SYSTEM AND METHOD FOR CONTENT CREATION
In a first aspect, a system for creating fact-based content is presented. The system includes an application service provider operating on a network. The application service provider is configured to receive a user prompt and generate a web query for content based on the user prompt. The system includes a fact-based language model in communication with the application service provider. The fact-based language model is configured to receive the web query from the application service provider and retrieve, from a electronic library, relevant fact-based content based on the web query. The electronic library includes proprietary data. The fact-based language model is configured to provide the relevant fact-based content to the application service provider. The application service provider communicates content to a user based on the user prompt. The content includes at least a portion of the relevant fact-based content from the electronic library.
1 - 18 . (canceled)
19 . A system for distributing fact-based electronic content, comprising:
a computing device configured to:
receive a user prompt;
generate a query requesting content based in response to the user prompt;
submit the query to one or more fact-based language models trained with training data correlating prompts to content, wherein the one or more fact-based language model are trained to receive prompts as input and provide fact-based content as output, wherein the one or more fact-based language models are configured to:
receive the query;
retrieve relevant fact-based content based on the query; and
provide the relevant fact-based content to the computing device;
and
display content to a user based on the user prompt, the content including at least a portion of the relevant fact-based content and one or more citations identifying one or more fact based sources associated with the fact-based content.
20 . The system of claim 19 , wherein the one or more fact-based language models retrieve relevant fact-based content from an electronic library.
21 . The system of claim 19 , wherein the electronic library includes proprietary data of one or more one of textbooks, journals, whitepapers, or professor notes.
22 . The system of claim 21 , wherein the content includes a ratio of proprietary content to non-proprietary content.
23 . The system of claim 19 , wherein the computing device is further configured to display a ratio of content to fact-based content to the user.
24 . The system of claim 19 , wherein user prompt is a request for fact-based content.
25 . The system of claim 19 , wherein the one or more fact-based language models are further configured to identify a level of knowledge of the user prompt and retrieve the relevant fact-based content based on the level of knowledge of the user prompt.
26 . The system of claim 19 , wherein the one or more fact-based language models are configured to extract linguistic data from the query.
27 . The system of claim 19 , wherein the fact-based content includes text, image, video, or a combination thereof.
28 . A method of distributing fact-based content, comprising:
receiving a user prompt;
generating a query requesting content in response to the user prompt;
submitting the query to one or more fact-based language models, the one or more fact-based language models trained with training data correlating prompts to content,
wherein the one or more fact-based language models are trained to receive prompts as input and provide fact-based content as output;
retrieving relevant fact-based content based on the query;
providing, by the fact-based language model, the relevant fact-based content; and
displaying content to a user based on the user prompt, the content including one or more citations identifying one or more fact based sources associated with the fact-based content.
29 . The method of claim 28 , wherein the one or more fact-based language models retrieve relevant fact-based content from an electronic library.
30 . The method of claim 28 , wherein the electronic library includes proprietary data of one or more one of textbooks, journals, whitepapers, or professor notes.
31 . The method of claim 30 , wherein the content includes a ratio of proprietary content to non-proprietary content.
32 . The method of claim 28 , the content is displayed in a ratio of content to fact-based content to the user.
33 . The method of claim 28 , wherein user prompt is a request for fact-based content.
34 . The method of claim 28 , further comprising identifying, by the one or more fact-based language models, a level of knowledge of the user prompt; and
retrieving, by the one more fact-based language models, the relevant fact-based content based on the level of knowledge of the user prompt.
35 . The method of claim 28 , further comprising extracting, by the one or more fact-based language models, linguistic data from the query.
36 . The method of claim 28 , wherein the fact-based content includes one or more of text, image, video, or a combination thereof.
37 . The method of claim 28 , further comprising classifying, by a classifier, the content into one or more categories.
38 . The method of claim 28 , wherein retrieving relevant a fact-based content based on the query comprises:
searching, by the one or more fact-based language models, the internet for content based on criteria; and
retrieving, by the one or more fact-based language models, content from the internet based on the criteria.