Abstract: Methods, apparatuses, and embodiments related to analyzing the content of digital images. A computer extracts multiple sets of visual features, which can be keypoints, based on an image of a selected object. Each of the multiple sets of visual features is extracted by a different visual feature extractor. The computer further extracts a visual word count vector based on the image of the selected object. An image query is executed based on the extracted visual features and the extracted visual word count vector to identify one or more candidate template objects of which the selected object may be an instance. When multiple candidate template objects are identified, a matching algorithm compares the selected object with the candidate template objects to determine a particular candidate template of which the selected object is an instance.

Abstract: A technique making use of a few-shot model to determine graphical features present in an image based on a small set of examples with known graphical features. Where a support set including a number of images that each have a known combination of graphical features, the image recognition can identify unknown combinations of those graphical features in any number of query images. In an embodiment of the present disclosure examples of a filled-out form are used to interpret any number of additional filled out versions of the form.

Abstract: A technique making use of a few-shot model to determine whether a query text content belongs to a same language as a small set of examples, or alternatively provide a next member in the same language to the small set of examples. The related few-shot model makes use of convolutional models that are trained in a “learning-to-learn” fashion such that the models know how to evaluate few-shots that belong to the same language. The term “language” in this usage is broader than spoken languages (e.g., English, Spanish, German, etc.). “Language” refers to a category, or data domain, of expression through characters. Belonging to a given language is not specifically based on what the language is, but the customs or traits expressed in that language.

Abstract: Disclosed are a method and apparatus for identifying versions of a form. In an example, clients of a medical company fill out many forms, and many of these forms have multiple versions. The medical company operates in 10 states, and each state has a different version of a client intake form, as well as of an insurance identification form. In order to automatically extract information from a particular filled out form, it may be helpful to identify a particular form template, as well as the version of the form template, of which the filled out form is an instance. A computer system evaluates images of filled out forms, and identifies various form templates and versions of form templates based on the images.

Abstract: Disclosed herein is a machine learning application for automatically reading filled-in forms. There are multiple steps involved in using a computer to accurately read a handwritten form. First, the system identifies the form. Second, the system identifies what parts of the form are important. Third, the important parts are extracted as image data (known as shreds). Finally, fourth, the system interprets the shreds. This application is focused on steps two and three of that overall process. The disclosed techniques relate to training a machine learning system on a given series of forms such that when provided future filled-in forms within that series, the system is able to extract the portions of the filled-in form that are important/relevant.

Abstract: A technique making use of a few-shot model to determine graphical features present in an image based on a small set of examples with known graphical features. Where a support set including a number of images that each have a known combination of graphical features, the image recognition can identify unknown combinations of those graphical features in any number of query images. In an embodiment of the present disclosure examples of a filled-out form are used to interpret any number of additional filled out versions of the form.

Abstract: A technique making use of a few-shot model to determine graphical features present in an image based on a small set of examples with known graphical features. Where a support set including a number of images that each have a known combination of graphical features, the image recognition can identify unknown combinations of those graphical features in any number of query images. In an embodiment of the present disclosure examples of a filled-out form are used to interpret any number of additional filled out versions of the form.

Abstract: Methods, apparatuses, and embodiments related to analyzing the content of digital images. A computer extracts multiple sets of visual features, which can be keypoints, based on an image of a selected object. Each of the multiple sets of visual features is extracted by a different visual feature extractor. The computer further extracts a visual word count vector based on the image of the selected object. An image query is executed based on the extracted visual features and the extracted visual word count vector to identify one or more candidate template objects of which the selected object may be an instance. When multiple candidate template objects are identified, a matching algorithm compares the selected object with the candidate template objects to determine a particular candidate template of which the selected object is an instance.

Abstract: A technique making use of a few-shot model to determine whether a query text content belongs to a same language as a small set of examples, or alternatively provide a next member in the same language to the small set of examples. The related few-shot model makes use of convolutional models that are trained in a “learning-to-learn” fashion such that the models know how to evaluate few-shots that belong to the same language. The term “language” in this usage is broader than spoken languages (e.g., English, Spanish, German, etc.). “Language” refers to a category, or data domain, of expression through characters. Belonging to a given language is not specifically based on what the language is, but the customs or traits expressed in that language.

Abstract: Introduced here is a machine learning related technique for supplying an observed model additional training data based upon previously received training data. To determine textual content of a character string based on a digital image that includes a handwritten version of the character string a substantial amount of training data is used. The character string can include one or more characters, and the characters can include any of letters, numerals, punctuation marks, symbols, spaces, etc. Disclosed herein is a technique to determine variations between different images of matching known character strings and substitute those variations into the images in order to create more images with the same known character string.

Abstract: A method comprising receiving a candidate image, including a first field, a second field, a first data element, and a second data element. The processor receives a definition of first and second form zones. The processor evaluates the first data element to determine a first measure of a similarity and the second data element to determine a second measure of a similarity. The processor computes a similarity value indicative of an overall similarity, iteratively compares the overall similarity to a previous iteration, modifies the positions of the form zones, and repeats the evaluating and computing based on the modified positions until the computed similarity value meets a threshold.

Abstract: A system for segregating handwritten information from typographic information on a document may include a memory, an interface, and a processor. The memory stores an electronic document image of a document where the electronic document image includes pixels and each pixel has a characteristic. The processor may receive, via the interface, the electronic document image and may identify first, second and third most frequently occurring characteristics of the pixels of the electronic document image. The pixels having the first most frequently occurring characteristic represent a background of the document. The processor may determine the typographic information of the document as represented by pixels having the second most frequently occurring characteristic. The processor may determine the handwritten information of the document as represented by pixels having the third most frequently occurring characteristic.

Abstract: A system and methods for progressive feature evaluation of an electronic document image to identify user supplied elements is disclosed. The system includes a controller in communication with a storage device configured to receive and accessibly store a generated plurality of candidate images. The controller is operable to analyze the electronic document image to identify a first feature set and a second feature set, wherein each of the first and second feature sets represent a different form feature, compare the first feature set to the second feature set, and define a third feature set based on the intersection of the first and second feature sets, wherein the third feature sets represents the user provided elements.

Abstract: A system for providing a managed virtual point to point communication service having a verified directory and providing secure transmission and controlled delivery of electronic document images may include a memory, an interface, and a processor. The memory may store a verified directory of users. The interface may communicate with devices of sending and receiving users in the verified directory. The processor may be operative to receive a request to deliver an electronic document image from a sending user to a receiving user. The processor may provide secure access to the electronic document image to the receiving user. The processor may provide a delivery confirmation to the sending device of the sending user upon determining that the electronic document image was securely accessed by the receiving user. The delivery confirmation may indicate that the electronic document image was securely transmitted to the receiving user.

Abstract: A system for extracting data from a document may include a memory, an interface, and a processor. The processor may identify one or more landmarks based on predefined characteristics that are associated with known landmarks. After one or more landmarks are defined, business rules may indicate one or more areas that may contain data to be extracted. The business rules may also indicate the type of data in the area and processing methods that may be used to extract the data. After determining the business rules, the processor may determine whether the data is in the area and/or extract the data.

Abstract: A system for contextualizing machine indeterminable information based on machine determinable information may include a memory, an interface, and a processor. The memory may store an electronic document image which may include information determinable by a machine and information indeterminable by a machine. The processor may be operative to receive, via the interface, the electronic document image. The processor may determine the machine determinable information of the electronic document image and may identify the machine indeterminable information of the electronic document image. The processor may contextualize the machine indeterminable information based on the machine determinable information. The processor may present the contextualized machine indeterminable information to the user to facilitate interpretation thereof. In response thereto, the processor may receive, via the interface, data representative of a user determination associated with the machine indeterminable information.

Abstract: A system for contextualizing machine indeterminable information based on machine determinable information may include a memory, an interface, and a processor. The memory may store an electronic document image which may include information determinable by a machine and information indeterminable by a machine. The processor may be operative to receive, via the interface, the electronic document image. The processor may determine the machine determinable information of the electronic document image and may identify the machine indeterminable information of the electronic document image. The processor may contextualize the machine indeterminable information based on the machine determinable information. The processor may present the contextualized machine indeterminable information to the user to facilitate interpretation thereof. In response thereto, the processor may receive, via the interface, data representative of a user determination associated with the machine indeterminable information.

Abstract: A system and methods for progressive feature evaluation of an electronic document image to identify user supplied elements is disclosed. The system includes a controller in communication with a storage device configured to receive and accessibly store a generated plurality of candidate images. The controller is operable to analyze the electronic document image to identify a first feature set and a second feature set, wherein each of the first and second feature sets represents a different form feature, compare the first feature set to the second feature set, and define a third feature set based on the intersection of the first and second feature sets, wherein the third feature set represents the user provided elements.

Abstract: A social networking system for deployment within an enterprise and a method for sharing data among a plurality of users of the enterprise-deployed social networking system are provided. The social networking system provides a database including a plurality of data records. Each data record of the plurality of data records includes data relating to one user of the plurality of users. A permission matrix defining, for each user of the plurality of users, first permissions and second permission is also provided. Upon receipt of a request for a first data record of the plurality of data records, a source of the request is identified, and a portion of the first data record accessible to the identified source of the request is determined using the permission matrix. The portion of the first data record is transmitted to the identified source based on the determination.

Abstract: Candidate identification utilizing fingerprint identification is disclosed.