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 job allocation are described hereon. In an example, a job for allocation may be received. The job may be analyzed to obtain information pertaining to the job. The information may comprise at least one of a domain of the job and a priority level of the job. Further, performance of resources may be determined to provide resource information. The resource information may be determined using a supervised learning model comprising a job vector for each job type and a resource vector corresponding to each resource. The resource information may include a list of resources with at least one of a corresponding probability of each resource completing the job and a performance score of each resource. Based on the job information and the resource information, the resource may be recommended for the job using an expertise-estimation modeling technique and the job may be assigned to the recommended resource, accordingly.

Abstract: Examples of a character recognition system are provided. In an example, the system may receive an object detection requirement pertaining to a video clip. The system may identify a visual media feature map from visual media data to process the object detection requirement. The system may implement an artificial intelligence component to segment the visual media feature map into a plurality of regions, and identify a plurality of image proposals therein. The system may implement a first cognitive learning operation to allocate a human face identity for a human face and an object name for an object present in the video clip. The system may determine a face identity model for the human face present in the plurality of image proposals and generate a tagged face identity model. The system may implement a second cognitive learning operation to assemble the plurality of frames with an appurtenant tagged face identity model.

Abstract: A system for determining a response to a query includes a receiver to receive a query along with a plurality of potential responses to the query. A detector detects a topic and a type of the query based on information extracted from text and structure. Further, a selector selects at least one of a plurality of techniques for processing the query and the plurality of potential responses, based on the topic and the type of the query. An obtainer obtains an answer by execution of each of the selected techniques for processing the query and the plurality of potential responses along with an associated confidence score. A determinator determines one of obtained answers as a correct response to the query, based on a comparison between confidence scores associated with the answers.

Abstract: Examples of cognitive procurement and proactive continuous sourcing are defined. In an example, the system receives a procurement request. The system implements an artificial intelligence component to sort the supplier data into a plurality of domains. The system modifies a domain from the plurality of data domains based on new supplier data being received. The system generates user procurement behavior data based on the procurement interaction and a domain from the plurality of data domains. The system establishes a user procurement behavior model corresponding to a guideline associated with the procurement interaction. The system determines whether the user procurement behavior model should be updated based on modification in the plurality of data domains and updates the same. The system notifies the user regarding change in the user procurement behavior model due to change in a domain of the received supplier data selected by the user.

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 a character recognition system are provided. In an example, the system may receive an object detection requirement pertaining to a video clip. The system may identify a visual media feature map from visual media data to process the object detection requirement. The system may implement an artificial intelligence component to segment the visual media feature map into a plurality of regions, and identify a plurality of image proposals therein. The system may implement a first cognitive learning operation to allocate a human face identity for a human face and an object name for an object present in the video clip. The system may determine a face identity model for the human face present in the plurality of image proposals and generate a tagged face identity model. The system may implement a second cognitive learning operation to assemble the plurality of frames with an appurtenant tagged face identity model.

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: A system for determining a response to a query includes a receiver to receive a query along with a plurality of potential responses to the query. A detector detects a topic and a type of the query based on information extracted from text and structure. Further, a selector selects at least one of a plurality of techniques for processing the query and the plurality of potential responses, based on the topic and the type of the query. An obtainer obtains an answer by execution of each of the selected techniques for processing the query and the plurality of potential responses along with an associated confidence score. A determinator determines one of obtained answers as a correct response to the query, based on a comparison between confidence scores associated with the answers.

Abstract: A classifier receives policy data corresponding to a new policy. Further, the classifier processes the policy data to classify the policy data into an obligation class and an informational class. An information extractor then extracts metadata from the policy data that is classified into the obligation class. Subsequently, a data translator determines if there is an incremental change in the policy data based on a comparison of the policy data with policy data corresponding to existing policies. On determining the incremental change in the policy data, the data translator translates the policy data that is classified into the obligation class into a rule based on the metadata. A rules engine then receives the rule from the data translator for claims adjudication.

Abstract: Examples of analyzing documents are defined. In an example, a request to analyze a document may be received. A knowledge model corresponding to a guideline associated with the document may be obtained. The knowledge model may include at least one of a hypothetical question and a logical flow to determine an inference to the hypothetical question. The hypothetical question relates to an element of the guideline. Based on the knowledge model, data from the document may be extracted for analysis using an artificial intelligence (AI) component. The Ai component may be configured to extract and analyze data, based on the knowledge model. Based on the analysis, a report indicating whether the document falls within a purview of the guideline may be generated.

Abstract: Examples of artificial intelligence-based reasoning explanation are described. In an example implementation, a knowledge model having a plurality of ontologies and a plurality of inferencing rules is generated. Once the knowledge model is generated, based on a real-world problem, a knowledge model from amongst various knowledge models is selected to be used for resolving a real-world problem. The data procured from the real-world problem is clustered and classified into an ontology of the determined knowledge model. Inferencing rules to be used for deconstructing the real-world problem are identified, and a machine reasoning is generated to provide a hypothesis for the problem and an explanation to accompany the hypothesis.

Abstract: A classifier receives policy data corresponding to a new policy. Further, the classifier processes the policy data to classify the policy data into an obligation class and an informational class. An information extractor then extracts metadata from the policy data that is classified into the obligation class. Subsequently, a data translator determines if there is an incremental change in the policy data based on a comparison of the policy data with policy data corresponding to existing policies. On determining the incremental change in the policy data, the data translator translates the policy data that is classified into the obligation class into a rule based on the metadata. A rules engine then receives the rule from the data translator for claims adjudication.

Abstract: The present disclosure describes a system and a method for producing patient-specific small diameter vascular grafts (SDVG) for coronary artery bypass graft (CABG) surgery. In some embodiments, the method for producing SDVGs includes non-invasive quantification of patient-specific coronary and vascular physiology by applying computational fluid dynamics (CFD), rapid prototyping, and in vitro techniques to medical images and coupling the quantified patient-specific coronary and vascular physiology from the CFD to computational fluid-structure interactions and SDVG structural factors to design a patient-specific SDVG.

Abstract: In an example, an ontology analyzer may generate an ontology, based on a claim adjudication request. The claim adjudication request may be processed, based on the ontology to provide an ontology based inference. A rule based analyzer may identify a predefined rule corresponding to the claim adjudication request and process the request, based on the predefined rule. A conflict resolver may resolve a conflict which may occur between the ontology based inference and the rule based inference. When a conflict is detected, a predefined criteria may be selected for resolving the conflict, the predefined criteria comprising rules to select one of the ontology based inference and the rule based inference to maximize a probability of accurately processing the claim adjudication request in case of a conflict.

Abstract: A curator captures input data corresponding to service tasks from an external source. Further, a browser extension collects intermediate service delivery data for the service tasks from the external source. Subsequently, a learner stores the input data and the intermediate service delivery data as training data. Then, a receiver receives a service request from a client. The service request is indicative of a service task to be performed and information associated with the service task. Further, an advisor processes the service request to generate an intermediate service response. Thereafter, the advisor determines a confidence level associated with the intermediate service response and ascertains whether the confidence level associated with service response is below pre-determined threshold level. If the confidence level is below a pre-determined threshold level, the advisor automatically generates a final service response corresponding to service request based on training data.

Abstract: A claims preprocessor processes claim data to identify claims that are to be adjudicated. Each claim includes at least one claim exception. The claims preprocessor further prioritizes the claim exception of each identified claim based on the claim data. A robotic process automator then orchestrates adjudication of the identified claims based on claim data. Further, a rules engine adjudicates the identified claims based on pre-defined rules. Subsequently, a fall out handler determines if any of the identified claims are incorrectly adjudicated and identify an issue associated with incorrect claims adjudication on determining that any of the identified claims are incorrectly adjudicated. A self learner then provides feedback to rules engine based on a decision tree and information received from fall out handler, the feedback being usable to resolve the issue. The information received from fall out handler is indicative of issue associated with incorrect claims adjudication.

Abstract: A system for orchestrating an operation is disclosed. The system includes an case orchestration engine to identify a discrepancy in the operation, and to generate a plurality of hypotheses for resolving the discrepancy. The case orchestration engine further collects evidence pertaining to the discrepancy in the operation, evaluates each of the plurality of hypotheses based on a dialogue-driven feedback received from a user, and selects one of the plurality of hypotheses for resolving the discrepancy based on the evidence and an expected outcome of the operation. The case orchestration engine provides reasons for the discrepancy along with remedial measures for resolving the discrepancy based on the selected hypothesis, and then generates a plan for performing the operation to achieve the expected outcome based on the remedial measures.

Abstract: Systems and methods for orchestrating a process are disclosed. In an implementation, a system is configured to extract process information associated with the process. Based on the process information, the system is configured to determine a current model of performing the process based on the process information. The system is further configured to retrieve regulatory information associated with the process, wherein the regulatory information is indicative of at least one of a predefined policy, a predefined rule, and a predefined regulation associated with the process. Further, the system is configured to update the current model based on at least one of the process information and the regulatory information for obtaining a predefined outcome of the process.

Abstract: Examples of job allocation are described hereon. In an example, a job for allocation may be received. The job may be analyzed to obtain information pertaining to the job. The information may comprise at least one of a domain of the job and a priority level of the job. Further, performance of resources may be determined to provide resource information. The resource information may be determined using a supervised learning model comprising a job vector for each job type and a resource vector corresponding to each resource. The resource information may include a list of resources with at least one of a corresponding probability of each resource completing the job and a performance score of each resource. Based on the job information and the resource information, the resource may be recommended for the job using an expertise-estimation modeling technique and the job may be assigned to the recommended resource, accordingly.