Apparatus and method for data ingestion for user specific outputs of one or more machine learning models

An apparatus for data ingestion and manipulation, the apparatus including at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive a resource data file from one or more data acquisition systems, classify the resource data file to one or more educational categorizations, generate an educational module as a function of the resource data file and the classification of the educational categorizations wherein the education module comprises one or more machine learning models, retrieve a user profile of a plurality of user profiles as a function of a user input, create user-specific outputs as a function of the educational module, the user profile, and a conversational input and generate a virtual avatar model as a function of the user specific outputs.

FIELD OF THE INVENTION

The present invention generally relates to the field of machine learning. In particular, the present invention is directed to data ingestion for user specific outputs of one or more machine learning models.

BACKGROUND

Current machine learning systems lack the proper structure to output accurate and dependable educational material. In addition, systems that can output some level of accuracy lack the capabilities to generate user specific outputs.

SUMMARY OF THE DISCLOSURE

In an aspect an apparatus for data ingestion and manipulation is described. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory contains instructions configuring the at least a processor to receive resource data file from one or more data acquisition systems, classify the resource data file to one or more educational categorizations, generate an educational module as a function of the resource data file and the classification of the educational categorizations wherein the educational module includes one or more machine learning models, retrieve a user profile of a plurality of user profiles as a function of a user input, create user-specific outputs as a function of the educational module, the user profile, and a conversational input and generate a virtual avatar model as a function of the user specific outputs.

In another aspect a method for data ingestion and manipulation is described. The method includes receiving, by at least a processor, resource data file from one or more data acquisition systems, classifying, by the at least a processor, the resource data file to one or more educational categorizations, generating by the at least a processor, an educational module as a function of the resource data file and the classification of the educational categorizations wherein the educational module includes one or more machine learning models, retrieving by the at least a processor, a user profile of a plurality of user profiles as a function of a user input, creating by the at least a processor, user-specific outputs as a function of the educational module, the user profile, and a conversational input; and generating, by the at least a processor, a virtual avatar model as a function of the user specific outputs.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed apparatuses and method for data manipulation for user specific outputs of one or more machine learning models. In one or more embodiments, apparatus includes one or more data acquisition systems used to retrieve resource data file and generate educational materials. In one or more embodiments, apparatus may receive user input in order to generate user specific outputs.

Aspects of the present disclosure can be used to generate educational material. Aspects of the present disclosure can also be used to determine the source and accuracy of the educational material. Aspects of the present disclosure can further be used to generate user specific outputs.

Aspects of the present disclosure allow for the generation of user specific outputs. This may be done through the receipt of educational materials and information associated with each specific user. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples.′

Referring now toFIG.1, in an embodiment, apparatus100and methods described herein may perform or implement one or more aspects of a cryptographic system. In one embodiment, a cryptographic system is a system that converts data from a first form, known as “plaintext,” which is intelligible when viewed in its intended format, into a second form, known as “ciphertext,” which is not intelligible when viewed in the same way. Ciphertext may be unintelligible in any format unless first converted back to plaintext. In one embodiment, a process of converting plaintext into ciphertext is known as “encryption.” Encryption process may involve the use of a datum, known as an “encryption key,” to alter plaintext. Cryptographic system may also convert ciphertext back into plaintext, which is a process known as “decryption.” Decryption process may involve the use of a datum, known as a “decryption key,” to return the ciphertext to its original plaintext form. In embodiments of cryptographic systems that are “symmetric,” decryption key is essentially the same as encryption key: possession of either key makes it possible to deduce the other key quickly without further secret knowledge. Encryption and decryption keys in symmetric cryptographic systems may be kept secret and shared only with persons or entities that the user of the cryptographic system wishes to be able to decrypt the ciphertext. One example of a symmetric cryptographic system is the Advanced Encryption Standard (“AES”), which arranges plaintext into matrices and then modifies the matrices through repeated permutations and arithmetic operations with an encryption key.

With continued reference toFIG.1, in embodiments of cryptographic systems that are “asymmetric,” either encryption or decryption key cannot be readily deduced without additional secret knowledge, even given the possession of a corresponding decryption or encryption key, respectively; a common example is a “public key cryptographic system,” in which possession of the encryption key does not make it practically feasible to deduce the decryption key, so that the encryption key may safely be made available to the public. An example of a public key cryptographic system is RSA, in which an encryption key involves the use of numbers that are products of very large prime numbers, but a decryption key involves the use of those very large prime numbers, such that deducing the decryption key from the encryption key requires the practically infeasible task of computing the prime factors of a number which is the product of two very large prime numbers. Another example is elliptic curve cryptography, which relies on the fact that given two points P and Q on an elliptic curve over a finite field, and a definition for addition where A+B=−R, the point where a line connecting point A and point B intersects the elliptic curve, where “0,” the identity, is a point at infinity in a projective plane containing the elliptic curve, finding a number k such that adding P to itself k times results in Q is computationally impractical, given correctly selected elliptic curve, finite field, and P and Q.

With continued reference toFIG.1, in some embodiments, apparatus100and methods described herein produce cryptographic hashes, also referred to by the equivalent shorthand term “hashes.” A cryptographic hash, as used herein, is a mathematical representation of a lot of data, such as files or blocks in a block chain as described in further detail below; the mathematical representation is produced by a lossy “one-way” algorithm known as a “hashing algorithm.” Hashing algorithm may be a repeatable process; that is, identical lots of data may produce identical hashes each time they are subjected to a particular hashing algorithm. Because hashing algorithm is a one-way function, it may be impossible to reconstruct a lot of data from a hash produced from the lot of data using the hashing algorithm. In the case of some hashing algorithms, reconstructing the full lot of data from the corresponding hash using a partial set of data from the full lot of data may be possible only by repeatedly guessing at the remaining data and repeating the hashing algorithm; it is thus computationally difficult if not infeasible for a single computer to produce the lot of data, as the statistical likelihood of correctly guessing the missing data may be extremely low. However, the statistical likelihood of a computer of a set of computers simultaneously attempting to guess the missing data within a useful timeframe may be higher, permitting mining protocols as described in further detail below.

Still referring toFIG.1, in an embodiment, hashing algorithm may demonstrate an “avalanche effect,” whereby even extremely small changes to lot of data produce drastically different hashes. This may thwart attempts to avoid the computational work necessary to recreate a hash by simply inserting a fraudulent datum in data lot, enabling the use of hashing algorithms for “tamper-proofing” data such as data contained in an immutable ledger as described in further detail below. This avalanche or “cascade” effect may be evinced by various hashing processes; persons skilled in the art, upon reading the entirety of this disclosure, will be aware of various suitable hashing algorithms for purposes described herein. Verification of a hash corresponding to a lot of data may be performed by running the lot of data through a hashing algorithm used to produce the hash. Such verification may be computationally expensive, albeit feasible, potentially adding up to significant processing delays where repeated hashing, or hashing of large quantities of data, is required, for instance as described in further detail below. Examples of hashing programs include, without limitation, SHA256, a NIST standard; further current and past hashing algorithms include Winternitz hashing algorithms, various generations of Secure Hash Algorithm (including “SHA-1,” “SHA-2,” and “SHA-3”), “Message Digest” family hashes such as “MD4,” “MD5,” “MD6,” and “RIPEMD,” Keccak, “BLAKE” hashes and progeny (e.g., “BLAKE2,” “BLAKE-256,” “BLAKE-512,” and the like), Message Authentication Code (“MAC”)-family hash functions such as PMAC, OMAC, VMAC, HMAC, and UMAC, Poly 1305-AES, Elliptic Curve Only Hash (“ECOH”) and similar hash functions, Fast-Syndrome-based (FSB) hash functions, GOST hash functions, the Grøstl hash function, the HAS-160 hash function, the JH hash function, the RadioGatun hash function, the Skein hash function, the Streebog hash function, the SWIFFT hash function, the Tiger hash function, the Whirlpool hash function, or any hash function that satisfies, at the time of implementation, the requirements that a cryptographic hash be deterministic, infeasible to reverse-hash, infeasible to find collisions, and have the property that small changes to an original message to be hashed will change the resulting hash so extensively that the original hash and the new hash appear uncorrelated to each other. A degree of security of a hash function in practice may depend both on the hash function itself and on characteristics of the message and/or digest used in the hash function. For example, where a message is random, for a hash function that fulfills collision-resistance requirements, a brute-force or “birthday attack” may to detect collision may be on the order of O(2n/2) for n output bits; thus, it may take on the order of 2256operations to locate a collision in a 512 bit output “Dictionary” attacks on hashes likely to have been generated from a non-random original text can have a lower computational complexity, because the space of entries they are guessing is far smaller than the space containing all random permutations of bits. However, the space of possible messages may be augmented by increasing the length or potential length of a possible message, or by implementing a protocol whereby one or more randomly selected strings or sets of data are added to the message, rendering a dictionary attack significantly less effective.

With continued reference toFIG.1, embodiments described in this disclosure may perform secure proofs. A “secure proof,” as used in this disclosure, is a protocol whereby an output is generated that demonstrates possession of a secret, such as device-specific secret, without demonstrating the entirety of the device-specific secret; in other words, a secure proof by itself, is insufficient to reconstruct the entire device-specific secret, enabling the production of at least another secure proof using at least a device-specific secret. A secure proof may be referred to as a “proof of possession” or “proof of knowledge” of a secret. Where at least a device-specific secret is a plurality of secrets, such as a plurality of challenge-response pairs, a secure proof may include an output that reveals the entirety of one of the plurality of secrets, but not all of the plurality of secrets; for instance, secure proof may be a response contained in one challenge-response pair. In an embodiment, proof may not be secure; in other words, proof may include a one-time revelation of at least a device-specific secret, for instance as used in a single challenge-response exchange.

Still referring toFIG.1, secure proof may include a zero-knowledge proof, which may provide an output demonstrating possession of a secret while revealing none of the secret to a recipient of the output; zero-knowledge proof may be information-theoretically secure, meaning that an entity with infinite computing power would be unable to determine secret from output. Alternatively, zero-knowledge proof may be computationally secure, meaning that determination of secret from output is computationally infeasible, for instance to the same extent that determination of a private key from a public key in a public key cryptographic system is computationally infeasible. Zero-knowledge proof algorithms may generally include a set of two algorithms, a prover algorithm, or “P,” which is used to prove computational integrity and/or possession of a secret, and a verifier algorithm, or “V” whereby a party may check the validity of P. Zero-knowledge proof may include an interactive zero-knowledge proof, wherein a party verifying the proof must directly interact with the proving party; for instance, the verifying and proving parties may be required to be online, or connected to the same network as each other, at the same time. Interactive zero-knowledge proof may include a “proof of knowledge” proof, such as a Schnorr algorithm for proof on knowledge of a discrete logarithm. in a Schnorr algorithm, a prover commits to a randomness r, generates a message based on r, and generates a message adding r to a challenge c multiplied by a discrete logarithm that the prover is able to calculate; verification is performed by the verifier who produced c by exponentiation, thus checking the validity of the discrete logarithm. Interactive zero-knowledge proofs may alternatively or additionally include sigma protocols. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various alternative interactive zero-knowledge proofs that may be implemented consistently with this disclosure.

Alternatively, and continuing to refer toFIG.1, zero-knowledge proof may include a non-interactive zero-knowledge, proof, or a proof wherein neither party to the proof interacts with the other party to the proof; for instance, each of a party receiving the proof and a party providing the proof may receive a reference datum which the party providing the proof may modify or otherwise use to perform the proof. As a non-limiting example, zero-knowledge proof may include a succinct non-interactive arguments of knowledge (ZK-SNARKS) proof, wherein a “trusted setup” process creates proof and verification keys using secret (and subsequently discarded) information encoded using a public key cryptographic system, a prover runs a proving algorithm using the proving key and secret information available to the prover, and a verifier checks the proof using the verification key; public key cryptographic system may include RSA, elliptic curve cryptography, ElGamal, or any other suitable public key cryptographic system. Generation of trusted setup may be performed using a secure multiparty computation so that no one party has control of the totality of the secret information used in the trusted setup; as a result, if any one party generating the trusted setup is trustworthy, the secret information may be unrecoverable by malicious parties. As another non-limiting example, non-interactive zero-knowledge proof may include a Succinct Transparent Arguments of Knowledge (ZK-STARKS) zero-knowledge proof. In an embodiment, a ZK-STARKS proof includes a Merkle root of a Merkle tree representing evaluation of a secret computation at some number of points, which may be 1 billion points, plus Merkle branches representing evaluations at a set of randomly selected points of the number of points; verification may include determining that Merkle branches provided match the Merkle root, and that point verifications at those branches represent valid values, where validity is shown by demonstrating that all values belong to the same polynomial created by transforming the secret computation. In an embodiment, ZK-STARKS does not require a trusted setup.

Further referring toFIG.1, zero-knowledge proof may include any other suitable zero-knowledge proof. Zero-knowledge proof may include, without limitation, bulletproofs. Zero-knowledge proof may include a homomorphic public-key cryptography (hPKC)-based proof. Zero-knowledge proof may include a discrete logarithmic problem (DLP) proof. Zero-knowledge proof may include a secure multi-party computation (MPC) proof. Zero-knowledge proof may include, without limitation, an incrementally verifiable computation (IVC). Zero-knowledge proof may include an interactive oracle proof (IOP). Zero-knowledge proof may include a proof based on the probabilistically checkable proof (PCP) theorem, including a linear PCP (LPCP) proof. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various forms of zero-knowledge proofs that may be used, singly or in combination, consistently with this disclosure.

With continued reference toFIG.1, in an embodiment, secure proof is implemented using a challenge-response protocol. In an embodiment, this may function as a one-time pad implementation; for instance, a manufacturer or other trusted party may record a series of outputs (“responses”) produced by a device possessing secret information, given a series of corresponding inputs (“challenges”), and store them securely. In an embodiment, a challenge-response protocol may be combined with key generation. A single key may be used in one or more digital signatures as described in further detail below, such as signatures used to receive and/or transfer possession of crypto-currency assets; the key may be discarded for future use after a set period of time. In an embodiment, varied inputs include variations in local physical parameters, such as fluctuations in local electromagnetic fields, radiation, temperature, and the like, such that an almost limitless variety of private keys may be so generated. Secure proof may include encryption of a challenge to produce the response, indicating possession of a secret key. Encryption may be performed using a private key of a public key cryptographic system or using a private key of a symmetric cryptographic system; for instance, trusted party may verify response by decrypting an encryption of challenge or of another datum using either a symmetric or public-key cryptographic system, verifying that a stored key matches the key used for encryption as a function of at least a device-specific secret. Keys may be generated by random variation in selection of prime numbers, for instance for the purposes of a cryptographic system such as RSA that relies prime factoring difficulty. Keys may be generated by randomized selection of parameters for a seed in a cryptographic system, such as elliptic curve cryptography, which is generated from a seed. Keys may be used to generate exponents for a cryptographic system such as Diffie-Helman or ElGamal that are based on the discrete logarithm problem.

With continued reference toFIG.1, embodiments described in this disclosure may utilize, evaluate, and/or generate digital signatures. A “digital signature,” as used herein, includes a secure proof of possession of a secret by a signing device, as performed on provided element of data, known as a “message.” A message may include an encrypted mathematical representation of a file or other set of data using the private key of a public key cryptographic system. Secure proof may include any form of secure proof as described above, including without limitation encryption using a private key of a public key cryptographic system as described above. Signature may be verified using a verification datum suitable for verification of a secure proof; for instance, where secure proof is enacted by encrypting message using a private key of a public key cryptographic system, verification may include decrypting the encrypted message using the corresponding public key and comparing the decrypted representation to a purported match that was not encrypted; if the signature protocol is well-designed and implemented correctly, this means the ability to create the digital signature is equivalent to possession of the private decryption key and/or device-specific secret. Likewise, if a message making up a mathematical representation of file is well-designed and implemented correctly, any alteration of the file may result in a mismatch with the digital signature; the mathematical representation may be produced using an alteration-sensitive, reliably reproducible algorithm, such as a hashing algorithm as described above. A mathematical representation to which the signature may be compared may be included with signature, for verification purposes; in other embodiments, the algorithm used to produce the mathematical representation may be publicly available, permitting the easy reproduction of the mathematical representation corresponding to any file.

With continued reference toFIG.1, in some embodiments, digital signatures may be combined with or incorporated in digital certificates. In one embodiment, a digital certificate is a file that conveys information and links the conveyed information to a “certificate authority” that is the issuer of a public key in a public key cryptographic system. Certificate authority in some embodiments contains data conveying the certificate authority's authorization for the recipient to perform a task. The authorization may be the authorization to access a given datum. The authorization may be the authorization to access a given process. In some embodiments, the certificate may identify the certificate authority. The digital certificate may include a digital signature.

With continued reference toFIG.1, in some embodiments, a third party such as a certificate authority (CA) is available to verify that the possessor of the private key is a particular entity; thus, if the certificate authority may be trusted, and the private key has not been stolen, the ability of an entity to produce a digital signature confirms the identity of the entity and links the file to the entity in a verifiable way. Digital signature may be incorporated in a digital certificate, which is a document authenticating the entity possessing the private key by authority of the issuing certificate authority and signed with a digital signature created with that private key and a mathematical representation of the remainder of the certificate. In other embodiments, digital signature is verified by comparing the digital signature to one known to have been created by the entity that purportedly signed the digital signature; for instance, if the public key that decrypts the known signature also decrypts the digital signature, the digital signature may be considered verified. Digital signature may also be used to verify that the file has not been altered since the formation of the digital signature.

With continued reference toFIG.1, computing device104may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine-learning processes. A “machine-learning process,” as used in this disclosure, is a process that automatedly uses a body of data known as “training data” and/or a “training set” (described further below in this disclosure) to generate an algorithm that will be performed by a Processor module to produce outputs given data provided as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. A machine-learning process may utilize supervised, unsupervised, lazy-learning processes and/or neural networks, described further below.

Still referring toFIG.1, apparatus100may include a database116. Database116may 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 database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Database may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. Database116may include a plurality of data entries and/or records as described above. Data entries in 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 database may store, retrieve, organize, and/or reflect data and/or records.

With continued reference toFIG.1, processor108is configured to receive resource data file120from one or more data acquisition systems124. “Resource data file” for the purposes of this disclosure is any information that can be used to gain an educational understanding of one or more topics. For example, resource data file120may include a digital book such as a biology textbook, a mathematics textbook, a history textbook and the like wherein information contained within the textbooks may be used to gain an educational understanding of their respective topics. In one or more embodiments, resource data file120may include a plurality information such as but not limited to, information associated with biology, information associated with medicine, information associated with law, information associated with history, information associated with patents, and/or any other information that may be used to gain an educational understanding of one or more topics. In one or more embodiments, resource data file120may include information which may be used to generate a teaching curriculum. “Teaching curriculum” for the purposes of this disclosure is a guide on how a particular set of information will be taught. For example, teaching curriculum may include a set of questions and answers on a topic such as mathematics. In some cases, teaching curriculum may include information in an organized manner wherein a first set of information is first introduced and/or taught, then a second set of information is introduced and/or taught. In one or more embodiments, teaching curriculum may include data visualized in various formats, such as but not limited to, quest and answer, multiple choice, digital flash cards, true and false and the like. In one or more embodiments, a teaching curriculum may include objectives, assignments, due dates associated with assignments and the like.

With continued reference toFIG.1, in one or more embodiments, resource data file120may include metadata128. “Metadata” for the purposes of this disclosure is information relating to the source of one or elements within resource data file120. For example, resource data file120containing information about biology may contain metadata128indicating that the source of the biology information was retrieved from a biology textbook. In one or more embodiments, metadata128may include the identifying source of the information. The identifying source of the information may include, but is not limited to, the website the information was retrieved from, the server and/or database116the information was retrieved from, the author who generated the information, the name of the textbook (if any), the publishers (if any), the date the information was published and/or released, the sources cited within the information, the database116the information was retrieved from, the website in which the information was retrieved, information associated with the particular individual who uploaded the information and the like. In one or more embodiments, metadata128may include one or more options to retrieve the original source of the information within resource data file120. This may include but is not limited to a hyperlink or weblink that can direct an individual to the source of the information, and/or information allowing a user to be directed to the source of the information. In one or more embodiments, resource data file120may be stored on an immutable sequential listing140wherein the resource data file120cannot be tampered with. In one or more embodiments, metadata128may include the source of the resource data file120on the immutable sequential listing140. In one or more embodiments, location of resource data file120on an immutable sequential listing140may allow for preservation of the original information contained within resource data file120.

With continued reference toFIG.1, resource data file120may include user input information. “User input information” for the purposes of this disclosure is educational information that is input by a user. “User” for the purposes of this disclosure is an individual who will be interacting and/or using apparatus100. User may include a student, a teacher and/or any other individual interested in obtaining educational information. “User input” for the purposes of this disclosure is any information received by computing device104from a user through one or more input devices. In one or more embodiments, user input information may include notes a user may have taken during an educational course, useful documents that a user had on their possession and/or any other educational information that has been input by a user. In one or more embodiments, user input information may be received by a remote device, such as but not limited to, a smartphone, a laptop, a desktop computer and/or any other computing systems.

In one or more embodiments, resource data file120may be retrieved from one or more data acquisition systems124. “Data acquisition system” for the purposes of this disclosure is software or an algorithm that is used to gather data from various sources. For example, data acquisition system124may include a web crawler136. A “web crawler,” as used herein, is a program that systematically browses the internet for the purpose of web indexing. Web crawler136may be seeded with platform URLs, wherein the crawler may then visit the next related URL, retrieve the content, index the content, and/or measures the relevance of the content to the topic of interest. In some embodiments, computing device104may generate a web crawler136to compile resource data file120and/or elements thereof. The web crawler136may be seeded and/or trained with a reputable website, such as educational websites. Web crawler136may be generated by computing device104. In some embodiments, the web crawler136may be trained with information received from a user through a user interface. In some embodiments, the web crawler136may be configured to generate a web query. A web query may include search criteria received from a user. For example, a user may submit a plurality of websites for the web crawler136to search to extract any data suitable for resource data file120. In one or more embodiments, data acquisition system124may include one or more data scrapers configured to use application program interfaces (API) for data retrieval. APIs allow applications to communicate with one another. In one or more embodiments, APIs may be used for data retrieval wherein the API facilitates data retrieval. In one or more embodiments, data acquisition system124may include a data scraper. A “data scraper” for the purpose of this disclosure is a system that extracts data from one or more websites or databases116. In contrast to a WebCrawler, a data scraper may be used to capture specific data sets whereas a Web crawler136may be used to capture entire webpages. In one or more embodiments, data acquisition system124may include an RSS (really simple syndicate) aggregator. “RSS aggregator” for the purposes of this disclosure is a system that is configured to systematically gather data from RSS websites. The RSS aggregator may be configured to retrieve data and store it on database116. In one or more embodiments, the RSS aggregator may be configured to retrieve resource data file120and/or any information associated with resource data file120. In one or more embodiments, data acquisition system124may include a data scraper configured to retrieve any data stored on database116. In one or more embodiments, any information input by user and/or any other individuals may be stored on database116and retrieved by data scraper. In one or more embodiments, data acquisition system124may include a chatbot system. A “chatbot system” for the purposes of this disclosure is a program configured to simulate human interaction with a user in order to receive or convey information. In some cases, chatbot system may be configured to receive resource data file120, elements thereof and any other data as described in this disclosure through interactive questions presented to the user.

With continued reference toFIG.1, in one or more embodiments, data acquisition system124may be configured to retrieve resource data file120. In one or more embodiments, data acquisition system124may be configured to retrieve metadata128associated with resource data file120.

With continued reference toFIG.1, data acquisition system124may include an optical character reader. In one or more embodiments, a user, a 3rd party, an educational institution, an educator and the like may input one or more data files into computing device104wherein the one or more data files may be converted to machine readable text. For example, a user may input digital records and/or scanned physical documents that have been converted to digital documents, wherein data set120may include data that have bene converted into machine readable text. In some embodiments, optical character recognition or optical character reader (OCR) includes automatic conversion of images of written (e.g., typed, handwritten, or printed text) 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 process 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 toFIGS.4-6. 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.4,5, and6.

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, in one or more embodiments, processor108is configured to classify the resource data file120to one or more educational categorizations144. “Educational categorization,” for the purposes of this disclosure, is a grouping of information that is associated with the same or similar educational topic. For example, educational categorization144may include medicine, history, and the like. In one or more embodiments, educational categorizations144may include but are not limited to, technology, history, medicine, law, orthodontics, surgery, mathematics, accounting, English literature, English grammar, languages, engineering and the like. In one or more embodiments, each educational categorization144may include a subcategorization, wherein the subcategorization is a specific category within the education categorization. For example, educational categorization144of law may include subcategorizations such as, but not limited to, patent law, criminal law, product liability, torts, medical malpractice and the like. Similarly, an educational categorization144of medicine may include subcategorizations such as medications, surgery, orthopedics, dentistry, chemotherapy and the like. In one or more embodiments, sub categorizations may be used to categorize resource data file120into smaller data sets that may allow for quicker processing of data. In one or more embodiments, computing device104may be configured to generate subcategorizations in situations wherein data classified to educational categorization144reaches a data threshold. “Data threshold” for the purposes of this disclosure is a predetermined threshold of a storage size of a data set. In one or more embodiments, data sets containing a larger storage size than data threshold may require longer processing due to the vast amount of data that needs to be processed. In one or more embodiments, computing device104may be configured to generate additional subcategorizations when data classified to a particular educational categorization144reaches data threshold. In one or more embodiments, subcategorization may allow for the generation of smaller data sets that may be used for processing.

With continued reference toFIG.1, a “classifier,” as used in this disclosure is a machine-learning model, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. Classifiers as described throughout this disclosure may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. In some cases, processor108may generate and train an educational classifier configured to receive resource data file120and output one or more educational categorizations144. Processor108and/or another device may generate a classifier using a classification algorithm, defined as a process whereby a computing device104derives a classifier from training data. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. An educational classifier may be trained with training data correlating resource data file120to educational categorizations144. Training data may include a plurality resource data file120correlated to a plurality of educational categorizations144. In an embodiment, training data may be used to show that a particular element within resource data file120may be correlated to a particular educational categorization144. Training data may be received from an external computing device104, input by a user, and/or previous iterations of processing. An educational classifier may be configured to receive as input and categorize components of resource data file120to one or more educational categorizations144. In some cases, processor108and/or computing device104may then select any elements resource data file120containing a similar label and/or grouping and group them together. In some cases, resource data file120may be classified using a classifier machine learning model. In some cases classifier machine learning model may be trained using training data correlating a plurality of resource data file120correlated to a plurality of educational categorizations144. In an embodiment, a particular element within resource data file120may be correlated to a particular educational categorization144. In some cases, classifying resource data file120may include classifying resource data file120as a function of the classifier machine learning model. In some cases, classifier training data may be generated through input by a user. In some cases, classifier machine learning model may be trained through user feedback wherein a user may indicate whether a particular element corresponds to a particular class. In some cases, classifier machine learning model may be trained using inputs and outputs based on previous iterations.

l=∑i=0nai2,
where aiis attribute number i of the vector. Scaling and/or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on 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.

With continued reference toFIG.1, processor108is configured to generate an educational module148. “Educational module” for the purposes of this disclosure is a system that receives one or more inputs and generates outputs containing educational material that are specific to a user. For example, a first user requesting information associated with biology may receive differing outputs than a second user requesting similar information. In one or more embodiments, educational module148may utilize one or more machine learning processes, one or more classification processes and the like in order to generate outputs. In one or more embodiments, machine learning models, machine learning modules, and/or any other systems or algorithms within educational module148may be used interchangeably and/or in differing orders. For example, educational module148may utilize a first machine learning processes first and then a second machine learning process second. However, educational module148may also utilize the second machine learning process first and the first machine learning process second.

With continued reference toFIG.1, processor108is configured to feed resource data file120into educational module148. In one or more embodiments, resource data file120may be used to generate training data for one or more processes within educational module148. In one or more embodiments, training data may be generated based on each educational categorization144. For example, a first training data set may be generated based on data classified to a medicine categorization, a second training data set may be generated based on data classified to a mathematics categorization and the like.

With continued reference toFIG.1, educational module148may include an educational machine learning model152. In one or more embodiments, processor108is configured to generate an educational machine learning model152as a function of the resource data file120. “Educational machine learning model” for the purposes of this disclosure is a machine learning model that is configured to receive an input and output corresponding educational information associated with the input. For example, the input may include a prompt such as “generate sample test questions related to biology” wherein an output may contain questions and answers associated with biology. In an embodiment, educational machine learning model152may be used to receive educational outputs164.

In one or more embodiments, processor108may use a machine learning module, such as an educational machine learning module for the purposes of this disclosure, to implement one or more algorithms or generate one or more machine-learning models, such as educational machine learning model152, to generate outputs. However, the machine learning module is exemplary and may not be necessary to generate one or more machine learning models and perform any machine learning described herein. In one or more embodiments, one or more machine-learning models may be generated using training data. Training data may include inputs and corresponding predetermined outputs so that a machine-learning model may use correlations between the provided exemplary inputs and outputs to develop an algorithm and/or relationship that then allows machine-learning model to determine its own outputs for inputs. Training data may contain correlations that a machine-learning process may use to model relationships between two or more categories of data elements. Exemplary inputs and outputs may come from database116, such as any database116described in this disclosure, or be provided by a user. In other embodiments, a machine-learning module may obtain a training set by querying a communicatively connected database116that includes past inputs and outputs. Training data may include inputs from various types of databases116, resources, and/or user inputs132and outputs correlated to each of those inputs so that a machine-learning model may determine an output. Correlations may indicate causative and/or predictive links between data, which may be modeled as relationships, such as mathematical relationships, by machine-learning models, as described in further detail below. In one or more embodiments, training data may be formatted and/or organized by categories of data elements by, for example, associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data may be linked to categories by tags, tokens, or other data elements. A machine learning module, such as educational machine learning module may be used to create educational machine learning model152and/or any other machine learning model using training data. Educational machine learning model152may be trained by correlated inputs and outputs of training data. Training data may be data sets that have already been converted from raw data whether manually, by machine, or any other method. Educational training data156may be stored in database116. Educational training data156may also be retrieved from database116. In some cases educational machine learning model152may be trained iteratively using previous inputs correlated to previous outputs. For example, processor108may be configured to store outputs from a current iteration and to train the machine learning model. In some cases, the machine learning model may be trained based on user input132. For example, a user may indicate that information that has been output is inaccurate wherein the machine learning model may be trained as a function of the user input132. In some cases, the machine learning model may allow for improvements to computing device104such as but not limited to improvements relating to comparing data items, the ability to sort efficiently, an increase in accuracy of analytical methods and the like.

With continued reference toFIG.1, in one or more embodiments, a machine-learning module may be generated using training data. Training data may include inputs and corresponding predetermined outputs so that machine-learning module may use the correlations between the provided exemplary inputs and outputs to develop an algorithm and/or relationship that then allows machine-learning module to determine its own outputs for inputs. Training data may contain correlations that a machine-learning process may use to model relationships between two or more categories of data elements. The exemplary inputs and outputs may come from database116or be provided by a user such as a prospective employee, and/or an employer and the like. In other embodiments, machine-learning module may obtain a training set by querying a communicatively connected database116that includes past inputs and outputs. Training data may include inputs from various types of databases116, resources, and/or user inputs132and outputs correlated to each of those inputs so that a machine-learning module may determine an output. Correlations may indicate causative and/or predictive links between data, which may be modeled as relationships, such as mathematical relationships, by machine-learning processes, as described in further detail below. In one or more embodiments, training data may be formatted and/or organized by categories of data elements by, for example, associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data may be linked to categories by tags, tokens, or other data elements.

With continued reference toFIG.1, educational machine learning model152may include training data containing a plurality of educational prompts160correlated to a plurality of educational outputs164. In one or more embodiments, educational outputs164may include elements of resource data file120. In one or more embodiments, processor108may use resource data file120to generate educational training data156. In one or more embodiments, educational prompts160may include inputs into the machine learning model. In one or more embodiments, educational prompts160may be received by a user, processor108and/or another computing device104, wherein one or more educational prompts160may be fed into educational machine learning model152in order to receive outputs such as educational output164. In one or more embodiments, educational training data156may include a plurality of educational prompts160correlated to a plurality of educational outputs164. In an embodiment, an input such as educational prompt160may be correlated to an element of resource data file120as indicated by educational output164. In one or more embodiments, processor108may classify educational prompts160to one or more educational categorizations144wherein outputs of educational machine learning model152may include outputs classified to the same educational categorization144. In one or more embodiments, resource data file120may be used to generate educational training data156and/or elements thereof. In one or more embodiments, educational training data156may be retrieved from a database116, generated by a third party, containing data from previous iterations and the like. In one or more embodiments, resource data file120may be used to append educational training data156wherein processor108may be configured to iteratively append educational training data156as a function of received resource data file120. In one or more embodiments, resource data file120may be continuously and/or systematically received wherein processor108may be configured to append resource data file120to educational training data156before and/or after each iteration of the processing. For example, processor108may be configured to retrieve resource data file120before and/or after each iteration of the processing by apparatus100wherein processor108may be configured to iteratively update educational training data156. In one or more embodiments, processor108may receive resource data file120and generate inputs and correlated outputs for educational training data156. In one or more embodiments, processor108may append resource data file120to educational training data156wherein educational training data is iteratively updated. In one or more embodiments processor may classify resource data file120to educational categorizations wherein the classified data may be used as outputs to similarly classified inputs. In one or more embodiments, training data may be generated based on classified inputs and classified outputs, wherein inputs classified to the same educational categorization may be correlated to outputs classified to the same educational categorization144. In one or more embodiments, processor108and/or educational module148may generate patterns within resource data file120to be used as training data. In one or more embodiments, processor108and/or educational module148may select a portion of resource data file120to be used as a validation set wherein the validation set may be used to evaluate educational machine learning model. In one or more embodiments the validation set may be used to determine how accurately education machine learning model may generate outputs. In one or more embodiments, processor108and/or educational module1487may use a test set to evaluate educational machine learning models accuracy after training. In one or more embodiments, educational training data156may be used to train education machine learning model. In one or more embodiments, educational outputs164, and/or alternatively user specific outputs168, may be generated as a function of education machine learning model.

With continued reference toFIG.1, educational training data156may be classified to one or more educational categorizations144, wherein each classified set of training data may be correlated to an educational categorization144. In one or more embodiments, educational module148and/or processor108may classify an educational prompt160to one or more education categorizations and select one or more classified training data corresponding to the educational categorization144. In one or more embodiments, educational machine learning model152and/or educational module may include a plurality of training data wherein each training data may be configured to generate a differing educational output164. For example, a first educational training data156set may be used to generate multiple choice questions and answers, wherein a second educational training data156set may be used to generate fill in the blank questions. In one or more embodiments, educational machine learning model152and/or educational module148may include a plurality of educational training data156sets wherein each data set may be correlated to a teaching curriculum categorization. In one or more embodiments, each training data set may include a plurality of educational prompts160correlated to a plurality of resource data file120that has been generated in a particular teaching curriculum format. For example, computing device104may select a particular training data set when a user requests multiple choice questions and answers, and another training data set when a user requests bullet points. In one or more embodiments, education training data may include a plurality educational prompt160and a plurality of resource data file120correlated to a plurality of educational outputs164. In an embodiment, educational training data156may be used to select elements of resource data file120and transform them into educational outputs164.

In one or more embodiments, educational machine learning model152may be used to generating teaching curriculum as described above. In one or more embodiments, educational machine learning model152may be configured to generate teaching curriculums as a function of resource data file120. In one or more embodiments, inputs of educational machine learning model152may include requests for specific sets of information and within a specific format wherein educational machine learning model152may be configured to output educational information in the desired format. For example, inputs may include requests to generate multiple choice questions wherein educational machine learning model152may be configured to output educational information in a multiple-choice format.

With continued reference toFIG.1, “educational output” for the purposes of this disclosure is a set of educational information that is associated with a request from educational prompt160. For example, an educational prompt160may indicate “generate questions and answers for medicine” wherein educational output164may include corresponding questions and answers associated with medicine. In one or more embodiments, educational outputs164may be generated in various teaching curriculum formats, such as, but not limited to question and answer, multiple choice, bullet points, true and false questions and/or any other format that may be used to facilitate the education of user. “Educational prompt” for the purposes of this disclosure is an input into educational machine learning model152requesting educational information in various teaching curriculum formats. For example, and without limitation, educational prompt160may include information indicating a particular educational categorization144to be received as well as a specific format.

With continued reference toFIG.1, educational output164may include metadata128. In one or more embodiments, educational output164may include elements of resource data file120wherein each element of resource data file120may include metadata128. “Element” for the purposes of this disclosure refers to a portion of an entire dataset. In one or more embodiments, educational machine learning model152may receive resource data file120and categorize them to one or more educational categorizations144, educational machine learning model152may then selection elements of resource data file120to be used as outputs of educational machine learning model152. In one or more embodiments, educational outputs164may include associated metadata128wherein the metadata128includes the source of the information within educational output164. In one or more embodiments, resource data file120may include a plurality of elements wherein each element contains associated metadata128. In one or more embodiments, educational machine learning model152may output educational outputs164wherein educational output164may contain one or more elements and one or more associated metadata128. In one or more embodiments, educational output164may include one or more elements stored on immutable sequential listing140as described herein.

With continued reference toFIG.1, educational machine learning model152may be configured to generate user specific outputs168. “User specific outputs” for the purposes of this disclosure are educational outputs164that are specific to a user based on information associated with the user. For example, two differing users may input the same information into educational module148and/or computing device104wherein educational module148may be configured to generate differing educational outputs164. In one or more embodiments, user specific output168may include educational output164that has been modified through one or more processes as described in this disclosure. In one or more embodiments, user specific output168may include an educational output164in which educational prompt160has been modified in order to create a differing educational output164. This will be described in further detail below.

With continued reference toFIG.1, in one or more embodiments, user specific outputs168may be generated as a function of user profile172. “User profile” for the purposes of this disclosure is information pertaining to a user and their interactions with apparatus100. In one or more embodiments, database116may be populated with a plurality of user profiles172wherein each user profile172is associated with a differing user. In one or more embodiments, user profile172may include but is not limited to, the age of the user, the geographical location of the user, the gender of the user and the like. In one or more embodiments, user profile172may include the educational background of a user, such as but not limited to, school attended, schools graduated, grades associated with the educational courses that the user attended, current educational courses the user is in, upcoming educational courses the user will be attending, previous exams taken, grades associated with previous exams taken, and the like. In one or more embodiments, user profile172may include information associated with previous interactions a user had with apparatus100. Previous interactions may include, but are not limited to, inputs made by the user, outputs generated by apparatus100as a function of user inputs132, and the like. In one or more embodiments, user profile172may include a dialect spoken by user. In one or more embodiments, user profile172may include words not understood by the user. In one or more embodiments, user profile172may include educational categorizations144that a user is proficient in, and/or educational categorizations144that a user is lacking. In one or more embodiments, user profile172may include preferences of teaching curriculum. For example, user profile172may include a preference to receive outputs in a question-and-answer format, outputs in a multiple-choice format and the like. In one or more embodiments, user profile172may include any data as indicated by user data in Non-provisional application Ser. No. 18/122,340 filed on Mar. 16, 2023 and entitled “APPARATUS AND METHOD FOR GENERATING AN EDUCATIONAL ACTION DATUM USING MACHINE-LEARNING” the entirety of which is incorporated herein by reference.

With continued reference toFIG.1, in some embodiments, user profile172may include virtual activity data pertaining to user. As used in this disclosure, “virtual activity data” is data related to one or more virtual actions, wherein the virtual action is an action performed by user in a virtual environment. As used in this disclosure, a “virtual environment” is a digital environment which allows users to interact with it and elements/devices thereof within the virtual environment digitally. In some embodiments, virtual environment may be one of a computer system, computer network, and the like. In a non-limiting example, virtual environment may include a user device such as a tablet, laptop, desktop, smart phone and the like connected to network. In some embodiments, virtual environment may also include any electronically based elements associated with the virtual environment, as described in this disclosure. In a non-limiting example, virtual environment may include computer programs, data, data stores, and the like thereof. In some cases, virtual environment may be local to processor108; for instance, and without limitation, virtual environment may be generated and hosted by processor108locally in a single computing device104. In other cases, virtual environment may be remote to processor108; for instance, virtual environment may be connected to the processor108by a network. Virtual environment may employ any type of network architecture. For example, and without limitation, virtual environment may employ a peer to peer (P2P) architecture where each computing device104in a computing network is connected with every computing device104in the network and every computing device104acts as a server for the data stored in the computing device104. In a further exemplary embodiment, virtual environment may also employ a client server architecture where a computing device104is implemented as a central computing device104(e.g., server) that is connected to each client computing device104and communication is routed through the central computing device104. However, the network architecture is not limited thereto. One skilled in the art, after having reviewed the entirety of this disclosure, will recognize the various network architectures that may be employed by the virtual environment. In other embodiments, any network topology may be used. In a non-limiting example, virtual environment may employ a mesh topology where a computing device104is connected to one or multiple other computing devices104using point to point connections. However, the network topology is not limited thereto. One skilled in the art, after having reviewed the entirety of this disclosure, will recognize the various network architectures that may be employed by the virtual environment. In a non-limiting example, virtual activity data may be received, by processor108, from virtual environment. Data related to user's activity in virtual environment such as, without limitation, online browsing, online shopping, social media posting, and the like may be collected by processor108as user profile172.

With continued reference toFIG.1, in some embodiments, virtual environment may include a cloud environment. As used in this disclosure, a “cloud environment” is a set of systems and/or processes acting together to provide services in a manner that is dissociated with underlaying hardware and/or software within apparatus100used for such purpose and includes a cloud. A “cloud,” as described herein, refers to one or more servers that are accessed over the internet. In some cases, cloud may include Hybrid Cloud, Private Cloud, Public Cloud, Community Cloud, any cloud defined by National Institute of Standards and Technology (NIST), and the like thereof. In some embodiments, cloud may be remote to apparatus100; for instance, cloud may include a plurality of functions distributed over multiple locations external to apparatus100. Location may be a data center, such as data store described in further detail below. In some embodiments, cloud environment may include implementation of cloud computing. As used in this disclosure, “cloud computing” is an on-demand delivery of information technology (IT) resources within a network through internet, without direct active management by user. In some embodiment, without limitation, cloud computing may include a Software-as-a-Service (SaaS). As used in this disclosure, a “Software-as-a-Service” is a cloud computing service model which make software available to the user using apparatus100directly; for instance, SaaS platform may provide partial or entire set of functionalities of apparatus100to user without direct installation of the entire set of functionalities. In a non-limiting example, virtual activity data may include data related to a user input132and/or corresponding received response from one or more clouds; for instance, and without limitation, processor108may receive a collection of user inputs132and/or corresponding response data contained in response body from one or more SaaS platforms as user profile172. In another non-limiting example, virtual activity data may include one or more documents created, modified, and/or otherwise saved by users in one or more clouds; for instance, and without limitation, electronic files stored in MICROSOFT365, DROPBOX, G SUITE, and the like by the user. Processor108may extract user data rom such documents using optical character recognition (OCR) as described below in this disclosure.

With continued reference toFIG.1, in some embodiments, receiving the user profile172may include receiving virtual activity data from a virtual avatar model176operated by the user. As used in this disclosure, a “virtual avatar model” is a component configured to generate, operate, and manage a virtual avatar based on user's command. A “virtual avatar,” as used in this disclosure is defined as an interactive character or entity in a virtual environment. In a non-limiting example, virtual avatar may include a virtual representation of the user in virtual environment. In an embodiment, a virtual avatar may be customizable. Virtual avatar may include, without limitation, an animal, human, robot, inanimate object, and the like, and may include one or more personalized characteristics, wherein personalized characteristics may be derived from user's behavior and/or activity in virtual environment. In a non-limiting example, virtual environment may include an extended reality space, such as, without limitation, augmented reality (AR) space, virtual reality (VR) space, and/or any other digital realities. For example, and without limitation, extended reality space may include a virtual classroom, virtual meeting room, virtual study room, and the like thereof. Virtual activity data may include data related to the behavior and/or activity of the virtual avatar of virtual avatar model176operated by user. User may have a unique study style which may be incorporated by virtual avatar of virtual avatar model176. For instance, and without limitation, virtual activity data of virtual avatar model176may include one or more image files, recorded animation files, and/or video clips that may include one or more files Virtual avatar may include one or more animation files and/or video clips and may include user's study activities indicating user's learning pattern. Virtual avatar and virtual avatar model176may include any virtual avatar and virtual avatar model176described in U.S. patent application Ser. No. 18/122,198 filed on Mar. 16, 2023 and titled “APPARATUS AND METHOD FOR EDUCATING AN ENTITY USING EXTENDED REALITY,” the entity of which is incorporated by reference herein.

With continued reference toFIG.1, apparatus100may include an optical device. As used in this disclosure, an “optical device” is a device that is configured to sense electromagnetic radiation, such as without limitation visible light, and generate an image representing the electromagnetic radiation. In some cases, optical device may include a camera, wherein the camera may include one or more optics. Exemplary non-limiting optics include spherical lenses, aspherical lenses, reflectors, polarizers, filters, windows, aperture stops, and the like. In some cases, at least a camera may include an image sensor. Exemplary non-limiting image sensors include digital image sensors, such as without limitation charge-coupled device (CCD) sensors and complimentary metal-oxide-semiconductor (CMOS) sensors, chemical image sensors, and analog image sensors, such as without limitation film. In some cases, a camera may be sensitive within a non-visible range of electromagnetic radiation, such as without limitation infrared. In a non-limiting example, optical device may include any device configured to capture visual representation of the user, or the environment surrounding user, from a portable webcam to a high-end camera configured to capture visual representations not visible to human eye, such as, without limitation, infra-red cameras. As used in this disclosure, “image data” is information representing at least a physical scene, space, and/or object. In some cases, user profile172may include image data, generated by camera, related to the user. “Image data” may be used interchangeably through this disclosure with “image,” where image is used as a noun. An image may be optical, such as without limitation where at least an optic is used to generate an image of an object. In a non-limiting example, optical device may be configured to use at least an optic to generate an image of the user. An image may be material, such as without limitation when film is used to capture an image. An image may be digital, such as without limitation when represented as a bitmap. Alternatively, an image may be comprised of any media capable of representing a physical scene, space, and/or object. Alternatively, where “image” is used as a verb, in this disclosure, it refers to generation and/or formation of an image. In other cases, external datum108may include video data related to the user, wherein video data is a recording of plurality of images generated by at least an optic.

With continued reference toFIG.1, in some embodiments, apparatus100may include or be communicatively connected to an eye sensor. As used in this disclosure, an “eye sensor” is any system or device that is configured or adapted to detect an eye parameter as a function of an eye phenomenon. In some cases, at least an eye sensor may be configured to detect at least an eye parameter as a function of at least an eye phenomenon. As used in this disclosure, an “eye parameter” is an element of information associated with an eye. Exemplary non-limiting eye parameters may include blink rate, eye-tracking parameters, pupil location, gaze directions, pupil dilation, and the like. Exemplary eye parameters are described in greater detail below. In some cases, an eye parameter may be transmitted or represented by an eye signal. An eye signal may include any signal described in this disclosure. As used in this disclosure, an “eye phenomenon” may include any observable phenomenon associated with an eye, including without limitation focusing, blinking, eye-movement, and the like. In a non-limiting example, eye sensor may include an electromyography sensor. Electromyography sensor may be configured to detect at least an eye parameter as a function of at least an eye phenomenon.

Still referring toFIG.1, in some embodiments, eye sensor may be embedded within optical device described above. In a non-limiting example, eye sensor may utilize a camera directed toward user's eyes. In some cases, eye sensor may include a light source, likewise directed to user's eyes. Light source may have a non-visible wavelength, for instance infrared or near-infrared. In some cases, a wavelength may be selected which reflects at an eye's pupil (e.g., infrared). Light that selectively reflects at an eye's pupil may be detected, for instance by camera. Images of eyes may be captured by camera of optical device. In some embodiments, optical device may be programmed with Python using a Remote Python/Procedure Call (RPC) library. Optical device may be used to operate computer vision model described below in this disclosure; for instance, and without limitation, optical device may be used to operate image classification and segmentation models, such as without limitation by way of TensorFlow Lite; detect motion, for example by way of frame differencing algorithms; detect markers, for example blob detection; detect objects, for example face detection; track eyes; detection persons, for example by way of a trained machine learning model; detect camera motion, for example by way of optical flow detection; detect and decode barcodes; capture images; and record video.

Still referring toFIG.1, in some cases, optical device with eye sensor may be used to determine eye patterns (e.g., track eye movements). For instance, and without limitation, camera of optical device may capture one or more images of the user and internal/external processor108of optical device may process images to track user's eye movements. External processor108may include a processor108in communication with apparatus100as described in further detail below. In some embodiments, a video-based eye tracker may use corneal reflection (e.g., first Purkinje image) and a center of pupil as features to track over time. A more sensitive type of eye-tracker, a dual-Purkinje eye tracker, may use reflections from a front of cornea (i.e., first Purkinje image) and back of lens (i.e., fourth Purkinje image) as features to track. A still more sensitive method of tracking may include use of image features from inside eye, such as retinal blood vessels, and follow these features as the eye rotates. In some cases, optical methods, particularly those based on video recording, may be used for gaze-tracking and may be non-invasive and inexpensive. In a non-limiting example, a relative position between camera of optical device and the user may be known or estimable. Pupil location may be determined through analysis of images (either visible or infrared images). In some cases, camera may focus on one or both eyes and record eye movement as viewer (i.e., user) looks. In some cases, eye sensor embedded within optical device may use center of pupil and infrared/near-infrared non-collimated light to create corneal reflections (CR). A vector between pupil center and corneal reflections can be used to compute a point of regard on surface (i.e., a gaze direction). In some cases, a simple calibration procedure with the user may be needed before using eye sensor. In some cases, two general types of infrared/near-infrared (also known as active light) eye-tracking techniques can be used: bright-pupil (light reflected by pupil) and dark-pupil (light not reflected by pupil). Difference between bright-pupil and dark pupil images may be based on a location of illumination source with respect to optics. For instance, if illumination is coaxial with optical path, then eye may act as a retroreflector as the light reflects off retina creating a bright pupil effect similar to red eye. If illumination source is offset from optical path, then pupil may appear dark because reflection from retina is directed away from camera. In some cases, bright-pupil tracking creates greater iris/pupil contrast, allowing more robust eye-tracking with all iris pigmentation, and greatly reduces interference caused by eyelashes and other obscuring features. In some cases, bright-pupil tracking may also allow tracking in lighting conditions ranging from total darkness to very bright.

Still referring toFIG.1, additionally, or alternatively, in some cases, a passive light optical eye tracking method may be employed by optical device. Passive light optical eye tracking may use visible light to illuminate user's eyes. In some cases, passive light optical tracking yields less contrast of pupil than with active light methods; therefore, in some cases, a center of iris may be used for calculating a gaze vector. In some cases, a center of iris determination requires detection of a boundary of iris and sclera (e.g., limbus tracking). In some case, eyelid obstruction of iris and our sclera may challenge calculations of an iris center. Further, one or more devices described herein may be head-mounted, some may require user's head to be stable, and some may function remotely and automatically track user's head during motion. Optical device may capture images at frame rate. Exemplary frame rates include 15, 30, 60, 120, 240, 350, 1000, and 1250 Hz.

With continued reference toFIG.1, user profile172may include actual activity data pertaining to the user. As used in this disclosure, “actual activity data” is data related to user's activity in a physical environment. In a non-limiting example, actual activity data includes “real-world” data related to user's activity that is taking place outside virtual environment as described above. In a non-limiting example, actual activity data may include data related to real-world activities such as, without limitation, exercising, playing sport, reading, drawing, studying, and the like performed by the user off-line. In some embodiments, actual activity data may be received from a user profile172. In one or more embodiments, user profile172may include one or more data submissions pertaining to actual activity data. As used in this disclosure, a “data submission” is an assemblage of data provided by the user as an input source. In a non-limiting example, data submission may include user uploading one or more documentations to processor108, wherein the documentations are sources of information related to user. In some cases, documentation may include electronic document, such as, without limitation, txt file, JSON file, word document, pdf file, excel sheet, image, video, audio, and the like thereof. For instance, and without limitation, electronic document may include electronic transcript, course schedule, homework assignment, and the like thereof. In other cases, documentation may include physical document (i.e., in form of paper) containing data related to user's real-world activity; for instance, and without limitation, such physical documentation may be a certificate awarded by user's educational establishment (i.e., diploma). Physical document may be scanned by processor10user profile1728, and data therein may be extracted by processor108using OCR as described in further detail above.

With continued reference toFIG.1, user profile172may include educational obstacle datum. As used in this disclosure, an “educational obstacle datum” is an element of data related to difficulties user currently facing that impede user's educational progress. In a non-limiting example, educational obstacle datum may include data related to questions that a user answered incorrectly and/or incompletely during an exam participated in by the user. In another non-limiting example, educational obstacle datum may include a particular activity, task, and/or otherwise exercise that user need practice with and/or improve on. In one or more embodiments, educational obstacle datum may be generated and/or input by a user. In one or more embodiments, a user may input educational obstacle datum into user profile172. In one or more embodiments, educational obstacle datum may be generated by processor108. In one or more embodiments, educational obstacle datum may be generated by elements of user profile172, previous inputs by the user and the like. A generated educational obstacle machine-learning model may be trained by correlated inputs and outputs of training data. Training data may be data sets that have already been converted from raw data whether manually, by machine, or any other method. Training data may include previous outputs such that educational obstacle machine-learning model iteratively produces outputs. Educational obstacle machine-learning model using a machine-learning process may output converted data based on input of training data. In a non-limiting example, generating educational obstacle machine-learning model may include training educational obstacle machine-learning model using educational obstacle training data, wherein the educational obstacle training data may include a plurality of user data sets and/or user profiles172correlated to a plurality of educational obstacle data. For example, educational obstacle training data may be used to show user profile172may indicate a particular educational obstacle datum. In one or more embodiments, educational obstacle training data may also include a plurality of virtual activity data that are each correlated to one of a plurality of educational obstacle data. In such an embodiment, educational obstacle training data may be used to show how virtual activity data may indicate a particular difficulty user currently facing in education based on virtual activity. In other embodiments, educational obstacle training data may also include a plurality of actual activity data that are correlated to one of a plurality of educational obstacle data. In such an embodiment, educational obstacle training data may be used to show how actual activity data may indicate a particular difficulty user currently facing in education based on actual activity. Processor108is configured to determine educational obstacle datum using trained educational obstacle machine-learning model as a function of user profile172.

With continued reference toFIG.1, in some embodiments, educational obstacle datum may include at least one educational obstacle category associated with educational obstacle datum. As used in this disclosure, an “educational obstacle category” is a class or division of educational obstacle data. In a non-limiting example, educational obstacle category may be an element of data related to a user's area of difficulty in the user's education. In some embodiments, educational obstacle category may include a subject; for instance, and without limitation, user profile172may be categorized by area of learning such as literature, math, chemistry, physics, biology, and the like thereof. In other embodiments, educational obstacle category may include a field in the subject; for instance, and without limitation, subject match may include one or more educational obstacle categories such as algebra, geometry, statistics, and the like thereof. In one or more embodiments, educational obstacle datum may be consistent with educational categorization144. In one or more embodiments, educational obstacle datum may be determined based on at least one educational obstacle category. In some embodiments, processor108may be configured to classify user profile172into at least one educational obstacle category and filter user profile172based on at least one educational obstacle category. For instance, and without limitation, processor108may be configured to only examine virtual activity data and/or actual activity data related to math subject and determine educational obstacle datum in field of math pertaining to the user. Such educational obstacle datum may include data indicating user is currently having difficulty in solving differential equations, understanding differentiation rules, and the like. In a further embodiment, educational obstacle machine-learning model 1 may be trained using educational obstacle training data, wherein educational obstacle training data may include a plurality of educational obstacle categories as input correlated to a plurality of educational obstacle data as output. In such embodiment, each educational obstacle category may show a particular educational obstacle datum pertaining to user. Such trained educational obstacle machine-learning model may be configured to take one or more educational obstacle categories and/or user data as input and output educational obstacle datum pertaining to user.

With continued reference toFIG.1, an educational obstacle classifier may classify user profile172into at least one educational obstacle category and/or educational categorization144, which may include any educational obstacle category and/or educational categorization144as described above. For instance, and without limitation, educational obstacle classifier may receive user data profile such as, without limitation, virtual activity data, actual activity data, and the like, and classify received user profile172to an educational obstacle category. Educational obstacle classifier may be trained using training data correlating user data to educational obstacle category. In a non-limiting example, training data used for training educational obstacle classifier may include a plurality of user data sets as input correlated to a plurality of educational obstacle categories as output. Each user profile172may show one or more educational obstacle categories user belongs to. Processor108may be configured to classify user profile172into educational obstacle category using trained educational obstacle classifier and determine educational obstacle datum as a function of the at least one educational obstacle category.

With continued reference toFIG.1, processor108may be configured to generate an educational action datum for the user as a function of the educational obstacle datum. As used in this disclosure, an “educational action datum” is an element of data related to actions user can adopt to eliminate or address educational obstacle datum; In a non-limiting example, educational action datum may include data related to actions that helps to overcome user's educational difficulties specified by educational obstacle datum. In some embodiments, actions may include virtual and/or actual actions. In a non-limiting example, educational action datum may include an online training course that helps user better understanding concepts in a given educational obstacle category of educational obstacle datum. In another non-limiting example, educational action datum may include detailed instructions for user to establish a healthy learning style. In some embodiments, educational action datum may include at least an educational action datum waypoint. As used in this disclosure, an “educational action datum waypoint” is a checkpoint of user's progress on completing educational action datum. In some cases, educational action datum waypoint may include a plurality of educational action datum waypoints; for instance, and without limitation, educational action datum waypoint may include a section, unit, or otherwise a chapter of a training course provided by educational action datum. In some embodiments, at least an educational action datum waypoint may include a user-oriented assignment. As used in this disclosure, a “user-oriented assignment” is a set of user-specific questions targeting educational obstacle datum of the user. In a non-limiting example, user-specific question may include a question asking user “what is the derivative of a given function f(x)” if user is currently struggling at passing exam and quizzes in calculus class. Educational action datum may further include at least a waypoint response associated with at least an educational action datum waypoint. As used in this disclosure, a “waypoint response” is a datum representing a completion of educational action datum waypoint. In a non-limiting example, educational action datum may include educational prompts160that are fed into educational machine learning model152and/or educational module148to produce user specific outputs168. In one or more embodiments, educational action datum, educational obstacle datum and the like may be fed into educational machine learning model152as education prompt, wherein educational outputs164(or in this instance, user specific outputs168) are generated by educational machine learning model152and/or educational module148. Model may include a component in communication with virtual avatar in virtual environment.

With continued reference toFIG.1., processor108and/or educational module148may determine educational action datum using a using a lookup table. A “lookup table,” for the purposes of this disclosure, is an array of data that maps input values to output values. A lookup table may be used to replace a runtime computation with an array indexing operation. In another non limiting example, an educational action datum lookup table may be able to correlate educational obstacle datum to an educational action datum. Processor108may be configured to “lookup” one or more educational obstacle datum 1in order to find a corresponding educational action datum. In some embodiments, educational action datum may be determined by processor108based on a plurality of lookup tables; for instance, and without limitation, a lookup table for each educational obstacle category may be initialized by processor108. Processor108may be configured to lookup one or more educational obstacle datum in corresponding lookup tables, determined based on educational obstacle category in order to find a corresponding educational action datum.

With continued reference toFIG.1, educational module148and/or processor108may be configured to retrieve user profile172of a plurality of user profiles172as function of user input132. In one or more embodiments, processor108may be configured to receive a unique identifier from a user. “Unique identifier” for the purposes of this disclosure is a unique set of characters or numbers that may be used to locate user profile172. In one or more embodiments, unique identifier may include an account number wherein the account number may be used to retrieve user profile172. In one or more embodiments, unique identifier may include a username and password, wherein each user profile172may be associated with a username and password. In one or more embodiments, processor108may receive a username and password and search database116containing a plurality of user profiles172associated with username and passwords having a corresponding match. In one or more embodiments, processor108may retrieve a user profile172that contains a corresponding match to a unique identifier input by users.

With continued reference toFIG.1, educational module148may be configured to generate user specific outputs168. In one or more embodiments, user specific outputs168may include educational outputs164that are specific to a user as described above. In one or more embodiments, user specific outputs168may include associated metadata128. In one or more embodiments, educational module148and/or educational machine learning model152may generate a plurality of educational outputs164by segmenting elements resource data file120. In one or more embodiments, user specific outputs168may include metadata128wherein the metadata128is associated with a received element from resource data file120.

In one or more embodiments, user specific outputs168may be generated as a function of educational prompt160. In one or more embodiments, educational prompt160may be fed into educational machine learning model152wherein use-specific outputs are generated. In one or more embodiments, educational prompt160by be generated by educational module148. In one or more embodiments, in instances wherein educational prompt160is generated by educational module148, outputs of educational machine learning model152may include user specific outputs168. In one or more embodiments, in stances where educational prompt160is generated by a user, such as through user input132, then outputs of educational machine learning model152may include educational prompts160. In one or more embodiments, user specific outputs168are outputs of machine learning model that have been generated based on user inputs132generated and/or modified by educational module148.

With continued reference toFIG.1, educational module148may receive user input132and generate one or more educational prompts160. In one or more embodiments, educational module148may receive conversational input180and generate one or more educational prompts160. “Conversational input” for the purposes of this disclosure is an input by a user into computing device104that is received for the purposes of generating one or more educational prompts160or educational outputs164. For example, educational input may include a request by a user to receive a particular set of educational information. In one or more embodiments, conversational input180may include user input132that is associated to the generation of educational prompts160, educational outputs164and/or user specific outputs168. In one or more embodiments, conversational input180may be consistent with user input132. In one or more embodiments, conversational input180may include user inputs132that are input for the purpose of generating educational prompts160, educational outputs164and/or user specific outputs168. In one or more embodiments, conversational inputs180may include inputs into a chatbot system and/or interactions within an avatar as described herein. In one or more embodiments, conversational input180may be consistent with educational prompt160. In one or more embodiments, conversational input180may be used to generate educational prompt160.

In one or more embodiments, conversational input180may include an input made by any input devices as described in this disclosure. In one or more embodiments, conversational input180may include audio received by a user. In one or more embodiments, a user may communicate with apparatus100, a chatbot and/or an avatar using a microphone. In one or more embodiments, conversational input180may include communications made to apparatus100either through text-based messages and/or through a microphone. In one or more embodiments, audio-based communication may simulate a conversation between user and apparatus100. In one or more embodiments, a user may speak, computing device104may receive communications through microphone as conversational input180. In one or more embodiments, microphone may be communicatively connected to computing device104.

Continuing in reference toFIG.1, apparatus100may include and/or be communicatively connected to a microphone. As used in this disclosure, a “microphone” is any transducer configured to transduce pressure change phenomenon to a signal, for instance a signal representative of a parameter associated with the phenomenon. Microphone, according to some embodiments, may include a transducer configured to convert sound into electrical signal. Exemplary non-limiting microphones include dynamic microphones (which may include a coil of wire suspended in a magnetic field), condenser microphones (which may include a vibrating diaphragm condensing plate), and a contact (or conductance) microphone (which may include piezoelectric crystal material). Microphone may include any microphone for transducing pressure changes, as described above; therefore, microphone may include any variety of microphone, including any of: condenser microphones, electret microphones, dynamic microphones, ribbon microphones, carbon microphones, piezoelectric microphones, fiber-optic microphones, laser microphones, liquid microphones, microelectromechanical systems (MEMS) microphones, and/or a speaker microphone.

With continued reference toFIG.1, an “audio signal,” as used in this disclosure, is a representation of sound. In some cases, an audio signal may include an analog electrical signal of time-varying electrical potential. In some embodiments, an audio signal may be communicated (e.g., transmitted and/or received) by way of an electrically transmissive path (e.g., conductive wire), for instance an audio signal path. Alternatively or additionally, audio signal may include a digital signal of time-varying digital numbers. In some cases, a digital audio signal may be communicated (e.g., transmitted and/or received) by way of any optical fiber, at least an electrically transmissive path, and the like. In some cases, a line code and/or a communication protocol may be used to aid in communication of a digital audio signal. Exemplary digital audio transports include, without limitation, Alesis Digital Audio Tape (ADAT), Tascam Digital Interface (TDIF), Toshiba Link (TOSLINK), Sony/Philips Digital Interface (S/PDIF), Audio Engineering Society standard 3 (AES3), Multichannel Audio Digital Interface (MADI), Musical Instrument Digital Interface (MIDI), audio over Ethernet, and audio over IP. Audio signals may represent frequencies within an audible range corresponding to ordinary limits of human hearing, for example substantially between about 20 and about 20,000 Hz. According to some embodiments, an audio signal may include one or more parameters, such as without limitation bandwidth, nominal level, power level (e.g., in decibels), and potential level (e.g., in volts). In some cases, relationship between power and potential for an audio signal may be related to an impedance of a signal path of the audio signal. In some cases, a signal path may single-ended or balanced.

With continued reference toFIG.1, microphone may be configured to transduce an environmental noise to an environmental noise signal. In some cases, environmental noise may include any of background noise, ambient noise, aural noise, such as noise heard by a user's ear, and the like. Additionally or alternatively, in some embodiments, environmental noise may include any noise present in an environment, such as without limitation an environment surrounding, proximal to, or of interest/disinterest to a user. Environmental noise may, in some cases, include substantially continuous noises, such as a drone of an engine. Alternatively or additionally, in some cases, environmental noise may include substantially non-continuous noises, such as spoken communication or a backfire of an engine. Environmental noise signal may include any type of signal, for instance types of signals described in this disclosure. For instance, environmental noise signal may include a digital signal or an analog signal.

With continued reference toFIG.1, educational module148may receive conversational input180and generate educational prompt160. In one or more embodiments, educational module148may contain a dialogue machine learning model184. In one or more embodiments, conversational input180may be fed into dialogue machine learning model184wherein educational prompt160may be generated as an output of the machine learning model. “Dialogue machine learning model” for the purposes of this disclosure is a system that that is configured to receive a conversational input180and generate an educational prompt160that is specific to the user. In one or more embodiments, a user may not fully understand what education materials are necessary and as a result, educational module148may generate tailored inputs to bed used within educational machine learning model152. In one or more embodiments, educational module148may receive conversational input180wherein a user may request particular educational information. In one or more embodiments, dialogue machine learning model184may modify generate educational prompt160for the user wherein educational prompt160may indicate the particular educational materials that a user needs as a well as the difficulties and the like.

With continued reference toFIG.1, educational module148may be configured to generate user specific outputs168as a function of educational machine learning model152, user profile172and conversational input180. In one or more embodiments, educational module148may be configured to generate educational prompt160as a function of user profile172and conversational input180, wherein user specific output168may be generated as a function of educational prompt160as described in further detail above. In one or more embodiments, educational module148may use dialogue machine learning model184to generate educational prompts160. In one or more embodiments, dialogue training may be used to train dialogue machine learning model184. In one or more embodiments, dialogue training data188may include a plurality of conversational inputs180and/or user profiles172correlated to a plurality of educational prompts160. In an embodiment, dialogue machine learning model184may be used to receive conversational input180from user, and using user profile172, generate a custom educational prompt160. In one or more embodiments, dialogue machine learning model184may be trained with user profile172wherein elements of user profile172may be used to generate educational prompt160. For example, user profile172may indicate that a user is suffering in chemistry wherein conversational input180may include a request to generate questions. Dialogue machine learning model184may use user profile172and conversational input180to generate educational prompt160indicating what questions should be used, the level of difficulty, the particular categories to be covered, the chose of words to be used and the like. In one or more embodiments, dialogue training data188may be used to output educational prompt160, wherein educational prompt160includes a unique list of inputs to be used for educational machine learning model152. In one or more embodiments, conversational input180and/or user profile172may be fed into dialogue machine learning model184wherein educational prompt160may be output as a result. In one or more embodiments, educational prompt160may include language that is familiar to user based on user profile172. In one or more embodiments, educational prompt160may include information that a user is struggling in based on user profile172as described above. In one or more embodiments, educational prompt160may include one or more elements, a user is familiar with, one or more topics a user is struggling in and the like. In one or more embodiments, conversational input180may be used to generate a broad request for educational machine learning model152wherein user profile172may be used to narrow the request to areas that are beneficial to the user. For example, a user may request sample questions wherein user profile172may indicate the topics a user is suffering in. In an embodiment, dialogue machine learning model184may receive conversational input180and user profile172in order to create tailored prompts for educational machine learning model152. In one or more embodiments, dialogue training data188may include a plurality of conversational inputs180and/or user profiles172correlated to a plurality of educational outputs164. In one or more embodiments, dialogue training data188may be iteratively updated and/or modified through one or more iterations of the processing. In one or more embodiments, user profile172may be iteratively updated and as a result, dialogue training data188may be iteratively updated as well. In one or more embodiments, a user may interact differently with apparatus100wherein movements, conversations, inputs, and the like may be used to update user profile172. In one or more embodiments, a user's interaction with apparatus100may indicate that the user has become more proficient in various topics wherein dialogue training data188may be updated in order to provide more accurate results. In an embodiment, educational module148may iteratively update dialogue training data188to ensure that dialogue training data188is changing and/or updating in response to the user. In one or more embodiments, the modifying of dialogue training data188may allow for dialogue machine learning model184to adapt to new situations with respect to the user and/or adapt to a user's current needs. In one of more embodiments, processor108may receive updated information either through interaction with apparatus100and/or through input wherein user profile172may be iteratively updated. In one or more embodiments, creating the user specific outputs168as a function of the educational machine learning model152, the user profile172, and/or the educational prompt160further includes updating, iteratively, the dialogue training data188input-output result generated by the trained dialogue machine learning model184for iterative retraining of the dialogue machine learning model184for subsequent use of the apparatus100. In one or more embodiments, educational prompts160may differ over times as changes to the user's intellect and/or habits may change over time. In one or more embodiments, generating educational prompt160may include generating educational prompt160as a function of dialogue machine learning model184.

With continued reference toFIG.1, educational module148may be configured to use one or more machine learning processes as described in this disclosure. For example, educational module148may receive conversational input180as an input into educational machine learning model152wherein outputs of educational machine learning model152may be output as educational outputs164and/or user specific outputs168. Similarly, educational module148may receive conversational input180and generate educational prompt160as a function of dialogue machine learning model184. It is to be understood that one or more processes may be used in educational module148.

With continued reference toFIG.1, one or more machine learning models may be used in sequence and/or out of sequence to generate differing but useful results. In sequence, educational module148may receive conversational input180to generate educational prompt160and in turn use educational prompt160to generate user specific output168. However, out of sequence, educational module148may receive conversational input180, place conversational input180into educational machine learning model152similar to that of education prompt and receive educational output164. Educational module148may then input educational output164into dialogue machine learning model184to receive educational prompt160. In an embodiment, processes done out of sequence may generate differing, but needed results. For example, out of sequence, educational module148may receive educational material (e.g. educational output164and/or user specific output168) and modify the material to meet the needs of the user. In an embodiment, dialogue machine learning model184may be used to modify inputs such that they meet the needs of user. For example, dialogue machine learning model184may be configured to remove elements generated by educational machine learning model152, in situations where the elements are not needed. Similarly, dialogue machine learning model184may modify words and/or portion to best fit the user, such as by using simpler words that the user understands and the like. In one or more embodiments, user specific output168may include an output that has been generated as a function of more than one machine learning processes. For example, educational module148may utilize educational machine learning model152first then dialogue machine learning model184second wherein an output of dialogue machine learning model184may include user specific output168. Similarly, educational model may first utilize dialogue machine learning model184first, then feed outputs of dialogue machine learning model184into educational machine learning model152to generate user specific outputs168as well. In an embodiment, the change in sequence of educational machine learning model152and dialogue machine learning model184may generate differing user specific outputs168. In one or more embodiments, educational module148may use one or more machine learning processes to determine a sequence of the one or more machine learning models as described herein. In one or more embodiments, inputs by the user may be used to indicate a sequence within educational module148. In one or more embodiments, training data containing a plurality of user inputs132correlated to a plurality of sequences wherein a user input132may be correlated to a sequence. For example, training data may indicate that user input132should first be fed into educational machine learning model152in one instance and training data may indicate that user input132(also known as conversational input180herein) should first be fed into conversational machine learning model in another instance.

In one or more embodiments, educational module148may receive feedback on outputs generated by educational module148through user input132. In one or more embodiments, a user may, through user input132, indicate that one or more outputs contain inaccurate information. For example, a user may indicate that an output retrieved from resource data file120may be inaccurate. As a result, educational module148may remove elements of resource data file120that have been indicated to contain inaccurate data as a result of user input132. In one or more embodiments, processor108may remove elements of resource data file120based on user input wherein elements associated with the user input132may be removed. In one or more embodiments, user input132may be used to continuously assess the accuracy of resource data file120and/or educational module148wherein processor108and/or educational module148may be configured to remove inaccurate data.

With continued reference toFIG.1, in one or more embodiments, processor108is configured to generate virtual avatar model176. In one or more embodiments, virtual avatar model176may include chatbot system as described above and in further detail in reference toFIG.3. In one or more embodiments, communication between education module and at least one virtual avatar and/or virtual avatar model176may be in real time. For example, the communications above may be in high time resolution (i.e., low latency). “High time resolution” as used in this disclosure is defined as a computer network that is optimized to process a very high volume of data messages with minimal delay (latency).

Still referring toFIG.1, apparatus100may be configured to instantiate, in a user interface, virtual avatar model176. In an embodiment, the virtual avatar may include a base image and a plurality of animations of the base image, wherein the virtual avatar may be configured to receive an educational response and display an animation of the plurality of animations as a function of the educational response. “Virtual avatar” as used in this disclosure is defined as an interactive character/entity in a virtual world. For example, a virtual avatar may include a base image consisting of a computer-generated image associated with the user/entity. “Animation” as used in this disclosure is defined as a form of digital medica production that includes using computer software to create moving images. For example, an avatar may be a 3-dimensional model that is capable of changing its shape with animations, such as human simulation animations like walking or falling down. The animation may also include video clips and animated clips, such as short videos used on a website, which may be a part of a longer recording. For example, the animation may be stitched together into sequences by splicing together multiple animations (e.g., short videos) to create a new, original video/animation. In an embodiment, there may be one or more post-sequence static set ups for the virtual avatar which may be still or in video format. For example, a virtual avatar may have a resting, default face (e.g., not showing any sign of emotion) and an expression corresponding to a previous sequence may be added. For example, a virtual avatar may initially be present with a resting, default face devoid of emotion and a smiling, happy expression corresponding to a previous sequence may be added. In yet another embodiment, each sequence may include a label representing each sequence to which responses and/or contexts could be matched. In an embodiment, instantiating the virtual avatar model176may further include generating a plurality of rules linking user input132to animations. This may be accomplished by generating a response model, which includes generating a plurality of responses and a plurality of rules matching inputs to responses (i.e. the response generated based on the input) and then generating pairs of all potential responses to animations or rules associating groups of responses to animations. For example, a response may be positive which may be linked to an animation of hands clapping. In another embodiment, generating a plurality of rules may also include receiving a plurality of training examples correlating response data to animations and training a classifier using the plurality of training examples, wherein the response classifier is configured to input a response and output a rule linking the educational response to an animation. The response classifier machine learning model may be supervised and trained with training data. Training data may include inputs and corresponding predetermined outputs so that a machine-learning module may use the correlations between the provided exemplary inputs and outputs to develop an algorithm and/or relationship that then allows the machine-learning module to determine its own outputs for inputs. Training data may contain correlations that a machine-learning process may use to model relationships between two or more categories of data elements. The exemplary inputs and outputs may come from a database116, such as any database116described in this disclosure, or be provided by a user. In other embodiments, a machine-learning module may obtain a training set by querying a communicatively connected database116that includes past inputs and outputs. Training data may include inputs from various types of databases116, resources, and/or user inputs132and outputs correlated to each of those inputs so that a machine-learning module may determine an output, such as labels of animations or sequences of labels, for an input, such as response from a chatbot, user inputs132and/or a combination of one or more inputs with one or more inputs from previous iterations. For example, labels of animations or sequences of labels may be utilized to retrieve and display a animations or sequences of animations. By way of a further example, the way in which the virtual avatar responds may be based on the context of an earlier conversation and/or an earlier exchange in a conversation. Correlations may indicate causative and/or predictive links between data, which may be modeled as relationships, such as mathematical relationships, by machine-learning processes, as described in further detail below. In an embodiment, various classifiers may be utilized depending on where a geographic location of a user, the time of day and/or other circumstances. Alternatively or additionally the geographic location of a user, the time of day and/or other circumstances may be utilized as training data for the classifier. In an embodiment, the geographic location of a user, the time of day and/or other circumstances may be utilized as inputs for the classifier. Each of the above steps may be performed by classifier or other machine learning model which are described in further detail below.

In an embodiment, virtual avatar model176may be customizable. For example, the user may be able to cosmetically design an avatar and choose personalized characteristics. Virtual avatar model176may include, without limitation, an animal, human, robot, inanimate object, and the like. In an embodiment, personalized characteristics may be also derived from user's behavior. For example, user may have a unique gait which may be incorporated by the virtual avatar. Virtual avatar model176may include one or more animation files and/or video clips and may include one or more files and/or video clips of the user. In an embodiment, generating the virtual avatar model176may include creating a digital representative for simulating one or more interactions in the extended reality space. Extended reality is discussed in more detail below. In one or more embodiments, virtual avatar model176may be customizable based on elements within user profile172. In one or more embodiments, virtual avatar model176may be generated as a function of user profile. In one or more embodiments, outputs of processor108may be presented to a user through interaction with virtual avatar model176. In one or more embodiments, virtual avatar model176may be configured to generate speech though one or more text-to-speech software. In one or more embodiments, the text to speech software may contain various configurations wherein audio generated by the text to speech software may contain differing voices, differing dialects and the like. In one or more embodiments, virtual avatar model176may present any outputs generated by apparatus100such as, but not limited to, educational prompt160, educational output164, user specific output168, and the like.

With continued reference toFIG.1, in one or more embodiments, virtual avatar model176may be used to simulate human interaction between a user and a computing device104. In one or more embodiments, virtual avatar model176may be used to simulate a teacher-classroom setting wherein virtual avatar model176may present information or request inputs from the user. In one or more embodiments, user specific outputs168may include questions and answers, and/or any other teaching curriculums that may require responses. In one or more embodiments, a user may input a response through one or more input devices as described above wherein virtual avatar model176may be configured to determine the accuracy of the responses from the user.

With continued reference toFIG.1, processor108may be configured to modify a graphical user interface as a function of data generated by educational module148, such as but not limited to user specific outputs168and. In some cases, processor108may be configured to create a user interface data structure. As used in this disclosure, “user interface data structure” is a data structure representing a specialized formatting of data on a computer configured such that the information can be effectively presented for a user interface. User interface data structure may include any data as described in this disclosure. In one or more embodiments, user interface data structure may include information associated virtual avatar model176. In one or more embodiments, processor108may be configured to retrieve data associated with virtual avatar model176.

With continued reference toFIG.1, processor108may be configured to transmit the user interface data structure to the graphical user interface. Transmitting may include, and without limitation, transmitting using a wired or wireless connection, direct, or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and/or transmittance of data and/or signal(s) therebetween. Data and/or signals therebetween may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio, and microwave data and/or signals, combinations thereof, and the like, among others. Processor108may transmit the data described above to database116wherein the data may be accessed from database116. Processor108may further transmit the data above to a device display or another computing device104.

With continued reference toFIG.1, apparatus100may include a graphical user interface (GUI). For the purposes of this disclosure, a “user interface” is a means by which a user and a computer system interact. For example, through the use of input devices and software. In some cases, processor108108may be configured to modify graphical user interface as a function of the data described above by populating user interface data structure with any data as described herein, such as but not limited to, user specific outputs168and educational outputs164, and visually presenting the data modification of the graphical user interface. A user interface may include graphical user interface, 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. In some embodiments, a user may interact with the user interface using a computing device104104distinct from and communicatively connected to processor108. For example, a smart phone, smart tablet, or laptop operated by the user and/or participant. A user interface may include one or more graphical locator and/or cursor facilities allowing a user to interact with graphical models and/or combinations thereof, for instance using a touchscreen, touchpad, mouse, keyboard, and/or other manual data entry device. A “graphical user interface,” as used herein, is a user interface that allows users to interact with electronic devices through visual representations. 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 graphical user interface. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which a graphical user interface and/or elements thereof may be implemented and/or used as described in this disclosure.

With continued reference toFIG.1, apparatus100may further include a display device communicatively connected to at least a processor108. “Display device” for the purposes of this disclosure, is a device configured to show visual information. In some cases, display device may include a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display device may include, but is not limited to, a smartphone, tablet, laptop, monitor, tablet, and the like. Display device may include a separate device that includes a transparent screen configured to display computer generated images and/or information. In some cases, display device may be configured to visually present one or more data through the GUI to a user, wherein a user may interact with the data through GUI. In some cases, a user may view GUI through display.

Referring now toFIG.2, an exemplary embodiment of a GUI200on a display device204is illustrated. GUI200is configured to receive the user interface structure as discussed above and visually present any data described in this disclosure. Display device204may include, but is not limited to, a smartphone, tablet, laptop, monitor, tablet, and the like. Display device204may further include a separate device that includes a transparent screen configured to display computer generated images and/or information. In some cases, GUI200may be displayed on a plurality of display devices. In some cases, GUI200may display data on separate windows208. A “window” for the purposes of this disclosure is the information that is capable of being displayed within a border of device display. A user may navigate through different windows208wherein each window208may contain new or differing information or data. For example, a first window208may display information relating to receiving user inputs132, whereas a second window may educational outputs164and/or user specific outputs168as described in this disclosure. A user may navigate through a first second, third and fourth window (and so on) by interacting with GUI200. For example, a user may select a button or a box signifying a next window on GUI200, wherein the pressing of the button may navigate a user to another window. In some cases, GUI may further contain event handlers, wherein the placement of text within a textbox may signify to computing device104to display another window. An “event handler” as used in this disclosure is a callback routine that operates asynchronously once an event takes place. Event handlers may include, without limitation, one or more programs to perform one or more actions based on user input132, such as generating pop-up windows, submitting forms, requesting more information, and the like. For example, an event handler may be programmed to request more information or may be programmed to generate messages following a user input132. User input132may include clicking buttons, mouse clicks, hovering of a mouse, input using a touchscreen, keyboard clicks, an entry of characters, entry of symbols, an upload of an image, an upload of a computer file, manipulation of computer icons, and the like. For example, an event handler may be programmed to generate a notification screen following a user input132wherein the notification screen notifies a user that the data was properly received. In some embodiments, an event handler may be programmed to request additional information after a first user input132is received. In some embodiments, an event handler may be programmed to generate a pop-up notification when a user input132is left blank. In some embodiments, an event handler may be programmed to generate requests based on the user input132. In this instance, an event handler may be used to navigate a user through various windows208wherein each window208may request or display information to or from a user. In this instance, window208may display a virtual avatar model212, such as the virtual avatar model176as described in reference toFIG.1. In one or more embodiments, virtual avatar model212may display visual animations to a user, such as simulations replicating human interaction. In one or more embodiments, virtual avatar model212may simulate speech and/or human emotion through manipulation of virtual facial features of virtual avatar model212. In one or more embodiments, virtual avatar model212may simulate speech through one or more text-to-speech software as described in this disclosure. In one or more embodiments, virtual avatar model212may interact with a user through one or more dialogue boxes216wherein the dialogue boxes may include speech generated by virtual avatar model212and corresponding responses by the user. In one or more embodiments, GUI200may include a textbox220wherein the textbox220may be configured to receive user input132. In one or more embodiments, textbox200may receive user input132such as any input as described above, such as but not limited to, educational prompt160, user input132, conversational input180and the like.

Referring toFIG.3, a chatbot system300is schematically illustrated. According to some embodiments, a user interface304may be communicative with a computing device308that is configured to operate a chatbot. In some cases, user interface304may be local to computing device308. Alternatively or additionally, in some cases, user interface304may remote to computing device308and communicative with the computing device308, by way of one or more networks, such as without limitation the internet. Alternatively or additionally, user interface304may communicate with user device308using telephonic devices and networks, such as without limitation fax machines, short message service (SMS), or multimedia message service (MMS). Commonly, user interface304communicates with computing device308using text-based communication, for example without limitation using a character encoding protocol, such as American Standard for Information Interchange (ASCII). Typically, a user interface304conversationally interfaces a chatbot, by way of at least a submission312, from the user interface308to the chatbot, and a response316, from the chatbot to the user interface304. In many cases, one or both of submission312and response316are text-based communication. Alternatively or additionally, in some cases, one or both of submission312and response316are audio-based communication.

Continuing in reference toFIG.3, a submission312once received by computing device308operating a chatbot, may be processed by a processor320. In some embodiments, processor320processes a submission312using one or more of keyword recognition, pattern matching, and natural language processing. In some embodiments, processor employs real-time learning with evolutionary algorithms. In some cases, processor320may retrieve a pre-prepared response from at least a storage component324, based upon submission312. Alternatively or additionally, in some embodiments, processor320communicates a response316without first receiving a submission312, thereby initiating conversation. In some cases, processor320communicates an inquiry to user interface304; and the processor is configured to process an answer to the inquiry in a following submission312from the user interface304. In some cases, an answer to an inquiry present within a submission312from a user device304may be used by computing device104as an input to another function, for example without limitation at least a feature or at least a preference input.

Further referring toFIG.4, training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier416. Training data classifier416may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. A distance metric may include any norm, such as, without limitation, a Pythagorean norm. Machine-learning module400may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data404. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a non-limiting example, training data classifier416may classify elements of training data to educational categorizations and/or teaching curriculum. In an embodiment, each input may be classified to educational categorization and/or teaching curriculum wherein correlated outputs may be classified to the same categorization. In an embodiment, classification may allow for quicker processing wherein a smaller batch of training data may be used and selected from. In an embodiment, classification may allow for quicker processing wherein less data is used in each iteration of the machine learning model. In an embodiment, computing device may be configured to generate subcategorizations when classified training data has reached a data threshold wherein an increase in data in the machine learning model may result in an increase of subcategorization. In an embodiment, the continuous receipt of resource data file as described above may create a large amount of training data that may take longer to process. In one or more embodiments, computing device may be configured to classify training data to an initial educational categorization set, and then classify training data to subcategorizations to allow for quicker processing.

f⁡(x)=11-e-x
given input x, a tanh (hyperbolic tangent) function, of the form

ex-e-xex+e-x,
a tanh derivative function such as ƒ(x)=tanh2(x), a rectified linear unit function such as ƒ(x)=max (0,x), a “leaky” and/or “parametric” rectified linear unit function such as ƒ(x)=max (ax,x) for some a, an exponential linear units function such as

f⁡(x)={xfor⁢x≥0α⁡(ex-1)for⁢x<0
for some value of α (this function may be replaced and/or weighted by its own derivative in some embodiments), a softmax function such as

f⁡(xi)=ex∑ixi
where the inputs to an instant layer are xi, a swish function such as ƒ(x)=x*sigmoid(x), a Gaussian error linear unit function such as f(x)=a(1+tanh(√{square root over (2/π)}(x+bxr))) for some values of a, b, and r, and/or a scaled exponential linear unit function such as

Referring now toFIG.7, an exemplary embodiment of an immutable sequential listing700is illustrated. Data elements are listing in immutable sequential listing700; data elements may include any form of data, including textual data, image data, encrypted data, cryptographically hashed data, and the like. Data elements may include, without limitation, one or more at least a digitally signed assertion. In one embodiment, a digitally signed assertion704is a collection of textual data signed using a secure proof as described in further detail below; secure proof may include, without limitation, a digital signature as described above. Collection of textual data may contain any textual data, including without limitation American Standard Code for Information Interchange (ASCII), Unicode, or similar computer-encoded textual data, any alphanumeric data, punctuation, diacritical mark, or any character or other marking used in any writing system to convey information, in any form, including any plaintext or cyphertext data; in an embodiment, collection of textual data may be encrypted, or may be a hash of other data, such as a root or node of a Merkle tree or hash tree, or a hash of any other information desired to be recorded in some fashion using a digitally signed assertion704. In an embodiment, collection of textual data states that the owner of a certain transferable item represented in a digitally signed assertion704register is transferring that item to the owner of an address. A digitally signed assertion704may be signed by a digital signature created using the private key associated with the owner's public key, as described above.

Still referring toFIG.7, a digitally signed assertion704may describe a transfer of virtual currency, such as crypto-currency as described below. The virtual currency may be a digital currency. Item of value may be a transfer of trust, for instance represented by a statement vouching for the identity or trustworthiness of the first entity. Item of value may be an interest in a fungible negotiable financial instrument representing ownership in a public or private corporation, a creditor relationship with a governmental body or a corporation, rights to ownership represented by an option, derivative financial instrument, commodity, debt-backed security such as a bond or debenture or other security as described in further detail below. A resource may be a physical machine e.g. a ride share vehicle or any other asset. A digitally signed assertion704may describe the transfer of a physical good; for instance, a digitally signed assertion704may describe the sale of a product. In some embodiments, a transfer nominally of one item may be used to represent a transfer of another item; for instance, a transfer of virtual currency may be interpreted as representing a transfer of an access right; conversely, where the item nominally transferred is something other than virtual currency, the transfer itself may still be treated as a transfer of virtual currency, having value that depends on many potential factors including the value of the item nominally transferred and the monetary value attendant to having the output of the transfer moved into a particular user's control. The item of value may be associated with a digitally signed assertion704by means of an exterior protocol, such as the COLORED COINS created according to protocols developed by The Colored Coins Foundation, the MASTERCOIN protocol developed by the Mastercoin Foundation, or the ETHEREUM platform offered by the Stiftung Ethereum Foundation of Baar, Switzerland, the Thunder protocol developed by Thunder Consensus, or any other protocol.

Still referring toFIG.7, in one embodiment, an address is a textual datum identifying the recipient of virtual currency or another item of value in a digitally signed assertion704. In some embodiments, address is linked to a public key, the corresponding private key of which is owned by the recipient of a digitally signed assertion704. For instance, address may be the public key. Address may be a representation, such as a hash, of the public key. Address may be linked to the public key in memory of a computing device, for instance via a “wallet shortener” protocol. Where address is linked to a public key, a transferee in a digitally signed assertion704may record a subsequent a digitally signed assertion704transferring some or all of the value transferred in the first a digitally signed assertion704to a new address in the same manner. A digitally signed assertion704may contain textual information that is not a transfer of some item of value in addition to, or as an alternative to, such a transfer. For instance, as described in further detail below, a digitally signed assertion704may indicate a confidence level associated with a distributed storage node as described in further detail below.

In an embodiment, and still referring toFIG.7immutable sequential listing700records a series of at least a posted content in a way that preserves the order in which the at least a posted content took place. Temporally sequential listing may be accessible at any of various security settings; for instance, and without limitation, temporally sequential listing may be readable and modifiable publicly, may be publicly readable but writable only by entities and/or devices having access privileges established by password protection, confidence level, or any device authentication procedure or facilities described herein, or may be readable and/or writable only by entities and/or devices having such access privileges. Access privileges may exist in more than one level, including, without limitation, a first access level or community of permitted entities and/or devices having ability to read, and a second access level or community of permitted entities and/or devices having ability to write; first and second community may be overlapping or non-overlapping. In an embodiment, posted content and/or immutable sequential listing700may be stored as one or more zero knowledge sets (ZKS), Private Information Retrieval (PIR) structure, or any other structure that allows checking of membership in a set by querying with specific properties. Such database may incorporate protective measures to ensure that malicious actors may not query the database repeatedly in an effort to narrow the members of a set to reveal uniquely identifying information of a given posted content.

Still referring toFIG.7, immutable sequential listing700may preserve the order in which the at least a posted content took place by listing them in chronological order; alternatively or additionally, immutable sequential listing700may organize digitally signed assertions704into sub-listings708such as “blocks” in a blockchain, which may be themselves collected in a temporally sequential order; digitally signed assertions704within a sub-listing708may or may not be temporally sequential. The ledger may preserve the order in which at least a posted content took place by listing them in sub-listings708and placing the sub-listings708in chronological order. The immutable sequential listing700may be a distributed, consensus-based ledger, such as those operated according to the protocols promulgated by Ripple Labs, Inc., of San Francisco, Calif., or the Stellar Development Foundation, of San Francisco, Calif, or of Thunder Consensus. In some embodiments, the ledger is a secured ledger; in one embodiment, a secured ledger is a ledger having safeguards against alteration by unauthorized parties. The ledger may be maintained by a proprietor, such as a system administrator on a server, that controls access to the ledger; for instance, the user account controls may allow contributors to the ledger to add at least a posted content to the ledger, but may not allow any users to alter at least a posted content that have been added to the ledger. In some embodiments, ledger is cryptographically secured; in one embodiment, a ledger is cryptographically secured where each link in the chain contains encrypted or hashed information that makes it practically infeasible to alter the ledger without betraying that alteration has taken place, for instance by requiring that an administrator or other party sign new additions to the chain with a digital signature. Immutable sequential listing700may be incorporated in, stored in, or incorporate, any suitable data structure, including without limitation any database, datastore, file structure, distributed hash table, directed acyclic graph or the like. In some embodiments, the timestamp of an entry is cryptographically secured and validated via trusted time, either directly on the chain or indirectly by utilizing a separate chain. In one embodiment the validity of timestamp is provided using a time stamping authority as described in the RFC 3161 standard for trusted timestamps, or in the ANSI ASC x9.95 standard. In another embodiment, the trusted time ordering is provided by a group of entities collectively acting as the time stamping authority with a requirement that a threshold number of the group of authorities sign the timestamp.

In some embodiments, and with continued reference toFIG.7, immutable sequential listing700, once formed, may be inalterable by any party, no matter what access rights that party possesses. For instance, immutable sequential listing700may include a hash chain, in which data is added during a successive hashing process to ensure non-repudiation. Immutable sequential listing700may include a block chain. In one embodiment, a block chain is immutable sequential listing700that records one or more new at least a posted content in a data item known as a sub-listing708or “block.” An example of a block chain is the BITCOIN block chain used to record BITCOIN transactions and values. Sub-listings708may be created in a way that places the sub-listings708in chronological order and link each sub-listing708to a previous sub-listing708in the chronological order so that any computing device may traverse the sub-listings708in reverse chronological order to verify any at least a posted content listed in the block chain. Each new sub-listing708may be required to contain a cryptographic hash describing the previous sub-listing708. In some embodiments, the block chain contains a single first sub-listing708sometimes known as a “genesis block.”

Still referring toFIG.7, the creation of a new sub-listing708may be computationally expensive; for instance, the creation of a new sub-listing708may be designed by a “proof of work” protocol accepted by all participants in forming the immutable sequential listing700to take a powerful set of computing devices a certain period of time to produce. Where one sub-listing708takes less time for a given set of computing devices to produce the sub-listing708protocol may adjust the algorithm to produce the next sub-listing708so that it will require more steps; where one sub-listing708takes more time for a given set of computing devices to produce the sub-listing708protocol may adjust the algorithm to produce the next sub-listing708so that it will require fewer steps. As an example, protocol may require a new sub-listing708to contain a cryptographic hash describing its contents; the cryptographic hash may be required to satisfy a mathematical condition, achieved by having the sub-listing708contain a number, called a nonce, whose value is determined after the fact by the discovery of the hash that satisfies the mathematical condition. Continuing the example, the protocol may be able to adjust the mathematical condition so that the discovery of the hash describing a sub-listing708and satisfying the mathematical condition requires more or less steps, depending on the outcome of the previous hashing attempt. Mathematical condition, as an example, might be that the hash contains a certain number of leading zeros and a hashing algorithm that requires more steps to find a hash containing a greater number of leading zeros, and fewer steps to find a hash containing a lesser number of leading zeros. In some embodiments, production of a new sub-listing708according to the protocol is known as “mining.” The creation of a new sub-listing708may be designed by a “proof of stake” protocol as will be apparent to those skilled in the art upon reviewing the entirety of this disclosure.

Continuing to refer toFIG.7, in some embodiments, protocol also creates an incentive to mine new sub-listings708. The incentive may be financial; for instance, successfully mining a new sub-listing708may result in the person or entity that mines the sub-listing708receiving a predetermined amount of currency. The currency may be fiat currency. Currency may be cryptocurrency as defined below. In other embodiments, incentive may be redeemed for particular products or services; the incentive may be a gift certificate with a particular business, for instance. In some embodiments, incentive is sufficiently attractive to cause participants to compete for the incentive by trying to race each other to the creation of sub-listings708Each sub-listing708created in immutable sequential listing700may contain a record or at least a posted content describing one or more addresses that receive an incentive, such as virtual currency, as the result of successfully mining the sub-listing708.

With continued reference toFIG.7, where two entities simultaneously create new sub-listings708, immutable sequential listing700may develop a fork; protocol may determine which of the two alternate branches in the fork is the valid new portion of the immutable sequential listing700by evaluating, after a certain amount of time has passed, which branch is longer. “Length” may be measured according to the number of sub-listings708in the branch. Length may be measured according to the total computational cost of producing the branch. Protocol may treat only at least a posted content contained the valid branch as valid at least a posted content. When a branch is found invalid according to this protocol, at least a posted content registered in that branch may be recreated in a new sub-listing708in the valid branch; the protocol may reject “double spending” at least a posted content that transfer the same virtual currency that another at least a posted content in the valid branch has already transferred. As a result, in some embodiments the creation of fraudulent at least a posted content requires the creation of a longer immutable sequential listing700branch by the entity attempting the fraudulent at least a posted content than the branch being produced by the rest of the participants; as long as the entity creating the fraudulent at least a posted content is likely the only one with the incentive to create the branch containing the fraudulent at least a posted content, the computational cost of the creation of that branch may be practically infeasible, guaranteeing the validity of all at least a posted content in the immutable sequential listing700.

Still referring toFIG.7, additional data linked to at least a posted content may be incorporated in sub-listings708in the immutable sequential listing700; for instance, data may be incorporated in one or more fields recognized by block chain protocols that permit a person or computer forming a at least a posted content to insert additional data in the immutable sequential listing700. In some embodiments, additional data is incorporated in an unspendable at least a posted content field. For instance, the data may be incorporated in an OP_RETURN within the BITCOIN block chain. In other embodiments, additional data is incorporated in one signature of a multi-signature at least a posted content. In an embodiment, a multi-signature at least a posted content is at least a posted content to two or more addresses. In some embodiments, the two or more addresses are hashed together to form a single address, which is signed in the digital signature of the at least a posted content. In other embodiments, the two or more addresses are concatenated. In some embodiments, two or more addresses may be combined by a more complicated process, such as the creation of a Merkle tree or the like. In some embodiments, one or more addresses incorporated in the multi-signature at least a posted content are typical crypto-currency addresses, such as addresses linked to public keys as described above, while one or more additional addresses in the multi-signature at least a posted content contain additional data related to the at least a posted content; for instance, the additional data may indicate the purpose of the at least a posted content, aside from an exchange of virtual currency, such as the item for which the virtual currency was exchanged. In some embodiments, additional information may include network statistics for a given node of network, such as a distributed storage node, e.g. the latencies to nearest neighbors in a network graph, the identities or identifying information of neighboring nodes in the network graph, the trust level and/or mechanisms of trust (e.g. certificates of physical encryption keys, certificates of software encryption keys, (in non-limiting example certificates of software encryption may indicate the firmware version, manufacturer, hardware version and the like), certificates from a trusted third party, certificates from a decentralized anonymous authentication procedure, and other information quantifying the trusted status of the distributed storage node) of neighboring nodes in the network graph, IP addresses, GPS coordinates, and other information informing location of the node and/or neighboring nodes, geographically and/or within the network graph. In some embodiments, additional information may include history and/or statistics of neighboring nodes with which the node has interacted. In some embodiments, this additional information may be encoded directly, via a hash, hash tree or other encoding.

With continued reference toFIG.7, in some embodiments, virtual currency is traded as a crypto-currency. In one embodiment, a crypto-currency is a digital, currency such as Bitcoins, Peercoins, Namecoins, and Litecoins. Crypto-currency may be a clone of another crypto-currency. The crypto-currency may be an “alt-coin.” Crypto-currency may be decentralized, with no particular entity controlling it; the integrity of the crypto-currency may be maintained by adherence by its participants to established protocols for exchange and for production of new currency, which may be enforced by software implementing the crypto-currency. Crypto-currency may be centralized, with its protocols enforced or hosted by a particular entity. For instance, crypto-currency may be maintained in a centralized ledger, as in the case of the XRP currency of Ripple Labs, Inc., of San Francisco, Calif. In lieu of a centrally controlling authority, such as a national bank, to manage currency values, the number of units of a particular crypto-currency may be limited; the rate at which units of crypto-currency enter the market may be managed by a mutually agreed-upon process, such as creating new units of currency when mathematical puzzles are solved, the degree of difficulty of the puzzles being adjustable to control the rate at which new units enter the market. Mathematical puzzles may be the same as the algorithms used to make productions of sub-listings708in a block chain computationally challenging; the incentive for producing sub-listings708may include the grant of new crypto-currency to the miners. Quantities of crypto-currency may be exchanged using at least a posted content as described above.

Referring now toFIG.8, a method800of data ingestion and manipulation is described. At step805, method800includes receiving, by at least a processor, resource data file from one or more data acquisition systems. In one or more embodiments, at least one of the one or more data acquisition systems includes a Web Crawler. In one or more embodiments, resource data file is stored on an immutable sequential listing. In one or more embodiments, resource data file includes metadata, the metadata containing a source for each data element within resource data file. This may be implemented within reference toFIGS.1-7and without limitation.

With continued reference toFIG.8, at step810method800includes classifying, by the at least a processor, the resource data file to one or more educational categorizations. This may be implemented within reference toFIGS.1-7and without limitation.

With continued reference toFIG.8, at step815method800includes generating by the at least a processor, an educational module as a function of the resource data file and the classification of the educational categorizations. In one or more embodiments, generating by the at least a processor, the educational module includes generating an educational machine learning model as a function of the resource data file and the classification of the educational categorizations and appending resource data file to educational training data having a plurality of educational prompts correlated to a plurality of user specific outputs. In one or more embodiments, the user-specific outputs include associated metadata. This may be implemented within reference toFIGS.1-7and without limitation.

With continued reference toFIG.8, at step820method800includes retrieving by the at least a processor, a user profile of a plurality of user profiles as a function of a user input. In one or more embodiments, user profile includes an educational obstacle datum. This may be implemented within reference toFIGS.1-7and without limitation.

With continued reference toFIG.8, at step825method800includes, creating by the at least a processor, user-specific outputs as a function of the educational module, the user profile, and a conversational input. In one or more embodiments, creating by the at least a processor, the user-specific outputs as a function of the educational module, the user profile, and the conversational input includes receiving dialogue training data having a plurality of user profiles and conversational inputs correlated to a plurality of educational prompts, training a dialogue machine learning model as a function of the dialogue training data, and generating an educational prompt as a function of the dialogue machine learning model. In one or more embodiments, creating by the at least a processor, the user-specific outputs as a function of the educational module, the user profile, and the conversational input further includes feeding the educational prompt to an educational machine learning model. In one or more embodiments, feeding the educational prompt to the educational machine learning model includes receiving educational training data having a plurality of educational prompts correlated to a plurality of user-specific outputs, training the educational machine learning model as a function of the educational training data, and generating the user specific output as a function of the educational machine learning model and the educational prompt. In one or more embodiments, creating by the at least a processor, the user-specific outputs as a function of the educational machine learning model, the user profile, and the educational prompt further includes updating, iteratively, the dialogue training data input-output result generated by the trained dialogue machine learning model for iterative retraining of the dialogue machine learning model for subsequent use of the method. This may be implemented within reference toFIGS.1-7and without limitation.

With continued reference toFIG.8, at step830method800includes generating by the at least a processor, a virtual avatar model as a function of the user specific outputs. In one or more embodiments, generating the virtual avatar model further includes generating the virtual avatar model as a function of the user profile. This may be implemented within reference toFIGS.1-7and without limitation.

Processor904may include any suitable processor108, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor904may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor904may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor108, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor108, Trusted Platform Module (TPM), a floating point unit (FPU), system on module (SOM), and/or system on a chip (SoC).

Computer system900may also include a storage device924. Examples of a storage device (e.g., storage device924) 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 device924may be connected to bus912by 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 device924(or one or more components thereof) may be removably interfaced with computer system900(e.g., via an external port connector (not shown)). Particularly, storage device924and an associated machine-readable medium928may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system900. In one example, software920may reside, completely or partially, within machine-readable medium928. In another example, software920may reside, completely or partially, within processor904.

Computer system900may also include an input device932. In one example, a user of computer system900may enter commands and/or other information into computer system900via input device932. Examples of an input device932include, 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 device932may be interfaced to bus912via 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 bus912, and any combinations thereof. Input device932may include a touch screen interface that may be a part of or separate from display936, discussed further below. Input device932may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.

A user may also input commands and/or other information to computer system900via storage device924(e.g., a removable disk drive, a flash drive, etc.) and/or network interface device940. A network interface device, such as network interface device940, may be utilized for connecting computer system900to one or more of a variety of networks, such as network944, and one or more remote devices948connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network944, may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software920, etc.) may be communicated to and/or from computer system900via network interface device940.

Computer system900may further include a video display adapter952for communicating a displayable image to a display device, such as display device936. 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 adapter952and display device936may be utilized in combination with processor904to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system900may 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 bus912via a peripheral interface956. 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.