Abstract: Systems and methods for data analytics include retrieving a first data model that includes a first set of one or more entities. A respective entity of the first set of one or more entities relates to a data subset of a first set of one or more databases, and corresponds to a metric, a dimension, or a filter. Based on the first data model, a training set is generated for training a first agent. The first agent is configured to respond to user input queries formulated in natural language. The training set for training the first agent includes a plurality of sample requests, and a plurality of database queries for the one or more databases. At least one respective database query of the plurality of database queries corresponds to at least one respective sample request of the plurality of sample requests.

Abstract: A data analytics system comprises a data source configured to store a plurality of data sets. A dialog manager generates a context-based data query responsive to identifying a context of a natural language query based, at least in part, on a prior natural language query within a conversation. An analytics engine is configured to run queries on the data source responsive to the context-based data query and then generate an output result. The dialog manager generates an interactive data visualization of the output result with a prompt for a follow-up query on the data source. The prompt for the follow-up query is related to the context of the natural language query.

Abstract: A method to monitor a data of interest of an individual (U) and give advice to said individual, said method comprising: a—collect said data of interest and a plurality of life parameters, b—monitor the evolution over time of said data of interest, c—detect an unexpected/inadvertent deviation in the evolution of data of interest, forming a break event, occurred at a first instant (T1), d—store the previously collected values of the plurality of life parameters over a first time window preceding said break event, to form a reference values collection of life parameters, attached to said individual and to the break event, and later: e—collect over time, the values of the plurality of life parameters, f—compare said lately collected values of life parameters to the reference values collection of life parameters stored, and if they are similar, give a notice to the user, whereby the user can be warned of a possible imminent deviation related to the data of interest.

Abstract: A method implemented in a system which comprises a lightweight personal wearable monitoring device, supplied by a battery, comprising an accelerometer, a processing unit, a display, a remote server, said method comprising the steps: /a/ collecting, from a plurality of individuals caused to practice various physical activities from a set of predefined activities, acceleration data from sensing devices placed on each of said individuals, /b/ defining N small data-size specific metrics, computed from acceleration signals, which allow to define a global activity classifier, /c/ acquiring, at a first monitoring device worn by a first user, acceleration signals from the accelerometer of the first monitoring device, /d/ calculating, at the first monitoring device, over each lapsed time unit T1, specific metrics values from the sensed signals, to form a series of specific metrics values, /e/ send them to the processing unit, /f/ allocate an activity type for each time unit together with corresponding specific metric

Abstract: A method for determining the weight of an entity/item to be weighed on a weighing device (1), the weighing device comprising at least a weight sensor and a control unit (4), the method comprising the following steps: /a/ collecting weight raw samples (WSi) of the total weight sensed at the weight sensor(s), at a sampling frequency (F0), and converting each of the weight raw samples (WSi) into digitalized weight raw samples (DSi), /b/ entering sequentially each of the digitalized weight raw samples (DSi) into a Butterworth filter, the latter issuing filtered weight samples (FSi), /c/ defining a rolling window (RW) containing a parametrized number NS of latest filtered weight samples (FSi), /d1/ determining, in the rolling window, the minimum (MIN) and maximum (MAX) values of filtered weight samples, /d2/ comparing the value of MAX?MIN with regard to a parametrized Threshold (T), /e/ if MAX?MIN is greater than Threshold (T), repeat steps /a/ to /d2/, and as soon as MAX?MIN is less than Threshold (T), output a

Abstract: A method for providing personalized recommendations to an individual, relating to monitoring his or her weight, comprising the steps of /a/ obtaining a plurality of prior weighings for the individual concerned, each weighing giving the total weight and the body fat percentage, /b/ deriving a characteristic curve (S1) for the individual by associating at least the total weight and the fat body mass, /c/ obtaining a target total weight (P2) for the user to achieve by the end of a target period, and therefore a target total weight change, /d/ deducing a target number of calories to burn, corresponding to target changes in the fat body mass and lean body mass deduced from the total weight change, /e/ providing the user with personalized instructions on exercise and/or diet.

Abstract: A method for determining the weight of an entity/item to be weighed on a weighing device (1), the weighing device comprising at least a weight sensor and a control unit (4), the method comprising the following steps: /a/ collecting weight raw samples (WSi) of the total weight sensed at the weight sensor(s), at a sampling frequency (F0), and converting each of the weight raw samples (WSi) into digitalized weight raw samples (DSi), /b/ entering sequentially each of the digitalized weight raw samples (DSi) into a Butterworth filter, the latter issuing filtered weight samples (FSi), /c/ defining a rolling window (RW) containing a parametrized number NS of latest filtered weight samples (FSi), /d1/ determining, in the rolling window, the minimum (MIN) and maximum (MAX) values of filtered weight samples, /d2/ comparing the value of MAX?MIN with regard to a parametrized Threshold (T), /e/ if MAX?MIN is greater than Threshold (T), repeat steps/a/ to /d2/, and as soon as MAX?MIN is less than Threshold (T), outpu

Abstract: A method implemented in a system which comprises a lightweight personal wearable monitoring device, supplied by a battery, comprising an accelerometer, a processing unit, a display, a remote server, said method comprising the steps: /a/ collecting, from a plurality of individuals caused to practice various physical activities from a set of predefined activities, acceleration data from sensing devices placed on each of said individuals, /b/ defining N small data-size specific metrics, computed from acceleration signals, which allow to define a global activity classifier, /c/ acquiring, at a first monitoring device worn by a first user, acceleration signals from the accelerometer of the first monitoring device, /d/ calculating, at the first monitoring device, over each lapsed time unit T1, specific metrics values from the sensed signals, to form a series of specific metrics values, /e/ send them to the processing unit, /f/ allocate an activity type for each time unit together with corresponding specific metric

Abstract: A method implemented in a system which comprises a lightweight personal wearable monitoring device, supplied by a battery, comprising an accelerometer, a processing unit, a display, a remote server, said method comprising the steps: /a/ collecting, from a plurality of individuals caused to practice various physical activities from a set of predefined activities, acceleration data from sensing devices placed on each of said individuals, /b/ defining N small data-size specific metrics, computed from acceleration signals, which allow to define a global activity classifier, /c/ acquiring, at a first monitoring device worn by a first user, acceleration signals from the accelerometer of the first monitoring device, /d/ calculating, at the first monitoring device, over each lapsed time unit T1, specific metrics values from the sensed signals, to form a series of specific metrics values, /e/ send them to the processing unit, /f/ allocate an activity type for each time unit together with corresponding specific metric

Abstract: The method for optimizing light and sound programs for a falling asleep phase and for an awakening phase of a first user, in a system comprising, for the first user and other users, a bedside device, a bio parameter sensor, a smartphone, and additionally for all users a central server, with: /a1/ collecting, with regard to the first user, sleep data and sleep context data, said sleep data comprising at least light and sound program played for the falling asleep phase and for the awakening phase, bio parameters and sleep patterns sequence deduced therefrom, said sleep context data comprising at least previous daytime activity such as, sending this data to the central server, /a2/ repeat /a1/ for other users, /b1/ building a user-specific model of each user sleep behavior, /c/ comparing user-specific models to define groups of similar users, each group of users being allocated with a group meta-model with decision rules and preferred playlist of sound tracks, /d/ sending the group meta-model from the server to

Abstract: A method implemented in a system which comprises a lightweight personal wearable monitoring device, supplied by a battery, comprising an accelerometer, a processing unit, a display, a remote server, said method comprising the steps: /a/ collecting, from a plurality of individuals caused to practice various physical activities from a set of predefined activities, acceleration data from sensing devices placed on each of said individuals, /b/ defining N small data-size specific metrics, computed from acceleration signals, which allow to define a global activity classifier, /c/ acquiring, at a first monitoring device worn by a first user, acceleration signals from the accelerometer of the first monitoring device, /d/ calculating, at the first monitoring device, over each lapsed time unit T1, specific metrics values from the sensed signals, to form a series of specific metrics values, /e/ send them to the processing unit, /f/ allocate an activity type for each time unit together with corresponding specific metric

Abstract: A method to monitor a data of interest of an individual (U) and give advice to said individual, said method comprising: a—collect said data of interest and a plurality of life parameters, b—monitor the evolution over time of said data of interest, c—detect an unexpected/inadvertent deviation in the evolution of data of interest, forming a break event, occurred at a first instant (T1), d—store the previously collected values of the plurality of life parameters over a first time window preceding said break event, to form a reference values collection of life parameters, attached to said individual and to the break event, and later: e—collect over time, the values of the plurality of life parameters, f—compare said lately collected values of life parameters to the reference values collection of life parameters stored, and if they are similar, give a notice to the user, whereby the user can be warned of a possible imminent deviation related to the data of interest.

Abstract: A method for providing personalized recommendations to an individual, relating to monitoring his or her weight, comprising the steps of /a/ obtaining a plurality of prior weighings for the individual concerned, each weighing giving the total weight and the body fat percentage, /b/ deriving a characteristic curve (S1) for the individual by associating at least the total weight and the fat body mass, /c/ obtaining a target total weight (P2) for the user to achieve by the end of a target period, and therefore a target total weight change, /d/ deducing a target number of calories to burn, corresponding to target changes in the fat body mass and lean body mass deduced from the total weight change, /e/ providing the user with personalized instructions on exercise and/or diet.