Apparatus and methods for screening users

An apparatus for screening users. The apparatus includes a processor communicatively connected to a user device and a memory communicatively connected to the processor. The memory contains instructions configuring the processor to receive verbal communication associated with a user and parse, using a chatbot, at least a user characteristic from the verbal communication. The processor also screens the user as a function of the user characteristic. Screening the user includes generating a compatibility score based on a compatibility of the at least a user characteristic and a posting and determining a confidence score wherein the confidence score comprises a quantitative value reflecting a confidence in the screening.

FIELD OF THE INVENTION

The present invention generally relates to the field of parsing communications. In particular, the present invention is directed to an apparatus and methods for screening users.

BACKGROUND

Screening users for compatibility with postings can be labor intensive. However, automating this process is difficult due to the nuances and complexities related to determining a compatibility with postings.

SUMMARY OF THE DISCLOSURE

In an aspect of the present disclosure is an apparatus for screening users, the apparatus including at least a processor communicatively connected to a user device; and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to receive verbal communication associated with a user; parse, using a chatbot, at least a user characteristic from the verbal communication; screen the user as a function of the user characteristic, wherein screening includes: generating a compatibility score based on a compatibility of the at least a user characteristic and a posting, and determining a confidence score, wherein the confidence score comprises a quantitative value reflecting a confidence in the screening.

In another aspect of the present disclosure is a method for screening users, the method including: receiving, at a processor, verbal communication associated with a user; parsing, by the processor using a chatbot, at least a user characteristic from the verbal communication; screening, by the processor, the user as a function of the user characteristic, wherein screening includes: generating, by the processor, a compatibility score based on a compatibility of the at least a user characteristic and a posting; and determining, by the processor, a confidence score, wherein the confidence score comprises a quantitative value reflecting a confidence in the screening.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to apparatus and methods for screening users. In an embodiment, an apparatus includes a processor communicatively connected to a user device and a memory communicatively connected to the processor. The memory contains instructions configuring the processor to receive verbal communication associated with a user and parse, using a chatbot, at least a user characteristic from the verbal communication. The processor also screens the user as a function of the user characteristic. Screening the user includes generating a compatibility score based on a compatibility of the at least a user characteristic and a posting and determining a confidence score wherein the confidence score comprises a quantitative value reflecting a confidence in the screening. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples.

In an embodiment, methods and systems 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.

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.

In some embodiments, systems and 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.

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, Poly1305-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 RadioGatún 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.

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.

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, 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.

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.

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.

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.

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.

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.

Now referring toFIG.1, an apparatus for screening users is illustrated. Apparatus100includes a processor104. Screening users, as discussed in this disclosure, may be an automated process executed by processor104. Screening may include determining whether one or more prospective employees may be suitable candidates for employment for a specific job position, a group of job positions, and/or in general. Screening may be performed at anytime during an employer's search for candidates. In some embodiments, screening may be performed early in an application process, such as before a prospective employee interviews with a hiring manager. Screening may be a prerequisite for prospective employees to apply for a position. Processor104may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, graphical user interface (GUI), digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Processor104may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. Processor104may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting processor104to one or more of a variety of networks, and one or more devices. 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 may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and/or from a computer and/or a computing device. Processor104may include but is not limited to, for example, a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. Processor104may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Processor104may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. Processor104may be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of apparatus100and/or computing device.

Still referring toFIG.1, processor104is configured to receive a verbal communication112associated with a user. As used in this disclosure, a “verbal communication” is information and/or data that is transmitted to a processor from another device, such as a user device116, and includes verbal content. Verbal communication112may typically be from user, although processor104may receive the verbal communication112from another party or device. As used in this disclosure, “verbal content” is comprehensible language-based communication including oral communication and written communication. Processor104may be communicatively connected to a user device116. As used in this disclosure, “communicatively connected” means connected by way of a connection, attachment or linkage between two or more relata which allows for reception and/or transmittance of information therebetween. For example, and without limitation, this connection may be wired or wireless, 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. A communicative connection may be achieved, for example and without limitation, through wired or wireless electronic, digital or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, and without limitation, via a bus or other facility for intercommunication between elements of a computing device. Communicative connecting may also include indirect connections via, for example and without limitation, wireless connection, radio communication, low power wide area network, optical communication, magnetic, capacitive, or optical coupling, and the like. In some instances, the terminology “communicatively coupled” may be used in place of communicatively connected in this disclosure.

As used in this disclosure, a “user device” is a computing device controlled and/or operated by a user. Computing device may be any computing device described in this disclosure, such as a processor communicatively connected to a memory. User device116may be a personal computer such as a desktop, laptop, smart phone, and/or the like. Processor104may be configured to require credentials from user device116, such as a username and a password, to verify the identity of user. Processor104may send verification to user, such as an email to user's email address and/or a text message to user's phone saved on a memory and/or database to which processor104has access. Verification may include a link to click that sends a verification to processor104. Verification may include a temporary code for user to then input from user device116to confirm that the device is user device116. Any description of communication and/or data transmitted to and/or from a user is understood to include embodiments wherein the communication and/or the data is transmitted to and/or from user device116, respectively. User may be a prospective employee seeking employment.

Processor104may receive verbal communication112from user device116, another computing device, an educational institution such as a university or high school, an employer, memory108, and/or a database. Communication between user and processor104may be initiated by the user or processor104. Verbal communication112may include at least a user document120. As used in this disclosure, a “user document” is a document that includes information about the user. User document120may include at least a user characteristic124. User document120may include a transcript from an educational institution the user attended such as a college transcript, a written resume, a letter of recommendation, and/or the like. As used in this disclosure, a “user characteristic” is information about a prospective employee pertaining to qualifications of the prospective employee and may include, for example, skills such as communication skills and managerial skills, accreditations, a minimum grade point average (GPA), degree, major and/or focus of study, prior employment, experience, and/or the like.

Still referring toFIG.1, processor104may parse, using a chatbot128, at least a user characteristic from the verbal communication112. As used in the current disclosure, a “chatbot” is a computer program designed to simulate conversation with users. A chatbot may accomplish this by presenting the user with questions. In some embodiments, chatbot128may be designed to convincingly simulate the way a human would behave/respond as a conversational partner. In an embodiment, chatbot128may be configured to ask questions from a questionnaire to user. Chatbot128questions may also be generated as a function of an employer's input. For example, an employer seeking candidates for a job position may provide input to processor104, and the processor104may generate chatbot128questions based on the input. Additionally, chatbot128may be configured to respond to user based on the user's verbal communication112.

With continued reference toFIG.1, at least a user document120may be received by processor104via chatbot128. Chatbot128may prompt user with at least a request132. As used in this disclosure, a “request” is a request, such as a question, to a user for information about the user. A request may seek information about a user characteristic124. Request132prompted by chatbot128may be consistent with disclosure of an initial request and/or a subsequent request as disclosed in U.S. patent application Ser. No. 17/690,451 filed on Mar. 9, 2022, and entitled “APPARATUS AND METHODS FOR CREATING A VIDEO RECORD”, the entirety of which in incorporated herein by reference. In some embodiments, chatbot128may prompt user to respond to request132, such as questions, in text or a video format. For example, chatbot128may verbally, audially and/or with text, ask user questions prompting the user to respond orally with verbal communication112, which processor104may record. Processor104may convert user's verbal communication112to text as described in this disclosure. A transcript of verbal communication112to chatbot128may be displayed to an employer.

Chatbot128may be configured to provide user with a plurality of options as an input into the chatbot128. Chatbot128entries may include multiple choice, short answer response, true or false responses, and the like. In some embodiments, chatbot128communication to user, including requests132, may be based on a specific posting136. With continued reference toFIG.1, processor104may be configured to receive a selection from user device116of a posting136. A “posting,” as used in this disclosure, is a communication of a job position for which a prospective employer is seeking or may be seeking one or more candidates to potentially fill the job position. Processor104may receive a plurality of postings136. A posting may include information about the employer such as the employer's name and address; compensation such as a salary, an hourly wage, and/or benefits; a title of the job position; geographical location of where the job will be performed and/or whether the job is to be performed remotely; a description of the job position such as a listing of responsibilities, expectations, and/or goals to be accomplished; criteria140; and/or the like. A job position may be part-time and/or full-time. Job position may be as an employee and/or contractor. As used in this disclosure, “criteria,” are skills, accreditations, a minimum grade point average (GPA), degree, major and/or focus of study, and/or experience. Criteria140may include requirements and/or preferences. As used in this disclosure, a “requirement” is a criterion that must be satisfied for a prospective employee to be eligible for consideration for a posting. As used in this disclosure, a “preference” is a desired criterion, but it is not required for a prospective employee to be considered for a posting. Request132may be based on a specific posting136, such as a posting136for which user has selected to apply. For example, user may select posting136for a position as a professor in philosophy at a community college, and request132may be, without limitation, “How many years have you taught philosophy?”, “Have you written any published articles?”, or “Please explain your educational background in philosophy.” Additional disclosure pertaining to posting can be found in U.S. patent application Ser. No. 17/582,059 filed on Jan. 24, 2022, and entitled “APPARATUS AND METHODS FOR MATCHING VIDEO RECORDS WITH POSTINGS USING AUDIOVISUAL DATA PROCESSING”, the entirety of which in incorporated herein by reference. Employer may decide on what type of requests132are appropriate. In some embodiments, chatbot128may be configured to allow user to input verbal communication112as a freeform response into the chat box. Chatbot128may then use a decision tree, data base, or other data structure to respond to the users entry into the chatbot128as a function of a chatbot128input. As used in the current disclosure, “chatbot input” is any response that a candidate or employer inputs in to a chatbot128as a response to a prompt or question, such as verbal communication112.

With continued reference toFIG.1, processor104may be configured to analyze verbal communication112submitted into chatbot128. Chatbot128may be able to analyze verbal communication112based on pre-determined set of factors, employer input, keywords, composition, and clarity of user's verbal communication112. In some embodiments, processor104may search for a synonyms or other equivalent words to the keywords and correlate those responses to the keywords. User classification may occur by identifying keywords within verbal communication112and/or by employing a machine learning model. Request132may be based on at least a user document120, such as user's transcript and/or written resume. For example, processor104may be configured to utilize optical character recognition (OCR) or any word recognition process discussed in this disclosure to identify one or more keywords in user document120. As used in this disclosure, a “keyword” is an element of word or syntax used to identify and/or match elements to each other. Keyword may be at least a user characteristic124. Keywords may be stored in a database, such as keyword database, from which processor104may retrieve the keywords. Keyword database may be implemented, without limitation, as a relational database, a key-value retrieval datastore such as a NOSQL database, or any other format or structure for use as a datastore that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Keyword may include locations such as cities, states, and regions; use characteristic152; criteria140as discussed below; and/or the like. In some embodiments, request132may be based on at least a keyword processor104identifies in user document120. For example, processor104may identify “electrical engineering” in user's university transcript and create an initial request132concerning electrical engineering such as, “Are you looking for a job in electrical engineering?” or “How many years of experience do you have working as an electrical engineer?” In some embodiments, user may select at least a keyword to indicate a type of job the user is interested in. For example, user may select “teacher” and a subcategory “kindergarten teacher” to identify that the user is seeking employment as a kindergarten teacher. Request132may include asking user for name, address, email address, phone number, whether they are currently employed, etc.

With continued reference toFIG.1, processor104may be configured to recognize at least a keyword in user document120as a function of visual verbal content. In some cases, recognizing a plurality of keywords in user document120may include using a language processing module172. In some embodiments, samples, and examples of keywords submitted by a hiring entity or apparatus100administrator may be used to train language processing module172in identifying keywords. For instance, a sample may be a list of synonyms used for common words used by hiring entities, such as “advocate”, “attorney-at-law”, “counsel”, “counselor”, “counselor-at-law”, “lawyer”, and “legal eagle”. These samples and examples may additionally be used to classify keywords to similar keywords contained in a plurality of user identifiers, as described further below. Language processing module172may include any hardware and/or software module. For example, language processing module172may be used to extract from user document120all information pertaining to “truck driver”. Language processing module172may be configured to extract, from user document120, one or more words. One or more words may include, without limitation, strings of one or more characters, including without limitation any sequence or sequences of letters, numbers, punctuation, diacritic marks, engineering symbols, geometric dimensioning and tolerancing (GD&T) symbols, chemical symbols and formulas, spaces, whitespace, and other symbols, including any symbols usable as textual data as described above. Textual data may be parsed into tokens, which may include a simple word (sequence of letters separated by whitespace) or more generally a sequence of characters as described previously. The term “token,” as used herein, refers to any smaller, individual groupings of text from a larger source of text; tokens may be broken up by word, pair of words, sentence, or other delimitation. These tokens may in turn be parsed in various ways. Textual data may be parsed into words or sequences of words, which may be considered words as well. Textual data may be parsed into “n-grams”, where all sequences of n consecutive characters are considered. Any or all possible sequences of tokens or words may be stored as “chains”, for example for use as a Markov chain or Hidden Markov Model.

Still referring toFIG.1, language processing module172may operate to produce a language processing model. Language processing model may include a program automatically generated by processor104and/or language processing module172to produce associations between one or more words extracted from at least a user document120and detect associations, including without limitation mathematical associations, between such words. Associations between language elements, where language elements include for purposes herein extracted words, relationships of such categories to other such term may include, without limitation, mathematical associations, including without limitation statistical correlations between any language element and any other language element and/or language elements. Statistical correlations and/or mathematical associations may include probabilistic formulas or relationships indicating, for instance, a likelihood that a given extracted word indicates a given category of semantic meaning. As a further example, statistical correlations and/or mathematical associations may include probabilistic formulas or relationships indicating a positive and/or negative association between at least an extracted word and/or a given semantic meaning; positive or negative indication may include an indication that a given document is or is not indicating a category semantic meaning. Whether a phrase, sentence, word, or other textual element in a document or corpus of documents constitutes a positive or negative indicator may be determined, in an embodiment, by mathematical associations between detected words, comparisons to phrases and/or words indicating positive and/or negative indicators that are stored in memory at computing device, or the like.

Still referring to 1, language processing module172and/or diagnostic engine may generate the language processing model by any suitable method, including without limitation a natural language processing classification algorithm; language processing model may include a natural language process classification model that enumerates and/or derives statistical relationships between input terms and output terms. Algorithm to generate language processing model may include a stochastic gradient descent algorithm, which may include a method that iteratively optimizes an objective function, such as an objective function representing a statistical estimation of relationships between terms, including relationships between input terms and output terms, in the form of a sum of relationships to be estimated. In an alternative or additional approach, sequential tokens may be modeled as chains, serving as the observations in a Hidden Markov Model (HMM). HMMs as used herein are statistical models with inference algorithms that that may be applied to the models. In such models, a hidden state to be estimated may include an association between an extracted words, phrases, and/or other semantic units. There may be a finite number of categories to which an extracted word may pertain; an HMM inference algorithm, such as the forward-backward algorithm or the Viterbi algorithm, may be used to estimate the most likely discrete state given a word or sequence of words. Language processing module172may combine two or more approaches. For instance, and without limitation, machine-learning program may use a combination of Naive-Bayes (NB), Stochastic Gradient Descent (SGD), and parameter grid-searching classification techniques; the result may include a classification algorithm that returns ranked associations.

Continuing to refer toFIG.1, generating language processing model may include generating a vector space, which may be a collection of vectors, defined as a set of mathematical objects that can be added together under an operation of addition following properties of associativity, commutativity, existence of an identity element, and existence of an inverse element for each vector, and can be multiplied by scalar values under an operation of scalar multiplication compatible with field multiplication, and that has an identity element is distributive with respect to vector addition, and is distributive with respect to field addition. Each vector in an n-dimensional vector space may be represented by an n-tuple of numerical values. Each unique extracted word and/or language element as described above may be represented by a vector of the vector space. In an embodiment, each unique extracted and/or other language element may be represented by a dimension of vector space; as a non-limiting example, each element of a vector may include a number representing an enumeration of co-occurrences of the word and/or language element represented by the vector with another word and/or language element. Vectors may be normalized, scaled according to relative frequencies of appearance and/or file sizes. In an embodiment associating language elements to one another as described above may include computing a degree of vector similarity between a vector representing each language element and a vector representing another language element; vector similarity may be measured according to any norm for proximity and/or similarity of two vectors, including without limitation cosine similarity, which measures the similarity of two vectors by evaluating the cosine of the angle between the vectors, which can be computed using a dot product of the two vectors divided by the lengths of the two vectors. Degree of similarity may include any other geometric measure of distance between vectors.

Still referring toFIG.1, language processing module172may use a corpus of documents to generate associations between language elements in the language processing module172, and diagnostic engine may then use such associations to analyze words extracted from one or more documents and determine that the one or more documents indicate significance of a category. In an embodiment, language module and/or processor104may perform this analysis using a selected set of significant documents, such as documents identified by one or more experts as representing good information; experts may identify or enter such documents via graphical user interface or may communicate identities of significant documents according to any other suitable method of electronic communication, or by providing such identity to other persons who may enter such identifications into processor104. Documents may be entered into processor104by being uploaded by an expert or other persons using, without limitation, file transfer protocol (FTP) or other suitable methods for transmission and/or upload of documents; alternatively or additionally, where a document is identified by a citation, a uniform resource identifier (URI), uniform resource locator (URL) or other datum permitting unambiguous identification of the document, diagnostic engine may automatically obtain the document using such an identifier, for instance by submitting a request to a database or compendium of documents such as JSTOR as provided by Ithaka Harbors, Inc. of New York.

With continued reference toFIG.1, identifying keywords in user document120may include matching a plurality of keywords to words in user document120. In some embodiments, matching may include classifying keywords contained in keyword database to similar words contained in user document120. For example, keywords relating “web developer internship experience” may be matched to similar words in user document120. Similar words may be based on synonyms of keywords as described above. Matching may occur through a classifier176. 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. Classifier176may 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. Processor104and/or another device may generate classifier176using a classification algorithm, defined as a processes whereby processor104derives classifier176from training data. In an embodiment, training data may include data from a database as described in this disclosure including keyword database, sample and examples of keywords and words similar to keywords, language processing module172, and any other training data described throughout this disclosure. Classifier176may take the plurality of keywords from keyword database as algorithm inputs. Classifier176may then use the training data disclosed above to output data bins of words similar to keywords matched to keywords. Each data bin may be categorized to each keyword and labeled with the keyword. 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. Words similar to keywords that are classified and labeled according to the corresponding keywords may become keywords and/or be added to keyword database.

With continued reference toFIG.1, processor104may be configured to generate classifier176using a K-nearest neighbors (KNN) algorithm. A “K-nearest neighbors algorithm” as used in this disclosure, includes a classification method that utilizes feature similarity to analyze how closely out-of-sample-features resemble training data to classify input data to one or more clusters and/or categories of features as represented in training data; this may be performed by representing both training data and input data in vector forms, and using one or more measures of vector similarity to identify classifications within training data, and to determine a classification of input data. K-nearest neighbors algorithm may include specifying a K-value, or a number directing the classifier176to select the k most similar entries training data to a given sample, determining the most common classifier176of the entries in a database, and classifying the known sample; this may be performed recursively and/or iteratively to generate classifier176that may be used to classify input data as further samples. For instance, an initial set of samples may be performed to cover an initial heuristic and/or “first guess” at an output and/or relationship, which may be seeded, without limitation, using expert input received according to any process as described herein. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data. Heuristic may include selecting some number of highest-ranking associations and/or training data elements.

l=∑i=0nai2,
where ai is 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. Keyword may be consistent with disclosure of keyword in U.S. patent application Ser. No. 17/690,424 filed on Mar. 9, 2022, and entitled “APPARATUSES AND METHODS FOR LINKING POSTING DATA”, which is incorporated herein by reference in its entirety.

Still referring toFIG.1, processor104may implement one or more algorithms or generate one or more machine-learning modules, such as request module144, to generate request132to user. In one or more embodiments, the machine-learning module may be generated using 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 database, such as any database described in this disclosure, or be provided by a user such as a prospective employee, and/or an employer. In other embodiments, a machine-learning module may obtain a training set by querying a communicatively connected database that includes past inputs and outputs. Training data may include inputs from various types of databases, resources, and/or user inputs and 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 descriptors of categories by tags, tokens, or other data elements. Request module144may be generated using training data, such as request data. Request module144may be trained by correlated inputs and outputs in request data. Inputs of request data may include keywords, verbal communication112containing at least a keyword, and/or user documents120containing at least a keyword. Outputs of request data may include requests132corresponding to the inputs. Request data may be inputs and corresponding outputs that have already been determined whether manually, by machine, or any other method. Request data may include previous outputs such that request module144iteratively produces outputs. In some embodiments, criteria140in request data may be requirements and not preferences. In embodiments in which request132is based on posting136, inputs of request data may include criteria140and/or postings136containing criteria140, and outputs of request data may include requests132corresponding to the inputs. Request module144using a machine-learning process may output request132based on request data and input of criteria140, postings136including criteria140, keywords, and/or user documents120containing at least a keyword. Chatbot128may utilize request module144and prompt user with request132based on output of the request module144. For example, chatbot128may prompt user with request132, wherein the chatbot128is configured to output the request132, utilizing request module144, based on an input of at least a keyword, verbal communication112containing at least a keyword, and/or user documents120containing at least a keyword and request data. As another example, chatbot128may prompt user with request132, wherein the chatbot128is configured to output the request132, utilizing request module144, based on an input of criteria140and/or postings136containing criteria140and request data.

With continuing reference toFIG.1, processor104may be configured to respond to verbal communication112with request132using a decision tree. A “decision tree,” as used in this disclosure, is a data structure that represents and combines one or more determinations or other computations based on and/or concerning data provided thereto, as well as earlier such determinations or calculations, as nodes of a tree data structure where inputs of some nodes are connected to outputs of others. Decision tree may have at least a root node, or node that receives data input to the decision tree, corresponding to at least a user input into a chatbot128. decision tree has at least a terminal node, which may alternatively or additionally be referred to herein as a “leaf node,” corresponding to at least an exit indication; in other words, decision and/or determinations produced by decision tree may be output at the at least a terminal node. decision tree may include one or more internal nodes, defined as nodes connecting outputs of root nodes to inputs of terminal nodes. Processor104may generate two or more decision trees, which may overlap; for instance, a root node of one tree may connect to and/or receive output from one or more terminal nodes of another tree, intermediate nodes of one tree may be shared with another tree, or the like.

Still referring toFIG.1, processor104may build decision tree by following relational identification; for example, relational indication may specify that a first rule module receives an input from at least a second rule module and generates an output to at least a third rule module, and so forth, which may indicate to processor104an in which such rule modules will be placed in decision tree. Building decision tree may include recursively performing mapping of execution results output by one tree and/or subtree to root nodes of another tree and/or subtree, for instance by using such execution results as execution parameters of a subtree. In this manner, processor104may generate connections and/or combinations of one or more trees to one another to define overlaps and/or combinations into larger trees and/or combinations thereof. Such connections and/or combinations may be displayed by visual interface to user, for instance in first view, to enable viewing, editing, selection, and/or deletion by user; connections and/or combinations generated thereby may be highlighted, for instance using a different color, a label, and/or other form of emphasis aiding in identification by a user. In some embodiments, subtrees, previously constructed trees, and/or entire data structures may be represented and/or converted to rule modules, with graphical models representing them, and which may then be used in further iterations or steps of generation of decision tree and/or data structure. Alternatively or additionally subtrees, previously constructed trees, and/or entire data structures may be converted to APIs to interface with further iterations or steps of methods as described in this disclosure. As a further example, such subtrees, previously constructed trees, and/or entire data structures may become remote resources to which further iterations or steps of data structures and/or decision trees may transmit data and from which further iterations or steps of generation of data structure receive data, for instance as part of a decision in a given decision tree node.

Continuing to refer toFIG.1, decision tree may incorporate one or more manually entered or otherwise provided decision criteria. decision tree may incorporate one or more decision criteria using an application programmer interface (API). decision tree may establish a link to a remote decision module, device, system, or the like. decision tree may perform one or more database lookups and/or look-up table lookups. decision tree may include at least a decision calculation module, which may be imported via an API, by incorporation of a program module in source code, executable, or other form, and/or linked to a given node by establishing a communication interface with one or more exterior processes, programs, systems, remote devices, or the like; for instance, where a user operating system has a previously existent calculation and/or decision engine configured to make a decision corresponding to a given node, for instance and without limitation using one or more elements of domain knowledge, by receiving an input and producing an output representing a decision, a node may be configured to provide data to the input and receive the output representing the decision, based upon which the node may perform its decision.

Still referring toFIG.1, verbal communication120may be a video record148of user. As used in this disclosure, a “video record” is data including an audio recording of a prospective employee for purposes of potentially acquiring a job. The audio recording may include verbal content152. For example, verbal content152may include a monologue. Video record148may also include a visual recording of the prospective employee. Visual recording may include an image component156. As used in this disclosure, “image component” may be a visual representation of information, such as a plurality of temporally sequential frames and/or pictures, related to video record148. For example, image component156may include animations, still imagery, recorded video, and the like. In some cases, at least a user characteristic124may be explicitly conveyed within video record148. Alternatively, or additionally, in some cases, at least a user characteristic124may be conveyed implicitly in video record148. Video record148may be communicated by way of digital signals, for example between computing devices which are communicatively connected with at least a wireless network. Video record148may be compressed to optimize speed and/or cost of transmission of video. Video record148may be compressed according to a video compression coding format (i.e., codec). Exemplary video compression codecs include H.26x codecs, MPEG formats, VVC, SVT-AV1, and the like. In some cases, compression of a digital video may be lossy, in which some information may be lost during compression. Alternatively, or additionally, in some cases, compression of a video record148may be substantially lossless, where substantially no information is lost during compression. Processor104may receive posting136and/or video record148from a user, such as an employer, hiring agency, recruiting firm, and/or a prospective employee. Processor104may receive posting136and/or video record148from a computing device through a network, from a database, and or store posting136and/or video record148in a memory and retrieve from the memory. Apparatus100may include a memory108. Memory108may be communicatively connected to processor104and may be configured to store information and/or datum related to apparatus100, such as request132, posting136including criteria140, user document120, keywords selected by user, video record148including verbal communication112with at least a user characteristic124.

Still referring toFIG.1, processor104may be configured to extract a plurality of textual elements from video record148of verbal communication112, which may include at least a user characteristic124. Processor104may include audiovisual speech recognition (AVSR) processes to recognize verbal content152in video records140. For example, processor104may use image component156to aid in recognition of audible verbal content152such as viewing prospective employee move their lips to speak on video to process the audio content of video record148. AVSR may use image component156to aid the overall translation of the audio verbal content152of video records140. In some embodiments, AVSR may include techniques employing image processing capabilities in lip reading to aid speech recognition processes. In some cases, AVSR may be used to decode (i.e., recognize) indeterministic phonemes or help in forming a preponderance among probabilistic candidates. In some cases, AVSR may include an audio-based automatic speech recognition process and an image-based automatic speech recognition process. AVSR may combine results from both processes with feature fusion. Audio-based speech recognition process may analysis audio according to any method described herein, for instance using a Mel frequency cepstral coefficients (MFCCs) and/or log-Mel spectrogram derived from raw audio samples. Image-based speech recognition may perform feature recognition to yield an image vector. In some cases, feature recognition may include any feature recognition process described in this disclosure, for example a variant of a convolutional neural network. In some cases, AVSR employs both an audio datum and an image datum to recognize verbal content152. For instance, audio vector and image vector may each be concatenated and used to predict speech made by prospective employee, who is ‘on camera.’

With continued reference toFIG.1, processor104may be configured to analyze verbal communication112. Analyzing verbal communication112may include identifying at least a user characteristic124from video record148. In some cases, processor104may be configured to recognize at least a keyword as a function of visual verbal content152. In some cases, recognizing at least keyword may include optical character recognition. In some cases, processor104may generate a transcript of much or even all verbal content152from video record148. Processor104may use transcript to analyze the content of video record148and extract at least a user characteristic124.

Still refereeing toFIG.1, 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 component156may 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 to 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 may 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 component156. 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 image component156to 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 a background of image component156. 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 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 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 aspect ratio and/or scale of image component156.

Still referring toFIG.1, in some embodiments an OCR process may 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 case, 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 component156. Matrix matching may also rely on a stored glyph being in a similar font and at a 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 at least a feature. 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 may be compared with an abstract vector-like representation of a character, which might reduce to one or more glyph prototypes. General techniques of feature detection in computer vision are applicable to this type of OCR. In some embodiments, machine-learning processes like nearest neighbor classifiers (e.g., k-nearest neighbors algorithm) may be used to compare image features with stored glyph features and choose a nearest match. OCR may employ any machine-learning process described in this disclosure, for example machine-learning processes described with reference toFIG.2. Exemplary non-limiting OCR software includes Cuneiform and Tesseract. Cuneiform is a multi-language, open-source optical character recognition system originally developed by Cognitive Technologies of Moscow, Russia. Tesseract is free OCR software originally developed by Hewlett-Packard of Palo Alto, Calif., United States.

Still referring toFIG.1, in some cases, OCR may employ a two-pass approach to character recognition. A first pass may try to recognize a character. Each character that is satisfactory is passed to an adaptive classifier as training data. The adaptive classifier then gets a chance to recognize characters more accurately as it further analyzes image components124. Since the adaptive classifier may have learned something useful a little too late to recognize characters on the first pass, a second pass is run over the image components148. Second pass may include adaptive recognition and use characters recognized with high confidence on the first pass to recognize better remaining characters on the second pass. In some cases, two-pass approach may be advantageous for unusual fonts or low-quality image components148where visual verbal content152may 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.

Still referring toFIG.1, in some cases, OCR may include post-processing. For example, OCR accuracy may 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 content152. 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 us of apriori 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, request132may include a plurality of requests132. For example, processor104may be configured to prompt user with at least a request132based on verbal communication112. A second request132may be a follow up to a preceding request132. For example, if an initial request132is “Are you open to relocating for employment?” and verbal communication112is in the affirmative such as “yes” or “I think so,” then subsequent request132may be “Which cities, states, or regions would you consider moving to?”. As another example, if user indicates in initial verbal communication112that the user was once employed as a manager of a team of people, subsequent request132may ask the user to describe a scenario where the user gave critical feedback to a team member when the team member fell below expectations. Subsequent request132may also be based on a specific posting136such as, “Would you be willing to relocate to Chicago?” if the posting136was for a position located in Chicago. Subsequent request132may seek additional information related to initial verbal communication112. For example, if initial verbal communication112is an affirmative response to initial request132of whether user has written any published articles, subsequent request132may be, for example, “How many published articles have you authored?”, “What are the subject matters of the articles?”, “In which journals or publications were the articles published”, or “what are the titles of the articles?”.

Requests132may be included in a set of requests for a subset of users. For example, each industry, type of profession, and/or field of work may have an associated set of requests to procure a consistent set of information from user. As example, Processor104may determine that user is an accountant by user document120, selected keyword, and/or verbal communication112the user has already submitted. Processor104may then prompt user with requests112from a set of requests associated with accountant. Processor104may ask “Are you a Certified Public Accountant?” If user's response is affirmative, processor104may ask user how many years of experience the user has as a CPA. Additional requests in the set of requests associated with accountants may include, for example, “What is your area of expertise?” and “Do you have experience in Accounts Receivable?” In some embodiments, processor104may be configured to determine which set of requests apply to user based on verbal communication112. Processor104may determine a category corresponding to user. For example, categories may include accountant, mechanical engineer, elementary school teacher, etc., and each category may have an associated set of requests. In these embodiments, inputs of request data verbal communication112, and outputs of request data may be categories corresponding to the verbal communication112, wherein the categories identify which set of requests apply to user. For example, categories may include accountant, mechanical engineer, elementary school teacher, etc., and each category may have an associated set of requests. Sets of requests may be stored in memory108or a database.

Still referring toFIG.1, processor104is configured to screen user as a function of user characteristic124. Screening may include generating a compatibility score160based on a compatibility of at least a user characteristic124with posting136, employer input, selected keyword, and/or user document120. Compatibility score160may be based on at least a requirement of criteria140for posting136. For example, if a requirement of criteria140is five years' experience as a sales manager and user characteristic124includes seven years' experience as a sales manager, then compatibility score160may be high. Compatibility score160may be numerical, for example out of a score of one hundred. In some embodiments, compatibility score160may include qualitative descriptors, such as great, good, average, lacking, and/or poor. Compatibility score160may be consistent with disclosure of compatibility score in U.S. patent application Ser. No. 17/582,087 filed on Jan. 24, 2022, and entitled “DIGITAL POSTING MATCH RECOMMENDATION APPARATUS AND METHODS”, which is incorporated by reference herein in its entirety.

Screening a user may include determining a confidence score164. As used in this disclosure, a “confidence score” is a score reflecting a confidence in the screening process. In some embodiments, confidence score164may be reflective of a level of confidence in the accuracy of a compatibility score. Similar to compatibility score160, confidence score164may include a quantitative value, for example out of a score of one hundred or a value from1-10. In some embodiments, confidence score164may include qualitative descriptors, such as great, good, average, lacking, and/or poor. Confidence score164may be based on an amount of the verbal communication112, such as a length of time of verbal communication112, a word count of the verbal communication112, and/or the number of requests132to which user responded. For example, a higher word count of verbal communication112may imply a more complete response by user to one or more requests132, which may correlate with a greater understanding of user and of user characteristics124. Confidence score164may be based on whether processor receives at least a user document120and/or an amount of details included in the user document120. For example, transcripts may corroborate user characteristic124parsed from oral verbal communication112. Confidence score164may be based on an amount of criteria140in posting136. For example, few criteria140in posting136may reduce the certainty that user is compatible with the posting136. A large number of criteria140and/or a high level of detail for the criteria140may provide greater incite as to whether user is compatible with posting136. Confidence score164may be based on an amount of requirements for criteria140of posting136. Processor104may transmit to employer of posting136all verbal communications112and/or user documents120of users with a threshold compatibility score160and/or a threshold confidence score164for the posting136.

Still referring toFIG.1, processor104may implement one or more algorithms or generate one or more machine-learning modules, such as confidence module168, to generate confidence score164. Confidence module168may be generated using training data, such as confidence data. Confidence module168may be trained by correlated inputs and outputs in confidence data. Inputs of confidence data may include factors of confidence score164discussed above such as verbal communications112, amounts or word counts of verbal communications112, numbers of requests132, amount of user documents120, levels of detail of user documents120, and/or criteria140of postings136. Outputs of confidence data may include confidence scores164corresponding to the inputs. Confidence data may be inputs and corresponding outputs that have already been determined whether manually, by machine, or any other method. Confidence data may include previous outputs such that confidence module168iteratively produces outputs. Confidence module168using a machine-learning process may output confidence score164based on confidence data and input of verbal communications112, amounts or word counts of verbal communications112, numbers of requests132, amount of user documents120, levels of detail of user documents120, and/or criteria140of postings136.

With continued reference toFIG.1, processor104may be configured to post user document120, posting136, verbal communication112, video record148, compatibility score160, and/or confidence score164on an immutable sequential listing, such as blockchain, as discussed below with reference toFIG.4.

Referring now toFIG.2, an exemplary embodiment of neural network200is illustrated. A neural network200also known as an artificial neural network, is a network of “nodes,” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes may be organized in a network, such as without limitation a convolutional neural network, including an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. Connections may run solely from input nodes toward output nodes in a “feed-forward” network or may feed outputs of one layer back to inputs of the same or a different layer in a “recurrent network.”

Referring now toFIG.4, an exemplary embodiment of an immutable sequential listing400is illustrated. Data elements are listing in immutable sequential listing400; 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 assertion404is 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. 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 assertion404. In an embodiment, collection of textual data states that the owner of a certain transferable item represented in a digitally signed assertion404register is transferring that item to the owner of an address. A digitally signed assertion404may be signed by a digital signature created using the private key associated with the owner's public key, as described above.

Still referring toFIG.4, in some embodiments, an address is a textual datum identifying the recipient of virtual currency or another item of value, such as verbal communication112and/or user document120, in a digitally signed assertion404. In some embodiments, address may be linked to a public key, the corresponding private key of which is owned by the recipient of a digitally signed assertion404. 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 assertion404may record a subsequent a digitally signed assertion404transferring some or all of the value transferred in the first a digitally signed assertion404to a new address in the same manner. A digitally signed assertion404may 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 assertion404may indicate a confidence level associated with a distributed storage node as described in further detail below.

In an embodiment, and still referring toFIG.4immutable sequential listing400records 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 listing400may 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.4, immutable sequential listing400may preserve the order in which the at least a posted content took place by listing them in chronological order; alternatively or additionally, immutable sequential listing400may organize digitally signed assertions404into sub-listings408such as “blocks” in a blockchain, which may be themselves collected in a temporally sequential order; digitally signed assertions404within a sub-listing408may or may not be temporally sequential. User document120, posting136, verbal communication112, video record148, compatibility score160, and/or confidence score164may be posted on immutable sequential listing400, such as blockchain. Training data for any machine-learning module discussed in this disclosure may be posted on immutable sequential listing400, such as blockchain. A master list may be included. Master list may include a hash-table and/or distributed hash table which may be used to locate a requestor-linked data store. For example, a public key associated with a requestor containing location information pertaining to requestor-linked data store may be converted into a series of hash functions. This may occur by converting an entry into a series of integers by using a hash function. A hash function may include any function that may be used to map a set of data which falls into the hash table. Hash functions may be stored in a hash table, where it can be quickly retrieved using a hashed key. The hashed key may then be used to access requestor-linked data store when prompted. Using the hashed key, a hash function may compute an index that may suggest where requestor-linked data store may be found. Locating may also be performed by linking the at least an encrypted data record to a digital signature associated with the requestor. Requestor may produce a digital signature, which may then be linked to the at least an encrypted data record and locate to the location of the at least an encrypted data record. When the digital signature is presented, it may contain location information of the at least an encrypted data record and allow access control regulator to locate the precise location of encrypted data record. For example, digital signature may be generated using a public and/or private key linked to requestor which may contain location information of encrypted data record. In an embodiment, encrypted data record may be linked to a requestor key, so that when a requestor key is presented, location of encrypted data record becomes apparent. Locating may also be performed by information that may be contained in data access request. For example, a data access request associated with a user may contain location information of encrypted data record that requestor is attempting to access. When generating a data access request, requestor may specify the location of encrypted data record that may then be transmitted to access control regulator. Additional disclosure pertaining to immutable sequential listing can be found in U.S. patent application Ser. No. 17/486,461 filed on Sep. 27, 2021, and entitled “SYSTEMS AND METHODS FOR SCORE GENERATION FOR APPLICANT TRACKING”, the entirety of which in incorporated herein by reference.

With continued reference toFIG.4, the ledger may preserve the order in which at least a posted content took place by listing them in sub-listings408and placing the sub-listings408in chronological order. The immutable sequential listing400may 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 listing400may 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. Immutable sequential listing400and/or any component of the immutable sequential listing400, such as sub-listing408and digitally signed assertions404, may be validated by processor104consistent with disclosure of validation in U.S. patent application Ser. No. 16/698,182 filed on Nov. 27, 2019 and titled “SYSTEMS AND METHODS FOR BIOMETRIC KEY GENERATION IN DATA ACCESS CONTROL, DATA VERIFICATION, AND PATH SELECTION IN BLOCK CHAIN-LINKED WORKFORCE DATA MANAGEMENT”, which is incorporated by reference herein in its entirety.

In some embodiments, and with continued reference toFIG.4, immutable sequential listing400, once formed, may be inalterable by any party, no matter what access rights that party possesses. For instance, immutable sequential listing400may include a hash chain, in which data is added during a successive hashing process to ensure non-repudiation. Immutable sequential listing400may include a block chain. In one embodiment, a block chain is immutable sequential listing400that records one or more new at least a posted content in a data item known as a sub-listing408or “block.” An example of a block chain is the BITCOIN block chain used to record BITCOIN transactions and values. Sub-listings408may be created in a way that places the sub-listings408in chronological order and link each sub-listing408to a previous sub-listing408in the chronological order so that any computing device may traverse the sub-listings408in reverse chronological order to verify any at least a posted content listed in the block chain. Each new sub-listing408may be required to contain a cryptographic hash describing the previous sub-listing408. In some embodiments, the block chain may contain a single first sub-listing408sometimes known as a “genesis block.”

Still referring toFIG.4, the creation of a new sub-listing408may be computationally expensive; for instance, the creation of a new sub-listing408may be designed by a “proof of work” protocol accepted by all participants in forming the immutable sequential listing400to take a powerful set of computing devices a certain period of time to produce. Where one sub-listing408takes less time for a given set of computing devices to produce the sub-listing408, protocol may adjust the algorithm to produce the next sub-listing408so that it will require more steps; where one sub-listing408takes more time for a given set of computing devices to produce the sub-listing408, protocol may adjust the algorithm to produce the next sub-listing408so that it will require fewer steps. As an example, protocol may require a new sub-listing408to contain a cryptographic hash describing its contents; the cryptographic hash may be required to satisfy a mathematical condition, achieved by having the sub-listing408contain 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-listing408and 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-listing408according to the protocol is known as “mining.” The creation of a new sub-listing408may 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.4, in some embodiments, protocol also creates an incentive to mine new sub-listings408. The incentive may be financial; for instance, successfully mining a new sub-listing408may result in the person or entity that mines the sub-listing408receiving 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-listings408. Each sub-listing408created in immutable sequential listing400may 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-listing408.

With continued reference toFIG.4, where two entities simultaneously create new sub-listings408, immutable sequential listing400may develop a fork; protocol may determine which of the two alternate branches in the fork is the valid new portion of the immutable sequential listing400by evaluating, after a certain amount of time has passed, which branch is longer. “Length” may be measured according to the number of sub-listings408in 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 in 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-listing408in 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 listing400branch 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 listing400.

Still referring toFIG.4, additional data linked to at least a posted content may be incorporated in sub-listings408in the immutable sequential listing400; 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 listing400. 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.

Referring toFIG.5, a chatbot system500is schematically illustrated. According to some embodiments, a user interface504may be communicative with a computing device508that is configured to operate a chatbot. In some cases, user interface504may be local to computing device508. Alternatively or additionally, in some cases, user interface504may remote to computing device508and communicative with the computing device508, by way of one or more networks, such as without limitation the internet. Alternatively or additionally, user interface504may communicate with user device508using telephonic devices and networks, such as without limitation fax machines, short message service (SMS), or multimedia message service (MMS). Commonly, user interface504communicates with computing device508using text-based communication, for example without limitation using a character encoding protocol, such as American Standard for Information Interchange (ASCII). Typically, a user interface504conversationally interfaces a chatbot, by way of at least a submission512, from the user interface508to the chatbot, and a response516, from the chatbot to the user interface504. In many cases, one or both of submission512and response516are text-based communication. Alternatively or additionally, in some cases, one or both of submission512and response516are audio-based communication.

Continuing in reference toFIG.5, a submission512once received by computing device508operating a chatbot, may be processed by a processor520. In some embodiments, processor520processes a submission5112using 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, processor520may retrieve a pre-prepared response from at least a storage component524, based upon submission512. Alternatively or additionally, in some embodiments, processor520communicates a response516without first receiving a submission512, thereby initiating conversation. In some cases, processor520communicates an inquiry to user interface504; and the processor is configured to process an answer to the inquiry in a following submission512from the user interface504. In some cases, an answer to an inquiry present within a submission512from a user device504may be used by computing device104as an input to another function, for example without limitation at least a feature108or at least a preference input112

Now referring toFIG.6, an exemplary embodiment of a method600for screening users is illustrated. At step605, receives verbal communication associated with user; this may be implemented, without limitation, as described above in reference toFIGS.1-6. Verbal communication may include audio recording of user and/or visual recording of the user.

At step610, processor parses, using chatbot, at least a user characteristic from verbal communication; this may be implemented, without limitation, as described above in reference toFIGS.1-6. Chatbot may utilize a machine-learning module. Chatbot may prompt user with request, wherein the request is based on posting.

At step615, processor screens user as a function of user characteristic; this may be implemented, without limitation, as described above in reference toFIGS.1-6. Screening user includes generating a compatibility score based on a compatibility of the at least a user characteristic and a posting. Screening user also includes determining confidence score of compatibility score. Compatibility score may be based on a requirement of the posting. Confidence score may be based on an amount of verbal communication. Confidence score may be based on at least a user characteristic. Confidence score may be based on whether processor receives user document. Confidence score may be based on an amount of requirements for criteria of posting. Processor may transmit to employer of posting all verbal communications and/or user documents of users with a threshold compatibility score and/or a threshold confidence score for the posting.

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

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

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

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