Abstract: Sensors are used to generate sample set data representing objects in a sample set. A computer system analyzes the sample set data to determine the frequencies with which features in a feature set are observed in the objects in the sample set. An example of such output is a bar chart representing the frequency of observation of features in the feature set in a particular object. The feature output may be used to identify one or more obscure (i.e., low frequency) features in the particular object. Machine learning may be used to learn associations between sample set data and features in the feature set.

Abstract: A user provides a query that includes at least two of a subject, a predicate, and an object. A computer system identifies synonyms of one or more of the subject, predicate, and object, and forms new queries from the identified synonyms. The system searches a dataset using the new queries, and possibly also using the user-provided query, to produce search results. The system may process the search results, such as by filtering and/or sorting them. The system provides output representing the search results to the user. The user may use the search result output to identify answers that are analogous to answers to the query originally provided by the user.

Abstract: Sensors are used to generate sample set data representing objects in a sample set. A computer system analyzes the sample set data to determine the frequencies with which features in a feature set are observed in the objects in the sample set. An example of such output is a bar chart representing the frequency of observation of features in the feature set in a particular object. The feature output may be used to identify one or more obscure (i.e., low frequency) features in the particular object. Machine learning may be used to learn associations between sample set data and features in the feature set.

Abstract: Sensors are used to generate sample set data representing objects in a sample set. A computer system analyzes the sample set data to determine the frequencies with which features in a feature set are observed in the objects in the sample set. An example of such output is a bar chart representing the frequency of observation of features in the feature set in a particular object. The feature output may be used to identify one or more obscure (i.e., low frequency) features in the particular object. Machine learning may be used to learn associations between sample set data and features in the feature set.

Abstract: A user provides a query that includes at least two of a subject, a predicate, and an object. A computer system identifies synonyms of one or more of the subject, predicate, and object, and forms new queries from the identified synonyms. The system searches a dataset using the new queries, and possibly also using the user-provided query, to produce search results. The system may process the search results, such as by filtering and/or sorting them. The system provides output representing the search results to the user. The user may use the search result output to identify answers that are analogous to answers to the query originally provided by the user.

Abstract: A user provides a query that includes at least two of a subject, a predicate, and an object. A computer system identifies synonyms of one or more of the subject, predicate, and object, and forms new queries from the identified synonyms. The system searches a dataset using the new queries, and possibly also using the user-provided query, to produce search results. The system may process the search results, such as by filtering and/or sorting them. The system provides output representing the search results to the user. The user may use the search result output to identify answers that are analogous to answers to the query originally provided by the user.

Abstract: Sensors are used to generate sample set data representing objects in a sample set. A computer system analyzes the sample set data to determine the frequencies with which features in a feature set are observed in the objects in the sample set. An example of such output is a bar chart representing the frequency of observation of features in the feature set in a particular object. The feature output may be used to identify one or more obscure (i.e., low frequency) features in the particular object. Machine learning may be used to learn associations between sample set data and features in the feature set, thereby improving the accuracy and efficiency of future uses of the computer system.

Abstract: A user provides a query that includes at least two of a subject, a predicate, and an object. A computer system identifies synonyms of one or more of the subject, predicate, and object, and forms new queries from the identified synonyms. The system searches a dataset using the new queries, and possibly also using the user-provided query, to produce search results. The system may process the search results, such as by filtering and/or sorting them. The system provides output representing the search results to the user. The user may use the search result output to identify answers that are analogous to answers to the query originally provided by the user.

Abstract: Objects in a sample set, and/or data representing those objects, are analyzed to determine whether the objects have features in a feature set. Each object may be analyzed more than once to produce multiple determinations of whether the object has features in the feature set. The frequencies with which features in the feature set are observed in the objects in the object set may be used to produce output representing the frequencies of observation. An example of such output is a bar chart representing the frequency of observation of features in the feature set in a particular object. The feature output may be used to identify one or more obscure (i.e., low frequency) features in the particular object. Various operations performed by the system may be performed by computers, humans, or a combination thereof.