Product and service finder website

Abstract

Embodiments directed to automatically locating goods or services for potential use in a film production event are described. In one embodiment, a method includes executing a search of digital platforms having data associated with property items of a defined type. The method further includes identifying, using a machine learning model, a property item having a defined film production attribute in the property items based on the search. The method further includes extracting data associated with the property item from the data associated with the property items. The method further includes generating a database including the data associated with the property item. The method further includes providing a visualization of the data associated with the property item to a client device based on a request obtained from the client device to query the database for data associated with at least one property item having the defined film production attribute.

Description

PRODUCT AND SERVICE FINDER WEBSITECROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of and priority to U.S. Provisional Application Serial No. 63 / 562,913, filed March 8, 2024, and titled “PRODUCT AND SERVICE FINDER WEBSITE,” the entire contents of which are hereby incorporated herein by reference.BACKGROUND

[0002] Members of some industries such as the film production industry often require unique or uncommon and authentic products or services for use in connection with a film production event such as filming a movie, commercial, or documentary. For instance, various types of vehicles, real estate, and skillsets (e.g., stunt acting) are often required in connection with such film production events. Embodiments described herein provide solutions in the form of systems and methods that can be implemented to seek out, attract, and establish a relationship between various users seeking certain goods or services for use in a film production event and users offering such goods or services for such a purpose.SUMMARY

[0003] The present disclosure is directed to embodiments of systems and methods that can be implemented to locate certain goods or services for potential use in a film production event, as well as attract and establish relationships between various users seeking certain goods or services for use in a film production event and users offering such goods or services for such a purpose. For instance, among other operations, the embodiments can prompt users to register products or services to be offered for use in connection with a film production event by way of various user interfaces.

[0004] Aspects and advantages of embodiments of the present disclosure will be set forth in part in the following description or can be learned from the description or through practice of the embodiments. Other aspects and advantages of embodiments of the present disclosure will become better understood with reference to the appended claims and the accompanying drawings, all of which are incorporated in and constitute a part of this specification. The drawings illustrate example embodiments of the present disclosure and, together with the description, serve to explain the related concepts of the present disclosure.

[0005] According to one example embodiment, a computing device includes a memory device to store computer-readable instructions thereon and at least one processing deviceconfigured through execution of the computer-readable instructions to execute a search of at least one digital platform having data associated with one or more property items of a defined type. The at least one processing device is further configured to identify, using a machine learning model, a property item having a defined film production attribute in the one or more property items based on the search. The at least one processing device is further configured to extract data associated with the property item from the data associated with the one or more property items. The at least one processing device is further configured to generate a database comprising the data associated with the property item. The at least one processing device is further configured to provide a visualization of the data associated with the property item to a client device based on a request obtained from the client device to query the database for data associated with at least one property item having the defined film production attribute.

[0006] According to another example embodiment, a method of automating location of goods and services having certain film production attributes includes executing, by at least one computing device, a search of at least one digital platform having data associated with one or more property items of a defined type. The method further includes identifying, by the at least one computing device using a machine learning model, a property item having a defined film production attribute in the one or more property items based on the search. The method further includes extracting, by the at least one computing device, data associated with the property item from the data associated with the one or more property items. The method further includes generating, by the at least one computing device, a database comprising the data associated with the property item. The method further includes providing, by the at least one computing device, a visualization of the data associated with the property item to a client device based on a request obtained from the client device to query the database for data associated with at least one property item having the defined film production attribute.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Many aspects of the present disclosure can be better understood with reference to the following figures. The components in the figures are not necessarily to scale, with emphasis instead being placed upon clearly illustrating the concepts of the disclosure. Moreover, repeated use of reference characters or numerals in the figures is intended to represent the same or analogous features, elements, or operations across different figures. Repeated description of such repeated reference characters or numerals is omitted for brevity.

[0008] FIG. 1 illustrates an example networked environment according to various aspects and embodiments of the present disclosure.

[0009] FIG. 2 illustrates a flow diagram of an example, computer-implemented method according to various aspects and embodiments of the present disclosure.

[0010] FIGS. 3A, 3B, 4A, 4B, 5A, 5B, 6A, and 6B each illustrate an example user interface according to various aspects and embodiments of the present disclosure.

[0011] FIG. 7 illustrates a flow diagram of another example, computer-implemented method according to various aspects and embodiments of the present disclosure.DETAILED DESCRIPTION

[0012] The present disclosure is directed to systems and methods that can be implemented to locate certain goods or services for potential use in a film production event, as well as attract and establish relationships between various users seeking certain goods or services for use in a film production event and users offering such goods or services for such a purpose. The embodiments can prompt some users to register products or services for use in a film production event by way of various user interfaces. The embodiments can prompt other users to post a request for certain goods or services needed for an upcoming film production event by way of various other user interfaces. The embodiments can prompt users to exchange information concerning the goods and services with one another by way of still other user interfaces. The embodiments can generate a searchable database including data indicative of a variety of different product and service types available for use in a film production event, as well as data indicative of a variety of different upcoming film production events for which certain goods or services are needed. The embodiments can generate the searchable database such that it associates (e.g., matches, maps) data indicative of a good or service with a user offering such a good or service. The embodiments can also generate the searchable database such that it associates (e.g., matches, maps) data indicative of an upcoming film production event with a user seeking a certain good or service for use in the upcoming film production event. The embodiments can then query the database to match and connect users having certain goods or services with users seeking such goods or services. Some embodiments automatically query the database continuously or periodically to match and connect users having certain goods or services with users seeking such goods or services. Other embodiments query the database upon a request from a user to match and connect the user with one or more other users that have certain goods or services or that are searching for certain goods or services.

[0013] The embodiments can search different databases such as digital platforms having data associated with different products or services of various types such as, for instance, digitalmarket platforms (e.g., online commercial marketplace), social media platforms (e.g., social media marketplace), public record platform (e.g., tax assessor records, department of motor vehicles records), and / or other platforms. The embodiments can utilize different types of technology (e.g., machine learning models) to identify potential products or services, or corresponding owners or providers of potential products or services that have a certain likelihood of being desired for use in a particular film production event. The embodiments can determine (e.g., using a regression model) such a probability of the products or services being desired for use currently or at some later time in a particular film production event based on a collection of historical film prop data that includes a listing of various products or services used in or requested for use in different past film production events. The embodiments can then perform various operations to match and connect users having certain goods or services with users seeking such goods or services.

[0014] The embodiments can use image recognition technology (e.g., machine learning or artificial intelligence models) to identify attributes of goods or services by analyzing one or more images that have been obtained from a search of at least one digital platform or have been uploaded by a user that is an owner or provider of the goods or services. The embodiments can use navigation technology (e.g., global positioning system (GPS)) and other forms of data to identify locations (e.g., locations of potential goods or services) based on many variables including terrain and demographics, for instance, as it relates to satisfying the needs in film production.

[0015] The embodiments can further allow for a user to create and use a “digital replica” of a particular product or service the user is offering for use with a film production event. For example, the product or service offered by the user in some cases can be the “digital replica” itself, which can substitute and be offered for use in the same manner as the actual product or service. For instance, a user such as an owner or provider of a product or service can capture one or more types of digital data such as image data, video data, scan data, audio data, or other digital data indicative of a particular product or service offered by the user. The embodiments allow for the user to further create a “digital replica” such as a non-fungible token (NFT) or some other unique and authenticated digital representation of a tangible, real-world product or service offered by the user. The embodiments can allow for a user operating a three- dimensional (3D) laser scanner in some cases to capture a 3D laser scan of a certain tangible product, use the 3D laser scan data to create an NFT as a digital representation of the tangible product, store and document the NFT as being owned by the user (e.g., in a database or in a chain of a blockchain), and offer the NFT itself for use with a film production event in the samemanner as the tangible product would otherwise be offered for use with such an event. The embodiments can record creation and ownership data corresponding to such a digital representation in a database or in a chain of a blockchain, track transaction activity data associated with the digital representation, and perform one or more operations based at least in part on the transaction activity data in some cases. For instance, the embodiments can track different transactions involving use of a certain product across various film production events, determine when the product will be available for use, identify a user interested in using the product, and send a notification to a computing device associated with such a user indicating the availability of the product.

[0016] The embodiments can also determine a probability (e.g., using a regression model) of an owner or provider of such products or services actually offering or providing the products or services for a particular film production event. For instance, the embodiments can determine a probability that an owner or provider of such products or services will actually offer or provide them based at least in part on historical product or services transaction data corresponding to at least one of the products or services themselves, other products or services of the same or similar type, the owner or provider of the products or services, or one or more other owners or providers of the same or similar products or services. For example, such historical product or services transaction data can be indicative of or include information indicating that the products or services themselves or other products or services of the same or similar type have or have not been offered or provided for such a film production event or similar event in the past. In another example, such historical product or services transaction data can be indicative of or include information indicating that the owner or provider of the products or services, or other owners or providers of the same or similar products or services, has or has not offered or provided such products or services for such a film production event or similar event in the past on one or more occasions.

[0017] The embodiments can further propose one or more commercial contract or transaction terms that can be used (e.g., in a legally binding manner) to commercially offer or provide a certain product or service for a particular film production event. For instance, the embodiments can propose commercial contract or transaction terms such as a recommended product sale value or rental fee, service fee, scope of service, delivery date and time or contract duration, insurance terms, cancellation or breach of contract terms, other commercial transaction related terms, or any combination thereof. The embodiments can recommend such commercial contract or transaction terms based at least in part on historical commercial contract or transaction data corresponding to at least one of the product or service itself or otherproducts or services of the same or similar type. For example, such historical commercial contract or transaction data can be indicative of or include information indicating one or more terms used in at least one commercial contract or transaction involving the product or service itself that has been at least one of offered, fully executed, or otherwise completed in the past. In another example, such historical commercial contract or transaction data can be indicative of or include information indicating various terms used in different commercial contracts or transactions involving other products or services that have been at least one of offered, fully executed, or otherwise completed in the past.

[0018] The embodiments can further generate a proposed product or service agreement that can be used (e.g., in a legally binding manner) to commercially offer or provide a certain product or service for a particular film production event. For example, the embodiments can generate a proposed product or service agreement that includes one or more of the aforementioned proposed commercial contract or transaction terms that can be used to commercially offer or provide a certain product or service for a particular film production event.

[0019] For context, FIG. 1 illustrates an example networked environment 100 according to various aspects and embodiments of the present disclosure. The networked environment 100 includes a computing environment 102, one or more first client computing devices 134, one or more second client computing devices 144, a network 150, and a number of data sources. In the example shown, the data sources include one or more commercial markets data sources 160, one or more social network data sources 170, one or more public records data sources 180, and one or more historical film prop data sources 190, among possibly other data sources or digital platforms. The computing environment 102, the first client computing devices 134, and the second client computing devices 144 can access and are in data communication with the data sources via the network 150. The network 150 includes, for example, the Internet, intranets, extranets, wide area networks (WANs), local area networks (LANs), wired networks, wireless networks, cable networks, satellite networks, or other suitable networks, etc., or any combination of two or more such networks.

[0020] The computing environment 102 in the example shown includes one or more computing devices 104. Each computing device 104 includes at least one processor circuit having a processor 106 and a memory 108, both of which are coupled to a local interface 110. Each computing device 104 can be embodied as or include at least one server computer or likedevice. The local interface 110 can be embodied as or include a data bus with an accompanying address control bus or other bus structure.

[0021] The computing environment 102 can be embodied as a server computer or related computing system providing computing capability in many examples. The computing environment 102 can employ a plurality of computing devices arranged in one or more server banks, computer banks, or other arrangement. Such computing devices may be located in a single installation or may be distributed among many different geographical locations. For example, the computing environment 102 can include a plurality of computing devices implemented as at least one of a hosted computing resource, a grid computing resource, or any other distributed computing arrangement. In some cases, the computing environment 102 may correspond to an elastic computing resource where the allotted capacity of processing, network, storage, or other computing-related resources may vary over time.

[0022] A number of applications, services, processes, and related components may be executed in the computing environment 102 according to various embodiments. Also, a range of different data, datatypes, etc. is stored in a data store 112 accessible to the computing environment 102. The data store 112 may be representative of a plurality of data stores 112 as can be appreciated. The data stored in the data store 112 in many examples can be associated with the operation of the various applications and functional entities described herein.

[0023] Stored in the memory 108 are both data and several components that are executable by the processor 106. In particular, stored in the memory 108 and executable by the processor 106 are a product and service finder application 114, one or more machine learning models 116, a communications stack 118, and potentially other applications. Also stored in the memory 108 may be the data store 112 and other data. In addition, an operating system may be stored in the memory 108 and executable by the processor 106.

[0024] The data stored in the data store 112 can include a searchable database including data indicative of a variety of different product and service types available for use in a film production event. The database further includes data indicative of a variety of different upcoming film production events for which certain goods or services are needed. In some cases, the database can also include one or more of the historical film prop data, historical product or services transaction data, historical commercial contract or transaction data, digital representations (e.g., NFT) of products or services, or ownership and transaction activity data corresponding to such digital representations described in examples herein.

[0025] The first client computing device 134 and the second client computing device 144 are each representative of one of a plurality of client devices that may be coupled to the network150. The first client computing device 134 and the second client computing device 144 may each include or be embodied as a processor-based system such as a computer system in many examples. Such a computer system may be embodied in the form of a desktop computer, a laptop computer, personal digital assistants, cellular telephones, smartphones, set-top boxes, music players, web pads, tablet computer systems, game consoles, electronic book readers, smartwatches, head mounted displays, voice interface devices, or other devices. The first client computing device 134 and the second client computing device 144 may each include a display device such as, for example, one or more liquid crystal displays (LCD), gas plasma-based flat panel displays, organic light emitting diode (OLED) displays, electrophoretic ink (E ink) displays, LCD projectors, or other types of display devices.

[0026] The first client computing device 134 and the second client computing device 144 may each be configured to execute various applications such as discrete instances of a client application 136a, 136b and / or other applications. The client application 136 may be executed in each of the first client computing device 134 and the second client computing device 144, for example, to access network content served up by the computing environment 102 and / or other servers, thereby rendering one or more user interfaces on the respective display of each device 134, 144. To this end, the client application 136 may include or be embodied as, for example, a browser, a dedicated application, etc., and the user interface may include or be embodied as a network page, an application screen, etc. The client application 136 may be used to interact with the product and service finder application 114 to perform various functions including locating and informing users of certain products or services for use in a film production event, identifying upcoming film production events for which certain goods or services are needed, and / or other functions. The first client computing device 134 and the second client computing device 144 may each be configured to execute applications beyond the client application 136 such as, for example, email applications, digital platform applications (e.g., social media or networking applications), word processors, spreadsheets, and / or other applications.

[0027] To perform various operations described in examples herein, the computing device 104 (e.g., via the product and service finder application 114) can be implemented to search a variety of data sources (e.g., digital sources, digital platforms) and automatically evaluate, extract, and / or convert (e.g., to another format) various types of data indicative of different goods and services that are included in such data sources. The computing device 104 (e.g., via the product and service finder application 114) can refer to a variety of data sources, including the commercial markets data source 160, the social network data source 170, the public recordsdata source 180, the media data source 190, (or “the data sources 160, 170, 180, 190”), and / or other data sources. The product and service finder application 114 can obtain data from the data sources 160, 170, 180, 190 by way of application programming interfaces (API), scraping data from web pages (e.g., using a web crawler or spider application, or a search engine bot), and / or other approaches, and from data previously stored in the networked environment 100 (e.g., the data store 112). The computing device 104 (e.g., via the product and service finder application 114) may obtain data from the data sources 160, 170, 180, 190 on a continuous (e.g., in real-time, uninterrupted) or periodically according to a defined time interval (e.g., once per day, month, week, and so on).

[0028] The commercial markets data source 160 can provide or include publicly available data from commercial markets, marketplaces, or networks such as, for instance, CRAIGSLIST, FACEBOOK MARKETPLACE, AMAZON MARKETPLACE, ZILLOW, and REDFIN, among others. In some examples, a commercial market may provide data identifying various goods and services that are commercially offered (e.g., for purchase or rent) to the public or a network of users, as well data identifying various users that are commercially (e.g., for purchase or rent) seeking certain goods and services.

[0029] The social network data source 170 may provide or include publicly available data from social networks such as, for instance, LINKEDIN and FACEBOOK, among others. In some examples, a social network may provide data identifying various goods and services that are commercially or otherwise offered (e.g., for purchase, rent, gift, or donation) to the public or a network of users, as well as data identifying various users that are commercially or otherwise (e.g., for purchase, rent, gift, or donation) seeking certain goods and services.

[0030] The public records data source 180 may provide or include publicly available data from various public entities such as, for instance, tax assessor agencies (e.g., Internal Revenue Service), real and personal property registration, title, and taxing agencies (e.g., state, city, county agencies), and licensing agencies (e.g., Department of Motor Vehicles), among others. In some examples, a public record may provide data identifying various goods and services, as well as the respective owners or providers of such goods or services, or even their contact information in some cases.

[0031] The media data source 190 may provide or include publicly available data from various media networks or providers such as, for instance, NETFLIX, MAX, YOUTUBE, DISNEY, HULU, APPLE TV, and AMAZON PRIME, among others. In some examples, a media network or provider may provide data identifying various goods and services used in past film production events such as movies, documentaries, commercials, news programs, oranother type of film production event. For instance, a media network or provider may provide data (e.g., movie scenes, videos, images) identifying certain goods or services that appear to have been used relatively more frequently or less frequently than others in certain types of film production events, by certain film producers, and / or during certain periods of history.

[0032] In many examples, any or all of the commercial markets data source 160, the social network data source 170, the public records data source 180, or the media data source 190 may provide historical commercial contract or transaction data indicative of or including various terms used in different commercial contracts or transactions involving a variety of products and services that have been at least one of offered, fully executed, or otherwise completed in the past. In other examples, any or all of the commercial markets data source 160, the social network data source 170, the public records data source 180, or the media data source 190 may provide historical product or services transaction data indicative of or including information indicating that various products and services have or have not been offered or provided for different film production events in the past. In yet other examples, any of such historical data described above may be provided (e.g., via the first client computing device 134, the client application 136, the second client computing device 144) by a user such as an owner or a provider of a product or service or another user seeking such a product or service for use in a film production event.

[0033] The computing environment 102 (e.g., the computing device 104) executes the product and service finder application 114, the machine learning models 116, the communications stack 118, and possibly other applications, services, processes, systems, engines, or components. The product and service finder application 114 can be embodied as one or more software applications or services executing on the computing device 104. The product and service finder application 114 can be executed by the processor 106 to perform the various product or service finder operations described in examples herein. For instance, the product and service finder application 114 can be implemented to locate certain goods or services having a certain likelihood of being used in a film production event.

[0034] To identify goods or services of a particular type that have certain attributes, and to determine a likelihood that such goods or services would be used in a film production event, the product and service finder application 114 can use the machine learning models 116. For instance, the machine learning models 116 can include one or more machine-vision models, classifier models (e.g., support vector machine (SVM)), regression models, or another machine learning or artificial intelligence model (e.g., neural network, deep neural network). The product and service finder application 114 can employ such models to, for instance, identifycertain goods or services in images or other data (e.g., textual, graphical, numerical, visual, audio, video). The product and service finder application 114 can further employ such models to, for example, to determine a probability of the goods or services being desired for use in a particular film production event based on a collection of historical film prop data (e.g., obtained from the media data source 190) that includes a listing of various products or services used in or requested for use in different past film production events.

[0035] In automatically identifying certain goods and services and determining the probability they would be used in a film production event, the product and service finder application 114 may train and utilize the machine learning models 116. The machine learning models 116 may be trained on a variety of data (e.g., obtained from the media data source 190) in order to ascertain patterns in the data through regression analysis. For example, the machine learning models 116 may determine that a certain type of good or service has been more frequently used in certain types of past film production events, and thus, may be associated with a higher likelihood of being desired for use in these types of film production events in the future. The machine learning models 116 may be continuously or periodically updated based upon new information, thereby further refining and improving the machine learning models 116. For instance, the machine learning models 116 may be updated with data that is continuously or periodically obtained by the product and service finder application 114 from one or more data sources. In some cases, the product and service finder application 114 can then use the machine learning models 116 to automatically determine updated probabilities associated with any previously evaluated good or service based on new information obtained by the product and service finder application 114 during a recent search. In other examples, the product and service finder application 114 can then use the machine learning models 116 to automatically determine probabilities associated with any potential or new good or service that may be identified based on new information obtained by the product and service finder application 114 during a recent search.

[0036] The product and service finder application 114 can be further implemented to attract and establish relationships between various users seeking certain goods or services for use in a film production event and users offering such goods or services for such a purpose. To attract such users and establish relationships between them for the purposes of locating goods or services for use in a film production event, the product and service finder application 114 can employ the communications stack 118 and the network 150 to prompt the first client computing device 134 and the second client computing device 144 (e.g., via the client applications 136a, 136b) to perform one or more operations described herein by way of one ormore user interfaces generated by the product and service finder application 114. For example, the product and service finder application 114 can prompt the first client computing device 134 and the second client computing device 144 to perform such operations by way of user interfaces 300a, 300b, 400a, 400b, 500a, 500b, 600a, or 600b described herein and illustrated in FIGS. 3 A, 3B, 4A, 4B, 5A, 5B, 6A, and 6B.

[0037] The product and service finder application 114 can further allow for a user by way of the client application 136 to create and use a “digital replica” of a particular product or service the user is offering for use with a film production event. For example, the product or service offered by the user in some cases can be the “digital replica” itself, which can substitute and be offered for use in the networked environment 100 in the same manner as the actual product or service. For instance, a user such as an owner or provider of a product or service can capture one or more types of digital data such as image data, video data, scan data, audio data, or other digital data indicative of a particular product or service offered by the user. The user can employ the second client computing device 144 to create a “digital replica” such as a non-fungible token (NFT) or some other unique and authenticated digital representation of a tangible, real -world product or service offered by the user. For example, the user can employ a three-dimensional (3D) laser scanner in some cases to capture a 3D laser scan of a certain tangible product and use the 3D laser scan data to create (e.g., via the second client computing device 144) an NFT as a digital representation of the tangible product. The user can provide the digital representation or NFT to the computing device 104, which can store and document (e.g., in a database or in a chain of a blockchain) the digital representation or NFT as being owned by the user. The computing device 104 (e.g., via the product and service finder application 114) can also facilitate any offer of the digital representation or NFT itself in the networked environment 100 for use with a film production event in the same manner as the tangible product would otherwise be offered for use with such an event. The computing device 104 (e.g., via the product and service finder application 114) can further record creation and ownership data corresponding to the digital representation or NFT in a database or in a chain of a blockchain in some cases. The computing device 104 (e.g., via the product and service finder application 114) can also track transaction activity data associated with the digital representation and perform one or more operations based at least in part on the transaction activity data in some cases. For instance, the computing device 104 can track different transactions involving use of a certain product across various film production events, determine when the product will be available for use, identify a user interested in using the product, andsend a notification to a computing device (e.g., the first client computing device 134) associated with such a user indicating the availability of the product.

[0038] The computing device 104 (e.g., via the product and service finder application 114) can also determine a probability (e.g., using the machine learning models 116) of an owner or provider of such products or services actually offering or providing the products or services for a particular film production event. For instance, the computing device 104 can determine a probability that an owner or provider of such products or services will actually offer or provide them based at least in part on historical product or services transaction data corresponding to at least one of the products or services themselves, other products or services of the same or similar type, the owner or provider of the products or services, or one or more other owners or providers of the same or similar products or services. For example, such historical product or services transaction data can be indicative of or include information indicating that the products or services themselves or other products or services of the same or similar type have or have not been offered or provided for such a film production event or similar event in the past. In another example, such historical product or services transaction data can be indicative of or include information indicating that the owner or provider of the products or services, or other owners or providers of the same or similar products or services, has or has not offered or provided such products or services for such a film production event or similar event in the past on one or more occasions.

[0039] The computing device 104 (e.g., via the product and service finder application 114 and the machine learning models 116) can further propose one or more commercial contract or transaction terms that can be used (e.g., in a legally binding manner) to commercially offer or provide a certain product or service for a particular film production event. For instance, the computing device 104 can propose commercial contract or transaction terms such as a recommended product sale value or rental fee, service fee, scope of service, delivery date and time or contract duration, insurance terms, cancellation or breach of contract terms, other commercial transaction related terms, or any combination thereof. The computing device 104 can recommend such commercial contract or transaction terms based at least in part on historical commercial contract or transaction data corresponding to at least one of the product or service itself or other products or services of the same or similar type. For example, such historical commercial contract or transaction data can be indicative of or include information indicating one or more terms used in at least one commercial contract or transaction involving the product or service itself that has been at least one of offered, fully executed, or otherwise completed in the past. In another example, such historical commercial contract or transactiondata can be indicative of or include information indicating various terms used in different commercial contracts or transactions involving other products or services that have been at least one of offered, fully executed, or otherwise completed in the past.

[0040] The computing device 104 (e.g., via the product and service finder application 114 and the machine learning models 116) can further generate a proposed product or service agreement that can be used (e.g., in a legally binding manner) to commercially offer or provide a certain product or service for a particular film production event. For example, the computing device 104 can generate a proposed product or service agreement that includes one or more of the aforementioned proposed commercial contract or transaction terms that can be used to commercially offer or provide a certain product or service for a particular film production event.

[0041] The communications stack 118 can include software and hardware layers to implement data communications such as, for instance, Bluetooth®, Bluetooth® Low Energy (BLE), WiFi®, cellular data communications interfaces, or a combination thereof. Thus, the communications stack 118 can be relied upon by the computing device 104 to establish cellular, Bluetooth®, WiFi®, and other communications channels with the networks 150 and with at least one of the first client computing device 134, the second client computing device 144, or a device of any or all of the data sources 160, 170, 180.

[0042] The communications stack 118 can include the software and hardware to implement Bluetooth®, BLE, and related networking interfaces, which provide for a variety of different network configurations and flexible networking protocols for short-range, low-power wireless communications. The communications stack 118 can also include the software and hardware to implement WiFi® communication, and cellular communication, which also offers a variety of different network configurations and flexible networking protocols for mid-range, long-range, wireless, and cellular communications. The communications stack 118 can also incorporate the software and hardware to implement other communications interfaces, such as X10®, ZigBee®, Z-Wave®, and others. The communications stack 118 can be configured to communicate various data or information amongst the computing device 104, the first client computing device 134, the second client computing device 144, and / or a device of any or all of the data sources 160, 170, 180. Examples of such data or information can include, but are not limited to, data indicative of products or services offered or sought for use in a film production event.

[0043] FIG. 2 illustrates a flow diagram of an example, computer-implemented method 200 (or “method 200”) according to various aspects and embodiments of the present disclosure.It is understood that the flowchart of FIG. 2 provides merely an example of the many different types of functional arrangements that may be employed to implement the operation of the portion of the product and service finder application 114 as described herein. As an alternative, the flowchart of FIG. 2 may be viewed as depicting an example of elements of a method implemented in the computing environment 102 (FIG. 1) according to one or more embodiments.

[0044] At 202, the method 200 includes executing a search of at least one digital platform having data associated with one or more property items of a defined type. As referenced herein, the term “property item” includes various types of products, goods, and services (e.g., vehicles, real estate, skillsets (e.g., stunt acting)) that may be desired for use in a film production event. In one example, the computing device 104 (e.g., via the product and service finder application 114) can execute a search of any or all of the data sources 160, 170, 180, 190 for data associated with vehicles of a defined type such as, for instance, sports cars.

[0045] At 204, the method 200 includes identifying (e.g., using the machine learning models 116), a property item having a defined film production attribute in the one or more property items based on the search. For example, the computing device 104 (e.g., via the product and service finder application 114 and the machine learning models 116) can identify a sports car of a certain make, model, year, or color, among possibly other defined film production attributes, in image or video data (e.g., images, or videos) obtained from any or all of the data sources 160, 170, 180, 190.

[0046] At 206, the method 200 includes extracting data associated with the property item from the data associated with the one or more property items. For example, the computing device 104 (e.g., via the product and service finder application 114 and the machine learning models 116) can extract data from any or all of the data sources 160, 170, 180, 190 that identifies, for instance, the aforementioned sports car (e.g., the actual make, model, year, color, owner) or its owner (e.g., contact information for the owner of the sports car).

[0047] At 208, the method 200 includes generating a database comprising the data associated with the property item. For example, the computing device 104 (e.g., via the product and service finder application 114) can generate a database comprising various data collected across the data sources 160, 170, 180, 190 that identifies different goods and services, as well respective owners of such goods and services, and / or contact information for such owners. The computing device 104 can store such a database in the data store 112, for instance.

[0048] At 210, the method 200 includes providing a visualization of the data associated with the property item to a client device based on a request obtained from the client device toquery the database for data associated with at least one property item having the defined film production attribute. For example, in response to receiving a request from the second client computing device 144 (e.g., the client application 136b) to query the aforementioned database for data associated with certain goods having a certain attribute, the computing device 104 (e.g., via the product and service finder application 114) can render one or both of the user interfaces 600a, 600b described herein and illustrated in FIGS. 6A,and 6B, respectively.

[0049] FIGS. 3A and 3B respectively illustrate an example user interface 300a, 300b according to various aspects and embodiments of the present disclosure. In one example, the computing device 104 (e.g., via the product and service finder application 114) can generate the user interfaces 300a, 300b to prompt a user to register with and create a user profile within a network of users offering and seeking goods or services for use in film production events. For example, the computing device 104 can generate the user interfaces 300a, 300b to prompt a user to begin a process of registering an item or creating a profile for an item the user may want to offer for use in a film production event. For instance, the computing device 104 can generate the user interfaces 300a, 300b to prompt a user to begin a process of providing data associated with one or more property items owned by the user that the user wants to offer for use in a film production event.

[0050] In another example, the computing device 104 can generate the user interfaces 300a, 300b to prompt a user to begin a process of providing data associated with one or more property items that the computing device 104 has determined (e.g., via data from a search of the data sources 160, 170, 180, 190 and use of the machine learning models 116) are associated with (e.g., owned by) the user and have a certain probability of being desired for use in a film production event. For instance, based on making such a determination, the computing device 104 can send the first client computing device 134 an electronic mail (e-mail) or text message having a hyperlink that directs the first client computing device 134 to one or both of the user interfaces 300a, 300b. In another example, based on making such a determination, the computing device 104 can send the first client computing device 134 an e-mail or text message having a hyperlink that directs the first client computing device 134 to one or both of the user interfaces 400a, 400b to prompt the user to register the property item and offer it for use.

[0051] FIGS. 4 A and 4B respectively illustrate another example user interface 400a, 400b according to various aspects and embodiments of the present disclosure. In one example, the computing device 104 (e.g., via the product and service finder application 114) can generate the user interfaces 400a, 400b to prompt a user to complete a property item registration and offering process to offer a property item to a network of film production users for use in a filmproduction event. For example, the computing device 104 can generate the user interfaces 400a, 400b to prompt a user to complete a property item registration and offering process to offer a property item based on the user’s completion of the aforementioned user registration and profile creation process via the user interfaces 300a, 300b.

[0052] In another example, the computing device 104 can generate the user interfaces 400a, 400b to prompt a user to complete a property item registration and offering process to offer a property item based on a determination by the computing device 104 (e.g., via data from a search of the data sources 160, 170, 180, 190 and use of the machine learning models 116) that property item is associated with (e.g., owned by) the user and has a certain probability of being desired for use in a film production event. For instance, based on making such a determination, the computing device 104 can send the first client computing device 134 an e- mail or text message having a hyperlink that directs the first client computing device 134 to one or both of the user interfaces 400a, 400b.

[0053] FIGS. 5A and 5B respectively illustrate another example user interface 500a, 500b according to various aspects and embodiments of the present disclosure. In one example, the computing device 104 (e.g., via the product and service finder application 114) can generate the user interface 500a to provide a registered user with a dashboard of the user’s account. In another example, the computing device 104 (e.g., via the product and service finder application 114) can generate the user interface 500b in response to the user’s completion of aforementioned property item registration and offering process via the user interfaces 400a, 400b. For instance, the computing device 104 can generate the user interface 500b to provide the user with a preview of a property item to be posted or that is currently posted amongst the network of users seeking and offering various goods and services for use in different film production events.

[0054] FIGS. 6 A and 6B respectively illustrate another example user interface 600a, 600b according to various aspects and embodiments of the present disclosure. In one example, the computing device 104 (e.g., via the product and service finder application 114) can generate the user interface 600a to prompt a user via the second client computing device 144 to submit one or more requests to query a database of various property items (e.g., goods, services) offered for use in film production events to locate data associated with certain property items of a certain type that have certain attributes. In this example, the computing device 104 can further use the interface 600a to prompt the user via the second client computing device 144 to contact another user associated with the first client computing device 134 such as an owner of a certain property item based on return data obtained from searching the aforementioneddatabase. In this example, upon election by the user via the second client computing device 144 to contact the other user associated with the first client computing device 134 or to view details of the property item via the user interface 600a, the computing device 104 (e.g., via the product and service finder application 114) can generate the user interface 600b illustrated in FIG. 6B to provide information about the property item and / or contact information for such other user or the first client computing device 134.

[0055] Although not illustrated, in some cases, the computing device 104 can generate a user interface to prompt a user via the second client computing device 144 to provide data indicative of an upcoming film production event for which a defined property item is desired. For instance, the computing device 104 can generate a user interface to prompt a film producer user via the second client computing device 144 to provide details of one or more property items the user wants to use in an upcoming film production event. The computing device 104 can further record such data in the aforementioned database, which can be queried, for instance, based on a request received via the first client computing device 134 to locate upcoming film production events for which certain goods or services are desired.

[0056] FIG. 7 illustrates a flow diagram of another example, computer-implemented method 700 (or “method 700”) according to various aspects and embodiments of the present disclosure. It is understood that the flowchart of FIG. 7 provides merely an example of the many different types of functional arrangements that may be employed to implement the operation of the portion of the product and service finder application 114 as described herein. As an alternative, the flowchart of FIG. 7 may be viewed as depicting an example of elements of a method implemented in the computing environment 102 (FIG. 1) according to one or more embodiments.

[0057] At 702, the method 700 includes identifying (e.g., using the machine learning models 116), a property item having a defined film production attribute amongst one or more property items returned in a search. For example, the computing device 104 (e.g., via the product and service finder application 114 and the machine learning models 116) can search any or all of the data sources 160, 170, 180, 190 and identify a sports car of a certain make, model, year, or color, among possibly other defined film production attributes, in image or video data (e.g., images, or videos) obtained from any or all of the data sources 160, 170, 180, 190.

[0058] At 704, the method 700 includes determining whether there is a relatively high likelihood (e.g., above a defined threshold) that the property item will be desired for use in a film production event. For example, the computing device 104 (e.g., via the product and servicefinder application 114 and the machine learning models 116) can determine a probability of the aforementioned sports car being desired by some users in the networked environment 100 for use in a particular film production event. For instance, the computing device 104 can determine a probability that the car will be wanted by some users in the networked environment 100 based at least in part on one or more of historical film prop data or historical product or services transaction data that can be obtained from any or all of the data sources 160, 170, 180, 190 as described herein.

[0059] If it is determined at step 704 that there is a relatively high likelihood that the property item will be desired, at 706 the method 700 includes prompting the owner of the property item identified at 702 to register with the networked environment 100 and offer the item to the networked environment 100. For instance, the computing device 104 (e.g., via the product and service finder application 114) can prompt the owner of the aforementioned sports car by way of the second client computing device 144 (e.g., via the client application 136) to register with the networked environment 100 and offer the item to users in the networked environment 100 using any or all of the user interfaces 300a, 300b, 400a, 400b, 500a, 500b, 600a, 600b described herein and illustrated in FIGS. 3 A to 6B.

[0060] At 708, the method 700 includes determining whether there is a relatively high likelihood (e.g., above a defined threshold) that an owner of the property item will actually offer or provide the item for a particular film production event. For example, the computing device 104 (e.g., the product and service finder application 114 and the machine learning models 116) can determine a probability of an owner of the aforementioned sports car actually offering or providing the car for a particular film production event. For instance, the computing device 104 can determine a probability that the owner will actually offer or provide the car based at least in part on historical product or services transaction data that can be obtained from any or all of the data sources 160, 170, 180, 190 or from the owner via the second client computing device 144 (e.g., the client application 136) as described herein.

[0061] If it is determined at step 708 that there is a relatively high likelihood that the owner of the property item will actually offer or provide the item for use, at 710 the method 700 includes generating and providing a proposed product agreement that can be used (e.g., in a legally binding manner) to commercially offer or provide the product item for a particular film production event. For example, the computing device 104 (e.g., via the product and service finder application 114 and the machine learning models 116) can generate a proposed product agreement that can include suggested commercial contract or transaction terms such as a recommended product sale value or rental fee, service fee, scope of service, delivery date andtime or contract duration, insurance terms, cancellation or breach of contract terms, other commercial transaction related terms, or any combination thereof. The computing device 104 can recommend such commercial contract or transaction terms based at least in part on historical commercial contract or transaction data that can be obtained from any or all of the data sources 160, 170, 180, 190 or from at least one of the owner via the second client computing device 144 (e.g., the client application 136) or another user via the first client computing device 134 (e.g., the client application 136) as described herein. The computing device 104 can provide such a proposed product agreement to at least one of the owner via the second client computing device 144 (e.g., the client application 136) or another user via the first client computing device 134 (e.g., the client application 136).

[0062] At 712, the method 700 includes updating the machine learning models 116 using any new data or information obtained by the computing device 104 as a result of performing any or all of steps 702, 704, 706, 708, 710 described above. Following such updates, the method 700 returns to step 702 and repeats steps 702 to 710 until ended.

[0063] Referring now to FIG. 1, the memory 108 can store other executable-code components for execution by the processor 106. For example, an operating system can be stored in the memory 108 for execution by the processor 106. Where any component discussed herein is implemented in the form of software, any one of a number of programming languages can be employed such as, for example, C, C++, C#, Objective C, JAVA®, JAVASCRIPT®, Perl, PHP, VISUAL BASIC®, PYTHON®, RUBY, FLASH®, or other programming languages.

[0064] As discussed above, the memory 108 can store software for execution by the processor 106. In this respect, the terms “executable” or “for execution” refer to software forms that can ultimately be run or executed by the processor 106, whether in source, object, machine, or other form. Examples of executable programs include, for instance, a compiled program that can be translated into a machine code format and loaded into a random access portion of the memory 108 and executed by the processor 106, source code that can be expressed in an object code format and loaded into a random access portion of the memory 108 and executed by the processor 106, source code that can be interpreted by another executable program to generate instructions in a random access portion of the memory 108 and executed by the processor 106, or other executable programs or code. An executable program can be stored in any portion or component of the memory 108. The memory 108 can be embodied as, for example, a random access memory (RAM), read-only memory (ROM), magnetic or other hard disk drive, solid- state, semiconductor, universal serial bus (USB) flash drive, memory card, optical disc (e.g.,compact disc (CD) or digital versatile disc (DVD)), floppy disk, magnetic tape, or other types of memory devices.

[0065] In various embodiments, the memory 108 can include both volatile and nonvolatile memory and data storage components. Volatile components are those that do not retain data values upon loss of power. Nonvolatile components are those that retain data upon a loss of power. Thus, the memory 108 can include, for example, a RAM, ROM, magnetic or other hard disk drive, solid-state, semiconductor, or similar drive, USB flash drive, memory card accessed via a memory card reader, floppy disk accessed via an associated floppy disk drive, optical disc accessed via an optical disc drive, magnetic tape accessed via an appropriate tape drive, and / or other memory component, or any combination thereof. In addition, the RAM can include, for example, a static random-access memory (SRAM), dynamic random-access memory (DRAM), or magnetic random-access memory (MRAM), and / or other similar memory device. The ROM can include, for example, a programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or other similar memory devices.

[0066] Also, the processor 106 may represent multiple processors 106 and / or multiple processor cores and the memory 108 may represent multiple memories 108 that operate in parallel processing circuits, respectively. In such a case, the local interface 110 may be an appropriate network that facilitates communication between any two of the multiple processors 106, between any processor 106 and any of the memories 108, or between any two of the memories 108, etc. The local interface 110 may include additional systems designed to coordinate this communication, including, for example, performing load balancing. The processor 106 may be of electrical or of some other available construction.

[0067] Any or all of the product and service finder application 114, the machine learning models 116, and the communications stack 118 can be embodied, at least in part, through software or program instructions. The program instructions may be embodied in the form of source code that comprises human-readable statements written in a programming language or machine code that comprises numerical instructions recognizable by a suitable execution system such as a processor 106 in a computer system or other system. The machine code may be converted from the source code, etc. If embodied in hardware, each block may represent a circuit or a number of interconnected circuits to implement the specified logical function(s).

[0068] Further, any logic or application described herein, including the product and service finder application 114, the machine learning models 116, and the communications stack 118, may be implemented and structured in a variety of ways. For example, one or moreapplications described may be implemented as modules or components of a single application. Further, one or more applications described herein may be executed in shared or separate computing devices or a combination thereof. For example, a plurality of the applications described herein may execute in the same computing device 104, or in multiple computing devices 104 in the same the computing environment 102.

[0069] As discussed above, the product and service finder application 114, the machine learning models 116, and the communications stack 118 can each be embodied, at least in part, by software or executable-code components for execution by general purpose hardware. Alternatively, the same can be embodied in dedicated hardware or a combination of software, general, specific, and / or dedicated purpose hardware. If embodied in such hardware, each can be implemented as a circuit or state machine, for example, that employs any one of or a combination of a number of technologies. These technologies can include, but are not limited to, discrete logic circuits having logic gates for implementing various logic functions upon an application of one or more data signals, application specific integrated circuits (ASICs) having appropriate logic gates, field-programmable gate arrays (FPGAs), or other components.

[0070] Referring now to FIGS. 2 and 7, the flowchart or process diagram shown in each of FIGS. 2 and 7 is representative of certain processes, functionality, and operations of the embodiments discussed herein. Each block can represent one or a combination of steps or executions in a process. Alternatively, or additionally, each block can represent a module, segment, or portion of code that includes program instructions to implement the specified logical function(s). The program instructions can be embodied in the form of source code that includes human-readable statements written in a programming language or machine code that includes numerical instructions recognizable by a suitable execution system such as the processor 106. The machine code can be converted from the source code. Further, each block can represent, or be connected with, a circuit or a number of interconnected circuits to implement a certain logical function or process step.

[0071] Although the flowchart or process diagram shown in each of FIGS. 2 and 7 illustrates a specific order, it is understood that the order can differ from that which is depicted. For example, an order of execution of two or more blocks can be scrambled relative to the order shown. Also, two or more blocks shown in succession can be executed concurrently or with partial concurrence. Further, in some embodiments, one or more of the blocks can be skipped or omitted. In addition, any number of counters, state variables, warning semaphores, or messages might be added to the logical flow described herein, for purposes of enhanced utility, accounting, performance measurement, or providing troubleshooting aids. Such variations, asunderstood for implementing the process consistent with the concepts described herein, are within the scope of the embodiments.

[0072] Also, any logic or application described herein, including the product and service finder application 114, the machine learning models 116, and the communications stack 118 can be embodied, at least in part, by software or executable-code components and / or stored in any tangible or non-transitory computer-readable medium or device for execution by an instruction execution system such as a general-purpose processor. In this sense, the logic can be embodied as, for example, software or executable-code components that can be fetched from the computer-readable medium and executed by the instruction execution system. Thus, the instruction execution system can be directed by execution of the instructions to perform certain processes such as those illustrated in each of FIGS. 2 and 7. In the context of the present disclosure, a non-transitory computer-readable medium can be any tangible medium that can contain, store, or maintain any logic, application, software, or executable-code component described herein for use by or in connection with an instruction execution system.

[0073] The computer-readable medium can include any physical media such as, for example, magnetic, optical, or semiconductor media. More specific examples of suitable computer-readable media include, but are not limited to, magnetic tapes, magnetic floppy diskettes, magnetic hard drives, memory cards, solid-state drives, USB flash drives, or optical discs. Also, the computer-readable medium can include a RAM including, for example, an SRAM, DRAM, or MRAM. In addition, the computer-readable medium can include a ROM, a PROM, an EPROM, an EEPROM, or other similar memory device.

[0074] Disjunctive language, such as the phrase “at least one of X, Y, or Z,” unless specifically stated otherwise, is to be understood with the context as used in general to present that an item, term, or the like, can be either X, Y, or Z, or any combination thereof (e.g., X, Y, and / or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to be each present. As referenced herein in the context of quantity, the terms “a” or “an” are intended to mean “at least one” and are not intended to imply “one and only one.”

[0075] As referred to herein, the terms “include,” “includes,” and “including” are each intended to be inclusive in a manner similar to the term “comprising.” As referenced herein, the terms “or” and “and / or” are generally intended to be inclusive, that is (i.e.), “A or B” or “A and / or B” are each intended to mean “A or B or both.” As referred to herein, the terms “first,” “second,” “third,” and so on, can be used interchangeably to distinguish one component or entity from another and are not intended to signify location, functionality, or importance of theindividual components or entities. As referenced herein, the terms “couple,” “couples,” “coupled,” and / or “coupling” refer to chemical coupling (e.g., chemical bonding), communicative coupling, electrical and / or electromagnetic coupling (e.g., capacitive coupling, inductive coupling, direct and / or connected coupling), mechanical coupling, operative coupling, optical coupling, and / or physical coupling.

[0076] It should be emphasized that the above-described embodiments of the present disclosure are merely possible examples of implementations set forth for a clear understanding of the principles of the disclosure. Many variations and modifications can be made to the abovedescribed embodiment s) without departing substantially from the spirit and principles of the disclosure. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claim.

Claims

CLAIMSTherefore, the following is claimed:

1. A computing device, comprising: a memory device to store computer-readable instructions thereon; and at least one processing device configured through execution of the computer-readable instructions to: execute a search of at least one digital platform having data associated with one or more property items of a defined type; identify, using a machine learning model, a property item having a defined film production attribute among the one or more property items based on the search; extract data associated with the property item from the data associated with the one or more property items; generate a database comprising the data associated with the property item; and provide a visualization of the data associated with the property item to a client device based on a request obtained from the client device.

2. The computing device of claim 1, wherein the at least one processing device is further configured through execution of the computer-readable instructions to: obtain at least one of the data associated with the property item or additional data associated with the property item from a second client device associated with an owner of the property item.

3. The computing device of claim 1, wherein the at least one processing device is further configured through execution of the computer-readable instructions to: prompt the client device to submit one or more requests to query the database for other data associated with at least one additional property item being of the defined type or another type and having the defined film production attribute or another attribute.

4. The computing device of claim 1, wherein the at least one processing device is further configured through execution of the computer-readable instructions to: prompt the client device to contact a second client device associated with an owner of the property item based on return of the data associated with the property item from the query.

5. The computing device of claim 1, wherein the at least one processing device is further configured through execution of the computer-readable instructions to: determine, using a second machine learning model and based on historical film prop data, a probability of the property item being desired for use in a film production event; and prompt a second client device associated with an owner of the property item to provide at least one of the data associated with the property item or additional data associated with the property item based on the probability of the property item being desired for use in the film production event.

6. The computing device of claim 1, wherein the at least one processing device is further configured through execution of the computer-readable instructions to: determine, using a second machine learning model and based on historical film prop data, a probability of the property item being desired for use in a film production event; and prompt a second client device associated with an owner of the property item to complete a property item registration and offering process to offer the property item to a network of film production users for use in the film production event based on the probability of the property item being desired for use in the film production event.

7. The computing device of claim 1, wherein the at least one processing device is further configured through execution of the computer-readable instructions to: prompt the client device to provide data indicative of an upcoming film production event for which a defined property item is desired.

8. The computing device of claim 1, wherein the at least one processing device is further configured through execution of the computer-readable instructions to: create a digital representation of the property item based at least in part on digital data indicative of the property item obtained from a second client device associated with an owner of the property item, the digital representation comprising a unique and authenticated digital representation or a non-fungible token; and send an offer notification to the client device, the offer notification comprising data indicative of at least one of the owner, the digital representation, or offer terms for purchasing or leasing the digital representation for use with a film production event.

9. The computing device of claim 1, wherein the at least one processing device is further configured through execution of the computer-readable instructions to: create a digital representation of the property item based at least in part on digital data indicative of the property item obtained from a second client device associated with an owner of the property item, the digital representation comprising a unique and authenticated digital representation or a non-fungible token; record creation and ownership data corresponding to the digital representation in the database; track transaction activity data associated with the digital representation; and perform one or more operations based at least in part on the transaction activity data.

10. The computing device of claim 1, wherein the at least one processing device is further configured through execution of the computer-readable instructions to: determine, using a second machine learning model and based at least in part on historical product or services transaction data, a probability of an owner of the property item offering the property item for use in a film production event; and generate a proposed product agreement for use of the property item in the film production event based at least in part on the probability, the proposed product agreement comprising one or more commercial contract or transaction terms defined based at least in part on historical commercial contract or transaction data, the historical product or services transaction data and the historical commercial contract or transaction data each being associated with at least one of the property item or another property item of same or similar type as the property item.

11. A method of automating location of goods and services having certain film production attributes, the method comprising: executing, by at least one computing device, a search of at least one digital platform having data associated with one or more property items of a defined type; identifying, by the at least one computing device using a machine learning model, a property item having a defined film production attribute among the one or more property items based on the search; extracting, by the at least one computing device, data associated with the property item from the data associated with the one or more property items; andgenerating, by the at least one computing device, a database comprising the data associated with the property item.

12. The method of claim 11, further comprising: obtaining, by the at least one computing device, at least one of the data associated with the property item or additional data associated with the property item from a second client device associated with an owner of the property item.

13. The method of claim 11, further comprising: prompting, by the at least one computing device, the client device to submit one or more requests to query the database for other data associated with at least one additional property item being of the defined type or another type and having the defined film production attribute or another attribute.

14. The method of claim 11, further comprising: prompting, by the at least one computing device, the client device to contact a second client device associated with an owner of the property item based on return of the data associated with the property item from the query.

15. The method of claim 11, further comprising: determining, by the at least one computing device using a second machine learning model and based on historical film prop data, a probability of the property item being desired for use in a film production event; and prompting, by the at least one computing device, a second client device associated with an owner of the property item to provide at least one of the data associated with the property item or additional data associated with the property item based on the probability of the property item being desired for use in the film production event.

16. The method of claim 11, further comprising: determining, by the at least one computing device using a second machine learning model and based on historical film prop data, a probability of the property item being desired for use in a film production event; and prompting, by the at least one computing device, a second client device associated with an owner of the property item to complete a property item registration and offering process tooffer the property item to a network of film production users for use in the film production event based on the probability of the property item being desired for use in the film production event.

17. The method of claim 11, further comprising: prompting, by the at least one computing device, the client device to provide data indicative of an upcoming film production event for which a defined property item is desired.

18. The method of claim 11, further comprising: creating, by the at least one computing device, a digital representation of the property item based at least in part on digital data indicative of the property item obtained from a second client device associated with an owner of the property item, the digital representation comprising a unique and authenticated digital representation or a non-fungible token; and sending, by the at least one computing device, an offer notification to the client device, the offer notification comprising data indicative of at least one of the owner, the digital representation, or offer terms for purchasing or leasing the digital representation for use with a film production event.

19. The method of claim 11, further comprising: creating, by the at least one computing device, a digital representation of the property item based at least in part on digital data indicative of the property item obtained from a second client device associated with an owner of the property item, the digital representation comprising a unique and authenticated digital representation or a non-fungible token; recording, by the at least one computing device, creation and ownership data corresponding to the digital representation in the database; tracking, by the at least one computing device, transaction activity data associated with the digital representation; and performing, by the at least one computing device, one or more operations based at least in part on the transaction activity data.

20. The method of claim 11, further comprising: determining, by the at least one computing device using a second machine learning model and based at least in part on historical product or services transaction data, a probabilityof an owner of the property item offering the property item for use in a film production event; and generating, by the at least one computing device, a proposed product agreement for use of the property item in the film production event based at least in part on the probability, the proposed product agreement comprising one or more commercial contract or transaction terms defined based at least in part on historical commercial contract or transaction data, the historical product or services transaction data and the historical commercial contract or transaction data each being associated with at least one of the property item or another property item of same or similar type as the property item.

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