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. T his action is responsive to the Application filed on July 31, 2023 . Claims 14-33 are pending in the case. Claims 14, 22, and 30 are the independent claims. This action is non-final. 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. Claim s 1 4 - 33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental steps) without significantly more. This judicial exception is not integrated into a practical application because any additional elements amount to implementing the abstract idea on a generic computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Regarding independent claim 1 4 , and relying on the evaluation flowchart in MPEP 2106: Step 1 (Is the claim to a process, machine, manufacture, or composition of matter?) : Yes. Claim 14 is a method (process). Step 2a Prong One (Does the claim recite an abstract idea?) : Yes. Claim 1 4 recite s : inferring location information of at least one object on the screen image (a mental process of observation and determination , such as of a human mentally evaluating a screen image and determining a location of at least one object ) . Under the broadest reasonable interpretation, these steps may be performed mentally, using mental observation and mental determination, including by a human using a physical aid such as pen and paper, including a human mentally performing observations and mentally performing mathematical calculations, and therefore correspond to the Mental Processes grouping. Step 2a Prong Two (Does the claim recite additional elements that integrate the judicial exception into a practical application?) : No. Claims 1 and 11 additionally recite: performed by a server for generating an event on an object on a screen based on Artificial Intelligence (AI) (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)); receiving, via a communication interface, a screen image of a user terminal from an agent installed on the user terminal (insignificant extra-solution activity as discussed in MPEP 2106.05(g)); inferring…by inputting the received screen image into a pre-trained Al model provided in the server (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)); transmitting result data indicating a location of the at least one object to the agent via the communication interface, wherein the transmitted result data causes the agent to generate an event for the at least one object on the screen of the user terminal (insignificant extra-solution activity as discussed in MPEP 2106.05(g)). Therefore, in view of the considerations set forth in MPEP 2106.04(d), 2106.05(a)-(c) and (e)-(h), the additional elements as disclosed above alone or in combination do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity, combined with implementing the abstract idea using generic computer components. Step 2b (Does the claim recite additional elements that amount to siqnificantly more than the judicial exception) : No. Relying on the same analysis as Step 2a Prong Two (see MPEP 2106.05.I.A: Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:…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., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP 2106.05(f));…Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception...; Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g);…) ), claim 14 do es not recite any additional elements that amount to significantly more than the abstract idea. As discussed above, Claim 14 recites : performed by a server for generating an event on an object on a screen based on Artificial Intelligence (AI) (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)); receiving, via a communication interface, a screen image of a user terminal from an agent installed on the user terminal (insignificant extra-solution activity as discussed in MPEP 2106.05(g) , reevaluated to include well-understood, routine, conventional activity as discussed in MPEP 2106.05(d), such as receiving or transmitting data over at network ); inferring…by inputting the received screen image into a pre-trained Al model provided in the server (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)); transmitting result data indicating a location of the at least one object to the agent via the communication interface, wherein the transmitted result data causes the agent to generate an event for the at least one object on the screen of the user terminal (insignificant extra-solution activity as discussed in MPEP 2106.05(g) , including mere data outputting, and further reevaluated to include well-understood, routine, conventional activity as discussed in MPEP 2106.05(d), such as receiving or transmitting data over at network; Examiner notes that although the claim recites generating an event for at least one object on the screen, the claim recites no additional details regarding how this is performed or indicating that generation of the event causes any particular meaningful change in the system or object on the screen; therefore this appears to merely recite output of a result/data ). The additional elements as discussed above , in combination with the abstract idea , are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination with generic computer functions and components used to implement the abstract idea . Regarding independent claim s 22 and 30 , and relying on the evaluation flowchart in MPEP 2106: Step 1 (Is the claim to a process, machine, manufacture, or composition of matter?) : Yes. Claim 22 is a method (process). Claim 30 is a computer-readable storage medium (manufacture). Step 2a Prong One (Does the claim recite an abstract idea?) : Yes. Claim 14 recites: object location information inferr ed from the screen image …result data is inferred…from the transmitted screen image (a mental process of observation and determination, such as of a human mentally evaluating a screen image and determining a location and result data for at least one object). Under the broadest reasonable interpretation, these steps may be performed mentally, using mental observation and mental determination, including by a human using a physical aid such as pen and paper, including a human mentally performing observations and mentally performing mathematical calculations, and therefore correspond to the Mental Processes grouping. Step 2a Prong Two (Does the claim recite additional elements that integrate the judicial exception into a practical application?) : No. Claims 22 and 30 additionally recite: generating an event on an object on a screen based on artificial intelligence (AI) (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)); transmitting, by an agent installed on a user terminal, a screen image of the user terminal to a server (insignificant extra-solution activity as discussed in MPEP 2106.05(g)); requesting, by the agent, object location information inferred from the screen image by an Al model provided in the server (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)); receiving, by the agent via a communication interface, result data indicating a location of at least one object on the screen (insignificant extra-solution activity as discussed in MPEP 2106.05(g)) , wherein the result data is inferred by the Al model from the transmitted screen image (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)); generating, by the agent, an event for the at least one object on the screen of the user terminal based on the received result data (insignificant extra-solution activity as discussed in MPEP 2106.05(g)) . In addition, claim 30 also recites a non-transitory computer -readable storage medium storing a computer program, which when executed by a processor of a user terminal, causes the processor to perform operations comprising the steps above (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Therefore, in view of the considerations set forth in MPEP 2106.04(d), 2106.05(a)-(c) and (e)-(h), the additional elements as disclosed above alone or in combination do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity, combined with implementing the abstract idea using generic computer components. Step 2b (Does the claim recite additional elements that amount to siqnificantly more than the judicial exception) : No. Relying on the same analysis as Step 2a Prong Two (see MPEP 2106.05.I.A: Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:…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., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP 2106.05(f));…Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception...; Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g);…) ), claims 22 and 30 do not recite any additional elements that amount to significantly more than the abstract idea. Claims 22 and 30 additionally recite: generating an event on an object on a screen based on artificial intelligence (AI) (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)); transmitting, by an agent installed on a user terminal, a screen image of the user terminal to a server (insignificant extra-solution activity as discussed in MPEP 2106.05(g) reevaluated to include well-understood, routine, conventional activity as discussed in MPEP 2106.05(d), such as receiving or transmitting data over at network); requesting, by the agent, object location information inferred from the screen image by an Al model provided in the server (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) reevaluated to include well-understood, routine, conventional activity as discussed in MPEP 2106.05(d), such as receiving or transmitting data over at network); receiving, by the agent via a communication interface, result data indicating a location of at least one object on the screen (insignificant extra-solution activity as discussed in MPEP 2106.05(g) reevaluated to include well-understood, routine, conventional activity as discussed in MPEP 2106.05(d), such as receiving or transmitting data over at network) , wherein the result data is inferred by the Al model from the transmitted screen image (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)); generating, by the agent, an event for the at least one object on the screen of the user terminal based on the received result data (insignificant extra-solution activity as discussed in MPEP 2106.05(g), including mere data outputting; Examiner notes that although the claim recites generating an event for at least one object on the screen, the claim recites no additional details regarding how this is performed or indicating that generation of the event causes any particular meaningful change in the system or object on the screen; therefore this appears to merely recite output of a result/data). In addition, claim 30 also recites a non-transitory computer -readable storage medium storing a computer program, which when executed by a processor of a user terminal, causes the processor to perform operations comprising the steps above (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). The additional elements as discussed above , in combination with the abstract idea , are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination with generic computer functions and components used to implement the abstract idea . Regarding dependent claim 15 : Step 2a Prong One : incorporates the rejection of claim 14 . Step 2a Prong Two: the claims additionally recite receiving a registration of a scheduler from the agent (insignificant extra-solution activity as discussed in MPEP 2106.05(g)) ; and transmitting a start signal to the agent via the communication interface at a scheduled time, wherein the receiving of the screen image is performed in response to the start signal (insignificant extra-solution activity as discussed in MPEP 2106.05(g)) . Step 2b: the claims additionally recite receiving a registration of a scheduler from the agent (insignificant extra-solution activity as discussed in MPEP 2106.05(g) reevaluated to include well-understood, routine, conventional activity as discussed in MPEP 2106.05(d), such as receiving or transmitting data over at network) ; and transmitting a start signal to the agent via the communication interface at a scheduled time, wherein the receiving of the screen image is performed in response to the start signal (insignificant extra-solution activity as discussed in MPEP 2106.05(g) reevaluated to include well-understood, routine, conventional activity as discussed in MPEP 2106.05(d), such as receiving or transmitting data over at network) . Regarding dependent claim 16 : Step 2a Prong One : incorporates the rejection of claim 14 . Step 2a Prong Two: the claims additionally recite wherein the pre-trained Al model is trained using a Deep Learning algorithm including at least one of a Convolutional Neural Network (CNN), a Region-based Convolutional Neural Network (R-CNN), Fast R-CNN, Faster R- CNN, or Mask R-CNN (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)) . Step 2b: he claims additionally recite wherein the pre-trained Al model is trained using a Deep Learning algorithm including at least one of a Convolutional Neural Network (CNN), a Region-based Convolutional Neural Network (R-CNN), Fast R-CNN, Faster R- CNN, or Mask R-CNN (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Regarding dependent claim 17 : Step 2a Prong One : incorporates the rejection of claim 14 . Step 2a Prong Two: the claims additionally recite wherein the pre-trained Al model is trained using training data comprising full screen images and labeled location data of objects within the full screen images (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to the training of the pre-trained model and field of use an technological environment as discussed in MPEP 2106.05(h) with respect to the training data comprising full screen images and labeled location data of objects ). Step 2b: the claims additionally recite wherein the pre-trained Al model is trained using training data comprising full screen images and labeled location data of objects within the full screen images (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to the training of the pre-trained model and field of use an technological environment as discussed in MPEP 2106.05(h) with respect to the training data comprising full screen images and labeled location data of objects). Regarding dependent claim 1 8 : Step 2a Prong One : incorporates the rejection of claim 1 4 . Step 2a Prong Two: the claims additionally recite wherein the at least one object includes at least one of a program window, a search bar of a browser, a login button, a company name, an ID input field, or a password input field (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and field of use an technological environment as discussed in MPEP 2106.05(h) ). Step 2b: the claims additionally recite wherein the at least one object includes at least one of a program window, a search bar of a browser, a login button, a company name, an ID input field, or a password input field (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and field of use an technological environment as discussed in MPEP 2106.05(h)). Regarding dependent claim 19 : Step 2a Prong One : incorporates the rejection of claim 14 . Step 2a Prong Two: the claims additionally recite wherein the communication interface is configured to support TCP/IP socket communication (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and a field of use and technological environment as discussed in MPEP 2106.05(h)). Step 2b: the claims additionally recite wherein the communication interface is configured to support TCP/IP socket communication (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and a field of use and technological environment as discussed in MPEP 2106.05(h)). Regarding dependent claim 20 : Step 2a Prong One : incorporates the rejection of claim 14. Step 2a Prong Two: the claims additionally recite wherein the result data transmitted to the agent comprises coordinate data represented in a JSON (JavaScript Object Notation) or XML format, and further comprises a confidence score indicating a probability of recognition for the at least one object (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and a field of use and technological environment as discussed in MPEP 2106.05(h)). Step 2b: the claims additionally recite wherein the result data transmitted to the agent comprises coordinate data represented in a JSON (JavaScript Object Notation) or XML format, and further comprises a confidence score indicating a probability of recognition for the at least one object (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and a field of use and technological environment as discussed in MPEP 2106.05(h)). Regarding dependent claim 21 : Step 2a Prong One : incorporates the rejection of claim 14 . The claim further recites determining whether the confidence score meets a predetermined threshold (a mental process of evaluation). Step 2a Prong Two: the claims additionally recite transmitting an error notification or a retry request to the agent if the confidence score is below the predetermined threshold ( insignificant extra-solution activity as discussed in MPEP 2106.05(g)) . Step 2b: the claims additionally recite transmitting an error notification or a retry request to the agent if the confidence score is below the predetermined threshold (insignificant extra-solution activity as discussed in MPEP 2106.05(g) reevaluated to include well-understood, routine, conventional activity as discussed in MPEP 2106.05(d), such as receiving or transmitting data over at network). Regarding dependent claim 23 : Step 2a Prong One : incorporates the rejection of claim 22 . Step 2a Prong Two: the claims additionally recite re gistering a scheduler with the server (insignificant extra-solution activity as discussed in MPEP 2106.05(g)) ; and receiving a start signal from the server via the communication interface at a scheduled time, wherein the transmitting of the screen image is performed in response to the start signal (insignificant extra-solution activity as discussed in MPEP 2106.05(g)) . Step 2b: the claims additionally recite re gistering a scheduler with the server (insignificant extra-solution activity as discussed in MPEP 2106.05(g) reevaluated to include well-understood, routine, conventional activity as discussed in MPEP 2106.05(d), such as storing and retrieving information in memory) ; and receiving a start signal from the server via the communication interface at a scheduled time, wherein the transmitting of the screen image is performed in response to the start signal (insignificant extra-solution activity as discussed in MPEP 2106.05(g) reevaluated to include well-understood, routine, conventional activity as discussed in MPEP 2106.05(d), such as receiving or transmitting data over at network) . Regarding dependent claim 24 : Step 2a Prong One: incorporates the rejection of claim 22 . Step 2a Prong Two: the claims additionally recite: wherein the generating the event comprises controlling an input device to perform a text data input or a mouse button click at a screen coordinate corresponding to the received location information (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and a field of use and technological environment as discussed in MPEP 2106.05(h)). Step 2b: the claims additionally recite: wherein the generating the event comprises controlling an input device to perform a text data input or a mouse button click at a screen coordinate corresponding to the received location information (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and a field of use and technological environment as discussed in MPEP 2106.05(h)). Regarding dependent claim 25: Step 2a Prong One: incorporates the rejection of claim 22. Step 2a Prong Two: the claims additionally recite: wherein the agent operates in an environment including at least one of a Web environment, a Command Line Interface (CLI) environment, or a Remote Desktop Protocol (RDP) environment (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and a field of use and technological environment as discussed in MPEP 2106.05(h)). Step 2b: the claims additionally recite: wherein the agent operates in an environment including at least one of a Web environment, a Command Line Interface (CLI) environment, or a Remote Desktop Protocol (RDP) environment (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and a field of use and technological environment as discussed in MPEP 2106.05(h)). Regarding dependent claim 26: Step 2a Prong One: incorporates the rejection of claim 22. Step 2a Prong Two: the claims additionally recite: wherein the capturing the screen image and the generating the event are repeated to automatically perform a series of tasks performed by a user (insignificant extra-solution activity as discussed in MPEP 2106.05(g)) . Step 2b: the claims additionally recite: wherein the capturing the screen image and the generating the event are repeated to automatically perform a series of tasks performed by a user (insignificant extra-solution activity as discussed in MPEP 2106.05(g) such as data outputting, reevaluated to include well-understood, routine, conventional activity as discussed in MPEP 2106.05(d), such as selecting information for display; Examiner notes that although the claim recites that the sets are to automatically perform a series of tasks performed by a user, the claims do not appear to provide any meaningful limitation or detail regarding how the capturing and generating steps are related to the series of tasks performed by the user, such that the claim appears to potentially recite performing the capturing and generating of the event in response to the user interaction with the device, i.e. merely outputting results based on user inputs). Regarding dependent claim 27: Step 2a Prong One: incorporates the rejection of claim 22. Step 2a Prong Two: the claims additionally recite wherein the agent recognizes the at least one object even if a Class ID of the object in a source code is changed (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and a field of use and technological environment as discussed in MPEP 2106.05(h)) . Step 2b: the claims additionally recite wherein the agent recognizes the at least one object even if a Class ID of the object in a source code is changed (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) and a field of use and technological environment as discussed in MPEP 2106.05(h)). Regarding dependent claim 28: Step 2a Prong One: incorporates the rejection of claim 22. Step 2a Prong Two: the claims additionally recite displaying a visual indicator, including a bounding box or a highlight overlay, on the at least one object on the screen of the user terminal based on the received result data prior to or simultaneously with the generating the event (insignificant extra-solution activity as discussed in MPEP 2106.05(g)). Step 2b: the claims additionally recite displaying a visual indicator, including a bounding box or a highlight overlay, on the at least one object on the screen of the user terminal based on the received result data prior to or simultaneously with the generating the event (insignificant extra-solution activity as discussed in MPEP 2106.05(g) such as data outputting, reevaluated to include well-understood, routine, conventional activity as discussed in MPEP 2106.05(d), such as selecting information for display). Regarding dependent claim 29: Step 2a Prong One: incorporates the rejection of claim 22. Step 2a Prong Two: the claims additionally recite wherein the transmitting the screen image comprises converting the captured screen image into a grayscale image or compressing the screen image to a predetermined resolution to reduce network traffic load via the communication interface (insignificant extra-solution activity as discussed in MPEP 2106.05(g) and a field of use and technological environment as discussed in MPEP 2106.05(h)). Step 2b: the claims additionally recite displaying a visual indicator, including a bounding box or a highlight overlay, on the at least one object on the screen of the user terminal based on the received result data prior to or simultaneously with the generating the event (insignificant extra-solution activity as discussed in MPEP 2106.05(g) with respect to transmitting the screen image and converting it into a particular format such as receiving or transmitting data over a network and data outputting, reevaluated to include well-understood, routine, conventional activity as discussed in MPEP 2106.05(d), such as storing and retrieving information in memory, selecting information for display and a field of use and technological environment as discussed in MPEP 2106.05(h) with respect to the format in which the image is converted being grayscale or compressed). Regarding dependent claim 31: Step 2a Prong One: incorporates the rejection of claim 30. Step 2a Prong Two: the claims additionally recite wherein the pre-trained AI model is updated by learning new training data comprising screen images and labeled object locations periodically or at a specific timing (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Step 2b: the claims additionally recite wherein the pre-trained AI model is updated by learning new training data comprising screen images and labeled object locations periodically or at a specific timing (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Regarding dependent claim 32: Step 2a Prong One: incorporates the rejection of claim 30. Step 2a Prong Two: the claims additionally recite wherein the operations further comprise creating a log upon completion of the event generation or upon occurrence of an error (insignificant extra-solution activity as discussed in MPEP 2106.05(g)). Step 2b: the claims additionally recite wherein the operations further comprise creating a log upon completion of the event generation or upon occurrence of an error (insignificant extra-solution activity as discussed in MPEP 2106.05(g) such as data outputting, reevaluated to include well-understood, routine, conventional activity as discussed in MPEP 2106.05(d), such as storing and retrieving information in memory, selecting information for display). Regarding dependent claim 33: Step 2a Prong One: incorporates the rejection of claim 30. Step 2a Prong Two: the claims additionally recite wherein the user terminal includes any one of a desktop computer, a laptop, an IoT device, a connected car terminal, or a kiosk, and the operations are performed in a non-Windows Operating System (OS) environment or a remote terminal environment (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Step 2b: the claims additionally recite wherein the user terminal includes any one of a desktop computer, a laptop, an IoT device, a connected car terminal, or a kiosk, and the operations are performed in a non-Windows Operating System (OS) environment or a remote terminal environment (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Therefore, in view of the considerations set forth in MPEP 2106.04(d), 2106.05(a)-(c) and (e)-(h), the additional elements as recited in the dependent claims discussed above alone or in combination do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity, combined with implementing the abstract idea using generic computer components, and limitations describing a field of use or technological environment. The additional elements as discussed above , in combination with the abstract idea , are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination with generic computer functions and components used to implement the abstract idea , and limitations describing a field of use or technological environment. 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. Claim s 14, 15, 18, 22-2 7 , 30, 32, and 33 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Petursson (US 20180197103 A1) . With respect to claim 14, Petursson teaches a method performed by a server for generating an event on an object on a screen based on Artificial Intelligence (AI), the method comprising: (a) receiving, via a communication interface, a screen image of a user terminal from an agent installed on the user terminal ( e.g. paragraph 0054, Fig. 1B, learning engine/system taking screenshot of GUI 102 and storing in database; paragraph 0069, Fig. 2, AI learning engine consisting of client module 202 and server module 220; client 202 is in charge of collecting screenshots/screen captures of GUI of SUT and performing coordinate based GUI operations to monitor changes; paragraph 0071, client 202 starts/initiates SUT via start app function 204 which constructs the initial action array data structure containing actions performed by client 202 on GUI and the results of those actions in the form of corresponding screenshots/captures; it then communicates the initialized action array to server 220 by invoking send action array function 206 which communicates action array to screen store function 224 of server 220; paragraph 0072, sending empty action array with initial screenshot; paragraph 0073, sending new screenshot of detected changes in updated action array; paragraph 0081, action array construct communicated by client 202 to screen store function 224 of AI server 220 ) ; (b) inferring location information of at least one object on the screen image by inputting the received screen image into a pre-trained Al model provided in the server ( e.g. paragraph 0055, Fig. 1B, detecting objects on the screenshot by detect objects from screenshot function that is a chain of successive algorithms applied to detect objects from the screenshot including use of canny edge detection, contour detection, and user supplied algorithms ; paragraph 0072, Fig. 2, screenshot undergoes processing from various functional modules of the sever; paragraph 0084, triggering screen detection function 226; paragraph 0093, object detection function 228 (in server 202) performing zero-metadata detection by scanning the image/screenshot of the GUI and detecting the edges/contours of the object; paragraph 0094, once object is detected, storing X and Y coordinates of the location of the object on the screen and the height and width of the object ; paragraph 0096, system/learning engine 200B learning objects having any shape, gathering more and more information/knowledge about screen objects until they are fully identified by object types with a high level of likelihood ) ; and (c) transmitting result data indicating a location of the at least one object to the agent via the communication interface, wherein the transmitted result data causes the agent to generate an event for the at least one object on the screen of the user terminal ( e.g. paragraph 0061, Fig. 1B, suggest next action functionality implemented by determining objects/object groups to utilize; checking to ensure object is indeed interactive or actionable and then determining number of actions applied to object or object group, and proceeding with object with the lowest action-count; paragraph 0062, determining weight/priority of object, selecting object with highest weight/priority, and invoking input generator to supply the input for the object; determining next action to perform on the object; paragraph 0064, performing the next action identified ; paragraph 0072, Fig. 2, last of the sever side modules, action suggester function 246, sends populated action array with suggested actions to perform on various objects back to client 202 ; paragraph 0073, action execution function 210 performing suggested actions on the various objects; paragraph 0113-0116, after objects on pages/screens of GUI are detected, passing control to application mapping function 230 which models behavior of application as it changes states due to actions/events, including actions triggered by inputs in the form of GUI interactions; actions caused by action execution 210 which are automated/simulated actions by learning engine; paragraph 0144, action suggester 246 populates action array with actions to be performed by client 202, picking screen objects and populating action array fields object_id , object_info , an action_group_id , invoking guesser to determine what action to be guessed or tried on the screen object; i.e. information about the detected object (such as location/coordinates) and a suggested action/event for the object are transmitted by the server back to the client, and the action/event is performed/generated for the object by the client’s action execution function 210, as shown in Fig. 2 ) . With respect to claims 22 and 30, Petursson teaches a non-transitory computer-readable storage medium storing a computer program, which when executed by a processor of a user terminal, causes the processor to perform operations comprising and method, and the method for generating an event on an object on a screen based on artificial intelligence (AI) ( e.g. claim 1, program instructions stored in non-transitory storage medium ) , the method comprising: (a) transmitting, by an agent installed on a user terminal, a screen image of the user terminal to a server and (b) requesting, by the agent, object location information inferred from the screen image by an Al model provided in the server ( e.g. paragraph 0054, Fig. 1B, learning engine/system taking screenshot of GUI 102 and storing in database; paragraph 0069, Fig. 2, AI learning engine consisting of client module 202 and server module 220; client 202 is in charge of collecting screenshots/screen captures of GUI of SUT and performing coordinate based GUI operations to monitor changes; paragraph 0071, client 202 starts/initiates SUT via start app function 204 which constructs the initial action array data structure containing actions performed by client 202 on GUI and the results of those actions in the form of corresponding screenshots/captures; it then communicates the initialized action array to server 220 by invoking send action array function 206 which communicates action array to screen store function 224 of server 220; paragraph 0072, sending empty action array with initial screenshot; paragraph 0073, sending new screenshot of detected changes in updated action array; paragraph 0081, action array construct communicated by client 202 to screen store function 224 of AI server 220 ; i.e. where sending the screenshots to the server ultimately triggers the server sending back detected object information, including location information, as cited below, the transmission of the screenshot also acts as/constitutes as request for the location information ) ; (c) receiving, by the agent via a communication interface, result data indicating a location of at least one object on the screen, wherein the result data is inferred by the Al model from the transmitted screen image ( e.g. paragraph 0061, Fig. 1B, suggest next action functionality implemented by determining objects/object groups to utilize; checking to ensure object is indeed interactive or actionable and then determining number of actions applied to object or object group, and proceeding with object with the lowest action-count; paragraph 0062, determining weight/priority of object, selecting object with highest weight/priority, and invoking input generator to supply the input for the object; determining next action to perform on the object; paragraph 0072, Fig. 2, last of the sever side modules, action suggester function 246, sends populated action array with suggested actions to perform on various objects back to client 202; paragraph 0113-0116, after objects on pages/screens of GUI are detected, passing control to application mapping function 230 which models behavior of application as it changes states due to actions/events, including actions triggered by inputs in the form of GUI interactions; paragraph 0144, action suggester 246 populates action array with actions to be performed by client 202, picking screen objects and populating action array fields object_id , object_info , an action_group_id , invoking guesser to determine what action to be guessed or tried on the screen object; Table 1, showing that object_info field of the action array includes coordinates and size information of the objects; i.e. information about the detected object (such as location/coordinates) and a suggested action/event for the object are transmitted by the server back to the client, and the action/event is performed/generated for the object by the client’s action execution function 210, as shown in Fig. 2 ) ; and (d) generating, by the agent, an event for the at least one object on the screen of the user terminal based on the received result data ( e.g. paragraph 0062, Fig. 1B, invoking input generator to supply the input for the object; determining next action to perform on the object; paragraph 0064, performing the next action identified; paragraph 0073, action execution function 210 performing suggested actions on the various objects; paragraph 0115, actions caused by action execution 210 which are automated/simulated actions by learning engine; paragraph 0144, actions to be performed by client 202 ) . With respect to claim 15, Petursson teaches all of the limitations of claim 14 as previously discussed, and further taches the method further comprising: receiving a registration of a scheduler from the agent ( e.g. paragraph 0064, indicating use of a predetermined timer for executing the process, including executing the process until the predetermined timer has run out; paragraph 0233, unmanned execution going until predetermined time has passed; i.e. a user or setting provides predetermined timing details for performing the execution/testing by the AI engine/system ) ; and transmitting a start signal to the agent via the communication interface at a scheduled time, wherein the receiving of the screen image is performed in response to the start signal ( e.g. paragraph 0064, indicating use of a predetermined timer for executing the process, including executing the process until the predetermined timer has run out; paragraph 0071, client 202 starts or initiates SUT via start app function; paragraph 0072, start app function initializing empty action array, taking first screenshot of GUI and sending to sever by send action array function; paragraph 0232-0233, system running unmanned, until predetermined time has passed; i.e. where a system running unmanned according to a predetermined timer, until a predetermined amount of time has passed would include beginning/starting the execution at a scheduled time, such as a designated time for the starting of the predetermined timer/time period ) . With respect to claim 23, Petursson teaches all of the limitations of claim 22 as previously discussed, and further teaches the method further comprising: registering a scheduler with the server ( e.g. paragraph 0064, indicating use of a predetermined timer for executing the process, including executing the process until the predetermined timer has run out; paragraph 0233, unmanned execution going until predetermined time has passed; i.e. a user or setting provides predetermined timing details for performing the execution/testing by the AI engine/system ) ; and receiving a start signal from the server via the communication interface at a scheduled time, wherein the transmitting of the screen image is initiated in response to the start signal ( e.g. paragraph 0064, indicating use of a predetermined timer for executing the process, including executing the process until the predetermined timer has run out; paragraph 0071, client 202 starts or initiates SUT via start app function; paragraph 0072, start app function initializing empty action array, taking first screenshot of GUI and sending to sever by send action array function; paragraph 0232-0233, system running unmanned, until predetermined time has passed; i.e. where a system running unmanned according to a predetermined timer, until a predetermined amount of time has passed would include beginning/starting the execution at a scheduled time, such as a designated time for the starting of the predetermined timer/time period ) . With respect to claim 18, Petursson teaches all of the limitations of claim 14 as previously discussed, and further teaches w herein the at least one object includes at least one of a program window, a search bar of a browser, a login button, a company name, an ID input field, or a password input field ( e.g. paragraphs 0076-0077, password field; paragraph 0077, login button ) . With respect to claim 24, Petursson teaches all of the limitations of claim 22 as previously discussed, and further teaches wherein the generating the event comprises controlling an input device to perform a text data input or a mouse button click at a screen coordinate corresponding to the received location information ( e.g. paragraph 0160, Tables 1 and 2, action on object including click of mouse, etc. ) . With respect to claim 25, Petursson teaches all of the limitations of claim 22 as previously discussed, and further teaches wherein the agent operates in an environment including at least one of a Web environment, a Command Line Interface (CLI) environment, or a Remote Desktop Protocol (RDP) environment ( e.g. paragraph 0048, SUT/application implemented with thin client such as browser based client that communicates over web to backend webserver ; i.e. the system operates in at least a web environment ) . With respect to claim 26, Petursson teaches all of the limitations of claim 22 as previously discussed, and further teaches wherein the capturing the screen image and the generating the event are repeated to automatically perform a series of tasks performed by a user ( e.g. as shown in Figs. 1B and 2, after taking the screenshot, sending to server, receiving suggested action, and performing suggested action, the process repeats; paragraphs 0075-0077, when more than one actions belong to group, action execution performs all actions in the group; grouping functionality useful for screens containing grouped GUI objects; in the absence of grouping functionality, multiple screenshots sent to server; as each action is executed, new screenshot sent to server and server determines next suggested action; object grouper groups username filed, password field, and login button into object group and then client executes all of the above described three actions successively, determines change thresholds are triggered, and takes screenshot and sends to the server; paragraphs 0148-0150, object grouper detecting which stored objects function together , groups them into functional sets, and assigns them a same action_group_id in action array; all actions having same action_group_id performed by action execution of client and then latest screenshot sent to sever side ) . With respect to claim 27, Petursson teaches all of the limitations of claim 22 as previously discussed, and further teaches wherein the agent recognizes the at least one object even if a Class ID of the object in a source code is changed ( e.g. paragraph 0093, object detection function performs zero-metadata detection; there is no prior knowledge or metadata needed about a screen object before it is detected, and detection is based solely on visual indicators in the GUI; i.e. because the detection/recognition occurs with no dependency on any metadata such as an object/class ID in source code, the detection/recognition will occur regardless of whether the Class ID of the object in source code is changed, or not changed ). With respect to claim 32, Petursson teaches all of the limitations of claim 30 as previously discussed, and further teaches wherein the operations further comprise creating a log upon completion of the event generation or upon occurrence of an error ( e.g. paragraph 0061, determining total number of actions applied to object/object group thus far, referred to as the action-count; i.e. the system tracks the number of completed events/actions with respect to the different GUI objects, analogous to creating/keeping a log of completed events/actions ). With respect to claim 33, Petursson teaches all of the limitations of claim 30 as previously discussed, and further teaches wherein the user terminal includes any one of a desktop computer, a laptop, an IoT device, a connected car terminal, or a kiosk, and the operations are performed in a non-Windows Operating System (OS) environment or a remote terminal environment ( e.g. paragraph 0049, computing platform may be a desktop computer, laptop computer, tablet, mobile/smartphone, etc.; paragraph 0160, discussing implementation on different operating systems, including non-Windows operating systems ) . Claim Rejections – 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set f