Abstract: Techniques and solutions are provided for obtaining a suggested configuration for a configurable object. Typically, a particular object and object configuration are recommended based on technical characteristics of the object. However, a user or process wishing to obtain a recommendation may be more familiar with their operational requirements. Disclosed techniques can include an overall solutions category containing solutions of different solutions category subtypes. Sets of requirements attributes and configuration (technical) attributes can be defined for the solutions category. In some cases, a first machine learning model is trained using input values for the requirements attributes and the configuration attributes, and is used to recommend a particular solution in response to a set of input requirement attribute values.

Abstract: Techniques and solutions are provided for obtaining a suggested configuration for a configurable object. Typically, a particular object and object configuration are recommended based on technical characteristics of the object. However, a user or process wishing to obtain a recommendation may be more familiar with their operational requirements. Disclosed techniques can include an overall solutions category containing solutions of different solutions category subtypes. Sets of requirements attributes and configuration (technical) attributes can be defined for the solutions category. In some cases, a first machine learning model is trained using input values for the requirements attributes and the configuration attributes, and is used to recommend a particular solution in response to a set of input requirement attribute values.

Abstract: Techniques and solutions are provided for obtaining a suggested configuration for a configurable object. Typically, a particular object and object configuration are recommended based on technical characteristics of the object. However, a user or process wishing to obtain a recommendation may be more familiar with their operational requirements. Disclosed techniques can include an overall solutions category containing solutions of different solutions category subtypes. Sets of requirements attributes and configuration (technical) attributes can be defined for the solutions category. In some cases, a first machine learning model is trained using input values for the requirements attributes and the configuration attributes, and is used to recommend a particular solution in response to a set of input requirement attribute values.

Abstract: A non-transitory computer-readable storage medium includes instructions stored thereon. When executed by at least one processor, the instructions may be configured to cause a computing system to at least generate a graphical user interface (GUI), receive selections of characters and directional input, and compare the selections of the characters and the directional input to a stored passcode combination. The GUI may include at least one face and a plurality of buttons inside the at least one face. At least some of the plurality of buttons may include characters in a random or pseudorandom sequence. The GUI may be configured to receive selections of the characters and directional input to the at least one face.

Abstract: A system and method, the method including determining a quality score for at least one feature of a product, the quality score being a function of a priority of the at least one feature to the product, a relationship between the at least one feature and at least one design element comprising the at least one feature, and a complexity of the at least one design element; determining an overall quality score for the product based on a summation of each determined quality score for each of the at least one feature; and producing a report including the overall quality score for the product.

Abstract: A non-transitory computer-readable storage medium includes instructions stored thereon. When executed by at least one processor, the instructions may be configured to cause a computing system to at least generate a graphical user interface (GUI), receive selections of characters and directional input, and compare the selections of the characters and the directional input to a stored passcode combination. The GUI may include at least one face and a plurality of buttons inside the at least one face. At least some of the plurality of buttons may include characters in a random or pseudorandom sequence. The GUI may be configured to receive selections of the characters and directional input to the at least one face.

Abstract: A system and method, the method including determining a quality score for at least one feature of a product, the quality score being a function of a priority of the at least one feature to the product, a relationship between the at least one feature and at least one design element comprising the at least one feature, and a complexity of the at least one design element; determining an overall quality score for the product based on a summation of each determined quality score for each of the at least one feature; and producing a report including the overall quality score for the product.