The structure of an untagged document can be derived using a predictive model that is trained in a supervised learning framework based on a corpus of tagged training documents. Analyzing the training documents results in a plurality of document part feature vectors, each of which correlates a category defining a document part (for example, “title” or “body paragraph”) with one or more feature-value pairs (for example, “font=Arial” or “alignment=centered”). Any suitable machine learning algorithm can be used to train the predictive model based on the document part feature vectors extracted from the training documents. Once the predictive model has been trained, it can receive feature-value pairs corresponding to a portion of an untagged document and make predictions with respect to the how that document part should be categorized. The predictive model can therefore generate tag metadata that defines a structure of the untagged document in an automated fashion.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A document structure extraction method comprising: accessing, by a document structure analytics server, an untagged document that comprises a plurality of document parts, wherein certain of the document parts have a visual appearance that is defined by formatting information included in the untagged document, and wherein at least two of the document parts are distinguishable from each other based on having distinctive visual appearances; extracting at least a portion of the formatting information from the untagged document; for a particular one of the plurality of document parts, generating one or more feature-value pairs using the extracted formatting information, wherein each of the generated feature-value pairs characterizes the visual appearance of the particular document part by associating a particular value with a particular formatting feature; using a predictive model to predict a categorization for the particular document part based on the one or more feature-value pairs, wherein the predictive model applies a machine learning algorithm to make predictions based on a collection of categorized feature-value pairs aggregated from a corpus of tagged training documents; and defining tag metadata that associates the particular document part with the predicted categorization generated by the predictive model. 2. The document structure extraction method of claim 1, wherein one of the generated feature-value pairs associates a font size formatting feature with a particular font size value. 3. The document structure extraction method of claim 1, further comprising: identifying a characteristic of the untagged document; and selecting the predictive model based on the corpus of tagged training documents also having the identified characteristic. 4. The document structure extraction method of claim 1, wherein accessing the untagged document further comprises receiving the untagged document from a client computing device; and the method further comprises applying the tag metadata to the untagged document to produce a tagged document, and sending the tagged document to the client computing device. 5. The document structure extraction method of claim 1, wherein one of the generated feature-value pairs associates a font size formatting feature with a particular value that is selected from a group consisting of a largest font in the untagged document, an intermediate-sized font in the untagged document, and a smallest font in the untagged document. 6. The document structure extraction method of claim 1, wherein one of the generated feature-value pairs associates a font size formatting feature with a particular value that is selected from a group consisting of a font size that is larger than a preceding paragraph, a font size that is smaller than the preceding paragraph, a font size that is larger than a following paragraph, and a font size that is smaller than the following paragraph. 7. The document structure extraction method of claim 1, wherein one of the generated feature-value pairs associates a formatting feature with a particular value that defines the formatting feature for a first document part in relation to the formatting feature for a second document part. 8. The document structure extraction method of claim 1, wherein one of the generated feature-value pairs associates a particular value selected from a group consisting of left justification, center justification, right justification, and full justification with a paragraph alignment formatting feature. 9. The document structure extraction method of claim 1, further comprising using the predictive model to determine a confidence level in the categorization for the particular document part. 10. The document structure extraction method of claim 1, wherein accessing the untagged document further comprises receiving, from a document viewer executing on a client computing device, the plurality of document parts and the formatting information. 11. The document structure extraction method of claim 1, wherein accessing the untagged document further comprises receiving, by the document structure analytics server, a plurality of untagged documents from a document management system. 12. The document structure extraction method of claim 1, further comprising sending the tag metadata from the document structure analytics server to a client computing device, wherein the untagged document is stored at the client computing device. 13. The document structure extraction method of claim 1, further comprising embedding the tag metadata into the untagged document to produce a tagged document, wherein sending the tag metadata to the client computing device comprises sending the tagged document to the client computing device. 14. The document structure extraction method of claim 1, further comprising modifying the untagged document such that the visual appearance of the particular document part is further defined by the predicted categorization generated by the predictive model. 15. A non-transitory computer readable medium encoded with instructions that, when executed by one or more processors, cause a document structure analysis process to be invoked, the process comprising: identifying a plurality of training documents; accessing a particular one of the training documents, the particular training document comprising a plurality of document parts, wherein a particular one of the document parts has (a) a visual appearance defined by formatting information included in the particular training document, and (b) a document part categorization; generating, for the particular document part, one or more feature-value pairs using the formatting information, wherein each of the generated one or more feature-value pairs characterizes the visual appearance of the particular document part by correlating a particular value with a particular formatting feature; defining a document part feature vector that links the generated one or more feature-value pairs with the document part categorization; storing the document part feature vector in a memory resource hosted by a document structure analytics server; and using the document part feature vector to train a predictive model in a supervised learning framework, wherein the predictive model is configured to establish a predicted document part categorization based on at least one feature-value pair received from a client computing device. 16. The non-transitory computer readable medium of claim 15, wherein: a particular one of the generated feature-value pairs defines a proportion of the particular training document; and the document part categorization is selected from a group consisting of a heading, a title, and a body paragraph. 17. The non-transitory computer readable medium of claim 15, wherein: the plurality of training documents are identified on the basis of a common characteristic that is selected from a group consisting of an author and a topic keyword; and the predictive model is associated with the common characteristic. 18. A document structure evaluation system that comprises a memory device and a processor that is operatively coupled to the memory device, wherein the processor is configured to execute instructions stored in the memory that, when executed, cause the processor to carry out a document structure evaluation process that comprises: displaying, in a document viewer, an untagged document that comprises a plurality of document parts, wherein certain of the document parts have a visual appearance that is defined by formatting information included in the untagged document, and wherein at least two of the document parts are distinguishable from each other based on having distinctive visual appearances; sending, to a document structure analytics server, a particular one of the document parts and formatting information that characterizes the visual appearance of the particular document part; receiving, from the document structure analytics server, a predicted categorization for the particular document part; and embedding into the untagged document metadata that correlates the particular document part with the predicted categorization received from the document structure analytics server. 19. The document structure evaluation system of claim 18, wherein the process further comprises: receiving, from the document structure analytics server, a confidence level associated with the predicted categorization; and displaying, in the document viewer, the predicted categorization and the confidence level. 20. The document structure evaluation system of claim 18, wherein the process further comprises: displaying, in the document viewer, the predicted categorization; and receiving, from a user of the document viewer, an acceptance of the predicted categorization, wherein the acceptance is received before the metadata is embedded into the untagged document.