Abstract: A computer-implemented method for inferring an emerging problem in product manufacturing. The method comprises obtaining a time-ordered set comprising one or more snapshots of a product and one or more similarity thresholds. The method also comprises obtaining at least one recent snapshot, the at least one recent snapshot being time-ordered after at least one snapshot of the time-ordered set. The method also comprises retrieving a subset of one or more snapshots from the time-ordered set, the one or more snapshots being time-ordered before the at least one recent snapshot and satisfying, with respect to the at least one recent snapshot, a similarity above at least one of the one or more similarity thresholds. The method also comprises determining a trend from the retrieved subset and a baseline, the trend being a time distribution of the snapshots of the retrieved subset with respect to the baseline.

Abstract: A computer implemented method for improving search engine queries including receiving a text corpus, determining a list of n-gram candidates, each being a series of consecutive words of said text corpus, the number of said consecutive words within said series being an integer n superior or equal to two, modifying at least partially said text corpus based on said list of n-gram candidates, performing a machine learning embedding on the resulting text corpus, for each element in said list of n-gram candidates, computing a score based on the embedding of said element and the embeddings of the words making up said element, adding one or more of the n-gram candidates to a search engine queries items list based on their respective scores.

Abstract: A computer implemented method for comparing unsupervised embedding methods for a similarity based industrial component model requesting system including obtaining a text corpus relating to industrial component models and a list of testing words, modifying by altering some of the occurrences of each testing word, the modified text corpus containing, for each testing word, occurrences of a first version of each testing word, and occurrences of a second version of each testing word, running an unsupervised embedding method on the modified text corpus and obtaining vector representations, determining a scoring value, by comparing, for at least some of the testing words, the vector representations of the first version of these testing words, and the vector representations the second version of these testing words, running the obtaining, modifying with the text corpus and the list of testing words with another unsupervised embedding method and returning the respective scoring values.

Abstract: The invention notably relates to a computer-implemented method for updating a model of a machine learning system. The method comprises providing a first set of observations of similar events, each observation being associated with one or more variables, each variable being associated with a value, and with a target value; indexing each observation of the first set with its corresponding one or more variables and target value; receiving, on the index, a query allowing a selection of a subset of the first set of observations; returning, as a result of the query, a subset of the first set of observations; providing a second model; training the provided second model with the returned subset of the first set of observations; and loading the trained second model.

Abstract: A computer-implemented method for assessing a potential divergence of an outcome predicted by a machine learning system including training a model on a first set of observations, each observation being associated with a target value, randomly generating a second set of observations, applying the trained model to the second set thereby obtaining a target value associated with each observation of the second set, indexing the first and second sets of observations and their associated target values into an index, receiving a first query allowing a selection of a subset of the first and second sets of observations, generating a second query, generating a third query that comprises the first query and an additional constraint, querying the index using the second and third queries, and returning a response to the second and third queries.

Abstract: A computer implemented method for improving a similarity search of an industrial component model including obtaining a set of industrial component models, each having associated attributes and a similarity embedding, receiving a similarity request using a given industrial component model as an input, the output of said similarity request being a first subset of industrial component models selected from the set of industrial component models based on the comparison between similarity embeddings and the similarity embedding of the input industrial component model, receiving a second subset of industrial component models from said first subset of industrial component models based on an interchangeability criteria of the input industrial component model with any industrial component model of said second subset of industrial component models, associating a similarity attribute to the input industrial component model, and computing a new set of similarity embeddings.

Abstract: The invention notably relates to a computer-implemented method for selecting an appropriate decision by constraining options assessed with a model. The method comprises selecting a model capable of receiving inputs and providing output in response to an input; training a model with a set of data representing similar events; generating options that represent hypothetical events; computing target values by applying the trained model on the generated options; computing index for indexing the generated options and target values associated with the trained model; querying the said index for obtaining a selection of a set of options, the selection being performed according to a specific constraint; returning, as a result of the query, a subset of the set of the generated options, the subset being ranked according to the target values associated with each option.

Abstract: The invention notably relates to a computer-implemented method for retrieving model outcomes in the course of an event. The method comprises providing variables of a first set of observations of similar events, providing a multiplicity of models, indexing the multiplicity of models with the variables of the first set of observations, querying a model according to one or more variables, and returning, as a result of the query, a model.

Abstract: The invention notably relates to a computer-implemented method, performed by a server storing an index of a search engine, for sending, to a client, the URLs of pages of a Web corpus that relates to a theme. The method comprises receiving, from the client, a structured query that corresponds to the theme, the structured query consisting of a disjunction of at least one keyword; determining in the index the group that consists of the URLs of all pages that match the query; and sending to the client the URLs of the group as a stream. Such a method improves the building of a thematic Web corpus.

Abstract: The disclosure notably relates to a computer-implemented method for predicting new occurrences of an event of a physical system. The method comprises providing a first set of past events of the physical system, each past event comprising several attributes, providing a signature for each past event of the first set, providing a new event comprising several attributes, computing a signature of the new event, computing a similarity measure between the signature of the new event and each signature of each past event of the first set, determining the past events closest to the new event according to the similarity measures thereby forming a second set of past events, computing a score of relevance for each attribute of the second set, providing a set of attributes by selecting the attributes having the greater scores of relevance.

Abstract: The invention notably relates to a computer-implemented method for selecting an appropriate decision by constraining options assessed with a model. The method comprises selecting a model capable of receiving inputs and providing output in response to an input; training a model with a set of data representing similar events; generating options that represent hypothetical events; computing target values by applying the trained model on the generated options; computing index for indexing the generated options and target values associated with the trained model; querying the said index for obtaining a selection of a set of options, the selection being performed according to a specific constraint; returning, as a result of the query, a subset of the set of the generated options, the subset being ranked according to the target values associated with each option.

Abstract: The invention notably relates to a computer-implemented method for assessing a potential divergence of an outcome predicted by a machine learning system.

Abstract: The invention notably relates to a computer-implemented method for retrieving model outcomes in the course of an event. The method comprises providing variables of a first set of observations of similar events, providing a multiplicity of models, indexing the multiplicity of models with the variables of the first set of observations, querying a model according to one or more variables, and returning, as a result of the query, a model.

Abstract: The invention notably relates to a computer-implemented method, performed by a server storing an index of a search engine, for sending, to a client, the URLs of pages of a Web corpus that relates to a theme. The method comprises receiving, from the client, a structured query that corresponds to the theme, the structured query consisting of a disjunction of at least one keyword; determining in the index the group that consists of the URLs of all pages that match the query; and sending to the client the URLs of the group as a stream. Such a method improves the building of a thematic Web corpus.

Abstract: Computer-implemented method for building a web corpus (WCD) comprising the steps of: sending by a web crawler (WC) a query to a reference web crawl agent (RWCA), this query containing a least one identifier of a resource, receiving by the web crawler (WC) a response from the reference web crawl agent (RWCA); if this response does not contain the resource identified by the identifier, downloading by the web crawler (WC) the resource from the website (WS) corresponding to the identifier and adding the resource to the web corpus (WCD; and if this response contains the resource identified by the identifier, adding the resource to the web corpus (WCD).

Abstract: Computer-implemented method for building a web corpus (WCD) comprising the steps of: sending by a web crawler (WC) a query to a reference web crawl agent (RWCA), this query containing a least one identifier of a resource, receiving by the web crawler (WC) a response from the reference web crawl agent (RWCA); if this response does not contain the resource identified by the identifier, downloading by the web crawler (WC) the resource from the website (WS) corresponding to the identifier and adding the resource to the web corpus (WCD; and if this response contains the resource identified by the identifier, adding the resource to the web corpus (WCD).