The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/05/2025 has been entered.
Notice to Applicant
In response to the communication received on 06/05/2025, the following is a Non-Final Office Action for Application No. 17247714.
Status of Claims
Claims 1-3, 6-9, 14-21, 24-27 and 32-36 are pending.
Claims 10-13 and 28-31 are withdrawn.
Claims 4, 5, 22, and 23 are cancelled.
Priority
As required by M.P.E.P. 201.14(c), acknowledgement is made of applicant’s claim for priority based on: 17247714 filed 12/21/2020 Claims Priority from Provisional Application 62992661, filed 03/20/2020.
Response to Amendments
Applicant’s amendments have been fully considered.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot in light of the new grounds of rejection, as necessitated by amendment.
As per the 101 rejection, Applicant argues that the claims are in favor of eligibility per Prong One of Step 2A, however Examiner respectfully disagrees. Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Since the recitation of the claims falls into at least one of the above Groupings, there is a basis for providing further analysis with regard to Prong Two of Step 2A to determine whether the recitation of an abstract idea is deduced to being directed to an abstract idea. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Prong Two of Step 2A, however Examiner respectfully disagrees. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The memory, system computing assets, and/or hardware assets is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic processor server limitation is no more than mere instructions to apply the exception using a generic computer component. Further, memory, system computing assets, and/or hardware assets to inter alia perform the function of calculate a system computing asset evaluation rating of the system computing asset that is underperforming is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. In other words, the present claims use a generic processing device and memory medium to inter alia perform the function of calculate a system computing asset evaluation rating of the system computing asset that is underperforming which is a concept that can be performed in the human mind. The processor is merely used to perform the function(s), and the processor does not integrate the abstract idea into a practical application since there are no meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Step 2B, however Examiner respectfully disagrees. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: memory, system computing assets, and/or hardware assets. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, memory, system computing assets, and/or hardware assets to inter alia perform the function of calculate a system computing asset evaluation rating of the system computing asset that is underperforming is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include the non-limiting or non-exclusive examples of MPEP § 2106.05. Thus, the rejection is maintained.
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 1-3, 6-9, 14-21, 24-27 and 32-36 are rejected under 35 U.S.C. 101 as directed to non-statutory subject matter.
Claims 1-3, 6-9, 14-21, 24-27 and 32-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In adhering to the 2019 PEG, Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, the claims fall within statutory class of process or machine or manufacture. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, the 2019 PEG flowchart is directed to Step 2. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
identifying, by an analysis system computing entity, a system sector of a system under test for an asset evaluation of the system sector;analyzing, by the analysis computing entity, the system sector to determine one or more system computing assets of the system sector via a secure data connection, wherein the system computing assets include hardware assets that are configured to execute operational tasks that support system operations of the system sector;determining, by the analysis system computing entity,an operational evaluation perspective for use in performing the asset evaluation on the system sector, wherein the operational evaluation perspective is in regard to implementational operation self-analysis;retrieving, by the analysis system computing entity, asset data regarding the one or more of system computing assets based on at least one system criteria, the operational evaluation perspective, an evaluation viewpoint and at least one evaluation rating metrics;determining, by the analysis computing entity, that a system computing asset of the one or more system computing assets is performing unfavorably compared to predetermined operational standards; andcalculating, by the analysis system computing entity, a system computingasset evaluation rating of the system computing asset that is underperforming, wherein when the system computing asset evaluation rating compares unfavorably to a predetermine threshold:classifying the system computing asset, by the analysis computing entity, a liability to the system sector; andproviding, by the analysis system computing entity, remediation techniques to bring the system computing asset into accordance with the predetermined operational standards.
[or]
non-transitory computer readable memory comprises: a first memory section for storing operational instructions that, when executed by a computing entity, cause the computing entity to:identify a system sector of a system under test for an asset evaluation of the system sector;analyze the system sector to determine one or more of system computing assets of the system sector via a secure data connection, wherein the system computing assets include hardware assets that are configured to execute operational tasks that support system operations of the system sector;determine an operational evaluation perspective for use in performing the asset evaluation on the system sector, wherein the operational evaluation perspective is in regard to implementational operation self-analysis;a second memory section for storing operational instructions that, when executed by the computing entity, cause the computing entity to:retrieve asset data regarding the one or more of system computing assets based on at least one system criteria, the operational evaluation perspective, an evaluation viewpoint and at least one evaluation rating metrics;determining, by the analysis computing entity, that a system computing asset of the one or more system computing assets is performing unfavorably compared to predetermined operational standards; anda third memory section for storing operational instructions that, when executed by the computing entity, cause the computing entity to:calculate a system computing asset evaluation rating of the system computing asset that is underperforming, wherein when the system computing asset evaluation rating compares unfavorably to a predetermine threshold:classifying the system computing asset as a liability to the system sector; andprovide remediation techniques to bring the system computing asset into accordance with the predetermined operational standards.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The memory, system computing assets, and/or hardware assets is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic memory, system computing assets, and/or hardware assets limitation is no more than mere instructions to apply the exception using a generic computer component. Further, calculate a system computing asset evaluation rating of the system computing asset that is underperforming by a memory, system computing assets, and/or hardware assets is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: memory, system computing assets, and hardware assets. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, calculate a system computing asset evaluation rating of the system computing asset that is underperforming by a memory, system computing assets, and/or hardware assets is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure at ¶0188 wherein “Figure 3B is schematic block diagram of an embodiment of a computing entity 16 that includes two or more computing devices 40 (e.g., two or more from any combination of the embodiments of Figures 2A - 2D)”. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
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ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
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iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
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v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine/manufacture for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 6-9, 14-21, 24-27 and 32-36 are rejected under 35 U.S.C. 103 as being unpatentable over Lovy et al. (US 20130208880 A1) hereinafter referred to as Lovy in view of Lu (US 20190197442 A1) hereinafter referred to as Lu.
Lovy teaches:
Claim 1. A method comprises:
identifying, by an analysis system computing entity, a system sector of a system under test for an asset evaluation of the system sector (¶0174 Status Plug-Ins 391 conduct specific, individual object tests. Bulk Plug-In Poller 392 makes it possible to conduct multiple simultaneous tests of plug-in objects. Unlike many network management systems that rely solely on individual object tests, the Bulk Plug-In Poller 392 enables a level of monitoring efficiency that allows appliance 300 to effectively scale to address larger network environments, including monitoring via SNMP (Simple Network Management Protocol). Used almost exclusively in TCP/IP networks, SNMP provides a means to monitor and control network devices, and to manage configurations, statistics collection, performance, and security.);
analyzing, by the analysis computing entity, the system sector to determine one or more system computing assets of the system sector via a secure data connection, wherein the system computing assets include hardware assets that are configured to execute operational tasks that support system operations of the system sector (¶0039 Specifically, a packet-switched data network 202 comprises a network appliance 300, a plurality of processes 302-306, plurality of monitored devices 314a-n, external databases 310a-n, external services 312 represented by their respective TCP port, and a global network topology 220, illustrated conceptually as a cloud. One or more of the elements coupled to global network topology 220 may be connected directly through a dedicated connection, such as a T1, T2, or T3 connection or through an Internet Service Provider (ISP), such as America On Line, Microsoft Network, CompuServe, etc. ¶0155 Appliance 300 monitors network objects, locates the source of problems, and facilitates diagnostics and repair of network infrastructure across the core, edge and access portions of the network. In the illustrative embodiment, appliance 300 comprises a status monitoring module 318, a performance monitoring module 316, a decision engine 324, a case management module 326 and database 348. The implementations of these modules as well as their interaction with each other and with external devices is described hereafter in greater detail.);
determining, by the analysis system computing entity, an operational evaluation perspective for use in performing the asset evaluation on the system sector, wherein the operational evaluation perspective is in regard to implementational operation self-analysis (¶0155 Appliance 300 monitors network objects, locates the source of problems, and facilitates diagnostics and repair of network infrastructure across the core, edge and access portions of the network. In the illustrative embodiment, appliance 300 comprises a status monitoring module 318, a performance monitoring module 316, a decision engine 324, a case management module 326 and database 348. The implementations of these modules as well as their interaction with each other and with external devices is described hereafter in greater detail. ¶0156 The present invention uses a priori knowledge of devices to be managed. For example, a list of objects to be monitored may be obtained from Domain Name Server. The desired objects are imported into the appliance 300. The relationships between imported objects may be entered manually or detected via an existing automated process application. In accordance with the paradigm of the invention, any deviation from the imported network configuration is considered a fault condition requiring a modification of the source data. In this manner the network management appliance 300 remains in synchronization with the source data used to establish the network configuration.);
retrieving, by the analysis system computing entity, asset data regarding the one or more of system computing assets based on at least one system criteria, the operational evaluation perspective, an evaluation viewpoint and at least one evaluation rating metrics (¶0158 A Status Monitoring Module 318 comprises a collection of processes that perform the activities required to dynamically maintain the network service level, including the ability to quickly identify problems and areas of service degradation. Specifically, Status Monitoring Module 318 comprises Status Puller Module 330, On-Demand Status Puller 335, Status Plug-Ins 391, Bulk Plug-In Puller 392, Bulk UDP Puller 394, Bulk ifOperStatus Puller 396, Bulk TCP Puller 398, Bulk ICMP Puller 397, Trap Receiver 332, Status View Maintenance Module 385, and Status Maps and Tables Module 387. ¶0159 Polling and trapping are the two primary methods used by appliance 300 to acquire data about a network's status and health. Polling is the act of asking questions of the monitored objects, i.e., systems, services and applications, and receiving an answer to those questions. The response may include a normal status indication, a warning that indicates the possibility of a problem existing or about to occur, or a critical indication that elements of the network are down and not accessible. The context of the response determines whether further appliance 300 action is necessary. Trapping is the act of listening for a message (or trap) sent by the monitored object to appliance 300. These trap messages contain information regarding the object, its health, and the reason for the trap being sent.);
determining, by the analysis computing entity, that a system computing asset of the one or more system computing assets is performing unfavorably compared to predetermined operational standards (¶0159 Polling and trapping are the two primary methods used by appliance 300 to acquire data about a network's status and health. Polling is the act of asking questions of the monitored objects, i.e., systems, services and applications, and receiving an answer to those questions. The response may include a normal status indication, a warning that indicates the possibility of a problem existing or about to occur, or a critical indication that elements of the network are down and not accessible. The context of the response determines whether further appliance 300 action is necessary. Trapping is the act of listening for a message (or trap) sent by the monitored object to appliance 300. These trap messages contain information regarding the object, its health, and the reason for the trap being sent. ¶0162 Fault detection capability in appliance 300 is performed by Status Poller 330 and various poller modules, working to effectively monitor the status of a network. Status Poller 330 controls the activities of the various plug-ins and pollers in obtaining status information from managed devices, systems, and applications on the network. FIG. 6 illustrates the status flow between network appliance 300 and external network elements. Status Poller 330 periodically polls one or more monitored devices 314A-N. Status Poller 330 generates a fault poll query to a monitor device 314 and receives in return, a fault poll response. The fault poll queries may be in the form of any of a ICMP Echo, SNMP Get, TCP Connect or UDP Query. The fault poll response may be in the form of any of a ICMP Echo Reply, SNMP Response, TCP Ack or UDP Response.); and
calculating, by the analysis system computing entity, a system computing asset evaluation rating of the system computing asset that is underperforming, wherein when the system computing asset evaluation rating compares unfavorably to a predetermine threshold (¶0163 Status Poller 330 determines the needed poll types, segregates managed objects accordingly, and batch polls objects where possible. A Scheduler 373 triggers the Status Poller 330 to request polling at routine intervals. During each polling cycle, each monitored object is polled once. If any objects test critical, all remaining normal objects are immediately polled again. A Dependency Checker module which is part of the Root Cause Analysis Module determines which objects have changed status from the last time the Status Poller 330 was run, and determines, using the current state objects and the parent/child relation data, which objects are "dependency down" based on their reliance on an upstream object that has failed. ¶0166 A trap is a message sent by an SNMP agent to appliance 300 to indicate the occurrence of a significant event. An event may be a defined condition, such as a link failure, device or application failure, power failure, or a threshold that has been reached. Trapping provides a major incremental benefit over the use of polling alone to monitor a network. The data is not subject to an extended polling cycle and is as real-time as possible. Traps provide information on only the object that sent the trap, and do not provide a complete view of network health.):
Although not explicitly taught by Lovy, Lu teaches in the analogous art of systems for artificial intelligence based risk and knowledge management:
classifying the system computing asset, by the analysis computing entity, a liability to the system sector (¶0018 The disclosure creates intelligent agent assistants based upon internet of things and artificial intelligence across the entire risk management cycles and every aspect of the risk control management and knowledge management, which aims to proactively prevent any potential damages, intelligently handle the risk management, appropriately generate and select the knowledge, fairly determine the damages and payments to the loss claims, promptly repair the damages or recover the services, and accurately provide protections against any losses, including financial losses, intangible assets losses, and opportunity costs ¶0076 The agent 135 includes the functions to predict and categorize risks of quotes corresponding to a user request. It implements the AI techniques, such as Random Forest, Lasso Regression, and Deep Neural Network to produce the quotes based on user historical data and or monitoring sensor data if available. The risks includes various levels, such as high risk, medium risk, and low risk, where multiclass classification methods are used… The application is accepted and evaluate premium by the agent 135, if it is ascertained that the risk is low. In case the risk is medium, the AI agent assistant conducts the evaluation of the application to determine if it is within the maximum liabilities limit. If it is within the maximum liabilities limit, the application is accepted, else rejected. When the user's application is accepted, the AI assistant to agents 260 includes the estimation and prediction of the risk premiums by using AI or machine learning techniques, such as random forest, lasso regression, and deep neural network.); and
providing, by the analysis system computing entity, remediation techniques to bring the system computing asset into accordance with the predetermined operational standards (¶0018 The disclosure creates intelligent agent assistants based upon internet of things and artificial intelligence across the entire risk management cycles and every aspect of the risk control management and knowledge management, which aims to proactively prevent any potential damages, intelligently handle the risk management, appropriately generate and select the knowledge, fairly determine the damages and payments to the loss claims, promptly repair the damages or recover the services, and accurately provide protections against any losses, including financial losses, intangible assets losses, and opportunity costs ¶0066 The risk control and knowledge management agent 135 includes the notification generator 225, which is to determine if notifications are provided to users of the entity(s) 110 to prevent the entity(s) 110 from damages, or to minimize the damages proactively that may be caused, if a remedial measure is not taken, based on the comparison of the reference parameters. The notification generator 225 provides the alerts and advises via Internet of things (IoT) devices, which may be associated with the entity 110 and the system 105. If the damages are detected, the remediation executions are provided promptly if it is not fraudulent. The reference parameters include similar or different representations for various entity types. If the entity 110 is a bridge, the reference parameters may be vectors such as, depth of a crack, scope of the crack, and texture of an exterior bridge surface. In case the entity 110 is a vehicle, the reference parameters is a vector or matrix such as engine temperature, fuel rate, and a potential anomaly in a control unit of the vehicle. ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the systems for artificial intelligence based risk and knowledge management of Lu with the system for evolutionary contact center business intelligence of Lovy for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Lovy ¶0004 teaches that a need exists for a scalable contact center state engine providing a trustworthy, uninterrupted, unified reality;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Lovy Abstract teaches a web-based contact center state engine provides data describing the state of the contact center system and actionable intelligence including key performance indicators, and Lu Abstract teaches a risk management instrument that includes analysis with respect to entity data from multiple domains and/or various external factors; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Lovy at least the above cited paragraphs, and Lu at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the systems for artificial intelligence based risk and knowledge management of Lu with the system for evolutionary contact center business intelligence of Lovy. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Lovy teaches:
Claim 2. The method of claim 1, wherein the determining the system sector comprises: determining at least one system element of the system under test; and determining the system sector based on the at least one system element and the at least one system criteria (¶0180 The primary component of performance monitoring module 316 is performance poller 322. Performance poller 322 is the main device by which appliance 300 interacts with monitored device(s) 314a-n and is responsible for periodically monitoring such devices and reporting performance statistics thereon. Performance poller 322 is operatively coupled to application(s) 312, monitored device(s) 314, decision engine 334 and web process(es) 302. FIG. 10 illustrates the communication flow between the performance poller 322 and decision engine 334, as well as external elements. Performance poller 322 polls monitored device(s) 314a-n periodically for performance statistics. Specifically, performance poller 322 queries each device 314 with an SNMP Get call in accordance with the SNMP standard. In response, the monitored device 314 provides a performance poll response to performance poller 322 in the form of an SNMP Response call, also in accordance with the SNMP standard.).
Lovy teaches:
Claim 3. The method of claim 2 further comprises: a system element of the at least one system element includes one or more system computing assets; a system computing asset of the one or more system computing assets includes one or more of a physical asset and a conceptual asset, wherein one or more of the system element and the system computing asset is identified by one of an organization identifier, a division identifier, a department identifier, a group identifier, a sub-group identifier, a device identifier, a software identifier, or an internet protocol address identifier; and a system criteria of the at least one system criteria being system guidelines, system requirements, system design, system build, or resulting system (¶0180 The primary component of performance monitoring module 316 is performance poller 322. Performance poller 322 is the main device by which appliance 300 interacts with monitored device(s) 314a-n and is responsible for periodically monitoring such devices and reporting performance statistics thereon. Performance poller 322 is operatively coupled to application(s) 312, monitored device(s) 314, decision engine 334 and web process(es) 302. FIG. 10 illustrates the communication flow between the performance poller 322 and decision engine 334, as well as external elements. Performance poller 322 polls monitored device(s) 314a-n periodically for performance statistics. Specifically, performance poller 322 queries each device 314 with an SNMP Get call in accordance with the SNMP standard. In response, the monitored device 314 provides a performance poll response to performance poller 322 in the form of an SNMP Response call, also in accordance with the SNMP standard.).
Lovy teaches:
Claim 6. The method of claim 1 further comprises: an evaluation rating metric of the at least one evaluation rating metric being a process rating metric, a policy rating metric, a procedure rating metric, a certification rating, a documentation rating metric, or an automation rating metric (¶0080 FIG. 3I illustrates conceptually an exemplary agent scorecard user interface in accordance with the disclosure. Generally the audience for scorecards would be executive expertise and the focus would be strategic. Scorecards can be hybrid dashboards coupled with traditional reporting, including operational and financial performance trends, exceptions, and forecasting. Scorecards usually have predefined structure based on overall business objectives, but can also be created with the Sextant application Intelligence Wizard. Like dashboards, scorecards provide drilldown capability. Sextant application provides a built-in agent ranking scorecard capability that leverages distribution organizational layer definitions.).
Lovy teaches:
Claim 7. The method of claim 1, wherein the retreiving the asset data comprises: determining data gathering parameters regarding the system sector in accordance with the operational evaluation perspective, and the least one evaluation rating metric; identifying at least one system element of the system sector based on the data gathering parameters; obtaining asset information from one or more system computing assets of the at least one system element in accordance with the data gathering parameters; and recording the asset information from the one or more system computing assets to produce the asset data (¶0210 The command module 382 retrieves records from the command table, performs the task defined in a database record, and, based on the result returned by the command, places a message in the message queue, i.e. the Message Table. In the illustrative embodiment, a command can be any executable program, script or utility that can be run using the system( ) library function. ¶0218 The on demand status poller module 388 retrieves records from the status_request table with a user defined frequency, e.g. every 10 seconds. The module improves efficiency by batching status requests which will all be "launched" at the same time. The retrieved status requests are "farmed out" to the appropriate poller module. The on demand status poller module 388 waits for the results of the status requests to be returned by the pollers. Based on the result, the appropriate message is inserted into the message queue. The timer module 390 retrieves records from the active_timers table, performs the tasks defined in the record, and, upon completion of the task, puts the associated message into the message queue.).
Lovy teaches:
Claim 8. The method of claim 7, wherein the determining the data gathering parameters comprises: for the system sector, ascertaining identity of a system element of the at least one system element; and for the system element:determining a first sub-data gathering parameter of the data gather parameters based on the at least one system criteria; determining a second sub-data gathering parameter of the data gather parameters based on the at least one evaluation perspective; and determining a fourth sub-data gathering parameter of the data gather parameters based on the at least one evaluation rating metric (¶0151 As noted previously, Sextant application 50 works in conjunction with a network appliance 100, such as the CaseSentry network appliance commercially available from ShoreGroup Corporation, New York, N.Y. as described herein, for identifying, diagnosing, and documenting problems in computer networks. The devices and process available on a network, as well as grouping of the same, are collectively referred to hereafter as "objects". ¶0158 A Status Monitoring Module 318 comprises a collection of processes that perform the activities required to dynamically maintain the network service level, including the ability to quickly identify problems and areas of service degradation.).
Lovy teaches:
Claim 9. The method of claim 8, wherein the determining the data gathering parameters further comprises: for the system sector, ascertaining identity of a system computing asset of the one or more system computing assets; and for the system computing asset:determining a first data gathering parameter of the data gather parameters based on the at least one system criteria; determining a second data gathering parameter of the data gather parameters based on the at least one evaluation perspective; and determining a fourth data gathering parameter of the data gather parameters based on the at least one evaluation rating metric (¶0190 The Root Cause Analysis Module 383 works directly with the Decision Engine 334 during the event evaluation process. Appliance 300 first validates the existence of an event and then identifies the root cause responsible for that event. This process entails an evaluation of the parent/child relationships of the monitored object within the network. The parent/child relationships are established during the implementation process of appliance 300, where discovery and other means are used to identify the managed network topology.).
Lovy teaches:
Claim 14. The method of claim 7, wherein the obtaining the asset information from a system computing asset of the one or more system computing assets comprises: probing the system computing asset in accordance with the data gathering parameters to obtain a system computing asset data response; identifying vendor information from the system computing asset data response; and tagging the system computing asset data response with the vendor information (¶0162 Fault detection capability in appliance 300 is performed by Status Poller 330 and various poller modules, working to effectively monitor the status of a network. Status Poller 330 controls the activities of the various plug-ins and pollers in obtaining status information from managed devices, systems, and applications on the network. FIG. 6 illustrates the status flow between network appliance 300 and external network elements. Status Poller 330 periodically polls one or more monitored devices 314A-N. ¶0196 A complete set of options is available to amend or supplement a case including: changing case priority; setting the case status; assigning or re-assigning the case to specific personnel; correlating the case to a specific vendor case or support tracking number, and updating or adding information to provide further direction on actions to be taken or to supplement the case history.).
Lovy teaches:
Claim 15. The method of claim 1, wherein the calculating the system computing asset evaluation rating comprises: selecting and performing at least two of:based on the asset data and process analysis parameters, generating a process rating for the system sector in accordance with the at least one evaluation perspective, and at least one evaluation rating metric;based on the asset data and policy analysis parameters, generating a policy rating for the system sector in accordance with the at least one evaluation perspective, and at least one evaluation rating metric;based on the asset data and documentation analysis parameters, generating a documentation rating for the system sector in accordance with the at least one evaluation perspective, and at least one evaluation rating metric;based on the asset data and automation analysis parameters, generating an automation rating for the system sector in accordance with the at least one evaluation perspective, andat least one evaluation rating metric;based on the asset data and procedure analysis parameters, generating a procedure rating for the system sector in accordance with the at least one evaluation perspective, and at least one evaluation rating metric; andbased on the asset data and certification analysis parameters, generating a certification rating for the system sector in accordance with the at least one evaluation perspective, andat least one evaluation rating metric; andgenerating the system computing asset evaluation rating based on the selected and performed at least two of the process rating, the policy rating, the documentation rating, the automation rating, the procedure rating, and the certification rating (¶0064 Distribution layer 58 can also provide certain automatic-pilot intelligence to the underlying subsystems in an enterprise. Based on a Key Performance Indicator (KPI), an Intelligent Call Manager (ICM) script can modify call distribution or an IVR can update service announcements. Workforce management systems can be updated with accurate and reliable facts.).
Lovy teaches:
Claim 16. The method of claim 15, where the generating the process rating comprises: generating a first process rating based on a first combination of a system criteria of the system sector, of an evaluation perspective of the least one evaluation perspective, and of an evaluation viewpoint of the at least one evaluation viewpoint; generating a second process rating based on a second combination of a system criteria of the system aspect, of an evaluation perspective of the least one evaluation perspective, and of an evaluation viewpoint of the at least one evaluation viewpoint; and generating the process rating based on the first and second process ratings (¶0190 The Root Cause Analysis Module 383 works directly with the Decision Engine 334 during the event evaluation process. Appliance 300 first validates the existence of an event and then identifies the root cause responsible for that event. This process entails an evaluation of the parent/child relationships of the monitored object within the network.).
Lovy teaches:
Claim 17. The method of claim 1 further comprises at least one of: determining, by the analysis system, a system criteria deficiency of the system sector based on the asset evaluation rating and the asset data; determining, by the analysis system, a system asset deficiency of the system sector based on the asset evaluation rating and the asset data; determining, by the analysis system, an evaluation perspective deficiency of the system sector based on the asset evaluation rating and the asset data; and determining, by the analysis system, an evaluation viewpoint deficiency of the system sector based on the asset evaluation rating and the asset data (¶0196 The main Case Management screen of the user interface provides a portal through web server application 381 from which all current case activity can be viewed, including critical cases, current priority status, and all historical cases associated to the specific object. Case data is retained in appliance 300 to serve as a valuable knowledge-base of past activity and the corrective actions taken. This database is searchable by several parameters, including the ability to access all cases that have pertained to a particular device.).
Lovy teaches:
Claim 18. The method of claim 1 further comprises: determining, by the analysis system computing entity, a deficiency of the system sector based on the asset evaluation rating and the asset data;determining, by the analysis system computing entity, whether the deficiency is auto-correctable; and when the deficiency is auto-correctable, auto-correcting, by the analysis system computing entity, the deficiency (¶0196 The main Case Management screen of the user interface provides a portal through web server application 381 from which all current case activity can be viewed, including critical cases, current priority status, and all historical cases associated to the specific object. Case data is retained in appliance 300 to serve as a valuable knowledge-base of past activity and the corrective actions taken. This database is searchable by several parameters, including the ability to access all cases that have pertained to a particular device.).
As per claims 19-21, 24-27 and 32-36, the non-transitory computer readable memory tracks the method of claims 1-3, 6-9 and 14-18, respectively, resulting in substantially similar limitations. The same cited prior art and rationale of claims 1-3, 6-9 and 14-18 are applied to claims 19-21, 24-27 and 32-36, respectively. Lovy discloses that the embodiment may be found as a computer readable memory (Fig. 2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/KURTIS GILLS/ Primary Examiner, Art Unit 3624