Abstract: Software that uses personalized information pertaining to a user to determine how familiar (or “novel” or “surprising”) a new artifact will be to the user, by performing the following steps: (i) receiving a first dataset pertaining to a first user; (ii) building, utilizing the first dataset, an ontology of artifacts known to the first user, where the ontology includes a domain of food and a plurality of artifacts that include food recipes, and where the artifacts have corresponding characteristics that include food ingredients; (iii) calculating a prior probability distribution for each artifact of the ontology using a probabilistic familiarity algorithm; and (iv) calculating a probabilistic familiarity value for the first artifact with respect to the first user by adding the first artifact to the set of artifacts and calculating the first artifact's prior probability distribution using the probabilistic familiarity algorithm.

Abstract: A method for planning preparation of a plurality of dishes according to a plurality of corresponding recipes includes automatically identifying, by a processor, a plurality of candidate sub-recipes, wherein each candidate sub-recipe in the plurality of candidate sub-recipes comprises an intermediate component that is required by at least two of the plurality of corresponding recipes, and automatically selecting, by the processor, at least one candidate sub-recipe in the plurality of candidate sub-recipes for preparation, in accordance with at least one constraint on the preparation of the plurality of dishes.

Abstract: A method for planning preparation of a plurality of dishes according to a plurality of corresponding recipes includes automatically identifying, by a processor, a plurality of candidate sub-recipes, wherein each candidate sub-recipe in the plurality of candidate sub-recipes comprises an intermediate component that is required by at least two of the plurality of corresponding recipes, and automatically selecting, by the processor, at least one candidate sub-recipe in the plurality of candidate sub-recipes for preparation, in accordance with at least one constraint on the preparation of the plurality of dishes.

Abstract: A method includes receiving a set of parameters for a given project and generating, using information from a knowledge database, a plurality of combinations of group members based at least in part on the set of parameters. The method also includes evaluating a set of metrics for each of the combinations of group members, the set of metrics comprising at least one novelty metric and at least one collective intelligence metric. The method further includes generating one or more strategy matrices for each of at least a subset of the combinations of group members using information from the knowledge database, evaluating the combinations of group members in the subset using the strategy matrices to determine respective predicted success values, and selecting a given one of the combinations of group members for the given project based at least in part on the sets of metrics and predicted success values.

Abstract: A method includes predicting availability of a plurality of constituents for one or more future epochs, obtaining one or more metrics for each of a plurality of existing work products, each of the plurality of existing work products using at least one constituent, and generating at least one work product for each of the one or more future epochs based in part on the predicted availability of the constituents and the one or more metrics for the existing work products. The metrics for the existing work products may include quality metrics and novelty metrics.

Abstract: A method for designing a new product includes automatically identifying a plurality of components of stored products, wherein the plurality of components is associated with an existing component included in a design of the new product, automatically selecting a new component from among the plurality of components, wherein a relationship between the existing component and the new component optimizes a design objective for the new product, automatically adding the new component to the design for the new product, and iteratively repeating the identifying, the selecting, and the adding until a stopping criterion is met, wherein the design for the new product is complete when the stopping criterion is met.

Abstract: A method for designing a new product includes automatically identifying a plurality of components of stored products, wherein the plurality of components is associated with an existing component included in a design of the new product, automatically selecting a new component from among the plurality of components, wherein a relationship between the existing component and the new component optimizes a design objective for the new product, automatically adding the new component to the design for the new product, and iteratively repeating the identifying, the selecting, and the adding until a stopping criterion is met, wherein the design for the new product is complete when the stopping criterion is met.

Abstract: A method for designing a new product includes automatically identifying a plurality of components of stored products, wherein the plurality of components is associated with an existing component included in a design of the new product, automatically selecting a new component from among the plurality of components, wherein a relationship between the existing component and the new component optimizes a design objective for the new product, automatically adding the new component to the design for the new product, and iteratively repeating the identifying, the selecting, and the adding until a stopping criterion is met, wherein the design for the new product is complete when the stopping criterion is met.

Abstract: A method for designing a new product includes automatically identifying a plurality of components of stored products, wherein the plurality of components is associated with an existing component included in a design of the new product, automatically selecting a new component from among the plurality of components, wherein a relationship between the existing component and the new component optimizes a design objective for the new product, automatically adding the new component to the design for the new product, and iteratively repeating the identifying, the selecting, and the adding until a stopping criterion is met, wherein the design for the new product is complete when the stopping criterion is met.

Abstract: A method for designing a new product includes automatically identifying a plurality of components of stored products, wherein the plurality of components is associated with an existing component included in a design of the new product, automatically selecting a new component from among the plurality of components, wherein a relationship between the existing component and the new component optimizes a design objective for the new product, automatically adding the new component to the design for the new product, and iteratively repeating the identifying, the selecting, and the adding until a stopping criterion is met, wherein the design for the new product is complete when the stopping criterion is met.

Abstract: A method for designing a new product includes automatically identifying a plurality of components of stored products, wherein the plurality of components is associated with an existing component included in a design of the new product, automatically selecting a new component from among the plurality of components, wherein a relationship between the existing component and the new component optimizes a design objective for the new product, automatically adding the new component to the design for the new product, and iteratively repeating the identifying, the selecting, and the adding until a stopping criterion is met, wherein the design for the new product is complete when the stopping criterion is met.

Abstract: Software that uses personalized information pertaining to a user to determine how familiar (or “novel” or “surprising”) a new artifact will be to the user, by performing the following steps: (i) receiving a first dataset pertaining to a first user; (ii) building, utilizing the first dataset, an ontology of artifacts known to the first user, where the ontology includes a domain of food and a plurality of artifacts that include food recipes, and where the artifacts have corresponding characteristics that include food ingredients; (iii) calculating a prior probability distribution for each artifact of the ontology using a probabilistic familiarity algorithm; and (iv) calculating a probabilistic familiarity value for the first artifact with respect to the first user by adding the first artifact to the set of artifacts and calculating the first artifact's prior probability distribution using the probabilistic familiarity algorithm.

Abstract: Software that uses machine logic to generate novel work products by combining known work product constituents to create constituent combinations, by performing the following steps: (i) maintaining a constituent data store, the constituent data store including identifying information for a plurality of constituents used to make combination products; (ii) receiving a set of combination preferences relating to a desired type of combination product; and (iii) applying a set of eliminational rules to the set of combination preferences and the plurality of constituents to define a raw set of proposed combination product(s), where each combination product of the raw set of proposed combination product(s) meets all the eliminational rules.

Abstract: Software that uses personalized information pertaining to a user to determine how familiar (or “novel” or “surprising”) a new artifact will be to the user, by performing the following steps: (i) receiving the identity of a first user; (ii) receiving a first dataset pertaining to the first user; (iii) receiving the identity of a first artifact; and (iv) applying a probabilistic familiarity algorithm to the first dataset with respect to the first artifact to yield a probabilistic familiarity value for the first artifact with respect to the first user. The first dataset is received over a computer network, and the first dataset includes at least one piece of personalized information for the first user.

Abstract: The present disclosure relates to methods and systems for calculating a food additive. A first method includes identifying chemical compounds of an averse food ingredient, identifying chemical compounds of a flavorful food ingredient and calculating a set of chemical compounds for the food additive such that an olfactory perception of a mixture of the averse food ingredient, the flavorful food ingredient and the food additive is the same as an olfactory perception of only the flavorful food ingredient. A first device includes a database storing information identifying chemical compounds of an averse food ingredient and identifying chemical compounds of a flavorful food ingredient, and a processor for calculating a food additive such that an olfactory perception of flavors of a mixture of the averse food ingredient, the flavorful food ingredient and the food additive is the same as an olfactory perception of only the flavorful food ingredient.

Abstract: Receiving, by a computer, data identifying one or more flavor compounds and their respective quantities in a food and an enhancement objective. Identifying one or more flavor compounds associated with a distinct flavor of the food. Creating a vector of physicochemical properties associated with the one or more flavor compounds associated with the distinct flavor of the food. Creating a vector of physicochemical properties associated with each of one or more additive flavor compounds. Identifying an additive flavor compound to enhance the food based on the angle between the physicochemical property vector associated with the distinct flavor of the food and the physicochemical property vectors for the one or more additive flavor compounds, and the enhancement objective.

Abstract: A method includes receiving a set of parameters for a given project and generating, using information from a knowledge database, a plurality of combinations of group members based at least in part on the set of parameters. The method also includes evaluating a set of metrics for each of the combinations of group members, the set of metrics comprising at least one novelty metric and at least one collective intelligence metric. The method further includes generating one or more strategy matrices for each of at least a subset of the combinations of group members using information from the knowledge database, evaluating the combinations of group members in the subset using the strategy matrices to determine respective predicted success values, and selecting a given one of the combinations of group members for the given project based at least in part on the sets of metrics and predicted success values.

Abstract: A method includes receiving a set of parameters for a given project and generating, using information from a knowledge database, a plurality of combinations of group members based at least in part on the set of parameters. The method also includes evaluating a set of metrics for each of the combinations of group members, the set of metrics comprising at least one novelty metric and at least one collective intelligence metric. The method further includes generating one or more strategy matrices for each of at least a subset of the combinations of group members using information from the knowledge database, evaluating the combinations of group members in the subset using the strategy matrices to determine respective predicted success values, and selecting a given one of the combinations of group members for the given project based at least in part on the sets of metrics and predicted success values.

Abstract: The present disclosure relates to methods, devices and systems for calculating or selecting a first set of chemical compounds for an odor additive. For example, a method identifies a second set of chemical compounds present in a sample and intensities of individual chemical compounds in the second set of chemical compounds, and calculates the first set of chemical compounds for the odor additive such that an olfactory perception of a mixture of the sample and the odor additive is an olfactory white.

Abstract: The present disclosure relates to methods, devices and systems for calculating or selecting a first set of chemical compounds for an odor additive. For example, a method identifies a second set of chemical compounds present in a sample and intensities of individual chemical compounds in the second set of chemical compounds, and calculates the first set of chemical compounds for the odor additive such that an olfactory perception of a mixture of the sample and the odor additive is an olfactory white.