Abstract: Computer-readable media, methods, and systems are disclosed for applying machine learning mechanisms to classify and validate documents based on expense rule sets and external data validation services. Document images associated with expenses are received in connection with a reimbursable event. For each received document image data associated with the received document image is transmitted to an optical character recognition image processor that can recognize contents and associated coordinates. OCR data is received and transmitted to a text tokenizer. Tokenized text is received corresponding to expense details, and the tokenized text and coordinates are sent to a text feature generator. Text feature vectors are received and transmitted to a document classifier and a document classification received. Document fields are extracted and based thereon a document is validates and a corresponding reimbursement instruction generated.

Abstract: Embodiments relate to file-based sharing of content through a content distribution network. A request is received from a source to disseminate content to a target consumer. The request does not include target consumer URL information. Based upon the request, content metadata stored in a database with content data, is referenced to generate a file including metadata comprising a portion of the content metadata. The file does not include the content data. The file is exported to the source. The source delivers the file to the target consumer through a communication channel (e.g., shared folder, email) located outside of the distribution network. The file is imported from the target consumer. The metadata of the file is processed to examine its integrity. The metadata may comprise a key for reference during this process. Upon confirming integrity of the file, the content data and the content metadata are distributed to the target consumer.

Abstract: A distribution network communicates content to tenant groups in a secure manner. An engine of the distribution network receives content created utilizing an application having different customers and partners. The engine also receives: a first identifier indicating a customer of the application with which a tenant is associated, and a second identifier indicating a partner of the application with which the tenant is associated. The engine references a stored database table to correlate the first identifier and the second identifier. Based upon the first identifier and the second identifier, the engine evaluates whether the tenant is to be provided access to the content. The engine may provide the tenant with the content according to an access right determined from the first identifier and the second identifier. Certain embodiments may find particular use disseminating content to new tenants of a customer, based upon prior distribution to other tenants of that customer.

Abstract: Embodiments relate to systems and methods that create a final model by parallel training of models executed within separate containers. A master job present within one container, performs pre-processing (e.g., noise reduction; duplicate removal) of incoming data. The master job orchestrates the training of individual models by child jobs that are executed in parallel within respective separate containers. After checking the status of completion of the child jobs (e.g., via HTTP or by reading local progress files) the master job references the trained models in order to determine a final model. This final model determination may comprise aggregating the trained models, or selecting one model based upon a metric (such as a f1 score). Parallel training of models by child jobs executed within separate containers, streamlines and accelerates model creation. Particular embodiments may be suited to train a model that identifies unique entities from incoming data including names and addresses.

Abstract: An employee of a large organization sends a human-readable document such as an email or text message to another employee of the organization to inform the other employee of a change in availability. A trained machine-learning model extracts, from the human-readable document, data used by a leave management system (LMS) to formalize and memorialize the leave request. For example, the employee name, manager name, date leave begins, date leave ends, reason for the leave request, or any suitable combination thereof may be determined by the machine-learning model based on the human-readable document. The extracted data is provided to the LMS and the leave request is created.

Abstract: Methods, systems, and computer-readable storage media for receiving, by an analytics content network (ACN), a configuration request through a tenant configuration application programming interface (TC-API) of a plurality of APIs, the configuration request including an identifier that uniquely identifies an analytics system tenant that is embedded in an application tenant within a customer landscape, the ACN distributing analytics content to multiple customer landscapes, determining, by the ACN, one or more analytics content that is to be distributed to the analytics system tenant at least partially based on the identifier, providing, by the ACN, a content package that includes the one or more analytics content within an API payload for response to the request, and transmitting, by the ACN, a response to the request through the TC-API, the response comprising the API payload.

Abstract: A distribution network may efficiently communicate items/item details in a private manner, with different access rights tailored to various tenants residing within a same or different customer landscape. A first input comprising a flat file with items/item details, is received. A second input comprising permissions entries for per-user, per-item direct access rights (e.g., view, read, write, delete) is also received. The first and second inputs are recursively processed to find nearest ancestors having the direct access rights, with a hash maintained including the nearest ancestors. An effective permitted structure (e.g., tree comprising root and leaf nodes) is generated by recursively adding descendant items having inherited access rights, to the nearest ancestors. Ultimately, descendant item(s) are privately distributed to a user with an access right according to the effective permitted structure. Embodiments may be particularly suited to the private distribution of analytics content (e.g.

Abstract: A distribution network may efficiently communicate items/item details in a private manner, with different access rights tailored to various tenants residing within a same or different customer landscape. A first input comprising a flat file with items/item details, is received. A second input comprising permissions entries for per-user, per-item direct access rights (e.g., view, read, write, delete) is also received. The first and second inputs are recursively processed to find nearest ancestors having the direct access rights, with a hash maintained including the nearest ancestors. An effective permitted structure (e.g., tree comprising root and leaf nodes) is generated by recursively adding descendant items having inherited access rights, to the nearest ancestors. Ultimately, descendant item(s) are privately distributed to a user with an access right according to the effective permitted structure. Embodiments may be particularly suited to the private distribution of analytics content (e.g.

Abstract: A distribution network communicates content to tenant groups in a secure manner. An engine of the distribution network receives content created utilizing an application having different customers and partners. The engine also receives: a first identifier indicating a customer of the application with which a tenant is associated, and a second identifier indicating a partner of the application with which the tenant is associated. The engine references a stored database table to correlate the first identifier and the second identifier. Based upon the first identifier and the second identifier, the engine evaluates whether the tenant is to be provided access to the content. The engine may provide the tenant with the content according to an access right determined from the first identifier and the second identifier. Certain embodiments may find particular use disseminating content to new tenants of a customer, based upon prior distribution to other tenants of that customer.

Abstract: Embodiments relate to file-based sharing of content through a content distribution network. A request is received from a source to disseminate content to a target consumer. The request does not include target consumer URL information. Based upon the request, content metadata stored in a database with content data, is referenced to generate a file including metadata comprising a portion of the content metadata. The file does not include the content data. The file is exported to the source. The source delivers the file to the target consumer through a communication channel (e.g., shared folder, email) located outside of the distribution network. The file is imported from the target consumer. The metadata of the file is processed to examine its integrity. The metadata may comprise a key for reference during this process. Upon confirming integrity of the file, the content data and the content metadata are distributed to the target consumer.

Abstract: Methods, systems, and computer-readable storage media for receiving, by an analytics content network (ACN), a configuration request through a tenant configuration application programming interface (TC-API) of a plurality of APIs, the configuration request including an identifier that uniquely identifies an analytics system tenant that is embedded in an application tenant within a customer landscape, the ACN distributing analytics content to multiple customer landscapes, determining, by the ACN, one or more analytics content that is to be distributed to the analytics system tenant at least partially based on the identifier, providing, by the ACN, a content package that includes the one or more analytics content within an API payload for response to the request, and transmitting, by the ACN, a response to the request through the TC-API, the response comprising the API payload.

Abstract: Embodiments relate to methods and apparatuses providing contextual suggestion in the environment of a user interface to a planning spreadsheet. Particular embodiments feature an interface engine that is in communication with an underlying data model. The data model may be hierarchical in nature (e.g., organized according to tree structure). In response to user input to the spreadsheet interface, the engine is configured to parse metadata associated with the data model, and construct therefrom suggestion strings prompting a user to interact with the data of the data model. The interface engine may consider a variety of factors in providing relevant suggestion, including but not limited to cell type and user context (e.g., access rights) so as to further target the offered suggestions offered to user expectation. This approach can desirably enhance efficiency of user interaction with the spreadsheet application, resulting in less typing, fewer corrections, faster workflows, and greater satisfaction.

Abstract: Embodiments relate to methods and apparatuses providing contextual suggestion in the environment of a user interface to a planning spreadsheet. Particular embodiments feature an interface engine that is in communication with an underlying data model. The data model may be hierarchical in nature (e.g., organized according to tree structure). In response to user input to the spreadsheet interface, the engine is configured to parse metadata associated with the data model, and construct therefrom suggestion strings prompting a user to interact with the data of the data model. The interface engine may consider a variety of factors in providing relevant suggestion, including but not limited to cell type and user context (e.g., access rights) so as to further target the offered suggestions offered to user expectation. This approach can desirably enhance efficiency of user interaction with the spreadsheet application, resulting in less typing, fewer corrections, faster workflows, and greater satisfaction.