Apparatus and a method for generating a confidence score associated with a scanned label

An apparatus for generating a confidence score associated with a scanned label is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a profile comprising at least a label containing a plurality of metadata associated with the at least a label. The memory instructs the processor to generate a scanned label as a function of the at least a label, wherein generating a scanned label comprises scanning the at least a label using a text recognition module. The memory instructs the processor to determine a confidence score associated with the at least a label as a function of a comparison between the scanned label and a plurality of historical scanned labels. The memory instructs the processor to display the confidence score using a display device.

FIELD OF THE INVENTION

The present invention generally relates to the field of data analysis. In particular, the present invention is directed to an apparatus and a method for generating a confidence score associated with a scanned label.

BACKGROUND

In various fields, such as healthcare, pathology, logistics, and document management, the need for accurate and reliable extraction of information from scanned labels is crucial. However, OCR technology, which converts text within images or documents into machine-readable format, is not always perfect and can introduce errors due to factors like image quality, font variations, or handwriting inconsistencies.

SUMMARY OF THE DISCLOSURE

In an aspect, an apparatus for generating a confidence score associated with a scanned label is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a profile comprising at least a label containing a plurality of metadata associated with the at least a label. The memory instructs the processor to generate a scanned label as a function of the at least a label, wherein generating a scanned label comprises scanning the at least a label using a text recognition module. The memory instructs the processor to determine a confidence score associated with the scanned label as a function of a comparison between the scanned label and a plurality of historical scanned labels. The memory instructs the processor to display the confidence score using a display device.

In another aspect, a method for generating a confidence score associated with a scanned label is disclosed. The method includes receiving, using at least a processor, a profile comprising at least a label containing a plurality of metadata associated with the at least a label. The method includes generating, using the at least a processor, a scanned label as a function of the at least a label, wherein generating a scanned label comprises scanning the at least a label using a text recognition module. The method includes determining, using the at least a processor, a confidence score associated with the scanned label as a function of a comparison between the scanned label and a plurality of historical scanned labels. The method includes displaying the confidence score using a display device.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to an apparatus and a method for generating a confidence score associated with a scanned label is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a profile comprising at least a label containing a plurality of metadata. The memory instructs the processor to generate a scanned label as a function of the at least a label, wherein generating a scanned label comprises scanning the at least a label using a text recognition module. The memory instructs the processor to determine a confidence score as a function of a comparison between the scanned label and a plurality of historical scanned labels. The memory instructs the processor to display the confidence score using a display device. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples.

With continued reference toFIG.1, processor104may be configured to receive a profile108from a user. For the purposes of this disclosure, a “profile” is a representation of information and/or data describing information an individual or a group slides. A profile108may be made up of a plurality of slide data. As used in the current disclosure, “data” is information associated with slide. The profile108may be a digital representation of a histology slide. As used in the current disclosure, a “histology slide” is a slide containing a portion of biopsied tissue. A histology slide may include biopsied tissue from a patient, wherein the biopsied tissue is sliced into very thin layers and placed on a glass slide. A digital representation of the histology slide may include digital photos of the histology slide. These photos may include digital photos taken under a microscope. A profile108may additionally include paperwork surrounding the histology slide. Current disclosure may include information regarding testing, analysis, storage, and disposal of histology slides by medical professionals. A profile108may be created by a processor104, a user, medical professional, or a third party (e.g. Spouse, Support Staff, Family Member, and the like). The profile108may include any of the following personal information: age, weight, height, gender, geographical location, diagnostic information, medical history, test result, lab result, and the like. A profile108may include information medical information associated with patient and/or tissue.

With continued reference toFIG.1, a profile108contains a label112associated with the histology slide. As used in the current disclosure, a “label” is a descriptive tag or identifier that is assigned to an individual or a group histology slides. A label112may contain a plurality of information regarding the histology slide. A label112may contain information regarding a patient or caser identifier, wherein a unique identifier is assigned to a specific patient or case, which helps in tracking and referencing slides related to that case. A label112may contain information regarding the type of biological sample, disease, tissue, morphology, and the like contained within the histology slide. This may include they type of tissue or sample that the slide represents, such as breast tissue, lung biopsy, skin lesion, cardiac tissue, liver tissue, and the like. In some cases, a label112may contain information regarding a pathological diagnosis or condition being investigated or identified on the slide, such as carcinoma, lymphoma, or infectious disease. Labels112may contain information regarding the specific staining or analysis techniques that have been applied to the histology slide. The specific staining or preparation technique used on the slide, which can provide additional information about the slide's characteristics, including but not limited to techniques such as hematoxylin and eosin (H&E) staining, immunohistochemistry (IHC), or special stains. Labels112may include additional notes, observations, or annotations made by the pathologist that may be relevant to the slide, such as the presence of specific features or abnormalities. Labels112may additionally include grading labels, staging labels, quality assessment labels, and the like. A grading label may reflect the severity or stage of the disease based on certain criteria. A quality assessment label may reflect the reliability or suitability of the slide for analysis. The specific use of labels may vary depending on the pathology laboratory's protocols and practices. The purpose of labels is to enhance the organization, communication, and retrieval of histology slides within a laboratory or medical facility.

With continued reference toFIG.1, a label112may come in a plurality formats. The labels112may include both printed labels, digital labels, handwritten labels, photographs of labels, scans of labels, and the like. In some cases, a label may take the form of an identification code. As used in the current disclosure, an “identification code” is a visual representation of the label. An identification code may include a barcode. A barcode may consist of a series of parallel lines, bars, or squares of varying widths and spacings. The barcode may serve as a unique identifier for the slide and contains encoded data that provides relevant information about the slide. Identification codes are designed to be scanned or read by barcode scanners or readers, which can quickly decode the encoded information. Identification codes are widely used in various industries for purposes such as product identification, inventory management, and tracking. Identification codes may include both one-dimensional (1D) barcodes and two-dimensional (2D) barcodes. A linear or one-dimensional (1D) barcode, which is composed of a sequence of vertical bars and spaces. The width and spacing of these bars and spaces represent specific patterns that encode alphanumeric or numeric data. Examples of 1D barcodes include the Universal Product Code (UPC) and the Code 39 barcode. A two-dimensional (2D) barcode, which can encode more complex information in a smaller space. 2D barcodes use patterns of squares, dots, or other geometric shapes to represent data. Examples of 2D barcodes include the QR code (Quick Response code) and the Data Matrix code. By scanning the barcode associated with the histology slide, healthcare professionals can quickly access the associated information in a digital database or Laboratory Information System (LIS). This facilitates efficient tracking, identification, and retrieval of slides during diagnostic processes, consultations, or research.

With continued reference toFIG.1, a profile108includes a plurality of metadata116. As used in the current disclosure, “metadata” refers to descriptive information or attributes that provide context, structure, and meaning to data. Metadata116is essentially data about data. Metadata116helps in understanding and managing various aspects of data, such as its origin, content, format, quality, and usage. It plays a crucial role in organizing, searching, and interpreting data effectively. Metadata116may include descriptive metadata, structural metadata, administrative metadata, technical metadata, provenance metadata, usage metadata, and the like. Metadata116may be organized and managed through metadata schemas, standards, or frameworks. These provide guidelines and specifications for capturing, storing, and exchanging metadata in a consistent and structured manner. Common metadata116standards include Dublin Core, Metadata Object Description Schema (MODS), and the Federal Geographic Data Committee (FGDC) metadata standard. In some cases, metadata116may be associated with a label112for a histology slide. Metadata116may provide additional descriptive information or attributes that are linked to the slide's label. This metadata116provides context and relevant details about the slide, aiding in its identification, categorization, and management within a pathology laboratory or medical facility. The specific metadata116associated with a label112can vary based on the requirements and practices of the medical facility. Metadata116associated with the label may include patient information. Patient information may include data such as the patient's name, unique patient identifier (ID), age, gender, and any other relevant demographic information. Patient information helps in identifying and associating the slide with the correct individual's medical records. Metadata116may also include case specific details, wherein case specific details may include information about the specific case or clinical scenario related to the slide. Case specific details may include information about the case number, referring physician, clinical history, relevant symptoms, or any other pertinent details that aid in understanding the context of the slide. In some cases, metadata116may include information related to the specific specimen type of the slide. This may include the type of tissue or sample that the slide represents. Metadata116may contain notes, comments, or observations made by the pathologist or other medical professional. These annotations might highlight specific features, anomalies, or noteworthy aspects of the slide that are important for interpretation or follow-up analysis. For instance a timestamp reflecting when and where the slide was prepared, analyzed, or labeled can be associated as metadata116. This information helps in tracking and maintaining a chronological record of slide-related activities. It could be breast tissue, lung biopsy, skin lesion, or any other anatomical or pathological specimen. In some embodiments, metadata may contain information regarding staining or preparation technique, pathological diagnosis, and the like.

With continued reference toFIG.1, processor104is configured to generate a scanned label120as a function of the at least a label112. As used in the current disclosure, a “scanned label” is a label112that has been converted from a physical document or an image to machine encoded text or binary code. A scanned label120may refer to the process of capturing or digitizing the information on a label using a scanning device, such as a barcode scanner or an optical character recognition (OCR) system. Scanning the label112allows for the automatic extraction and interpretation of the label's content for further processing or integration into a digital system. When a label112is scanned, the scanning device captures the visual representation of the label, whether it is a barcode, text, or a combination of both. Processor104then then converts the scanned image into a scanned label120that can be read and interpreted by a computer or software system. This include converting the image associated with the label112into machine encoded text. In a non-limiting example, if the label112scanned using a barcode scanner, assuming the label112is a barcode. The scanner converts the barcode into a scanned label120which includes binary code that represents the encoded data. In another non-limiting example, if the label112contains text or alphanumeric characters, an OCR system scans the label and uses image recognition algorithms to identify and convert the characters into a scanned label120that includes machine encoded text. The OCR software analyzes the scanned image, identifies the shapes and patterns of the characters, and applies character recognition techniques to convert them into digital text. In some embodiments, once the scanned label120is generated textual data may be extracted from the label. Textual data may be associated with the corresponding histology slide in a digital database or Laboratory Information System (LIS). This enables efficient tracking, retrieval, and management of histology slides, as data from the scanned label120can be used for various purposes such as patient identification, case management, and slide categorization.

With continued reference toFIG.1, processor104is configured to generate a scanned label120using a text recognition module124. As used in the current disclosure, a “text recognition module” is a software designed to automatically recognize and extract text from images or scanned document. It is a technology that enables computers to understand and interpret printed or handwritten text characters. The output of a text recognition module124may be the extracted text in a machine-readable format, which can be further processed, stored, or analyzed by other applications or systems. The accuracy and performance of a text recognition module124may depend on factors such as the quality of the input image, the complexity of the text, the language being recognized, and the robustness of the recognition algorithms. In some embodiments, text recognition modules124may find application in various fields, including document digitization, data entry, automated form processing, intelligent character recognition (ICR), and automated reading of printed or handwritten text in areas such as optical mark recognition (OMR), invoice processing, and text-based searching within images or scanned documents.

Still referring toFIG.1, a text recognition module124may include an optical character recognition (OCR) system. An optical character recognition system or optical character reader (OCR) may be configured to convert of images of written text (e.g., typed, handwritten, or printed) into machine-encoded text. In some cases, recognition of at least a keyword from an image component may include one or more processes, including without limitation optical character recognition (OCR), optical word recognition, intelligent character recognition, intelligent word recognition, and the like. In some cases, OCR may recognize written text, one glyph or character at a time. In some cases, optical word recognition may recognize written text, one word at a time, for example, for languages that use a space as a word divider. In some cases, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine learning processes. In some cases, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine learning processes.

Still referring toFIG.1, in some cases, OCR may be an “offline” process, which analyses a static document or image frame. In some cases, handwriting movement analysis can be used as input for handwriting recognition. For example, instead of merely using shapes of glyphs and words, this technique may capture motions, such as the order in which segments are drawn, the direction, and the pattern of putting the pen down and lifting it. This additional information can make handwriting recognition more accurate. In some cases, this technology may be referred to as “online” character recognition, dynamic character recognition, real-time character recognition, and intelligent character recognition.

Still referring toFIG.1, in some cases, OCR processes may employ pre-processing of image components. Pre-processing process may include without limitation de-skew, de-speckle, binarization, line removal, layout analysis or “zoning,” line and word detection, script recognition, character isolation or “segmentation,” and normalization. In some cases, a de-skew process may include applying a transform (e.g., homography or affine transform) to the image component to align text. In some cases, a de-speckle process may include removing positive and negative spots and/or smoothing edges. In some cases, a binarization process may include converting an image from color or greyscale to black-and-white (i.e., a binary image). Binarization may be performed as a simple way of separating text (or any other desired image component) from the background of the image component. In some cases, binarization may be required for example if an employed OCR algorithm only works on binary images. In some cases. a line removal process may include the removal of non-glyph or non-character imagery (e.g., boxes and lines). In some cases, a layout analysis or “zoning” process may identify columns, paragraphs, captions, and the like as distinct blocks. In some cases, a line and word detection process may establish a baseline for word and character shapes and separate words, if necessary. In some cases, a script recognition process may, for example in multilingual documents, identify a script allowing an appropriate OCR algorithm to be selected. In some cases, a character isolation or “segmentation” process may separate signal characters, for example, character-based OCR algorithms. In some cases, a normalization process may normalize the aspect ratio and/or scale of the image component.

Still referring toFIG.1, in some embodiments, an OCR process will include an OCR algorithm. Exemplary OCR algorithms include matrix-matching process and/or feature extraction processes. Matrix matching may involve comparing an image to a stored glyph on a pixel-by-pixel basis. In some cases, matrix matching may also be known as “pattern matching,” “pattern recognition,” and/or “image correlation.” Matrix matching may rely on an input glyph being correctly isolated from the rest of the image component. Matrix matching may also rely on a stored glyph being in a similar font and at the same scale as input glyph. Matrix matching may work best with typewritten text.

Still referring toFIG.1, in some embodiments, an OCR process may include a feature extraction process. In some cases, feature extraction may decompose a glyph into features. Exemplary non-limiting features may include corners, edges, lines, closed loops, line direction, line intersections, and the like. In some cases, feature extraction may reduce dimensionality of representation and may make the recognition process computationally more efficient. In some cases, extracted feature can be compared with an abstract vector-like representation of a character, which might reduce to one or more glyph prototypes. General techniques of feature detection in computer vision are applicable to this type of OCR. In some embodiments, machine-learning processes like nearest neighbor classifiers (e.g., k-nearest neighbors algorithm) can be used to compare image features with stored glyph features and choose a nearest match. OCR may employ any machine-learning process described in this disclosure, for example machine-learning processes described with reference toFIG.2. Exemplary non-limiting OCR software includes Cuneiform and Tesseract. Cuneiform is a multi-language, open-source optical character recognition system originally developed by Cognitive Technologies of Moscow, Russia. Tesseract is free OCR software originally developed by Hewlett-Packard of Palo Alto, California, United States.

Still referring toFIG.1, in some cases, OCR may employ a two-pass approach to character recognition. The second pass may include adaptive recognition and use letter shapes recognized with high confidence on a first pass to recognize better remaining letters on the second pass. In some cases, two-pass approach may be advantageous for unusual fonts or low-quality image components where visual verbal content may be distorted. Another exemplary OCR software tool include OCRopus. OCRopus development is led by German Research Centre for Artificial Intelligence in Kaiserslautern, Germany. In some cases, OCR software may employ neural networks, for example neural networks as taught in reference toFIGS.2,4, and5.

Still referring toFIG.1, in some cases, OCR may include post-processing. For example, OCR accuracy can be increased, in some cases, if output is constrained by a lexicon. A lexicon may include a list or set of words that are allowed to occur in a document. In some cases, a lexicon may include, for instance, all the words in the English language, or a more technical lexicon for a specific field. In some cases, an output stream may be a plain text stream or file of characters. In some cases, an OCR process may preserve an original layout of visual verbal content. In some cases, near-neighbor analysis can make use of co-occurrence frequencies to correct errors, by noting that certain words are often seen together. For example, “Washington, D.C.” is generally far more common in English than “Washington DOC.” In some cases, an OCR process may make use of a priori knowledge of grammar for a language being recognized. For example, grammar rules may be used to help determine if a word is likely to be a verb or a noun. Distance conceptualization may be employed for recognition and classification. For example, a Levenshtein distance algorithm may be used in OCR post-processing to further optimize results.

With continued reference toFIG.1, processor may be configured to transform data extracted from a label112into a digital format, wherein the digital format may be stored and manipulated electronically by the processor104. Examples of a digital format may include a textural representation (e.g., plain text, XML, JSON, and the like) or/and a graphical representation (scanned labels that contain graphical elements, such as hand-drawn annotations or symbols, may require this representation, e.g., digital vector graphics or bitmap images). Additionally, or alternatively, digital representation may be further structured using specific data formats, for example, and without limitation, scanned label may be represented using standard formats like DICOM (Digital Imaging and Communications in Medicine) or HL7 (Health Level 7) to ensure interoperability with existing healthcare systems (list some examples here if any), enabling seamless integration with databases300and facilitating efficient data exchange and interoperability.

With continued reference toFIG.1, processor104may be configured to generate a plurality of named entities128as a function of the scanned label120using a named entity recognition process. As used in the current disclosure, a “named entity” is a specific type of word or phrase that represents a real-world object with a unique identity. Named entities are typically people, places, ideas, concepts, or things that denote specific individuals, organizations, locations, dates, times, products, events, quantities, diseases, tissue samples, and other entities that can be uniquely identified. These entities play a significant role in understanding the context and extracting meaningful information from text. Named entities may provide contextual information and serve as reference points for understanding the meaning and relationships within a text. Recognizing and extracting named entities from textual data is a fundamental task in natural language processing (NLP), information extraction, text mining, and various other applications where understanding the semantics and identifying key elements of text is important.

With continued reference toFIG.1, processor104may be configured to generate a plurality of named entities128using a named entity recognition (NER) system132. As used in the current disclosure, a “named entity recognition (NER) system” is software that identifies a plurality of named entities128in from text. A NER system132may be configured to identify a plurality of named entities from a scanned label120. Inputs of a NER system132may include a profile108, label112, scanned label120, metadata, and the like. The output of a named entity recognition system132may include a plurality of named entities128. Named entities128may include a structured representation of the identified named entities, typically in the form of annotations or tags attached to the original text.

With continued reference toFIG.1, a NER system132may generate a plurality of named entities128using a natural language processing model. As used in the current disclosure, a “natural language processing (NLP) model” is a computational model designed to process and understand human language. It leverages techniques from machine learning, linguistics, and computer science to enable computers to comprehend, interpret, and generate natural language text. The NLP model may preprocess the input text, wherein the input text may include the label112and the scanned label120, or any other data mentioned herein. Preprocessing the input text may involve tasks like tokenization (splitting text into individual words or sub-word units), normalizing the text (lowercasing, removing punctuation, etc.), and encoding the text into a numerical representation suitable for the model. The NLP model may include transformer architecture, wherein the transformers are deep learning models that employ attention mechanisms to capture the relationships between words or sub-word units in a text sequence. They consist of multiple layers of self-attention and feed-forward neural networks. The NLP model may weigh the importance of different words or sub-word units within a text sequence while considering the context. It enables the model to capture dependencies and relationships between words, taking into account both local and global contexts. This process may be used to identify a plurality of named entities128. Language processing model may include a program automatically generated by processor104and/or named entity recognition system to produce associations between one or more significant terms extracted from a scanned label120and detect associations, including without limitation mathematical associations, between such significant terms. Associations between language elements, where language elements include for purposes herein extracted significant terms, relationships of such categories to other such term may include, without limitation, mathematical associations, including without limitation statistical correlations between any language element and any other language element and/or language elements. Statistical correlations and/or mathematical associations may include probabilistic formulas or relationships indicating, for instance, a likelihood that a given extracted significant term indicates a given category of semantic meaning. As a further example, statistical correlations and/or mathematical associations may include probabilistic formulas or relationships indicating a positive and/or negative association between at least an extracted significant term and/or a given semantic relationship; positive or negative indication may include an indication that a given document is or is not indicating a category semantic relationship. Whether a phrase, sentence, word, or other textual element in scanned label120constitutes a positive or negative indicator may be determined, in an embodiment, by mathematical associations between detected significant terms, comparisons to phrases and/or words indicating positive and/or negative indicators that are stored in memory at processor104, or the like.

With continued reference toFIG.1, processor104may classify a plurality of named entities128into a plurality of entity categories. As used in the current disclosure, “entity categories” is a category that is representative of one or more predefined classes or types of data. Entity categories may be related to one or more aspects of the histology slide. Entity categories may include broad areas or aspects of a histology slide. Non-limiting examples of entity categories may include patient name, patient identification code, slide identification code, specimen identification code, specimen type, stains or preparation techniques, alphanumeric identification codes, pathological diagnosis, scoring or ranking of the histology slide, annotations, observations, notes, medical facility name, medical facility identification number, and the like. In an embodiment, a processor104may be configured to generate a plurality of entity categories based on the available the scanned label120. Processor104may generate a plurality of entity categories based on historical versions of the entity categories. Processor104may generate a plurality of entity categories by extracting relevant features, characteristics, or traits associated with the scanned label120. Identification of features may depend on the nature and type of histology slide. In a non-limiting example, plurality of named entities128may be classified into a plurality of entity categories based on their semantic meaning. In other embodiments, a processor104be configured to receive a plurality of entity categories from a database such as database300. In an embodiment, entity categories may be used to retrieve regular expression associated with each named entity from the database.

With continued reference toFIG.1, processor104may be configured to classify each named entity into the plurality of entity categories based on the named entities128spatial position on the scanned label120. As used in the current disclosure, “spatial position” refers to the positing of the named entity on the label112. Spatial positioning may be a form of template base NER. Template-based Named Entity Recognition (NER) is an approach to identifying named entities in text using predefined templates or patterns. It relies on a set of fixed patterns that represent the structure or characteristics of the named entities you want to extract. In the current case, the template may include a plurality of fields in fixed spatial positions so that processor104is able assign the content of each of those fields to an entity category. The spatial position on the label112may be associated with one or more entity categories. The fields on a label112may contain important information related to the slide and its associated data. These fields may provide identification, categorization, and contextual details about the slide. The specific fields on a label112may be configured to have the same spatial positioning. In a non-limiting example, a label112may include a spatial position or a field for a person's name. To capture person names, processor104may consult the lookup table, mentioned herein below, to retrieve a list of know person names, populating the persons name in the spatial position for a persons name. can vary depending on the laboratory or institution. Each field on a label112may be associated with an entity category. Thus, processor104may be configured to classify each named entity128into entity categories based on the spatial position on a label112. Processor104may be configured to identify spatial positions on the scanned label as a function of the medical facility that the scanned label120is associated with. Processor104the medical facility may be identified using an alphanumeric code or another identifier on the label112or within its associated metadata116.

With continued reference toFIG.1, a processor104may determine a plurality of named entities using a lookup table. A “lookup table,” for the purposes of this disclosure, is a data structure, such as without limitation an array of data, that maps input values to output values. A lookup table may be used to replace a runtime computation with an indexing operation or the like, such as an array indexing operation. A look-up table may be configured to pre-calculate and store data in static program storage, calculated as part of a program's initialization phase or even stored in hardware in application-specific platforms. Data within the lookup table may include previous examples of named entities compared to the scanned label120. Data within the lookup table may be received from database300. Lookup tables may also be used to generate a plurality of named entities128by matching an input value to an output value by matching the input against a list of valid (or invalid) items in an array. In a non-limiting example, a scanned label120include a plurality of fields containing text that describes various aspects of the slide. Examples of named entities may indicate that a list of named entities from this particular medical facility includes a patient name, patient identification number, sample identification number, and a listing of the staining or preparation techniques used on the slide. A lookup table may look up the scanned label120as an input and output a list of named entities128. Processor104may be configured to “lookup” or input one or more scanned label120, label112, profile108, metadata116, examples of named labels, and the like. Whereas the output of the lookup table may include a list of named entities. Alternatively or additionally, a query representing elements of scanned label120may be submitted to the lookup table and/or a database, and an associated data fault identifier stored in a data record within the lookup table and/or database may be retrieved using the query.

With continued reference toFIG.1, processor104may be configured to generate a scanned label120using a label machine-learning model. As used in the current disclosure, a “label machine-learning model” is a machine-learning model that is configured to generate a scanned label. The label machine-learning model may be consistent with the machine-learning model or a classifier as described below inFIG.2. Inputs to the label machine-learning model may include profile108, label112, metadata116, a plurality of entity categories, examples of scanned labels120, examples of named entities128, and the like. Outputs to the label machine-learning model may include a scanned label120and a listing of named entities. In some embodiments, a label machine learning model may be configured to sort a listing of named entities128into one or more entity categories. Label training data is a plurality of data entries containing a plurality of inputs that are correlated to a plurality of outputs for training a processor by a machine-learning process. In an embodiment, label training data may comprise a plurality of labels112correlated to examples of scanned labels120. In another embodiment, label training data may comprise a plurality of labels112correlated to examples of named entities. Label training data may be received from database300. Label training data may contain information regarding profile108, label112, metadata116, a plurality of entity categories, examples of scanned labels120, examples of named entities128, and the like. Machine-learning models may be performed using, without limitation, linear machine-learning models such as without limitation logistic regression and/or naive Bayes machine-learning models, nearest neighbor machine-learning models such as k-nearest neighbors machine-learning models, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic machine-learning models, decision trees, boosted trees, random forest machine-learning models, learning vector quantization, and/or neural network-based machine-learning models.

With continued reference toFIG.1, processor104determines a confidence score136as a function of a comparison between the scanned label120and a plurality of previously scanned labels120. As used in the current disclosure, a “confidence score” is quantitative measurement of the accuracy of the content generated from scanned label120. Accuracy of the content of the scanned label120may refer to an accurate translation of the text of the label112when generating the scanned label120. Accuracy of the content of the scanned label120may also refer to the likelihood that text of the scanned label120accurately reflects the content of the histology slide. A processor104may generate A confidence score136for each attribute of each entity. A confidence score136may be used to normalize one or more scanned labels120to bring all scanned labels120onto a comparable scale. This step is important to eliminate any bias introduced by different qualities or views of the scanned labels. Normalization techniques can include min-max scaling, z-score normalization, or logarithmic transformation. In an embodiment, if content that is generated from the scanned label120is accurate then the confidence score136may be high, conversely if content that is generated from the scanned label120is likely inaccurate then the confidence score136may be low. A confidence score136may be expressed as a numerical score, a linguistic value, alphanumeric score, or an alphabetical score. Confidence score136may be represented as a score used to reflect the level of accuracy of the content of the scanned label120. A non-limiting example, of a numerical score, may include a scale from 1-10, 1-100, 1-1000, and the like, wherein a rating of 1 may represent an inaccurate scanned label120, whereas a rating of 10 may represent an accurate scanned label120. In another non-limiting example, linguistic values may include, “Highly Accurate,” “Moderately Accurate,” “Moderately Inaccurate,” “Highly Inaccurate,” and the like. In some embodiments, linguistic values may correspond to a numerical score range. For example, a scanned label120that receives a score between 50-75, on a scale from 1-100, may be considered “Moderately Accurate.”

With continued reference toFIG.1, a confidence score136may be generated by comparing the current scanned label120to historically scanned labels. As used in the current disclosure, “historically scanned labels” are scanned labels120that have been generated prior to the current iteration of scanned labels120. Processor104may identify a plurality of historically scanned labels as a function of the metadata116associated with the label112. The metadata116may contain information regarding patient identifiers or medical facility identifiers. Based on the patient identifiers and/or medical facility identifiers processor104may generate a plurality of historically scanned labels from the same facility and/or patient of the current scanned label120. Processor104may compare the historically scanned labels the current scanned label120may be comparing their content, spatial position, font, and the like. To compare a scanned label120to historically scanned labels to verify the accuracy of the scanned label120, a processor104may implement a process that involves data matching and comparison. Processor104may extract relevant features or attributes are extracted from the current scanned label120and the historically scanned labels. These features could include specific fields or data elements such as slide IDs, patient IDs, specimen types, dates of collection, or any other relevant information present on the label. Processor104may then apply a comparison algorithm to evaluate the similarity or differences between the extracted features of the current scanned label120and the historically scanned labels. The choice of the comparison algorithm depends on the specific requirements and nature of the data. For example, it could be a simple string comparison, machine learning model, fuzzy matching algorithm, or more advanced techniques like Levenshtein distance or token-based similarity measures. Processor104may determine if there is a match or similarity threshold that indicates the accuracy of the current scanned label. If a match is found, it can be considered as a verification of the accuracy of the label. If there are discrepancies or differences, it may require further investigation or manual verification. Processor104may assign a confidence score136to indicate the level of accuracy or similarity between the current scanned label120and the historically scanned labels. This score can be based on the results of the comparison algorithm and can be used to make decisions or trigger appropriate actions based on predefined thresholds. In an embodiment a historically scanned label may be a barcode as mentioned herein above.

With continued reference toFIG.1, a confidence score136may include a derivation score. As used in the current disclosure, a “derivation score” is a score that addresses variations in scanned label120as compared to historical scanned labels. These variations may be caused due to errors in the OCR system performance due to text attributes like font, boldness, italics, handwriting, etc. If the OCR system does not provide the same result for several OCR scans associated with the same user, the derivation score may be affected negatively. Processor104may be configured to preprocess the scanned label120. Preprocessing may include steps to normalize the text attributes within the scanned label120. This could involve standardizing the font, removing unnecessary formatting, or converting the text to a consistent style (e.g., removing italics or boldness). Processor104may then extract relevant features or attributes are extracted from the scanned label and the previously scanned labels. These features could include the text content, font type, font size, boldness, italics, or any other relevant attributes that may impact OCR performance. Processor104may then compare the OCR results of the scanned label120with the OCR results of the historically scanned labels. It assesses the variations or differences in the OCR output, taking into account text attributes like font, boldness, italics, or handwriting styles. Based on the comparison results, a derivation score may be calculated to quantify the level of variation or inconsistency in the OCR performance. This score takes into account the frequency and magnitude of variations in the OCR output associated with different text attributes. The derivation score may serve as an indicator of the OCR system's performance consistency. A lower derivation score suggests a lower likelihood of variations in OCR results due to text attributes, which may decrease confidence in the accuracy of the detection. Conversely, a higher derivation score indicates more consistent OCR performance across different scans of the same user, increasing confidence in the detection.

With continued reference toFIG.1, a confidence score136may include an expression score. As used in the current disclosure, an “expression score” is a score that represents OCR system's translation of one or more words within scanned label. An expression score may be reflected for each word in the scanned label120or it may be aggregated to provide one score for all of the words included in the scanned label120. Processor104may be configured to preprocess the scanned label120. Preprocessing may include steps to normalize the text attributes within the scanned label120. This could involve standardizing the font, removing unnecessary formatting, or converting the text to a consistent style (e.g., removing italics or boldness). Processor104may then extract a plurality of words from the scanned label120. In some embodiments, an expression score may include a classification of each word of the scanned label120to a named entity128. This classification can be performed by mapping the expression scores to a predefined set of named entities or categories. For each extracted word, processor104calculates an expression score that reflects the match or alignment with the expected named entity category. This score takes into account the presence or absence of the named entity's associated with the extracted word. The expression scores may serve as a confidence metric for the accuracy of the named entity classification. If the text extracted by OCR does not match the a named entity128of the plurality of named entities128, then the confidence goes down. A higher expression score indicates a better alignment with the expected named entity, while a lower score suggests a potential error or mismatch.

With continued reference toFIG.1, a confidence score136may include a consistency score. As used in the current disclosure, a “consistency score” is a score that represents confidence in a barcode. As mentioned herein above, a scanned label120may include one or more barcodes. The scanned label120(barcode) obtained through the OCR process may undergo preprocessing steps to normalize and clean the data. This could involve removing noise, correcting errors, or formatting the text. Processor104applies barcode recognition techniques to extract the barcode information from the scanned label120. This process can involve decoding the barcode using specific algorithms or libraries designed for barcode recognition. Processor104then extracts any accompanying text or information present along with the barcode using the text recognition system, mentioned herein above. Processor104may compare the content described by the extracted barcode with the historically scanned label or the OCR scan. It evaluates if there is a difference or mismatch between the information derived from the barcode and historically scanned labels. Based on the content comparison, the processor104may generate a consistency score that reflects the level of consistency between the barcode and the historically scanned label or other OCR generated text. If there is a difference in the content described by the barcode and the OCR scan, the confidence goes down, resulting in a lower consistency score. The generated Consistency score provides a measure of the accuracy between the scanned label120(barcode) and historically scanned label or/and the text obtained through OCR recognition. It helps assess the reliability and confidence in the accuracy of the scanned label. By evaluating the consistency score, the computer can make informed decisions, trigger appropriate actions, or initiate further verification steps based on predefined confidence thresholds.

With continued reference toFIG.1, a confidence score136may include a temporal consistency score. As used in the current disclosure, a “temporal consistency score” is a score that addresses errors incurred due to limitations inconsistencies in handwriting or printed text. A temporal consistency score may be generated by comparing the current OCR scan to other documents associated with the same user to catch the degradation and help lower confidence. For example, if a handwritten document contains the letter “R” but the OCR system interprets the letter as “K” due to unclear handwriting. Processor104may compare the handwritten document to other documents in the set to identify a discrepancy between the scanned label120and historically scanned labels. The temporal consistency score may reflect the confidence processor104has in the scanned label. In the above mentioned example, the temporal consistency score may be negatively effected due to the misinterpretation of the letter “R.” Processor104may place the scanned label120through preprocessing steps to normalize and clean the data. This includes removing noise, correcting errors, and formatting the text for comparison. Processor104then compares the current scanned label120to historically scanned labels. This comparison aims to identify any inconsistencies or discrepancies in the text due to limitations in handwriting or printed text quality. Based on the text comparison, the processor104may calculate a temporal consistency score that reflects the level of consistency or inconsistency between the current scanned label120and the historically scanned labels. This score helps assess the temporal consistency and potential degradation of text recognition accuracy.

With continued reference toFIG.1, Processor104may generate a confidence score136as a function of the temporal consistency score, consistency score, expression score, and a derivation score. This embodiment of a confidence score136may be an overall reflection of the confidence the content generated from scanned label120. In an embodiment, a confidence score136may be generated by averaging two or more of the above mentioned scores. This may include assigning each score a weighted average. In another embodiment, a confidence score136may be generated by evaluating the highest or lowest score among the four as the confidence score136.

With continued reference toFIG.1, processor104may generate the confidence score136using a score machine-learning model140. As used in the current disclosure, an “score machine-learning model” is a machine-learning model that is configured to generate a confidence score136. The score machine-learning model140may be consistent with the machine-learning model described below inFIG.2. Inputs to the score machine-learning model140may include profile108, label112, metadata116, a plurality of entity categories, scanned labels120, named entities128, examples of confidence scores136, and the like. Outputs to the score machine-learning model140may include a confidence score136tailored to one or more scanned labels120. Outputs to the score machine learning model may additionally include a variation score, expression score, consistency score, and temporal consistency score. Score training data may include a plurality of data entries containing a plurality of inputs that are correlated to a plurality of outputs for training a processor by a machine-learning process. In an embodiment, score training data may include a plurality of scanned labels120correlated to examples of confidence scores136. Examples of confidence scores136may include historical confidence scores136that have been generated from previous iterations of score machine learning model or apparatus100. Score training data may be received from database300. Score training data may contain information regarding profile108, label112, metadata116, a plurality of entity categories, scanned labels120, named entities128, examples of confidence scores136, and the like. In an embodiment, a score machine-learning model140may be iteratively updated with the input and output results of past score machine-learning models. The machine-learning model may be performed using, without limitation, linear machine-learning models such as without limitation logistic regression and/or naive Bayes machine-learning models, nearest neighbor machine-learning models such as k-nearest neighbors machine-learning models, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic machine-learning models, decision trees, boosted trees, random forest machine-learning model, and the like.

With continued reference toFIG.1, processor104may generate the confidence score136as a function of a comparison between the scanned label120and the historically scanned labels using a comparison fuzzy inference. As used in the current disclosure, a “comparison fuzzy inference” is a method that interprets the values in the input vector (i.e., scanned label120and historically scanned labels) and, based on a set of rules, assigns values to the output vector. A set of fuzzy rules may include a collection of linguistic variables that describe how the system should make a decision regarding classifying an input or controlling an output. Fuzzy inference rules operate on fuzzy sets and provide a framework for mapping input variables to output variables through linguistic rules. Fuzzy inference rules may operate using linguistic variables, which represent imprecise or vague concepts rather than precise numerical values. Linguistic variables are defined by membership functions, which describe the degree of membership or truth for different linguistic terms or categories. In a non-limiting example, a linguistic variable associated with the confidence score136may have linguistic terms like “High Confidence,” “Moderate Confidence,” and/or “Low Confidence,” each with its corresponding membership function. A fuzzy inference rule typically follows a conditional “IF-THEN” structure. It consists of an antecedent (IF part) and a consequent (THEN part). The antecedent specifies the conditions or criteria based on which the rule will be applied, and the consequent determines the output or conclusion of the rule. In an embodiment. the confidence score136may be determined by a comparison of the degree of match between a first fuzzy set and a second fuzzy set, and/or single values therein with each other or with either set, which is sufficient for purposes of the matching process.

Still referring toFIG.1, confidence score136may be determined as a function of the intersection between two fuzzy sets, wherein each fuzzy set may be representative of a scanned label120and a historically scanned labels respectively. Comparing the scanned label120and a historically scanned labels may include utilizing a fuzzy set inference system as described herein below, or any scoring methods as described throughout this disclosure. For example, without limitation, processor104may use a fuzzy logic model to determine confidence score136as a function of fuzzy set comparison techniques as described in this disclosure. In some embodiments, each piece of information associated with a scanned label120may be compared to a historically scanned labels, wherein the confidence score136may be represented using a linguistic variable on a range of potential numerical values, where values for the linguistic variable may be represented as fuzzy sets on that range; a “good” or “ideal” fuzzy set may correspond to a range of values that can be characterized as ideal, while other fuzzy sets may correspond to ranges that can be characterized as mediocre, bad, or other less-than-ideal ranges and/or values. In embodiments, these variables may be used to compare a scanned label120and a historically scanned labels to determine the confidence score136specific to the scanned label120. A fuzzy inferencing system may combine such linguistic variable values according to one or more fuzzy inferencing rules, including any type of fuzzy inferencing system and/or rules as described in this disclosure, to determine a degree of membership in one or more output linguistic variables having values representing ideal overall performance, mediocre or middling overall performance, and/or low or poor overall performance; such mappings may, in turn, be “defuzzified” as described in further detail below to provide an overall output and/or assessment.

l=∑i=0n⁢ai2,
where aiis attribute number experience of the vector. Scaling and/or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on the similarity of attributes; this may, for instance, be advantageous where cases represented in training data are represented by different quantities of samples, which may result in proportionally equivalent vectors with divergent values.

Still referring toFIG.1, processor104may be configured to display confidence score136using a display device144. As used in the current disclosure, a “display device” is a device that is used to display content. A display device144may include a user interface. A “user interface,” as used herein, is a means by which a user and a computer system interact; for example, through the use of input devices and software. A user interface may include a graphical user interface (GUI), command line interface (CLI), menu-driven user interface, touch user interface, voice user interface (VUI), form-based user interface, any combination thereof, and the like. A user interface may include a smartphone, smart tablet, desktop, or laptop operated by the user. In an embodiment, the user interface may include a graphical user interface. A “graphical user interface (GUI),” as used herein, is a graphical form of user interface that allows users to interact with electronic devices. In some embodiments, GUI may include icons, menus, other visual indicators, or representations (graphics), audio indicators such as primary notation, and display information and related user controls. A menu may contain a list of choices and may allow users to select one from them. A menu bar may be displayed horizontally across the screen such as pull down menu. When any option is clicked in this menu, then the pull down menu may appear. A menu may include a context menu that appears only when the user performs a specific action. An example of this is pressing the right mouse button. When this is done, a menu may appear under the cursor. Files, programs, web pages and the like may be represented using a small picture in a graphical user interface. For example, links to decentralized platforms as described in this disclosure may be incorporated using icons. Using an icon may be a fast way to open documents, run programs etc. because clicking on them yields instant access. Information contained in user interface may be directly influenced using graphical control elements such as widgets. A “widget,” as used herein, is a user control element that allows a user to control and change the appearance of elements in the user interface. In this context a widget may refer to a generic GUI element such as a check box, button, or scroll bar to an instance of that element, or to a customized collection of such elements used for a specific function or application (such as a dialog box for users to customize their computer screen appearances). User interface controls may include software components that a user interacts with through direct manipulation to read or edit information displayed through user interface. Widgets may be used to display lists of related items, navigate the system using links, tabs, and manipulate data using check boxes, radio boxes, and the like.

Now referring toFIG.3, an exemplary score database300is illustrated by way of block diagram. In an embodiment, any past or present versions of data disclosed herein may be stored within including profile108, label112, metadata116, a plurality of entity categories, scanned labels120, named entities128, confidence scores136, and the like. Processor104may be communicatively connected with score database300. For example, in some cases, database300may be local to processor104. Alternatively or additionally, in some cases, database300may be remote to processor104and communicative with processor104by way of one or more networks. Network may include, but not limited to, a cloud network, a mesh network, or the like. By way of example, a “cloud-based” system, as that term is used herein, can refer to a system which includes software and/or data which is stored, managed, and/or processed on a network of remote servers hosted in the “cloud,” e.g., via the Internet, rather than on local servers or personal computers. A “mesh network” as used in this disclosure is a local network topology in which the infrastructure processor104connect directly, dynamically, and non-hierarchically to as many other computing devices as possible. A “network topology” as used in this disclosure is an arrangement of elements of a communication network. Score database300may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NOSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Score database300may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. Score database300may include a plurality of data entries and/or records as described above. Data entries in a database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and/or reflect data and/or records as used herein, as well as categories and/or populations of data consistently with this disclosure. In an embodiment, score database300may be a generic storage mechanism. A generic storage mechanism may be a storage system or method that is not specific to any particular type or format of data, that is, a storage solution that provides a flexible and adaptable way to store and retrieve data without being tied to a specific data format, schema, or domain.

Now referring toFIG.6, an exemplary embodiment of fuzzy set comparison600is illustrated. In a non-limiting embodiment, the fuzzy set comparison. In a non-limiting embodiment, fuzzy set comparison600may be consistent with fuzzy set comparison inFIG.1. In another non-limiting the fuzzy set comparison600may be consistent with the name/version matching as described herein. For example and without limitation, the parameters, weights, and/or coefficients of the membership functions may be tuned using any machine-learning methods for the name/version matching as described herein. In another non-limiting embodiment, the fuzzy set may represent a scanned label120and historically scanned label fromFIG.1.

Alternatively or additionally, and still referring toFIG.6, fuzzy set comparison600may be generated as a function of determining data compatibility threshold. The compatibility threshold may be determined by a computing device. In some embodiments, a computing device may use a logic comparison program, such as, but not limited to, a fuzzy logic model to determine the compatibility threshold and/or version authenticator. Each such compatibility threshold may be represented as a value for a posting variable representing the compatibility threshold, or in other words a fuzzy set as described above that corresponds to a degree of compatibility and/or allowability as calculated using any statistical, machine-learning, or other method that may occur to a person skilled in the art upon reviewing the entirety of this disclosure. In some embodiments, determining the compatibility threshold and/or version authenticator may include using a linear regression model. A linear regression model may include a machine learning model. A linear regression model may map statistics such as, but not limited to, frequency of the same range of version numbers, and the like, to the compatibility threshold and/or version authenticator. In some embodiments, determining the compatibility threshold of any posting may include using a classification model. A classification model may be configured to input collected data and cluster data to a centroid based on, but not limited to, frequency of appearance of the range of versioning numbers, linguistic indicators of compatibility and/or allowability, and the like. Centroids may include scores assigned to them such that the compatibility threshold may each be assigned a score. In some embodiments, a classification model may include a K-means clustering model. In some embodiments, a classification model may include a particle swarm optimization model. In some embodiments, determining a compatibility threshold may include using a fuzzy inference engine. A fuzzy inference engine may be configured to map one or more compatibility threshold using fuzzy logic. In some embodiments, a plurality of computing devices may be arranged by a logic comparison program into compatibility arrangements. A “compatibility arrangement” as used in this disclosure is any grouping of objects and/or data based on skill level and/or output score. Membership function coefficients and/or constants as described above may be tuned according to classification and/or clustering algorithms. For instance, and without limitation, a clustering algorithm may determine a Gaussian or other distribution of questions about a centroid corresponding to a given compatibility threshold and/or version authenticator, and an iterative or other method may be used to find a membership function, for any membership function type as described above, that minimizes an average error from the statistically determined distribution, such that, for instance, a triangular or Gaussian membership function about a centroid representing a center of the distribution that most closely matches the distribution. Error functions to be minimized, and/or methods of minimization, may be performed without limitation according to any error function and/or error function minimization process and/or method as described in this disclosure.

Still referring toFIG.6, inference engine may be implemented according to input a plurality of scanned labels120and a plurality of historically scanned labels. For instance, an acceptance variable may represent a first measurable value pertaining to the classification of a plurality of scanned label120to an historically scanned label. Continuing the example, an output variable may represent a confidence score136. In an embodiment, a plurality of scanned label120and/or an historically scanned label may be represented by their own fuzzy set. In other embodiments, an evaluation factor may be represented as a function of the intersection two fuzzy sets as shown inFIG.6, An inference engine may combine rules, such as any semantic versioning, semantic language, version ranges, and the like thereof. The degree to which a given input function membership matches a given rule may be determined by a triangular norm or “T-norm” of the rule or output function with the input function, such as min (a, b), product of a and b, drastic product of a and b, Hamacher product of a and b, or the like, satisfying the rules of commutativity (T(a, b)=T(b, a)), monotonicity: (T(a, b)≤T(c, d) if a≤c and b≤d), (associativity: T(a, T(b, c))=T(T(a, b), c)), and the requirement that the number 1 acts as an identity element. Combinations of rules (“and” or “or” combination of rule membership determinations) may be performed using any T-conorm, as represented by an inverted T symbol or “⊥,” such as max(a, b), probabilistic sum of a and b (a+b-a*b), bounded sum, and/or drastic T-conorm; any T-conorm may be used that satisfies the properties of commutativity: ⊥(a, b)=⊥(b, a), monotonicity: ⊥(a, b)≤⊥(c, d) if a≤c and b≤d, associativity: ⊥(a, ⊥(b, c))=⊥(⊥(a, b), c), and identity element of 0. Alternatively or additionally T-conorm may be approximated by sum, as in a “product-sum” inference engine in which T-norm is product and T-conorm is sum. A final output score or other fuzzy inference output may be determined from an output membership function as described above using any suitable defuzzification process, including without limitation Mean of Max defuzzification, Centroid of Area/Center of Gravity defuzzification, Center Average defuzzification, Bisector of Area defuzzification, or the like. Alternatively or additionally, output rules may be replaced with functions according to the Takagi-Sugeno-King (TSK) fuzzy model.

A first fuzzy set604may be represented, without limitation, according to a first membership function608representing a probability that an input falling on a first range of values612is a member of the first fuzzy set604, where the first membership function608has values on a range of probabilities such as without limitation the interval [0,1], and an area beneath the first membership function608may represent a set of values within first fuzzy set604. Although first range of values612is illustrated for clarity in this exemplary depiction as a range on a single number line or axis, first range of values612may be defined on two or more dimensions, representing, for instance, a Cartesian product between a plurality of ranges, curves, axes, spaces, dimensions, or the like. First membership function608may include any suitable function mapping first range612to a probability interval, including without limitation a triangular function defined by two linear elements such as line segments or planes that intersect at or below the top of the probability interval. As a non-limiting example, triangular membership function may be defined as:

y⁡(x,a,b,c,d)=max⁡(min(x-ab-a,1,d-xd-c),0)
a sigmoidal function may be defined as:

y⁡(x,a,c)=11-e-a⁡(x-c)
a Gaussian membership function may be defined as:

y⁡(x,c,σ)=e-12⁢(x-cσ)2
and a bell membership function may be defined as:

y⁡(x,a,b,c,)=[1+❘"\[LeftBracketingBar]"x-ca❘"\[RightBracketingBar]"2⁢b]-1
Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various alternative or additional membership functions that may be used consistently with this disclosure.

First fuzzy set604may represent any value or combination of values as described above, including any a plurality of scanned label120and historically scanned label. A second fuzzy set616, which may represent any value which may be represented by first fuzzy set604, may be defined by a second membership function620on a second range624; second range624may be identical and/or overlap with first range612and/or may be combined with first range via Cartesian product or the like to generate a mapping permitting evaluation overlap of first fuzzy set604and second fuzzy set616. Where first fuzzy set604and second fuzzy set616have a region636that overlaps, first membership function608and second membership function620may intersect at a point632representing a probability, as defined on probability interval, of a match between first fuzzy set604and second fuzzy set616. Alternatively or additionally, a single value of first and/or second fuzzy set may be located at a locus636on first range612and/or second range624, where a probability of membership may be taken by evaluation of first membership function608and/or second membership function620at that range point. A probability at628and/or632may be compared to a threshold640to determine whether a positive match is indicated. Threshold640may, in a non-limiting example, represent a degree of match between first fuzzy set604and second fuzzy set616, and/or single values therein with each other or with either set, which is sufficient for purposes of the matching process; for instance, confidence score136may indicate a sufficient degree of overlap with fuzzy set representing a plurality of scanned label120and an historically scanned label for combination to occur as described above. Each threshold may be established by one or more user inputs. Alternatively or additionally, each threshold may be tuned by a machine-learning and/or statistical process, for instance and without limitation as described in further detail below.

In an embodiment, a degree of match between fuzzy sets may be used to rank one resource against another. For instance, if both a plurality of scanned label120and a historically scanned label have fuzzy sets, confidence score136may be generated by having a degree of overlap exceeding a predictive threshold, processor104may further rank the two resources by ranking a resource having a higher degree of match more highly than a resource having a lower degree of match. Where multiple fuzzy matches are performed, degrees of match for each respective fuzzy set may be computed and aggregated through, for instance, addition, averaging, or the like, to determine an overall degree of match, which may be used to rank resources; selection between two or more matching resources may be performed by selection of a highest-ranking resource, and/or multiple notifications may be presented to a user in order of ranking.

Referring now toFIG.7, a flow diagram of an exemplary method700for generating a confidence score associated with a scanned label is illustrated. At step705, method700includes receiving, using at least a processor, a profile comprising at least a label containing a plurality of metadata. This may be implemented as described and with reference toFIGS.1-7. The method further includes generating, using the at least a processor, a plurality of named entities as a function the at least a label. The method then includes classifying, using the at least a processor, the plurality of named entities into a plurality of entity categories. In an embodiment, a label include a barcode.

Still referring toFIG.7, at step710, method700includes generating, using the at least a processor, a scanned label as a function of the at least a label, wherein generating a scanned label comprises scanning the at least a label using a text recognition module. This may be implemented as described and with reference toFIGS.1-7.

Still referring toFIG.7, at step715, method700includes determining, using the at least a processor, a confidence score as a function of a comparison between the scanned label and a plurality of historical scanned labels. This may be implemented as described and with reference toFIGS.1-7. In some embodiments, the confidence score may include a derivation score, expression score, consistency score, and/or temporal consistency score. In other embodiments, generating the confidence score comprises generating the confidence score using a confidence machine learning model. Generating the confidence score using the confidence machine learning model comprises training the confidence machine-learning model using confidence training data, wherein the confidence training data contains a plurality of data entries containing the plurality of scanned label and the plurality of historical scanned labels as inputs correlated to the confidence score as an output and generating the confidence score as a function of a comparison between the scanned label and the plurality of historical scanned labels using a trained confidence machine-learning model.

Still referring toFIG.7, at step720, method700includes displaying the confidence score using a display device. This may be implemented as described and with reference toFIGS.1-7.

Memory808may include various components (e.g., machine-readable media) including, but not limited to, a random-access memory component, a read only component, and any combinations thereof. In one example, a basic input/output system816(BIOS), including basic routines that help to transfer information between elements within computer system800, such as during start-up, may be stored in memory808. Memory808may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software)820embodying any one or more of the aspects and/or methodologies of the present disclosure. In another example, memory808may further include any number of program modules including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combinations thereof.

Computer system800may also include a storage device824. Examples of a storage device (e.g., storage device824) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device824may be connected to bus812by an appropriate interface (not shown). Example interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device824(or one or more components thereof) may be removably interfaced with computer system800(e.g., via an external port connector (not shown)). Particularly, storage device824and an associated machine-readable medium828may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system800. In one example, software820may reside, completely or partially, within machine-readable medium828. In another example, software820may reside, completely or partially, within processor804.

Computer system800may also include an input device832. In one example, a user of computer system800may enter commands and/or other information into computer system800via input device832. Examples of an input device832include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device832may be interfaced to bus812via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus812, and any combinations thereof. Input device832may include a touch screen interface that may be a part of or separate from display836, discussed further below. Input device832may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.

Computer system800may further include a video display adapter852for communicating a displayable image to a display device, such as display device836. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter852and display device836may be utilized in combination with processor804to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system800may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus812via a peripheral interface856. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof.