Analytical report generation

Abstract

A method for generating an analysis report is described. According to one example, an analysis report having analysis data associated with an organization and including one or more sections may be obtained. Each section may include at least a subset of the analysis data. A first set of attributes corresponding to the analysis report and a second set of attributes corresponding to each section may be obtained. The first set of attributes may be analyzed to identify a set of visual indicators associated with the analysis report. For each section, the first set of attributes, the second set of attributes, and a third set of attributes corresponding to each visual indicator in the set of visual indicators may be analyzed to determine a visual indicator for representing the subset within the section. For each section, the visual indicator may be incorporated into the section to generate a final analysis report.

Description

Background Art

[0001] Analysis reports are widely used in organizations for various purposes. For example, analysis reports can be used to analyze the financial condition of an organization. Analysis reports can also be used to analyze the operational performance of an organization. Analysis reports can also be used to analyze market trends, new products, consumer preferences, etc. Examples of analysis reports can include audit reports, Food and Drug Administration (FDA) approval reports, product sales reports, etc. In addition to providing analysis data associated with an organization in text form, analysis reports can also include visual representations of the analysis data in the form of charts or graphs. Brief Description of the Drawings

[0002] Now, a system and / or method will be described according to examples of the present subject matter with reference to the accompanying drawings, in which:

[0003] Figure 1 A communication environment of a system for generating an analysis report according to one example is shown;

[0004] Figure 2 A communication environment of a system for generating an analysis report according to another example is shown;

[0005] Figure 3A And Figure 3B Different stages of generating an analysis report using an analysis report generation system according to one example are shown;

[0006] Figure 4 A method for generating an analysis report according to one example is shown;

[0007] Figures 5A to 5C A method for generating an analysis report based on an association mapping between various attributes related to the analysis report according to another example is shown;

[0008] Figures 6A to 6D A method for generating an analysis report based on probability scores associated with various visual indicators available for incorporation into the analysis report according to another example is shown;

[0009] Figure 7A And Figure 7B A method for generating an analysis report based on weight scores associated with various attributes related to the analysis report and probability scores associated with various visual indicators available for incorporation into the analysis report according to another example; and

[0010] Figure 8 A computing environment of a non - transient computer - readable medium for generating an analysis report according to one example is shown. Detailed Description

[0011] An analysis report is a document that may include one or more visual indicators that graphically represent analysis data relevant to the purpose of the analysis report. Examples of visual indicators may include, but are not limited to, charts, graphs, dashboards, etc. In one example, a dashboard may also include one or more charts or graphs. Typically, each analysis report may include multiple subsections or chapters, where each chapter has one or more visual indicators. For example, if the analysis report is a product sales report, the analysis report may include multiple visual indicators representing the sales data of the organization's products. The analysis report may include multiple chapters, such as a chapter depicting the sales data of the organization in different regions, a chapter depicting the sales data of different products of the organization, and a chapter highlighting the sales and revenue of different departments of the organization. Each such chapter may include a combination of text and visual indicators (such as dashboards) to represent the sales data of different regions or different products.

[0012] Typically, an analysis report is manually prepared by one or more individuals. When individuals use different business intelligence (BI) tools as sources of one or more visual indicators, the user needs to manually identify, select different visual indicators and associate them with different chapters of the analysis report. The manual preparation of an analysis report is a time-consuming and tedious task that consumes a large amount of processing resources. For example, when preparing an analysis report on an electronic device, the individual preparing the analysis report may have to configure and customize various visual indicators before finally determining the one or more visual indicators to be used in the analysis report. This may result in a waste of human and processing resources that are consumed to generate and analyze various visual indicators that may not even be used in the analysis report ultimately.

[0013] In addition, the analysis report needs to represent the analysis data in an easily understandable manner so that any decision can be easily made based on the analysis data. For example, if the analysis report is a product sales report, an individual referring to the product sales report should be able to easily make a decision regarding changing or maintaining the replenishment rate of a product to one of multiple regions based on the represented sales data. Currently, if the user preparing the analysis report cannot find the most suitable visual indicator to incorporate into the analysis report, the user may select any visual indicator based on their understanding and skills. Thus, currently, for the analysis report to have an acceptable quality, the user must be a professional who is skilled at preparing analysis reports. Even if the analysis report is prepared by a skilled professional, the quality of the analysis report highly depends on the skill level of the user, which is undesirable as different individuals may have different skill levels, thus affecting the quality of the analysis report.

[0014] Describes a method for generating an analysis report including one or more chapters. The analysis report may have analysis data associated with an organization. Each of the one or more chapters may include at least one subset of the analysis data.

[0015] This subject matter facilitates automatically identifying, selecting one or more visual indicators and assigning them to different chapters of an analysis report. In one example, a first set of attributes corresponding to the analysis report may initially be analyzed to identify a set of visual indicators associated with the analysis report. The visual indicators may provide a graphical representation of the analysis data present in the analysis report. For each of the one or more chapters, this subject matter uses the first set of attributes, a second set of attributes corresponding to the chapter, and a third set of attributes corresponding to each visual indicator in the set of visual indicators to determine at least one visual indicator from the set of visual indicators for representing the subset of the analysis data within the chapter. In one example, the first set of attributes, the second set of attributes, and the third set of attributes may be processed by a visual indicator assignment model to determine at least one visual indicator. One or more historical analysis reports may be used to train the visual indicator assignment model. For each chapter of the analysis report, at least one visual indicator may be incorporated into the chapter to generate the final analysis report. Thus, a simple and robust method for quickly assigning visual indicators to the chapters of an analysis report is provided.

[0016] In one example, the first set of attributes, the second set of attributes, and the third set of attributes may be processed based on an association map generated by the visual indicator assignment model. The association map may indicate the relationships between the chapters, the visual indicators, and the analysis report.

[0017] In another example, the first set of attributes, the second set of attributes, and the third set of attributes may be processed based on a probability score corresponding to each visual indicator in the set of visual indicators. The probability score may indicate the probability of the visual indicator appearing in the chapter of the analysis report. In one example, for each attribute in the first set of attributes, the second set of attributes, and the third set of attributes, a pre-assigned weighted score may be determined. The pre-assigned weighted score may indicate the comparative weight of the attribute for calculating the probability score. The probability score corresponding to each visual indicator may be calculated considering the pre-assigned weighted score of each attribute. For each chapter, the association map or the probability score corresponding to each visual indicator may be analyzed to determine at least one visual indicator for representing the subset of the analysis data within the chapter. Subsequently, the at least one visual indicator selected for different chapters may be incorporated into these chapters to generate the final analysis report.

[0018] Accordingly, the described method automatically identifies at least one visual indicator and assigns it to each section of the analysis report without requiring input from the user. Thus, an analysis report can be generated easily and quickly without wasting processing resources. The described method significantly reduces the power consumed by the electronic device to generate the analysis report, thereby increasing the battery life of the electronic device.

[0019] In addition, the described method does not depend on an individual's skill level. The probability of a visual indicator appearing in a section is calculated based on a statistical analysis of one or more attributes corresponding to the analysis report, the section, and the visual indicator. Thus, the interconnection between the visual indicator and the section is accurately identified. The described method takes into account the context of the analysis report and the sections of the analysis report. Thus, a final analysis report including the desired visual indicators can be generated, which is easy to understand and comprehend.

[0020] Reference Figures 1 to 8 The present subject matter is further described. It should be noted that the specification and the drawings only illustrate the principles of the present subject matter. Although not explicitly described or illustrated herein, various arrangements that cover the principles of the present subject matter can be designed. In addition, all statements herein reciting the principles, aspects, and examples of the present subject matter and their specific examples are intended to cover their equivalents.

[0021] Figure 1 A communication environment 100 for implementing a system 102 for generating an analysis report according to one example is shown. The analysis report may have analysis data associated with an organization. In one example, the analysis report may include one or more sections, where each of the one or more sections includes at least one subset of the analysis data. For example, a commercial company can be an organization, and a product sales report can be an analysis report on the sales of products sold by the organization. The product sales report may include a plurality of visual indicators representing the sales data of the products. The visual indicators may provide a graphical representation of the analysis data present in the analysis report. The analysis report may also include a plurality of sections describing different aspects or details of the product sales. For example, the analysis report may include sections depicting sales data in different regions, sections depicting the sales of different products of the organization, and sections highlighting the sales and revenues of different departments of the organization. Each such section may include a combination of text and visual indicators (such as dashboards) to represent the sales data of different regions or different products.

[0022] The communication environment 100 may also include a business intelligence (BI) tool server 104 and a report generation application server 106. The system 102 can be a device, such as an electronic device, which can be operated by a user to generate an analysis report. Examples of electronic devices may include, but are not limited to, laptop computers, desktop computers, tablet computers, and smart phones.

[0023] The BI tool server 104 can be configured to host BI tools. The BI tools can be software applications installable on the system 102. The BI tools can be accessed by a user through the system 102 to configure and customize various visual indicators. The customized visual indicators can be stored in the BI tool server for future use in various analysis reports. In one example, the BI tool server 104 can store and maintain data associated with the BI tools and grant authorized users access to the data. In one example, the BI tool server 104 can be virtually hosted on a cloud-based platform, for example, located at or away from the site. In another example, the BI tool server 104 can be an independent physical system geographically located on or away from the site. Examples of the site can include, but are not limited to, a company's building or any other working environment of any industry or enterprise. The building can be a commercial construction, for example, a commercial complex, an industrial construction, a data center, and a warehousing facility. In addition, the building can also refer to a combination of two or more structures or courtyards.

[0024] The report generation application server 106 can be configured to host a report generation application. The report generation application can be a software application installable on the system 102. The report generation application can be accessed by a user through the system 102 to finalize and generate an analysis report after incorporating visual indicators as needed. In one example, the report generation application server 106 can store and maintain data associated with the analysis report and grant authorized users access to the data. In one example, the report generation application server 106 can be virtually hosted on a cloud-based platform, for example, located at or away from the site. In another example, the report generation application server 106 can be an independent physical system geographically located on or away from the site. Examples of the site can include, but are not limited to, a company's building or any other working environment of any industry or enterprise. The building can be a commercial construction, for example, a commercial complex, an industrial construction, a data center, and a warehousing facility. In addition, the building can also refer to a combination of two or more structures or courtyards.

[0025] In one example, the BI tool server 104 and the report generation application server 106 can be managed and owned by different entities and can be located at different geographical locations. In another example, the BI tool server 104 and the report generation application server 106 can be managed and owned by the same entity and can be co-located at the same geographical location.

[0026] System 102, BI tool server 104, and report generation application server 106 may be communicatively coupled to each other via network 108 and may exchange data and signals via network 108. Network 108 may be a wireless network, a wired network, or a combination thereof. Network 108 may also be a single network or a collection of many such individual networks that are interconnected with each other and function as a single large network, such as the Internet or an intranet. Examples of such individual networks include, but are not limited to, local area network (LAN), wide area network (WAN), Internet, global system for mobile communications (GSM) network, universal mobile telecommunications system (UMTS) network, personal communications service (PCS) network, time division multiple access (TDMA) network, code division multiple access (CDMA) network, next generation network (NGN), public switched telephone network (PSTN), and integrated services digital network (ISDN). Depending on the technology, network 108 may include various network entities, such as transceivers, gateways, and routers. In one example, network 108 may include any communication network that uses any common protocol (e.g., hypertext transfer protocol (HTTP) and transmission control protocol / internet protocol (TCP / IP)).

[0027] System 102 may include system module 110 and system data 112. System 102 may also include components other than those depicted, such as a display, input / output interfaces, an operating system, applications, and other software or hardware components (not shown in the figure).

[0028] System module 110 may be implemented as a combination of hardware and programming (e.g., programmable instructions for implementing the various functionalities of system module 110). In the examples described herein, such a combination of hardware and programming may be implemented in several different ways. For example, the programming for system module 110 may be executable instructions. Such instructions may be stored on a non-transitory machine-readable storage medium that may be directly or indirectly (e.g., via networking means) coupled to system 102. In one example, system module 110 may include processing resources (e.g., a single processor or a combination of multiple processors) to execute such instructions. In this example, the non-transitory machine-readable storage medium may store instructions that, when executed by the processing resources, implement system module 110. In other examples, system module 110 may be implemented as an electronic circuit.

[0029] In one example, system module 110 may include data acquisition module 114, visual indicator assignment module 116, report generation module 118, and other system modules 120. Other system modules 120 may also implement functionalities that supplement the functions performed by system 102 or any system module 110.

[0030] System data 112 includes data received, stored, or generated as a result of functions implemented by any system module 110 or system 102. It may further be noted that system module 110 may utilize the information stored and available in system data 112 for system 102 to perform various functions. In one example, system data 112 may include analysis data 122 and other system data 124. Other system data 124 may include data generated by system module 110. It may be noted that such examples are merely illustrative. Without departing from the scope of the subject matter, the method may be applicable to other examples. Analysis data 122 may be defined as data associated with an organization, such as sales data, and at least a portion of the data is expected to be graphically represented by visual indicators in an analysis report.

[0031] In operation, data acquisition module 114 of system 102 may obtain an analysis report having analysis data associated with an organization. The organization may be a specific organization for which the analysis report is to be finalized. The analysis data may be analysis data 122. The analysis data may be pre-stored in system 102. In one example, the analysis report may include one or more chapters, where each of the one or more chapters includes at least one subset of the analysis data. Hereinafter, the one or more chapters may be collectively referred to as chapters and individually referred to as a chapter.

[0032] Visual indicator assignment module 116 of system 102 may implement a visual indicator assignment model to identify visual indicators that may be included in different chapters of the analysis report. For example, visual indicator assignment module 116 may identify a first set of attributes corresponding to the analysis report and a second set of attributes corresponding to each chapter. In one example, the first set of attributes may include organization attributes corresponding to a specific organization, user attributes corresponding to a specific user associated with the specific organization and using a specific report generation application to finalize the analysis report, and application attributes corresponding to the specific report generation application. Examples of the second set of attributes corresponding to a specific chapter among these chapters may include, but are not limited to, the ID, name, type, and description of the specific chapter.

[0033] In one example, visual indicator assignment module 116 may analyze the first set of attributes to identify a set of visual indicators associated with the analysis report. In one example, the set of visual indicators may include visual indicators that may be accessed by a specific user of a specific organization in a specific report generation application.

[0034] In one example, the visual indicator assignment module 116 may receive an association map between a first set of attributes, a second set of attributes, and a third set of attributes corresponding to each visual indicator in the set of visual indicators. Examples of the third set of attributes of the visual indicator may include, but are not limited to, the ID of the visual indicator, the type of the visual indicator, the x-axis field of the visual indicator, the y-axis field of the visual indicator, and the BI tool ID of the BI tool for customizing the visual indicator. In one example, the association map may indicate the relationship between the first set of attributes, the second set of attributes, and the third set of attributes based on the statistical analysis of one or more historical analysis reports. For example, the association map may indicate whether a particular user of a particular organization uses a particular visual indicator in a particular section of a particular analysis report when using a particular report generation application.

[0035] In one example, for each section, the visual indicator assignment module 116 may analyze the association map to determine at least one visual indicator for representing a subset of the analysis data within the section. For the sake of brevity, at least one visual indicator may alternatively be referred to hereinafter as the determined visual indicator. For example, if the association map indicates that a particular user of a particular organization uses a particular visual indicator in a particular section of a particular analysis report when using a particular report generation application, the particular visual indicator may be determined as at least one visual indicator.

[0036] For each section of the analysis report, the report generation module 118 of the system 102 may incorporate the determined visual indicator into the section to generate a final analysis report. Thus, the visual indicators for each section of the analysis report are automatically identified in real time and incorporated into the analysis report without the need for input from the user.

[0037] Figure 2 A communication environment 200 for implementing the system 102 is shown according to another example. In one example, the communication environment 200 may include the system 102, a BI tool server 104, and a report generation application server 106. The system 102, the BI tool server 104, and the report generation application server 106 may be communicatively coupled to each other via a network 108.

[0038] In one example, the BI tool server 104 may include a BI tool module 202 and BI tool data 204. The BI tool server 104 may include components other than the depicted components, such as a display, a processor, an input / output interface, an operating system, applications, and other software or hardware components (not shown in the figure).

[0039] The BI tool module 202 can be implemented as a combination of hardware and programming (e.g., programmable instructions for implementing the various functionalities of the BI tool module 202). In the examples described herein, such a combination of hardware and programming can be implemented in several different ways. For example, the programming for the BI tool module 202 can be executable instructions. Such instructions can be stored on a non-transitory machine-readable storage medium that can be directly or indirectly (e.g., via networking means) coupled to the BI tool server 104. In one example, the BI tool module 202 can include processing resources (e.g., a single processor or a combination of multiple processors) to execute such instructions. In this example, the non-transitory machine-readable storage medium can store instructions that, when executed by the processing resources, implement the BI tool module 202. In other examples, the BI tool module 202 can be implemented as an electronic circuit.

[0040] In one example, the BI tool module 202 can include a BI tool communication module 206 and other BI tool modules 208. The other BI tool modules 208 can also implement functionalities that complement the functions performed by the BI tool server 104 or any of the BI tool modules 202. The BI tool communication module 206 can be a wireless communication module. Examples of the BI tool communication module 206 can include, but are not limited to, a Global System for Mobile Communications (GSM) module, a Code Division Multiple Access (CDMA) module, a Bluetooth module, a Network Interface Card (NIC), a Wi-Fi module, a dial-up module, an Integrated Services Digital Network (ISDN) module, a Digital Subscriber Line (DSL) module, and a cable module. In one example, the BI tool communication module 206 can also include one or more antennas to enable wireless transmission and reception of data and signals.

[0041] The BI tool data 204 includes data received, stored, or generated as a result of functions implemented by any BI tool module 202 or the BI tool server 104. It may further be noted that the BI tool module 202 may utilize the information stored and available in the BI tool data 204 for the BI tool server 104 to perform various functions. The BI tool data 204 may include visual indicator data 210 and other BI tool data 212. The visual indicator data 210 may include various visual indicators customized by users of various organizations and the attributes associated with each visual indicator. Examples of the attributes of the visual indicator may include, but are not limited to, the ID of the visual indicator, the type of the visual indicator, the x-axis field of the visual indicator, the y-axis field of the visual indicator, or the BI tool ID of the BI tool used to customize the visual indicator. For example, the type of the visual indicator may be a bar chart, a pie chart (pirchart), etc. The attributes of the visual indicator may alternatively be referred to herein as the third set of attributes. The other BI tool data 212 includes data received, stored, or generated as a result of functions implemented by any BI tool module 202 or the BI tool server 104.

[0042] In one example, the report generation application server 106 may include a server module 214 and server data 216. The report generation application server 106 may include components other than those depicted, such as a display, a processor, an input / output interface, an operating system, applications, and other software or hardware components (not shown in the figure).

[0043] The server module 214 may be implemented as a combination of hardware and programming (e.g., programmable instructions for implementing the various functionalities of the server module 214). In the examples described herein, such a combination of hardware and programming may be implemented in several different ways. For example, the programming for the server module 214 may be executable instructions. Such instructions may be stored on a non-transitory machine-readable storage medium that may be directly or indirectly (e.g., via networking means) coupled to the report generation application server 106. In one example, the server module 214 may include processing resources (e.g., a single processor or a combination of multiple processors) to execute such instructions. In this example, the non-transitory machine-readable storage medium may store instructions that, when executed by the processing resources, implement the server module 214. In other examples, the server module 214 may be implemented as an electronic circuit.

[0044] In one example, the server module 214 may include a server communication module 218 and other server modules 220. The other server modules 220 may also implement functionality that supplements the functionality performed by the reporting generation application server 106 or any of the server modules 214. The server communication module 218 may be a wireless communication module. Examples of the server communication module 218 may include, but are not limited to, a Global System for Mobile Communications (GSM) module, a Code Division Multiple Access (CDMA) module, a Bluetooth module, a Network Interface Card (NIC), a Wi-Fi module, a dial-up module, an Integrated Services Digital Network (ISDN) module, a Digital Subscriber Line (DSL) module, and a cable module. In one example, the server communication module 218 may also include one or more antennas to enable wireless transmission and reception of data and signals.

[0045] Server data 216 includes data received, stored, or generated as a result of the functionality implemented by any of the server modules 214 or the reporting generation application server 106. It may further be noted that the server module 214 may utilize the information stored and available in the server data 216 for the reporting generation application server 106 to perform various functions. The server data 216 may include analysis report data 222, section data 224, and other server data 226. The analysis report data 222 may include attributes associated with the analysis report and a section of the analysis report. For example, the analysis report data 222 may include organization attributes corresponding to various organizations accessing the various reporting generation applications hosted by the reporting generation application server 106. In addition, the analysis report data 222 may include user attributes corresponding to various users associated with the organization. The analysis report data 222 may also include application attributes corresponding to the reporting generation application. Examples of organization attributes may include, but are not limited to, an ID associated with a particular organization and the name of the particular organization. Examples of user attributes may include, but are not limited to, an ID associated with a particular user, the name of the particular user, the name of the particular organization with which the particular user is associated, and the department of the particular organization in which the particular user works. Examples of application attributes may include, but are not limited to, an ID associated with a particular reporting generation application and the name of the particular reporting generation application. The section data 224 may include attributes associated with a section of the analysis report. Examples of attributes associated with a section may include, but are not limited to, the ID of the analysis report that includes the particular section, an ID associated with the particular section, a title associated with the particular section, the type of the particular section, and a description of the particular section. The other server data 226 includes data received, stored, or generated as a result of the functionality implemented by any of the server modules 214 or the reporting generation application server 106.

[0046] In one example, system 102 may include a processor 228, an interface 230, a memory 232, a system communication module 234, a system module 110, and system data 112. System 102 may include components other than those depicted, such as a display, an input / output interface, an operating system, application programs, and other software or hardware components (not shown in the figure).

[0047] Processor 228 may be implemented as a microprocessor, a microcomputer, a microcontroller, a digital signal processor, a central processing unit, a state machine, logic circuitry, and / or other devices that manipulate signals based on operational instructions. Interface 230 may allow system 102 to connect or couple to one or more other devices (such as BI tool server 104 and report generation application server 106) via a wired connection (e.g., a local area network, i.e., LAN) or via a wireless connection (e.g., Wi-Fi). Interface 230 may also enable communication between different logical components and hardware components of system 102.

[0048] Memory 232 may be a computer-readable medium, examples of which include volatile memory (e.g., RAM) and / or non-volatile memory (e.g., erasable programmable read-only memory, i.e., EPROM, flash memory, etc.). Memory 232 may be external memory or internal memory, such as a flash drive, a compact disc drive, an external hard disk drive, etc. Memory 232 may also include system data 112 and / or other system data that may be received, utilized, or generated during the operation of system 102.

[0049] System communication module 234 may be a wireless communication module. Examples of system communication module 234 may include, but are not limited to, a Global System for Mobile Communications (GSM) module, a Code Division Multiple Access (CDMA) module, a Bluetooth module, a Network Interface Card (NIC), a Wi-Fi module, a dial-up module, an Integrated Services Digital Network (ISDN) module, a Digital Subscriber Line (DSL) module, and a cable module. In one example, system communication module 234 may also include one or more antennas to enable wireless transmission and reception of data and signals. System communication module 234 may allow system 102 to transmit data and signals to one or more other devices (such as BI tool server 104 and report generation application server 106) and receive data and signals from one or more other devices.

[0050] In one example, system module 110 may include a data acquisition module 114, a visual indicator assignment module 116, a report generation module 118, and other system modules 120, as explained with reference to Figure 1 In addition, system data 112 may include analysis data 122 and other system data 124, as explained with reference to Figure 1 as explained.

[0051] As previously mentioned, one or more users who intend to include one or more visual indicators in an analysis report can use System 102 to automatically populate visual indicators in different sections of the analysis report. In one example, a user (e.g., a first user of an organization) can initially access a BI tool associated with System 102 and the organization to customize and create various visual indicators for use by users of the organization associated with the first user. Subsequently, other users (such as a second user) can use System 102 to automatically populate visual indicators in different sections of the analysis report.

[0052] In operation, to create a visual indicator, the first user can access System 102 to log in to the BI tool installed in System 102. Thus, System 102 can receive a BI tool login request from the first user to log in to the BI tool installed in System 102. The BI tool login request can include BI tool login credentials entered by the first user, such as a user ID and password. System 102 can query the BI tool server 104 for the BI tool login credentials to check if the first user is authorized to access the BI tool. Upon successful authentication of the first user, System 102 can allow the first user to use the BI tool to configure and customize various visual indicators. In one example, the first user can be associated with an organization. The first user can configure and customize visual indicators for future use by the organization in various analysis reports associated with the organization. The customized visual indicators can be stored in the BI tool server along with the attributes associated with each customized visual indicator. These attributes can be stored as the third set of attributes previously referenced Figure 1 and explained. Examples of the third set of attributes can include, but are not limited to, the user ID of the first user who has configured and customized the visual indicator, the name of the organization of the first user, the ID of the visual indicator, the type of the visual indicator, the x-axis field of the visual indicator, the y-axis field of the visual indicator, and the BI tool ID of the BI tool used to configure and customize the visual indicator.

[0053] Once the visual indicator is created and customized, users of the organization can access the system to update one or more analysis reports. In one example, system 102 can receive a report generation application login request from a user (e.g., a second user) to log in to the report generation application installed in system 102. The report generation application login request can include report generation application login credentials entered by the second user, such as a user ID and password. System 102 can query the report generation application server 106 for the report generation application login credentials to check whether the second user is authorized to access the report generation application. Upon successful authentication of the second user, system 102 can allow the second user to use the report generation application to finalize the analysis report and generate a final analysis report. In one example, the first user and the second user can be associated with the same organization. In one example, the first user can be the same as the second user. In another example, the first user can be different from the second user.

[0054] Although, for the sake of brevity, only a single system 102 for accessing both the BI tool and the report generation application is shown, those skilled in the art should understand that the BI tool and the report generation application can also be accessed separately by the same or different users through separate systems. Additionally, although the BI tool and the report generation application have been explained as being logged in separately by the user, in one example, the BI tool can be embedded within the report generation application, and the user can use single sign-on (SSO) to access the report generation application in which the BI tool is embedded. In the case of SSO, different users can be authorized to access different options within the report generation application and the BI tool. For example, one user may only be allowed to read the visual indicator and use the visual indicator in the analysis report, while another user may be allowed to customize the visual indicator and also use the visual indicator in the analysis report.

[0055] In one example, upon successful authentication, the data acquisition module 114 of system 102 can obtain the analysis report that the second user wishes to customize. In one example, the analysis report can be pre-stored in the report generation application server 106. To obtain the analysis report, system 102 can send a request to the report generation application server 106. In another example, the user can provide the analysis report to the data acquisition module 114. The analysis report can have analysis data associated with the organization with which the second user is associated. In one example, the analysis report can include one or more chapters, where each of the one or more chapters includes at least one subset of the analysis data. For the sake of brevity, hereinafter, the one or more chapters can be collectively referred to as chapters and individually referred to as a chapter. It has been Figure 3AAn exemplary analysis report 302 having one or more chapters 304-1, 304-2, … 304-N is shown herein. Here, N can be greater than or equal to 1. In one example, the analysis report and the chapters can be pre-created by a user using a report generation application. The analysis data can be raw data that is desired to be graphically represented in the analysis report by one or more visual indicators.

[0056] Once the analysis report is obtained, a second user can instruct the system 102 to customize the analysis report by including various visual indicators in different chapters of the analysis report. In one example, the visual indicator assignment module 116 of the system 102 can implement a visual indicator assignment model to identify visual indicators that can be included in different chapters of the analysis report.

[0057] In one example, the visual indicator assignment model can be pre-trained based on a statistical analysis of at least one historical analysis report. Hereinafter, the at least one historical analysis report can alternatively be referred to as a historical analysis report. The historical analysis report can be a final analysis report generated and used by an organization historically. During the training of the visual indicator assignment model, the visual indicator assignment module 116 can obtain the historical analysis report. In one example, the historical analysis report can be obtained from the memory 232 of the system 102. The historical analysis report can be received from the report generation application and can be pre-stored in the memory 232. In another example, the historical analysis report can be obtained directly from the report generation application. The historical analysis report can include one or more predefined chapters having historical analysis data. Each of the one or more predefined chapters can include at least one assigned visual indicator that graphically represents the historical analysis data.

[0058] Subsequently, for each historical analysis report in the at least one historical analysis report, the visual indicator assignment module 116 can identify a first set of historical attributes corresponding to each historical analysis report. In addition, for each historical analysis report, the visual indicator assignment module 116 can identify a second set of historical attributes corresponding to each of the one or more predefined chapters. In addition, for each historical analysis report, the visual indicator assignment module 116 can identify a third set of historical attributes corresponding to each of the at least one assigned visual indicators. The visual indicator assignment module 116 can identify the first set of historical attributes, the second set of historical attributes, and the third set of historical attributes based on various attributes or information about the historical analysis report, the one or more predefined chapters, and the at least one assigned visual indicator available at the report generation application server 106. The visual indicator assignment model can be trained on the first set of historical attributes, the second set of historical attributes, and the third set of historical attributes to understand the relationship between the chapters, the visual indicators, and the historical analysis report.

[0059] In one example, during real-time operation, to identify visual indicators that may be included in different sections of an analysis report, the visual indicator assignment module 116 may implement a visual indicator assignment model to identify a first set of attributes corresponding to the analysis report and a second set of attributes corresponding to each section. The visual indicator assignment module 116 may identify the first set of attributes corresponding to the analysis report from various available attributes or information about the analysis report at the report generation application server 106. The visual indicator assignment module 116 may also identify the second set of attributes corresponding to each section from various available attributes or information about one or more sections at the report generation application server 106.

[0060] The first set of attributes and the second set of attributes may be attributes related to the purpose of automatically assigning visual indicators to each section of the analysis report. The visual indicator assignment model may be trained based on statistical analysis of one or more historical analysis reports to identify attributes related to the purpose of automatically assigning visual indicators to each section of the analysis report. In one example, the first set of attributes and the second set of attributes may be stored in the memory 232 of the system 102 or in an external memory of an external system. The first set of attributes and the second set of attributes may be obtained from the memory 232 or the external memory for analysis when determining visual indicators for inclusion in the sections of the analysis report.

[0061] In one example, the first set of attributes may include organizational attributes corresponding to the organization, user attributes corresponding to a user associated with the organization and using a specific report generation application to finalize the analysis report, and application attributes corresponding to the specific report generation application. Examples of the second set of attributes corresponding to a specific section among these sections may include, but are not limited to, the ID, name, type, and description of the specific section.

[0062] Once at least a first set of attributes is identified, the visual indicator assignment module 116 of system 102 can analyze the organizational attributes and a third set of attributes to identify a first plurality of predefined visual indicators assigned to the organization. The third set of attributes can be defined as attributes corresponding to a visual indicator inventory stored in the BI tool server 104. The visual indicator inventory can include visual indicators customized for various organizations. In one example, the third set of attributes for a particular visual indicator can include the ID of the organization associated with the user who customized the particular visual indicator. Thus, the visual indicator assignment module 116 can retrieve the organization ID from the organizational attributes and can identify from the visual indicator inventory the visual indicator associated with the same organization ID as the organization ID retrieved from the organizational attributes of the second user. In one example, the organization ID can be part of the third set of attributes of the identified visual indicator. Thus, the visual indicator assignment module 116 can identify a first plurality of predefined visual indicators assigned to the organization based on the organization ID.

[0063] The visual indicator assignment module 116 can then analyze the user attributes and the third set of attributes to identify a second plurality of predefined visual indicators assigned to the user for use in an analysis report associated with the organization from the first plurality of predefined visual indicators. In one example, the visual indicator assignment module 116 can analyze the user attributes and the third set of attributes corresponding to the first plurality of predefined visual indicators to identify the second plurality of predefined visual indicators. In one example, the third set of attributes for a particular visual indicator can include the ID of at least one user granted access to the particular visual indicator. The visual indicator assignment module 116 can retrieve the user ID from the user attributes. The visual indicator assignment module 116 can then identify certain visual indicators as part of the second plurality of predefined visual indicators from the first plurality of predefined visual indicators based on determining that the ID of at least one user in the third set of attributes of certain visual indicators is the same as the user ID retrieved from the user attributes.

[0064] The visual indicator assignment module 116 may also analyze the application properties and a third set of properties to identify, from a second plurality of predefined visual indicators, a set of visual indicators assigned to the user for use by the report generation application in an analysis report. In one example, the visual indicator assignment module 116 may analyze the application properties and a third set of properties corresponding to the second plurality of predefined visual indicators to identify the set of visual indicators. In one example, the third set of properties for a particular visual indicator may include the ID of at least one report generation application having permission to access the particular visual indicator. The visual indicator assignment module 116 may retrieve the report generation application ID from the application properties. The visual indicator assignment module 116 may then compare, for each visual indicator in the second plurality of predefined visual indicators, the ID of at least one report generation application in the third set of properties with the report generation application ID retrieved from the application properties. A visual indicator having the ID of at least one report generation application that is the same as the report generation application ID retrieved from the application properties may be selected as part of the set of visual indicators.

[0065] Thus, as explained, the visual indicator assignment module 116 may implement a visual indicator assignment model to analyze a first set of properties, such as organizational properties, user properties, and application properties, to identify the set of visual indicators associated with the analysis report.

[0066] The visual indicator assignment module 116 may then determine, for each section, at least one visual indicator for representing a subset of the analysis data within the section. In one example, the at least one visual indicator may be determined based on an association mapping between the first set of properties, the second set of properties, and the third set of properties. In another example, the at least one visual indicator may be determined based on a probability score associated with each visual indicator in the set of visual indicators. The probability score may indicate the probability that the visual indicator appears in the section of the analysis report.

[0067] In one example, the visual indicator assignment module 116 may implement a visual indicator assignment model to generate an association map and calculate probability scores. As previously described, the visual indicator assignment model may be trained based on a statistical analysis of one or more historical analysis reports. In one example, the visual indicator assignment module 116 may analyze a first set of historical attributes, a second set of historical attributes, and a third set of historical attributes. Based on the analysis of the first set of historical attributes, the second set of historical attributes, and the third set of historical attributes, the visual indicator assignment module 116 may calculate probability scores or generate an association map between the first set of attributes, the second set of attributes, and the third set of attributes corresponding to each visual indicator in the set of visual indicators. The association map may indicate the relationship between a section, a visual indicator, and an analysis report. For example, based on determining that a particular user of a particular organization has historically used a particular visual indicator in a particular section of an analysis report of a particular type of analysis report when generating an analysis report using a particular report generation application, the association map may link the particular visual indicator to the particular user, the particular type of analysis report, the particular section, the particular organization, and the particular report generation application. In one example, the visual indicator assignment module 116 may implement a visual indicator assignment model to generate the association map as explained. The association map may be stored in the memory 232 of the system 102 or in an external memory.

[0068] When using the association map in real time to determine at least one visual indicator, the visual indicator assignment module 116 may receive the association map between the first set of attributes, the second set of attributes, and the third set of attributes corresponding to each visual indicator in the set of visual indicators. In one example, the association map may be obtained from the memory 232 of the system 102. In another example, the association map may be obtained from an external memory.

[0069] In one example, for each section, the visual indicator assignment module 116 may analyze the association map to determine at least one visual indicator for representing a subset of the analysis data within the section. The visual indicator assignment module 116 may determine at least one visual indicator from the set of visual indicators. For the sake of brevity, at least one visual indicator may alternatively be referred to hereinafter as the determined visual indicator.

[0070] When using probability scores to determine at least one visual indicator, in one example, the visual indicator assignment module 116 may analyze the first set of attributes, the second set of attributes, and the third set of attributes corresponding to each visual indicator in the set of visual indicators to calculate probability scores for each visual indicator. The visual indicator assignment model may utilize a regression algorithm (such as random forest, linear regression, naive Bayes, etc.) to determine the probability scores.

[0071] In one example, to calculate a probability score, the visual indicator assignment module 116 may receive input from a user to assign pre-assigned weighted scores to each attribute in a first set of attributes, a second set of attributes, and a third set of attributes corresponding to each visual indicator in the set of visual indicators. The pre-assigned weighted scores may indicate the relative weights of the attributes used to calculate the probability score. For example, based on the input received from the user, a weighted score of 0.3 may be assigned to user attributes, a weighted score of 0.1 may be assigned to application attributes, a weighted score of 0.2 may be assigned to organizational attributes, a weighted score of 0.2 may be assigned to the second set of attributes, and a weighted score of 0.2 may be assigned to the third set of attributes. In other examples, the weighted scores for each attribute may vary due to various factors such as user input.

[0072] In another example, to calculate a probability score, the visual indicator assignment module 116 may implement a visual indicator assignment model to determine the pre-assigned weighted scores for each attribute in the first set of attributes, the second set of attributes, and the third set of attributes. The visual indicator assignment model may automatically determine the pre-assigned weighted scores without requiring input from the user. For example, the visual indicator assignment model may determine that assigning the highest weighted score to user attributes compared to the remaining attributes results in generating an analysis report that is least modified by the user. Thus, the visual indicator assignment model may assign the highest weighted score to user attributes.

[0073] Subsequently, the visual indicator assignment module 116 may process the first set of attributes, the second set of attributes, the third set of attributes, and the pre-assigned weighted scores corresponding to each attribute in the first set of attributes, the second set of attributes, and the third set of attributes to calculate the probability score for each visual indicator. Thus, the probability score may depend on the relevance of the user's attributes. The relevance may be determined based on the input user or may be automatically determined by the visual indicator assignment model without input from the user. For example, if it is determined that the user primarily wants to use visual indicators that the user typically uses, the highest weighted score may be assigned to user attributes compared to the remaining attributes. Similarly, if it is determined that the user primarily wants to use visual indicators that are typically used in a specific section of the analysis report, the highest weighted score may be assigned to the second set of attributes.

[0074] Once the probability scores for each visual indicator in the set of visual indicators have been calculated, for each chapter, the visual indicator assignment module 116 may identify at least one visual indicator from the set of visual indicators to represent a subset of the analysis data within the chapter based on the probability scores. The probability scores of the determined visual indicators may meet a predetermined selection criterion. In one example, the predetermined selection criterion may require that the probability score of at least one visual indicator be greater than a threshold probability score. The threshold probability score may be predetermined and pre-stored in the memory 232 of the system or in an external memory. In another example, the predetermined selection criterion may require that the probability score of at least one visual indicator be equal to the maximum probability score. The maximum probability score may have the maximum value among the probability scores calculated for each visual indicator in the set of visual indicators.

[0075] In one example, to identify at least one visual indicator, for each visual indicator, the visual indicator assignment module 116 may compare the probability score of the visual indicator with the threshold probability score. In one example, the threshold probability score may be automatically predetermined by the visual indicator assignment model without input from the user. In another example, the threshold probability score may be manually assigned by the user. Subsequently, if the probability score of the visual indicator is greater than the threshold probability score, the visual indicator assignment module 116 may determine the visual indicator as at least one visual indicator to represent a subset of the analysis data in the chapter. In the case where the probability score of the visual indicator is not greater than the threshold probability score, the visual indicator may not be determined as at least one visual indicator.

[0076] In another example, to identify at least one visual indicator, the visual indicator assignment module 116 may process the probability scores of each visual indicator in the set of visual indicators to identify the maximum probability score. The maximum probability score may have the maximum value among the probability scores calculated for each visual indicator in the set of visual indicators. In one example, the maximum probability score may be identified by the visual indicator assignment model. Subsequently, the visual indicator assignment module 116 may determine the visual indicator with the maximum probability score as at least one visual indicator to represent a subset of the analysis data in the chapter.

[0077] In yet another example, to identify at least one visual indicator, for each section, the visual indicator assignment module 116 may sort the visual indicators in descending order of the corresponding probability scores. Subsequently, for each section, the visual indicator assignment module 116 may identify a predefined number of visual indicators according to the sorting starting from the highest ranking. The predefined number for a section may indicate a specific number of visual indicators to be assigned to that section. In one example, the predefined number may be automatically predetermined by the visual indicator assignment model without input from the user, and the predefined number may be pre-stored in the system's memory or an external memory. In another example, the predefined number may be manually assigned by the user. For each section, the visual indicator assignment module 116 may assign the predefined number of visual indicators to that section of the analysis report.

[0078] In one example, for each section of the analysis report, the report generation module 118 of the system 102 may incorporate the identified visual indicators into that section to generate a final analysis report. The final analysis report may be displayed on a display (not shown) of the system 102. An exemplary final analysis report 306 having one or more sections 308-1, 308-2,... 308-N that can be generated has been shown in Figure 3B Here, N may be greater than or equal to 1. Visual indicators 310-1, 310-2, 310-3, 310-4, 310-5, 310-6, 310-7, 310-8, 310-9, 310-10 have been automatically incorporated to generate the final analysis report.

[0079] In one example, the report generation module 118 may display the set of visual indicators on a user interface of a report generation application. In one example, the user interface may be presented on a display of the system 102. In one example, the report generation module 118 may receive input from the user through the user interface. The input may have instructions for incorporating at least one desired visual indicator from the set of visual indicators into that section of the analysis report. The report generation module 118 may incorporate the at least one desired visual indicator into that section of the analysis report when the input is received from the user.

[0080] In another example, the visual indicator assignment module 116 of the system 102 may receive feedback input from the user. The feedback input may assign at least one desired visual indicator from the set of visual indicators to that section of the analysis report. In one example, the feedback input may remove an undesired visual indicator from the visual indicators assigned to that section of the analysis report.

[0081] Subsequently, the visual indicator assignment module 116 may identify a fourth set of attributes corresponding to at least one desired visual indicator or an undesired visual indicator. The fourth set of attributes may be identified in a similar manner as the third set of attributes were interpreted as being identified.

[0082] In addition, the visual indicator assignment module 116 may analyze the first set of attributes, the second set of attributes corresponding to the chapter, and the fourth set of attributes to improvise an association mapping between the first set of attributes, the second set of attributes, and the third set of attributes. The improvised association mapping may then be used to generate a further analysis report.

[0083] In another example, when using probability scores to determine at least one visual indicator for the chapter, the visual indicator assignment module 116 may receive feedback inputs from the user for each of the first set of attributes, the second set of attributes, and the third set of attributes. The feedback inputs may assign desired weighted scores to the attributes. The visual indicator assignment module 116 may improvise a visual indicator assignment model based on a comparison of the pre-assigned weighted scores and the desired weighted scores. In addition, the visual indicator assignment module 116 may re-train the visual indicator assignment model to optimize the visual indicator assignment model based on the feedback inputs.

[0084] In one example, the visual indicator assignment module 116 may periodically update the pre-assigned weighted scores using the improvised visual indicator assignment model. In another example, the visual indicator assignment module 116 may update the pre-assigned weighted scores when it determines that the pre-assigned weighted scores are different from the desired weighted scores.

[0085] Figure 3A and Figure 3B illustrates different stages of an analysis report generated and customized by the system 102 according to one example.

[0086] Figure 3A illustrates an analysis report 302 in an initial stage according to one example. The analysis report 302 may include one or more chapters 304-1, 304-2, … 304-N. Here, N may be greater than or equal to 1. The one or more chapters 304-1, 304-2, … 304-N may be collectively referred to as chapters 304 and individually as a chapter 304. The initial stage is a stage where no visual indicators are included in the chapters 304 of the analysis report 302. The analysis report 302 may be obtained by the data acquisition module 114 of the system 102, as explained with reference to Figure 1 and Figure 2 In one example, the analysis report 302 and the chapters 304 may be pre-created by the user using a report generation application.

[0087] Figure 3B shows a final analysis report 306 of a subsequent phase according to an example. The final analysis report 306 may include one or more chapters 308-1, 308-2, … 308-N. Here, N may be greater than or equal to 1. The one or more chapters 308-1, 308-2, … 308-N may be collectively referred to as chapters 308 and individually as chapter 308. The subsequent phase is the phase in which visual indicators 310-1, 310-2, 310-3, 310-4, 310-5, 310-6, 310-7, 310-8, 310-9, 310-10 are incorporated into chapter 304 of the final analysis report 306. The final analysis report 306 may be generated by the report generation module 118 of the system 102, as explained with reference to Figure 1 and Figure 2 As shown, visual indicators 310-1, 310-2, and 310-3 have been incorporated into chapter 308-1 of the final analysis report 306. In addition, visual indicators 310-4 and 310-5 have been incorporated into chapter 308-2 of the final analysis report 306. As shown, visual indicators 310-6, 310-7, 310-8, 310-9, and 310-10 have been incorporated into chapter 308-N of the final analysis report 306. The number of visual indicators 310 assigned to a chapter 308 may be equal to or greater than 1.

[0088] Figure 4 、 Figures 5A to 5C 、 Figures 6A to 6D and Figure 7A and Figure 7B respectively show exemplary methods 400, 500, 600, and 700 for generating an analysis report. The order in which these methods are described is not intended to be construed as limiting, and any number of the described method blocks may be combined in any order to implement these methods or alternative methods. In addition, methods 400, 500, 600, and 700 may be implemented by processing resources or computing devices through any suitable hardware, non-transitory machine-readable instructions, or a combination thereof.

[0089] It may also be understood that methods 400, 500, 600, and 700 may be implemented by a programmed computing device (such as Figure 1 and Figure 2The system depicted in (102) executes. Additionally, as will be readily understood, the methods 400, 500, 600, and 700 can be executed based on instructions stored in a non-transitory computer-readable medium. The non-transitory computer-readable medium can include, for example, digital memory, magnetic storage media (such as one or more disks and tapes), hard disk drives, or optically readable digital data storage media. Although the methods 400, 500, 600, and 700 are described below with reference to the system 102 as described above; other suitable systems for executing these methods can also be used. Additionally, the implementation of these methods is not limited to such examples.

[0090] Figure 4 A method 400 for generating an analysis report according to one example is shown.

[0091] At block 402, an analysis report can be obtained. The analysis report can have analysis data associated with an organization. In one example, the analysis report can include one or more chapters, where each of the one or more chapters includes at least one subset of the analysis data. In one example, the analysis report can be the same as the analysis report explained with reference to Figure 1 and Figure 2 In one example, the analysis report can be obtained by the data acquisition module 114 in a manner similar to that explained with reference to Figure 1 and Figure 2

[0092] At block 404, a first set of attributes corresponding to the analysis report can be obtained. Additionally, a second set of attributes corresponding to each of the one or more chapters can be obtained. In one example, the first set of attributes and the second set of attributes can be the same as the first set of attributes and the second set of attributes explained with reference to Figure 1 and Figure 2 The first set of attributes and the second set of attributes can be obtained by the visual indicator assignment module 116 explained with reference to Figure 1 and Figure 2 The first set of attributes and the second set of attributes can be identified by the visual indicator assignment module 116 in a manner similar to that explained with reference to Figure 1 and Figure 2 In one example, the first set of attributes and the second set of attributes identified by the visual indicator assignment module 116 can be stored in the memory of the system, such as the memory 232 of the system 102. Thus, the first set of attributes and the second set of attributes can be obtained from the memory of the system. In another example, the first set of attributes and the second set of attributes identified by the visual indicator assignment module 116 can be stored in an external memory. Thus, the first set of attributes and the second set of attributes can be obtained from the external memory.

[0093] ​At block 406, a first set of attributes may be analyzed to identify a set of visual indicators associated with the analysis report. The first set of attributes may be analyzed by the visual indicator assignment module 116 in a manner similar to that explained with reference to Figure 1 and Figure 2 The first set of attributes may be analyzed in a manner similar to that explained with reference to

[0094] At block 408, for each section of the analysis report, the first set of attributes, a second set of attributes, and a third set of attributes corresponding to each visual indicator in the set of visual indicators may be analyzed to determine at least one visual indicator for representing a subset of the analysis data within that section. In one example, the first set of attributes, the second set of attributes, and the third set of attributes may be analyzed by the visual indicator assignment module 116 in a manner similar to that explained with reference to Figure 1 and Figure 2 The first set of attributes, the second set of attributes, and the third set of attributes may be analyzed in a manner similar to that explained with reference to

[0095] In one example, at least one visual indicator may be determined based on an association map. Determining at least one visual indicator based on an association map has been shown and explained with reference to Figure 1 、 Figure 2 and Figures 5A to 5C Determining at least one visual indicator based on an association map has been shown and explained with reference to

[0096] In another example, at least one visual indicator may be determined based on a probability score associated with each visual indicator in the set of visual indicators. Determining at least one visual indicator based on a probability score has been shown and explained in detail with reference to Figures 6A to 6D Determining at least one visual indicator based on a probability score has been shown and explained in detail with reference to

[0097] In another example, at least one visual indicator may be determined based on a weight score associated with each attribute in the first set of attributes, the second set of attributes, and the third set of attributes and a probability score associated with each visual indicator in the set of visual indicators. Determining at least one visual indicator based on a weight score and a probability score has been shown and explained in detail with reference to Figure 7A 、 Figure 7B and Figure 8 Determining at least one visual indicator based on a weight score and a probability score has been shown and explained in detail with reference to

[0098] At block 410, for each section of the analysis report, at least one visual indicator may be incorporated into the section to generate a final analysis report. In one example, at least one visual indicator may be incorporated by the report generation module 118 in a manner similar to that explained with reference to Figure 1 and Figure 2 At least one visual indicator may be incorporated in a manner similar to that explained with reference to

[0099] Figure 5A 、 Figure 5B and Figure 5C shows a method 500 for generating an analysis report based on an association map between various attributes related to the analysis report, according to one example.

[0100] At block 502, an analysis report can be obtained. The analysis report can have analysis data associated with an organization. The analysis report can include one or more sections. Each of the one or more sections can include at least one subset of the analysis data. In one example, the analysis report can be the same as the analysis report explained with reference to Figure 1 and Figure 2 In one example, the analysis report can be obtained by the data acquisition module 114 in a similar manner as explained with reference to Figure 1 and Figure 2

[0101] At block 504, a first set of attributes corresponding to the analysis report and a second set of attributes corresponding to each of the one or more sections can be identified. The first set of attributes can include organization attributes corresponding to the organization, user attributes corresponding to a user associated with the organization and using a specific report generation application to finalize the analysis report, and application attributes corresponding to the specific report generation application. The first set of attributes and the second set of attributes can be identified by the visual indicator assignment module 116 in a similar manner as explained with reference to Figure 1 and Figure 2

[0102] At block 506, the organization attributes and a third set of attributes can be analyzed to identify a first plurality of predefined visual indicators assigned to the organization. The first plurality of predefined visual indicators can be customized for use by the organization. The organization attributes and the third set of attributes can be analyzed by the visual indicator assignment module 116 in a similar manner as explained with reference to Figure 2

[0103] At block 508, the user attributes and the third set of attributes can be analyzed to identify a second plurality of predefined visual indicators from the first plurality of predefined visual indicators. The second plurality of predefined visual indicators can be assigned to the user for use in the analysis report associated with the organization. The user attributes and the third set of attributes can be analyzed by the visual indicator assignment module 116 in a similar manner as explained with reference to Figure 2

[0104] At block 510, the application attributes and the third set of attributes can be analyzed to identify a set of visual indicators from the second plurality of predefined visual indicators. The set of visual indicators can be assigned to the user for use by the report generation application in the analysis report. The application attributes and the third set of attributes can be analyzed by the visual indicator assignment module 116 in a similar manner as explained with reference to Figure 2

[0105] ​​​​​At block 512, at least one historical analysis report can be obtained. Each historical analysis report in the at least one historical analysis report can include one or more predefined sections with historical analysis data. Each section in the one or more predefined sections can include at least one assigned visual indicator that graphically represents the historical analysis data. The at least one historical analysis report can be the same as the at least one historical analysis report explained with reference to Figure 2 In one example, the at least one historical analysis report can be obtained by the visual indicator assignment module 116 in a similar manner as explained with reference to Figure 2 .

[0106] At block 514, for each historical analysis report in the at least one historical analysis report, a first set of historical attributes corresponding to the historical analysis report, a second set of historical attributes corresponding to each section in the one or more predefined sections, and a third set of historical attributes corresponding to each visual indicator in the at least one assigned visual indicator can be identified. In one example, the first set of historical attributes, the second set of historical attributes, and the third set of historical attributes can be identified by the visual indicator assignment module 116 in a similar manner as explained with reference to Figure 2 .

[0107] At block 516, the first set of historical attributes, the second set of historical attributes, and the third set of historical attributes can be analyzed to generate an association map between the first set of attributes, the second set of attributes, and the third set of attributes. In one example, the first set of historical attributes, the second set of historical attributes, and the third set of historical attributes can be analyzed by the visual indicator assignment module 116 in a similar manner as explained with reference to Figure 2 .

[0108] At block 518, for each section, the association map can be analyzed to determine at least one visual indicator for representing a subset of the analysis data within the section. In one example, the association map can be analyzed by the visual indicator assignment module 116 in a similar manner as explained with reference to Figure 1 and Figure 2 .

[0109] At block 520, for each section of the analysis report, at least one visual indicator can be incorporated into the section to generate a final analysis report. In one example, the at least one visual indicator can be incorporated and the final analysis report can be generated by the report generation module 118 in a similar manner as explained with reference to Figure 1 and Figure 2 .

[0110] At block 522, the set of visual indicators can be displayed on the user interface of the report generation application. In one example, the set of visual indicators can be displayed by the report generation module 118 in a similar manner as explained with reference to Figure 2Display the set of visual indicators in a similar manner as explained.

[0111] At block 524, feedback input may be received from the user. The feedback input may assign at least one desired visual indicator from the set of visual indicators to the section of the analysis report. In one example, the feedback input may remove an undesired visual indicator from the visual indicators assigned to the section of the analysis report. In one example, the feedback input may be received by the visual indicator assignment module 116 in a similar manner as explained with reference Figure 2 and as explained.

[0112] At block 526, when the feedback input is received, at least one desired visual indicator may be incorporated into the section of the analysis report. In one example, at least one desired visual indicator may be incorporated into the section by the visual indicator assignment module 116 in a similar manner as explained with reference Figure 2 and as explained.

[0113] At block 528, a fourth set of attributes corresponding to at least one desired visual indicator or undesired visual indicator may be identified. In one example, the fourth set of attributes may be identified by the visual indicator assignment module 116 in a similar manner as explained with reference Figure 2 and as explained.

[0114] At block 530, the first set of attributes, the second set of attributes corresponding to the section, and the fourth set of attributes may be analyzed to temporarily establish an association mapping between the first set of attributes, the second set of attributes, and the third set of attributes. In one example, the first set of attributes, the second set of attributes corresponding to the section, and the fourth set of attributes may be analyzed by the visual indicator assignment module 116 in a similar manner as explained with reference Figure 2 and as explained.

[0115] Figures 6A to 6D A method 600 for generating an analysis report based on probability scores associated with various visual indicators available for incorporation into an analysis report is shown according to one example.

[0116] At block 602, an analysis report may be obtained. The analysis report may have analysis data associated with an organization. The analysis report may include one or more sections. Each of the one or more sections may include at least one subset of the analysis data. In one example, the analysis report may be the same as the analysis report explained with reference Figure 1 and Figure 2 and as explained. In one example, the analysis report may be obtained by the data acquisition module 114 in a similar manner as explained with reference Figure 1 and Figure 2 and as explained.

[0117] At block 604, a first set of attributes corresponding to the analysis report can be obtained. Additionally, a second set of attributes corresponding to each of one or more sections can be obtained. The first set of attributes can include organization attributes corresponding to the organization, user attributes corresponding to a user associated with the organization and using a specific report generation application to finalize the analysis report, and application attributes corresponding to the specific report generation application. In one example, the first set of attributes and the second set of attributes can be the same as the first set of attributes and the second set of attributes explained with reference to Figure 1 and Figure 2 The first set of attributes and the second set of attributes can be obtained by the visual indicator assignment module 116 that implements the visual indicator assignment model explained with reference to Figure 1 and Figure 2 The visual indicator assignment model can be trained based on a statistical analysis of one or more historical analysis reports. The first set of attributes and the second set of attributes can be identified by the visual indicator assignment module 116 in a similar manner as explained with reference to Figure 1 and Figure 2 In one example, the first set of attributes and the second set of attributes identified by the visual indicator assignment module 116 can be stored in the memory of the system, such as the memory 232 of the system 102. Thus, the first set of attributes and the second set of attributes can be obtained from the memory of the system. In another example, the first set of attributes and the second set of attributes identified by the visual indicator assignment module 116 can be stored in an external memory. Thus, the first set of attributes and the second set of attributes can be obtained from the external memory.

[0118] At block 606, user details of the user of the organization that used the report generation application to finalize the analysis report can be retrieved from the first set of attributes. In one example, the user details can be retrieved by the visual indicator assignment module 116 as explained with reference to Figure 1 and Figure 2 Examples of user details can include, but are not limited to, an ID associated with the user, the name of the user, the name of the organization with which the user is associated, and the department of the organization where the user works. In one example, the user details can be similar to the user attributes explained with reference to Figure 1 and Figure 2

[0119] At block 608, the organization attributes and a third set of attributes can be analyzed to identify a first plurality of predefined visual indicators assigned to the organization. The first plurality of predefined visual indicators can be customized for use by the organization. The organization attributes and the third set of attributes can be analyzed by the visual indicator assignment module 116 in a similar manner as explained with reference to Figure 2

[0120] ​​At block 610, user attributes and a third set of attributes can be analyzed to identify a second plurality of predefined visual indicators from a first plurality of predefined visual indicators. The second plurality of predefined visual indicators can be assigned to the user for use in an analysis report associated with the organization. The visual indicator assignment module 116 can analyze the user attributes and the third set of attributes in a manner similar to that Figure 2 explained by reference

[0121] At block 612, application attributes and a third set of attributes can be analyzed to identify a set of visual indicators from the second plurality of predefined visual indicators. The set of visual indicators can be assigned to the user for use by a report generation application in an analysis report. The visual indicator assignment module 116 can analyze the application attributes and the third set of attributes in a manner similar to that Figure 2 explained by reference

[0122] At block 614, a third set of attributes corresponding to each visual indicator in the set of visual indicators can be obtained. In one example, the third set of attributes can be obtained by the visual indicator assignment module 116 Figure 1 and Figure 2 explained by reference. Examples of the third set of attributes of a visual indicator can include, but are not limited to, the ID of the visual indicator, the type of the visual indicator, the x-axis field of the visual indicator, the y-axis field of the visual indicator, and the BI tool ID of the BI tool for customizing the visual indicator. In one example, the third set of attributes can be identified based on the visual indicator data 210 stored in the BI tool server 104 Figure 1 and Figure 2 explained by reference. The identified third set of attributes can be stored in the memory of the system or an external memory. The third set of attributes can be obtained from the BI tool server 104, the memory of the system, or the external memory accordingly.

[0123] At block 616, for each section, the first set of attributes, the second set of attributes, and the third set of attributes corresponding to each visual indicator in the set of visual indicators can be analyzed to calculate a probability score for each visual indicator. The probability score can indicate the probability that the visual indicator appears in that section of the analysis report. In one example, the probability score can be calculated by the visual indicator assignment module 116 implementing the visual indicator assignment model. The visual indicator assignment model can be trained based on the statistical analysis of one or more historical analysis reports to calculate the probability score. The visual indicator assignment model can utilize regression algorithms (such as random forest, linear regression, naive Bayes, etc.) to determine the probability score. In one example, the visual indicator assignment model can understand the relationship between the section, the visual indicator, and the analysis report based on the statistical analysis of one or more historical analysis reports to calculate the probability score.

[0124] In one example, to calculate a probability score for each visual indicator, at block 618, input may be received from a user to assign pre-assigned weighted scores to each attribute in a first set of attributes, a second set of attributes, and a third set of attributes corresponding to each visual indicator in the set of visual indicators. The pre-assigned weighted scores may indicate the relative weights of the attributes used to calculate the probability score. For example, based on the input received from the user, a weighted score of 0.3 may be assigned to user attributes, a weighted score of 0.1 may be assigned to application attributes, a weighted score of 0.2 may be assigned to organizational attributes, a weighted score of 0.2 may be assigned to the second set of attributes, and a weighted score of 0.2 may be assigned to the third set of attributes. In other examples, the weighted scores for each attribute may vary due to various factors such as user input.

[0125] Subsequently, at block 620, the first set of attributes, the second set of attributes, the third set of attributes, and the pre-assigned weighted scores corresponding to each attribute in the first set of attributes, the second set of attributes, and the third set of attributes may be processed to calculate the probability score for each visual indicator. Thus, the probability score may depend on the relevance of the user's attributes. For example, if the user primarily wants to use visual indicators that the user typically uses, the user may assign the highest weighted score to the user attributes compared to the remaining attributes. Similarly, if the user primarily wants to use visual indicators that are typically used in a specific section of an analysis report, the user may assign the highest weighted score to the second set of attributes.

[0126] In another example, to calculate a probability score for each visual indicator, at block 622, for each attribute in the first set of attributes, the second set of attributes, and the third set of attributes, a pre-assigned weighted score may be determined. The pre-assigned weighted scores may indicate the relative weights of the attributes used to calculate the probability score. In one example, the pre-assigned weighted scores may be determined by the visual indicator assignment module 116 that implements the visual indicator assignment model. The visual indicator assignment model may automatically determine the pre-assigned weighted scores without requiring input from the user. For example, the visual indicator assignment model may determine that assigning the highest weighted score to the user attributes compared to the remaining attributes results in generating an analysis report that is least modified by the user. Thus, the visual indicator assignment model may assign the highest weighted score to the user attributes.

[0127] Subsequently, at block 624, the first set of attributes, the second set of attributes, the third set of attributes, and the pre-assigned weighted scores corresponding to each attribute in the first set of attributes, the second set of attributes, and the third set of attributes may be processed to calculate the probability score for each visual indicator. Thus, the probability score may depend on the relevance of the attributes automatically determined for the user by the visual indicator assignment model.

[0128] At block 626, for each chapter, at least one visual indicator for representing a subset of the analysis data within the chapter can be identified from the set of visual indicators. The probability score of the at least one visual indicator can meet a predetermined selection criterion. In one example, the at least one visual indicator can be identified by the visual indicator assignment module 116. In one example, the predetermined selection criterion can require that the probability score of the at least one visual indicator be greater than a threshold probability score. The threshold probability score can be predetermined and pre-stored in the memory of the system or an external memory. In another example, the predetermined selection criterion can require that the probability score of the at least one visual indicator be equal to the maximum probability score. The maximum probability score can have the maximum value among the probability scores calculated for each visual indicator in the set of visual indicators.

[0129] In one example, to identify the at least one visual indicator, at block 628, for each visual indicator, the probability score of the visual indicator can be compared with the threshold probability score. In one example, the threshold probability score can be automatically predetermined by the visual indicator assignment model without input from the user. In another example, the threshold probability score can be manually assigned by the user.

[0130] Subsequently, at block 630, for the probability scores of the visual indicators greater than the threshold probability score, the visual indicators can be determined as the at least one visual indicator for representing the subset of the analysis data in the chapter. In the case where the probability score of a visual indicator is not greater than the threshold probability score, the visual indicator can not be determined as the at least one visual indicator.

[0131] In one example, to identify the at least one visual indicator, at block 632, the probability scores calculated for each visual indicator in the set of visual indicators can be processed to identify the maximum probability score. The maximum probability score can have the maximum value among the probability scores calculated for each visual indicator in the set of visual indicators. In one example, the maximum probability score can be identified by the visual indicator assignment model.

[0132] Subsequently, at block 634, the visual indicator with the maximum probability score can be determined as the at least one visual indicator for representing the subset of the analysis data in the chapter.

[0133] In another example, to identify the at least one visual indicator, at block 636, for each chapter, the visual indicators can be sorted in descending order of the corresponding probability scores.

[0134] Subsequently, at block 638, for each chapter, a predefined number of visual indicators can be identified according to the ranking starting from the highest ranking. The predefined number for a chapter indicates the specific number of visual indicators to be assigned to that chapter. In one example, the predefined number can be automatically predetermined by a visual indicator assignment model without input from the user, and the predefined number can be pre-stored in the system's memory or external memory. In another example, the predefined number can be manually assigned by the user.

[0135] Subsequently, at block 640, for each chapter, the predefined number of visual indicators can be assigned to that chapter of the analysis report.

[0136] At block 642, for each chapter of the analysis report, at least one visual indicator can be incorporated into that chapter to generate a final analysis report. In one example, the report generation module 118 can incorporate at least one visual indicator and generate a final analysis report in a similar manner as explained with reference Figure 1 and Figure 2 In a similar manner as explained with reference.

[0137] At block 644, the set of visual indicators can be displayed on the user interface of the report generation application. In one example, the report generation module 118 can display the set of visual indicators in a similar manner as explained with reference Figure 2 In a similar manner as explained with reference. In one example, the set of visual indicators can be sorted in descending order of the corresponding probability scores. Additionally, the set of visual indicators can be displayed on the user interface according to the ranking starting from the highest ranking.

[0138] At block 646, at least one user-selected visual indicator from the set of visual indicators can be incorporated into that chapter of the analysis report. In one example, when input is received from the user via the user interface, at least one user-selected visual indicator can be incorporated into that chapter. The input can have instructions for incorporating at least one user-selected visual indicator into that chapter. In one example, the visual indicator assignment module 116 can incorporate at least one user-selected visual indicator into that chapter in a similar manner as explained with reference Figure 2 In a similar manner as explained with reference.

[0139] At block 648, feedback input can be received from the user for each of the first set of attributes, the second set of attributes, and the third set of attributes. The feedback input can assign a desired weighted score to the attribute.

[0140] At block 650, a visual indicator assignment model can be temporarily established based on the comparison of the pre-assigned weighted scores and the desired weighted scores. In one example, the visual indicator assignment model can be re-trained to optimize the visual indicator assignment model based on the feedback input.

[0141] In one example, at block 652, the pre-assigned weighted scores may be updated periodically using a temporarily established visual indicator assignment model. In another example, the pre-assigned weighted scores may be updated when it is determined that the pre-assigned weighted scores are different from the desired weighted scores.

[0142] Figure 7A and Figure 7B FIG. 7 shows a method 700 for generating an analysis report based on weight scores associated with various attributes related to an analysis report and probability scores associated with various visual indicators available for incorporation into the analysis report, according to one example.

[0143] At block 702, an analysis report may be obtained. The analysis report may have analysis data associated with an organization. The analysis report may include one or more chapters. Each of the one or more chapters may include at least one subset of the analysis data. In one example, a report generation application may be used to obtain the analysis report. In one example, the analysis report may be the same as the analysis report explained in reference Figure 1 and Figure 2 In one example, the analysis report may be obtained by the data acquisition module 114 in a similar manner as explained in reference Figure 1 and Figure 2 In a similar manner as explained in reference

[0144] At block 704, a first set of attributes corresponding to the analysis report may be identified. Additionally, a second set of attributes corresponding to each of the one or more chapters may be identified. In one example, the first set of attributes and the second set of attributes may be the same as the first set of attributes and the second set of attributes explained in reference Figure 1 and Figure 2 The first set of attributes and the second set of attributes may be identified by the visual indicator assignment module 116 in a similar manner as explained in reference Figure 1 and Figure 2 In a similar manner as explained in reference

[0145] At block 706, user details of the user of the organization that ultimately determines the analysis report using the report generation application may be retrieved from the first set of attributes. In one example, the user details may be retrieved by the visual indicator assignment module 116 as explained in reference Figure 1 and Figure 2 Examples of user details may include, but are not limited to, an ID associated with the user, the name of the user, the name of the organization with which the user is associated, and the department of the organization where the user works. In one example, the user details may be similar to the user attributes explained in reference Figure 1 and Figure 2 In a similar manner as explained in reference

[0146] At block 708, a first set of attributes and user details may be analyzed to identify a set of visual indicators. The set of visual indicators may be assigned to a user for use in an analysis report when using a reporting generation application. The visual indicators may provide a graphical representation of the analysis data present in the analysis report. The set of visual indicators may be identified by the visual indicator assignment module 116 in a manner similar to that explained with reference to Figure 2 as explained.

[0147] At block 710, a third set of attributes corresponding to each visual indicator in the set of visual indicators may be identified. In one example, the third set of attributes may be identified by the visual indicator assignment module 116 as explained with reference to Figure 1 and Figure 2 Examples of the third set of attributes of the visual indicators may include, but are not limited to, the ID of the visual indicator, the type of the visual indicator, the x-axis field of the visual indicator, the y-axis field of the visual indicator, and the BI tool ID of the BI tool used to customize the visual indicator. In one example, the third set of attributes may be identified based on the visual indicator data 210 stored in the BI tool server 104 as explained with reference to Figure 1 and Figure 2 as explained.

[0148] At block 712, a pre-assigned weighted score may be obtained for each attribute in the first set of attributes, the second set of attributes, and the third set of attributes corresponding to each visual indicator in the set of visual indicators. The pre-assigned weighted score may indicate the comparative weight of the attribute for calculating a probability score. The pre-assigned weighted score may be manually assigned by a user or may be automatically determined by a visual indicator assignment model, as explained in blocks 618, 620, 622, and 624 of Figure 6B as explained.

[0149] In one example, at block 714, an input may be received from the user to assign the pre-assigned weighted scores to each attribute in the first set of attributes, the second set of attributes, and the third set of attributes corresponding to each visual indicator in the set of visual indicators. For example, based on the input received from the user, a weighted score of 0.3 may be assigned to the user attributes, a weighted score of 0.1 may be assigned to the application attributes, a weighted score of 0.2 may be assigned to the organization attributes, a weighted score of 0.2 may be assigned to the second set of attributes, and a weighted score of 0.2 may be assigned to the third set of attributes. In other examples, the weighted scores for each attribute may vary due to various factors such as user input.

[0150] In another example, at block 716, for each attribute in the first set of attributes, the second set of attributes, and the third set of attributes, a pre-assigned weighted score can be determined using a visual indicator assignment model. The visual indicator assignment model can automatically determine the pre-assigned weighted score without requiring input from the user. For example, the visual indicator assignment model can determine that assigning the highest weighted score to the user attribute compared to the remaining attributes results in generating an analysis report that is least modified by the user. Thus, the visual indicator assignment model can assign the highest weighted score to the user attribute.

[0151] At block 718, for each section, the first set of attributes, the second set of attributes, the third set of attributes, and the pre-assigned weighted scores corresponding to each attribute in the first set of attributes, the second set of attributes, and the third set of attributes can be processed to calculate a probability score for each visual indicator. The probability score can indicate the probability that the visual indicator appears in that section of the analysis report. In one example, the probability score can be calculated by the visual indicator assignment module 116 that implements the visual indicator assignment model. The visual indicator assignment model can be trained based on the statistical analysis of one or more historical analysis reports to calculate the probability score. The visual indicator assignment model can use a regression algorithm (such as random forest, linear regression, naive Bayes, etc.) to determine the probability score. In one example, the visual indicator assignment model can understand the relationship between the section, the visual indicator, and the analysis report based on the statistical analysis of one or more historical analysis reports to calculate the probability score.

[0152] At block 720, for each section, at least one visual indicator can be determined from the set of visual indicators to represent a subset of the analysis data within that section. The probability score of the at least one visual indicator can meet a predetermined selection criterion. In one example, the at least one visual indicator can be determined by the visual indicator assignment module 116. In one example, the predetermined selection criterion can require that the probability score of the at least one visual indicator is greater than a threshold probability score. The threshold probability score can be predetermined and pre-stored in the memory of the system or an external memory. In another example, the predetermined selection criterion can require that the probability score of the at least one visual indicator is equal to the maximum probability score. The maximum probability score can have the maximum value among the probability scores calculated for each visual indicator in the set of visual indicators. The at least one visual indicator can be determined or identified in a similar manner as explained for blocks 626 to 640 of Figure 6C reference.

[0153] At block 722, for each section of the analysis report, the at least one visual indicator can be incorporated into that section to generate a final analysis report. In one example, the report generation module 118 can perform this operation in a manner similar to that of Figure 1 reference Figure 2In a similar manner as explained, incorporate at least one visual indicator and may generate a final analysis report.

[0154] At block 724, feedback input may be received from the user. The feedback input may assign a corresponding desired weighted score to each attribute in the first group of attributes, the second group of attributes, and the third group of attributes.

[0155] At block 726, a visual indicator assignment model may be temporarily established based on a comparison of the pre-assigned weighted scores with the corresponding desired weighted scores. In one example, the visual indicator assignment model may be retrained to optimize the visual indicator assignment model based on the feedback input.

[0156] In one example, at block 728, the pre-assigned weighted scores may be periodically updated using the temporarily established visual indicator assignment model. In another example, the pre-assigned weighted scores may be updated when it is determined that the pre-assigned weighted scores are different from the desired weighted scores.

[0157] Figure 8 A computing environment 800 for implementing a non-transitory computer-readable medium for generating an analysis report according to one example is shown. In one example, the computing environment 800 includes a processor 802 that is communicatively coupled to a non-transitory computer-readable medium 804 via a communication link 806. In one example, the communication link 806 may be similar to the network 108 as described in connection with the previous figures. In an exemplary embodiment, the computing environment 800 may be, for example, the communication environment 100 or the communication environment 200. In one example, the processor 802 may have one or more processing resources for obtaining and executing computer-readable instructions from the non-transitory computer-readable medium 804. The processor 802 and the non-transitory computer-readable medium 804 may be implemented, for example, in the system 102 (as described in connection with the previous figures).

[0158] The non-transitory computer-readable medium 804 may be, for example, an internal memory device or an external memory device. In an exemplary embodiment, the communication link 806 may be a network communication link. The processor 802 and the non-transitory computer-readable medium 804 may also be communicatively coupled to one or more servers 808 via a network 810. The one or more servers 808 may be the BI tool server 104 or the report generation application server 106 as described in connection with Figure 1 and Figure 2 The network 810 may be similar to the network 108 as described in connection with Figure 1 and Figure 2 and

[0159] In an exemplary embodiment, the non-transitory computer-readable medium 804 may include a set of computer-readable instructions 812 accessible by the processor 802 via a communication link 806. Referring to FIG. 6, in one example, the non-transitory computer-readable medium 804 may include instructions 812 that may cause the processor 802 to obtain an analysis report having analysis data associated with an organization using a report generation application. The analysis report may include one or more sections. Each of the one or more sections may include a subset of the analysis data. In one example, the analysis report may be obtained in a manner similar to that explained with reference to Figure 7A block 702 of FIG.

[0160] In one example, the instructions 812 may further cause the processor 802 to identify a first set of attributes corresponding to the analysis report and a second set of attributes corresponding to each of the one or more sections. In one example, the first set of attributes and the second set of attributes may be identified in a manner similar to that explained with reference to Figure 7A block 704 of FIG.

[0161] In one example, the instructions 812 may further cause the processor 802 to retrieve user details of a user of the organization that used the report generation application to finalize the analysis report from the first set of attributes. In one example, the user details may be retrieved in a manner similar to that explained with reference to Figure 7A block 706 of FIG.

[0162] In one example, the instructions 812 may further cause the processor 802 to analyze the first set of attributes and the user details to identify a set of visual indicators assigned to the user to be used in the analysis report when using the report generation application. The visual indicators may provide a graphical representation of the analysis data present in the analysis report. In one example, the first set of attributes and the user details may be analyzed in a manner similar to that explained with reference to Figure 7A block 708 of FIG.

[0163] In one example, the instructions 812 may further cause the processor 802 to identify a third set of attributes corresponding to each of the visual indicators in the set of visual indicators. In one example, the third set of attributes may be identified in a manner similar to that explained with reference to Figure 7A block 710 of FIG.

[0164] In one example, the instructions 812 may further cause the processor 802 to obtain a pre-assigned weighted score for each of the attributes in the first set of attributes, the second set of attributes, and the third set of attributes corresponding to each of the visual indicators in the set of visual indicators. In one example, the pre-assigned weighted scores may be obtained in a manner similar to that explained with reference to Figure 7A block 712 of FIG.

[0165] In one example, instruction 812 may also cause processor 802 to receive input from a user to assign pre-assigned weighted scores to each of the first set of attributes, the second set of attributes, and the third set of attributes corresponding to each of the visual indicators in the set. The pre-assigned weighted scores may indicate the comparative weights of the attributes used to calculate the probability scores. In one example, the pre-assigned weighted scores may be assigned in a similar manner as explained in reference Figure 7A for box 714.

[0166] In one example, for each of the first set of attributes, the second set of attributes, and the third set of attributes, instruction 812 may also cause processor 802 to determine the pre-assigned weighted scores using a visual indicator assignment model. In one example, the pre-assigned weighted scores may be determined in a similar manner as explained in reference Figure 7A for box 716.

[0167] For each section, in one example, instruction 812 may cause processor 802 to process the first set of attributes, the second set of attributes, the third set of attributes, and the pre-assigned weighted scores corresponding to each of the first set of attributes, the second set of attributes, and the third set of attributes through a visual indicator assignment model to calculate the probability scores for each visual indicator. The probability scores of the visual indicators may indicate the probability that the visual indicators appear in that section of the analysis report. The visual indicator assignment model may be trained based on the statistical analysis of one or more historical analysis reports. In one example, the probability scores may be calculated in a similar manner as explained in reference Figure 7B for box 718.

[0168] For each section, in one example, instruction 812 may also cause processor 802 to determine at least one visual indicator from the set of visual indicators for representing a subset of the analysis data within that section using a visual indicator assignment model. The probability scores of the at least one visual indicator may meet a pre-determined selection criterion. In one example, the at least one visual indicator may be determined in a similar manner as explained in reference Figure 7B for box 720.

[0169] For each section of the analysis report, in one example, instruction 812 may also cause processor 802 to incorporate the at least one visual indicator into that section to generate a final analysis report. In one example, the at least one visual indicator may be incorporated and the final analysis report may be generated in a similar manner as explained in reference Figure 7B for box 722.

[0170] In one example, instruction 812 may also cause processor 802 to receive feedback input from a user. The feedback input may assign corresponding desired weighted scores to each of the first set of attributes, the second set of attributes, and the third set of attributes.

[0171] In one example, instruction 812 may also cause processor 802 to temporarily establish a visual indicator assignment model based on a comparison of a pre-assigned weighted score with a corresponding desired weighted score. The visual indicator assignment model may be temporarily established in a manner similar to that explained in block 726 of the reference Figure 7B .

[0172] In one example, instruction 812 may also cause processor 802 to periodically update the pre-assigned weighted score using the temporarily established visual indicator assignment model. The pre-assigned weighted score may be updated in a manner similar to that explained in block 728 of the reference Figure 7B .

[0173] Although the examples of the present disclosure have been described in language specific to structural features and / or methods, it should be understood that the appended claims are not necessarily limited to the specific features or methods described. Instead, these specific features and methods are disclosed and explained as examples of the present disclosure.

Claims

1. A system, comprising: A data acquisition module, wherein the data acquisition module is used to: obtaining an analytical report having analytical data associated with an organization, the analytical report comprising one or more sections, wherein each section of the one or more sections comprises at least a subset of the analytical data; and A visual indicator assignment module that implements a visual indicator assignment model to: identifying a first set of attributes corresponding to the analytical report and a second set of attributes corresponding to each of the one or more sections; analyzing the first set of attributes to identify a set of visual indicators associated with the analytical report, wherein the visual indicators will provide a graphical representation of the analytical data present in the analytical report; receiving an association mapping between the first set of attributes, the second set of attributes, and a third set of attributes corresponding to each visual indicator in the set of visual indicators; as well as for each section, analyzing the association map to determine at least one visual indicator for representing the subset of the analytical data within the section; and A report generation module, wherein the report generation module is used to: For each section of the analysis report, the at least one visual indicator is incorporated into the section to generate a final analysis report.

2. The system of claim 1 , wherein the first set of attributes includes organization attributes corresponding to the organization, user attributes corresponding to a user associated with the organization and using a specific report generating application to finalize the analytical report, and application attributes corresponding to the specific report generating application, and wherein the visual indicator assignment module is to: analyzing the organizational attributes and the third set of attributes to identify a first plurality of predefined visual indicators assigned to the organization, wherein the first plurality of predefined visual indicators are customized for use by the organization; analyzing the user attributes and the third set of attributes to identify, from the first plurality of predefined visual indicators, a second plurality of predefined visual indicators assigned to the user for use in an analytical report associated with the organization; as well as The application attributes and the third set of attributes are analyzed to identify the set of visual indicators from the second plurality of predefined visual indicators that are assigned to the user for use by the particular report generating application in the analysis report.

3. The system of claim 1 , wherein the visual indicator assignment module is to: obtaining at least one historical analysis report, each of the at least one historical analysis report comprising one or more predefined sections having historical analysis data, wherein each of the one or more predefined sections comprises at least one assigned visual indicator that graphically represents the historical analysis data; for each of the at least one historical analysis report, identifying a first set of historical attributes corresponding to the historical analysis report, a second set of historical attributes corresponding to each of the one or more predefined sections, and a third set of historical attributes corresponding to each of the at least one assigned visual indicator; as well as The first set of historical attributes, the second set of historical attributes, and the third set of historical attributes are analyzed to generate the association map between the first set of attributes, the second set of attributes, and the third set of attributes.

4. The system of claim 2, wherein the report generation module: displaying the set of visual indicators on a user interface of the particular report generating application; and At least one desired visual indicator from the set of visual indicators is incorporated into the section of the analysis report upon receiving input from the user through the user interface, the input having instructions for incorporating the at least one desired visual indicator into the section.

5. A method comprising: obtaining, by a data acquisition module associated with a report generation application, an analytical report having analytical data associated with an organization, the analytical report comprising one or more sections, wherein each section of the one or more sections comprises at least a subset of the analytical data; obtaining, by a visual indicator assignment module implementing a visual indicator assignment model, a first set of attributes corresponding to the analysis report and a second set of attributes corresponding to each of the one or more sections; retrieving, by the visual indicator assignment module, from the first set of attributes, user details of a user of the organization who uses the report generation application to finalize the analytical report; analyzing, by the visual indicator assignment module, the first set of attributes and the user details to identify a set of visual indicators assigned to the user for use in the analytical report when using the report generation application, wherein the visual indicators will provide a graphical representation of the analytical data present in the analytical report; obtaining, by the visual indicator assignment module, a third set of attributes corresponding to each visual indicator in the set of visual indicators; for each section, analyzing, by the visual indicator assignment model, the first set of attributes, the second set of attributes, and the third set of attributes corresponding to each visual indicator in the set of visual indicators to calculate a probability score for each visual indicator, the probability score indicating a probability of the visual indicator appearing in the section of the analytical report, wherein the visual indicator assignment model is trained based on a statistical analysis of one or more historical analytical reports; For each chapter, the visual indicator allocation model identifies at least one visual indicator from the set of visual indicators for representing the subset of the analysis data within the chapter, wherein the probability score of the at least one visual indicator satisfies a predetermined selection criterion; and for each chapter of the analysis report, the report generation module incorporates the at least one visual indicator into the chapter to generate a final analysis report.

6. The method of claim 5, wherein the identifying at least one visual indicator by the visual indicator assignment model comprises: for each visual indicator, comparing the probability score for the visual indicator to a threshold probability score; as well as For the probability score of the visual indicator that is greater than the threshold probability score, the visual indicator is determined to be the at least one visual indicator for representing the subset of the analytical data in the section.

7. The method of claim 5, wherein the identifying, by the visual indicator assignment model, at least one visual indicator comprises: processing the probability scores calculated for each of the set of visual indicators to identify a maximum probability score, wherein the maximum probability score has a maximum value among the probability scores calculated for each of the visual indicators in the set of visual indicators; as well as The visual indicator having the maximum probability score is determined as the at least one visual indicator for representing the subset of the analytical data in the section.

8. The method of claim 5, wherein the identifying, by the visual indicator assignment model, at least one visual indicator comprises: For each chapter, sorting the visual indicators in descending order of the corresponding probability scores; for each chapter, identifying a predefined number of visual indicators according to the ranking starting with a highest ranking, wherein the predefined number for a chapter indicates a specific number of visual indicators to be assigned to the chapter; as well as For each section, the predefined number of visual indicators is assigned to the section of the analytical report.

9. The method of claim 5, wherein calculating the probability score for each visual indicator comprises: for each attribute in the first set of attributes, the second set of attributes, and the third set of attributes, determining, by the visual indicator assignment model, a pre-assigned weighting score indicating a comparative weight of the attribute for calculating the probability score; as well as The first set of attributes, the second set of attributes, the third set of attributes, and the pre-assigned weighted scores corresponding to each of the first set of attributes, the second set of attributes, and the third set of attributes are processed to calculate the probability score for each visual indicator.

10. A non-transitory computer readable medium comprising instructions for generating an analysis report, the instructions being executable by a processing resource to: obtaining, using a report generation application, an analytical report having analytical data associated with an organization, the analytical report comprising one or more sections, wherein each section of the one or more sections comprises a subset of the analytical data; identifying a first set of attributes corresponding to the analytical report and a second set of attributes corresponding to each of the one or more sections; retrieving, from the first set of attributes, user details of a user of the organization who uses the report generation application to finalize the analytical report; analyzing the first set of attributes and the user details to identify a set of visual indicators assigned to the user for use in the analytical report when using the report generating application, wherein the visual indicators will provide a graphical representation of the analytical data present in the analytical report; identifying a third set of attributes corresponding to each visual indicator in the set of visual indicators; obtaining a pre-assigned weighted score for each attribute in the first set of attributes, the second set of attributes, and the third set of attributes corresponding to each visual indicator in the set of visual indicators; for each section, processing the first set of attributes, the second set of attributes, the third set of attributes, and the pre-assigned weighted scores corresponding to each of the first set of attributes, the second set of attributes, and the third set of attributes by a visual indicator assignment model to calculate a probability score for each visual indicator, wherein the probability score for a visual indicator indicates a probability of the visual indicator appearing in the section of the analytical report, and wherein the visual indicator assignment model is trained based on a statistical analysis of one or more historical analytical reports; for each chapter, determining, using the visual indicator assignment model, at least one visual indicator from the set of visual indicators for representing the subset of the analytical data within the chapter, wherein the probability score of the at least one visual indicator satisfies a predetermined selection criterion; as well as For each section of the analysis report, the at least one visual indicator is incorporated into the section to generate a final analysis report.

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