the user may take no action regardless of the abnormality notification, based on his/her preference, experience or expertise.” Also, see Fig. 2 and description of separate blocks as described above);
performing the call restriction on the target abnormal call action based on monitoring the target abnormal call action (Huang, Fig. 2, blocks 202-208 and corresponding description as given above).
Regarding claim 6, the rejection of claim 5 is incorporated as given above. Huang teaches displaying second notification information during a process of the first operation, wherein the second notification information prompts the user the optimization function for the target abnormal application is able to be closed (Huang, paragraph 0053 and Table 1, Actions:Restart that requires application to close. “if an abnormality of an application with respect to a resource usage persists for a period of time longer than a threshold, an action to be taken may be changed.” i.e. second notification); and
stopping executing the first operation based on receiving a closing instruction for the optimization function (Huang, paragraph 0053 and Table 1, Action:Restart that requires application to close. Also Action:Freeze stops executing the first operation).
Regarding claim 7, the rejection of claim 4 is incorporated as given above. Huang teaches wherein, after the identifying of the target abnormal call action based on the interactive data corresponding to the plurality of call actions, further comprises:
obtaining a label indicating whether the decision result is correct (Huang, paragraph 0075 teaches labeling of data samples used in the abnormality detection model. Paragraph 0076 “labeled data of the combination of applications and abnormality parameters are used to build one or more abnormality detection models that are capable of detecting abnormality at once for a plurality of applications and a plurality of abnormality parameters or prediction outputs.”); and
updating the decision tree model based on the label and the feature data corresponding to the target abnormal application (Huang, paragraph 0076 “labeled data of the combination of applications and abnormality parameters are used to build one or more abnormality detection models that are capable of detecting abnormality at once for a plurality of applications and a plurality of abnormality parameters or prediction outputs.”).
Regarding claim 10, the rejection of claim 1 is incorporated as given above. Huang teaches wherein, the performing of the call restriction on the target abnormal call action based on monitoring the target abnormal call action comprises:
suspending execution of the target abnormal call action based on monitoring the target abnormal call action(Huang, paragraph 0053 and Table 1, Action:Restart that requires application to stop and close. Also Action:Freeze stops executing the first operation); and
releasing the suspension of the target abnormal call action after preset suspension time has elapsed (Huang, paragraph 0053 “if an abnormality of an application with respect to a resource usage persists for a period of time longer than a threshold, an action to be taken may be changed.”).
Regarding claim 12, Huang teaches:
An electronic apparatus for running at least one application (Huang, Abstract “detecting anomaly of one or a plurality of applications …abnormality detection model is built … at the remote device based on data collected from multiple devices” Fig. 2 is a flowchart for anomaly detection in an application that allows as shown in paragraph 0053 in Table 1 freeze or restart of the application based on level of abnormality), the electronic apparatus comprising:
memory storing one or more computer programs (Huang, paragraphs 0033-0034 software program stored in memory of the computing device); and
one or more processors communicatively coupled to the memory (Huang, paragraph 0033 “a central processing unit (CPU) or multiple processing units”);
wherein the one or more computer programs include computer-executable instructions that, when executed by the one or more processors individually or collectively (Huang, paragraph 0124 “as computer instructions stored in a non-transitory computer-readable medium and executable by one or more processors.”), cause the electronic apparatus to:
obtain feature data corresponding to a plurality of running applications (Huang, Fig. 1, paragraph 0035 current state of the system “application in an abnormal state may keep running …in a state without stopping or changing to a different state.” Paragraph 0034 and Fig. 1 “computing device may also include one or more applications 106. … “data (e.g., usage data) about one or more resources of the computing device … that are accessed or used by each application executed on the computing device, and associate usage of each of the resources to the corresponding application.” [i.e. feature data for multiple running applications]. Running system state data is taught in paragraph 0035 “This may also be the case when the application keeps attempting to transit from operating mode or state to another operating mode or state but fails.”), wherein the feature data comprises current system state data and running state data corresponding to an application (Huang, Fig. 1, paragraph 0035 current state of the system “application in an abnormal state may keep running …in a state without stopping or changing to a different state.” Running system state data is taught in paragraph 0035 “This may also be the case when the application keeps attempting to transit from operating mode or state to another operating mode or state but fails.”),
identify a target abnormal application based on the feature data corresponding to the plurality of running applications (Huang, Fig. 2 paragraph 0038 application anomaly detection. Paragraph 0047 “At step 206, the method 200 detects anomaly of the application using the abnormality detection model based on the obtained resource usage data….whether the application has anomaly in resource usage of resources such as CPU BG, CPU FG …”),
obtain interactive data corresponding to a plurality of call actions of the target abnormal application within a preset duration (Examiner under BRI interprets “plurality of call actions” as calls to procedures or functions within application to use resources. Huang teaches in paragraph 0041 calls to procedures or functions for resources “resource usage may refer to usage … of a resource that is used … by the application … a frequency that the resource is used or accessed (for executing an application), a duration that the resource is used or accessed …Examples of resource usages may include usage … of CPU in background (BG), CPU in foreground (FG) …”),
identify a target abnormal call action based on the interactive data corresponding to the plurality of call actions (Huang, paragraph 0047 “the computing device may monitor the resources used by the application, collect the resource usage data of the resources according to requirements of the abnormality detection model, and provide the collected resource usage data to the abnormality detection model. In another example, the abnormality detection model may be configured to monitor and collect the resource usage data of the resources used by the application. … The device may monitor and collect resource usage data corresponding to each and every … process used or accessed within a device …The abnormality detection model may be configured to detect anomaly of multiple applications in resource usages simultaneously. The abnormality detection model may predict or detect abnormality in multiple resource usages or abnormality parameters simultaneously. Multiple levels or classes of abnormality may be detected or identified by the abnormality detection model simultaneously.”), and
perform call restriction on the target abnormal call action based on monitoring the target abnormal call action (Huang, Fig. 2, block 208 paragraph 0052 “Take an action in response to detection of the anomaly …the computing device is configured to perform an operation in response. The operation …involves …limiting resource usage or limiting access to one or more resources [i.e. call restriction] by an application that is detected to be behaving abnormally. …an action may be taken to limit some resources and or services available to the application.”).
Claims 13-18, the apparatus that implement the method of claims 2-7, respectively, are rejected on the same grounds as claims 2-7, respectively.
Regarding claim 19, Huang teaches:
One or more non-transitory computer-readable storage media storing computer-executable instructions that, when executed by one or more processors individually or collectively (Huang, paragraph 0124 “as computer instructions stored in a non-transitory computer-readable medium and executable by one or more processors.”), cause an electronic apparatus to perform operations (Huang, Abstract “detecting anomaly of one or a plurality of applications …abnormality detection model is built … at the remote device based on data collected from multiple devices” Fig. 2 is a flowchart for anomaly detection in an application that allows as shown in paragraph 0053 in Table 1 freeze or restart of the application based on level of abnormality), the operations comprising:
obtaining feature data corresponding to a plurality of running applications (Huang, Fig. 1, paragraph 0035 current state of the system “application in an abnormal state may keep running …in a state without stopping or changing to a different state.” Paragraph 0034 and Fig. 1 “computing device may also include one or more applications 106. … “data (e.g., usage data) about one or more resources of the computing device … that are accessed or used by each application executed on the computing device, and associate usage of each of the resources to the corresponding application.” [i.e. feature data for multiple running applications]. Running system state data is taught in paragraph 0035 “This may also be the case when the application keeps attempting to transit from operating mode or state to another operating mode or state but fails.”), wherein the feature data comprises current system state data and running state data corresponding to the at least one application (Huang, Fig. 1, paragraph 0035 current state of the system “application in an abnormal state may keep running …in a state without stopping or changing to a different state.” Running system state data is taught in paragraph 0035 “This may also be the case when the application keeps attempting to transit from operating mode or state to another operating mode or state but fails.”);
identifying a target abnormal application based on the feature data corresponding to the plurality of the running applications (Huang, Fig. 2 paragraph 0038 application anomaly detection. Paragraph 0047 “At step 206, the method 200 detects anomaly of the application using the abnormality detection model based on the obtained resource usage data….whether the application has anomaly in resource usage of resources such as CPU BG, CPU FG …”);
obtaining interactive data corresponding to a plurality of call actions of the target abnormal application within a preset duration (Examiner under BRI interprets “plurality of call actions” as calls to procedures or functions within application to use resources. Huang teaches in paragraph 0041 calls to procedures or functions for resources “resource usage may refer to usage … of a resource that is used … by the application … a frequency that the resource is used or accessed (for executing an application), a duration that the resource is used or accessed …Examples of resource usages may include usage … of CPU in background (BG), CPU in foreground (FG) …”);
identifying a target abnormal call action based on the interactive data corresponding to the plurality of the call actions (Huang, paragraph 0047 “the computing device may monitor the resources used by the application, collect the resource usage data of the resources according to requirements of the abnormality detection model, and provide the collected resource usage data to the abnormality detection model. In another example, the abnormality detection model may be configured to monitor and collect the resource usage data of the resources used by the application. … The device may monitor and collect resource usage data corresponding to each and every … process used or accessed within a device …The abnormality detection model may be configured to detect anomaly of multiple applications in resource usages simultaneously. The abnormality detection model may predict or detect abnormality in multiple resource usages or abnormality parameters simultaneously. Multiple levels or classes of abnormality may be detected or identified by the abnormality detection model simultaneously.”); and
performing call restriction on the target abnormal call action based on monitoring the target abnormal call action (Huang, Fig. 2, block 208 paragraph 0052 “Take an action in response to detection of the anomaly …the computing device is configured to perform an operation in response. The operation …involves …limiting resource usage or limiting access to one or more resources [i.e. call restriction] by an application that is detected to be behaving abnormally. …an action may be taken to limit some resources and or services available to the application.”).
Claim 20, the article of manufacture that implement the method of claim 2 is rejected on the same grounds as claim 2.
No prior art rejection is given for claims 8-9, 11.
Conclusion
The prior art made of record in Form PTO-892 and not relied upon is considered pertinent to Applicants’ disclosure.
Young et al. (U.S. Patent Publn. No. 2012/0166869 A1) teaches differentiating normal operation of an application program from error conditions to predict, diagnose, and recover from application failures. Access to resources by the application program is monitored, and resource access events are logged. Resource access patterns are established from the logged resource access events utilizing computer pattern recognition techniques. If subsequent access to resources by the application program deviates from the established patterns, then a user and/or administrator of the application program is notified of a potential error condition based on the detected deviation.
Applicants are required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
In the interests of compact prosecution, Applicants are invited to contact the examiner via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicants may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicants may wish to request an interview using the Interview Practice website: http://www.uspto.gov/patent/laws-and-regulations/interview-practice.
Applicants are reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicants to the USPTO via Internet e-mail. If such a reply is submitted by Applicants via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to INDRANIL CHOWDHURY whose telephone number is (571)272-0446. The examiner can normally be reached on M-Fri 9:30-7:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ashish Thomas can be reached on 571-272-0631. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/INDRANIL CHOWDHURY/ Examiner, Art Unit 2114
/MARK D FEATHERSTONE/Supervisory Patent Examiner, Art Unit 2111