System and method for data analysis and reporting

Abstract

Systems and methods for automated data analysis and reporting of pharmaceutical batch production records are provided. In response to receiving a user command to generate a batch report, the method may include: automatically performing one or more statistical analyses on raw batch data extracted from one or more batch manufacturing records; generating one or more graphs and one or more tables using the analyzed data; identifying one or more discussion boxes related to the analyzed data; compiling a batch report including the one or more identified discussion boxes; storing the batch report in a memory; and displaying the batch report on the user equipment.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 336,035, filed on April 28, 2022, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure relates to systems and methods for automated data analysis and reporting of pharmaceutical batch production records. Background Art

[0004] When manufacturing pharmaceutical drug ingredients, part of drug product development is to perform a feasibility study of the manufacturing process. Performing a feasibility study involves reviewing and analyzing batch production records, which provide a complete history of each stage of the manufacturing process for a given drug lot or batch produced. Batch production records are used to ensure health, safety, and quality, and provide an auditable record detailing compliance with federal regulations. The results of a given feasibility study will identify whether the manufactured drug product meets the specifications for that drug product, or deviates from the specifications for that drug product. Therefore, a feasibility study can be used to demonstrate compliance with the specifications for a given drug product, or to assess deviations from those specifications. Summary of the invention

[0005] The process of creating a feasibility report generally includes batch testing, batch data collection, data analysis, and report drafting. A given feasibility report may contain the batch data analyzed and a discussion of the results, including a discussion of compliance with specifications and an explanation of the manufacturing and analytical processes.

[0006] Currently, each of the batch data collection, data analysis, and report drafting steps in the report drafting process is performed manually. Batch data is collected from batch production records, which are generally in the form of printed documents or portable document format (PDF) documents. This document format is necessary to ensure that batch production records meet regulatory requirements and can be reviewed regularly. Collecting batch record data from printed documents or PDF documents requires manually reading the document, identifying relevant data, and then copying the data into a computing system so that it can be analyzed. This process is generally performed by two people, one of whom manually copies the data values ​​into a spreadsheet and the other checks both the batch production record and the spreadsheet values ​​to ensure that the person accurately copies the values.

[0007] After the batch record data is copied into the electronic form, one or more personnel can then perform data analysis. Such data analysis can include, for example, evaluating physical properties (such as tablet weight, hardness and / or thickness), and evaluating blend uniformity, layered content uniformity, solubility, water content and / or disintegration time. The complexity of data analysis is individual-driven. That is, highly skilled data scientists will provide more complex analysis than less skilled data scientists. Since more complex analysis will result in higher quality reports, more highly trained data scientists are necessary to ensure that high quality reports are produced. This leads to increased training costs and increased demand for more highly trained personnel. Furthermore, since manual data analysis is personnel-driven, there is a lack of consistency between data analyses performed by different personnel.

[0008] Because the variability of the human-driven nature of manual data analysis carries over to the report drafting phase of the feasibility reporting process, the human-driven nature of the feasibility reporting process can cause a lack of consistency in both the data analysis included in the report and any discussion of that analysis in the report. Such inconsistency can undermine both the scientific quality of the feasibility report and the commercial reputation of the entity creating the report.

[0009] Therefore, the system and method for automated data analysis and reporting of drug batch production records are presented herein. By automating the report production process, the company can realize the saving of a large amount of time and improve the quality and consistency of the report, and ensure that the report meets the scientific standards for analysis and format. Additionally, the company can improve customer satisfaction by shortening the turnaround time of generating reports and delivering higher-quality reports. The company can also minimize the demand for highly skilled personnel, reduce training costs, increase productivity, and promote its commercial reputation.

[0010] In one or more examples, a computer-implemented method for generating a batch report includes: extracting raw batch data from one or more batch manufacturing reports received from one or more manufacturing devices and storing the extracted raw batch data in a memory, receiving a user command on a user device to automatically generate a batch report, and in response to receiving the user command: performing one or more statistical analyses on the stored raw batch data; generating one or more graphs and one or more tables using the analyzed data and storing the one or more graphs and the one or more tables in a memory; identifying one or more discussion boxes related to the analyzed data; compiling a batch report including the one or more identified related discussion boxes, the one or more graphs, and the one or more tables; saving the compiled batch report in the memory; and displaying the compiled batch report on the user device.

[0011] Optionally, the computer-implemented method includes identifying one or more outliers in the analyzed data and determining whether the one or more outliers are caused by a manufacturing process error or a post-manufacturing data analysis error.

[0012] In one or more examples, when one or more batch manufacturing reports are received, raw batch data can be extracted from the one or more batch manufacturing reports in real time, and the computer-implemented method can include: if the outliers are caused by manufacturing process errors, providing one or more guidelines to adjust one or more settings of one or more manufacturing equipment.

[0013] The one or more batch manufacturing reports may include a plurality of batch pages, and extracting the raw batch data may include: receiving one or more image coordinates corresponding to one or more bounded regions within a page, optically scanning each of the plurality of batch pages, collecting raw batch data within the one or more bounded regions on each of the plurality of optically scanned batch pages, and generating a ledger containing the collected raw batch data.

[0014] Optionally, compiling a batch report includes generating one or more instruction codes, which include instructions for: filling one or more modifiable handles in one or more relevant discussion boxes with one or more statistical data values ​​from the analyzed data; embedding one or more graphs and one or more tables into one or more appropriate locations based on the relevant discussion boxes; and generating a data report including one or more filled relevant discussion boxes, one or more embedded graphs, and one or more embedded tables.

[0015] In one or more examples, the one or more instruction codes include LaTeX code.

[0016] Optionally, the one or more modifiable handles may each correspond to a data metric, the one or more discussion boxes include pre-written text regarding the one or more data metrics, and populating the one or more modifiable handles includes replacing each of the modifiable handles with a corresponding data metric from the analyzed data.

[0017] In one or more examples, the compiled batch report can indicate whether the analyzed data is within one or more defined batch specifications.

[0018] In one or more examples, a system for generating a batch report is provided, the system comprising: a memory, one or more processors, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and when the one or more programs are executed by the one or more processors, the one or more programs cause the processors to: extract raw batch data from one or more batch manufacturing reports received from one or more manufacturing devices and store the extracted raw batch data in the memory, receive a user command on a user device to automatically generate a batch report, and in response to receiving the user command: perform one or more statistical analyses on the stored raw batch data; generate one or more graphs and one or more tables using the analyzed data and store the one or more graphs and the one or more tables in the memory; identify one or more discussion boxes related to the analyzed data, compile a batch report including one or more related discussion boxes, one or more graphs, and one or more tables; save the compiled batch report in the memory; and display the compiled batch report on the user device.

[0019] Optionally, when the one or more programs are executed by one or more processors, the one or more programs cause the processors to: identify one or more outliers in the analyzed data, and determine whether the one or more outliers are caused by a manufacturing error or a post-manufacturing data analysis error.

[0020] In one or more examples, raw batch data can be extracted from the one or more batch manufacturing reports in real time as the one or more batch manufacturing reports are received.

[0021] If the outlier is caused by a manufacturing process error, the one or more programs, when executed by the one or more processors, may cause the processor to provide one or more guidelines to adjust one or more settings of one or more manufacturing equipment.

[0022] Optionally, the one or more batch manufacturing reports may include a plurality of batch pages, and extracting the raw batch data may include: receiving one or more image coordinates corresponding to one or more bounded regions within a page, optically scanning each of the plurality of batch pages, collecting raw batch data within the one or more bounded regions on each of the plurality of optically scanned batch pages, and generating a ledger containing the collected raw batch data.

[0023] In one or more examples, compiling a batch report includes generating one or more instruction codes, which include instructions for: filling one or more modifiable handles in one or more relevant discussion boxes with one or more statistical data values ​​from the analyzed data; embedding one or more graphs and one or more tables into one or more appropriate locations based on the relevant discussion boxes; and generating a data report including one or more populated relevant discussion boxes, one or more embedded graphs, and one or more embedded tables.

[0024] Optionally, the one or more instruction codes may include LaTeX code.

[0025] In one or more examples, the one or more modifiable handles may each correspond to a data metric, one or more discussion boxes may include pre-written text regarding the one or more data metrics, and populating the one or more modifiable handles may include replacing each of the modifiable handles with a corresponding data metric from the analyzed data.

[0026] In one or more examples, the compiled batch report can indicate whether the analyzed data is within one or more defined batch specifications.

[0027] In one or more examples, a computer-readable storage medium stores one or more programs for generating a batch report, the one or more programs including instructions, which, when executed by an electronic device having a display and a user input interface, cause the device to: extract raw batch data from one or more batch manufacturing reports received from one or more manufacturing devices and store the extracted raw batch data in a memory, receive a user command on a user device to automatically generate a batch report, and in response to receiving the user command: perform one or more statistical analyses on the stored raw batch data; generate one or more graphs and one or more tables using the analyzed data and store the one or more graphs and the one or more tables in a memory, identify one or more discussion boxes related to the analyzed data, compile a batch report including one or more related discussion boxes, one or more graphs, and one or more tables, save the compiled batch report in a memory, and display the compiled batch report on the user device.

[0028] Optionally, when one or more programs are executed by an electronic device, the one or more programs cause the device to: identify one or more outliers in the analyzed data, and determine whether the one or more outliers are caused by a manufacturing error or a post-manufacturing data analysis error.

[0029] In one or more examples, raw batch data can be extracted from the one or more batch manufacturing reports in real time as the one or more batch manufacturing reports are received.

[0030] In one or more examples, if the outlier is caused by a manufacturing process error, when the electronic device executes one or more programs, the one or more programs can cause the device to provide one or more guidelines to adjust one or more settings of one or more manufacturing devices.

[0031] The one or more batch manufacturing reports may include a plurality of batch pages, and extracting the raw batch data may include: receiving one or more image coordinates corresponding to one or more bounded regions within a page, optically scanning each of the plurality of batch pages, collecting raw batch data within the one or more bounded regions on each of the plurality of optically scanned batch pages, and generating a ledger containing the collected raw batch data.

[0032] In one or more examples, compiling a batch report may include generating one or more instruction codes, which include instructions for: filling one or more modifiable handles in one or more relevant discussion boxes with one or more statistical data values ​​from the analyzed data; embedding one or more graphs and one or more tables into one or more appropriate locations based on the relevant discussion boxes; and generating a data report including one or more populated relevant discussion boxes, one or more embedded graphs, and one or more embedded tables.

[0033] Optionally, the one or more instruction codes may include LaTeX code.

[0034] In one or more examples, the one or more modifiable handles may each correspond to a data metric, one or more discussion boxes may include pre-written text regarding the one or more data metrics, and populating the one or more modifiable handles may include replacing each of the modifiable handles with a corresponding data metric from the analyzed data.

[0035] In one or more examples, the compiled batch report can indicate whether the analyzed data is within one or more defined batch specifications.

[0036] It will be appreciated that any of the variations, aspects, features and options described with respect to the system may be combined.

[0037] Additional advantages will be apparent to those skilled in the art from the following detailed description.The aspects and descriptions herein are to be considered illustrative in nature and not restrictive.

[0038] All publications (including patent documents, scientific papers and databases) referred to in this application are incorporated herein by reference in their entirety, for all purposes, to the same extent as if each individual publication were individually incorporated herein by reference. If the limitations set forth herein conflict or are otherwise inconsistent with the limitations set forth in the patents, applications, published applications and other publications incorporated herein by reference, the limitations set forth herein take precedence over the limitations incorporated herein by reference. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

[0040] Figure 1 An exemplary automated process for generating batch reports according to one or more examples of the present disclosure is depicted.

[0041] Figure 2 An exemplary user interface for a user to command automated generation of batch reports according to one or more examples of the present disclosure is depicted.

[0042] Figure 3 An exemplary user interface for a user to input data required for an automated process to generate batch reports is depicted in accordance with one or more examples of the present disclosure.

[0043] Figure 4 An exemplary user interface including a graph generated using an automated data analysis method according to one or more examples of the present disclosure is depicted.

[0044] Figure 5 An exemplary user interface according to some examples of the present disclosure is depicted, the user interface including an exemplary table of content page generated using an automated data analysis method.

[0045] Figure 6 An exemplary user interface including exemplary pages of manufacturing steps and results sections from a report generated using an automated data analysis method according to some examples of the present disclosure is depicted.

[0046] Figure 7 An exemplary user interface including an exemplary page from an analytical results section of a report generated using an automated data analysis method is depicted according to one or more examples of the present disclosure.

[0047] Figure 8An exemplary automated process to extract raw batch data from a printed batch production report or a PDF batch production report according to one or more examples of the present disclosure is depicted.

[0048] Fig. 9 An exemplary user interface including a batch production report according to one or more examples of the present disclosure is depicted.

[0049] Fig.10 An exemplary process for a user to command automated generation of batch reports according to one or more examples of the present disclosure is depicted.

[0050] Fig.11 An example of a computing device according to one or more examples of the present disclosure is illustrated. DETAILED DESCRIPTION

[0051] As explained above, current methods for generating feasibility reports require manual data collection, analysis, and report drafting. The human-driven nature of these methods can result in substandard data analysis, substandard graphics and / or layout quality, compromised scientific quality, and long turnaround times for generating a given report.

[0052] Previously, the manual process for generating a feasibility report required a person to: (1) manually collect data values ​​from batch production reports, (2) verify the manually collected data values, (3) perform data analysis, (4) create graphs and tables, and (5) draft the feasibility report. Whereas previously manual report generation required the user to follow a five-step process, automated report generation can streamline the process to require only two steps for a person: (1) execute the procedure, and (2) review the generated batch data report. This automation can significantly reduce the time any given user must spend generating a report. For example, manually drafting a given report from start to finish may take weeks, but automatically generating the same report may only take a few hours to a few days.

[0053] An automated report generator can perform each step required to generate a high-quality report without requiring extensive input from the user. For example, an automated report generator can accurately extract data values ​​from batch production records, automatically perform complex data analysis, automatically generate high-quality graphs and tables based on the analysis, incorporate relevant discussions of the analysis, and compile a report that includes the automatically generated graphs and tables, and the data analysis and discussion. Thus, an automated report generator can reduce the required human interaction to simply executing the program and viewing the report.

[0054] In addition to reducing the time and effort required to draft reports, automated report generators can also improve the quality and consistency of the reports generated. For example, as explained above, a highly skilled data scientist will provide more complex analysis than a less skilled or newer data scientist. Furthermore, manually drafted reports lack consistency when drafted by multiple people because the data analysis and writing discussions performed by the people may vary. However, this problem can be completely eliminated via an automated report generator because the automated report generator can repeat the same complex analysis each time it is executed and discuss the results in the same way, thereby producing a consistent, high-quality report.

[0055] By automating the report production process, companies can therefore achieve significant time savings and improve the quality and consistency of reports, and ensure that reports meet scientific standards for analysis and format. Additionally, companies can improve customer satisfaction by shortening the turnaround time for generating reports and delivering higher quality reports. Companies can also minimize the need for highly skilled personnel, reduce training costs, increase productivity, and enhance their business reputation.

[0056] In the following description of the present disclosure and examples, reference is made to the accompanying drawings, which show by way of illustration specific examples that can be practiced. It will be understood that other examples can be practiced and that changes can be made without departing from the scope of the present disclosure.

[0057] In addition, it should be understood that, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" used in the following description are intended to include the plural forms as well. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more associated listed items. It should also be understood that when the terms "comprises", "comprising", "including", and / or "comprising" are used herein, the presence of stated features, integers, steps, operations, elements, components, and / or units is detailed, but the presence or addition of one or more additional features, integers, steps, operations, elements, components, units, and / or combinations thereof is not excluded.

[0058] Some parts of the following detailed description are presented in the form of algorithms and symbolic representations of operations performed on data bits in a computer memory. These algorithmic descriptions and representations are used by those skilled in the art of data processing to most effectively convey the substance of their work to other technicians in the field. An algorithm is here and generally considered to be a self-consistent sequence of steps (instructions) that lead to a desired result. These steps are those steps that require physical manipulation of physical quantities. Typically, although not necessarily, these quantities take the form of electrical, magnetic or optical signals that can be stored, transferred, combined, compared and otherwise manipulated. Sometimes, mainly for common purposes, it is convenient to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, etc. Further, sometimes it is also convenient to refer to the arrangement of certain steps that require physical manipulation of physical quantities as modules or code devices, without loss of generality.

[0059] However, all of these and similar terms should be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless otherwise clearly apparent from the following discussion, it should be understood that throughout the description, discussions utilizing terms such as "processing," "computing," "calculating," "determining," "displaying," etc. refer to the actions and processes of a computer system or similar electronic computing device that manipulates and transforms data represented as physical (electronic) quantities within a computer system memory or register or other information storage device, transfer device, or display device.

[0060] Certain aspects of the present disclosure include process steps and instructions described in the form of algorithms. It should be noted that the process steps and instructions of the present disclosure can be embodied in software, firmware or hardware, and when embodied in software, it can be downloaded to reside on different platforms used by a variety of operating systems and operated therefrom.

[0061] The present disclosure also relates to a device for performing the operations herein. The device may be specially constructed for the desired purpose, or it may include a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transient computer-readable storage medium, such as but not limited to any type of disk (including floppy disk, optical disk, CD-ROM, magneto-optical disk), read-only memory (ROM), random access memory (RAM), EPROM, EEPROM, magnetic card or optical card, application-specific integrated circuit (ASIC), or any type of medium suitable for storing electronic instructions and each coupled to a computer system bus. Further, the computer involved in the specification may include a single processor, or may be an architecture using a multi-processor design to increase computing power.

[0062] The methods, devices and systems described herein are not inherently related to any particular computer or other device. Various general purpose systems may also be used together with the programs taught herein, or it may be convenient to construct more specialized devices to perform the required method steps. The required structure for various of these systems will emerge from the following description. In addition, the present disclosure is not described with reference to any particular programming language. It will be appreciated that various programming languages ​​may be used to implement the teachings of the present disclosure as described herein.

[0063] Figure 1 An exemplary automated process 100 for generating batch reports according to one or more examples is depicted. The automated process 100 may be performed by any suitable type of microprocessor-based device, such as a personal computer, workstation, server, or handheld computing device (portable electronic device) such as a cell phone or tablet.

[0064] like Figure 1 As shown, the automated process 100 starts at step 102, where raw batch data is extracted. The raw batch data can be extracted from batch production reports received from a variety of manufacturing equipment. In a non-limiting example, the example equipment may include a tablet tester, a fluidized bed processor, a vertical granulator, a V-type mixer / box mixer, a capsule weight sorter, a roller press, an extruder, a pan coating process tester, and / or a high performance liquid chromatography (HPLC) device. In a non-limiting example, the extracted raw data may include: tablet weight, thickness, and / or hardness measurements; air flow, inlet air temperature, product temperature, discharge temperature, and / or dew point measurements; blade speed, shredder speed, water addition rate, and / or blade / shredder torque process (in-process) measurements. In one or more examples, the raw data extracted at step 102 can be stored in a memory in a location and / or format that can be accessed by scientists and / or users. In one or more examples, extracting raw batch data at step 102 can occur in response to a user command to extract batch data. Extracting raw batch data at step 102 may also occur automatically upon receipt of a batch production report.

[0065] After extracting the raw batch data at step 102, the automated process 100 can move to step 104 and receive a user command to generate a batch report. The user command received at step 104 of the automated process 100 can be received via a command from a user interacting with a user interface of any suitable computing device. The computing device receiving the user command can be the same computing device executing the automated process 100, or can be communicatively coupled to the computing device executing the automated process 100. In one or more examples, extracting the raw batch data at step 102 can occur in response to receiving a user command to generate a batch report.

[0066] Figure 2 Depicted is an exemplary user interface for a user to command automated generation of batch reports according to one or more examples. Figure 2 The user interface shown in FIG. 1 may be used by a user to generate the user command received at step 104 of the automation process 100. Figure 2 As shown, user interface 202 enables a user to select an analysis type of manufacturing 201 or analytical 202. Then, manufacturing user interface 204 and analytical user interface 206 enable the user to select a specific analysis based on the selected analysis. For example, if the user selects manufacturing 201, then the user may be presented with a manufacturing user interface 204 having a list of possible specific manufacturing analyses (such as analyzing mixing, granulation, extrusion, spheronization, drying, compression, pan coating, encapsulation, roller pressing, packaging, etc.). If the user selects analytical 202, then the user may be presented with an analytical user interface 206 having a list of possible specific analytical analyses (such as analyzing content uniformity, layered content uniformity, solubility, water content, impurities, X-ray powder diffraction (XRPD) analysis, etc.).

[0067] In one or more examples, after selecting an analysis type, the user may be prompted to provide some project information related to the batch report that the user commanded to be generated at step 104 . Figure 3 An exemplary user interface 300 is depicted for a user to input data required for an automated process for generating a batch report according to one or more examples. The user interface 300 may be displayed after the user selects an analysis type and a specific analysis as discussed above. Figure 3As shown, user interface 300 includes window 301, which includes data field 302, product name field 304 and directory field 306 for users to add required data. The required data that the user can add in data field 302 can vary based on the specific analysis type. Directory field 306 can enable the user to specify the location where the files generated by the automated process will be saved. Such files can include, for example, electronic tables, diagrams, documents, etc. After entering information in window 301, the user can select a button or area of ​​user interface 300 to run the automated process. After selecting the button to run the automated process, a command can be received at step 104 of automated process 100.

[0068] Return to reference Figure 1 In response to the user command received at step 104, the automated process 100 can move to step 106 and perform statistical analysis on the stored raw batch data extracted at step 102. The statistical analysis can be predetermined by one or more scientists before being implemented by the automated process 100. The statistical analysis can include, for example, evaluating changes in physical properties (such as tablet weight, hardness and / or thickness) over time. In one or more examples, the statistical analysis can include evaluating blend uniformity, layered content uniformity, solubility, water content and / or disintegration time. In one or more examples, data corresponding to the statistical analysis performed at step 106 can be stored in a memory, and its location and / or format can be accessed by scientists and / or users to enable those scientists and / or users to view the data used to generate the final batch report. Therefore, scientists and / or users can make any necessary or desired edits to the analysis, which can be used to generate a new version of the batch report.

[0069] After performing the statistical analysis on the stored raw batch data at step 106, the automated process 100 may move to step 108 and generate one or more graphs and / or tables. In one or more examples, generating the graphs and / or tables at step 108 may be included as part of performing the statistical analysis at step 106. In one or more examples, the one or more tables created by the automated process 100 may tabulate the data used in the graphs, various parameters, analytical summaries, etc. In non-limiting examples, the one or more graphs may include a visualization of a particle size analysis, various in-process parameters and / or physical property statistics over time.

[0070] Figure 4 An exemplary user interface including a graph page 400 generated using an automated data analysis method is depicted according to one or more examples. Graph page 400 can be generated via automated process 100. Figure 4As shown, the graph page 400 provides a visualization of the tablet thickness characteristics for a given batch. For example, the graph page 400 includes a graph 402 of tablet weight over time, which graphically depicts the change in tablet thickness values ​​over time compared to the target tablet thickness specifications (including upper and lower specification limits). In one or more examples, the graphs and / or tables generated at step 108 can be saved in a memory in a location and / or format that can be accessed by a scientist and / or user. Therefore, the scientist and / or user can view the table and / or graph separately from the final report, and can make any necessary or desired edits to the table and / or graph, which can be used to generate a new version of the batch report.

[0071] As explained above, the complexity of data analysis previously performed via manual processes was personnel driven, where the complexity was related to the skill level of a given person. However, in the automated process 100, the data analysis can be designed by one or more highly skilled personnel, where the automated process 100 is configured to repeat the same analysis each time it generates a report. Therefore, the automated process 100 can ensure a high level of complexity in the analysis. In addition, by standardizing the analysis methods and scope and repeating the same analysis in an automated manner, the automated process 100 eliminates the inconsistencies inherent in the previous personnel-driven manual process. By eliminating these inconsistencies, the automated process 100 can improve customer satisfaction and thereby improve business reputation.

[0072] Return to reference Figure 1 After generating the graphs and / or tables at step 108, the automated process 100 can move to step 110 and identify the relevant discussion boxes. Discussion boxes are pre-written text boxes. In one or more examples, the discussion boxes can be written to generally discuss common analyses performed. For example, if a feasibility report of a given type commonly includes a discussion of tablet weight, the discussion of tablet weight can be standardized so that the discussion included in the report will always follow the same format and discuss the tablet weight results in the same manner.

[0073] In one or more examples, a discussion box can include one or more modifiable handles. The modifiable handles can act as replaceable blanks, thereby defining a specific location within a given discussion box where a certain data value should be discussed. Thus, the modifiable handles can act as variables to be filled with appropriate data values. For example, a discussion box can include the following:

[0074] Table ___ shows the tablet weights for ___. The observed tablet weights across sampling times were ___, with an average of ___ (target: ___).”

[0075] Thus, when the modifiable handles of the discussion box are populated, the appropriate information and data may be automatically populated into the appropriate locations within the discussion box text.

[0076] In one or more examples, identifying relevant discussion boxes at step 110 can include evaluating what types of data were collected and what analyses have been performed, and selecting a corresponding discussion box associated with the data and / or analysis.

[0077] In one or more examples, the automated process 100 can generate various types of reports based on the type of analysis performed, as discussed above. Discussion boxes can be written to provide appropriate discussions for each type of report. In one or more examples, certain discussion boxes will only be relevant to certain types of reports. Therefore, in one or more examples, not all pre-written discussion boxes will be identified as relevant in step 110.

[0078] In one or more examples, the discussion boxes may be written in advance by one or more highly skilled personnel, wherein the automated process 100 is configured to include each relevant discussion box each time it generates a report. Thus, by including the discussions written by the highly skilled personnel, the automated process 100 can improve the quality of the generated reports. Furthermore, by standardizing the discussions included in each report and automatically including relevant discussions in each report, the automated process 100 eliminates the inconsistencies inherent in the previous manual process driven by personnel. By eliminating these inconsistencies and generating high-quality reports, the automated process 100 can improve customer satisfaction and thereby improve business reputation.

[0079] After identifying the relevant discussion box at step 110, the automated process 100 can move to step 112 and compile a batch report (e.g., a feasibility report). In one or more examples, in order to compile the batch report at step 112, the automated process 100 can develop one or more typesetting instructions. The typesetting instructions may include specific instructions related to filling the relevant discussion box identified at step 110, embedding the graphs and / or tables generated at step 108 into appropriate locations based on the relevant discussion box, and generating the batch report. In one or more examples, the typesetting instructions can be compiled using a typesetting document preparation system. The document preparation system can be configured to standardize the format of the batch report according to scientific standards and formats, which can improve the quality of the report. In one or more examples, the typesetting instructions can be stored in a memory in a location and / or format that can be accessed by scientists and / or users. Therefore, scientists and / or users can view the typesetting instructions used to generate the final batch report and make any necessary or desired edits to the instructions, which can be used to generate a new version of the batch report. For example, the typesetting instructions can be in LaTeX format. TM Code, Document or The data is stored in a format such as a spreadsheet.

[0080] After compiling the batch report at step 112, the automated process 100 can move to step 114 and display the batch report. The batch report can be displayed on the user device and / or any suitable computing device. The participation of any personnel in the automated process 100 can be limited to executing the automated process and viewing the displayed batch report. Therefore, the automated process 100 can greatly reduce the effort required by each personnel to generate high-quality reports. In addition, when generating reports, employees can focus their attention elsewhere and thereby increase productivity.

[0081] The batch report displayed at step 114 of the automated process 100 can include a variety of sections. Such sections can include, for example: an executive summary section; a section discussing process flow, equipment, and master recipes; a section discussing manufacturing steps and results; a section discussing analytical results, a section discussing conclusions; and a section containing references to the report. In one or more examples, the various sections included in the batch report can be presented in a table of contents.

[0082] Figure 5 An exemplary user interface including an exemplary catalog page 500 generated using an automated data analysis method is depicted according to some examples. The catalog page 500 may be part of a report generated and displayed via the automated process 100. Figure 5 As shown, the directory page 500 includes a list of chapters 502 of the batch report. The list of chapters of the batch report may include the page number of each chapter and any other relevant details. In one or more examples, the directory page 500 may be located before the list of figures and / or the list of tables contained in the report. The list of figures and the list of tables may include the figure / table number, the figure / table name, and the figure / table page number, and the corresponding figure / table can be found in the report at the page number.

[0083] The executive summary section of the report displayed at step 114 of the automated process 100 may include a discussion of the purpose of a given report, relevant background information, and information about the scope of the report. For example, the purpose of a report may include recording the results of a process development study for a specific drug ingredient. Alternatively, the purpose of a report may include summarizing observations during the manufacture of a specific drug ingredient. In a non-limiting example, the relevant background information may include information about the drug that is the focus of the report and / or a discussion or summary of the drug's manufacturing history. Information about the scope of a given report may include specific information about the batch number and / or batch size (number of tablets) of a specific drug. The scope section may include, for example, a table containing overview information for one or more manufacturing processes (e.g., granulation, compression, pan coating, etc.).

[0084] The section of the report discussing the process flow, equipment, and master recipe displayed at step 114 of the automated process 100 may include a diagram illustrating the process flow of a given manufacturing process. For example, the diagram illustrating the process flow may depict a timeline view of the various manufacturing steps, including the duration of each step, the constituent ingredients and percentages of the ingredients, the manufacturing process, etc. The process flow, equipment, and master recipe section may also include one or more tables containing information related to the number and type of equipment used, the drug formulation ingredients for each drug type (i.e., granular tablets, compressed tablets, and / or pan-coated tablets). The process flow, equipment, and master recipe section may include discussions, tables, and / or diagrams based on the data extracted at step 102 of the automated process 100, generated at step 108 of the process 100, and compiled at step 112 of the process 100.

[0085] The section of the report that discusses the manufacturing steps and results displayed at step 114 of the automated process 100 may include a discussion of mixing, grinding, granulation, and / or lubrication, etc. For example, Figure 6 An exemplary user interface including an example page 600 from a manufacturing steps and results section of a report generated using an automated data analysis method is depicted according to some examples. Page 600 may be part of a manufacturing steps and results section of a report generated and displayed via automated process 100.

[0086] like Figure 6 As shown, page 600 includes table 602, which shows various process parameters for a given drug batch, including the amount of solution, injection rate, atomization air pressure, inlet air temperature, product temperature, etc. In one or more examples, the process parameters can be graphically depicted in one or more graphs. For example, figure 604 of page 600 shows various parameters graphically depicted over time. In a non-limiting example, other graphs that may be included in the manufacturing steps and results section may include graphs related to particle size, compression force in the process, tablet weight statistics, tablet hardness statistics, tablet thickness statistics, etc. The manufacturing steps and results section may include discussions, tables, and / or graphs based on data extracted at step 102 of the automated process 100, generated at step 108 of the process 100, and compiled at step 112 of the process 100.

[0087] The section of the report discussing analytical results displayed at step 114 of the automated process 100 may include subsections related to blend uniformity, assays, layered content uniformity, solubility, moisture content, disintegration time, enhanced sampling, etc. In each subsection, the report may include a discussion, tables, and / or graphs based on the data extracted at step 102 of the automated process 100, generated at step 108 of the process 100, and compiled at step 112 of the process 100.

[0088] Figure 7 An exemplary user interface including an exemplary page 700 from an analytical results section of a report generated using an automated data analysis method is depicted according to some examples. Page 700 may be part of an analytical results section of a report generated and displayed via automated process 100. Figure 7 As shown, page 700 includes a subsection regarding blend uniformity 702 and a subsection regarding determinations 704. Each subsection of the analytical results section may include a discussion, tables, and / or graphs based on data extracted at step 102 of the automated process 100, generated at step 108 of the process 100, and compiled at step 112 of the process 100. In one or more examples, the subsections may also include analytical conclusions that have been automatically generated via the automated process 100. For example, as shown in subsection 704, this report page 700 provides a calculated average determination of a first percentage 706 that is characterized as "significantly improved" compared to the average of a second percentage 708 from different manufacturing batches. Thus, the report displayed at step 114 of the automated process 100 not only includes the results of the statistical analysis that was automatically performed, but also automatically provides a meaningful discussion of those results.

[0089] In one or more examples, the section of the report discussing the conclusions displayed at step 114 of the automated process 100 can include a discussion of one or more recommendations generated based on the conclusions of the report. For example, the report can determine that the data analysis revealed a low assay problem, and the report can include one or more recommendations to alter the granulation process in order to minimize the likelihood that the low assay problem will occur again.

[0090] The section of the report containing references for a given report displayed at step 114 of the automated process 100 may include references to one or more documents used to prepare the report. Such documents may include, for example, a specific batch report identified by a document number, regulatory and / or corporate protocols and / or standards identified by a document number.

[0091] As discussed above, due to regulatory necessities, the raw data necessary to generate a given batch report may be included in a batch production record in a printed document or PDF document format. Whereas previously, such raw data was extracted by two individuals, one of whom copied the values ​​into a spreadsheet and one of whom verified the accuracy of the process, the automated process 100 can perform such extraction completely independently. In one or more examples, the raw data necessary to generate a given batch report may be included in a batch production record exported from a machine in a comma separated value (CSV) format. The automated process 100 can be configured to extract such values ​​directly from a CSV file.

[0092] Figure 8 An exemplary automated process 800 for extracting raw batch data from a printed batch production report or a PDF batch production report according to one or more examples is depicted. The automated process 800 can be performed as part of step 102 of the automated process 100. The automated process 800 can begin at step 802 by receiving image coordinates corresponding to one or more bounded page regions. In one or more examples, a batch production record exported from a certain type of machine will be exported with associated raw data values ​​located within a certain region of the page. Thus, the image coordinates can correspond to a region within the page where the raw data values ​​are recorded. In one or more examples, a user can be able to edit the image coordinates corresponding to any changes to a certain region within the batch production record where the associated raw data values ​​are located. In one or more examples, a user can be able to perform such updates in real time upon receipt of the batch production record.

[0093] Fig. 9 An exemplary batch production report 900 is depicted according to one or more examples. Fig. 9 As shown, relevant raw data related to sample values ​​of weight, thickness, and hardness are included in a certain area 902 of the batch production report 900. Therefore, the image coordinates received at step 802 of the automated process 800 may include image coordinates corresponding to the area 902.

[0094] Return to reference Figure 8 After receiving the image coordinates at step 802, the automated process 800 may move to step 804 and scan each page of the batch production record. The scanning of the batch production record may be performed using any conventional scanning technique known in the art.

[0095] After scanning each page of the batch production record at step 804, the automated process 800 can move to step 806 and collect raw batch data located within one or more bounded page areas. The one or more bounded page areas can correspond to the image coordinates received at step 802.

[0096] After collecting the raw batch data at step 806, the automated process 800 can proceed to step 808 and generate a ledger containing the raw data. The ledger can be saved in a location and / or format accessible to scientists and / or users. For example, the raw data can be saved as a spreadsheet in a commercial spreadsheet software program such as ) in a spreadsheet.

[0097] In one or more examples, the automated process 800 and the automated process 100 can be performed in real time as data production records are output from one or more manufacturing devices. Thus, step 106 of the automated process 100 (performing statistical analysis on the stored raw batch data) can be performed in real time as a given batch is manufactured. As part of performing the statistical analysis at step 106, the automated process 100 can include identifying any anomalous data values ​​in the data. In one or more examples, identifying one or more anomalous data values ​​can involve performing a variance component analysis.

[0098] After identifying an abnormal data value, the automated process 100 can be configured to provide an alert to a user. In one or more examples, the automated process 100 can be configured to determine whether the abnormal value is caused by a manufacturing process error or a post-manufacturing data analysis error. In one or more examples, the automated process 100 can be configured to provide one or more guidelines to adjust the settings of one or more manufacturing equipment. For example, the automated process 100 can be configured to provide a guide to stop a manufacturing device after identifying an abnormal value that can be attributed to a manufacturing process error.

[0099] Previously, if the data analysis for a given production batch contained an anomalous data value, the entire batch may have been cleared. Therefore, by determining whether the anomalous value is due to a data analysis error, the automated process 100 can ensure that any batches with such values ​​are not cleared. In addition, by identifying the anomalous values ​​in real time, the automated process 100 can enable the user to immediately resolve any manufacturing process errors. Previously, an anomalous value due to a manufacturing error may not have been identified until after the entire batch was produced, resulting in the batch being cleared. However, by enabling the user to resolve the manufacturing error in real time and / or by providing guidance for adjusting one or more settings of the manufacturing equipment, the automated process 100 can pause a given process and ensure that no additional resources are used to produce the batch until the error is resolved.

[0100] As discussed above, whereas manually drafting reports has previously been a time-consuming and analytically difficult process for users, the use of an automated process such as process 100 may enable users to generate better, more consistent reports in a much shorter period of time. User interaction in an automated process may be reduced to simply entering some data, commanding the automated process to run, and viewing the output.

[0101] Fig.10 An exemplary process 1000 for a user to command automated generation of a batch report is depicted according to one or more examples. The process 1000 can be used to command automated generation of a batch report using the automated process 100. The process 1000 can be implemented using one or more suitable computing devices that can display a user interface to a user and record and / or transmit user input to the user interface. The computing device that enables a user to perform the method 1000 can be the same computing device that performs the automated process 100, or it can be communicatively coupled to the computing device that performs the automated process 100.

[0102] In one or more examples, process 1000 can begin at step 1002, where a user selects an analysis type. The user can use a user interface (such as Figure 2 After selecting the analysis type at step 1002, process 1000 may move to step 1004, where the user enters the required data based on the selected analysis type. The user interface for the user to enter the required data may be Figure 3 As discussed above, the user interface used by the user to enter the required data based on the selected analysis type at step 1004 can allow the user to enter, for example, a catalog location, a product name, one or more data fields based on the selected analysis type, etc.

[0103] After entering the required data based on the selected analysis type at step 1004, process 1000 can move to step 1006 where the user runs the program. Upon selecting to run the program at step 1006, a user command can be generated that triggers an automated process (such as process 100) to generate a batch report. In one or more examples, upon selecting to run the program at step 1006, a user command can be generated and executed in the process. Figure 1The process 1000 receives a user command at step 104 of the process 100. After running the program at step 1006, the process 1000 can move to step 1008 where the user views the generated report. As discussed above, the reports generated by the automated process 100 contain more complex analysis and more standardized analysis and presentation of the analysis, and can be generated in a fraction of the time that manually drafting the report would require.

[0104] Fig.11 An example of a computing device 1100 according to one example is illustrated. The device 1100 may be a host computer connected to a network. The device 1100 may be a client computer or a server. Fig.11 As shown, device 1100 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server, or handheld computing device (portable electronic device) such as a phone or tablet. The device may include, for example, one or more processors 1102, input devices 1106, output devices 1108, storage devices 1110, and communication devices 1104. Input devices 1106 and output devices 1108 may generally correspond to those described above and can be connected or integrated with a computer.

[0105] Input device 1106 may be any suitable device that provides input, such as a touch screen, a keyboard or keypad, a mouse, or a voice recognition device. Output device 1108 may be any suitable device that provides output, such as a touch screen, a haptic device, or a speaker.

[0106] Storage device 1110 may be any suitable device that provides storage, such as electronic, magnetic or optical storage, including RAM, cache, hard drive or removable storage disk. Communication device 1104 may include any suitable device capable of transmitting and receiving signals over a network (such as a network interface chip or device). The components of the computer may be connected in any suitable manner (such as via a physical bus or wirelessly).

[0107] Software 1112 may be stored in memory 1110 and executed by processor 1102 , which may include, for example, programming embodying the functionality of the present disclosure (eg, as embodied in the devices described above).

[0108] The software 1112 may also be stored and / or transferred to any non-transitory computer-readable storage medium for use by or in conjunction with an instruction execution system, apparatus, or device (such as those described above), which may retrieve instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of the present disclosure, a computer-readable storage medium may be any medium (such as storage device 1110) that may contain or store programming for use by or in conjunction with an instruction execution system, apparatus, or device.

[0109] The software 1112 may also be propagated in any transmission medium for use by or in conjunction with an instruction execution system, device, or apparatus (such as those described above), which may retrieve instructions associated with the software from the instruction execution system, device, or apparatus and execute the instructions. In the context of the present disclosure, a transmission medium may be any medium capable of communicating, propagating, or transmitting programming for use by or in conjunction with an instruction execution system, device, or apparatus. Transmission-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, or infrared wired or wireless propagation media.

[0110] Device 1100 may be connected to a network, which may be any suitable type of interconnected communication system. The network may implement any suitable communication protocol and may be protected by any suitable security protocol. The network may include any appropriately arranged network link that may implement transmission and reception of network signals, such as a wireless network connection, a T1 or T3 line, a cable network, DSL, or a telephone line.

[0111] Device 1100 may implement any operating system suitable for operating on a network. Software 1112 may be written in any suitable programming language, such as C, C++, Java, or Python. In various embodiments, for example, application software embodying the functionality of the present disclosure may be deployed in different configurations, such as in a client / server arrangement or through a web browser as a web-based application or web service.

[0112] Although the present disclosure and examples have been fully described with reference to the accompanying drawings, it should be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications should be understood to be included within the scope of the present disclosure and examples as defined by the claims.

[0113] For purposes of clarity and concise description, features are described herein as being part of the same or separate embodiments; however, it will be appreciated that the scope of the present disclosure includes embodiments having a combination of all or some of the described features.

[0114] Unless otherwise defined, all technical terms, symbols, and other technical and scientific terms or proper nouns used herein are intended to have the same meaning as commonly understood by one of ordinary skill in the art to which the claimed subject matter belongs. In some cases, terms with commonly understood meanings are defined herein for clarity and / or ease of reference, and the inclusion of such definitions herein should not necessarily be construed as indicating a substantial difference from the meaning generally understood in the art.

[0115] The above description is presented to enable those skilled in the art to make and use the present disclosure, and is provided in the context of specific applications and their requirements. Various modifications to the preferred embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments shown, but should be given the widest scope consistent with the principles and features disclosed herein.

Claims

1. A computer-implemented method for generating a batch report, comprising: extracting raw batch data from one or more batch manufacturing reports received from one or more manufacturing devices, and storing the extracted raw batch data in a memory; receiving, on a user device, a user command to automatically generate a batch report, and in response to receiving the user command: performing one or more statistical analyses on the stored raw batch data; generating one or more graphs and one or more tables using the analyzed data, and storing the one or more graphs and the one or more tables in the memory; identifying one or more discussion boxes related to the analyzed data; compiling a batch report including the one or more identified related discussion boxes, the one or more figures, and the one or more tables; storing the compiled batch report in the memory; as well as The compiled batch report is displayed on the user device.

2. The computer-implemented method of claim 1 , comprising: identifying one or more outliers in the analyzed data; as well as A determination is made as to whether the one or more outliers are caused by a manufacturing process error or a post-manufacturing data analysis error.

3. The computer-implemented method of claim 2, wherein: Upon receiving the one or more batch manufacturing reports, the raw batch data is extracted from the one or more batch manufacturing reports in real time. 4 . The computer-implemented method of claim 3 , comprising providing one or more guidelines to adjust one or more settings of the one or more manufacturing devices if the outlier is caused by a manufacturing process error.

5. The computer-implemented method of claim 1 , wherein: The one or more batch manufacturing reports include a plurality of batch pages, and extracting the raw batch data includes: receiving one or more image coordinates corresponding to one or more bounded regions within a page; optically scanning each page in the plurality of batches of pages; collecting raw batch data within the one or more bounded regions on each of a plurality of optically scanned batch pages; and Generate a ledger containing the raw batch data collected.

6. The computer-implemented method of claim 1 , wherein: Compiling the batch report includes generating one or more instruction codes, the one or more instruction codes including instructions for: populating one or more modifiable handles in one or more associated discussion boxes with one or more statistical data values ​​from the analyzed data; embedding the one or more figures and the one or more tables into one or more appropriate locations based on the relevant discussion boxes; as well as A data report is generated that includes one or more populated related discussion boxes, one or more embedded graphs, and one or more embedded tables.

7. The computer-implemented method of claim 6, wherein: The one or more instruction codes include LaTeX codes.

8. The computer-implemented method of claim 6, wherein: The one or more modifiable handles each correspond to a data metric, the one or more discussion boxes include pre-written text regarding the one or more data metrics, and populating the one or more modifiable handles includes replacing each of the modifiable handles with a corresponding data metric from the analyzed data.

9. The computer-implemented method of claim 1 , wherein: The compiled batch report indicates whether the analyzed data is within one or more defined batch specifications.

10. A system for generating a batch report, the system comprising: Memory; one or more processors; as well as one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the one or more programs are executed by the one or more processors, the one or more programs cause the processors to: extracting raw batch data from one or more batch manufacturing reports received from one or more manufacturing devices, and storing the extracted raw batch data in a memory; receiving, on a user device, a user command to automatically generate a batch report, and in response to receiving the user command: performing one or more statistical analyses on the stored raw batch data; generating one or more graphs and one or more tables using the analyzed data, and storing the one or more graphs and the one or more tables in the memory; identifying one or more discussion boxes related to the analyzed data; compiling a batch report including the one or more associated discussion boxes, the one or more figures, and the one or more tables; storing the compiled batch report in the memory; and The compiled batch report is displayed on the user device.

11. The system according to claim 10, wherein: When the one or more programs are executed by the one or more processors, the one or more programs cause the processors to: identifying one or more outliers in the analyzed data; and A determination is made as to whether the one or more outliers are caused by a manufacturing error or a post-manufacturing data analysis error.

12. The system according to claim 11, wherein: Upon receiving the one or more batch manufacturing reports, the raw batch data is extracted from the one or more batch manufacturing reports in real time.

13. The system according to claim 12, wherein: If the outlier is caused by a manufacturing process error, then when the one or more programs are executed by the one or more processors, the one or more programs cause the processors to provide one or more guidelines to adjust one or more settings of the one or more manufacturing devices.

14. The system according to claim 10, wherein: The one or more batch manufacturing reports include a plurality of batch pages, and extracting the raw batch data includes: receiving one or more image coordinates corresponding to one or more bounded regions within a page; optically scanning each page in the plurality of batches of pages; collecting raw batch data within the one or more bounded regions on each of a plurality of optically scanned batch pages; and Generate a ledger containing the raw batch data collected.

15. The system according to claim 10, wherein: Compiling the batch report includes generating one or more instruction codes, the one or more instruction codes including instructions for: populating one or more modifiable handles in one or more associated discussion boxes with one or more statistical data values ​​from the analyzed data; embedding the one or more figures and the one or more tables into one or more appropriate locations based on the relevant discussion boxes; as well as A data report is generated that includes one or more populated related discussion boxes, one or more embedded graphs, and one or more embedded tables.

16. The system of claim 15, wherein: The one or more instruction codes include LaTeX codes.

17. The system of claim 15, wherein: The one or more modifiable handles each correspond to a data metric, the one or more discussion boxes include pre-written text regarding the one or more data metrics, and populating the one or more modifiable handles includes replacing each of the modifiable handles with a corresponding data metric from the analyzed data.

18. The system of claim 10, wherein: The compiled batch report indicates whether the analyzed data is within one or more defined batch specifications.

19. A computer-readable storage medium storing one or more programs for generating a batch report, the one or more programs comprising instructions that, when executed by an electronic device having a display and a user input interface, cause the device to: extracting raw batch data from one or more batch manufacturing reports received from one or more manufacturing devices, and storing the extracted raw batch data in a memory; receiving, on a user device, a user command to automatically generate a batch report, and in response to receiving the user command: performing one or more statistical analyses on the stored raw batch data; generating one or more graphs and one or more tables using the analyzed data, and storing the one or more graphs and the one or more tables in the memory; identifying one or more discussion boxes related to the analyzed data; compiling a batch report including the one or more associated discussion boxes, the one or more figures, and the one or more tables; storing the compiled batch report in the memory; as well as The compiled batch report is displayed on the user device.

20. The computer-readable storage medium of claim 19, wherein: When the one or more programs are executed by the electronic device, the one or more programs cause the device to: identifying one or more outliers in the analyzed data; and A determination is made as to whether the one or more outliers are caused by a manufacturing error or a post-manufacturing data analysis error.

21. The computer-readable storage medium of claim 20, wherein: Upon receiving the one or more batch manufacturing reports, the raw batch data is extracted from the one or more batch manufacturing reports in real time.

22. The computer-readable storage medium of claim 21, wherein: If the outlier is caused by a manufacturing process error, then when the one or more programs are executed by the electronic device, the one or more programs cause the device to provide one or more guidelines to adjust one or more settings of one or more manufacturing devices.

23. The computer-readable storage medium of claim 19, wherein: The one or more batch manufacturing reports include a plurality of batch pages, and extracting the raw batch data includes: receiving one or more image coordinates corresponding to one or more bounded regions within a page; optically scanning each page in the plurality of batches of pages; collecting raw batch data within the one or more bounded regions on each of a plurality of optically scanned batch pages; and Generate a ledger containing the raw batch data collected.

24. The computer-readable storage medium of claim 19, wherein: Compiling the batch report includes generating one or more instruction codes, the one or more instruction codes including instructions for: populating one or more modifiable handles in one or more associated discussion boxes with one or more statistical data values ​​from the analyzed data; embedding the one or more figures and the one or more tables into one or more appropriate locations based on the relevant discussion boxes; as well as A data report is generated that includes one or more populated related discussion boxes, one or more embedded graphs, and one or more embedded tables.

25. The computer-readable storage medium of claim 24, wherein: The one or more instruction codes include LaTeX codes.

26. The computer-readable storage medium of claim 24, wherein: The one or more modifiable handles each correspond to a data metric, the one or more discussion boxes include pre-written text regarding the one or more data metrics, and populating the one or more modifiable handles includes replacing each of the modifiable handles with a corresponding data metric from the analyzed data.

27. The computer-readable storage medium of claim 19, wherein: The compiled batch report indicates whether the analyzed data is within one or more defined batch specifications.

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