Abstract: Examples of cognitive procurement are described. In an example embodiment, procurement-specific data sources associated with at least one of a process, an organization, and an industry relevant for procurement operations are monitored. From the monitored procurement-specific data, an operation behavioral pattern is identified. Subsequently, a behavior model of an order is constructed using the operation behavioral pattern and a pre-existing behavior model library. A procurement interaction indicating a query for processing the order is received from a user. The order is tracked by the cognitive order concierge. Using the behavior model, a potential event relating to the order is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order. In an example, the user is notified as per the remediation requirement.

Abstract: Examples of employee concierge are provided. In an example, an issue may be determined for an employee. The issue may be determined based on a query shared by the employee or upon occurrence of an unusual event. The unusual event may be indicative of a deviation in behaviour and routine of the employee. A session may be initiated and the issue may be parsed to determine a context. A bot may be selected from multiple bots for the issue where each bot includes information relating to a solution to address the issue. Data associated with the issue may be collected from a central database and other bots. The data may then be analyzed to determine a solution. The solution comprises a response to the query and a suggestion to mitigate the unusual event.

Abstract: Examples of automated order troubleshooting are described. In an example embodiment, sales-specific data sources associated with at least one of a process, an organization, and an industry relevant for sales operations are monitored. From the monitored sales-specific data, an operation behavioral pattern is identified, based on predefined rules. Subsequently, a behavior model capturing the operation behavioral pattern is constructed using a pre-existing behavior model library. Using the behavior model, a potential event relating to an order received to be fulfilled using the sales operation is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order.

Abstract: Examples of medical concierge are provided. In an example, an claim may be received. The claim may include data relating to service provided, by a provider, to multiple patients. The claim may be parsed to determine the provider, the multiple patients and the service provided. Additional information may then be fetched. The additional information may include one of a number of claims filed in the past, status of each claim, number of appeals filed, status of the appeals, and complaints registered by the provider. Thereafter, the claim and the additional information may be analyzed and a category may be determined for the provider. The category may be determined based on a behaviour model that may be computed based on the claim and the additional information. The category may be indicative of an issue in behaviour of the provider.

Abstract: Data from multiple sources may be gathered continuously to perform reconciliation operations. The data items in a first data set may be matched with those in the second data set using a data matching technique. Based on the matching, a confidence score indicative of an extent of match between the data items in the data sets may be generated. Based on the confidence score and predefined thresholds, it may be ascertained if the data items are reconciled. The non-reconciled items in at least one of the first data set and the second data set may be classified in a classification category, based on an artificial intelligence based technique, the classification category being indicative of an explanation of a non-reconciled data item being non-reconcilable. When the data item is not reconciled and classified, the data item is identified as an open item for further analysis.

Abstract: In an example, data, such as, a journal entry in a ledger, to be validated and associated supporting documents may be extracted. Further, an entity, indicative of a feature of the data may be extracted. Based on the extracted entity, one or more probable values for a field of the data may be determined. A probability score may be associated each of the probable values of the field. At least one of the probable values of the field may be compared with an actual value of the field of the data. Based on comparison, a notification indicative of a potential error in the data may generated. The data and historical data associated with the data may be processed, based on at least one of predefined rules and a machine learning technique, to detect an anomaly in the data, the anomaly being related to a contextual information associated with the data.

Abstract: Examples of cognitive procurement are described. In an example embodiment, procurement-specific data sources associated with at least one of a process, an organization, and an industry relevant for procurement operations are monitored. From the monitored procurement-specific data, an operation behavioral pattern is identified. Subsequently, a behavior model of an order is constructed using the operation behavioral pattern and a pre-existing behavior model library. A procurement interaction indicating a query for processing the order is received from a user. The order is tracked by the cognitive order concierge. Using the behavior model, a potential event relating to the order is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order. In an example, the user is notified as per the remediation requirement.

Abstract: A centralized data reconciliation system processes at least two data streams transmitting data related to one of a plurality of processes and executes a data reconciliation procedure. Unmatched data records identified during the data reconciliation procedure are further categorized into categorized records based on various reason categories and irreconcilable records which could not be categorized into the reason categories. The irreconcilable records are flagged for user input. The user input is recorded to further train the data reconciliation system. The at least two data streams are initially converted into self-describing data streams from which the entities and entity attributes are extracted using the data models received from the data streams. The data records from the first and second self-describing data streams are mapped. The matched pairs and unmatched pairs are selected from the mappings based on respective confidence scores that are estimated in accordance with the rules of data reconciliation.

Abstract: Examples of medical concierge are provided. In an example, an claim may be received. The claim may include data relating to service provided, by a provider, to multiple patients. The claim may be parsed to determine the provider, the multiple patients and the service provided. Additional information may then be fetched. The additional information may include one of a number of claims filed in the past, status of each claim, number of appeals filed, status of the appeals, and complaints registered by the provider. Thereafter, the claim and the additional information may be analyzed and a category may be determined for the provider. The category may be determined based on a behaviour model that may be computed based on the claim and the additional information. The category may be indicative of an issue in behaviour of the provider.

Abstract: Examples of automated order troubleshooting are described. In an example embodiment, sales-specific data sources associated with at least one of a process, an organization, and an industry relevant for sales operations are monitored. From the monitored sales-specific data, an operation behavioral pattern is identified, based on predefined rules. Subsequently, a behavior model capturing the operation behavioral pattern is constructed using a pre-existing behavior model library. Using the behavior model, a potential event relating to an order received to be fulfilled using the sales operation is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order.

Abstract: Examples of employee concierge are provided. In an example, an issue may be determined for an employee. The issue may be determined based on a query shared by the employee or upon occurrence of an unusual event. The unusual event may be indicative of a deviation in behaviour and routine of the employee. A session may be initiated and the issue may be parsed to determine a context. A bot may be selected from multiple bots for the issue where each bot includes information relating to a solution to address the issue. Data associated with the issue may be collected from a central database and other bots. The data may then be analyzed to determine a solution. The solution comprises a response to the query and a suggestion to mitigate the unusual event.

Abstract: Data from multiple sources may be gathered continuously to perform reconciliation operations. The data items in a first data set may be matched with those in the second data set using a data matching technique. Based on the matching, a confidence score indicative of an extent of match between the data items in the data sets may be generated. Based on the confidence score and predefined thresholds, it may be ascertained if the data items are reconciled. The non-reconciled items in at least one of the first data set and the second data set may be classified in a classification category, based on an artificial intelligence based technique, the classification category being indicative of an explanation of a non-reconciled data item being non-reconcilable. When the data item is not reconciled and classified, the data item is identified as an open item for further analysis.

Abstract: In an example, data, such as, a journal entry in a ledger, to be validated and associated supporting documents may be extracted. Further, an entity, indicative of a feature of the data may be extracted. Based on the extracted entity, one or more probable values for a field of the data may be determined. A probability score may be associated each of the probable values of the field. At least one of the probable values of the field may be compared with an actual value of the field of the data. Based on comparison, a notification indicative of a potential error in the data may generated. The data and historical data associated with the data may be processed, based on at least one of predefined rules and a machine learning technique, to detect an anomaly in the data, the anomaly being related to a contextual information associated with the data.

Abstract: A centralized data reconciliation system processes at least two data streams transmitting data related to one of a plurality of processes and executes a data reconciliation procedure. Unmatched data records identified during the data reconciliation procedure are further categorized into categorized records based on various reason categories and irreconcilable records which could not be categorized into the reason categories. The irreconcilable records are flagged for user input. The user input is recorded to further train the data reconciliation system. The at least two data streams are initially converted into self-describing data streams from which the entities and entity attributes are extracted using the data models received from the data streams. The data records from the first and second self-describing data streams are mapped. The matched pairs and unmatched pairs are selected from the mappings based on respective confidence scores that are estimated in accordance with the rules of data reconciliation.

Abstract: Provided is an antenna, a method for controlling an antenna and a mobile terminal. The antenna includes: a base plate, controlling apparatus, and driving apparatus, wherein M*N microcell units are provided in the base plate, each of the microcell units is in communication with neighboring microcell units, and liquid metal is provided in the multiple microcell units. The driving apparatus is connected with a controller and the base plate respectively, wherein the controlling apparatus generates a control signal according to one of the pre-stored control matrixes, and sends the control signal to the driving apparatus. Elements of the control matrix correspond to the microcell units in the base plate in a one-to-one manner, so as to control whether the liquid metal is kept in the corresponding microcell units. The driving apparatus drives, according to the control signal sent by the controlling apparatus, the liquid metal in the microcell units to flow in the microcell units.

Abstract: A thermometer includes a substrate; an optical resonator disposed on the substrate and including an optical resonance, the optical resonator being configured to receive a resonant frequency corresponding to the optical resonance; and a waveguide disposed on the substrate proximate to the optical resonator to receive input light, to communicate the resonant frequency to the optical resonator, and to transmit output light; wherein an aperture is interposed between: the substrate and the optical resonator, the substrate and the waveguide, or a combination comprising at least one of the foregoing, and the thermometer is configured to change the optical resonance in response to a change in temperature of the optical resonator.

Abstract: Provided is an antenna, a method for controlling an antenna and a mobile terminal. The antenna includes: a base plate, controlling apparatus, and driving apparatus, wherein M*N microcell units are provided in the base plate, each of the microcell units is in communication with neighbouring microcell units, and liquid metal is provided in the multiple microcell units. The driving apparatus is connected with a controller and the base plate respectively, wherein the controlling apparatus generates a control signal according to one of the pre-stored control matrixes, and sends the control signal to the driving apparatus. Elements of the control matrix correspond to the microcell units in the base plate in a one-to-one manner, so as to control whether the liquid metal is kept in the corresponding microcell units. The driving apparatus drives, according to the control signal sent by the controlling apparatus, the liquid metal in the microcell units to flow in the microcell units.

Abstract: A thermometer includes a substrate; an optical resonator disposed on the substrate and including an optical resonance, the optical resonator being configured to receive a resonant frequency corresponding to the optical resonance; and a waveguide disposed on the substrate proximate to the optical resonator to receive input light, to communicate the resonant frequency to the optical resonator, and to transmit output light; wherein an aperture is interposed between: the substrate and the optical resonator, the substrate and the waveguide, or a combination comprising at least one of the foregoing, and the thermometer is configured to change the optical resonance in response to a change in temperature of the optical resonator.