Abstract: Client instance data including a plurality of incidents and a plurality of knowledge elements comprising information relating to resolving one or more of the plurality of incidents is obtained. A validation set is built based on the obtained client instance data, the validation set including fingerprint data of plural fingerprints of known incident-knowledge relationships, each of fingerprint representing a link between one of the incidents and one of the knowledge elements used for resolving the incident. A knowledge element class is predicted from among plural knowledge element classes for each of knowledge element based on the built validation set, the plural knowledge element classes being defined based on respective threshold values indicating a quality of coverage provided by a knowledge element for resolving an incident. Classification data of the plural knowledge elements classified into the plural knowledge element classes is presented with the obtained client instance data.

Abstract: Client instance data including a plurality of incidents is obtained, each incident including a plurality of fields. A target field and an evaluation field are selected from among the plural fields. The plurality of incidents are grouped into a plurality of clusters based on a degree of a natural language text similarity of respective target fields in the plurality of incidents. A quality value is determined for each of the plurality of clusters based on the degree of the natural language text similarity of respective target fields in grouped incidents of the cluster from among the plurality of incidents, and based on respective evaluation fields. Each of the plurality of clusters is ranked based on the respective quality value of the cluster and a number of the grouped incidents of the cluster. At least one of the ranked plurality of clusters is identified to perform a service management operation.

Abstract: The technology disclosed relates to enforcing multi-part policies on data-deficient transactions of independent data stores. In particular, it relates to combining active analysis of access requests for the independent object stores with inspection of objects in the independent object stores, each of the analysis and inspection generating and persisting object metadata in a supplemental data store, actively processing data-deficient transactions that apply to the objects by accessing the supplemental data store to retrieve object metadata not available in transaction streams of the data-deficient transactions, and actively enforcing the multi-part policies using the retrieved object metadata.

Abstract: The technology disclosed relates to machine learning based anomaly detection. In particular, it relates to constructing activity models on per-tenant and per-user basis using an online streaming machine learner that transforms an unsupervised learning problem into a supervised learning problem by fixing a target label and learning a regressor without a constant or intercept. Further, it relates to detecting anomalies in near real-time streams of security-related events of one or more tenants by transforming the events in categorized features and requiring a loss function analyzer to correlate, essentially through an origin, the categorized features with a target feature artificially labeled as a constant. It further includes determining an anomaly score for a production event based on calculated likelihood coefficients of categorized feature-value pairs and a prevalencist probability value of the production event comprising the coded features-value pairs.

Abstract: Client instance data including a plurality of incidents is obtained, each incident including a plurality of fields. A target field and an evaluation field are selected from among the plural fields. The plurality of incidents are grouped into a plurality of clusters based on a degree of a natural language text similarity of respective target fields in the plurality of incidents. A quality value is determined for each of the plurality of clusters based on the degree of the natural language text similarity of respective target fields in grouped incidents of the cluster from among the plurality of incidents, and based on respective evaluation fields. Each of the plurality of clusters is ranked based on the respective quality value of the cluster and a number of the grouped incidents of the cluster. At least one of the ranked plurality of clusters is identified to perform a service management operation.

Abstract: The present disclosure is directed to a system and method for compressing keys of key-data item pairs for storage in a hash table to reduce power and/or area requirements of the memory used to implement the hash table. The system and method of the present disclosure use the hash function to compress a key of a key-data item pair. More specifically, the system and method of the present disclosure effectively remove information from the key that can be predicted using in the hash value of the key to generate a compressed key. Information in the key that is not predictable using the hash value of the key can be included in the compressed key to allow for recovery of the key.

Abstract: The technology disclosed relates to enforcing multi-part policies on data-deficient transactions of independent data stores. In particular, it relates to combining active analysis of access requests for the independent object stores with inspection of objects in the independent object stores, each of the analysis and inspection generating and persisting object metadata in a supplemental data store, actively processing data-deficient transactions that apply to the objects by accessing the supplemental data store to retrieve object metadata not available in transaction streams of the data-deficient transactions, and actively enforcing the multi-part policies using the retrieved object metadata.

Abstract: Disclosed herein is system, method and apparatus to capture and retrieve personalized mood icons. A personalized mood icon may be used to express a mood, tone, emotion, etc., and may comprise one or more components, including without limitation one or more visual and/or audible components. An icon may be generated from a media item depicting a user and a mood of the user. For example, an icon may be generated from a media item comprising one or more of still image, video, audio, multimedia, etc. content. An icon may comprise content from one or more media items and/or content portions of one or more media items. An icon may comprise a textual component, such as for example a textual title or description of the mood, tone, motion being portrayed using the icon.

Abstract: The technology disclosed relates to machine learning based anomaly detection. In particular, it relates to constructing activity models on per-tenant and per-user basis using an online streaming machine learner that transforms an unsupervised learning problem into a supervised learning problem by fixing a target label and learning a regressor without a constant or intercept. Further, it relates to detecting anomalies in near real-time streams of security-related events of one or more tenants by transforming the events in categorized features and requiring a loss function analyzer to correlate, essentially through an origin, the categorized features with a target feature artificially labeled as a constant. It further includes determining an anomaly score for a production event based on calculated likelihood coefficients of categorized feature-value pairs and a prevalencist probability value of the production event comprising the coded features-value pairs.

Abstract: An embodiment may involve receiving respective information technology performance data related to managed networks. The embodiment may further involve transmitting a web-based representation of a first graphical user interface. The first graphical user interface may be configurable to display a plurality of performance metrics related to the managed network. The embodiment may further involve receiving an indication to display a detailed representation of a particular performance metric of the plurality of performance metrics. The embodiment may further involve transmitting a web-based representation of a second graphical user interface.

Abstract: The present disclosure is directed to a system and method for compressing keys of key-data item pairs for storage in a hash table to reduce power and/or area requirements of the memory used to implement the hash table. The system and method of the present disclosure use the hash function to compress a key of a key-data item pair. More specifically, the system and method of the present disclosure effectively remove information from the key that can be predicted using in the hash value of the key to generate a compressed key. Information in the key that is not predictable using the hash value of the key can be included in the compressed key to allow for recovery of the key.

Abstract: The present invention relates to a method of treating allergic rhinitis in a subject (e.g., a pediatric human subject) in need thereof comprising nasally administering to the subject an effective amount of a fixed-dose pharmaceutical composition comprising mometasone or its salt and olopatadine or its salt.

Abstract: The present invention relates to a method of treating allergic rhinitis in a subject (e.g., a human) in need thereof comprising nasally administering to the subject an effective amount of a fixed-dose pharmaceutical composition comprising mometasone or its salt and olopatadine or its salt.

Abstract: The technology disclosed relates to enforcing multi-part policies on data-deficient transactions of independent data stores. In particular, it relates to combining active analysis of access requests for the independent object stores with inspection of objects in the independent object stores, each of the analysis and inspection generating and persisting object metadata in a supplemental data store, actively processing data-deficient transactions that apply to the objects by accessing the supplemental data store to retrieve object metadata not available in transaction streams of the data-deficient transactions, and actively enforcing the multi-part policies using the retrieved object metadata.

Abstract: Client instance data including a plurality of incidents is obtained, each incident including a plurality of fields. A target field and an evaluation field are selected from among the plural fields. The plurality of incidents are grouped into a plurality of clusters based on a degree of a natural language text similarity of respective target fields in the plurality of incidents. A quality value is determined for each of the plurality of clusters based on the degree of the natural language text similarity of respective target fields in grouped incidents of the cluster from among the plurality of incidents, and based on respective evaluation fields. Each of the plurality of clusters is ranked based on the respective quality value of the cluster and a number of the grouped incidents of the cluster. At least one of the ranked plurality of clusters is identified to perform a service management operation.

Abstract: Client instance data including a plurality of incidents is obtained, each incident including a plurality of fields. A target field and an evaluation field are selected from among the plural fields. The plurality of incidents are grouped into a plurality of clusters based on a degree of a natural language text similarity of respective target fields in the plurality of incidents. A quality value is determined for each of the plurality of clusters based on the degree of the natural language text similarity of respective target fields in grouped incidents of the cluster from among the plurality of incidents, and based on respective evaluation fields. Each of the plurality of clusters is ranked based on the respective quality value of the cluster and a number of the grouped incidents of the cluster. At least one of the ranked plurality of clusters is identified to perform a service management operation.

Abstract: Client instance data including a plurality of incidents is obtained, each incident including a plurality of fields. A target field and an evaluation field are selected from among the plural fields. The plurality of incidents are grouped into a plurality of clusters based on a degree of a natural language text similarity of respective target fields in the plurality of incidents. A quality value is determined for each of the plurality of clusters based on the degree of the natural language text similarity of respective target fields in grouped incidents of the cluster from among the plurality of incidents, and based on respective evaluation fields. Each of the plurality of clusters is ranked based on the respective quality value of the cluster and a number of the grouped incidents of the cluster. At least one of the ranked plurality of clusters is identified to perform a service management operation.

Abstract: The present invention relates to a method of treating allergic rhinitis in a subject (e.g., a human) in need thereof comprising nasally administering to the subject an effective amount of a fixed-dose pharmaceutical composition comprising mometasone or its salt and olopatadine or its salt.

Abstract: The present patent application relates to treatment of a respiratory disorder using retinoid-related orphan receptor gamma t (ROR-gamma) modulators. Particularly, the present patent application relates to treatment of a respiratory disorder using a ROR? inhibitor, wherein the ROR? inhibitor is administered by an inhalation route to a subject in need thereof.

Abstract: The technology disclosed relates to machine learning based anomaly detection. In particular, it relates to constructing activity models on per-tenant and per-user basis using an online streaming machine learner that transforms an unsupervised learning problem into a supervised learning problem by fixing a target label and learning a regressor without a constant or intercept. Further, it relates to detecting anomalies in near real-time streams of security-related events of one or more tenants by transforming the events in categorized features and requiring a loss function analyzer to correlate, essentially through an origin, the categorized features with a target feature artificially labeled as a constant. It further includes determining an anomaly score for a production event based on calculated likelihood coefficients of categorized feature-value pairs and a prevalencist probability value of the production event comprising the coded features-value pairs.