DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
1. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
2. 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 therefore, subject to the conditions and requirements of this title.
Claims 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of the “computer-implemented” encompasses signals per se. The specification discloses that Par. [0063]-[0064], “The memory may also be another form of computer-readable medium, such as a magnetic or optical disk, which may be embedded and/or may be removable. The computer program product contains instructions…” A claim whose BRI covers both statutory and non-statutory embodiments, since by using the “such as”, indicated not included “signal”, embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter. See MPEP 2106.03(II). Accordingly, claims 16-20 fail to recite statutory subject matter under 35 U.S.C. 101. It is suggested that claim 16 be amended to recite a “non-transitory” computer readable medium to overcome this rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
3. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
3.1 Claim(s) 1, 8-9, 11 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Paini (US 20230018965 A1) in view of Parasuraman (US 20230037297 A).
Regarding claims 1, 11 and 16, Paini (US 2023/0018965 A1) discloses a system for robotic process automation (Abstract, [0016], Robotic process automation (RPA) is a software technology that allows users to build, deploy, and manage software robots to emulate a human interacting with digital systems) and machine learning ([0016], machine learning models 220 to make complex decisions) for dynamic network monitoring ([0022] The computing environment 100 may comprise one or more networks (e.g., public networks and/or private networks), and packet validation ([0020], validate the data with the values captured from the actual robotic automated system) in electronic environments, the system which may interconnect one or more of the RPA smart change evaluator computing system 105), comprising:
a memory device (memory(s) 107) with computer-readable program code stored thereon ([0035], [0042], one or more program modules and/or stored in memory(s) 107) may share hardware and/or software elements);
at least one processing device ([0032], host processor(s) 106), wherein executing the computer-readable program code is configured to cause the at least one processing device to execute the computer-readable program code to ([0031]-[0032],[0042], (one or more processors (e.g., the host processor(s) 106, the MAC processor(s) 108, the PHY processor(s) 109, and/or the like) of the RPA smart change evaluator computing system 105 may be configured to execute machine readable instructions stored in memory 107):
extract data (a data extraction engine 207-1) from network data packets (the network 150) received from at least one or more data sources ([0036],[0037], extraction engine 207-1 may extract control information and wait for a request to run, a data capture database 207-3, a rules database 207-4; the data extraction engine may identify fields, buttons, data values, pages, reports, and the like).
Paini fails to discloses determine, using a machine learning (ML) engine, at least one network event trigger based on the data; determine, using the ML engine, bot criteria of a robotic process automation (RPA) bot based on the at least one network event trigger; assign the RPA bot to execute a network transmission protocol based on at least the bot criteria; and execute, via the RPA bot, a network transmission protocol.
However, Parasuraman discloses determine, using a machine learning (ML) engine ([0032], neural network and/or machine learning models), at least one network event ([0037], a sequence of events and application properties, such as visual clues in the user interface, properties of the components, and an input type) trigger based on the data ([0037], [0039], subsequently generate or trigger generation of the RPA scripts to automate the process; the application may process and validate the information and return a claim number if the information was entered correctly, or an error that may indicate incomplete or inaccurate entry of information or the like);
determine, using the ML engine ([0032], neural network and/or machine learning models), bot criteria of a robotic process automation bot ([0003], autonomous robotics process automation (RPA)) based on the at least one network event trigger ([0039], subsequently generate or trigger generation of the RPA scripts to automate the process);
assign the RPA bot (0018], an RPA robotic process (bot)) to execute a network transmission protocol [0031] Messages transmitted from and received at device in the computing environment 100 functions corresponding to the communication protocol) based on at least the bot criteria (0022], [0025]-[0026], the RPA automatic enhancement computing system 105 comprise one or more computing devices configured to perform one or more of the functions; and execution of various operations corresponding to the one or more computing devices (e.g., the computing device(s) 110) and/or servers); and
execute, via the RPA bot, a network transmission protocol ([0005], [0031], The RPA automatic enhancement system is an automation framework to automatically generate RPA scripts based on a user demonstration and protocols that generate an executable RPA script to perform user navigation).
Parasuraman and Paini are analogous art. They relate to a robotics process automation (RPA) automatic enhancement system. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify automatically generate an enhanced RPA scrip, taught by Parasuraman, incorporated with a smart change evaluation engine analyzes an existing RPA system, taught by Paini, in order to provide effective, efficient, scalable automatically augmenting RPA scripts. The RPA automatic enhancement system may capture a video demonstration of task performance and convert the activity into nodes by a task specification encoder and a task interpreter and then process the nodes by a reformer transformer to automatically generate an enhanced RPA script.
Regarding claim 8, Parasuraman discloses generate a user interface on a display for displaying information to the user ([0035], the user may navigate the user interface (UI) and then proceed to review entered credentials at 311. Once reviewed, the user may then navigate the UI to enter additional information at 313. When the information has been navigated and the credentials have been entered, the user may review the entered transaction information at 315 and may return to navigate the UI again at 317 to enter or modify certain information. When reviewed, the user may commit the data such as by using an input (e.g., an enter key, a mouse click, or the like),) wherein the user interface comprises at least one interactive dashboard ([0029], [0035], [0037], receiving input via a user interface, and may communicate the received input to one or more other computing devices), and generate at least one alert based on the network transmission protocol ([0018], the bot may stop and/or report an error, while humans would be able to adapt to changes identified on an application window. As such a need has been recognized for an improved RPA script generation system to automatically generate RPA scripts capable of performing actions with human-like efficiencies).
Regarding claim 9, Parasuraman discloses receive control signals from a user device ([0023]-[0027], information received from other computer systems comprising the computing environment 100), wherein the control signals comprise a revised mode of the ML engine and revised configurations of the RPA bot ([0023], [0032],[0035], neural network and/or machine learning models, and the RPA automatic, the code revision module 225, the code analyzer 226, and/or the report generation engine 230); and update the ML engine ([0036], deep learning neural network model to train (update) a complex learning system represented by a single model to represent the process) and update the RPA bot based on the control signals ([0025], [0036], [0041], configure operation of the RPA automatic enhancement computing system 105 and the RPA script generation engine 550 may generate an RPA script based on the optimized auto-transformed (update) NPG information and/or a configuration file associated with an RPA platform).
3.2 Claim(s) 2-3, 6, 12 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Paini (US 20230018965 A1) in view of Parasuraman (US 20230037297 A) furthers in view of Abu-Nimeh (US 20170026391 A1).
Regarding claims 2, 3, 6, 12 and 17, the combination of Paini and Parasuraman discloses the limitations of claims 1, 11 and 16, in addition, Parasuraman discloses robotic process automation, bot [0022], [0025]-[0026], the RPA automatic enhancement computing system 105 comprise one or more computing devices configured to perform one or more of the functions; and execution of various operations corresponding to the one or more computing devices (e.g., the computing device(s) 110) and/or servers), but fail to disclose the limitations of claims 2-3, 12 and 17. However, Abu-Nimeh discloses as follows:
Regarding claims 2, 12 and 17, Abu-Nimeh discloses determine, using the ML engine (machine learning service in platform 120), whether the data comprises network rule criteria (Abstract, [0002], monitoring online security threats comprising of a machine-learning service that receives data related to a plurality of features related to internet traffic metrics);
Reassign ([0007], a threat identification and detection engine) the RPA bot (the automated service 120) to execute a network threat protocol (Abstract, internet traffic metrics), based on at least the network rule criteria ([0024], compared against existing threat models 160 170 to identify and predict suspicious activity. In testing mode internet data 110 is fed into the platform 120, clustering and classification algorithms are run on the fly in a data categorizer 130, and are accumulated by either a network features aggregator 310 or a registry features aggregator 510; Fig. 5A, [0014], [0023], [0024], threat prediction engine and threat list generator; a threat plug and play platform 120 operating in a “training mode” in which threat models 160 170 are generated based on features aggregated from existing internet threat data 110);
execute, using the RPA bot (the automated service 120), the network threat protocol (Abstract, internet traffic metrics), wherein the network threat protocol comprises aggregating network packet threat data ([0025], the automated threat detection service 120 receives a package of data 110 from a user that comprises of any of a multiple sources of internet traffic data 100a-i);
generate a network threat map ([0008], Fig. 1, Fig. 2, threat model160, 170) based on at least the network rule criteria (Abstract, [0002], monitoring online security threats comprising of a machine-learning service that receives data related to a plurality of features related to internet traffic metrics) and the network packet threat data ([0007], [0025], the automated threat detection service 120 receives a package of data 110 from a user that comprises of any of a multiple sources of internet traffic data 100a-i); and
transmit, using the RPA bot (the automated service 120), a threat notification, wherein the threat notification comprises the network threat map ([0026], [0032], when prompted, the automated service 120 may produce a detailed output listing Malice Scores and Malicious Components 180 as well as Network Risk Reports 190. Malice Scores may be numbers ranging from 0, indicating benign traffic, and 1, indicating malicious traffic. Malicious Components may include IP addresses, domain names, network blocks, and URLs. The service may also include a reason why such traffic was classified as malicious. Network Risk Reports may include an updated list of IPs, domains, and CIDRs that have high threat scores).
Abu-Nimeh, Parasuraman and Paini are analogous arts. They relate to output metrics that aid in the detection, identification, and prediction of an attack. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify, understand the online threat landscape, taught by Abu-Nimeh, incorporated with teaching of Parasuraman and Paini, as stated above, in order to utilizes machine learning techniques in order to harness the information provided by the platform's users and partners in order to implement a scalable computer platform for dealing with online threats. The platform and its machine learning capacities culminate to create a machine learning service that may be trained to automatically recognize suspicious patterns in internet traffic and internet registry data and to alert the appropriate users and client systems.
Regarding claim 3, Abu-Nimeh discloses generate an event trigger forecast ML engine (Abstract, a machine-learning service that receives data related to a plurality of features related to internet traffic metrics, metrics that aid in the detection, identification, and prediction of an attack, which is equivalent to event trigger);
update the event trigger forecast ML engine with the data ([0023], [0032], features will be determined and updated every time a model is trained and built; implement a machine learning service in platform 120, the system training sessions in which the threat data 110 is fed into, processed and updated list of IPs, domains, and CIDRs that have high threat scores);
generate, using the event trigger forecast ML engine, an event trigger forecasting map ([0023], [0092], sorted by a data categorizer 130 (mapping). Categorizer 130 is a module in which relevant features are extracted from incoming data 110 and utilized by either the network features trainer 200 or registry features trainer 400);
generate, using the RPA bot, a forecast network transmission protocol based on at least the event trigger forecasting map ([0024], [0044], [0081], a threat plug and play platform 120 operating in a “testing mode” in which new incoming internet threat data and IPs 110 (internet protocol) are compared against existing threat models 160 170 to identify and predict suspicious activity. In testing mode internet data 110 is fed into the platform 120, clustering and classification algorithms (mapping) are run on the fly in a data categorizer 130); and
execute, using the RPA bot, the forecast network transmission protocol ([0024], [0032], Engine 300 then compares a set of features 140B of the current data 110 against the trained model 160 to determine scores and insights with respect to each IP 180 (internet protocol). Similarly, engine 500 then compares a set of features 150B of the current data 110 against the trained model 170 to generate a predictive threat list 190. This list 190 comprises networks and IP addresses that are expected to be used in future attacks).
Abu-Nimeh, Parasuraman and Paini are analogous arts. They relate to output metrics that aid in the detection, identification, and prediction of an attack. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify, understand the online threat landscape, taught by Abu-Nimeh, incorporated with teaching of Parasuraman and Paini, as stated above, in order to utilizes machine learning techniques in order to harness the information provided by the platform's users and partners in order to implement a scalable computer platform for dealing with online threats. The platform and its machine learning capacities culminate to create a machine learning service that may be trained to automatically recognize suspicious patterns in internet traffic and internet registry data and to alert the appropriate users and client systems.
Regarding claim 6, Abu-Nimeh discloses receive at least one historical dataset ([0069], Passive DNS is a historical replication of DNS data for IP addresses);
train the ML engine based on the at least one historical dataset ([0087], the model building 240 stage the categorizer 130 trains a random forest classifier for each of the clusters that were created in the previous section. The positive and negative set contains malicious samples that the classifier needs to learn the patterns (historical data).
wherein the at least one historical dataset comprises at least one of historical network resource transfer data associated with historical network resource transfer criteria, historical validation data associated with validation criteria, historical network rule data associated with historical network rule criteria, or historical network transmission protocols ([0069], one builds huge historical information database that can map all domains and IPs addresses on the Internet (network);
receive the network packet threat data ([0025], [0092], [0096], automated threat detection service 120 receives a package of data 110 from a user that comprises of any of multiple sources of internet traffic data 100a-I; threat data 110 is fed into the data categorizer 130);
update the at least one historical dataset with the network packet threat data (Abstract, receives data related to security threats comprising of a machine-learning service, include internet traffic metrics, the service then processes said data, an operation of ranking, an operation of classifying , an operation of predicting at least one feature, and an operation of clustering at least one feature); and
retrain the ML engine based on the network packet threat data ([0004], [0006], Abstract, classifiers can be retrained. This may be done simply by pushing a configuration file update to the platform. This configuration file may contain amendments to a classifier model or alternatively include an entirely new retrained model monitoring online security threats comprising of a machine-learning service that receives data related to a plurality of features related to internet traffic metrics, the machine learning service outputs metrics that aid in the detection, identification, and prediction of an attack).
3.3 Claim(s) 10, 15, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Paini (US 20230018965 A1) in view of Parasuraman (US 20230037297 A) furthers in view of Kurian US20190122200A1).
Regarding claims 10, 15 and 20, the combination of and Paini and Parasuraman disclose the limitations of claims 1, 11 and 16, in addition, Parasuraman discloses determine, machine learning unit (Abstract, [0037], neural network and/or machine learning models), that the at least one network event trigger ([0039], a specific technology and may subsequently generate or trigger generation of the RPA scripts to automate the process) comprises a distributed network resource transfer ([0002], [0021], managing resources of a cluster computing system and an intelligent resource management agent capable of determining a complexity of each input file of a plurality of input files and allocating computing resources based on that determination; and access one or more resources located within the private network 150), but the combination of Paini and Parasuraman fail to discloses using the RPA bots, distributed network resources from a distributed network resource transfer source; and transmit, via the RPA bot, the distributed network resources to a network resource transfer account.
However, Kurian discloses using the RPA bots (Abstract, a robotic process automation system), distributed network resources from a distributed network resource transfer source (Abstract, [0043], detects the triggering signal, an indication of the detection is automatically transmitted to the robotic process automation system. The processing device 220 is configured to use the network communication interface 210 to transmit and/or receive data and/or commands to and/or from the other devices connected to the network 150); and
transmit, via the RPA bot (Abstract, a robotic process automation system), the distributed network resources to a network resource transfer account (Abstract, [0038],[0083], the user's approval to use the account of the user to execute the transaction further comprises an approval for the system to transfer the known transaction amount or the transaction amount limit to a separate account or an escrow account).
Kurian, Parasuraman and Paini are analogous arts. They relate to robotic process automation systems execute one or more events. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify remotely triggering execution of events by a robotic process automation system, taught by Kurian, incorporated with teaching of Parasuraman and Paini, as stated above, in order to provide effective, efficient, scalable, and convenient technical solutions that address and overcome the technical problems associated with accurately and efficiently analyzing current and changed RPA processes, capturing information of components, elements, controls in the process flow and building new rules and defined for every component, element and control to provide intelligent robotic automation change evaluations.
Allowable Subject Matter
4. 4.1 Claims 4, 7, 13 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
4.2 Claim 18 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
As claims 5, 14 and 19 are directly or indirectly dependent on claims 4, 13 and 18, those claims are also allowable at least by virtue of their dependency.
Citation Pertinent prior art
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sturtivant US 2020/0074329 A1 discloses (e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor) the analytics module 116 provides reporting and dashboard visualizations for RPA support teams and receives data, alerts, events, and the like from the AP 104, and provides data reporting, and analytics across multiple RPA platforms.
Steele et al. (US 20190163916 A1) discloses data integration and threat assessment for triggering analysis of connection oscillations to improve data and connection security. The invention leverages a security threat assessment engine and an analytics engine to gather and process data from a combination of internal and external data sources for a third-party connection. The system continuously monitors and updates a generated threat level for a third-party connection to determine changes.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for allthat it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed wereinstead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1 009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. Biocraft Labs., Inc., 874 F.2d804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1, 215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163USPQ 545, 549 (CCPA 1969).
Conclusion
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kidest Worku whose telephone number is 571-272-3737. The examiner can normally be reached on Mon-Fri 9am to 5pm, ET.
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/KIDEST WORKU/Primary Examiner, Art Unit 2119
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