DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted 09/17/2024 and 04/20/2026 filed is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Foreign priority is not claimed.
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 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.
Claims 1-23 are rejected under 35 U.S.C. 103 as being unpatentable over Douthit et al. (EP 3830756 B1, as provided) in view of Dines et al. (US Pub No. 20230415338 A1).
Regarding Claim 1,
Douthit discloses One or more non-transitory computer-readable media storing one or more computer programs, the one or more computer programs configured to cause at least one processor to: extract information from one or more digital photographs of one or more documents, one or more screens of an application from a target computing system, or both, via one or more artificial intelligence (AI) models configured to perform Computer Vision (CV) and Optical Character Recognition (OCR); (Douthit, Detailed description, discloses the first stage classifier uses a computer vision-based convolutional neural network to find a bounding rectangle of forms or receipts within an image or video. This approach allows multiple documents to be identified within the same image or within frames of a video. The image classifier artificial intelligence identifies one or more bounding rectangles within the image and the type of document (such as a form or receipt). Given those results, the system extracts the sub-image within the bounding rectangle and uses a technique, such as optical character recognition (OCR) to extract the text. As used herein, the term "text" is defined as one or more alphanumeric characters arranged into strings (i.e., "words", "numbers", "letters", "special characters", or combinations thereof). The text is then fed to a natural language processing artificial intelligence capable of classifying the document as a specific form type, such as (but not limited to) a W2 form, a 1099-INT form, a 1098-T form, etc. Once classified, the image is then sent to a computerized software service where the form fields can be extracted based on the form type. Those extracted fields are then imported (as necessary) into a computerized software service; information from document is extracted from documents using OCR technique and AI models)
determine one or more target applications that the extracted information pertains to using a categorization AI model; (Douthit, Detailed description, discloses attention is now turned to the details of a natural language processing (NLP) artificial intelligence. There are several different stages of NLP. These include a lexical analysis, a syntactic analysis, a semantic analysis, a discourse integration, and a pragmatic analysis. A lexical analysis involves identifying and analyzing the structure of words. A lexicon is the collection of words and phrases in a language. Lexical analysis is dividing the whole chunk of text into paragraphs, sentences, and words. A syntactic analysis involves analysis of words in the sentence for grammar, and arranging words in a manner that shows the relationship among the words. A non-sensical sentence such as "Dust to the alpha house Greek" is rejected by an English syntactic analyzer. This analysis can be performed by a computer using a parse tree, which parses strings of text according to parts of speech (noun, verb, adjective article, adverb, etc.). A semantic analysis draws the exact meaning or the dictionary meaning from the text. The text is checked for meaningfulness. This check may be performed by mapping syntactic structures and objects in the task domain; extracted information is semantically matched with target application fields)
Douthit does not explicitly disclose transmit one or more identifications of the one or more target applications and the semantically matched information to a mobile device or the target computing system.
Dines discloses transmit one or more identifications of the one or more target applications and the semantically matched information to a mobile device or the target computing system. (Dines, [0076], discloses unattended robots 234 run unattended in virtual environments and can automate many processes. Unattended robots 234 may be responsible for remote execution, monitoring, scheduling, and providing support for work queues. Debugging for all robot types may be run in designer 210 in some embodiments. Both attended and unattended robots may automate various systems and applications including, but not limited to, mainframes, web applications, VMs, enterprise applications and computing system applications (e.g., desktop and laptop applications, mobile device applications, wearable computer applications, etc.); data is output to mobile device).
Regarding Claim 2,
The combination of Douthit and Dines further discloses wherein at least two of the one or more AI models configured to perform CV and OCR, the categorization AI model, and the semantic matching AI model are heads of a same generative AI model. (Dines, [0028-0029], [0032], [0163], discloses use multiple or many source examples to learn key-value pairs from the source, some embodiments essentially operate in reverse. Labels are determined from an empty target. Per the above, the source and the target can have different types. Even images of a graphical user interface (GUI) could be used by employing computer vision (CV), optical character recognition (OCR), and/or a document processing framework; 0029] Users may provide training information for building libraries for certain types of targets. For instance, CV, OCR, and document processing artificial intelligence (AI)/machine learning (ML) models may be provided “out of the box” that are capable of achieving an accuracy of 70%. As users provide corrections for incorrect values and/or label-value associations, these may be used to retrain the respective AI/ML model, increasing its accuracy; the type of the target document is determined, the type of the source is also determined if it is not already known, its key-value pairs extracted, and mapping performed from the source to the target. Similarity may be determine using fuzzy logic techniques in some embodiments, per the above. Matched and unmatched key-value pairs may be highlighted using different colors (e.g., green and red, respectively), allowing the user to see which graphical elements match and which do not. The matching interface may also have additional features in some embodiments, such as providing a confidence score of the individual matches, providing an overall mapping score, and allowing the user to hide/unhide the matched/unmatched graphical elements. Further, one or more RPA workflow activities are automatically created in some embodiments based on the target-based schema identification and semantic mapping that can be executed to perform the semantic AI functionality as part of an automation executed by an RPA robot; FIGS. 12A and 12B include a flowchart illustrating a process 1200 for performing target-based schema identification and semantic matching between a source and a target for RPA at design time (FIG. 12A) and runtime (FIG. 12B), according to an embodiment of the present invention. The process begins with receiving a selection of an empty target at 1205. The empty target may be a web page, a GUI of an application, an image, a file explorer, a list of data fields, such as from an Excel® spreadsheet, a relational database, a flat file source, etc., or any other suitable form of data, whether visible to the developer or not, without deviating from the scope of the invention. It should be noted that the source may be any such type as well, and potentially different from that of the target. The target schema and labels are then determined at 1210. This may be accomplished by calling a CV model, an OCR model, a document processing model, any combination thereof, etc.; CV, OCR and semantic matching, category AI are part of generative AI). Additionally, the rational and motivation to combine the references Douthit and Dines as applied in rejection of claim 1 apply to this claim.
Regarding Claim 3,
The combination of Douthit and Dines further discloses wherein the one or more computer programs are further configured to cause the at least one processor to: determine identifying information for the target computing system comprising an Internet Protocol (IP) address, a Media Access Control (MAC) address, or both; and
use the identifying information to transmit the one or more identifications of the one or more target applications and the semantically matched information to the target computing system. (Douthit, Detailed description, discloses computing system 600 in FIG. 6A may be connected to or be a part of a network. For example, as shown in FIG. 6B, the network 620 may include multiple nodes (e.g., node X 622, node Y 624). Each node may correspond to a computing system, such as the computing system shown in FIG. 6A, or a group of nodes combined may correspond to the computing system shown in FIG. 6A. By way of an example, embodiments of the disclosure may be implemented on a node of a distributed system that is connected to other nodes. By way of another example, embodiments of the disclosure may be implemented on a distributed computing system having multiple nodes, where each portion of the disclosure may be located on a different node within the distributed computing system. Further, one or more elements of the aforementioned computing system 600 may be located at a remote location and connected to the other elements over a network; Although not shown in FIG. 6B, the node may correspond to a blade in a server chassis that is connected to other nodes via a backplane. By way of another example, the node may correspond to a server in a data center. By way of another example, the node may correspond to a computer processor or micro-core of a computer processor with shared memory and/or resources; The nodes (e.g., node X 622, node Y 624) in the network 620 may be configured to provide services for a client device 626. For example, the nodes may be part of a cloud computing system. The nodes may include functionality to receive requests from the client device 626 and transmit responses to the client device 626. The client device 626 may be a computing system, such as the computing system shown in FIG. 6A. Further, the client device 626 may include and/or perform all or a portion of one or more embodiments of the disclosure; The computing system or group of computing systems described in FIG. 6A and FIG. 6B may include functionality to perform a variety of operations disclosed herein. For example, the computing system(s) may perform communication between processes on the same or different system. A variety of mechanisms, employing some form of active or passive communication, may facilitate the exchange of data between processes on the same device. Examples representative of these inter-process communications include, but are not limited to, the implementation of a file, a signal, a socket, a message queue, a pipeline, a semaphore, shared memory, message passing, and a memory-mapped file; The computing system in FIG. 6A may implement and/or be connected to a data repository. For example, one type of data repository is a database. A database is a collection of information configured for ease of data retrieval, modification, re-organization, and deletion. Database Management System (DBMS) is a software application that provides an interface for users to define, create, query, update, or administer databases; requests from client device is processed and data is transmitted to the device; memory, storage, query and file transfer applications are performed for retrieval and transmittal of exchange of data between the client device and server network with use of internet protocol address or mac of device). Additionally, the rational and motivation to combine the references Douthit and Dines as applied in rejection of claim 1 apply to this claim.
Regarding Claim 4,
The combination of Douthit and Dines further discloses wherein the one or more computer programs are further configured to cause the at least one processor to:
pair the mobile device with the target computing system using a short-range wireless communication protocol; determine identifying information for the target computing system; and transmit the identifying information for the target computing system to one or more computing systems that are configured to perform the transmitting of the one or more identifications of the one or more target applications and the semantically matched information to the mobile device or the target computing system. use the identifying information to transmit the one or more identifications of the one or more target applications and the semantically matched information to the target computing system. (Douthit, Detailed description, discloses computing system 600 in FIG. 6A may be connected to or be a part of a network. For example, as shown in FIG. 6B, the network 620 may include multiple nodes (e.g., node X 622, node Y 624). Each node may correspond to a computing system, such as the computing system shown in FIG. 6A, or a group of nodes combined may correspond to the computing system shown in FIG. 6A. By way of an example, embodiments of the disclosure may be implemented on a node of a distributed system that is connected to other nodes. By way of another example, embodiments of the disclosure may be implemented on a distributed computing system having multiple nodes, where each portion of the disclosure may be located on a different node within the distributed computing system. Further, one or more elements of the aforementioned computing system 600 may be located at a remote location and connected to the other elements over a network; Although not shown in FIG. 6B, the node may correspond to a blade in a server chassis that is connected to other nodes via a backplane. By way of another example, the node may correspond to a server in a data center. By way of another example, the node may correspond to a computer processor or micro-core of a computer processor with shared memory and/or resources; The nodes (e.g., node X 622, node Y 624) in the network 620 may be configured to provide services for a client device 626. For example, the nodes may be part of a cloud computing system. The nodes may include functionality to receive requests from the client device 626 and transmit responses to the client device 626. The client device 626 may be a computing system, such as the computing system shown in FIG. 6A. Further, the client device 626 may include and/or perform all or a portion of one or more embodiments of the disclosure; The computing system or group of computing systems described in FIG. 6A and FIG. 6B may include functionality to perform a variety of operations disclosed herein. For example, the computing system(s) may perform communication between processes on the same or different system. A variety of mechanisms, employing some form of active or passive communication, may facilitate the exchange of data between processes on the same device. Examples representative of these inter-process communications include, but are not limited to, the implementation of a file, a signal, a socket, a message queue, a pipeline, a semaphore, shared memory, message passing, and a memory-mapped file; The computing system in FIG. 6A may implement and/or be connected to a data repository. For example, one type of data repository is a database. A database is a collection of information configured for ease of data retrieval, modification, re-organization, and deletion. Database Management System (DBMS) is a software application that provides an interface for users to define, create, query, update, or administer databases; requests from client device is processed and data is transmitted to the device; memory, storage, query and file transfer applications are performed for retrieval and transmittal of exchange of data between the client device and server network with use of internet protocol address or mac of device). Additionally, the rational and motivation to combine the references Douthit and Dines as applied in rejection of claim 1 apply to this claim.
Regarding Claim 5,
The combination of Douthit and Dines further discloses wherein the one or more computer programs are further configured to cause the at least one processor to: receive the transmit one or more identifications of the one or more target applications and the semantically matched information, by the mobile device; and transmit the received one or more identifications of the one or more target applications and the semantically matched information to the target computing system. (Douthit, Detailed description, discloses computing system 600 in FIG. 6A may be connected to or be a part of a network. For example, as shown in FIG. 6B, the network 620 may include multiple nodes (e.g., node X 622, node Y 624). Each node may correspond to a computing system, such as the computing system shown in FIG. 6A, or a group of nodes combined may correspond to the computing system shown in FIG. 6A. By way of an example, embodiments of the disclosure may be implemented on a node of a distributed system that is connected to other nodes. By way of another example, embodiments of the disclosure may be implemented on a distributed computing system having multiple nodes, where each portion of the disclosure may be located on a different node within the distributed computing system. Further, one or more elements of the aforementioned computing system 600 may be located at a remote location and connected to the other elements over a network; Although not shown in FIG. 6B, the node may correspond to a blade in a server chassis that is connected to other nodes via a backplane. By way of another example, the node may correspond to a server in a data center. By way of another example, the node may correspond to a computer processor or micro-core of a computer processor with shared memory and/or resources; The nodes (e.g., node X 622, node Y 624) in the network 620 may be configured to provide services for a client device 626. For example, the nodes may be part of a cloud computing system. The nodes may include functionality to receive requests from the client device 626 and transmit responses to the client device 626. The client device 626 may be a computing system, such as the computing system shown in FIG. 6A. Further, the client device 626 may include and/or perform all or a portion of one or more embodiments of the disclosure; The computing system or group of computing systems described in FIG. 6A and FIG. 6B may include functionality to perform a variety of operations disclosed herein. For example, the computing system(s) may perform communication between processes on the same or different system. A variety of mechanisms, employing some form of active or passive communication, may facilitate the exchange of data between processes on the same device. Examples representative of these inter-process communications include, but are not limited to, the implementation of a file, a signal, a socket, a message queue, a pipeline, a semaphore, shared memory, message passing, and a memory-mapped file; The computing system in FIG. 6A may implement and/or be connected to a data repository. For example, one type of data repository is a database. A database is a collection of information configured for ease of data retrieval, modification, re-organization, and deletion. Database Management System (DBMS) is a software application that provides an interface for users to define, create, query, update, or administer databases; requests from client device is processed and data is transmitted to the device; memory, storage, query and file transfer applications are performed for retrieval and transmittal of exchange of data between the client device and server network with use of internet protocol address or mac of device). Additionally, the rational and motivation to combine the references Douthit and Dines as applied in rejection of claim 1 apply to this claim.
Regarding Claim 6,
The combination of Douthit and Dines further discloses wherein the one or more computer programs are further configured to cause the at least one processor to: receive the transmitted one or more identifications of the one or more target applications and the semantically matched information, by the target computing system; and copy the semantically matched information to the one or more target applications on the target computing system. (Douthit, Detailed description, discloses computing system 600 in FIG. 6A may be connected to or be a part of a network. For example, as shown in FIG. 6B, the network 620 may include multiple nodes (e.g., node X 622, node Y 624). Each node may correspond to a computing system, such as the computing system shown in FIG. 6A, or a group of nodes combined may correspond to the computing system shown in FIG. 6A. By way of an example, embodiments of the disclosure may be implemented on a node of a distributed system that is connected to other nodes. By way of another example, embodiments of the disclosure may be implemented on a distributed computing system having multiple nodes, where each portion of the disclosure may be located on a different node within the distributed computing system. Further, one or more elements of the aforementioned computing system 600 may be located at a remote location and connected to the other elements over a network; Although not shown in FIG. 6B, the node may correspond to a blade in a server chassis that is connected to other nodes via a backplane. By way of another example, the node may correspond to a server in a data center. By way of another example, the node may correspond to a computer processor or micro-core of a computer processor with shared memory and/or resources; The nodes (e.g., node X 622, node Y 624) in the network 620 may be configured to provide services for a client device 626. For example, the nodes may be part of a cloud computing system. The nodes may include functionality to receive requests from the client device 626 and transmit responses to the client device 626. The client device 626 may be a computing system, such as the computing system shown in FIG. 6A. Further, the client device 626 may include and/or perform all or a portion of one or more embodiments of the disclosure; The computing system or group of computing systems described in FIG. 6A and FIG. 6B may include functionality to perform a variety of operations disclosed herein. For example, the computing system(s) may perform communication between processes on the same or different system. A variety of mechanisms, employing some form of active or passive communication, may facilitate the exchange of data between processes on the same device. Examples representative of these inter-process communications include, but are not limited to, the implementation of a file, a signal, a socket, a message queue, a pipeline, a semaphore, shared memory, message passing, and a memory-mapped file; The computing system in FIG. 6A may implement and/or be connected to a data repository. For example, one type of data repository is a database. A database is a collection of information configured for ease of data retrieval, modification, re-organization, and deletion. Database Management System (DBMS) is a software application that provides an interface for users to define, create, query, update, or administer databases; requests from client device is processed and data is transmitted to the device; memory, storage, query and file transfer applications are performed for retrieval and transmittal of exchange of data between the client device and server network with use of internet protocol address or mac of device). Additionally, the rational and motivation to combine the references Douthit and Dines as applied in rejection of claim 1 apply to this claim.
Regarding Claim 7,
The combination of Douthit and Dines further discloses wherein the one or more computer programs are further configured to cause the at least one processor to:
receive one or more corrections to the copied information in the one or more target applications on the target computing system; and transmit the one or more corrections to one or more servers of a cloud AI system for retraining at least one of the one or more AI models configured to perform CV and OCR, the categorization AI model, and the semantic matching AI model. (Dines, [0029-0031, discloses users may provide training information for building libraries for certain types of targets. For instance, CV, OCR, and document processing artificial intelligence (AI)/machine learning (ML) models may be provided “out of the box” that are capable of achieving an accuracy of 70%. As users provide corrections for incorrect values and/or label-value associations, these may be used to retrain the respective AI/ML model, increasing its accuracy; analytics may be performed on the user interactions with the software to determine tasks that are used the most. These may be reported to a Center of Excellence (COE) and used as a form of task mining. In certain embodiments, both local and global AI/ML models may be included. For instance, the local AI/ML model may learn preferences of a given user while the global AI/ML model learns collective preferences from many or all users. A threshold may be required to use a result from an AI/ML model. For instance, if the local model is employed first and does not meet the suggestion threshold for one or more attributes, the global model may be tried to see if it comes up with a useful result; in order to perform target-based schema identification, a user may select a target that does not yet have values. Using CV, OCR, and/or document processing AI/ML models, the labels, or keys, are determined in the target, as well as their locations. This allows the system to determine the type of the target. For instance, a web form tends to have rectangular text fields to the right of the respective label for that text field. An invoice will tend to have certain fields, such as customer number, the word “invoice,” some variation of “amount,” etc. The location(s) of the labels may also be used to assist in the determination. For instance, text fields often tend to be arranged vertically with one above and one below, except at the top or bottom of the column of text fields; copies of incorrections are corrected by user and transferred to AI, OCR, CV or categorization models). Additionally, the rational and motivation to combine the references Douthit and Dines as applied in rejection of claim 1 apply to this claim.
Regarding Claim 8,
The combination of Douthit and Dines further discloses wherein the one or more AI models configured to perform CV and OCR, the categorization AI model, and the semantic matching AI model are executed in series, the categorization AI model receives output from the one or more AI models configured to perform CV and OCR, and the semantic matching AI model receives output from both the categorization AI model and the one or more AI models configured to perform CV and OCR. (Douthit, Detailed description, discloses computing system 600 in FIG. 6A may be connected to or be a part of a network. For example, as shown in FIG. 6B, the network 620 may include multiple nodes (e.g., node X 622, node Y 624). Each node may correspond to a computing system, such as the computing system shown in FIG. 6A, or a group of nodes combined may correspond to the computing system shown in FIG. 6A. By way of an example, embodiments of the disclosure may be implemented on a node of a distributed system that is connected to other nodes. By way of another example, embodiments of the disclosure may be implemented on a distributed computing system having multiple nodes, where each portion of the disclosure may be located on a different node within the distributed computing system. Further, one or more elements of the aforementioned computing system 600 may be located at a remote location and connected to the other elements over a network; Although not shown in FIG. 6B, the node may correspond to a blade in a server chassis that is connected to other nodes via a backplane. By way of another example, the node may correspond to a server in a data center. By way of another example, the node may correspond to a computer processor or micro-core of a computer processor with shared memory and/or resources; The nodes (e.g., node X 622, node Y 624) in the network 620 may be configured to provide services for a client device 626. For example, the nodes may be part of a cloud computing system. The nodes may include functionality to receive requests from the client device 626 and transmit responses to the client device 626. The client device 626 may be a computing system, such as the computing system shown in FIG. 6A. Further, the client device 626 may include and/or perform all or a portion of one or more embodiments of the disclosure; The computing system or group of computing systems described in FIG. 6A and FIG. 6B may include functionality to perform a variety of operations disclosed herein. For example, the computing system(s) may perform communication between processes on the same or different system. A variety of mechanisms, employing some form of active or passive communication, may facilitate the exchange of data between processes on the same device. Examples representative of these inter-process communications include, but are not limited to, the implementation of a file, a signal, a socket, a message queue, a pipeline, a semaphore, shared memory, message passing, and a memory-mapped file; The computing system in FIG. 6A may implement and/or be connected to a data repository. For example, one type of data repository is a database. A database is a collection of information configured for ease of data retrieval, modification, re-organization, and deletion. Database Management System (DBMS) is a software application that provides an interface for users to define, create, query, update, or administer databases; requests from client device is processed and data is transmitted to the device; memory, storage, query and file transfer applications are performed for retrieval and transmittal of exchange of data between the client device and server network with use of internet protocol address or mac of device). Additionally, the rational and motivation to combine the references Douthit and Dines as applied in rejection of claim 1 apply to this claim.
Regarding Claim 9,
The combination of Douthit and Dines further discloses mapping elements found in the one or more digital photographs of the one or more documents, the one or more screens of the application, or both, to elements found in screens of one or more target applications; generating confidence scores for the matches; and identifying the one or more target applications based on the one or more confidence scores. (Dines, [0165-170] Indications of graphical elements associated with semantically matched labels in the target screen (e.g., locations, coordinates, type, etc.) and respective confidence scores from the one or more AI/ML models are received at 1220. The semantically matched labels, individual confidence scores, and a global confidence score are displayed in a matching interface at 1225. For instance, the source and target may be shown and matching elements may be highlighted or otherwise made obvious to the developer. In some embodiments, connections are drawing between matching fields in the source and the target. In certain embodiments, elements in the source for which no match was found are highlighted or otherwise indicated to the developer; Indications of graphical elements associated with semantically matched labels in the target screen (e.g., locations, coordinates, type, etc.) and respective confidence scores from the semantic matching model are received at 1270. The semantically matched labels, individual confidence scores, and a global confidence score are displayed in a matching interface at 1275. For instance, the source and target may be shown and matching elements may be highlighted or otherwise made obvious to the developer. In some embodiments, connections are drawing between matching fields in the source and the target. In certain embodiments, elements in the source for which no match was found are highlighted or otherwise indicated to the developer. In some embodiments, the confidence scores are not shown to the user; semantic fields are matched and their confidence score is determined to access target application to process). Additionally, the rational and motivation to combine the references Douthit and Dines as applied in rejection of claim 1 apply to this claim.
Claims 10-18 recite method with steps corresponding to the method steps recited in Claims 1, 9, 8, 2, 4, 5, (3, 6), 7, 9 respectively. Therefore, the recited steps of the method claims 1-9 are mapped to the proposed combination in the same manner as the corresponding instructions of Claims 1-9. Additionally, the rationale and motivation to combine the Douthit and Dines references presented in rejection of Claim 1, apply to these claims.
Claims 19-23 recite system with elements corresponding to the instructions steps recited in Claims 1, 4, 6 and 9 respectively. Therefore, the recited elements of the system claims 19-23 are mapped to the proposed combination in the same manner as the corresponding steps of Claims 1, 4, 6 and 9. Additionally, the rationale and motivation to combine the Douthit and Dines references presented in rejection of Claim 1, apply to these claims.
Furthermore, the combination of Douthit and Dines further discloses One or more computing systems, comprising: memory storing computer program instructions; and at least one processor configured to execute the computer program instructions, wherein the computer program instructions are configured to cause the at least one processor (Dines, [0106-0107], Fig. 5, discloses architectural diagram illustrating a computing system 500 configured to perform target-based schema identification and/or semantic mapping for RPA, according to an embodiment of the present invention. In some embodiments, computing system 500 may be one or more of the computing systems depicted and/or described herein. In certain embodiments, computing system 500 may be part of a hyper-automation system, such as that shown in FIGS. 1 and 2. Computing system 500 includes a bus 505 or other communication mechanism for communicating information, and processor(s) 510 coupled to bus 505 for processing information. Processor(s) 510 may be any type of general or specific purpose processor, including a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Graphics Processing Unit (GPU), multiple instances thereof, and/or any combination thereof. Processor(s) 510 may also have multiple processing cores, and at least some of the cores may be configured to perform specific functions. Multi-parallel processing may be used in some embodiments. In certain embodiments, at least one of processor(s) 510 may be a neuromorphic circuit that includes processing elements that mimic biological neurons. In some embodiments, neuromorphic circuits may not require the typical components of a Von Neumann computing architecture; Computing system 500 further includes a memory 515 for storing information and instructions to be executed by processor(s) 510. Memory 515 can be comprised of any combination of random access memory (RAM), read-only memory (ROM), flash memory, cache, static storage such as a magnetic or optical disk, or any other types of non-transitory computer-readable media or combinations thereof. Non-transitory computer-readable media may be any available media that can be accessed by processor(s) 510 and may include volatile media, non-volatile media, or both. The media may also be removable, non-removable, or both).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US Pub No. 20190005069-A1 (Filgueiras de Arauj, Systems and methods of the present disclosure can use machine-learned image descriptor models for image retrieval applications and other applications. A trained image descriptor model can be used to analyze a plurality of database images to create a large-scale index of keypoint descriptors associated with the database images. An image retrieval application can provide a query image as input to the trained image descriptor model, resulting in receipt of a set of keypoint descriptors associated with the query image. Keypoint descriptors associated with the query image can be analyzed relative to the index to determine matching descriptors (e.g., by implementing a nearest neighbor search). Matching descriptors can then be geometrically verified and used to identify one or more matching images from the plurality of database images to retrieve and provide as output (e.g., by providing for display) within the image retrieval application, Abstract)
US-20220067462-A1 (Laza, Techniques for training an optical character recognition (OCR) model to detect and recognize text in images for robotic process automation (RPA) are disclosed. A text detection model and a text recognition model may be trained separately and then combined to produce the OCR model. Synthetic data and a smaller amount of real, human-labeled data may be used for training to increase the speed and accuracy with which the OCR text detection model and the text recognition model can be trained. After the OCR model has been trained, a workflow may be generated that includes an activity calling the OCR model, and a robot implementing the workflow may be generated and deployed, Abstract)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PINALBEN V PATEL whose telephone number is (571)270-5872. The examiner can normally be reached M-F: 10am - 8pm.
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/Pinalben Patel/Examiner, Art Unit 2673
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