Abstract: Embodiments relate to an intelligent computer platform to identify and process communications across multiple languages. An originating communication is identified, including identification of language, action tokens, and linguistic features. A first map of the identified action tokens and linguistic features from the originating language to a second format is created and populated into identifying machine learning model (MLM). A second communication is identified, including the originating language, action tokens, and linguistic features, and a second map is created of the identified action tokens and linguistic features of the second communication. The second map and the MLM are leveraged to identify and return a predicted action token class of the identified action tokens in the second communication.

Abstract: An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Abstract: A computer-implemented method according to one embodiment includes receiving historical sales data, transforming a nonlinear, non-convex optimization model for weighting optimization into a linear optimization model, determining a number of historical periods of the historical sales data and weights to be applied to a historical conversion rate and a historical growth rate for the number of historical periods of the historical sales data, utilizing the nonlinear, non-convex optimization model or the linear optimization model, predicting a future optimal conversion rate and a future optimal growth rate by applying the weights to the historical conversion rate and the historical growth rate for the number of historical periods of the historical sales data, and applying the future optimal growth rate and the future optimal conversion rate to current sales pipeline data to determine future sales data.

Abstract: A data processing system identifies a first topic for a first table, identifies a second topic for a second table, collects at least one first table attribute comprising at least one row name for the first table, and collects at least one second table attribute comprising at least one row name for the second table. The at least one semantic vector for the first table is compared with the at least one semantic vector for the second table to identify as related at least one row of the first table and at least one row of the second table. The at least one row of the first table and the at least one row of the second table are provided to a communication device with an identification as related.

Abstract: A system for cognitive assessment of the competitiveness of deals may include a memory having stored thereon historical deal information for historical deals with each historical deal including a historical deal component. A historical deal component may include a historical work scope and associated historical work pricing. The system may also include a processor cooperating with the memory and configured to compare current deal information with the historical deal information. The current deal information may include a current deal component that may include a current work scope and associated current work pricing. The processor may use machine learning to determine whether the current deal component is non-competitive based upon the historical deal information, and for each non-competitive current deal component generate an alternative current deal component. The alternative current deal component may have at least one of a different current work scope and different associated current work pricing.

Abstract: A system for cognitive deal pricing may include a memory having stored thereon historical deal information that includes historical deal components and historical deal communication associated therewith for historical deals. The system may also include a processor cooperating with the memory and configured to use machine learning to analyze the historical deal information to determine a predicted client type for each current deal component of a current deal, and generate the deal pricing based upon the predicted client type for each current deal component of the current deal.

Abstract: An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Abstract: Embodiments relate to an intelligent computer platform to identify and process communications across multiple languages. An originating communication is identified, including identification of language, action tokens, and linguistic features. A first map of the identified action tokens and linguistic features from the originating language to a second format is created and populated into identifying machine learning model (MLM). A second communication is identified, including the originating language, action tokens, and linguistic features, and a second map is created of the identified action tokens and linguistic features of the second communication. The second map and the MLM are leveraged to identify and return a predicted action token class of the identified action tokens in the second communication.

Abstract: An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Abstract: In one embodiment, a method of recommending a production plan includes calculating a similarity score between an incoming order and each historical order in a historical order database, providing a list of most similar historical orders and corresponding historical production plans ranked according to highest similarity scores, receiving an election indicating a historical production plan as a selected production plan, and admitting the selected historical production plan to fulfill the incoming order.

Abstract: In one embodiment, a computer-implemented method for action-aware communication and conversation prioritization includes: identifying an importance of one or more parties to a communication or conversation; evaluating content of the communication or conversation to identify one or more features of the communication or conversation; assessing an importance of the content included in the communication or conversation based on one or more of the identified features; determining an urgency of the communication or conversation based on one or more of the identified features; and prioritizing the communication or conversation based at least in part on the importance of one or more of the parties, the importance of the content, and the urgency of the communication or conversation. The method is adaptive in a continuous manner based on user actions responsive to the communication or conversation. Related systems and computer program products are also disclosed.

Abstract: An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Abstract: A system for cognitive deal pricing may include a memory having stored thereon historical deal information that includes historical deal components and historical deal communication associated therewith for historical deals. The system may also include a processor cooperating with the memory and configured to use machine learning to analyze the historical deal information to determine a predicted client type for each current deal component of a current deal, and generate the deal pricing based upon the predicted client type for each current deal component of the current deal.

Abstract: A system for cognitive assessment of the competitiveness of deals may include a memory having stored thereon historical deal information for historical deals with each historical deal including a historical deal component. A historical deal component may include a historical work scope and associated historical work pricing. The system may also include a processor cooperating with the memory and configured to compare current deal information with the historical deal information. The current deal information may include a current deal component that may include a current work scope and associated current work pricing. The processor may use machine learning to determine whether the current deal component is non-competitive based upon the historical deal information, and for each non-competitive current deal component generate an alternative current deal component. The alternative current deal component may have at least one of a different current work scope and different associated current work pricing.

Abstract: A method of determining a probability of a procuring organization accepting a product/service offering of an offering organization may include using a processor to obtain a first collection of information items relating to the product/service offering and that may be generated internally of the offering organization. The method may include using the processor to obtain a second collection of information items relating to the first collection of information items and that may be generated externally of the offering organization. The method may further include using the processor to generate a respective relevance score for each second collection of information items relative to a corresponding first collection of information items and generate a respective sentiment score for each second collection of information items.

Abstract: A meeting scheduling system may include a processor and a memory cooperating therewith. The processor may be configured to obtain meeting attributes for a meeting to be scheduled including attributes of desired meeting participants, attributes about meetings already scheduled for the desired meeting participants, attributes of meetings already conducted, and attributes of communications by the desired meeting participants. The processor may also be configured to classify the meeting to be scheduled by determining respective probabilities of whether the meeting will be held, cancelled, or rescheduled based upon the meeting attributes and present, to a meeting scheduler, potential time slots for the meeting to be scheduled based upon the classifying. The processor may also determine, based upon selection by the meeting scheduler, a selected time slot from among the potential time slots and present, to each desired meeting participant, the selected time slot.

Abstract: A data processing system identifies a first topic for a first table, identifies a second topic for a second table, collects at least one first table attribute comprising at least one row name for the first table, and collects at least one second table attribute comprising at least one row name for the second table. The at least one first table attribute and the at least one second table attribute are placed in at least one semantic category. The at least one first table attribute is converted into at least one semantic vector for the first table, and the at least one second table attribute is converted into at least one semantic vector for the second table. The at least one semantic vector for the first table is compared with the at least one semantic vector for the second table to identify as related at least one row of the first table and at least one row of the second table.

Abstract: Identifying actionable statements in communications may include: extracting features from at least one training statement; training a pattern recognition module to identify one or more types of patterns in actionable statements based at least in part on the features; and generating an actionable statement identification model using the trained action verb module and the trained pattern recognition module. Identifying actionable statements in communications is preferably adaptive in a continuous manner (e.g. based on user feedback), and may also include: determining whether a statement includes an actionable statement; predicting an actionable statement class of the actionable statement based on a pattern represented in the statement; and outputting the predicted actionable statement class to a user. Corresponding systems and computer program products are also disclosed.

Abstract: A computer-implemented method according to one embodiment includes receiving historical sales data, transforming a nonlinear, non-convex optimization model for weighting optimization into a linear optimization model, determining a number of historical periods of the historical sales data and weights to be applied to a historical conversion rate and a historical growth rate for the number of historical periods of the historical sales data, utilizing the nonlinear, non-convex optimization model or the linear optimization model, predicting a future optimal conversion rate and a future optimal growth rate by applying the weights to the historical conversion rate and the historical growth rate for the number of historical periods of the historical sales data, and applying the future optimal growth rate and the future optimal conversion rate to current sales pipeline data to determine future sales data.

Abstract: In one general embodiment, a computer-implemented method includes, for each time period of two or more time periods, calculating a variance of a metric based on one or more values of the metric for the time period. For each time period of the two or more time periods the following are calculated: a lower bound of a historical value and an upper bound of the historical value. A first curve is fit to the two or more lower bounds of historical values. A second curve is fit to the two or more upper bounds of historical values. For each of one or more future points in time, a future lower bound and a future upper bound for the future value of the metric at the future point in time are predicted utilizing the first curve and the second curve.