Systems and methods for automated mathematical chatting. The systems and methods convert any identified non-numerical inputs into vectors and then perform the mathematical equation utilizing the vectors instead of the nonnumeric inputs along with any other identified numeric inputs to obtain a numerical vector result. The systems and methods decode the numerical vector result into a result feature and then search one or more databases for output based on the result feature. The systems and methods provide the selected output from the one or more databases in response to the mathematical query.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for a mathematical chat bot, the system comprising: at least one processor; and a memory for storing and encoding computer executable instructions that, when executed by the at least one processor is operative to: collect a mathematical query; identify a variable that corresponds to each image of images in an equation in the mathematical query utilizing a mathematical knowledge graph to form corresponding variables; identify one or more mathematical operators in the equation in the mathematical query utilizing the mathematical knowledge graph; extract potential features for each image based on at least one of world knowledge and a natural language understanding system; filter the potential features; assign confidence scores to the potential features based on the filtering of the potential features; select a feature from the potential features for each image based on the confidence scores; encode the feature for each of the images into a vector; substitute the vector for each of the images into the corresponding variables; execute the equation to determine a result; decode the result into a result feature; search an image database for result images that correspond to the result feature; filter the result images; assign probability scores to the result images based on the filtering of the result images; select an answer image from the result images based on the probability scores; and provide the answer image to a user in reply to the mathematical query. 2. The system of claim 1, wherein the feature is a keyword. 3. The system of claim 1, wherein the feature is a sentence. 4. The system of claim 1, wherein the feature is a knowledge graph of keywords. 5. The system of claim 1, wherein the potential features are keywords. 6. The system of claim 1, wherein the potential features are sentences. 7. The system of claim 1, wherein the potential features are knowledge graphs of keywords. 8. The system of claim 1, wherein filter the potential features further comprises: determining whether each of the potential features is in the foreground or background; determining a specificity level for each of the potential features; and determining a pixel percentage occupied by each of the potential features. 9. The system of claim 1, filter the result images further comprises: determining whether the result feature is in the foreground or background in each of the result images; determining a pixel percentage occupied by the result feature in each of the result images; and determining a popularity of each of the result images. 10. The system of claim 1, wherein extract the potential features for each of the images based on at least one of the world knowledge and the natural language understanding system is performed utilizing a deep learning algorithm, and wherein encode the feature for each of the images into the vector is performed utilizing deep learning techniques. 11. The system of claim 1, wherein the images and the answer image are products. 12. A method for automated mathematical chatting, the method comprising: collecting a mathematical query; identifying a variable that corresponds to each nonnumeric input of inputs in an equation in the mathematical query utilizing a mathematical knowledge graph to form corresponding variables; identifying a mathematical operator in the equation in the mathematical query utilizing the mathematical knowledge graph; extracting potential features for each nonnumeric input utilizing at least one of world knowledge and a natural language understanding system; filtering the potential features; assigning confidence scores to the potential features based on the filtering of the potential features; selecting a feature from the potential features for each nonnumeric input based on the confidence scores; converting the feature for each nonnumeric input into a vector; substituting the vector for nonnumeric input into the corresponding variables; executing the equation to determine a result; decoding the result into a result feature; searching a database for outputs that correspond to the result feature; filtering the outputs; assigning probability scores to the outputs based on the filtering of the outputs; selecting an answer from the outputs based on the probability scores; and providing the answer to a user in response to the mathematical query. 13. The method of claim 12, wherein any nonnumeric inputs in the equation are images and text. 14. The method of claim 12, wherein any nonnumeric inputs in the equation are text. 15. The method of claim 12, wherein any nonnumeric inputs in the equation are at least one of a uniform resource locator, an audio file, an application, a video, and a website. 16. The method of claim 12, wherein any nonnumeric inputs in the equation and the answer are products. 17. The method of claim 12, wherein the feature is a keyword, a sentence, or a knowledge graph of keywords. 18. The method of claim 12, further comprising: determining that the confidence scores for a first nonnumeric input do not meet a predetermined threshold; in response to the determining that the confidence scores do not meet the predetermined threshold: extracting new potential features for each nonnumeric input utilizing at least one of the world knowledge and the natural language understanding system; filtering the new potential features; assigning new confidence scores to the new potential features based on the filtering of the new potential features; wherein selecting the feature from the potential features for each nonnumeric input based on the confidence scores comprises selecting the feature from the new potential features based on the filtering of the new potential features. 19. The method of claim 12, wherein the mathematical operator is a square root operator, a cubed operator, a squared operator, a multiplication operator, or a division operator. 20. A system for a mathematical chat bot, the system comprising: a computing device including a processing unit and a memory, the processing unit implementing a search engine and a conversation layer, the computing device is operable to: collect a mathematical query with inputs, wherein at least one of the inputs is a nonnumeric input; identify a variable that corresponds to each nonnumeric input of the inputs in an equation in the mathematical query utilizing a mathematical knowledge graph to form corresponding variables; identify one or more mathematical operators in the equation in the mathematical query utilizing the mathematical knowledge graph; select a feature from potential features for each nonnumeric input utilizing a deep learning algorithm; convert the feature for each nonnumeric input into a vector; substitute the vector for each nonnumeric input into the corresponding variables; execute the equation to determine a result; decode the result into a result feature; search a database for outputs that correspond to the result feature; select an answer from the outputs; and provide the answer in response to the mathematical query.