System and method for generating graphical representation of search results

Abstract

Methods and systems for generating one or more graphical representations of search results are disclosed herein. In one embodiment, a method includes generating search results. The method includes extracting metadata from the search results to generate one or more classifiers. The method includes determining one or more graphical representations of the search results based on the one or more classifiers. The method includes displaying the one or more graphical representations to a user interface. The method also includes displaying a displayed graphical representation having an emphasized portion based on the selection of the data point in response to the selection of the data point. The method includes emphasizing additional portions based on further selection of other data points.

Description

Technical Field

[0001] The present disclosure generally relates to systems and methods for generating graphical representations of search results, and in particular to systems and methods for generating interactive and / or dynamic graphical representations of semantic search results. Background Art

[0002] Typically, a user can submit a search (such as a document search) by submitting a search query via a user interface and / or a web browser. After submitting the search query, the user may receive the search results in the form of a static list or a static table list. Document searches, particularly enterprise-based document searches, may generate multiple returned documents in different formats for a given search query. In some examples, since the user may need to spend a lot of time and effort to understand the search results, the large number of results and the format of those results may cause problems. In other words, in order to understand the search and / or find the desired document or result, the user may open and view each document and decide whether the document provides the information sought. In addition, some users may only view the first few search results, which may not provide the information sought. Summary of the invention

[0003] In view of the foregoing, Applicants have recognized these and other problems in the art, and have recognized systems and methods for generating graphical representations of search results, and in particular, systems and methods for generating interactive and / or dynamic graphical representations of semantic search results for search queries, enterprise search queries, and / or document search queries.

[0004] The present disclosure as a whole relates to a system for solving the above-mentioned related problems. In particular, the system can realize the basic real-time generation and display of interactive and / or dynamic graphical representations of semantic search results of search queries, enterprise search queries and / or document search queries. Such a system can be configured to receive a search query for a set of data or searchable data from one or more computing devices, for example, through a user interface (e.g., a graphical user interface or a web-based user interface). The search query can be submitted to a search circuit and / or a search instruction. The search circuit and / or the search instruction can generate a set of search results or semantic search results (e.g., a list of documents, a list of websites (e.g., internal and / or external) including links to such documents). The search circuit and / or the search instruction can then receive the extracted metadata and / or extract the metadata in real time (e.g., via a natural language processing (NLP) algorithm) from a set of pre-extracted or pre-processed metadata (e.g., based on the pre-processing of a document set or an entire document set) based on the set of data. In one embodiment, a machine learning model or an NLP algorithm can utilize the metadata extracted in real time and / or the pre-extracted data to generate, for example, the subject of each document. Using the extracted or pre-extracted metadata, the search circuitry and / or search instructions may generate one or more classifiers for each result in the semantic search results. In other words, each result may include multiple specified classifiers that describe and / or indicate various qualities and / or identifiers associated with the search results (e.g., who wrote the document, when the document was written, when the document was published, the region or country of origin of the document, the subject or topic of the document, keywords associated with the document, etc.).

[0005] Drawing circuits and / or drawing instructions can determine one or more graphical representations based on one or more classifiers of each result in the semantic search results. In an embodiment, for example, a drawing circuit can determine that a specific graphical representation can be generated for the search results based on one or more classifiers (e.g., a graphical representation of the number of documents based on each author, each publication year, each keyword and / or each theme). The determined one or more graphical representations can be displayed to the user via a user interface (e.g., a graphical user interface or a web user interface). In one embodiment, the user can select or choose which type of graphical representation to display before and / or after submitting a search query. One or more graphical representations can be interactive and / or dynamic (e.g., a drawing circuit and / or drawing instructions can determine and / or generate one or more interactive and / or dynamic graphical representations). For example, a user can select any data point and / or part of each displayed graphical representation. At the time of the first selection or in response to the first selection, a drawing circuit and / or drawing instructions can determine and / or generate one or more new graphical representations. One or more new graphical representations may include a highlighted portion of the selected data point and / or part of the graphical representation. For example, the selected author can highlight any document including the author in the graphical representation. Then, the user can select the same data point and / or part. Based on such selections, the drawing circuitry and / or drawing instructions may generate one or more new graphical representations that show the search results including the first selection and remove the results lacking the first selection. In one embodiment, the user may select different data points and / or portions, and in response, the drawing circuitry may determine one or more graphical representations with emphasized portions based on the first selection and the second selection, and in some examples, determine one or more graphical representations with emphasized portions based on overlapping portions of the first selection and the second selection.

[0006] In one embodiment, the search circuitry and / or search instructions may be configured to generate new semantic search results in response to a selection of a data point and / or portion of one of the one or more graphical representations. The search circuitry and / or search instructions may receive or access pre-extracted metadata and / or extract metadata and generate new one or more classifiers based on the metadata. The drawing circuitry and / or drawing instructions may then generate one or more new graphical representations.

[0007] In another embodiment, the graphical representation may include different types of graphics, each of which is displayed via the user interface, such as a bar graph, a pie chart, a line graph, a cluster, etc. The user interface may additionally include a list button and / or a download button. The drawing circuitry and / or drawing instructions may generate a tabular list of associated search results in response to the selection of the list button. When the user selects different data points in the graphical representation, the corresponding tabular list may be based on a subset of the search results representing the user's selection of the data point. When the user selects the download button, the drawing circuitry and / or drawing instructions may prompt or initiate the download of the associated search results in a default or selected format.

[0008] Therefore, embodiments of the present disclosure relate to a method for generating one or more dynamic graphical representations of semantic search results. The method may include: generating semantic search results substantially in real time via the search circuit in response to the search circuit receiving a search query. The method may include: receiving or accessing metadata or pre-extracted metadata and / or extracting metadata in real time, and generating one or more classifiers for each result in the semantic search results based on the metadata. The method may include: determining one or more graphical representations of the semantic search results based on one or more classifiers of each result in the semantic search results via the drawing circuit. The method may include: displaying each of the one or more graphical representations to a user interface via the drawing circuit. The method may include: in response to a first selection of a first data point in the displayed graphical representation by a user, displaying the displayed graphical representation with an emphasized portion based on the first selection via the user interface. The method may include: after the first selection of the first data point and in response to the user's selection of the second data point, displaying the displayed graphical representation with an emphasized portion based on the selection of the second data point and an emphasized portion based on the overlap between the first selection of the first data point and the selection of the second data point via the user interface. The method may include: in response to a second selection of the first data point, determining one or more subset graphical representations via a drawing circuit and a subset of one or more classifiers for each result in the semantic search results, the subset of one or more classifiers for each result in the semantic search results being based on the first data point. The method may include: displaying each of the one or more subset graphical representations to a user interface via a drawing circuit, thereby replacing and forming the displayed graphical representation. The method may include: in response to a first selection of a list button, displaying a tabular list of semantic search results corresponding to the displayed graphical representation to the user interface via a drawing circuit. In another embodiment, a list button or another button in the user interface may enable a user to download the semantic search results (e.g., as a spreadsheet, a comma separated value file, a text editing document, and / or another list or database type document, as will be understood by those skilled in the art) to a computing device corresponding to the user.

[0009] In one embodiment, one or more graphical representations may include graphical types, including one or more bar graphs, pie charts, line graphs, three-dimensional graphs, clusters, or other chart types. The drawing circuit may generate clusters based on text from documents included in the semantic search results and natural language processing algorithms. The drawing circuit may generate a second cluster based on a second selection and a subset of the second cluster. The drawing circuit may generate a set of additional one or more graphical representations corresponding to the semantic search results based on the selected data points in one or more different data points in response to additional selections of one or more different data points. The semantic search results or a subset of the semantic search results and the displayed graphical representations may be dynamically linked. The first selection of the first data point, the second selection of the first data point, and the selection of the second data point may be received from a user interface. The user interface may include a graphical user interface or a web user interface displayed on a computing device of the user. Multiple graphics may be displayed as one or more graphical representations. Each of the multiple graphics may be based on a classifier in one of the one or more classifiers of each result in the semantic search results. The one or more classifiers may include one or more of the following: a date the document was uploaded, an author of the document, a region associated with the author of the document, a subject of the document, a context corresponding to the document, a business unit associated with the document or the author of the document, a team associated with the document or the author of the document, a department associated with the document or the author of the document, a competitor associated with the context of the document, a project number associated with the document, or a keyword associated with the document.

[0010] Another embodiment of the present disclosure relates to a system for dynamic visualization of semantic search results. The system may include a search circuit. The search circuit may be configured to determine semantic search results based on a received search query for a set of data. The search circuit may be configured to receive and / or access metadata or pre-extracted metadata based on the set of data and / or extract metadata based on the entire set or a portion of the set of data. The search circuit may be configured to determine one or more classifiers for each result in the semantic search results based on the metadata. The system may include a drawing circuit. The drawing circuit may be configured to generate a set of one or more graphical representations of the search results based on one or more classifiers for each result, user input, or one or more user selections at substantially the same time as determining one or more classifiers for each result. The drawing circuit may be configured to determine a subset of one or more classifiers for each result of the semantic search results based on one or more user selections. The drawing circuit may be configured to dynamically link the subset to the set of one or more graphical representations. The system may include a display circuit. The display circuit may be configured to display the set of one or more graphical representations on a user interface of a remote computing device. The display circuitry may be configured to: in response to selection of a list button, display a dynamic link subset of one or more classifiers for each result in the semantic search results on a user interface of the remote computing device. The display circuitry may be configured to: in response to selection of a download button, prompt or initiate downloading of the dynamic link subset.

[0011] In another embodiment, the one or more user selections may include a selection of one or more portions of the set of one or more graphical representations. The drawing circuitry may be configured to, in response to the selection of one of the one or more portions of the set of one or more graphical representations, update the set of one or more graphical representations based on the selection of one of the one or more portions of the set of one or more graphical representations. The drawing circuitry may be configured to, in response to the updating of the set of one or more graphical representations, dynamically link the set of one or more graphical representations to a subset of the one or more classifiers for each result in the semantic search results based on the selection of one of the one or more portions of the set of one or more graphical representations.

[0012] One of the one or more graphical representations may be a cluster. The drawing circuitry may be configured to form the clusters via the natural language processing circuitry. The natural language processing circuitry may use text from each document in the semantic search results. The display circuitry may be configured to transmit, in response to selection of a download button, a list of documents stored in the dynamically linked set of search results to a remote computing device via the user interface.

[0013] Another embodiment of the present disclosure relates to a method for generating one or more dynamic graphical representations of semantic search results. The method may include: in response to the search circuit receiving a search query for a set of data, generating a first set of semantic search results via the search circuit in substantially real time. The method may include: receiving and / or accessing metadata or pre-extracted metadata based on the set of data and / or extracting metadata from the entire set or a portion of the set of data, thereby generating one or more classifiers for each result in the first set of semantic search results based on the metadata. The method may include: determining one or more graphical representations of the first set of semantic search results based on one or more classifiers of each result in the first set of semantic search results via the drawing circuit. The method may include: displaying each of the one or more graphical representations of the first set of semantic search results to a user interface via the drawing circuit. The method may include: generating a second set of semantic search results via the search circuit in response to a first selection of a first data point in the displayed graphical representation by a user. The method may include: receiving metadata based on the entire set or a full set of data and / or extracting metadata from each result in the second set of semantic search results, thereby generating one or more classifiers for each result in the second set of semantic search results based on the metadata. The method may include: determining via a drawing circuit that one or more graphical representations of the second set of semantic search results may be based on one or more classifiers of each result in the first set of semantic search results and one or more classifiers of each result in the second set of semantic search results. The method may include: displaying each of the one or more graphical representations of the second set of semantic search results to a user interface via a drawing circuit. The method may include: in response to a first selection of a list button, displaying to the user interface via a drawing circuit a tabular list of semantic search results based on the semantic search results corresponding to the one or more graphical representations currently displayed. In another embodiment, the method may include: in response to selection of another button, prompting the user interface via a graphics circuit to select a location on the corresponding computing device to send the result list to the result list and / or the format of the result list, and after selecting the location, downloading the result list to the selected location in the selected format and / or a default format.

[0014] In one embodiment, the one or more graphical representations of the second set of semantic search results may include an emphasized portion corresponding to the first selection. The drawing circuitry may base each portion of the one or more graphical representations of the first set of semantic search results on one or more classifiers for each result in the first set of semantic search results. Each portion may include a user-selectable region.

[0015] According to the drawings, specific embodiments and examples of this article, additional and / or alternative purposes, features and advantages of the present disclosure will become apparent to those skilled in the art. However, the applicant notes that the drawings, specific embodiments and examples, although indicating certain embodiments of the present disclosure, are provided for illustrative purposes only and are not intended to limit or imply specific limitations. In addition, according to the specific embodiments, certain changes and modifications within the spirit and scope of the disclosed technology will become apparent to those of ordinary skill in the relevant art. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The disclosed aspects, features and advantages of the present disclosure will be better understood with reference to the following description, examples, claims and drawings. Applicants note, however, that the drawings illustrate certain embodiments of the present disclosure and should not be considered as limiting the breadth and scope of the present disclosure:

[0017] Figure 1 is a schematic diagram of a system for generating one or more graphical representations of search results according to certain embodiments of the present disclosure;

[0018] Figure 2 is another schematic diagram of a system for generating one or more graphical representations of search results according to certain embodiments of the present disclosure;

[0019] Figure 3A , Figure 3B , Figure 3C , Figure 3D , Figure 3E and Figure 3F is a user interface (UI) for displaying one or more graphical representations of search results according to certain embodiments of the present disclosure;

[0020] Figure 4A , Figure 4B and Figure 4C is a flow chart for generating one or more graphical representations of search results according to certain embodiments of the present disclosure; and

[0021] Figure 5 is another flow chart for generating one or more graphical representations of search results according to certain embodiments of the present disclosure. DETAILED DESCRIPTION

[0022] In order that the features and advantages of the embodiments of the systems and methods disclosed herein, as well as other features and advantages that will become apparent, may be understood in greater detail, a more particular description of the embodiments of the systems and methods briefly summarized above may be obtained by reference to the following detailed description of the embodiments thereof, one or more of which are further illustrated in the accompanying drawings, which form a part of this specification. However, it should be noted that the drawings illustrate only various embodiments of the systems and methods disclosed herein, and therefore should not be considered to limit the scope of the systems and methods disclosed herein, as it may also include other effective embodiments.

[0023] The following definitions are provided for the purpose of clarifying certain terms and phrases of the present disclosure and are in no way intended to unnecessarily or unduly limit any embodiments or aspects related thereto.

[0024] When used in the claims and / or specification, the term "reduce" or any variation of these terms includes any measurable decrease or complete inhibition of achieving a desired result.

[0025] The term "effective" as used in the specification and / or claims means sufficient to achieve a desired, intended, or intended result.

[0026] The use of the word "a" or "an" when used in conjunction with the terms "comprising," "including," "containing," or "having" in the claims or the specification may mean "one," but it is also consistent with the meaning of "one or more," "at least one," and "one or more than one."

[0027] The words "comprising" (and any forms of comprising, such as "comprise" and "comprises"), "having" (and any forms of having, such as "have" and "has"), "including" (and any forms of including, such as "includes" and "include"), or "containing" (and any forms of containing, such as "contains" and "contain") are inclusive or open-ended, and do not exclude additional, unrecited elements or method steps.

[0028] Typically, a user may submit a search request, such as a document search request, by submitting a search query to receive a static list of search results. For example, a large number of results, such as documents and / or website links, may be returned in the static list of search results. In other words, many results may be returned. Therefore, a user submitting a search may obtain a large amount of data. Based on such a large number of results, some users may only view the first few search results, which may not provide the information sought.

[0029] The present disclosure as a whole relates to a system for solving the related problems as described above. In particular, the system can realize the basic real-time generation and display of interactive and / or dynamic graphical representations of semantic search results for a search query, enterprise search query and / or document search query for a set of data. Such a system can be configured to receive a search query for the set of data from one or more computing devices, for example, through a user interface (e.g., a graphical user interface or a web-based user interface). The search query can be submitted to a search circuit and / or a search instruction. The search circuit and / or the search instruction can generate a set of search results or semantic search results (e.g., a document list, a website list (e.g., internal and / or external) including links to such documents). Then, the search circuit and / or the search instruction can receive and / or access pre-extracted metadata from a pre-processing module, circuit, pipeline and / or instruction based on the entire group or full group of documents or other types of data to be searched (e.g., the group of data or searchable data). In another embodiment, the search circuit and / or the search instruction can extract metadata in real time. In such examples, the search circuitry and / or search instructions may utilize a natural language processing (NLP) algorithm, instruction, or model or other algorithm or model to extract metadata from the entire set of searchable data or a subset of the data (e.g., search results). Using the extracted data and, in some examples, a machine learning model, the search circuitry and / or search instructions may generate one or more classifiers for each result in the semantic search results. In other words, each result may include multiple specified classifiers that describe and / or indicate various qualities and / or identifiers associated with the search results (e.g., who wrote the document, when the document was written, when the document was published, the region or country of origin of the document, the subject or topic of the document, keywords associated with the document, etc.).

[0030] Drawing circuits and / or drawing instructions can determine one or more graphical representations based on one or more classifiers of each result in the semantic search results. In an embodiment, for example, a drawing circuit can determine that a specific graphical representation can be generated for the search results based on one or more classifiers (e.g., a graphical representation of the number of documents based on each author, each publication year, each keyword and / or each theme). The determined one or more graphical representations can be displayed to the user via a user interface (e.g., a graphical user interface or a web user interface). In one embodiment, the user can select or choose which type of graphical representation to display before and / or after submitting a search query. One or more graphical representations can be interactive and / or dynamic (e.g., a drawing circuit and / or drawing instructions can determine and / or generate one or more interactive and / or dynamic graphical representations). For example, a user can select any data point and / or part of each displayed graphical representation. At the time of the first selection or in response to the first selection, a drawing circuit and / or drawing instructions can determine and / or generate one or more new graphical representations. One or more new graphical representations may include a highlighted portion of the selected data point and / or part of the graphical representation. For example, the selected author can highlight any document including the author in the graphical representation. Then, the user can select the same data point and / or part. Based on such selections, the drawing circuitry and / or drawing instructions may generate one or more new graphical representations that show the search results including the first selection and remove the results lacking the first selection. In one embodiment, the user may select different data points and / or portions, and in response, the drawing circuitry may determine one or more graphical representations with emphasized portions based on the first selection and the second selection, and in some examples, determine one or more graphical representations with emphasized portions based on overlapping portions of the first selection and the second selection.

[0031] In an alternative embodiment, the search circuit and / or search instructions may be configured to generate new semantic search results in response to the selection of a data point and / or portion of one of the one or more graphical representations. The search circuit and / or search instructions may receive metadata from a pre-processed entire set or full set of metadata and / or may extract metadata in real time, and generate new one or more classifiers based on the metadata and in some examples using a machine learning model. Then, the drawing circuit and / or drawing instructions may generate one or more new graphical representations. Therefore, each time a data point or portion of one of the one or more graphical representations is selected, a new search result may be formed. Based on this generation of new search results, each instance of one or more graphical representations may be dynamically and continuously updated.

[0032] Thus, a user can quickly digest or view information (e.g., search results and / or documents in the search results) based on the presented format (e.g., one or more graphical representations). In addition, the user can refine the results via one or more different selections of different data points or portions of the one or more graphical representations. In other words, the one or more graphical representations can be interactive, allowing selection of various data points or portions of the one or more graphical representations. In addition, the described system can increase user productivity and allow a user to locate relevant documents and / or other results in a shorter amount of time than a typical amount of time.

[0033] Figure 1 1 is a schematic diagram of a system for generating one or more graphical representations of search results according to certain embodiments of the present disclosure. System 100 may include a dynamic mapping system 102. Dynamic mapping system 102 may include one or more processors 104 and a memory 106. Memory 106 may include and / or store instructions and / or models that may be executed by one or more processors 104. Memory 106 may include search instructions 108, drawing instructions 110, and display instructions 112.

[0034] The search instructions 108 or display instructions 112 can generate a user interface (UI) 114A, 114B to 114N including a search box when executed by one or more processors 104, and the search box is configured to receive a search query for a set of data from one or more computing devices 116A, 116B to 116N. When the search query is submitted by one of the computing devices 116A, 116B to 116N via the corresponding UI 114A, 114B to 114N, the search instructions 108 can generate search results or semantic search results based on the search query. The search results may include multiple documents (e.g., in a document search), a list of websites, and / or other results. Then, the search instructions 108 can extract metadata in real time from each of the search results or semantic search results (e.g., from each document and / or from each website). For example, the search instructions 108 can generate or produce metadata using an NLP algorithm. In another embodiment, metadata can be pre-extracted based on the entire set of searchable data (e.g., documents and / or other data in the search, such as websites) and using pre-processing instructions, and in addition to or in lieu of real-time data extraction, search instructions 108 can also receive and / or access pre-extracted metadata. Then, search instructions 108 can generate one or more classifiers based on the metadata of each result in the search results or semantic search results. In such embodiments, search instructions 108 may include a machine learning model configured to generate one or more classifiers. Metadata and / or one or more classifiers may be stored in memory 106 with each result. In addition, metadata and / or one or more classifiers corresponding to the results may include an indicator indicating which result corresponds to the particular metadata and / or one or more classifiers. Search instructions 108 may perform similar operations in response to receiving a selection of a data point or portion of a graphical representation. For example, if a user selects a data point or portion of a graphical representation, search instructions 108 may generate another set of search results or semantic search results based on the original search query and the selected data point or portion of the graphical representation. Search instructions 108 may extract metadata from another set of search results or semantic search results and / or receive or access corresponding pre-extracted metadata. The search instructions 108 may generate one or more classifiers based on the extracted and / or pre-extracted metadata. The one or more classifiers may include one or more of the following: the date the document was uploaded, the author of the document, the region associated with the author of the document, the subject of the document, the context corresponding to the document, the business unit associated with the document or the author of the document, the team associated with the document or the author of the document, the department associated with the document or the author of the document, the competitor associated with the context of the document, the project number associated with the document, the keywords associated with the document, and / or other factors based on the content and / or metadata associated with each document.

[0035] When executed by one or more processors 104, the drawing instructions 110 may determine one or more graphical representations or a set of one or more graphical representations of the search results or semantic search results based on one or more classifiers of each result in the search results or semantic search results. The drawing instructions 110 may determine the type of graphical representation to be generated based on various factors. For example, the drawing instructions 110 may determine the type of graphical representation based on the display settings selected by the user, the generated classifiers, and / or other factors. Once one or more types of graphical representations are determined, the drawing instructions 110 may generate one or more graphical representations or a set of one or more graphical representations. The types of graphical representations may include one or more bar charts, pie charts, line charts, three-dimensional graphs, clusters, or other chart types. In such examples, a clustering diagram may be generated based on an NLP algorithm, instruction, or model. The NLP algorithm, instruction, or model may be included in the memory 106 or in the dynamic drawing system 102 or in other storage devices in communication with the dynamic drawing system. Such clustering may be generated at the request of a user or in response to a user. In another example, a clustering diagram may be automatically generated. In another example, drawing instructions 110 may generate clusters using an NLP algorithm, instruction, or model and one or more classifiers, extracted and / or pre-extracted metadata, a portion of text from a document associated with each search result, and / or the full text from a document associated with each search result.

[0036] The drawing instructions 110 can be configured to generate interactive and dynamic graphical representations when executed. When determining or generating one or more graphical representations, the drawing instructions 110 can configure the one or more graphical representations to include optional data points and / or portions of the actual graphical representation. For example, each bar in a bar graph can be selectable, each portion of a pie chart can be selectable, and so on. Once the user selects a data point or portion of one or more graphical representations, the drawing instructions 110 can perform different actions. In one embodiment, the drawing instructions 110 can emphasize or highlight the portion of one or more graphical representations corresponding to the selected data point or portion. If the user selects the same data point or portion again, the drawing instructions 110 can generate a new set of one or more graphical representations based on the selected data point or portion. If the user selects a second data point or portion, in addition to emphasizing the first data point or portion in the one or more graphical representations, the drawing instructions 110 can also emphasize the second data point or portion. Overlapping portions in one or more graphical representations can be emphasized to indicate that this overlapping portion includes the first data point or portion and the second data point or portion. In another embodiment, drawing instructions 110 may generate new one or more graphical representations based on the data points or portions of the graphical representation in response to a first selection of the data points or portions of the graphical representation and a subsequent second selection. In such examples, the new one or more graphical representations may be a subset of the graphical representation.

[0037] In one embodiment, drawing instructions 110 may dynamically link the corresponding search results to one or more graphical representations currently displayed. For example, if the user selects a first data point or portion of one or more graphical representations so that one or more new graphical representations are generated, drawing instructions 110 may link the corresponding search results or a subset of the search results (e.g., documents and / or website links) to the one or more new graphical representations. Thus, if the user selects a list button or a download button, the corresponding search results (e.g., a subset of the search results) are displayed or downloaded to the user's computing device based on the one or more new graphical representations.

[0038] In one embodiment, the search instructions 108 and the drawing instructions 110 may perform the functions described herein in real time or substantially in real time. The search instructions 108 and / or the drawing instructions 110 may perform the functions of multiple computing devices (e.g., computing devices 116A, 116B to 116N) simultaneously or substantially simultaneously and continuously. For example, when determining or generating one or more classifiers, the system 100 may generate a set of one or more graphical representations of search results based on the one or more classifiers substantially simultaneously. The system 100 may link any subset of the search results to an updated graphical representation when a data point is selected.

[0039] Display instructions 112 may display one or more graphical representations at a corresponding UI (e.g., UI 114A, 114B to 114N corresponding to computing devices 116A, 116B to 116N that initiated a search query and / or selected data points or portions of one or more graphical representations) in response to determining one or more graphical representations.

[0040] In one embodiment, the search query can be an internal search query. The internal search query can be a search performed on a website or GUI within an organization or enterprise. The results from such examples may include internal and / or private documents and / or other results. In another embodiment, the system 100 may include a search via the Internet, thereby providing access to public documents and / or other results.

[0041] In some examples, the dynamic mapping system 102 may be a computing device. The term "computing device" is used herein to refer to any or all of a programmable logic controller (PLC), a programmable automation controller (PAC), an industrial computer, a server, a virtual computing device or environment, a desktop computer, a personal data assistant (PDA), a laptop computer, a tablet computer, a smartbook, a palmtop computer, a personal computer, a smart phone, a virtual computing device, a cloud-based computing device, and similar electronic devices equipped with at least one processor and any other physical components necessary to perform the various operations described herein. Devices such as smart phones, laptop computers, and tablet computers are generally collectively referred to as mobile devices.

[0042] The term "server" or "server device" is used to refer to any computing device that can be used as a server, such as a primary exchange server, a web server, a mail server, a document server, or any other type of server. A server can be a dedicated computing device or a server module (e.g., an application) hosted by a computing device that enables the computing device to act as a server. A server module (e.g., a server application) can be a full-featured server module, or a lightweight or auxiliary server module (e.g., a lightweight or auxiliary server application) configured to provide synchronization services among dynamic databases on a computing device. A lightweight server or auxiliary server can be a streamlined version of server-type functionality that can be implemented on a computing device such as a smart phone, enabling it to be used as an Internet server (e.g., a corporate email server) only to the extent necessary to provide the functionality described herein.

[0043] As used herein, "non-transitory machine-readable storage medium" or "memory" may be any electronic, magnetic, optical, or other physical storage device that includes or stores information such as executable instructions, data, etc. For example, any machine-readable storage medium described herein may be any one or a combination of random access memory (RAM), volatile memory, non-volatile memory, flash memory, storage drive (e.g., hard drive), solid-state drive, any type of storage disk, etc. The memory may store or include instructions that may be executed by a processor.

[0044] As used herein, a "processor" or "processing circuitry" may include, for example, one processor or multiple processors included in a single device or distributed across multiple computing devices. A processor (e.g., Figure 1 The processor 102 shown may be at least one of a central processing unit (CPU), a semiconductor-based microprocessor, a graphics processing unit (GPU), a field programmable gate array (FPGA) for retrieving and executing instructions, a real-time processor (RTP), other electronic circuitry suitable for retrieving and executing instructions stored on a machine-readable storage medium, or a combination thereof.

[0045] In one embodiment, the machine learning model or classifier (e.g., NLP algorithm, instruction or model) can be a supervised or unsupervised learning model. In one embodiment, the machine learning model or classifier can be based on a decision tree, a random forest model, a random forest using bagging or boosting (such as gradient boosting), a neural network method, a support vector machine (SVM), other supervised learning models, other semi-supervised learning models, other unsupervised learning models, or one or more of some combination thereof, as will be readily understood by those of ordinary skill in the art. In one embodiment, the NLP algorithm, instruction or model can generate a clustering graph.

[0046] Figure 2 2 is another schematic diagram of a system for generating one or more graphical representations of search results according to certain embodiments of the present disclosure. Device 200 may include processing circuitry 202, memory 204, communication circuitry 206, search circuitry 208, drawing circuitry 210, and display circuitry 212, each of which will be described in more detail below. Although the various components are only described in detail below, Figure 2 200 is shown as being connected to the processing circuit 202, but it should be understood that the device 200 may also include a bus ( Figure 2 The apparatus 200 may be configured to perform various operations described herein, such as the above combined Figure 1 And the following combination Figures 3A to 5 Describe the operations.

[0047] The processing circuit 202 (and / or a coprocessor or auxiliary processor or any other processor otherwise associated with the processor) may communicate with the memory 204 via a bus for passing information between components of the device. The processing circuit 202 may be embodied in a variety of different ways and may, for example, include one or more processing devices configured to execute independently. In addition, the processor may include one or more processors configured in series via a bus to enable independent execution of software instructions, pipelining and / or multithreading.

[0048] Processing circuit 202 may be configured to execute software instructions stored in memory 204 or otherwise accessible to processing circuit 202 (e.g., software instructions stored on a separate storage device). In some cases, processing circuit 202 may be configured to perform hard-coded functions. Thus, whether configured by hardware or software methods, or by a combination of hardware and software, processing circuit 202 represents an entity (e.g., physically embodied in a circuit) capable of performing operations according to various embodiments of the present disclosure when configured accordingly. Alternatively, as another example, when processing circuit 202 is embodied as an executor of software instructions, the software instructions may specifically configure processing circuit 202 to perform the algorithms and / or operations described herein when executing the software instructions.

[0049] The memory 204 is non-transitory and may include, for example, one or more volatile and / or non-volatile memories. In other words, for example, the memory 204 may be an electronic storage device (e.g., a computer-readable storage medium). The memory 204 may be configured to store information, data, content, applications, software instructions, etc. for enabling the apparatus 200 to perform various functions according to the example embodiments contemplated herein.

[0050] The communication circuit 206 can be any device configured to receive and / or transmit data from / to a network and / or any other device, circuit or module that communicates with the device 200, such as a device or circuit embodied in hardware or a combination of hardware and software. In this regard, the communication circuit 206 may include, for example, a network interface for implementing communication with a wired or wireless communication network. For example, the communication circuit 206 may include one or more network interface cards, antennas, buses, switches, routers, modems, and supporting hardware and / or software, or any other device suitable for implementing communication via a network. In addition, the communication circuit 206 may include processing circuits for causing such signals to be transmitted to the network or for processing the reception of signals received from the network. For example, the communication circuit 206 may receive a search query and / or transmit results and corresponding one or more graphical representations from and / or to a user interface associated with a computing device.

[0051] The apparatus 200 may include a search circuit 208 configured to receive (e.g., via the communication circuit 206) a search query, determine search results, extract metadata from the search results, receive and / or access pre-extracted metadata, and / or generate one or more classifiers based on the extracted and / or pre-extracted metadata (e.g., using, for example, a machine learning model). The search circuit 208 may be configured to generate additional search results based on a user selection of a data point or portion of one of the one or more graphical representations. The search circuit 208 may perform these operations using the processing circuit 202, the memory 204, or any other hardware component included in the apparatus 200, as described below in conjunction with FIGS. 3 to 7B. The search circuit 208 may also collect data from various sources using the communication circuit 206. The output of the search circuit 208 may be transmitted to other circuits of the apparatus 200 (e.g., the drawing circuit 210).

[0052] In addition, the apparatus 200 includes a drawing circuit 210 that can determine the type of one or more graphical representations, determine one or more graphical representations, and dynamically link each search result to the one or more graphical representations. The drawing circuit 210 can also utilize the communication circuit 206 to collect data (e.g., search results) from various sources (e.g., search circuit 208 or computing devices 118A, 118B, 118N), and in some embodiments, can utilize the processing circuit 202 and / or the memory 204 to determine one or more graphical representations. The output of the drawing circuit 210 can be transmitted to other circuits of the apparatus 200.

[0053] In addition, the device 200 also includes a display circuit 212, which can display one or more graphical representations and / or a tabular list of search results. The display circuit 212 can also collect data (e.g., one or more graphical representations) from various sources (e.g., drawing circuit 210) using the communication circuit 206, and in some embodiments, can display one or more graphical representations using the processing circuit 202 and / or the memory 204.

[0054] Although the components 202-212 are described in part using functional language, it should be understood that a particular implementation necessarily includes the use of specific hardware. It should also be understood that some of these components 202-212 may include similar or common hardware. For example, the search circuit 208, the drawing circuit 210, and the display circuit 212 may each sometimes utilize the use of the processing circuit 202, the memory 204, or the communication circuit 206, so that no repeated hardware is required to facilitate the operation of these physical elements of the device 200 (although in some embodiments, dedicated hardware elements may be used for any of these components, such as those where enhanced parallelism may be desired). Therefore, the use of the terms "circuit" and "engine" with respect to the elements of the device should be interpreted as necessarily including specific hardware configured to perform the functions associated with the specific elements described. Of course, although the terms "circuit" and "engine" should be broadly understood to include hardware, in some embodiments, the terms "circuit" and "engine" may additionally refer to software instructions that configure the hardware components of the device 200 to perform the various functions described herein.

[0055] Although the search circuit 208, the drawing circuit 210, and the display circuit 212 may utilize the processing circuit 202, the memory 204, or the communication circuit 206 as described above, it should be understood that any of these elements of the device 200 may include one or more dedicated processors, specially configured field programmable gate arrays (FPGAs), or application specific interface circuits (ASICs) to perform their corresponding functions, and the processor 202 may be used to execute software stored in the memory or storage 204, and the communication circuit 206 may be used to implement any function not performed by a dedicated hardware element. However, in all embodiments, it should be understood that the search circuit 208, the drawing circuit 210, and the display circuit 212 are implemented via a specific machine designed to perform the functions described herein in conjunction with such elements of the device 200.

[0056] In some embodiments, various components of the device 200 may be remotely hosted (e.g., hosted by one or more cloud servers), and thus need not physically reside on the corresponding device 200. Thus, some or all of the functionality described herein may be provided by third-party circuitry. For example, a given device 200 may access the third-party circuitry via any type of network connection that facilitates the transfer of data and electronic information between the device 200 and one or more third-party circuitry. In turn, the device 200 may communicate remotely with one or more of the other components described above as comprising the device 200.

[0057] As will be appreciated based on this disclosure, the example embodiments contemplated herein may be implemented by the apparatus 200. In addition, some example embodiments may take the form of a computer program product comprising software instructions stored on at least one non-transitory computer-readable storage medium (e.g., memory 204). Any suitable non-transitory computer-readable storage medium may be utilized in such embodiments, some examples of which are non-transitory hard disks, CD-ROMs, flash memories, optical storage devices, and magnetic storage devices. It should be understood that for example, the computer program product may be implemented by the apparatus 200. Figure 2 Certain devices embodied in the apparatus 200 described in the drawings may be such that loading the software instructions onto a computing device or apparatus creates a special purpose machine including means for implementing the various functions described herein.

[0058] Figure 3A , Figure 3B , Figure 3C , Figure 3D , Figure 3E and Figure 3F is a user interface (UI) for displaying one or more graphical representations of search results according to certain embodiments of the present disclosure. Figure 3A , a user may enter a search string (e.g., search string 1) in the search box 306. When the search string is entered into the search box 306, the user may select the search button 304. In response to the selection of the search button 304, the system or device (e.g., system 100 or device 200) may determine a set of semantic search results as described herein. The system or device may then extract metadata and / or receive and / or access pre-extracted metadata as described herein. The system or device may generate one or more graphical representations using the extracted and / or pre-extracted metadata. Such graphical representations may include various types of charts, including but not limited to bar charts, line charts, pie charts, clusters, three-dimensional charts, and the like. Each chart may be based on one or more classifiers, for example, chart 312 may be based on the author of each document or search result, chart 314 may be based on the region where each document was published or drafted or the region associated with the author, and chart 316 may be based on the year of publication. Other classifiers, such as topics, keywords, and other factors may be considered. Each chart may be displayed in the UI 302. Each chart under the results for search string 1 308 may include a selectable portion or an interactive portion.

[0059] Figure 3BOne or more graphical representations are shown after a user selects a data point or portion of a graphical representation. In one embodiment, the user may select, for example, Author 4 or any other author, region, date, or other classifier included in the graphical representation. After the user selects Author 4 (e.g., selecting a label and / or bar representing the number of documents authored by the author), the system or device may update the displayed or currently displayed one or more graphical representations. For example, if Author 4 is selected, the system or device may update the displayed or currently displayed one or more graphical representations so that documents including the selected author (e.g., Author 4) are emphasized or highlighted (e.g., see bar 320). Thus, regions where the author drafted, published, posted internally, or otherwise made documents available, or regions associated with the author, may be highlighted. Figure 3B As shown, chart 318 may include a number of highlighted and / or emphasized authors (e.g., selected authors and co-authors), chart 322 may include highlighted and / or emphasized regions, and chart 324 may include a number of highlighted and / or emphasized dates. Other charts utilizing other classifiers may include similar highlighting and / or emphasis.

[0060] Figure 3C One or more graphical representations after a second user selection of the same data point or portion of the graphical representation are shown. In one embodiment, the user may select the same data point or portion of the graphical representation more than once or at least twice. Upon a second or other subsequent selection of the same data point, the system or device may update the displayed or currently displayed one or more graphical representations. The updated one or more graphical representations may include data corresponding to the user selection. In other words, the chart may include data corresponding to the selection, rather than including highlighted and / or emphasized portions of each chart. For example, chart 326 may include selected authors and / or co-authors; chart 328 may include regions where the author drafted, published, posted internally, and / or otherwise made the document available; and chart 330 may include dates when the author drafted, published, posted internally, uploaded, and / or otherwise made the document available. In another embodiment, the system or device may determine or generate a chart after further determining search results, metadata extraction, and / or after accessing or receiving pre-extracted metadata. In another embodiment, the system or device may utilize a subset of the search results and the extracted metadata. In yet another embodiment, selecting list button 310 may display a subset of the search results (eg, search results corresponding to a user selection).

[0061] In another embodiment, in response to the selection of the download button 311, the system or device may prompt the user to select a format and / or location for storing or downloading a subset of search results and / or a graphical representation. The user may select a format including one or more of a spreadsheet, a comma separated value file, a text edit document, and / or another list or database type document, as will be understood by those skilled in the art. The user may select a location in the memory of the user's corresponding computing device. Upon selecting a location, the system or device may initiate downloading a subset of search results and / or a graphical representation in a selected format or a default format. In addition, the user may specify the type of results to be downloaded, such as a list, an actual document (e.g., downloaded as a file or compressed file), and / or a graphical representation.

[0062] Figure 3D One or more graphical representations are shown following a first user selection of first and second data points or portions of a graphical representation. In one embodiment, a user may select a first data point or portion of a graphical representation (e.g., author 4) and then select a second data point or portion of the graphical representation (e.g., author 5). In such embodiments, following the second selection, one or more graphical representations may be further updated to highlight or emphasize the second selected data point or portion of the graphical representation. Additionally, overlapping portions of the first and second selections may be highlighted and / or emphasized such that the overlapping portions (e.g., documents or other search results that include the first and second selections) are distinguished from the first and second selections (e.g., see overlapping portion 343). In another embodiment, in response to a further selection of the first and / or second selections, the system or device may update one or more graphical representations such that data not including the first and second selections is deleted. As Figure 3D As shown, chart 340 can highlight and / or emphasize the authors and / or co-authors (e.g., Author 4 and Author 5) including the first selection and the second selection, chart 344 can highlight and / or emphasize the regions corresponding to the first selection and the second selection, and chart 346 can highlight and / or emphasize the dates corresponding to the first selection and the second selection.

[0063] Figure 3EA list of search results after a user selects list button 310 is shown. In one embodiment, the user can select list button 310 at any time. The search results 332 displayed when list button 310 is selected can be based on one or more graphical representations currently displayed. In addition, the system or device responds to the selection of list button 310, when one or more data points and / or portions of the graphical representation include highlighted and / or emphasized portions, the displayed search results 332 may include similar highlighted and / or emphasized portions, or may include an indicator (e.g., such as a number, a text-based identifier, or other identifier) ​​indicating the user's selection. In another embodiment, the displayed search results 332 can be sorted based on the user's selection.

[0064] Figure 3F Cluster 334 formed based on search results and / or user selection is shown. In one embodiment, one of the one or more graphical representations can be cluster 334. NLP algorithms or models (e.g., included in a system or device) can form, generate or determine cluster 334 based on text included in a document or at a website. Cluster 334 may include one or more different parts (e.g., such as part 336). Each part may include a higher-level tag (e.g., tag 1) and one or more sub-tags (e.g., such as sub-tag 338). For example, a tag may be a high-level keyword describing one or more in a search result or document. Sub-tags may further distinguish or describe documents classified by tags. Tags may include topics, one or more keywords, phrases, contexts corresponding to documents, business units associated with documents or authors of documents, teams associated with documents or authors of documents, departments associated with documents or authors of documents, competitors associated with the context of documents, project numbers associated with documents and / or other factors. In such examples, cluster 334 may be included with one or more other graphical representations. In addition, cluster 334 may be dynamic and interactive. In other words, the user may select one or more different classifiers and / or sub-classifiers. Additionally, the user selections may be highlighted and / or emphasized, similar to highlighting and / or emphasizing portions of other charts as described herein.

[0065] Figure 4A , Figure 4B and Figure 4Cis a flow chart for generating one or more graphical representations of search results according to certain embodiments of the present disclosure. Unless otherwise indicated, the actions of method 400 may be performed within system 100 and / or device 200. Specifically, method 400 may be included in one or more programs, protocols, or instructions loaded into memory 106 of dynamic graphics system 102 and executed on processor 104 or one or more processors. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described blocks may be combined in any order and / or in parallel to implement these methods.

[0066] In box 402, the system or device can determine whether a search query has been received. Such indication can be determined based on the selection of the search button and / or the input of the search query. In addition to receiving the actual search query, the system or device can also receive the indication of the submission of the search query based on such input. In box 404, the system or device can generate search results or semantic search results in response to receiving the search query. In one example, the system or device can use one or more algorithms and / or models to find documents, links to websites and / or other information. The algorithm and / or model can analyze the search query, for example, via noun chunks and / or NLP, and generate a search result list based on such analysis. In one embodiment, the search results may include one or more documents within the enterprise (e.g., available via the intranet of the enterprise) or outside the enterprise (e.g., available via the Internet). In another embodiment, one or more documents included in the search results within the enterprise can be further based on the user submitting the search query (e.g., the search results can be based on the user's access to a specific document in the enterprise or organization).

[0067] In box 406, the system or device may extract metadata from each search result in the search results. The system or device may extract metadata in real time or substantially in real time via an algorithm and / or model such as an NLP algorithm or model. In another embodiment, the system or device may receive or access pre-extracted metadata from a storage location (e.g., a memory or a database). In addition, the system or device may determine or generate one or more classifiers based on the extracted and / or pre-extracted metadata. In one embodiment, a machine learning model may generate one or more classifiers. One or more classifiers may include the author of the document, the date on which the document was published or posted to an internal location, the region from which the document originated, keywords associated with the document, topics associated with the document, and / or other factors corresponding to the document. In box 408, the system or device may determine a graphical representation based on one or more determined or generated classifiers. In one embodiment, one or more of one or more potential graphical representations may be initially generated or generated by default. The user may select one or more graphical representations to be displayed at any time. After such selection, the system or device may initially generate the selected one or more graphical representations. In another embodiment, the system or device may generate one or more graphical representations based on one or more classifiers. In addition, the user can deselect any of the one or more graphical representations to delete the one or more graphical representations from the corresponding UI. In another embodiment, the system or device can link the one or more graphical representations to the search results. In other words, when adjusting the one or more graphical representations (e.g., based on user selections), the system or device can automatically reduce the search result list (e.g., determine a subset of the search result set). In block 410, the system or device can display the one or more graphical representations on the corresponding user's UI.

[0068] At block 412, the system or device may determine whether a first selection of a first data point or portion of one of the one or more graphical representations has been received. In other words, the system or device may determine whether the user has submitted the first selection. At block 414, if a selection has not been received, the system or device may determine whether a new search query has been received or submitted. If a new search query has been received, the system or device may determine a new set of search results and determine one or more new graphical representations.

[0069] In box 416, the system or device may display one or more graphical representations with corresponding emphasized portions in response to receiving a first selection of a first data point or portion of one or more graphical representations. In one embodiment, the system or device may determine and / or generate one or more new graphical representations in response to receiving the first selection, and then display the one or more new graphical representations. In box 418, the system or device may determine whether a second data point or portion of one or more graphical representations has been received or submitted. If the second selection has not been received, the system or device may determine in box 420 whether a search query has been submitted. If the second selection has been received, in box 422, the system may display one or more graphical representations in which the first and second data points or portions of the one or more graphical representations are emphasized. In addition, the system or device may also emphasize the overlapping portion of the first data point and the second data point.

[0070] In box 424, the system or device can determine whether a second selection of the first data point and / or the second data point has been received or submitted. If the second selection has not been received or submitted, then in box 426, the system or device can determine whether a new search query has been received or submitted. In box 428, the system or device can determine one or more new graphical representations based on the second selection of the first data point and / or the second data point. In box 430, the system or device can display the one or more graphical representations determined or generated. In one embodiment, the system or device can perform similar functions to further select different or the same data points. In other words, the user can select multiple different data points to generate a display of multiple portions of one or more graphical representations, including highlighted and / or emphasized portions.

[0071] In box 432, the system or device can determine whether the list button has been selected. If the list button has not been selected, then in box 434, the system or device can determine whether a new search query has been received or submitted. If the list button has been selected, then in box 436, the system or device can display a table list corresponding to one or more graphical representations currently displayed. As described above, the boxes described in method 400 can be executed in parallel. For example, the system and device can basically continuously determine whether the list button has been selected or whether any data point in one or more graphical representations has been selected. In addition, the system or device can basically continuously determine whether a new search query has been submitted. In another embodiment, a download button can be selected. In response to such selection, the system or device can prompt or initiate the download of the table list and / or graphical representation.

[0072] Figure 5is another flow chart for generating one or more graphical representations of search results according to certain embodiments of the present disclosure. Unless otherwise indicated, the actions of method 500 may be performed within system 100 and / or device 200. Specifically, method 500 may be included in one or more programs, protocols, or instructions loaded into memory 106 of dynamic mapping system 102 and executed on processor or processors 104. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described blocks may be combined in any order and / or in parallel to implement the methods.

[0073] In box 502, the system or device can determine whether a search query has been received. Such indication can be determined based on the selection of the search button and / or the input of the search query. In addition to receiving the actual search query, the system or device can also receive the indication of the submission of the search query based on such input. In box 504, the system or device can generate search results or semantic search results in response to receiving the search query. In one example, the system or device can use one or more algorithms and / or models to find documents, links to websites and / or other information. The algorithm and / or model can analyze the search query, for example, via noun chunks and / or NLP, and generate a search result list based on such analysis. In one embodiment, the search results may include one or more documents within the enterprise (e.g., available via the intranet of the enterprise) or outside the enterprise (e.g., available via the Internet). In another embodiment, one or more documents included in the search results within the enterprise can be further based on the user submitting the search query (e.g., the search results can be based on the user's access to a specific document in the enterprise or organization).

[0074] In box 506, the system or device may extract metadata from each search result in the search results. The system or device may extract metadata in real time or substantially in real time via an algorithm and / or model such as an NLP algorithm or model. In another embodiment, the system or device may receive or access pre-extracted metadata from a storage location (e.g., a memory or a database). In addition, the system or device may determine or generate one or more classifiers based on the extracted and / or pre-extracted metadata. In one embodiment, a machine learning model may generate one or more classifiers. One or more classifiers may include the author of the document, the date on which the document was published or posted to an internal location, the region from which the document originated, keywords associated with the document, topics associated with the document, and / or other factors corresponding to the document. In box 408, the system or device may determine a graphical representation based on one or more determined or generated classifiers. In one embodiment, one or more of one or more potential graphical representations may be initially generated or generated by default. The user may select one or more graphical representations to be displayed at any time. After such selection, the system or device may initially generate the selected one or more graphical representations. In another embodiment, the system or device may generate one or more graphical representations based on one or more classifiers. In addition, the user can deselect any of the one or more graphical representations to delete the one or more graphical representations from the corresponding UI. In another embodiment, the system or device can link the one or more graphical representations to the search results. In other words, when adjusting the one or more graphical representations (e.g., based on user selections), the system or device can automatically reduce the search result list (e.g., determine a subset of the search result set). In block 510, the system or device can display the one or more graphical representations on the corresponding user's UI.

[0075] In box 512, the system or device can determine whether a selection of a data point or portion of one or more graphical representations has been received or submitted. If a selection has not yet been received or submitted, then in box 514, the system or device can determine whether a new search query has been received or submitted. If a selection of a data point or portion of one or more graphical representations has been received, then in box 516, the system or device can determine or generate a second or subsequent search result or semantic search result. In box 518, the system or device can extract metadata from each search result and / or receive and / or access pre-extracted metadata to generate one or more new classifiers. In box 508, the system or device can determine one or more new graphical representations based on the one or more new classifiers. In box 510, the system or device can display one or more new graphical representations.

[0076] Although specific terms and concepts are incorporated in the present disclosure, applicants note that the disclosed terms and concepts are specifically used for description and therefore should not be understood or interpreted as limiting in any way. Certain embodiments and aspects of the disclosed systems, processes, and methods have been described in detail with particular reference to the illustrated embodiments. However, it is apparent that many and various modifications and changes may be made within the spirit and scope of the embodiments of the systems, processes, and methods described herein, and such modifications and changes are considered equivalent and within the breadth and scope of the present disclosure.

Claims

1. A method for generating one or more dynamic graphical representations of semantic search results, the method comprising: In response to the search circuitry receiving the search query, substantially in real time: generating, via the search circuit, a semantic search result for a set of data, receiving pre-extracted metadata based on the set of data, thereby generating one or more classifiers for each result in the semantic search results based on the metadata, determining, via a graphing circuit, one or more graphical representations of the semantic search results based on the one or more classifiers for each result in the semantic search results, and displaying each of the one or more graphical representations to a user interface via the drawing circuitry; in response to a first selection by a user of a first data point in the displayed graphical representation, displaying, via the user interface, the displayed graphical representation with an emphasized portion based on the first selection; subsequent to the first selection of the first data point and in response to the user selecting a second data point, displaying via the user interface the displayed graphical representation having an emphasized portion based on the selection of the second data point and an emphasized portion based on an overlap between the first selection of the first data point and the selection of the second data point; In response to the second selection of the first data point: determining, via the graphing circuit and the subset of the one or more classifiers for each result in the semantic search results, one or more subset graphical representations, the subset of the one or more classifiers for each result in the semantic search results being based on the first data point, and displaying each of the one or more subset graphical representations to the user interface via the drawing circuitry, thereby replacing and forming the displayed graphical representation; as well as In response to a first selection of a list button, a tabular list based on the semantic search results corresponding to the displayed graphical representation is displayed to a user interface via the drawing circuitry.

2. The method of claim 1, wherein the one or more graphical representations include graph types including one or more bar graphs, pie charts, line graphs, 3D graphs, clusters, or other chart types.

3. The method of claim 2, wherein the drawing circuitry comprises a natural language processing algorithm or model, and wherein the drawing circuitry generates the clusters based on text from documents included in the semantic search results via the natural language processing algorithm. The method of claim 3 , wherein the drawing circuit generates a second cluster based on the second selection. 5 . The method of claim 1 , wherein the graphing circuitry generates an additional set of one or more graphical representations corresponding to the semantic search results based on additional selections of one or more different data points.

6. The method of claim 1, wherein the semantic search results or a subset of the semantic search results and the displayed graphical representation are dynamically linked.

7. A method according to claim 1, wherein the first selection of the first data point, the second selection of the first data point, and the selection of the second data point are received from the user interface, and wherein the user interface comprises a graphical user interface or a web user interface displayed on a user's computing device.

8. The method of claim 1, wherein a plurality of graphs are displayed as the one or more graphical representations, each of the plurality of graphs being based on one of the one or more classifiers for each result in the semantic search results.

9. A method according to claim 8, wherein the one or more classifiers include one or more of the following: the date the document was uploaded, the author of the document, the region associated with the author of the document, the subject of the document; the context of the document; a business unit associated with the document or the author of the document; a team associated with the document or the author of the document; a department associated with the document or the author of the document; a competitor associated with the context of the document; a project number associated with the document; or a keyword associated with the document.

10. A system for dynamic visualization of semantic search results, the system comprising: A search circuit, wherein the search circuit is configured to: determining a semantic search result for a set of data based on the received search query, receiving pre-extracted metadata based on the set of data, and determining one or more classifiers for each result in the semantic search results based on the metadata; A drawing circuit, wherein the drawing circuit is configured to: At substantially the same time as determining the one or more classifiers for each outcome: generating a set of one or more graphical representations of the search results based on one or more of the one or more classifiers, a user input, or one or more user selections for each result, determining a subset of the one or more classifiers for each result in the semantic search results based on the one or more user selections, and dynamically linking the subset to the set of one or more graphical representations; as well as The display circuit is configured as follows: displaying the set of one or more graphical representations on a user interface of the remote computing device, and In response to selection of a list button, a dynamically linked subset of the one or more classifiers for each result in the semantic search results is displayed on the user interface of the remote computing device.

11. The system of claim 10, wherein the one or more user selections comprise a selection of one or more portions of the set of one or more graphical representations.

12. The system of claim 11 , wherein the drawing circuitry is configured to, in response to a selection of one of the one or more portions of the set of one or more graphical representations, update the set of one or more graphical representations based on the selection of one of the one or more portions of the set of one or more graphical representations.

13. A system according to claim 12, wherein the drawing circuit is configured to: in response to an update to the set of one or more graphical representations, dynamically link the set of one or more graphical representations to a subset of the one or more classifiers for each result in the semantic search results based on the selection of one of the one or more parts of the set of one or more graphical representations. The system of claim 10 , wherein one of the one or more graphical representations is a cluster.

15. The system of claim 14, wherein the mapping circuit is configured to form the clusters via a natural language processing circuit.

16. The system of claim 15, wherein the natural language processing circuit uses text from each document in the semantic search results.

17. The system of claim 10, wherein the display circuit is configured to: In response to selection of a download button, a list of documents stored in the dynamically linked set of search results is transmitted to the remote computing device via the user interface.

18. A method for generating one or more dynamic graphical representations of semantic search results, the method comprising: In response to the search circuitry receiving a search query for a set of data, substantially in real time: generating, via the search circuit, a first set of semantic search results, receiving pre-extracted metadata based on the set of data, thereby generating one or more classifiers for each result in the first set of semantic search results based on the metadata, determining, via a graphing circuit, one or more graphical representations of the first set of semantic search results based on the one or more classifiers for each result in the first set of semantic search results, and displaying, via the drawing circuitry, each of the one or more graphical representations of the first set of semantic search results to a user interface; In response to a first selection by the user of a first data point in the displayed graphical representation: generating, via the search circuit, a second set of semantic search results, receiving pre-extracted metadata based on the set of data, thereby generating one or more classifiers for each result in the second set of semantic search results based on the metadata, determining, via a graphing circuit, one or more graphical representations of the second set of semantic search results based on the one or more classifiers for each result in the first set of semantic search results and the one or more classifiers for each result in the second set of semantic search results, and displaying, via the drawing circuitry, each of the one or more graphical representations of the second set of semantic search results to a user interface; as well as In response to a first selection of a list button, a tabular list based on semantic search results corresponding to the currently displayed one or more graphical representations is displayed to the user interface via the drawing circuitry.

19. The method of claim 18, wherein the one or more graphical representations of the second set of semantic search results include an emphasized portion corresponding to the first selection.

20. The method of claim 18, wherein the drawing circuitry causes each portion of the one or more graphical representations of the first set of semantic search results to be based on the one or more classifiers for each result in the first set of semantic search results, and wherein each portion includes a user-selectable area.

Images