Abstract: A system and method for classifying digital images is presented. The method includes extracting a plurality of descriptive data items of a transaction evidence from a digital image indicating a plurality of purchased items; searching in data source for informative data based on the extracted plurality of descriptive data items, wherein the informative data includes a price; determining a correlated amount for each of at least one of the plurality of descriptive data items, wherein the correlated amount determined for one of the descriptive data items defines a paid price for the descriptive data item; determining, based on at least one expense type classification rule, a primary expense type of the transaction evidence, wherein the at least one expense type classification rule is applied to the plurality of descriptive data items and each of the correlated amount; and classifying the digital image based on the primary expense type.

Abstract: A system and method for classifying digital images of transaction evidences. The method includes extracting a plurality of descriptive data items and a plurality of numeric values of a transaction evidence from a digital image; determining an association between at least one of the plurality of numeric values and at least one of the plurality of descriptive data items; determining, based on at least one expense type classification rule, at least a primary expense type of the transaction evidence, wherein the at least one expense type classification rule is applied to the plurality of descriptive data items and the plurality of numeric values; and classifying the digital image based on the determined at least one primary expense type.

Abstract: A system and method for classifying digital images is presented. The method includes extracting a plurality of descriptive data items of a transaction evidence from a digital image indicating a plurality of purchased items; searching in data source for informative data based on the extracted plurality of descriptive data items, wherein the informative data includes a price; determining a correlated amount for each of at least one of the plurality of descriptive data items, wherein the correlated amount determined for one of the descriptive data items defines a paid price for the descriptive data item; determining, based on at least one expense type classification rule, a primary expense type of the transaction evidence, wherein the at least one expense type classification rule is applied to the plurality of descriptive data items and each of the correlated amount; and classifying the digital image based on the primary expense type.

Abstract: A system and method for classifying digital images of transaction evidences. The method includes extracting a plurality of descriptive data items and a plurality of numeric values of a transaction evidence from a digital image; determining an association between at least one of the plurality of numeric values and at least one of the plurality of descriptive data items; determining, based on at least one expense type classification rule, at least a primary expense type of the transaction evidence, wherein the at least one expense type classification rule is applied to the plurality of descriptive data items and the plurality of numeric values; and classifying the digital image based on the determined at least one primary expense type.

Abstract: A system and method for clustering an electronic document are provided. The method includes performing an analysis of the electronic document that includes transaction evidence and a plurality of items associated with a set of coordinates indicating positioning of each item of the plurality of items within the electronic document; determining for each item of the plurality of items a set of coordinates; analyzing the set of coordinates of each of the plurality of items; determining a first customized radius for the electronic document based on a result of the analysis of the set of coordinates; receiving an input indicating a predetermined minimum number of items required to form a cluster; processing the set of coordinates of each of the plurality of items, the first customized radius and the predetermined minimum number of items to detect a cluster in the document; and generating a electronic template for the cluster.

Abstract: A system and method for determining vendor identity based on electronic documents analysis. The method includes receiving, by a computing device, a first electronic document. The first electronic document includes a plurality of textual items. The method also includes extracting, using the computing device, at least a first sequence of textual items from the plurality of textual items, determining a first approximate identity of a vendor entity that issued the first electronic document by comparing the first sequence of textual items to at least a second sequence of textual items that is associated with at least a second electronic document of a plurality of second electronic documents that was previously associated with a unique vendor entity, and extracting at least a first multiset of textual items from the plurality of textual items of the first electronic document.

Abstract: A system and method for determining vendor identity based on electronic documents analysis. The method includes receiving, by a computing device, a first electronic document. The first electronic document includes a plurality of textual items. The method also includes extracting, using the computing device, at least a first sequence of textual items from the plurality of textual items, determining a first approximate identity of a vendor entity that issued the first electronic document by comparing the first sequence of textual items to at least a second sequence of textual items that is associated with at least a second electronic document of a plurality of second electronic documents that was previously associated with a unique vendor entity, and extracting at least a first multiset of textual items from the plurality of textual items of the first electronic document.

Abstract: The disclosure generally relates to various methods to discover, configure, and leverage relationships in Internet of Things (IoT) networks. More particularly, the methods disclosed herein may support automated processes to create configurable sub-divisions and access controls in an IoT network based on usage associated with objects that are registered in the IoT network and interactions among the registered objects. Furthermore, in one embodiment, relationships between IoT devices that belong to different users may be implicitly discovered and/or ranked based on meetings (e.g., interactions) between the IoT devices, and relationships between the different users may likewise be implicitly discovered and/or ranked. Moreover, locations and interactions associated with IoT devices may be tracked over time to further discover user-specific and potentially asymmetric relationships among the IoT devices and/or the users associated therewith (e.g.

Abstract: The disclosure generally relates to various methods to discover, configure, and leverage relationships in Internet of Things (IoT) networks. More particularly, the methods disclosed herein may support automated processes to create configurable sub-divisions and access controls in an IoT network based on usage associated with objects that are registered in the IoT network and interactions among the registered objects. Furthermore, in one embodiment, relationships between IoT devices that belong to different users may be implicitly discovered and/or ranked based on meetings (e.g., interactions) between the IoT devices, and relationships between the different users may likewise be implicitly discovered and/or ranked. Moreover, locations and interactions associated with IoT devices may be tracked over time to further discover user-specific and potentially asymmetric relationships among the IoT devices and/or the users associated therewith (e.g.

Abstract: Methods and apparatuses are disclosed for synchronizing data inputs generated at a plurality of frequencies by a plurality of data sources. A device receives a first set of data points from a first data source of the plurality of data sources generated at a first frequency of the plurality of frequencies, receives a second set of data points from a second data source of the plurality of data sources generated at a second frequency of the plurality of frequencies, selects a time window corresponding to a period of time during which at least a subset of the first set of data points and at least a subset of the second set of data points were generated, and generates a vector representing a first reduced form of the subset of the first set of data points and a second reduced form of the subset of the second set of data points.

Abstract: The disclosure relates to various distance metrics that may quantify semantic and syntactic relationships between devices. More particularly, a first grammar associated with a first device and a second grammar associated with a second device may each comprise a symbol sequence that re-expresses one or more sequenced data items and one or more rules that represent a repeated pattern in the symbol sequence. Accordingly, one or more distance metrics that quantify a similarity between the first grammar and the second grammar may be calculated according to a comparison between the rules in the first grammar and the rules in the second grammar such that a relationship between the first device and the second device can be determined according to the one or more distance metrics.

Abstract: Systems and methods are disclosed for determining a context-dependent virtual distance based on stigmergic interference. The method may include obtaining environmental status information relating to an environment in proximity to a client device, calculating, based on the obtained environmental status information, the context-dependent virtual distance between the client device and a user of the client device, and controlling a user signaling pattern of the client device based on the calculated context-dependent virtual distance.

Abstract: The disclosure generally relates to various methods to discover, configure, and leverage relationships in Internet of Things (IoT) networks. More particularly, the methods disclosed herein may support automated processes to create configurable sub-divisions and access controls in an IoT network based on usage associated with objects that are registered in the IoT network and interactions among the registered objects. Furthermore, in one embodiment, relationships between IoT devices that belong to different users may be implicitly discovered and/or ranked based on meetings (e.g., interactions) between the IoT devices, and relationships between the different users may likewise be implicitly discovered and/or ranked. Moreover, locations and interactions associated with IoT devices may be tracked over time to further discover user-specific and potentially asymmetric relationships among the IoT devices and/or the users associated therewith (e.g.

Abstract: The disclosure generally relates to various methods to discover, configure, and leverage relationships in Internet of Things (IoT) networks. More particularly, the methods disclosed herein may support automated processes to create configurable sub-divisions and access controls in an IoT network based on usage associated with objects that are registered in the IoT network and interactions among the registered objects. Furthermore, in one embodiment, relationships between IoT devices that belong to different users may be implicitly discovered and/or ranked based on meetings (e.g., interactions) between the IoT devices, and relationships between the different users may likewise be implicitly discovered and/or ranked. Moreover, locations and interactions associated with IoT devices may be tracked over time to further discover user-specific and potentially asymmetric relationships among the IoT devices and/or the users associated therewith (e.g.

Abstract: Methods and systems are disclosed for incrementally sharing increasing amounts of information between two or more user devices. An aspect detects a second user device that is proximate a first user device, receives first log data from the second user device, determines whether the first user device and the second user device interacted with each other during a first period of time based on the first log data, assigns a first trust level to the second user device based on the first log data, wherein the second log data is more private than the first log data, determines whether the first user device and the second user device interacted with each other during a second period of time based on the received second log data, and assigns a second trust level to the second user device based on the second log data.

Abstract: The disclosure generally relates to enabling communication among one or more Internet of Things (IoT) device groups. In particular, various heterogeneous IoT devices that may need to interact with one another in different ways may be organized into IoT device groups to support efficient interaction among the IoT devices. For example, pre-defined IoT device groups may be formed organize certain IoT devices that perform similar activities and certain IoT devices may be dynamically allocated to ad-hoc IoT device groups for certain contexts (e.g., the ad-hoc IoT device groups may include IoT devices that can implement a desired function and therefore be dynamically formed to implement the desired function). Furthermore, the IoT groups may communicate hierarchically, wherein messages may be exchanged among IoT group owners or ranking members to support efficient communication between different IoT groups.

Abstract: The disclosure is related to providing interconnectivity between a plurality of user devices. A wireless interconnectivity device connects to a first user device of the plurality of user devices over a first local wireless network, connects to a second user device of the plurality of user devices over a second local wireless network, receives a request from the first user device to transfer data from the first user device to the second user device, determines whether or not a third user device has granted permission to transfer the data from the first user device to the second user device, and transfers the data from the first user device to the second user device based on the third user device having granted permission to transfer the data from the first user device to the second user device.

Abstract: An aspect of the disclosure is directed to transmitting a reduced stream of encoded video frames. An original stream of encoded video frames is analyzed, a plurality of frames are removed without re-encoding encoded video frames to generate the reduced stream of encoded video frames, and the reduced stream and metadata describing the plurality of removed frames are transmitted. An aspect of the disclosure is directed to creating a new version of an original stream of encoded video frames from a reduced stream of encoded video frames. The reduced stream of encoded video frames is received, the plurality of removed frames is identified based on metadata related to the reduced stream, a plurality of replacement frames are generated, and the plurality of replacement frames are added to the reduced stream of encoded video frames to recreate the new version of the original stream of encoded video frames.

Abstract: A client device requests to retrieve a computer software application that includes a set or core files including a core file executable and at least one data file that can be used by the core file executable. The client device creates a placeholder file in place of the data file. The client device installs the set of core files and the placeholder file. The data file is retrieved at a later time, such as in response to a user's interaction with the computer software application or automatically as a background task.

Abstract: A first computer-implemented process writes a first dataset to data storage locations on at least one data storage device. A request by the first or a second computer-implemented process to write a second dataset to one of the data storage locations where the first computer-implemented process has written some or all of the first dataset is identified. A list of protected or unprotected data portions is checked before the first or second computer-implemented process writes the second dataset. The first or second computer-implemented process writes a portion of the second dataset to a selected data storage location in the data storage device only if the resident data portion stored in the selected data storage location is (a) included in the list of unprotected data portions or (b) not included in the list of protected data portions.