Abstract: List-based entity name detection implementations are described that detect entity names in electronic textural documents. In one implementation, unknown entity names are detected. In another implementation, ambiguous entity names are detected and disambiguated. In yet another implementation, generic entity names are detected and associated with an applicable species entity name.

Abstract: Entity functional area and product use identification implementations are described that identify functional areas of an entity and products used by these functional areas. In general, this involves generating a functional area classifier to identify one or more functional areas of the entity inferred in a document, identifying one or more product names found in the document, identifying an entity name in the document (if not already known), and establishing at least one combination of the entity name, the functional area, and a product name for each product name found in the document. In some implementations, at least one of the entity location or the vendor of the named product or the technology designation of the product, is identified in the document and included in the combinations. Multiple documents are processed, and the resulting established combinations are output.

Abstract: Job profile data analysis implementations that are described herein generally analyzes job profile data, and in various implementations uses the analysis to identify marketing and sales opportunities and threats. More particularly, the job profile data is used to identify individuals who have recently changed jobs and have taken on a decision maker or influencer role in their new job with regard to the purchase of products and/or services. Marketing and sales opportunities and threats are gleaned from this knowledge and the knowledge of what products and services were used in the individual's previous and new jobs.

Abstract: AI maturity scoring implementations that are described herein generally assess the degree of immersion an entity has in AI matters. The AI maturity score for an entity is a combination of three components, namely an AI component, a data science component, and a data maturity component. The AI component quantifies the level of use of AI technologies at the entity. The data science component quantifies the level of an entity's data science expertise on a location basis. And the data maturity component quantifies the degree to which the entity is involved in using data technologies. An AI maturity report is also generated that includes a listing of, for each entity of interest, the AI maturity score computed for that entity.

Abstract: List-based entity name detection implementations are described that detect entity names in electronic textural documents. In one implementation, unknown entity names are detected. In another implementation, ambiguous entity names are detected and disambiguated. In yet another implementation, generic entity names are detected and associated with an applicable species entity name.

Abstract: Job profile data analysis implementations that are described herein generally analyzes job profile data, and in various implementations uses the analysis to identify marketing and sales opportunities and threats. More particularly, the job profile data is used to identify individuals who have recently changed jobs and have taken on a decision maker or influencer role in their new job with regard to the purchase of products and/or services. Marketing and sales opportunities and threats are gleaned from this knowledge and the knowledge of what products and services were used in the individual's previous and new jobs.

Abstract: Entity functional area and product use identification implementations are described that identify functional areas of an entity and products used by these functional areas. In general, this involves generating a functional area classifier to identify one or more functional areas of the entity inferred in a document, identifying one or more product names found in the document, identifying an entity name in the document (if not already known), and establishing at least one combination of the entity name, the functional area, and a product name for each product name found in the document. In some implementations, at least one of the entity location or the vendor of the named product or the technology designation of the product, is identified in the document and included in the combinations. Multiple documents are processed, and the resulting established combinations are output.

Abstract: The probability that an entity will purchase a product is predicted. This prediction involves receiving input data in the form of entries each of which represents an interest event. Through a series of iterations, a final matrix is generated from the input data entries by assigning an entity-product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event occurred from a prescribed date of interest. A supervised machine learning technique is employed to create a final prediction model for each product of interest using the final matrix as input. For each product, its prediction model is applied to the input data to estimate the probability that an entity will purchase the product within a future time period.

Abstract: Resume data identification implementations are described that identify anonymized resume corpus data pertaining to the same individual. In one implementation, identifying this data in the anonymized resume corpus involves segmenting the corpus into resume snippets and clustering the resume snippets into groups. Within each group the resume snippets potentially pertain to the same individual. In addition, one or more optional filtering operations can be performed to remove snippets from a group that are less likely to be associated with the same person. This filtering is especially useful when the resume corpus is large.

Abstract: Job profile data analysis implementations that are described herein generally analyzes job profile data, and in various implementations uses the analysis to identify marketing and sales opportunities and threats. More particularly, the job profile data is used to identify individuals who have recently changed jobs and have taken on a decision maker or influencer role in their new job with regard to the purchase of products and/or services. Marketing and sales opportunities and threats are gleaned from this knowledge and the knowledge of what products and services were used in the individual's previous and new jobs.

Abstract: Entity functional area and product use identification implementations are described that identify functional areas of an entity and products used by these functional areas. In general, this involves generating a functional area classifier to identify one or more functional areas of the entity inferred in a document, identifying one or more product names found in the document, identifying an entity name in the document (if not already known), and establishing at least one combination of the entity name, the functional area, and a product name for each product name found in the document. In some implementations, at least one of the entity location or the vendor of the named product or the technology designation of the product, is identified in the document and included in the combinations. Multiple documents are processed, and the resulting established combinations are output.

Abstract: Resume data identification implementations are described that identify anonymized resume corpus data pertaining to the same individual. In one implementation, identifying this data in the anonymized resume corpus involves segmenting the corpus into resume snippets and clustering the resume snippets into groups. Within each group the resume snippets potentially pertain to the same individual. In addition, one or more optional filtering operations can be performed to remove snippets from a group that are less likely to be associated with the same person. This filtering is especially useful when the resume corpus is large.

Abstract: List-based entity name detection implementations are described that detect entity names in electronic textural documents. In one implementation, unknown entity names are detected. In another implementation, ambiguous entity names are detected and disambiguated. In yet another implementation, generic entity names are detected and associated with an applicable species entity name.

Abstract: The probability that an entity will purchase a product is predicted. This prediction involves receiving input data in the form of entries each of which represents an interest event. Through a series of iterations, a final matrix is generated from the input data entries by assigning an entity-product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event occurred from a prescribed date of interest. A supervised machine learning technique is employed to create a final prediction model for each product of interest using the final matrix as input. For each product, its prediction model is applied to the input data to estimate the probability that an entity will purchase the product within a future time period.

Abstract: An apparatus for removing solids from a liquid/solid mixture disposed in a tank and rising to a mixture level, the apparatus including a suction assembly defining a suction opening, the suction assembly linked to a vacuum that causes suction at the opening, a support assembly formed about the suction surface including first and second essentially circular housing walls having wall edges and a filter belt loop sealed to the edges and sized such that the belt is slack and subject to deformation between the edges, a belt section disposed to cover the suction opening and a processor controlling a motivator to periodically alter belt position with respect to the suction opening.

Abstract: A volumetric feed hopper is provided with a variable speed drive to vary the feed rate of the hopper. The hopper is further provided with adjustable legs with pivoting feet to enable the hopper to be installed a various heights and on uneven surfaces.

Abstract: A tipping pit water reclaiming system for concrete reclaimers which includes one or more tanks with a pivot support and hydraulic pistons for tipping the tanks to remove the settled solids. Clarified water from the tipping tanks overflows into a pump tank where it is returned by pump to the reclaimer. The pump tank includes a second pump for pumping excess clarified water into a storage tank for return to the tipping tank after the cement solids have been emptied out of the tipping tank. The second pump prevents the overflowing of water onto the ground from the tipping tank or pump tank as solids accumulate in the tipping tank. The second pump is controlled by float activated switches positioned at predetermined levels within the pump tank.

Abstract: A volumetric feed hopper is provided with a variable speed drive to vary the feed rate of the hopper. The hopper is further provided with adjustable legs with pivoting feet to enable the hopper to be installed a various heights and on uneven surfaces.

Abstract: A tipping pit water reclaiming system for concrete reclaimers which includes one or more tanks with a pivot support and hydraulic pistons for tipping the tanks to remove the settled solids. Clarified water from the tipping tanks overflows into a pump tank where it is returned by pump to the reclaimer. The pump tank includes a second pump for pumping excess clarified water into a storage tank for return to the tipping tank after the cement solids have been emptied out of the tipping tank. The second pump prevents the overflowing of water onto the ground from the tipping tank or pump tank as solids accumulate in the tipping tank. The second pump is controlled by float activated switches positioned at predetermined levels within the pump tank.