MACHINE-LEARNING BASED PROCESSING AND REPORTING OF PROPOSAL DATA

An embodiment parses request data representative of a request for proposal (RFP) and extracts attribute data representative of an RFP attribute that corresponds to an entity of interest in a qualification taxonomy using a cognitive process to evaluate the RFP using natural language processing. The embodiment generates answer data representative of an answer to a first question of a qualification questionnaire related to the entity of interest using the RFP attribute. The embodiment constructs the answer data based at least in part on a response pattern associated with the first question, and computes a score for the answer to the first question based at least in part on a confidence value generated by the cognitive process. The embodiment then outputs the qualification questionnaire, including the answer and the score, allowing for review by a Subject Matter Expert (SME), and allowing the cognitive process to learn from the SME review.

TECHNICAL FIELD

The present invention relates generally to a method, system, and computer program product for digital file evaluations. More particularly, the present invention relates to a method, system, and computer program product for machine-learning based processing and reporting of proposal data.

BACKGROUND

Artificial intelligence (AI) technology has evolved significantly over the past few years. Modern AI systems are achieving human level performance on cognitive tasks like converting speech to text, recognizing objects and images, or translating between different languages. This evolution holds promise for new and improved applications in many industries.

An Artificial Neural Network (ANN)—also referred to simply as a neural network—is a computing system made up of a number of simple, highly interconnected processing elements (nodes), which process information by their dynamic state response to external inputs. ANNs are processing devices (algorithms and/or hardware) that are loosely modeled after the neuronal structure of the mammalian cerebral cortex but on much smaller scales. A large ANN might have hundreds or thousands of processor units, whereas a mammalian brain has billions of neurons with a corresponding increase in magnitude of their overall interaction and emergent behavior.

A Deep Learning Neural Network, referred to herein as a Deep Neural Network (DNN) is an artificial neural network (ANN) with multiple hidden layers of units between the input and output layers. Similar to shallow ANNs, DNNs can model complex non-linear relationships. DNN architectures, e.g., for object detection and parsing, generate compositional models where the object is expressed as a layered composition of image primitives. The extra layers enable composition of features from lower layers, giving the potential of modeling complex data with fewer units than a similarly performing shallow network. DNNs are typically designed as feedforward networks.

SUMMARY

The illustrative embodiments provide for machine-learning based processing and reporting of proposal data. An embodiment includes parsing, by a processor, request data representative of a request for proposal (RFP). The embodiment also includes extracting, by the processor, attribute data representative of an attribute of the RFP that corresponds to an entity of interest in a qualification taxonomy using a cognitive process to evaluate the RFP using natural language processing (NLP). The embodiment also includes generating, by the processor, answer data representative of an answer to a first question of a qualification questionnaire related to the entity of interest in the qualification taxonomy using the attribute of the RFP, wherein the generating of the answer data includes constructing the answer data based at least in part on a response pattern associated with the first question of the qualification questionnaire. The embodiment also includes computing, by the processor, a score for the answer to the first question of the qualification questionnaire based at least in part on a confidence value generated by the cognitive process. The embodiment also includes outputting, by the processor, the qualification questionnaire including the answer and the score for the answer. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the embodiment.

An embodiment includes a computer usable program product. The computer usable program product includes a computer-readable storage medium, and program instructions stored on the storage medium.

An embodiment includes a computer system. The computer system includes a processor, a computer-readable memory, and a computer-readable storage medium, and program instructions stored on the storage medium for execution by the processor via the memory.

DETAILED DESCRIPTION

A Request for Proposal (RFP) is a document that invites a vendor to submit a bid for goods or services. RFPs can come in a variety of formats, but usually provide detailed information that outlines key elements of a project. A typical RFP process generally involves a customer submitting RFPs to pre-selected vendors in order to receive competing bids for a given project. For example, a typical RFP process begins with a customer drafting an RFP and sending it to multiple vendors. The vendors review the RFP and respond with their proposals. The customer then selects a vendor to handle the project based on the responses to the RFP.

Thus, for a vendor that receives an RFP, it represents a new potential business opportunity, so the preparation of a proposal in response to the RFP is an important task. The process of preparing a response to an RFP usually involves carefully evaluating the project requirements and preparing a detailed proposal that explains how the vendor will complete the project. A proposal typically includes project parameters, such as schedule and pricing information, that will be compared to other proposals. Importantly, if a proposal is selected by the requesting customer, the vendor will be expected to be contractually bound by the proposed project parameters. Therefore, it is important for the vendor to carefully review the RFP and develop a comprehensive understanding of the proposed project in order to be able to prepare a proposal that is both feasible and competitive.

The use of RFPs is pervasive in many industries, including those involving new or emerging technologies. For example, companies transitioning towards cloud-based solutions to take advantage of economical and agile deployment options frequently issue RFPs to multiple vendors they deem qualified to complete the project. Such RFPs are typically very complex and unique. A customer preparing an RFP typically has its own personalized, unique manner of organizing the RFP and explaining the scope of the project sought to be completed. Currently, reviewing an RFP for the purpose of preparing a proposal is a manual process that requires gaining a comprehensive understanding of the customer's business requirements and developing a solution that satisfies those requirements. This is a highly time-consuming and manual process that is expensive and susceptible to human error. Thus, the illustrative embodiments recognize that the size, complexity, and diverse styles of RFPs present an obstacle to efficiently reviewing and responding to RFPs.

A proposal request, or “RFP,” is one of a several different types of sourcing documents. An RFP is primarily used for requesting proposals for a defined product or service. Other types of sourcing documents include a Requests for Information (RFI), which is typically used to select a supplier, and a Request for Quote (RFQ), which is sometimes synonymous with an RFP and is typically used to request a bid on a project or product. Other types of sourcing documents include an expression of interest (EOI), an invitation for bids (IFB), an invitation to tender (ITT), an invitation to vendors (ITV), a request for applications (RFA), a request for documentation (RFD), a request for offers (RFO), a request for negotiation (RFN), and a request for services (RFS). However, for the sake of simplicity, a “proposal request” or “RFP,” as used herein, refers to any type of sourcing document. Also, for the sake of simplicity, a “customer,” as used herein, refers to an entity that issues an RFP, and a “vendor,” as used herein, refers to an entity that receives and responds to an RFP, where an “entity” refers to an individual or organization, including any kind of business organization.

The illustrative embodiments recognize that there is a need to reduce the susceptibility to errors concomitant with current RFP review procedures. For example, the illustrative embodiments include an artificial intelligence (AI) based system that cognitively evaluates an incoming RFP and generates a qualification questionnaire (also referred to herein as a “qualification form”) that correlates with the RFP in a standardized format. The standardized qualification document presents the details of an RFP organized in a consistent manner to allow reviewers to gradually gain familiarity with the organization of the qualification documents. This allows a reviewer to gain an element of efficiency at reviewing the standardized qualification documents that cannot otherwise be achieved when reviewing RFPs that are different every time. Thus, a “qualification questionnaire,” as used herein, includes documents that include questions and answers, as well as documents that lack questions but include information described herein as being collected as answers for a qualification questionnaire, such as a qualification outline, summary, form, or other type of document.

The illustrative embodiments also recognize that RFPs are often time-sensitive requests that require immediate attention and efficient processing. The degree of urgency specified by the RFP sometimes results in time constraints that add to the challenging nature of reviewing and responding to RFPs. Therefore, the illustrative embodiments recognize that there is a need to reduce the amount of time required to review an RFP by employing AI-based processes to automate some aspects of the review process. For example, the illustrative embodiments include an AI-based system that cognitively evaluates an incoming RFP and generates a qualification questionnaire that includes AI-generated answers for addressing one or more aspects of the RFP. In some embodiments, the AI-generated answers include respective confidence scores based at least in part on the entities associated with the corresponding questions and context from the RFP according to processes described herein. The scoring system allows a reviewer to quickly identify aspects where the AI-generated response can likely be adopted and other aspects that require more of the reviewer's attention. Aspects requiring reviewer's attention in-turn become learning opportunities for the system to further improve the scoring based on the reviewer's explicit or implicit feedback, where explicit feedback is provided in form of clicking buttons like “useful” or “not useful,” while implicit feedback could be gathered based on the sections of the documents that the reviewer spends time on, including the duration spent on the corresponding section.

An embodiment can be implemented as a software application. The application implementing an embodiment can be configured as a modification of an existing image analysis system, as a separate application that operates in conjunction with an existing image analysis system, a standalone application, or some combination thereof.

The illustrative embodiments employ an AI-based system or process that includes Natural language processing (NLP), which is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. NLP involves natural language understanding, i.e. enabling computers to derive meaning from human or natural language input. Modern NLP algorithms are based on machine learning, especially statistical machine learning. Machine-learning NLP algorithms involve automatically learning rules through the analysis of large corpora of typical real-world examples. A corpus (plural, “corpora”) is a collection of textual material, such as a set of documents, portions of one or more documents, or sometimes, individual sentences. A training corpus may be such a collection of textual material that has been annotated with the correct values to be learned during a machine learning process.

In some embodiments, an AI-based system or process includes Natural language understanding (NLU), which is a subset of NLP. NLU is a subset of NLP. NLU uses algorithms to transform text into a structured ontology. Ontology functions as a structural framework to organize information and concepts. In one embodiment, the ontology includes an ontology of entities constructed from a taxonomy of a qualification questionnaire, which serves as a standardized document for summarizing requirements of, and automated responses to, a proposal request. NLU uses algorithms to disassemble and parse natural language input to determine appropriate syntactic and semantic schemes in order to derive meaning from the inputted language. In an embodiment, NLU relies on computational models that are based on linguistics to develop word meanings and correlations. In an embodiment, the NLU and NLP use a series of words to identify information about a proposal request and generate an answer for a qualification questionnaire, where the answer can be used as part of a proposal or other response to the proposal request. For example, in an embodiment, the NLP processes the series of words to identify the information about the proposal request and construct an answer for at least a part of a response, where the information is stored in a non-transitory memory and the answer is constructed utilizing NLU.

In an embodiment, the AI-based system further includes higher level natural language processing capabilities such as inferencing and deep semantic processing. In an embodiment, the AI-based system parses the information about the proposal request via one or more of a slot grammar parser, a predicate-argument structure (PAS) builder, and higher level natural language processing capabilities. In an embodiment, the AI-based system parses out insignificant language (e.g., articles, conjunctions, auxiliary verbs, pronouns, and prepositions), and, upon parsing the information about the proposal request, identifies one or more terms or entities from the proposal request corresponding to one or more entities of interest for a qualification questionnaire in order to facilitate reviewing and responding to the proposal request.

In some embodiments, an AI-based system or process facilitates parsing of information about a proposal request to determine one or more entities or terms associated with a qualification taxonomy or qualification questionnaire. In some embodiments, the AI-based system includes an English slot grammar (ESG) parser or Abstract Meaning Representation (AMR) or other natural language parsers to parse the information about the proposal request and thereby determine one or more terms or entities in the proposal request. In some embodiments, the AI-based system includes language-specific grammars for languages other than English. In an embodiment, the AI-based system further includes a PAS builder. The PAS builder simplifies results generated by the ESG, resulting in a more general form that is logically approximate to the result of the ESG parse. For example, ESG produces different parse trees for passive and active voice, whereas PAS results are the same for active and passive voice (e.g., “regular updates shall be provided by the vendor” and “the vendor shall provide regular updates” yield different part trees from ESG but reduce to the same result from PAS).

In some embodiments, an AI-based system or process extracts information from an RFP in order to identify attributes of the RFP that correspond to entities of interest in a qualification questionnaire. For example, in some embodiments, a vendor has a previously-prepared qualification questionnaire that helps the vendor assess if the requirements specified by the customer in the RFP can be satisfied by the vendor's available products or services. In some embodiments, the qualification questionnaire also serves as an input for creating solutions. In some embodiments, an AI-based system scans an RFP document from a customer and generates a qualification questionnaire ready to be output in a physical or electronic form. In some embodiments, this output can help in qualifying a proposal and can help sales personnel respond to the customer with questions quickly and also accurately gather data that is still required to complete the qualification questionnaire.

In some embodiments, a qualification taxonomy corresponding to a qualification questionnaire defines a response pattern for entities of interest in the qualification questionnaire. In some such embodiments, an AI-based system extracts information from an RFP in order to identify attributes of the RFP that correspond to the response patterns of the qualification taxonomy associated with entities of interest in a qualification questionnaire. For example, in an embodiment, a non-limiting example of an entity of interest is an Application Name, which is required information for a response to an RFP. Also, since the vendor offers a discrete list of applications, the questionnaire lists the application options and requires the answer to be a selection of one of the listed applications. In this example, the qualification taxonomy would indicate that the response pattern is something equivalent to PREDEFINED LIST, MUST RESPOND. As another non-limiting example, an entity of interest is a database size, which is not a required piece of information for a response, but can be included as a range of values, so the qualification taxonomy would indicate that the response pattern equivalent to MAX SIZE, MIN SIZE values.

In some embodiments, a qualification taxonomy corresponding to a qualification questionnaire includes an indication of question interdependencies. For example, a first non-limiting example of a question Qi relates to whether a database is needed, and a second non-limiting example of a question Qj relates to size information for a database (if included). Thus, the question Qj depends on the answer to question Qi because question Qj only needs to be answered if the answer to question Qi indicates that a database is required.

In some embodiments, a qualification taxonomy includes one or more possible weights that can be assigned to each answer depending on a product of service being proposed in response to an RFP, or depending on a context of a product or service being proposed in response to an RFP. In an embodiment, a qualification taxonomy includes one or more possible weights that can be assigned to each answer depending on a type of cloud computing service being proposed in response to the RFP. For example, in an embodiment, a vendor's offerings include cloud platform services, or Platform as a Service (PaaS), and cloud infrastructure services, known as Infrastructure as a Service (IaaS). The qualification taxonomy in this example includes a value for “Total Number of Users,” which factors into customer cost for a PaaS offering but does not factor into customer cost for an IaaS offering. Thus, for a PaaS offering, this question is assigned a weight that increases this question's significance, such as 0.5, whereas for an IaaS offering, this question is assigned a weight that diminishes this question's significance, such as 0.

In some embodiments, the illustrative embodiments include an AI-based system that cognitively evaluates an incoming RFP and generates a qualification questionnaire that includes AI-generated answers for addressing one or more aspects of the RFP. In some embodiments, the AI-generated answers include respective confidence scores based at least in part on the entities associated with the corresponding questions and context from the RFP according to processes described herein. In some such embodiments, the AI-based system detects more than one possible answer to a question. In such embodiments, the AI-based system includes respective confidence scores based at least in part on the entities associated with the corresponding questions and context from the RFP according to processes described herein, and then selects the answer with the highest possible score for use in the answer included in the qualification questionnaire. In an embodiment, the AI-based system detects multiple possible answers to a question, and the AI-based system includes respective confidence scores generated by the AI-based system for each possible answer, and then selects the top n answers having the highest scores.

In an embodiment, the illustrative embodiments include an AI-based system that cognitively evaluates an incoming RFP and generates a qualification questionnaire that includes AI-generated answers for addressing one or more aspects of the RFP and respective confidence scores based at least in part on the entities associated with the corresponding questions and context from the RFP according to processes described herein. In some embodiments, the completed qualification questionnaire with the AI-generated answers and scores is presented to a subject matter expert (SME) for manual review and as an aid for preparing a proposal or other type of response to the RFP. In some embodiments, answers with a confidence score below a configurable threshold are highlighted for SME review.

In some embodiments, an SME reviews all answers or at least the highlighted answers and provides feedback based on the SME review to the AI-based system as to the accuracy of the AI-generated answers such that the AI-based system learns from the SME review of the answers. In some embodiments, the AI-based system automatically makes adjustments based on the SME's feedback, or the SME makes adjustments and/or modifications based on the SME's review, for example adjustments to the scoring thresholds and/or modifications to response patterns and/or attributes. In some embodiments, the AI-based system increases or decreases the attribute weight for an answer because of consistent positive or negative feedback from users.

Furthermore, simplified diagrams of the data processing environments are used in the figures and the illustrative embodiments. In an actual computing environment, additional structures or component that are not shown or described herein, or structures or components different from those shown but for a similar function as described herein may be present without departing the scope of the illustrative embodiments.

Furthermore, the illustrative embodiments are described with respect to specific actual or hypothetical components only as examples. The steps described by the various illustrative embodiments can be adapted for providing explanations for decisions made by a machine-learning classifier model, for example

Any specific manifestations of these and other similar artifacts are not intended to be limiting to the invention. Any suitable manifestation of these and other similar artifacts can be selected within the scope of the illustrative embodiments.

Clients or servers are only example roles of certain data processing systems connected to network102and are not intended to exclude other configurations or roles for these data processing systems. Data processing system104couples to network102. Software applications may execute on any data processing system in data processing environment100. Any software application described as executing in processing system104inFIG. 1can be configured to execute in another data processing system in a similar manner. Any data or information stored or produced in data processing system104inFIG. 1can be configured to be stored or produced in another data processing system in a similar manner. A data processing system, such as data processing system104, may contain data and may have software applications or software tools executing computing processes thereon. In an embodiment, data processing system104includes memory124, which includes application105A that may be configured to implement one or more of the data processor functions described herein in accordance with one or more embodiments.

Server106couples to network102along with storage unit108. Storage unit108includes a database109configured to store data as described herein with respect to various embodiments, for example image data and attribute data. Server106is a conventional data processing system. In an embodiment, server106includes neural network application105B that may be configured to implement one or more of the processor functions described herein in accordance with one or more embodiments.

Clients110,112, and114are also coupled to network102. A conventional data processing system, such as server106, or client110,112, or114may contain data and may have software applications or software tools executing conventional computing processes thereon.

Device132is an example of a conventional computing device described herein. For example, device132can take the form of a smartphone, a tablet computer, a laptop computer, client110in a stationary or a portable form, a wearable computing device, or any other suitable device. In an embodiment, device132sends requests to server106to perform one or more data processing tasks by neural network application105B such as initiating processes described herein of the neural network. Any software application described as executing in another conventional data processing system inFIG. 1can be configured to execute in device132in a similar manner. Any data or information stored or produced in another conventional data processing system inFIG. 1can be configured to be stored or produced in device132in a similar manner.

Server106, storage unit108, data processing system104, and clients110,112, and114, and device132may couple to network102using wired connections, wireless communication protocols, or other suitable data connectivity. Clients110,112, and114may be, for example, personal computers or network computers.

In the depicted example, server106may provide data, such as boot files, operating system images, and applications to clients110,112, and114. Clients110,112, and114may be clients to server106in this example. Clients110,112,114, or some combination thereof, may include their own data, boot files, operating system images, and applications. Data processing environment100may include additional servers, clients, and other devices that are not shown.

In the depicted example, memory124may provide data, such as boot files, operating system images, and applications to processor122. Processor122may include its own data, boot files, operating system images, and applications. Data processing environment100may include additional memories, processors, and other devices that are not shown.

In an embodiment, one or more of application105A of data processing system104and application105B of server106implements an embodiment of an AI-based system, such as an NLP system, as described herein. In a particular embodiment, the AI-based system is implemented using one of network application105A and network application105B within a single server or processing system. In another particular embodiment, the AI-based system is implemented using both network application105A and network application105B within a single server or processing system. Server106includes multiple GPUs107including multiple nodes in which each node may include one or more GPUs as described herein.

With reference toFIGS. 3A and 3B, these figures depict an example configuration300in accordance with an illustrative embodiment. More specifically,FIG. 3Adepicts a block diagram of an example configuration for a qualification form generation system in accordance with an illustrative embodiment, andFIG. 3Bdepicts a block diagram of an example qualification taxonomy configuration for a qualification form generation system in accordance with an illustrative embodiment. The example embodiment includes a qualification form generation system302. In a particular qualification form generation system302is an example of application105A/105B ofFIG. 1.

In the illustrated embodiment, the qualification form generation system302includes a taxonomy data memory304for storing a qualification taxonomy306. In the illustrated embodiment, a non-limiting example of a qualification taxonomy306is shown inFIG. 3Bthat includes four entities of interest listed in the first column: APPLICATION, COMPUTE, STORAGE, and STEADY STATE INSTANCES.

The APPLICATION entity has two attributes: NAME and VERSION. The NAME attribute and VERSION attribute are required attributes that must be selected from respective predefined lists of values (i.e., NAME 1-NAMEn; VERSION 1-VERSIONn).

The COMPUTE entity has one attribute: CORES. The CORES attribute is an EITHER/OR attribute that must be CORES or vCPU and can specify ANY NUMBER as a NUMERIC VALUE associated with a number of cores or vCPUs.

The STORAGE entity has two attributes: SAN and DB SIZE. The SAN attribute is an EITHER/OR attribute that must be SAN or NAS and can specify ANY NUMBER as a NUMERIC VALUE attribute associated with a SAN or NAS. The DB SIZE is not required, and can be ANY NUMBER as a NUMERIC VALUE.

The STEADY STATE INSTANCES entity has one attribute: OPERATING SYSTEM PREFERENCE. The OPERATING SYSTEM PREFERENCE is a required attribute that must be selected from a PREDEFINED LIST OF VALUES.

In the illustrated embodiment, the qualification form generation system302includes an interface308for receiving an RFP310from a customer or other entity. In an embodiment, the interface308includes a web interface that allows customers to upload their RFP documents. In an embodiment, upon receiving the RFP310, the interface308automatically initiates a process for generating a qualification form or questionnaire and populating the questionnaire or form with answers using an AI-based cognitive process. For example, in an embodiment, the interface308transmits the RFP to an RFP entity data extraction module312. In an embodiment, the RFP entity data extraction module312performs NLP processing as described herein to extract attributes of the RFP that correspond to entities of interest in the qualification questionnaire. In an embodiment, the RFP entity data extraction module312includes a machine-learning algorithm that is trained using training data314to recognize salient aspects of the RFP310for extraction.

In the illustrated embodiment, the attributes extracted from the RFP are transmitted to a data association module316, which also receives taxonomy data, such as a qualification taxonomy306from the taxonomy data memory304. In an embodiment, the data association module316associates the extracted RFP entity data with entries of the qualification form by matching RFP entity data with predefined entities and response patterns of qualification form. For example, in an embodiment, the data association module316associates the extracted RFP entity data with entries of the qualification form according to a process shown inFIG. 5.

In the illustrated embodiment, the associated RFP entity data is transmitted to a scoring module318for scoring and to a form population module320for populating a qualification form. In an embodiment, the scoring module318calculates scores for each of the questions of the qualification questionnaire based at least in part on the entities associated with the corresponding questions and context from the RFP according to processes described herein. For example, in an embodiment, the scoring module318calculates a score for a question based at least in part on a number of other questions upon which the question depends, and a number of other questions that depend on the question. In an embodiment, the scoring module318calculates a score for a question based at least in part on one or more scores of questions upon which the question depends. In an embodiment, the scoring module318calculates a score for a question based at least in part on whether the question is required. In an embodiment, the scoring module318calculates a score for a question based at least in part on a context of the question, for example based on a weight assigned to the question, such as a first weight if the question indicates a PaaS offering and a second weight if the question indicates an IaaS offering.

In an embodiment, the form population module320appends the associated RFP entity data and the scores to a qualification form, which is then output as output qualification form322to an SME or other reviewing entity.

In the illustrated embodiment, the qualification form generation system302includes a taxonomy update module326that allows a user to make changes to the qualification taxonomy306. For example, an SME may wish to update the qualification taxonomy306by providing form feedback324to the system302after reviewing the output form322.

With reference toFIGS. 4A and 4B, these figures depict first and second portions, respectively, of a flowchart of an example process400for automatically preparing a qualification questionnaire responsive to receiving an RFP in accordance with an illustrative embodiment. In a particular embodiment, the qualification form generation system302carries out the process400using software instructions for a taxonomy builder.

In an embodiment, at block402, taxonomy builder stores taxonomy data associated with a qualification form. In an embodiment, at block404, taxonomy builder stores taxonomy data associating one of a plurality of predefined response patterns with each entry. For example, in the illustrated embodiment, a pattern table406shows a plurality of possible response patterns that the taxonomy builder detects for each question or entry of the qualification form associated with the taxonomy data. In an embodiment, non-limiting examples of possible response patterns that the taxonomy builder detects include the patterns P1-P13 explained below.P1. PREDEFINED, MUST: The response selects from predefined options and the response is required.P2. PREDEFINED: The response selects from predefined options (optional response).P3. FREE TEXT, MUST: The response can include free-form text and the response is required.P4. FREE TEXT: The response can include free-form text (optional response).P5. COUNT, MIN, MAX, MUST: The response includes a range of total amounts, including minimum and maximum totals, and the response is required.P6. COUNT, MIN, MAX: The response includes a range of total amounts, including minimum and maximum totals (optional response).P7. COUNT, MAX, MUST: The response includes a total maximum amount, e.g., total cap value, and the response is required.P8. COUNT, MAX: The response includes a total maximum amount, e.g., total cap value (optional response).P9. COUNT, MIN, MUST: The response includes a total minimum amount, and the response is required.P10. COUNT, MIN: The response includes a total minimum amount (optional response).P11. DATE, MUST: The response must be a data and is required.P12. PREDEFINED, ACTION: The response involves a predefined action.P13. FREE TEXT, ACTION: The response involves free-form action.

In an embodiment, at block404, taxonomy builder identifies and associates each entry or question with one of the response patterns P1-P13 as shown in block408. As a non-limiting example, question pattern table408shows that questions Q1-Q2 both require response pattern P1, question Q3 requires pattern P5, and the last question Qn (where n is any value) requires response pattern P11. In an embodiment, the process at block404includes an AI-based system, such as NLP to detect the patterns for each of the questions.

At block410, the taxonomy builder stores taxonomy data representative of interdependencies between entries. As a non-limiting example, question dependency table412shows that the answers to questions Q2-Q3 and Q5 depend on the answer to question Q1, the answer to question Q3 also depends on the answer to question Q2, and the answer to question Q5 also depends on the answer to question Q4. At block414, the taxonomy builder receive a new RFP. At block416, the taxonomy builder generates a new qualification form associated with new RFP.

At block418, the taxonomy builder extracts RFP entity data from the RFP corresponding to predefined entities of interest. At block420, the taxonomy builder associates the extracted RFP entity data with entries of the qualification form by matching RFP entity data with predefined entities and response patterns of qualification form. In an embodiment, the taxonomy builder associates the extracted RFP entity data with entries of the qualification form according to the process shown inFIG. 5. At block422, the taxonomy builder populates the new qualification form using extracted RFP entity data associated with each entry and formatted according a corresponding response pattern. At block424, the taxonomy builder scores the answers based at least in part on the entities associated with the corresponding questions and context from the RFP. At block426, the taxonomy builder populates the new qualification form with the scores. At block428, the taxonomy builder outputs the completed qualification form. Finally, at block430, the taxonomy builder updates the taxonomy data associated with the qualification form based on user input after user review of qualification form.

With reference toFIG. 5, this figure depicts a flowchart of an example process500for associating the extracted RFP entity data with entries of the qualification form by matching RFP entity data with predefined entities and response patterns of a qualification form in accordance with an illustrative embodiment. In a particular embodiment, the process500is an embodiment of the process at block420shown inFIG. 4performed by the taxonomy builder.

At block502, the taxonomy builder loads a taxonomy keyword list from taxonomy data stored in memory. Next, at block504, the taxonomy builder gets a keyword from the taxonomy keyword list. At block506, the taxonomy builder searches RFP entity data for the keyword from the taxonomy data. At block508, the taxonomy builder extracts values from the RFP entity data using a cognitive process to identify values related to the keyword from the taxonomy data. At block510, the taxonomy builder stores a list of extracted values from RFP entity data with respective confidence values from the cognitive process. At block512, the taxonomy builder computes an overall confidence score for the list of extracted values. At block514, the taxonomy builder checks whether the current keyword is the last keyword in list. If not, the process continues back to block504. Otherwise, the process ends.

Embodiments of the present invention may also be delivered as part of a service engagement with a client corporation, nonprofit organization, government entity, internal organizational structure, or the like. Aspects of these embodiments may include configuring a computer system to perform, and deploying software, hardware, and web services that implement, some or all of the methods described herein. Aspects of these embodiments may also include analyzing the client's operations, creating recommendations responsive to the analysis, building systems that implement portions of the recommendations, integrating the systems into existing processes and infrastructure, metering use of the systems, allocating expenses to users of the systems, and billing for use of the systems. Although the above embodiments of present invention each have been described by stating their individual advantages, respectively, present invention is not limited to a particular combination thereof. To the contrary, such embodiments may also be combined in any way and number according to the intended deployment of present invention without losing their beneficial effects.