Abstract: Provided are methods and systems for enabling seamless integration between multiple products as a common layer by using a common visual modeler. An example system may include an application server and a common visual modeler. The common visual modeler may include a visual modeling environment for building an application by an end user. The visual modeling environment may include a modeler for creating the application, wherein the modeler comprises at least one component from at least one enterprise software product, the at least one component comprising at least one UI-based modeling tool for creating at least one artifact and a configuration module for managing settings of the at least one component. The common visual modeler may also include a server runtime environment for executing the application, the server runtime environment comprising at least one runtime engine for the at least one artifact created by the modeler.

Abstract: Provided are methods and systems for combining business process management (BPM) and robotic process automation (RPA) into a unified BPM and RPA process. An example method commences with creating a BPM process flow. The BPM process flow can include one or more BPM models for executing BPM tasks. The method then proceeds with creating an RPA process flow. The RPA process flow can include one or more RPA models for executing the one or more RPA tasks. The method can further include linking the one or more RPA models to the BPM process flow. Thereafter, the method proceeds with creating an application package associated with the unified BPM and RPA process. The application package can include the one or more BPM models and the one or more RPA models. The method can further include exporting the application package for deployment.

Abstract: Provided are methods and systems for combining business process management (BPM) and robotic process automation (RPA) into a unified BPM and RPA process. An example method commences with creating a BPM process flow. The BPM process flow can include one or more BPM models for executing BPM tasks. The method then proceeds with creating an RPA process flow. The RPA process flow can include one or more RPA models for executing the one or more RPA tasks. The method can further include linking the one or more RPA models to the BPM process flow. Thereafter, the method proceeds with creating an application package associated with the unified BPM and RPA process. The application package can include the one or more BPM models and the one or more RPA models. The method can further include exporting the application package for deployment.

Abstract: Provided are methods and systems for combining business process management (BPM) and robotic process automation (RPA) into a unified BPM and RPA process. An example method commences with creating a BPM process flow. The BPM process flow can include one or more BPM models for executing BPM tasks. The method then proceeds with creating an RPA process flow. The RPA process flow can include one or more RPA models for executing the one or more RPA tasks. The method can further include linking the one or more RPA models to the BPM process flow. Thereafter, the method proceeds with creating an application package associated with the unified BPM and RPA process. The application package can include the one or more BPM models and the one or more RPA models. The method can further include exporting the application package for deployment.

Abstract: Provided are methods and systems for combining business process management (BPM) and robotic process automation (RPA) into a unified BPM and RPA process. An example method commences with creating a BPM process flow. The BPM process flow can include one or more BPM models for executing BPM tasks. The method then proceeds with creating an RPA process flow. The RPA process flow can include one or more RPA models for executing the one or more RPA tasks. The method can further include linking the one or more RPA models to the BPM process flow. Thereafter, the method proceeds with creating an application package associated with the unified BPM and RPA process. The application package can include the one or more BPM models and the one or more RPA models. The method can further include exporting the application package for deployment.

Abstract: Provided are methods and systems for combining business process management (BPM) and robotic process automation (RPA) into a unified BPM and RPA process. An example method commences with creating a BPM process flow. The BPM process flow can include one or more BPM models for executing BPM tasks. The method then proceeds with creating an RPA process flow. The RPA process flow can include one or more RPA models for executing the one or more RPA tasks. The method can further include linking the one or more RPA models to the BPM process flow. Thereafter, the method proceeds with creating an application package associated with the unified BPM and RPA process. The application package can include the one or more BPM models and the one or more RPA models. The method can further include exporting the application package for deployment.

Abstract: A prediction of the cost associated with translating textual content in a source language can be determined. A first quantity estimation of first textual content may be determined. The first textual content is to be translated via human translation. A second quantity estimation of second textual content may also be determined. The second textual content is to be translated via machine translation. An indication of a target language is obtained, wherein the source language and the target language form a language pair. The prediction of the cost associated with translating the first textual content and the second textual content from the source language to the target language is then determined. The prediction is based at least in part on the first quantity estimation, the second quantity estimation, and the language pair.

Abstract: Provided are methods and systems for enabling seamless integration between multiple products as a common layer by using a common visual modeler. An example system may include an application server and a common visual modeler. The common visual modeler may include a visual modeling environment for building an application by an end user. The visual modeling environment may include a modeler for creating the application, wherein the modeler comprises at least one component from at least one enterprise software product, the at least one component comprising at least one UI-based modeling tool for creating at least one artifact and a configuration module for managing settings of the at least one component. The common visual modeler may also include a server runtime environment for executing the application, the server runtime environment comprising at least one runtime engine for the at least one artifact created by the modeler.

Abstract: A prediction of the cost associated with translating textual content in a source language can be determined. A first quantity estimation of first textual content may be determined. The first textual content is to be translated via human translation. A second quantity estimation of second textual content may also be determined. The second textual content is to be translated via machine translation. An indication of a target language is obtained, wherein the source language and the target language form a language pair. The prediction of the cost associated with translating the first textual content and the second textual content from the source language to the target language is then determined. The prediction is based at least in part on the first quantity estimation, the second quantity estimation, and the language pair.

Abstract: A prediction of the cost associated with translating textual content in a source language can be determined. A first quantity estimation of first textual content may be determined. The first textual content is to be translated via human translation. A second quantity estimation of second textual content may also be determined. The second textual content is to be translated via machine translation. An indication of a target language is obtained, wherein the source language and the target language form a language pair. The prediction of the cost associated with translating the first textual content and the second textual content from the source language to the target language is then determined. The prediction is based at least in part on the first quantity estimation, the second quantity estimation, and the language pair.

Abstract: A document containing text in a source language may be translated into a target language based on content associated with that document, in conjunction with the present technology. An indication to perform an optimal translation of a document into a target language may be received via a user interface. The document may then be accessed by a computing device. The optimal translation is executed by a preferred translation engine of a plurality of available translation engines. The preferred translation engine is the most likely to produce the most accurate translation of the document among the plurality of available translation engines. Additionally, the preferred translation engine may be identified based on content associated with the document. The document is translated into the target language using the preferred translation engine to obtain a translated document, which may then be outputted by a computing device.

Abstract: The present technology may translate a content of a web page such as content locator (e.g., a uniform resource locator (URL)) from a source language to a target language. The content locator may be associated with a content page. The translation may involve dividing the content locator into segment tokens in a first language, followed by translating, transliterating or not changing a segment token. The processed tokens are then reassembled in a second language. The translation may be provided by a translation module through a content page provided by a network browser.

Abstract: Customers having a translation project to select a translation method from a variety of options, ranging from a completely human translation to a completely automated translation. For human translations, translation job information may be communicated through one or more network service modules which execute within a network service application, such as a web-based networking application. A network service module may register a user having an account with the network service application as a translator and communicate translation jobs to the user. One or more users who express interest in performing the translation are selected to perform a translation job, each job comprising at least a portion of the translation project. After a user provides a translation for the translation job, the translation is analyzed to generate a trust level prediction for the translation. A user translation profile may be updated after each translation to reflect the user's performance.

Abstract: The present technology may translate a content of a web page such as content locator (e.g., a uniform resource locator (URL)) from a source language to a target language. The content locator may be associated with a content page. The translation may involve dividing the content locator into segment tokens in a first language, followed by translating, transliterating or not changing a segment token. The processed tokens are then reassembled in a second language. The translation may be provided by a translation module through a content page provided by a network browser.

Abstract: A quality-prediction engine predicts a trust level associated with translational accuracy of a machine-generated translation. Training a quality-prediction may include translating a document in a source language to a target language by executing a machine-translation engine stored in memory to obtain a machine-generated translation. The training may further include comparing the machine-generated translation with a human-generated translation of the document. The human-generated translation is in the target language. Additionally, the training may include generating a mapping between features of the machine-generated translation and features of the human-generated translation based on the comparison.

Abstract: A prediction of the cost associated with translating textual content in a source language can be determined. A first quantity estimation of first textual content may be determined. The first textual content is to be translated via human translation. A second quantity estimation of second textual content may also be determined. The second textual content is to be translated via machine translation. An indication of a target language is obtained, wherein the source language and the target language form a language pair. The prediction of the cost associated with translating the first textual content and the second textual content from the source language to the target language is then determined. The prediction is based at least in part on the first quantity estimation, the second quantity estimation, and the language pair.

Abstract: A quality-prediction engine predicts a trust level associated with translational accuracy of a machine-generated translation. Training a quality-prediction may include translating a document in a source language to a target language by executing a machine-translation engine stored in memory to obtain a machine-generated translation. The training may further include comparing the machine-generated translation with a human-generated translation of the document. The human-generated translation is in the target language. Additionally, the training may include generating a mapping between features of the machine-generated translation and features of the human-generated translation based on the comparison. The mapping may allow determination of trust levels associated with translational accuracy of future machine-generated translations that lack corresponding human-generated translations.

Abstract: Customers having a translation project to select a translation method from a variety of options, ranging from a completely human translation to a completely automated translation. For human translations, translation job information may be communicated through one or more network service modules which execute within a network service application, such as a web-based networking application. A network service module may register a user having an account with the network service application as a translator and communicate translation jobs to the user. One or more users who express interest in performing the translation are selected to perform a translation job, each job comprising at least a portion of the translation project. After a user provides a translation for the translation job, the translation is analyzed to generate a trust level prediction for the translation. A user translation profile may be updated after each translation to reflect the user's performance.

Abstract: A document containing text in a source language may be translated into a target language based on content associated with that document, in conjunction with the present technology. An indication to perform an optimal translation of a document into a target language may be received via a user interface. The document may then be accessed by a computing device. The optimal translation is executed by a preferred translation engine of a plurality of available translation engines. The preferred translation engine is the most likely to produce the most accurate translation of the document among the plurality of available translation engines. Additionally, the preferred translation engine may be identified based on content associated with the document. The document is translated into the target language using the preferred translation engine to obtain a translated document, which may then be outputted by a computing device.

Abstract: This disclosure generally may disclose an efficient and easy to use system and method for sending combined captured data, and synchronized audio/visual annotation data.