408 lines
19 KiB
Markdown
408 lines
19 KiB
Markdown
file-path:: ../assets/SCICO-D-23-00080_R1_reviewer_1693666505321_0.pdf
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- file:: [SCICO-D-23-00080_R1_reviewer_1693666505321_0.pdf](../assets/SCICO-D-23-00080_R1_reviewer_1693666505321_0.pdf)
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file-path:: ../assets/SCICO-D-23-00080_R1_reviewer_1693666505321_0.pdf
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- Request for Proposals
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ls-type:: annotation
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hl-page:: 1
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hl-color:: yellow
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id:: 650b029c-659a-49e5-936c-9d54df4ba6c4
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- Request for Information
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ls-type:: annotation
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hl-page:: 1
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hl-color:: yellow
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id:: 650b02a0-f225-4f6b-b888-f2b6c11c605c
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- gap in leveraging model-based techniques in document-centric phases of proposal development, requirements analysis, etc
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ls-type:: annotation
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hl-page:: 1
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hl-color:: green
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id:: 650b037e-d40f-4ae5-96c3-0b3ba3534647
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- Large organizations respond to huge volumes of Request for Proposals (RFP)/ Request for Information (RFI) every year. The process of developing a proposal for an RFP/ RFI is completely manual and time-, effort-, and intellect-intensive.
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ls-type:: annotation
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hl-page:: 7
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hl-color:: green
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id:: 650f0245-8835-49dd-886b-085f88e61243
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- This paper presents an automated proposal development approach for a clientsupplied RFP/RFI using a combination of model-based and AI-enabled techniques and describes the case study of its successful deployment to hundreds of presales users across multiple geographies
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ls-type:: annotation
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hl-page:: 7
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hl-color:: green
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id:: 650f0267-1449-430f-97f5-907787d8f388
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- An RFP is a “collection of formal documents that includes a description of the desired form of response from a potential supplier, the relevant statement of work for the supplier, and required provisions in the supplier agreement” (ISO/IEC/IEEE 24765: 2017).
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ls-type:: annotation
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hl-page:: 7
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hl-color:: purple
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id:: 651085c3-3715-404e-9100-5c24ebef1976
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- An RFI typically precedes an RFP and seeks information about a product, service, or supplier capability.
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ls-type:: annotation
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hl-page:: 7
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hl-color:: purple
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id:: 651085d8-2efb-46fc-91c5-aae0fef6af0c
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- each business unit (BU) responds to hundreds of customer RFx yearly.
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ls-type:: annotation
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hl-page:: 7
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hl-color:: blue
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id:: 65108639-4906-446c-83bc-e145bb84cc15
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- An RFx has approximately 30 to 50 questions, and interpreting all these questions and developing a proposal by referring to various available sources of information is a time-consuming manual activity.
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ls-type:: annotation
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hl-page:: 7
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hl-color:: purple
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id:: 65108659-4b6e-4c10-b9ca-8f120a4bfc9e
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- Manual search is carried out by searching for specific documents in local/ shared repositories, searching for keywords, selecting relevant documents, and marking relevant portions of reusable tex
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ls-type:: annotation
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hl-page:: 7
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hl-color:: blue
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id:: 6510867c-9f0d-4e69-ba8a-191cf2321d93
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- no single source of truth and a lack of periodic knowledge validation resulting in inconsistent information and facts presented to customers
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ls-type:: annotation
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hl-page:: 7
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hl-color:: green
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id:: 65108685-8435-4b2e-b8bb-1953eba43623
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- Companies bid for multiple RFx and a lot of standardized content is reused across the proposals
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ls-type:: annotation
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hl-page:: 8
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hl-color:: green
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id:: 65108947-ab2c-447d-a1ff-9cebfb80faa6
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- A Day in the life of Bid
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ls-type:: annotation
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hl-page:: 8
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hl-color:: yellow
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id:: 65108970-9dce-43c0-aa87-60270a0002af
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- These documents are maintained in local repositories in word/ excel formats.
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ls-type:: annotation
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hl-page:: 8
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hl-color:: yellow
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id:: 651089e7-5fa9-44e7-a613-57e128bd7a56
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- AI and knowledge-aware software have remained largely untouched by MDE, and it has the potential to drastically change the way MDE is used and perceived in the software communit
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ls-type:: annotation
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hl-page:: 8
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hl-color:: green
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id:: 65108a16-94e2-4397-8a2f-678e48d87e2d
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- Here we propose an approach for automating the proposal development by exploring model-based techniques in combination with AI techniques.
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ls-type:: annotation
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hl-page:: 8
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hl-color:: purple
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id:: 65108a23-1148-47c8-8277-20e98018b7bd
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- uper-structure
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ls-type:: annotation
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hl-page:: 9
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hl-color:: yellow
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id:: 65108a8e-fcee-4d96-b310-5f4801a6f54a
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- This is a model-based AI intervention to generate a proposal in the desired format for a client-provided RFP/RFI. At the heart of the system is the “Proposal System Model” that enables a super-structure to be imposed on the information of interest lying around in a fragmented form as NL -text. This purpose-specific meta-model serves as the lens to extract information from the NL documents and facilitate document-to-model transformation in which the NL documents are parsed, and knowledge information is extracted into a machine-processable proposal knowledge model. Once a new client-provided RFP/RFI document is loaded, instantaneously on-click, leveraging the proposal knowledge model populated, solution recommendation can be generated for all RFx questions that have some relevant information in the knowledge model. As the complete information is in the model form, using model-to-text transformation, the proposal document can be automatically generated based on choices on recommendations.
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ls-type:: annotation
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hl-page:: 9
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hl-color:: yellow
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id:: 65108acf-c3a9-421a-b8ad-9112ec9489b0
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- document-to-models transformation for automatic parsing of knowledge documents into machineprocessable models
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ls-type:: annotation
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hl-page:: 9
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hl-color:: green
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id:: 65108b1d-6d99-40c8-ab82-343f20cbb123
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- knowledge-map recommendations for RFx questions
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ls-type:: annotation
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hl-page:: 9
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hl-color:: green
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id:: 65108b2c-2b81-4d84-bd02-d1a945d18c57
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- digitalized Proposal system by harmonizing multiple realization technologies
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ls-type:: annotation
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hl-page:: 9
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hl-color:: green
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id:: 65108e98-8b87-42d5-8fac-16ee5d4c9c0d
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- roposal document
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ls-type:: annotation
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hl-page:: 9
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hl-color:: yellow
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id:: 65108eb3-048e-470a-9f8b-1dc53786e00f
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hl-stamp:: 1695583925101
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- proposed evaluation metrics of interest? In general the evaluation is not presented in a systematic manner and it does not provide information on the evaluation set up, research questions, et
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ls-type:: annotation
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hl-page:: 6
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hl-color:: yellow
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id:: 65108f57-1e68-4490-8ea4-4de3c4972929
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- In the evaluation a quote from one of the users says"The RFx technology automates approximately 70% 75% " Which parts of the RFx development are not automated? What is the quality of the generated solutions?
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ls-type:: annotation
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hl-page:: 6
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hl-color:: yellow
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id:: 65108f66-78b3-45bb-b76d-a9d41d36884a
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- Proposal system model: A domain-agnostic meta-model for proposal development for any client-provided RFP/RFI is defined.
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ls-type:: annotation
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hl-page:: 10
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hl-color:: green
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id:: 65108f73-7d06-4be7-866f-881f06763107
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- DocToModels
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ls-type:: annotation
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hl-page:: 10
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hl-color:: green
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id:: 65108f82-5d4a-4709-bcf4-1167f01a40c7
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- it is used to populate knowledge data into the Proposal model.
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ls-type:: annotation
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hl-page:: 10
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hl-color:: green
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id:: 65108fc5-f8c5-40b7-858e-cfc575fd962b
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- K-Map recommende
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ls-type:: annotation
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hl-page:: 10
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hl-color:: green
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id:: 65108fca-3253-4bda-95f6-25d0cde25396
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- ModelToText
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ls-type:: annotation
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hl-page:: 10
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hl-color:: green
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id:: 65108fd8-8f38-472f-8085-98eb03074d80
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- unique harmonization of multiple technologies
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ls-type:: annotation
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hl-page:: 10
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hl-color:: yellow
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id:: 65108feb-8432-4876-ba35-3d1b9d57e49e
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- Lucene-based search engine facilitates knowledge search
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ls-type:: annotation
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hl-page:: 10
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hl-color:: yellow
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id:: 65109043-3284-4742-9433-d7967d3ebc4e
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hl-stamp:: 1695584325195
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- This Proposal_MM can be seen as a combination of proposal knowledge and solution model.
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ls-type:: annotation
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hl-page:: 11
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hl-color:: green
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id:: 65109098-d5a0-4429-8924-65c128a9d564
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- with data specific to the RFx to be responded to.
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ls-type:: annotation
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hl-page:: 11
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hl-color:: yellow
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id:: 6510a332-3685-46b1-a58b-a4bb49e0d8ff
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- banking, insurance, financial services, business process services, and so on
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ls-type:: annotation
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hl-page:: 11
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hl-color:: green
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id:: 6510a35e-5860-4bda-a116-731e8500e8fa
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- A KnowledgeType can have multiple KTypes. For the proposal process, solution knowledge for an offering can be classified into KnowledgeTypes such as product knowledge, process knowledge, facts knowledge, case knowledge and so on
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ls-type:: annotation
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hl-page:: 11
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hl-color:: green
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id:: 6510a381-f8d0-4354-971a-3491a38e428e
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- standard document structure in terms of organization of headings, styles,
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ls-type:: annotation
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hl-page:: 13
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hl-color:: yellow
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id:: 6510a3aa-5ac2-4adf-acb8-01a1aa695eb0
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- Using the meta-model as a lens, the document structural elements such as headings, paras, bullets, tables, etc. are analysed as per pattern mapping and corresponding model elements are automatically instantiated
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ls-type:: annotation
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hl-page:: 13
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hl-color:: green
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id:: 6510a3be-21e4-4bf4-8c6b-21a650c0a0ee
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- Pattern mapping is prepared based on the document structure and information organization of the document template used for capturing the solution knowledge.
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ls-type:: annotation
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hl-page:: 13
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hl-color:: green
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id:: 6510a3df-c453-48f7-bbc5-6ad6dddedc77
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- This content may have images, styled text, and tables.
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ls-type:: annotation
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hl-page:: 13
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hl-color:: green
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id:: 6510a435-94de-4e12-a9a4-f691e4beef09
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- ModelToText generates a proposal document as per desired document format using the reviewed K-Map. The KMap recommendations are composed as per compositional rules using model-to-text transformation
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ls-type:: annotation
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hl-page:: 15
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hl-color:: yellow
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id:: 6510a48e-5aad-4067-8fac-f0a6449b742e
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- The Proposal generation system depends largely on the proposal knowledge model.
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ls-type:: annotation
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hl-page:: 15
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hl-color:: green
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id:: 6510a4f5-e26a-4e2c-9ee9-1ace88fa5f23
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- A learning-based approach depicted in Fig. 7 is proposed to mine the relevant knowledge from submitted proposal documents
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ls-type:: annotation
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hl-page:: 15
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hl-color:: green
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id:: 6510a53f-03c4-4f28-81e6-38576362c58b
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- As proposal documents may have different document structures
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ls-type:: annotation
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hl-page:: 15
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hl-color:: yellow
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id:: 6510a554-e14d-4ec9-a62a-ea44c185aa75
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- a configurable extraction pattern-based approach is used for programmatically extracting question text and answer text. As per a standard pattern configuration, the question is extracted from the document heading and style information. The text after the question until the next question or the end of the document is considered an answer.
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ls-type:: annotation
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hl-page:: 15
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hl-color:: yellow
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id:: 6510a5df-3cd0-43b1-a38b-71343422bf55
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- he Maxent algorithm assumes the conditional dependence of the features. The extracted proposal questions are then classified by Question Classifier into concerns, and concern-specific knowledge data is generated. Automated checks are carried out on whether the question matches the existing Knowledge Element question. If so, the answer is checked for similarity, and the answer is marked for update. If it is a new question, then a new KnowledgeElement is proposed to be added with association to the appropriate concern. Human is in the loop to review/ refine these knowledge upgrade recommendations. This reviewed data is used for incremental knowledge upgrades to the proposal knowledge model.1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465
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ls-type:: annotation
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hl-page:: 15
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hl-color:: yellow
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id:: 6510a626-100c-4ef1-9fba-0b61bcd54da0
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- The Maxent algorithm assumes the conditional dependence of the features. The extracted proposal questions are then classified by Question Classifier into concerns, and concern-specific knowledge data is generated.
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ls-type:: annotation
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hl-page:: 15
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hl-color:: yellow
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id:: 6510a646-603b-439c-96ea-a77533810599
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hl-stamp:: 1695589960697
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- Initial knowledge documents are manually created by collating information from diverse sources. It is required to perform incremental knowledge updates in a periodic manner
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ls-type:: annotation
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hl-page:: 15
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hl-color:: blue
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id:: 65117186-079b-4295-b4e2-4055a364a186
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- sales and pre-sales teams (proposal writing experts) of a large-scale Business Unit (BU) that offers a suite of IT-enabled services to multiple customers across various geographies.
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ls-type:: annotation
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hl-page:: 16
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hl-color:: green
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id:: 65117266-7041-44f0-8b7f-2b0cf29b3ad6
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- Thousands of Requests for Proposal (RFP) / Requests for Information (RFI) floated by customers are responded to every year by that BU
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ls-type:: annotation
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hl-page:: 16
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hl-color:: green
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id:: 65117274-1718-4b1b-bfc1-b86dfd62f91f
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- It was a heavily expert-dependent process to select the solution fragments from documents relevant to a specific question in the RFx.
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ls-type:: annotation
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hl-page:: 16
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hl-color:: green
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id:: 65117495-a5c1-415b-84e2-3fc9113e652b
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- Manual search, copy-paste of content from individual documents, and formatting for appropriate styles was time-consuming and effort-intensive activit
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ls-type:: annotation
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hl-page:: 16
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hl-color:: green
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id:: 651174a4-2d25-43f6-bd37-6ad3b37c825b
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- 700 BU users from sales/ pre-sales teams
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ls-type:: annotation
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hl-page:: 16
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hl-color:: green
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id:: 651174ad-e656-4e13-b0cc-9b59fb1eb163
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- 3.1. Domain and Offerings
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ls-type:: annotation
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hl-page:: 16
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hl-color:: yellow
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id:: 6511750e-a50b-40d0-98ba-1ad38c3ceb0f
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- The Proposal system has been deployed and rolled out to over 700 BU users from sales/ pre-sales teams. Solution documents related to the capabilities of 31 offerings were collated and parsed using the DocToModels.
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ls-type:: annotation
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hl-page:: 16
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hl-color:: yellow
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id:: 65117555-0030-4899-9f5e-5e6704321216
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- Fig. 8 Knowledge Model View
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hl-page:: 17
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hl-color:: yellow
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id:: 651175a7-aece-49f3-b6e9-ad473abae0ee
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- Each Question in the RFP is analyzed and transformed into a query to retrieve relevant proposal Knowledge Elements for that question as a K-Map.
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ls-type:: annotation
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hl-page:: 18
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hl-color:: yellow
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id:: 6511769b-73ae-4190-8878-a335a888893c
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- Hundreds of proposals have been generated across seven geographies using the Proposal system.
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ls-type:: annotation
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hl-page:: 20
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hl-color:: blue
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id:: 65117731-fe4f-4e22-ba61-3e70c66f8ed1
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- ese 15 cases represent a wide range of RFx scenarios across domains and geographies
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ls-type:: annotation
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hl-page:: 20
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hl-color:: blue
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id:: 65117766-fbe8-42fd-9d49-b3cd5307317a
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- RFx and RFx Context are populated, and on search on any RFx, solution recommendations are generated question-wise
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ls-type:: annotation
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hl-page:: 20
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hl-color:: green
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id:: 65117b7d-e694-4276-8a0f-560edb751095
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- automates approximately 70% - 75% effort in preparation of the RFP/RI responses
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ls-type:: annotation
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hl-page:: 23
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hl-color:: green
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id:: 65117bd4-4128-4a69-bba5-41a1c4e23889
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- expert-driven proposal development for RFP/ RFIs, a model-based and AI-enabled approach is proposed to help the pre-sales teams in automatically generating a standard proposal document and provide a kick-start to the proposal process
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ls-type:: annotation
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hl-page:: 23
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hl-color:: purple
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id:: 65117bfa-52f9-44fd-8933-ef4941495454
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hl-stamp:: 1695733490005
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- abstracts the critical elements from diverse knowledge documents belonging to various domains and offerings and the key constructs of a proposal document
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ls-type:: annotation
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hl-page:: 23
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hl-color:: green
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id:: 65117c10-8703-4235-aaf3-cedc4ff70901
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- The Model-Driven Engineering (MDE) based approach offered a plethora of advantages for the proposal system, significantly improving its development and maintenance process.
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ls-type:: annotation
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hl-page:: 23
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hl-color:: yellow
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id:: 65117c30-8209-4797-a457-ff9e5bfe1ca9
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- This capability to generate a “proposal document” is a big value-add to the presales teams in the industry.
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ls-type:: annotation
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hl-page:: 23
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hl-color:: green
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id:: 6512e010-966f-4245-9e9a-5a01f95c1676
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- We could easily modify the models to reflect new information.
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hl-page:: 23
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hl-color:: green
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id:: 6512e04a-23e1-4982-bfb1-dd8a505e5b55
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- y leveraging existing modeling infrastructure for populating, querying we could rapidly create the proposal system
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ls-type:: annotation
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hl-page:: 23
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hl-color:: yellow
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id:: 6512e053-caf4-4086-aa3c-333adb8e98c6
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- The initial hurdle was in understanding the diverse knowledge document formats received from presales teams.
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ls-type:: annotation
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hl-page:: 23
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hl-color:: green
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id:: 6512e08d-dbc5-45db-8208-46d203804be0
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- proposal system meta-model
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ls-type:: annotation
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hl-page:: 23
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hl-color:: blue
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id:: 6512e09d-be01-4cb7-ac9b-ce7442926dbb
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hl-stamp:: 1695735972537
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- domain, offering, concern, knowledge element, and so on.
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ls-type:: annotation
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hl-page:: 23
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hl-color:: blue
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id:: 6512e0a2-20c9-4268-af1d-e42442a5399d
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hl-stamp:: 1695735974764
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- The meta-model enables a super-structure to be imposed on the information of interest lying around in fragmented form as NL text and is the central building block in our architecture to which every other component feeds/ retrieves model data
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ls-type:: annotation
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hl-page:: 23
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hl-color:: green
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id:: 6512e153-8ac2-48bf-a426-2907279191ae
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- The next key challenge was handling the diversity in document structures and formats while parsing information from NL documents. The structure of the document provided insights into the information organization and could be mapped to the meta-model concepts
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ls-type:: annotation
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hl-page:: 23
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hl-color:: yellow
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id:: 6512e165-d6e3-43b1-b085-a8e92e2c3555
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- defining extraction patterns for mapping information in the document to the meta-model.
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ls-type:: annotation
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hl-page:: 23
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hl-color:: yellow
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id:: 6512e184-4384-4e04-b59f-26e64d693569
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hl-stamp:: 1695736200053
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- This approach is both document structure agnostic and metamodel agnostic, thus paving the way forward to extend this technology for other business use cases that are manual and document-centric such as requirements.
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ls-type:: annotation
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hl-page:: 24
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hl-color:: yellow
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id:: 6512e1da-5919-417f-a514-0227aec42646
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- For question analysis, various matching heuristic such as exact matching, inexact matching, and fuzzy matching was implemented
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ls-type:: annotation
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hl-page:: 24
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hl-color:: green
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id:: 6512e2b7-67a0-4201-a25d-7d75ac617fc9
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- for an RFP from a UK Client, to respond to a question on providing references on past experience, the knowledge elements retrieved need to be past case studies from UK geography.
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ls-type:: annotation
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hl-page:: 24
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hl-color:: yellow
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id:: 6512e3c3-bbe3-420c-adce-a1e072e89e8c
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hl-stamp:: 1695736774387
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- Dynamic field population was another challenge faced during proposal document composition.
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ls-type:: annotation
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hl-page:: 24
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hl-color:: green
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id:: 6512e3f6-ffca-471d-8ce0-785cc2df7f12
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- There were many operational challenges during the deployment of this technology
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ls-type:: annotation
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hl-page:: 24
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hl-color:: green
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id:: 6512e436-c7ed-4d0e-aba8-edb7a87fccef
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- This proposal development is an expertise-driven, heavily manual activity, and is centered around various types of natural language documents prepared, referred, and maintained.
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ls-type:: annotation
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hl-page:: 25
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hl-color:: green
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id:: 6512e496-0525-418f-895f-4d9d7727451c
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- utomatic generation of formatted Proposal document with ~70% of questions answered
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ls-type:: annotation
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hl-page:: 25
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hl-color:: purple
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id:: 6512e4b5-7321-48f3-ad01-7b857813ba6e |