Apparatus and method for data conversion

An apparatus and method for converting data is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to extract, using a data extraction module, user activity data from user device data, classify the user activity data into one or more user activity data groups, convert, using a data converting module, the user activity data to system data as a function of the one or more user activity data groups and generate, using a report generation module, a user activity report as a function of the system data.

FIELD OF THE INVENTION

The present invention generally relates to the field of data conversion. In particular, the present invention is directed to apparatus and method for converting data.

BACKGROUND

The automotive industry has undergone significant growth in recent years, with millions of vehicles on the roads worldwide. However, existing technologies are often complex, time-consuming, and prone to errors. A new system and method for transforming data is needed.

SUMMARY OF THE DISCLOSURE

In an aspect, apparatus for converting data is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to extract, using a data extraction module, user activity data from user device data, classify the user activity data into one or more user activity data groups, convert, using a data converting module, the user activity data to system data as a function of the one or more user activity data groups and generate, using a report generation module, a user activity report as a function of the system data.

In another aspect, a method for converting data is disclosed. The method includes extracting, using at least a processor and a data extraction module, user activity data from user device data, classifying, using the at least a processor, the user activity data into one or more user activity data groups, converting, using the at least a processor and a data converting module, the user activity data to system data as a function of the one or more user activity data groups, generating, using the at least a processor and a report generation module, a user activity report as a function of the system data.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to apparatus and method for converting data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to extract, using a data extraction module, user activity data from user device data, classify the user activity data into one or more user activity data groups, convert, using a data converting module, the user activity data to system data as a function of the one or more user activity data groups and generate, using a report generation module, a user activity report as a function of the system data.

Referring now toFIG.1, an illustration of an exemplary embodiment of an apparatus100for converting data is illustrated. The apparatus100includes at least a processor104. The at least a processor104may include, without limitation, any processor described in this disclosure. The at least a processor104may be included in a computing device. The at least a processor104may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. The at least a processor104may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. The at least a 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. The at least a 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 the at least a 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. The at least a 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. The at least a processor104may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. The at least a 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. The at least a processor104may be implemented, as a non-limiting example, using a “shared nothing” architecture.

With continued reference toFIG.1, a memory108contains instructions configuring at least a processor104to extract user activity data112from user device data116using a data extraction module120. For the purposes of this disclosure, “extracting” is the process of analyzing data and identifying the relevant information contained within it. For the purposes of this disclosure, a “data extraction module” is a module or a software component that is configured to extract user activity data from user device data. For the purposes of this disclosure, “user device data” is data that is received from a user device. In some embodiments, the user device data may include image data as described below. As a non-limiting example, the user device data116may include document scanned or captured by the user device. As a non-limiting example, the user device data116may be received from a cash register that includes a POS. As another non-limiting example, when a user pays for a service in a vehicle maintenance system using a personal device of the user, the user device data116may be received. For example and without limitation, when the user uses a car wash service and a vending machine to buy a coolant for the user's car and pays for them using an application (app) the user's smartphone, the user device data116can be received from the smartphone and the user activity data112may be extracted, where the user activity data112may include user billing information and user system activity data that includes a list of services the user used, a name of the coolant the user bought, a price of the car wash service, a price of the coolant. For the purposes of this disclosure, an “application,” also called “app” of a mobile device is a software program designed to perform a specific function or set of functions on a mobile device. In some embodiments, the app may be configured to receive the user's input. As a non-limiting example, the user may input user information, user billing information and user system activity data into the app. As another non-limiting example, the user may make a payment using the app. In another embodiment, when the user purchases a product by tapping a credit card to a RFID reader for purchasing a tool for a vehicle cleaning, the user device data116may be received and be parsed, then the user activity data112may include user billing information, a name of the tool the user bought and a price of the tool. In some embodiments, a user manually input the user device data116using a user device. As a non-limiting example, the user may manually input user device data116using a shared device such as but not limited to a tablet.

With continued reference toFIG.1, for the purposes of this disclosure, a “user device” is any device a user uses to manage sales transactions or to process payments. For the purposes of this disclosure, a “user” is any individual, group or company that is using or has used an apparatus. As a non-limiting example, the user may include a driver of a vehicle, a passenger of the vehicle, a car wash employee, a car wash employer, a technician, a customer of the carwash, a sales manager, an accountant, a financial advisor, a cashier, and the like. In some embodiments, the user may input user device data116using the user device. In some embodiments, the user device may be configured to receive the user device data116. In some embodiments, the user device may be configured to display the user device data116as described below. In some embodiments, the user device may be configured to display a user activity report128as described below.

With continued reference toFIG.1, in an embodiment, a user device may include a personal device. For the purposes of this disclosure, a “personal device” is any device personally owned by a user. As a non-limiting example, the personal device may include a laptop, tablet, mobile phone, smart watch, or things of the like. In some embodiments, the user device may include an interface configured to receive inputs from the user. In some embodiments, the user may have a capability to process, store or transmit any information independently. In another embodiment, the user device may include a shared device. For the purposes of this disclosure, a “shared device” is a device that is designed for use by multiple users. In some embodiments, the shared device may be used by different users at different times. As a non-limiting example, the shared devices may include desktop computers, kiosks, screens, tablets, or the like.

With continued reference toFIG.1, in another embodiment, a user device may include a point of sale (POS). For the purposes this disclosure, a “point of sale” is a system that is configured to manage sales transactions and process payments. In some embodiments, the POS may include a software running on a hardware such as but not limited to a mobile phone, tablet, laptop, desktop, and the like. As a non-limiting example, the POS may be implemented in a personal device. As another non-limiting example, the POS may be implemented in a shared device. In some embodiments, the POS may be configured to receive user device data116from a user as described below. In some embodiments, the POS may be configured to retrieve the user activity data from a user database. In some embodiments, the POS may include a service price, service information, and the like. As a non-limiting example, the service may include a service provided by a vehicle maintenance system. For the purposes of this disclosure, a “vehicle maintenance system” is a system that conduct a service, repair, or maintenance of a vehicle. As a non-limiting example, the vehicle maintenance system may include washing a vehicle, wiping the vehicle, vacuuming, providing a product for cleaning, repairing and/or maintenance of the vehicle, inflating a tire of the vehicle, changing oil of the vehicle, and the like. For example and without limitation, the product for cleaning, repairing and/or maintenance of the vehicle may be provided using a vending machine. For the purposes of this disclosure, a “vending machine” is a machine that dispenses products for cleaning, repairing and/or maintenance of a vehicle. In some embodiments, the vending machine may dispense snacks, beverages, cigarettes, lottery tickets, or even personal hygiene products for the user. Additional disclosure related to the vehicle maintenance system may be found in U.S. patent application Ser. No. 18/196,147, filed on May 11, 2023, entitled as “METHODS AND APPARATUSES FOR CAR WASH USER EXPERIENCE,” the entirety of which is incorporated as a reference.

With continued reference toFIG.1, in some embodiments, a POS may include a cash register. For the purposes of this disclosure, a “cash register” is a mechanical or electronic device that is configured to process sales transactions and manage cash. As a non-limiting example, when a customer makes a purchase, a cashier may enter the details of the sale, such as the item(s) purchased, the price, and the amount of money tendered by the customer. The cash register, as a non-limiting example, then may calculate the total amount due, and the customer can pay by cash, credit/debit card, or other payment method. The cash register, as a non-limiting example, may keep a record of the transaction, including the date, time, and details of the sale, as well as any change given to the customer.

With continued reference toFIG.1, in another embodiment, a user device and/or apparatus100may include an accounting system. For the purposes of this disclosure, an “accounting system” is a system that is configured to manage financial transactions and related processes. In some embodiments, a user device may include the accounting system. In some embodiments, the accounting system may be configured to receive system data124from a user database. The system data124disclosed herein is further described below. In some embodiments, the accounting system may be configured to receive the system data124that is converted from user activity data112. In some embodiments, the accounting system may be configured to generate and/or display a user activity report128as described below. In some embodiments, the accounting system may be configured to display the system data124as described below. In some embodiments, a user may manually input the system data124into the accounting system using the user device.

With continued reference toFIG.1, in another embodiment, a user device and/or apparatus100may include a scanning device. For the purposes of this disclosure, a “scanning device” is a device for scanning a unique identifier. In some embodiments, the scanning device may be implemented in a POS. In some embodiments, the scanning device may include an illumination system, a sensor, and a decoder. The sensor in the scanning device may detect the reflected light from the illumination system and may generate an analog signal that is sent to the decoder. The decoder may interpret that signal, validate the unique identifier using the check digit, and convert it into text. This converted text may be delivered by the scanning device to a computing device holding a database of any information of a service, user, and the like. As a non-limiting example, the scanning device may include a pen-type reader, laser scanner, camera-based reader, charge-coupled device (CCD) reader, omni-directional barcode scanner, and the like. For example without limitation, the scanning device may include a mobile device with an inbuild camera such as without limitation, a phone, a tablet, a laptop, and the like. For example without limitation, a user may use a camera on a phone to scan a barcode. In some embodiments, the scanning device may include wired or wireless communication.

With continued reference toFIG.1, as used in this disclosure, a “camera” is a device that is configured to sense electromagnetic radiation, such as without limitation visible light, and generate an image representing the electromagnetic radiation. In some cases, a camera may include one or more optics. Exemplary non-limiting optics include spherical lenses, aspherical lenses, reflectors, polarizers, filters, windows, aperture stops, and the like. In some cases, at least a camera may include an image sensor. Exemplary non-limiting image sensors include digital image sensors, such as without limitation charge-coupled device (CCD) sensors and complimentary metal-oxide-semiconductor (CMOS) sensors, chemical image sensors, and analog image sensors, such as without limitation film. In some cases, a camera may be sensitive within a non-visible range of electromagnetic radiation, such as without limitation infrared.

With continued reference toFIG.1, in some embodiments, a camera may be configured to generate image data. As used in this disclosure, “image data” is information representing at least a physical scene, space, and/or object (e.g., a user or user's eyes). “Image data” may be used interchangeably through this disclosure with “image,” where image is used as a noun. An image may be optical, such as without limitation where at least an optic is used to generate an image of an object. An image may be material, such as without limitation when film is used to capture an image. An image may be digital, such as without limitation when represented as a bitmap. Alternatively, an image may be comprised of any media capable of representing a physical scene, space, and/or object. Alternatively where “image” is used as a verb, in this disclosure, it refers to generation and/or formation of an image. In some embodiments, the image data may be used for vehicle profiling. Additional disclosure related to the vehicle profiling may be found in U.S. patent application Ser. No. 18/195,760, filed on May 10, 2023, entitled “APPARATUS AND METHOD OF VEHICLE PROFILING,” the entirety of which is incorporated as a reference.

With continued reference toFIG.1, in some embodiments, image data may include an image of a receipt, document, credit card, debit card, unique identifier, and the like. For the purposes of this disclosure, a “receipt” is a document that provides evidence of a financial transaction. In some embodiments, the receipt may include a printed form, a digital form, and the like. As a non-limiting example, the camera may generate the image data by capturing the printed form of the receipt. For example, and without limitation, at least a processor104may extract user activity data112from the receipt using an optical character recognition (OCR) as described in the entirety of this disclosure. As another non-limiting example, the digital form of the receipt may be stored in a user database and retrieved from the user database. For the purposes of this disclosure, “document” is a recorded or written representation of information that can be used as evidence or as an official record. In some embodiments, the document may include a form of paper or electronic files. As a non-limiting example, the document may include text documents, spreadsheets, images, videos, audio recordings, or the like. As a non-limiting example, the document may include a survey, questionnaire, or any document thereof. For example, and without limitation, the at least a processor104may extract the user activity data112from the document using an optical character recognition (OCR) as described in the entirety of this disclosure.

With continued reference toFIG.1, in some embodiments, a scanning device may include a radio frequency identification (RFID) reader. For the purposes of this disclosure, a “radio frequency identification reader” is a device that emits radio waves and receives data transmitted by an RFID tag. In some embodiments, the RFID reader may be connected to a computer device that can process the data received from the RFID tag. For the purposes of this disclosure, “radio frequency identification tag” is a small electronic device that contains a unique identifier and can be attached to or embedded in an object or person. The unique identifier disclosed herein is further described below. The RFID tag may be also referred as an RFID transponder. In some embodiments, the RFID tag may include a passive RFID tag or active RFID tag. For the purposes of this disclosure, a “passive RFID tag” is an RFID tag that doesn't have its own power source and relies on the energy from a RFID reader to transmit its data. For the purposes of this disclosure, an “active RFID tag” is an RFID tag that has its own power source and can transmit its data without relying on an RFID reader's energy. In some embodiments, the RFID tag may be implemented on a vehicle. In some embodiments, the RFID tag may be implemented on a user device. In some embodiments, the RFID tag may be implemented on an identification (ID) card. When an RFID tag comes into the range of the RFID reader, the RFID tag may receive the radio waves emitted by the RFID reader and may use the energy from the waves to transmit its unique identifier back to the RFID reader. The RFID reader then may capture the unique identifier and may send it to the computer device and/or the at least a processor, which can use it for various purposes such as tracking sales transactions, payment process, and the like.

With continued reference toFIG.1, for the purposes of this disclosure, a “unique identifier” is an identifier that is unique for an object among others. As a non-limiting example, the unique identifier may include a universal product code (barcode), radio-frequency identification (RFID) cryptographic hashes, primary key, a unique sequencing of alpha-numeric symbols, or anything of the like that can be used to identify user activity user. For the purposes of this disclosure, a “universal product code” is a method of representing data in a visual, machine-readable form. In an embodiment, the universal product code may include linear barcode. For the purposes of this disclosure, “linear barcode,” also called “one-dimensional barcode” is a barcode that is made up of lines and spaces of various widths or sizes that create specific patterns. In another embodiment, the universal product code may include matrix barcode. For the purposes of this disclosure, “matrix barcode,” also called “two-dimensional barcode” is a barcode that is made up of two-dimensional ways to represent information. As a non-limiting example, the matrix barcode may include quick response (QR) code, and the like. The unique identifier may take the form of any identifier that uniquely corresponds to the purposes of apparatus100; this may be accomplished using methods including but not limited to Globally Unique Identifiers (GUIDs), Universally Unique Identifiers (UUIDs), or by maintaining a data structure, table, or database listing all transmitter identifiers and checking the data structure, table listing, or database to ensure that a new identifier is not a duplicate. In an embodiment, the unique identifier may include a printed form. As a non-limiting example, the unique identifier may be printed and sticked on a load. As another non-limiting example, the unique identifier may be printed and tagged on a load. As another non-limiting example, a user may have a printed unique identifier on a paper. In another embodiment, the unique identifier may include a digital form. As a non-limiting example, a user may find the unique identifier on a phone screen, tablet, computer screen, or any display device thereof. As a non-limiting example, a user may find the unique identifier on a phone screen. In an embodiment, the unique identifier may be used to keep track of a user. In another embodiment, the unique identifier may be used to keep track of sales transactions of one or more users. In an embodiment, the unique identifier may be used to keep track of the user's activity in the apparatus100. For example, without limitation, the unique identifier may be used to keep track of payments, vehicle maintenance, vehicle information, user information, and the like.

With continued reference toFIG.1, in some embodiments, a user device may be configured to display user device data116, user activity data112, system data124, user activity report128, and the like. In some embodiments, a user may input data related to the user device data116, the user activity data112, the system data124, the user activity report128using a user interface. For the purposes of this disclosure, a “user interface” is a means by which a user and a computer system interact; for example through the use of input devices and software. A user interface may include a graphical user interface (GUI), command line interface (CLI), menu-driven user interface, touch user interface, voice user interface (VUI), form-based user interface, any combination thereof and the like. In some embodiments, user interface may operate on and/or be communicatively connected to a decentralized platform, metaverse, and/or a decentralized exchange platform associated with the user. For example, the user may interact with user interface in virtual reality. In some embodiments, the user may interact with the user interface using a computing device distinct from and communicatively connected to an apparatus100. In an embodiment, user interface may include a graphical user interface. A “graphical user interface,” as used herein, is a graphical form of user interface that allows users to interact with electronic devices. In some embodiments, GUI may include icons, menus, other visual indicators or representations (graphics), audio indicators such as primary notation, and display information and related user controls. A menu may contain a list of choices and may allow users to select one from them. A menu bar may be displayed horizontally across the screen such as pull-down menu. When any option is clicked in this menu, then the pull-down menu may appear. A menu may include a context menu that appears only when the user performs a specific action. An example of this may include pressing the right mouse button. When this is done, a menu may appear under the cursor. Files, programs, web pages and the like may be represented using a small picture in a graphical user interface. For example, links to decentralized platforms as described in this disclosure may be incorporated using icons. Using an icon may be a fast way to open documents, run programs etc. because clicking on them yields instant access. Using an icon may be a fast way to open documents, run programs etc. because clicking on them yields instant access. Information contained in user interface may be directly influenced using graphical control elements such as widgets. A “widget,” as used herein, is a user control element that allows a user to control and change the appearance of elements in the user interface. In this context a widget may refer to a generic GUI element such as a check box, button, or scroll bar to an instance of that element, or to a customized collection of such elements used for a specific function or application such as without limitation a dialog box for the users to customize their computer screen appearances. User interface controls may include software components that a user interacts with through direct manipulation to read or edit information displayed through user interface. Widgets may be used to display lists of similar items, navigate the system using links, tabs, and manipulate data using check boxes, radio boxes, and the like.

With continued reference toFIG.1, for the purposes of this disclosure, “user activity data” is data related to a user and an activity related to the user. In an embodiment, the user activity data112may include user information. For the purposes of this disclosure, “user information” is information related to a user. As a non-limiting example, the user information may include name, gender, date of birth, residency, religion, organ donation, driver history, occupation, family, contact information, emergency contact, and the like. In another embodiments, the user activity data112may include user billing information. For the purposes of this disclosure, “user billing information” is information of a user's payment method. As a non-limiting example, the user billing information may include payment method preference, payment history, credit card information, debit card information, and the like. For the purposes of this disclosure, “card information” is information related to a user's card that can make a payment. As a non-limiting example, the card information may include card numbers, card security codes, the user's personal identification number (PIN) for the card, the card's expiration date, and the user's name on the card, and the like. In another embodiment, the user activity data112may include user vehicle information. For the purposes of this disclosure, “user vehicle information” is information of a user's vehicle. As a non-limiting example, the user vehicle information may include make, manufacture country, model, model version, model year, manufacturer, country of manufacturer, body type, color, coating, steering type, wheel type, tire size, tire type, number of wheels, standard seat number, optional seat number, engine, engine capacity, fuel type, fuel tank capacity, average fuel consumption, maximum permissible weight, vehicle height, vehicle length, vehicle width, vehicle status, such as but not limited to damage status, contamination status, and the like, presence of vehicle accessories, title records, theft records, accident records, insurance records, vehicle ID, interior fabric, and the like. Additional disclosure related to the user vehicle information may be found in U.S. patent application Ser. No. 18/195,760, filed on May 10, 2023, entitled “APPARATUS AND METHOD OF VEHICLE PROFILING,” the entirety of which is incorporated as a reference.

With continued reference toFIG.1, in some embodiments, user activity data112may include user system activity data. For the purposes of this disclosure, “user system activity data” is data related to user's activity in a vehicle maintenance system. As a non-limiting example, the user system activity data may include products purchased by the user. As another non-limiting example, the user system activity data may include a service of the vehicle maintenance system used by the user. As another non-limiting example, the user system activity data may include a price of services and/or products the user uses and/or purchases. For example and without limitation, the user system activity data may include data related to washing a vehicle, wiping the vehicle, vacuuming, purchasing products for cleaning, repairing and/or maintenance of the vehicle, using a vending machine, using a service from the vehicle maintenance system such as but not limited to cleaning, repairing and/or maintenance of the vehicle, using an automatic car wash, using a self car wash, inflating a tire of the vehicle, changing oil of the vehicle, and the like. In some embodiments, the user activity data112may include a date of a transaction, items or services purchased, quantity or price of the items, the payment method, the total amount paid, location of a user device, and the like.

With continued reference toFIG.1, in an embodiment, a data extraction module120may include tokenization. As a non-limiting example, the tokenization may break a document into smaller chunks called tokens, which can then be analyzed or manipulated. In some embodiments, the tokenization may break down sentences of the document into words or phrases. In another embodiment, the data extraction module120may include parsing libraries. As a non-limiting example, the parsing libraries may include XML, JSON, or the like. These libraries can be used to extract relevant information from a structured data file. In another embodiment, the data extraction module120may include string manipulation. As a non-limiting example, the string manipulation may use built-in functions or regular expressions to manipulate a string of user device data116and extract relevant information, user activity data112. For example, and without limitation, the at least a processor104may use string manipulation to extract price of a product purchased by a customer from a receipt. In another embodiment, the data extraction module120may use a machine learning model, the machine learning model disclosed in the entirety of this disclosure, where machine learning algorithms can be trained to recognize patterns in unstructured data and extract relevant information. In an embodiment, the data parsing may include tokenization. As a non-limiting example, the tokenization may break a document into smaller chunks called tokens, which can then be analyzed or manipulated. In some embodiments, the tokenization may break down sentences of the document into words or phrases. In an embodiment, the data parsing may include string manipulation. As a non-limiting example, the string manipulation may use built-in functions or regular expressions to manipulate a string of characters of user device data116and extract relevant information, user activity data112. For example, and without limitation, the at least a processor104may use string manipulation to extract price of a product purchased by a customer from a receipt.

With continued reference toFIG.1, in some embodiments, at least a processor104may be configured to extract user activity data112using an optical character recognition (OCR). For the purposes of this disclosure, an “optical character recognition” is a technology that enables the recognition and conversion of printed or written text into machine-encoded text. As a non-limiting example, the OCR may analyze image data. For example and without limitation, the image data may include an image of a receipt, document, credit card, debit card, and the like. For example and without limitation, the OCR may analyze the image of the receipt so that the data extraction module120may extract the user activity data112that includes a date of a transaction, items or services purchased, quantity or price of the items, the payment method, the total amount paid, location of a user device, and the like. In some cases, the data extraction module120may be configured to recognize a keyword using the OCR to find the user activity data112. As used in this disclosure, a “keyword” is an element of word or syntax used to identify and/or match elements to each other. In some cases, the data extraction module120may transcribe much or even substantially all user activity data112.

With continued reference toFIG.1, in some embodiments, optical character recognition or optical character reader (OCR) may include automatic conversion of images of written (e.g., typed, handwritten or printed text) into machine-encoded text. In some cases, recognition of a keyword from user activity data112may 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.

With continued reference 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.

With continued reference toFIG.1, in some cases, OCR processes may employ pre-processing of user activity data112. 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 convert (e.g., homography or affine convert) to the user activity data112to 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 component. In some cases, binarization may be required for example if an employed OCR algorithm only works on binary images. In some cases, a line removal process may include 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 component.

With continued reference 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 component. 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.

With continued reference toFIG.1, in some embodiments, an OCR process may include a feature extraction process. In some cases, feature extraction may decompose a glyph into 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.3. Exemplary non-limiting OCR software may include Cuneiform and Tesseract. Cuneiform may include a multi-language, open-source optical character recognition system originally developed by Cognitive Technologies of Moscow, Russia. Tesseract may include free OCR software originally developed by Hewlett-Packard of Palo Alto, California, United States.

With continued reference 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 may be passed to an adaptive classifier as training data. The adaptive classifier then may get a chance to recognize characters more accurately as it further analyzes user activity data112. Since the adaptive classifier may have learned something useful a little too late to recognize characters on the first pass, a second pass may be run over the user activity data112. 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 components where visual verbal content may be distorted. Another exemplary OCR software tool may 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.

With continued reference 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 content. In some cases, near-neighbor analysis can make use of co-occurrence frequencies to correct errors, by noting that certain words are often seen together. For example, “Washington, D.C.” is generally far more common in English than “Washington DOC.” In some cases, an OCR process may make us of a priori knowledge of grammar for a language being recognized. For example, grammar rules may be used to help determine if a word is likely to be a verb or a noun. Distance conceptualization may be employed for recognition and classification. For example, a Levenshtein distance algorithm may be used in OCR post-processing to further optimize results.

With continued reference toFIG.1, in some embodiments, a data extraction module120may be configured to extract user activity data112from data stored in a user database. As used in this disclosure, “user database” is a data structure configured to store data associated with a user. As a non-limiting example, the user database may store image data, data from a POS, data from a scanning device, data related to a vehicle maintenance system, and the like. In one or more embodiments, the user database may include inputted or calculated information and datum related to a user and the vehicle maintenance system. In some embodiments, a datum history may be stored in a user database. As a non-limiting example, the datum history may include real-time and/or previous inputted data related to the user and the vehicle maintenance system. As a non-limiting example, the user database may include instructions from a user, who may be an expert user, a past user in embodiments disclosed herein, or the like, where the instructions may include examples of the data related the user and the vehicle maintenance system.

With continued reference toFIG.1, in some embodiments, an apparatus100may be communicatively connected with a user database. For example, and without limitation, in some cases, the user database may be local to the apparatus100. In another example, and without limitation, the user database may be remote to the apparatus100and communicative with the at apparatus100by way of one or more networks. The network may include, but is not limited to, a cloud network, a mesh network, and the like. By way of example, a “cloud-based” system can refer to a system which includes software and/or data which is stored, managed, and/or processed on a network of remote servers hosted in the “cloud,” e.g., via the Internet, rather than on local severs or personal computers. A “mesh network” as used in this disclosure is a local network topology in which the infrastructure the apparatus100connect directly, dynamically, and non-hierarchically to as many other computing devices as possible. A “network topology” as used in this disclosure is an arrangement of elements of a communication network. The network may use an immutable sequential listing to securely store the user database. An “immutable sequential listing,” as used in this disclosure, is a data structure that places data entries in a fixed sequential arrangement, such as a temporal sequence of entries and/or blocks thereof, where the sequential arrangement, once established, cannot be altered or reordered. An immutable sequential listing may be, include and/or implement an immutable ledger, where data entries that have been posted to the immutable sequential listing cannot be altered.

With continued reference toFIG.1, in some embodiments, a user database may include keywords. For example, without limitation, the keyword may include “total amount” in the instance that a user, such as but not limited to a customer, a car wash employee, sales manager, and the like, is looking for total amount of money the customer spends in a vehicle maintenance system. In another non-limiting example, the keyword may include “SUV” in an example where a vehicle of the user is a sport utility vehicle (SUV).

With continued reference toFIG.1, in some embodiments, a user database may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NOSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Database may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. Database may include a plurality of data entries and/or records as described above. Data entries in a database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and/or reflect data and/or records as used herein, as well as categories and/or populations of data consistently with this disclosure.

With continued reference toFIG.1, a memory108contains instructions configuring at least a processor to classify user activity data112into one or more user activity data groups132. For the purposes of this disclosure, a “user activity data group” is a set of associative user activity data. As a non-limiting example, the one or more user activity data groups132may include user information group, user billing information group, user vehicle group, user system activity data group, and the like. In some embodiments, the one or more user activity data groups132may include one or more user activity data subgroups. For the purposes of this disclosure, a “user activity data subgroup” is a subset of a user activity data group. As a non-limiting example, the user information group may include a user information subgroup of a name group, gender group, date of birth group, residency group, religion group, organ donation group, driver history group, occupation group, family group, contact information group, emergency contact group, and the like. As a non-limiting example, the user billing information group may include a user billing information subgroup of a payment method preference group, payment history group, credit card information group, debit card information group, and the like. As another non-limiting example, the user vehicle information group may include a user vehicle information subgroup of a make group, manufacture country group, model group, model version group, model year group, manufacturer group, country of manufacturer group, body type group, color group, coating group, steering type group, wheel type group, tire size group, tire type group, number of wheels group, standard seat number group, optional seat number group, engine group, engine capacity group, fuel type group, fuel tank capacity group, average fuel consumption group, maximum permissible weight group, vehicle height group, vehicle length group, vehicle width group, vehicle status group, such as but not limited to damage status group, contamination status group, and the like, presence of vehicle accessories group, title records group, theft records group, accident records group, insurance records group, vehicle ID group, interior fabric group, and the like. As another non-limiting example, the user system activity data group may include a user system activity data subgroup of a price of services group, price of products group, a date of a transaction group, items purchased group, services used group, quantity of the items group, the payment method group, the total amount paid group, location group, and the like.

With continued reference toFIG.1, in some embodiments, user activity data112may be classified into the one or more user activity data groups using a user activity data classifier136trained with user activity data training data. For the purposes of this disclosure, a “user activity data classifier” 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,” that sorts system data related inputs into categories or bins of data, outputting a plurality of system groups associated therewith. The user activity data classifier136disclosed herein may be consistent with a classifier disclosed with respect toFIG.3. In some embodiments, a user activity data classifier136may be trained with user activity data training data correlating the system data124to one or more system groups. For the purposes of this disclosure, “training data” is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements. The training data disclosed herein is further disclosed with respect toFIG.3. In some embodiments, the system group training data may be received from one or more users, user database, external computing devices, and/or previous iterations of processing. As a non-limiting example, the system group training data may include instructions from a user, who may be an expert user, a past user in embodiments disclosed herein, or the like, which may be stored in memory and/or stored in the user database, where the instructions may include labeling of training examples.

With continued reference toFIG.1, as a non-limiting example, a user activity data classifier136may be trained with user activity data training data that correlates user information to a user information group. For example and without limitation, the user activity data training data may correlate contact information of the user information to a contact information group of the user information group. As another non-limiting example, the user activity data classifier may be trained with the user activity data training data that correlates user billing information to a user billing information group. For example and without limitation, the user activity data training data may correlate credit card information of the user billing information to a credit card information group of the user billing information group. As another non-limiting example, the user activity data classifier may be trained with the user activity data training data that correlates user vehicle information to a user vehicle information group. For example and without limitation, the user activity data training data may correlate a wheel type of a vehicle of the user vehicle information to a wheel type group of the user vehicle information group. As another non-limiting example, the user activity data classifier may be trained with the user activity data training data that correlates user system activity data to a user system activity data group. For example and without limitation, the user activity data training data may correlate a list of services a user used in a vehicle maintenance system of the user system activity data to a services used group of the user system activity data group.

With continued reference toFIG.1, a memory108contains instructions configuring at least a processor to convert user activity data112to system data124using a data as a function of one or more user activity data groups132. In some embodiments, as each of the one or more user activity data groups132includes different types of data, a method of converting the user activity data112to system data124may various. For example and without limitation, the data converting module140may convert a date of a transaction (user activity data112), MM/DD/YYYY, in a date of a transaction group in a user system activity data group (user activity data group132) to a date of a transaction (system data124), YYYY/MM/DD. For example and without limitation, the data converting module140may convert a price of services and number of services used (user activity data112), 4 dollars and 2 times, in a price of services group and a services used group in a user system activity data group (user activity data group132) to a total sale (system data124), 10 dollars, by multiplying the values. For the purposes of this disclosure, a “data converting module” is a module or a software component that is configured to convert the user activity data to system data.

With continued reference toFIG.1, for the purposes of this disclosure, “system data” is data that is converted user activity data in a desired format. In some embodiments, the system data124may be stored in a user database. In some embodiments, the system data124may be retrieved from the user database. In an embodiment, the system data124may include system user information. For the purposes of this disclosure, “system user information” is data that is converted from user information. As a non-limiting example, the system user information may include name, gender, date of birth, residency, religion, organ donation, driver history, occupation, family, contact information, emergency contact, and the like. In another embodiments, the system data124may include system user billing information. For the purposes of this disclosure, “system user billing information” is data that is converted from user billing information. As a non-limiting example, the system user billing information may include payment method preference, payment history, credit card information, debit card information, and the like. As a non-limiting example, the card information may include card numbers, card security codes, the user's personal identification number (PIN) for the card, the card's expiration date, and the user's name on the card, and the like. In another embodiment, the system data124may include system user vehicle information. For the purposes of this disclosure, “system user vehicle information” is data that is converted from user vehicle information. As a non-limiting example, the system user vehicle information may include make, manufacture country, model, model version, model year, manufacturer, country of manufacturer, body type, color, coating, steering type, wheel type, tire size, tire type, number of wheels, standard seat number, optional seat number, engine, engine capacity, fuel type, fuel tank capacity, average fuel consumption, maximum permissible weight, vehicle height, vehicle length, vehicle width, vehicle status, such as but not limited to damage status, contamination status, and the like, presence of vehicle accessories, title records, theft records, accident records, insurance records, vehicle ID, interior fabric, and the like.

With continued reference toFIG.1, in some embodiments, system data124may include system user system activity data. For the purposes of this disclosure, “system user system activity data” is data that is converted from user system activity data. As a non-limiting example, the system user system activity data may include products purchased by the user. As another non-limiting example, the system user system activity data may include a service of the vehicle maintenance system used by the user. As another non-limiting example, the user system activity data may include a price of services and/or products the user uses and/or purchases. For example and without limitation, the system user system activity data may include data related to washing a vehicle, wiping the vehicle, vacuuming, purchasing products for cleaning, repairing and/or maintenance of the vehicle, using a vending machine, using a service from the vehicle maintenance system such as but not limited to cleaning, repairing and/or maintenance of the vehicle, using an automatic car wash, using a self car wash, inflating a tire of the vehicle, changing oil of the vehicle, and the like. In some embodiments, the system data124may include a date of a transaction, items or services purchased, quantity or price of the items, the payment method, the total amount paid, location of a user device, and the like.

With continued reference toFIG.1, in some embodiments, a data converting module140may include reformatting user activity data116, translating the user activity data112into a different coding scheme, or applying other data converting techniques. In some embodiments, the data converting module140may be configured to ensure that the user activity data112can be converted to system data124within one entity and used to support various business processes or analytical activities. In some embodiments, the data converting module may be configured to ensure that the user activity data112can be effectively integrated into one entity to another entity and used to support various business processes or analytical activities. As a non-limiting example, the system data124may be used to generate a user activity report128as shown below. The process of data converting may include several steps, including data cleaning, data mapping, and data loading.

With continued reference toFIG.1, in some embodiments, a data converting module140may include data cleaning. For the purposes of this disclosure, “data cleaning” is the process of identifying and correcting or removing errors and inconsistencies in data. In some embodiments, data cleaning may include analyzing user activity data112to identify any missing or incomplete values, outliers, or inconsistencies. As a non-limiting example, the data converting module140may be configured to compare first user activity data112in a first user activity data group132with second user activity data112in a second user activity data group132to identify any discrepancies or inconsistencies. For example and without limitation, the data converting module140may compare a name of a user (first user activity data112) in a user information group (first user activity data group132) and a name of a user (second user activity data112) in a credit card information group (second user activity data group132) and identify the inconsistencies of the data. As another non-limiting example, the data converting module140may include predefined validation rules that check for common data errors or inconsistencies, such as formatting errors, invalid values, or out-of-range values. As another non-limiting example, the data converting module140may include statistical analysis that may detect outliers, unusual patterns or trends, or inconsistencies in the data. In some cases, the data converting module140may flag the missing data and prompt a user to input the necessary information. In other cases, the data converting module140may infer the missing data based on other available information or use statistical techniques to estimate the missing values. As a non-limiting example, the statistical techniques may include mean imputation, regression imputations, k-nearest neighbor imputation, expectation-maximization (EM) algorithm, or the like. In some cases, the data converting module140may exclude the data with missing values from analysis altogether to generate a user activity report128. In some embodiments, data cleaning may include removing duplicates, correcting spelling mistakes and formatting the user activity data112in a consistent manner. In some embodiments, data cleaning may include checking the user activity data112for accuracy and consistency, such as checking that all values fall within a specified range.

With continued reference toFIG.1, in some embodiments, the data converting module140may include data mapping. For the purposes of this disclosure, “data mapping” is the process of defining the relationships between data elements in different entities. In some embodiments, data mapping may include mapping fields, defining the data types and formats to be used, and identifying any transformations or conversions that are necessary. In some embodiments, the data converting module140may include data enrichment. For the purposes of this disclosure, “data enrichment” is adding additional data into system data. In some embodiments, the data enrichment may include collecting and analyzing additional data from various sources, such as social media, news articles, or demographic information, and integrating it with the system data124to create a more comprehensive dataset. As a non-limiting example, the data enrichment may include adding geographic data or demographic data.

With continued reference toFIG.1, in some embodiments, additional data for system data124may be derived from a web crawler. A “web crawler,” as used herein, is a program that systematically browses the internet for the purpose of Web indexing. The web crawler may be seeded with platform URLs, wherein the crawler may then visit the next related URL, retrieve the content, index the content, and/or measures the relevance of the content to the topic of interest. In some embodiments, an apparatus100may generate a web crawler to scrape the additional data for the system data124from social media sites, blogs, or the like. The web crawler may be seeded and/or trained with a reputable website, such as Twitter, to begin the search. A web crawler may be generated by a computing device of the apparatus100. In some embodiments, the web crawler may be trained with information received from a user through a user interface. In some embodiments, the web crawler may be configured to generate a web query. A web query may include search criteria received from the user. For example, the user may submit a plurality of websites for the web crawler to search to data statistics from and correlate to the additional data for the system data or user activity data112Additionally, the web crawler function may be configured to search for and/or detect one or more data patterns. A “data pattern” as used in this disclosure is any repeating forms of information. A data pattern may include repeating pecuniary strategies, repeating service strategies, and the like. In some embodiments, the web crawler may be configured to determine the relevancy of a data pattern. Relevancy may be determined by a relevancy score. A relevancy score may be automatically generated by the computing device of the apparatus100, received from a machine learning model, and/or received from the user. In some embodiments, the relevancy score may include a range of numerical values that may correspond to a relevancy strength of data received from a web crawler function. As a non-limiting example, a web crawler function may search the Internet for the additional data for the system data124. The web crawler may return the additional data for the system data124, such as, as non-limiting examples, pecuniary data, user's review of a service in a vehicle maintenance system, geographic information, demographic information, and the like.

With continued reference toFIG.1, an apparatus100includes a report generation module144. For the purposes of this disclosure, a “report generation module” is a module that is configured to generate a user activity report as a function of system data. In some embodiments, the report generation module144may include a variety of techniques and algorithms to analyze and summarize the system data124such as but not limited to data visualization, statistical analysis, and natural language processing. For the purposes of this disclosure, a “user activity report” is a compilation of user's activities that is formed to display or communicate the compilation of the user's activities. In some embodiments, the user activity report128may be configured for auditing or compliance purposes, such as tracking changes to the system data124. In some embodiments, the user activity report128may include a form of a text, a graph, a trend line, a chart, audio, animation, an image, a video, and the like. In some embodiments, the user activity report128may be configured to gain insights into how users interact with an apparatus100, identify trends or patterns in user behavior, and optimize system performance and usability. As a non-limiting example, the user activity report128may include an analysis of what kinds of services are mostly used by users in a specific location. As another non-limiting example, the user activity report128may include financial performance, marketing strategies, customer behavior, operational efficiency, and the like. As another non-limiting example, the user activity report128may include data insights related to sales revenue, expenses, profitability, market trends, customer demographics, competitive landscape, and more. As another non-limiting example, the user activity report128may include recommendations for improving the business's performance, based on the analysis conducted. As a non-limiting example, the recommendations may include changes to the company's marketing strategies, product offerings, customer experience, operations, financial management practices, and the like. In some embodiments, the report generation module144may be configured to generate the user activity report128as a function of one or more system groups. As a non-limiting example, the customer demographics of the user activity report128may be generated as a function of a user information group and/or user vehicle information group of the one or more system groups. As another non-limiting example, the sale revenue of the user activity report128may be generated as a function of a user system activity data group of the one or more system groups.

With continued reference toFIG.1, in an embodiment, a user activity report128may be read-only. In another embodiment, the user activity report128may be writable. In some embodiments, the writable user activity report may require authentication; for instance without limitation, the writable user activity report may be writable only given a unique identifier indicating that the device that will be modifying the user activity report128is authorized. In some embodiments, the user activity report128may include any combination of the above; for instance without limitation, the user activity report128may include a read-only section. For example without limitation, the user activity report128may include a writable section with limited access. In some embodiments, the user activity report128may include a writable section with general access, to which any user may be able to input data. The user activity report128may include the read-only section and the generally writable section, or the limited access writable section and the generally writable section, or the read-only section and the limited access section. The limited access section may be limited to users of the apparatus100, or in other words may be generally writable, but only to users of the apparatus100, who may have the unique identifier; the users may alternatively be granted the unique identifier by the apparatus100to update data only when authorized by the system, and otherwise be unable to update the user activity report128. In some embodiments, preventing users from being able to write over a user activity report128enables the user activity report128to be free from intentional or unintentional corruption or inaccuracy, and enables the apparatus100to ensure that certain information is always available to users. In some embodiments, writable sections enable the apparatus100itself or users of the apparatus100to correct, augment, or update information.

Referring now toFIG.2, a high-level system architecture of payment processing system200is shown. In some embodiments, payment processing system200may be implemented in any infrastructure or system. As a non-limiting example, payment processing system200may be implemented in a vehicle maintenance system, where the vehicle maintenance system is described further in detail above. Additionally, vehicle maintenance system disclosed herein may be consistent with a vehicle care site found in application Ser. No. 18/196,205, filed on May 11, 2023, entitled “SYSTEM AND METHOD FOR PROVIDING A SEAMLESS VEHICLE CARE AT A VEHICLE CARE SITE,” the entirety of which is disclosed as a reference. Payment processing system200may include plurality of on-premises system components204. As used in this disclosure, “on-premises system components” are components (e.g., computing infrastructure, software, or hardware) that are installed, hosted, and maintained within physical location of vehicle maintenance system. In an embodiment, vehicle maintenance system may be responsible for the management, maintenance, security, and operation of plurality of on-premises system components204. Exemplary embodiments of on-premises system components are described below in further detail.

With continued reference toFIG.2, in some embodiments, payment processing system200may include plurality of cloud system components208. As used in this disclosure, “cloud system components” are components (e.g., computing infrastructure, software, or hardware) that are installed, hosted, and maintained on a cloud environment. As used in this disclosure, a “cloud environment” is a set of systems and/or processes acting together to provide services in a manner that is dissociated with underlaying hardware and/or software within apparatus100used for such purpose and includes a cloud. A “cloud,” as described herein, refers to one or more servers that are accessed over the network. In some cases, cloud may include Hybrid Cloud, Private Cloud, Public Cloud, Community Cloud, any cloud defined by National Institute of Standards and Technology (NIST), and the like thereof. In some embodiments, cloud may be remote to apparatus100; for instance, cloud may include a plurality of functions distributed over multiple locations outside apparatus100. Location may be a data center. In a non-limiting example, data extraction module120, data converting module140, report generation module144, and/or user database212may run on one or more cloud servers.

With continued reference toFIG.2, cloud system components208may include implementation of cloud computing. As used in this disclosure, “cloud computing” is an on-demand delivery of information technology (IT) resources within a network through internet, without direct active management by either first entity or second entity. In an embodiment, without limitation, cloud system components208may include a Software-as-a-Service (SaaS). As used in this disclosure, a “Software-as-a-Service” is a cloud computing service model which make software available to apparatus100directly; for instance, SaaS may deliver ready-to-use software applications over the network, accessible via internet applications (e.g., web browsers or specialized client applications). In some cases, cloud computing may implement various security measure, such as data encryption, identify and access management, network security, and/or the like to protect the infrastructure and data such as, without limitation, user device data116. In a non-limiting example, apparatus100may include a network component that enable secure and reliable connectivity between data and end users, wherein the network component may include, without limitation, virtual private networks (VPNs), local balancers, content delivery networks (CDNs), and/or the like. Additionally, or alternatively, cloud system components208may include tools and/or services for monitoring, logging, and managing the performance, security and availability of cloud resources.

With continued reference toFIG.2, payment processing system200may include an operating system216. As used in this disclosure, an “operating system” refers to the fundamental software that manages and controls various system components. In an embodiment, operating system216may be responsible for executing any processing steps as described in this disclosure. In a non-limiting example, operating system216may allocate system resources (e.g., processing power, memory, and/or storage) among various system components and manage the execution of commands, coordinating the communication between a control module. The control module disclosed herein may be consistent with a control module found in application Ser. No. 18/196,205, filed on May 11, 2023, entitled “SYSTEM AND METHOD FOR PROVIDING A SEAMLESS VEHICLE CARE AT A VEHICLE CARE SITE,” the entirety of which is disclosed as a reference.

With continued reference toFIG.2, operating system216may communicate with a first control module220, wherein the first control module220may be a control module hosted on cloud environment as described above. User interface220may communicate with first control module220. In some embodiments, user interface220may be consistent with user device, where user device is further described in detail with respect toFIG.1. In a non-limiting example, user may use user interface220for user authentication to gain access to vehicle maintenance system and/or plurality of devices within the vehicle maintenance system. User interface220may allow user to enter user input containing user authentication datum, wherein the user authentication datum may be verified by a validation module. In some cases, validation module may be a cloud system component; for instance, and without limitation, validation module may include a cloud identify provider (IDP) configured to delivers identity and access management (IAM) functionality as a SaaS. First control module220may communicate with user database212to enable validation module to access plurality of data. In this case, user database212may include a cloud storage. Additional disclosure related to validation disclosed herein may be found in application Ser. No. 18/196,205, filed on May 11, 2023, entitled “SYSTEM AND METHOD FOR PROVIDING A SEAMLESS VEHICLE CARE AT A VEHICLE CARE SITE,” the entirety of which is disclosed as a reference.

With continued reference toFIG.2, operating system216may communicate with one or more token reading device224via Bluetooth Low Energy (BLE). In a non-limiting example, token reading device224may include a card reader, wherein the card reader is a device used to read and extract data from various types of cards, such as, without limitation, credit cards, debit cards, smart cards, magnetic strip cards, and/or the like. In some embodiments, token reading device224disclosed herein may be consistent with a scanning device described in detail with respect toFIG.1. In a non-limiting example, token reading device224may be configured to process an electronic payment transaction, enabling access to plurality of devices and/or cares within a vehicle maintenance system.

With continued reference toFIG.2, cloud system components208may include a payment processing platform228. For the purposes of this disclosure, a “payment processing platform” is a software or service that facilitate payment transactions between businesses and their customers. In some embodiments, payment processing platform228may facilitate payment transactions between a vehicle maintenance system and a user. In some embodiments, token reading device224may communicate with payment processing platform228. In a non-limiting example, token reading device224may receive and/or transmit user device data116and/or user activity data112to payment processing platform228. In a non-limiting example, payment processing platform228may receive and/or transmit user device data116to second control module232. In some cases, payment processing platform228may be a cloud system component; for instance, and without limitation, payment processing platform228may include a set of Application Programming Interfaces (APIs) configured for processing online payment. APIs may include a third-party (3rdparty) payment processing APIs. As another non-limiting example, payment processing platform228may include PayPal, Stripe, Square, Authorize.Net, Venmo, or the like.

With continued reference toFIG.2, in some embodiments, cloud system components208may include a second control module232, where second control module232may process or manage data between payment processing platform228and first control module220. In some embodiments, second control module232may receive any transactions, payments, user device data116and/or user activity data112and manage them. In some embodiments, second control module232may include apparatus100. In some embodiments, second control module232may be consistent with apparatus100. In a non-limiting example, second control module232may convert user device data116and/or user activity data112to system data124. In another non-limiting example, second control module232may transmit user data116, user activity data112, system data124and/or any data to first control module220. In another non-limiting example, second control module232may generate a command for first control module220. For example, and without limitation, user may make a single purchase in vehicle maintenance system, then second control module232may receive data related to the single purchase and process, store, transmit the data. For example, and without limitation, user may make multiple purchases in vehicle maintenance system, then second control module232may receive data related to the multiple purchases and process, store, transmit the data. For example, and without limitation, user may create a membership for using vehicle maintenance system, then second control module232may receive the data and process, store, transmit the data such as, but not limited to, generating a command to create a membership and transmit the command to first control module220. For example, and without limitation, user who has a membership for using vehicle maintenance system may make a single purchase, where information of the purchase is stored in the membership and retrieved for the user to allow for making the same purchase. As a non-limiting example, information of purchases may be stored in user database212. In a non-limiting example, information of purchases may be retrieved from user database212. For example, and without limitation, user who has a membership for using vehicle maintenance system may make a new single purchase, where information of the purchase is not stored in the membership. For example, and without limitation, user who has a membership for using vehicle maintenance system may make multiple purchases, where information of the purchases is stored in the membership and retrieved for the user to allow for making the same purchase. For example, and without limitation, user who has a membership for using vehicle maintenance system may make multiple purchases, where information of the purchases is not stored in the membership. As a non-limiting example, the command generated for first control module may include create/subscribe to a new membership, retrieve membership status, update membership status, deleting/unsubscribe membership, store payment or transaction, retrieve payment or transaction, process payment or transaction, or the like. In some embodiments, membership may allow user to access user activity data112, payment/transaction information, payment/transaction history, membership status, or the like.

With continued reference toFIG.2, in some embodiments, cloud system components208may include a first control module220. In a non-limiting example, first control module220may be configured to manage membership-related data (e.g., create/subscribe to a new membership, retrieving membership status, updating membership status, deleting/unsubscribe membership) using payment processing platform228and/or second control module232communicatively connected to first control module220by utilizing one or more STRIPE APIs. In another non-limiting example, first control module220may be configured to manage payment/transaction-related data using payment processing platform228and/or second control module232communicatively connected to first control module220by utilizing one or more STRIPE APIs. In some embodiments, cloud system components208may include user database212. User database212disclosed herein may be consistent with user database described with respect toFIG.1. In some embodiments, cloud system components208may include a user interface220. In some embodiments, user interface220may be consistent with user device described with respect toFIG.1. In some embodiments, user may view any data using user interface220. In some embodiments, user may make a payment using user interface220. In some embodiments, user may manage a membership for using a vehicle maintenance system using user interface220. Additional disclosure related to first control module220and user interface220may be found in application Ser. No. 18/196,205, filed on May 11, 2023, entitled “SYSTEM AND METHOD FOR PROVIDING A SEAMLESS VEHICLE CARE AT A VEHICLE CARE SITE,” the entirety of which is disclosed as a reference.

Referring now toFIG.4, an exemplary embodiment of neural network400is illustrated. A neural network400also 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 nodes404, one or more intermediate layers408, and an output layer of nodes412. 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.6, an exemplary embodiment of fuzzy set comparison600is illustrated. A first fuzzy set604may be represented, without limitation, according to a first membership function608representing a probability that an input falling on a first range of values612is a member of the first fuzzy set604, where the first membership function608has values on a range of probabilities such as without limitation the interval [0,1], and an area beneath the first membership function608may represent a set of values within first fuzzy set604. Although first range of values612is illustrated for clarity in this exemplary depiction as a range on a single number line or axis, first range of values612may be defined on two or more dimensions, representing, for instance, a Cartesian product between a plurality of ranges, curves, axes, spaces, dimensions, or the like. First membership function608may include any suitable function mapping first range612to a probability interval, including without limitation a triangular function defined by two linear elements such as line segments or planes that intersect at or below the top of the probability interval. As a non-limiting example, triangular membership function may be defined as:

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

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

y⁡(x,c,σ)=e-13⁢(x-cσ)3
and a bell membership function may be defined as:

y⁡(x,a,b,c,)=[1+❘"\[LeftBracketingBar]"x-ca❘"\[RightBracketingBar]"3⁢b]-1
Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various alternative or additional membership functions that may be used consistently with this disclosure.

With continued reference toFIG.6, first fuzzy set604may represent any value or combination of values as described above, including output from one or more machine-learning models. A second fuzzy set616, which may represent any value which may be represented by first fuzzy set604, may be defined by a second membership function620on a second range624; second range624may be identical and/or overlap with first range612and/or may be combined with first range via Cartesian product or the like to generate a mapping permitting evaluation overlap of first fuzzy set604and second fuzzy set616. Where first fuzzy set604and second fuzzy set616have a region628that overlaps, first membership function608and second membership function620may intersect at a point632representing a probability, as defined on probability interval, of a match between first fuzzy set604and second fuzzy set616. Alternatively or additionally, a single value of first and/or second fuzzy set may be located at a locus636on first range612and/or second range624, where a probability of membership may be taken by evaluation of first membership function608and/or second membership function620at that range point. A probability at628and/or632may be compared to a threshold640to determine whether a positive match is indicated. Threshold640may, in a non-limiting example, represent a degree of match between first fuzzy set604and second fuzzy set616, and/or single values therein with each other or with either set, which is sufficient for purposes of the matching process; for instance, threshold may indicate a sufficient degree of overlap between an output from one or more machine-learning models and/or an assessed vehicle status groups, alone or in combination. Alternatively or additionally, each threshold may be tuned by a machine-learning and/or statistical process, for instance and without limitation as described in further detail below.

With continued reference toFIG.6, in an embodiment, vehicle data and/or vehicle status and/or user interest data may be compared to multiple vehicle status groups fuzzy sets. For instance, user profile may be represented by a fuzzy set that is compared to each of the multiple vehicle status group fuzzy sets; and a degree of overlap exceeding a threshold between the vehicle status fuzzy set and any of the multiple vehicle status groups fuzzy sets may cause processor to rank vehicle status groups as a function of the vehicle data and/or vehicle status. For instance, in one embodiment there may be two vehicle status group fuzzy sets, representing respectively a first vehicle status group and a second vehicle status group. First vehicle status group may have a first fuzzy set; and Second vehicle status group may have a second fuzzy set. Processor104, for example, may compare a vehicle data and/or vehicle status fuzzy set with each of the first and second vehicle status group fuzzy sets, as described above, and classify a user profile to either, both, or neither of the first and second vehicle status groups. Machine-learning methods as described throughout may, in a non-limiting example, generate coefficients used in fuzzy set equations as described above, such as without limitation x, c, and σ of a Gaussian set as described above, as outputs of machine-learning methods. Likewise, vehicle data and/or vehicle status may be used indirectly to determine a fuzzy set, as vehicle data and/or vehicle status fuzzy set may be derived from outputs of one or more machine-learning models that take the vehicle data and/or vehicle status such as products and/or data directly or indirectly as inputs.

With continued reference toFIG.6, a computing device may use a logic comparison program, such as, but not limited to, a fuzzy logic model to determine a correlation between a plurality of vehicle data and/or vehicle status and a plurality of vehicle status groups. A correlation between vehicle data and/or vehicle status and vehicle status groups may include, but is not limited to, irrelevant, poor, average, high, and the like; each such designation may be represented as a value for a linguistic variable representing correlation, or in other words, a fuzzy set as described above that corresponds to a degree of positive correlations as calculated using any statistical, machine-learning, or other method that may occur to a person skilled in the art upon reviewing the entirety of this disclosure. In other words, a given element of vehicle data and/or vehicle status object may have a first non-zero value for membership in a first linguistic variable value such as a high correlation and a second non-zero value for membership in a second linguistic variable value such as average correlation. In some embodiments, determining a correlation may include using a linear regression model. A linear regression model may include a machine learning model. A linear regression model may be trained using a machine learning process. A linear regression model may map statistics such as, but not limited to, degree of similarity with respect to the type of vehicle data and/or vehicle status and vehicle status groups. In some embodiments, determining a correlation between vehicle data and/or vehicle status and vehicle status groups may include using a classification model. The classification model may be configured to input collected data and cluster data to a centroid based on, but not limited to, frequency of appearance, linguistic indicators of correlation, and the like.

Centroids may include scores assigned to them such that level of correlation of vehicle data and/or vehicle status and vehicle status groups may each be assigned a score. In some embodiments, the classification model may include a K-means clustering model. In some embodiments, the classification model may include a particle swarm optimization model. In some embodiments, determining the classification model may include using a fuzzy inference engine. A fuzzy inference engine may be configured to map one or more elements of vehicle data and/or vehicle status and vehicle status group data using fuzzy logic. In some embodiments, vehicle data and/or vehicle status and vehicle status groups may be arranged by a logic comparison program into various levels of correlation arrangements. A “correlation arrangement” as used in this disclosure is any grouping of objects and/or data based on degree of match based on vehicle status group assessment. This step may be implemented as described above inFIGS.1-6. Membership function coefficients and/or constants as described above may be tuned according to classification and/or clustering algorithms. For instance, and without limitation, a clustering algorithm may determine a Gaussian or other distribution of questions about a centroid corresponding to a given level, and an iterative or other method may be used to find a membership function, for any membership function type as described above, that minimizes an average error from the statistically determined distribution, such that, for instance, a triangular or Gaussian membership function about a centroid representing a center of the distribution that most closely matches the distribution. Error functions to be minimized, and/or methods of minimization, may be performed without limitation according to any error function and/or error function minimization process and/or method as described in this disclosure. Additionally and/or alternatively, the fuzzy set may be incorporated with vehicle data and/or vehicle status.

Referring now toFIG.7, a flow diagram of an exemplary method700for converting data is illustrated. The method700includes a step705of extracting, using at least a processor and a data extraction module, user activity data from user device data. In some embodiments, the user activity data may include user information. In some embodiments, the user activity data may include user activity information. In some embodiments, the user activity data may include user system activity data. In some embodiments, the user activity data may include user vehicle information. In some embodiments, the method700may further include extracting, using the at least a processor, the user activity data using an optical character recognition. In some embodiments, the method700may further include identifying, using the at least a processor, strings of characters of the user device data and extracting, using the at least a processor, the user activity data as a function of the strings of characters of the user device data. This may be implemented as a reference toFIGS.1-6.

With continued reference toFIG.7, a method700includes a step710of classifying, using at least a processor, user activity data into one or more user activity data groups. In some embodiments, the method700may further include classifying, using the at least a processor, the user activity data into the one or more user activity data groups using a user activity data classifier136trained with user activity data training data, wherein the user activity data training data correlates the user activity data with the one or more user activity data group. This may be implemented as a reference toFIGS.1-6.

With continued reference toFIG.7, a method700includes a step715of converting, using at least a processor and a data converting module, user activity data to system data as a function of one or more user activity data groups. In some embodiments, the method700may further include comparing, using the at least a processor, first user activity data of the user activity data and second user activity data of the user activity data and identifying, using the at least a processor, an inconsistency between the first user activity data and the second user activity data. This may be implemented as a reference toFIGS.1-6.

With continued reference toFIG.7, a method700includes a step720of generating, using at least a processor and a report generation module, a user activity report as a function of system data. In some embodiments, the method700may further include generating, using the at least a processor, the user activity report using a linear regression model. This may be implemented as a reference toFIGS.1-6.

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.