Systems and methods for positioning and locationing objects within a CUBE-based data model

Abstract

A cube-based locationing system may include a database storing a cube-based data model defining a plurality of cubes within a cube-based coordinate space mapped to a target environment. The plurality of cubes comprises multiple layers of nested cubes comprising first second and third cubes of different sizes, The cube-based data model implements a nested cube navigation format for selecting a cube from the multiple layers of nested cubes. The system may receive a request for data corresponding to a target position in the target environment, invoke a cube locationing API to access the cube-based data model with a nested cube navigation format based on a precision indication and to select an identified cube. The identified cube defines payload data, and a cube-based coordinate value defining a position of the identified cube within the cube-based coordinate space. The system may return the payload data of the identified cube.

Description

PATENT APPLICATION Attorney Docket No.: 34014-70817-PC SYSTEMS AND METHODS FOR POSITIONING AND LOCATIONING OBJECTS WITHIN A CUBE-BASED DATA MODELCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of the filing date of the following applications: provisional U.S. Patent Application No. 63 / 747,771 entitled ‘‘SYSTEMS AND METHODS FOR POSITIONING AND LOCATIONING OBJECTS WITHIN A CUBEBASED DATA MODEL,” filed on January 21, 2025; provisional U.S. Patent Application No.63 / 755,465 entitled “SYSTEMS AND METHODS FOR STORING SPATIOTEMPORAL DATA WITHIN A CUBE-BASED DATA MODEL,” filed on February 7, 2025; provisional U.S. Patent Application No. 63 / 755,487 entitled “SYSTEMS AND METHODS FOR ENCODING CUBE-BASED DATA WITHIN A CUBE-BASED DATA MODEL USING QUANTUM COMPUTING PROCESSING,” filed on February 7, 2025; provisional U.S. Patent Application No. 63 / 755,533 entitled “SYSTEMS AND METHODS FOR NAVIGATING AN OBJECT OR ENTITY USING A CUBE-BASED DATA MODEL,” filed on February 7, 2025; and provisional U.S. Patent Application No. 63 / 755,551 entitled “SYSTEMS AND METHODS FOR Al ANALYSIS OF DATA ENCODED WITHIN A CUBE-BASED DATA MODEL,” filed on February 7, 2025. The entire contents of each of the preceding applications are hereby expressly incorporated herein by reference.FIELD OF THE DISCLOSURE

[0002] The present invention is related to cube-based locationing systems and methods, and more particularly, to cube-based locationing systems and methods for updating and accessing cube-based data for positioning and locating objects.BACKGROUND

[0003] Traditional data storage and organizational systems are configured to store and retrieve data by assigning data elements to discrete locations in computer memories, database, etc.However, these techniques are not fully adaptable to storage and retrieval of payload data from within a cube-based data model that employs a hierarchical and recursive structure that is complete with time stamp components and mapped to real-world space.

[0004] Traditional locationing systems can be imprecise and error-prone when used to locate or otherwise correctly identify objects in the real world, virtual space, or otherwise environment,PATENT APPLICATION Attorney Docket No.: 34014-70817-PC such as an operating environment. Such traditional locationing systems can lack sufficient precision and fidelity and can cause difficulties in accurate measurements because a given environment may require different layers of precision.

[0005] For example, one traditional location system, Global Positioning System (GPS) technology, has received widespread adoption, and utility. However, GPS remains an imperfect tool for precise and reliable location tracking. One significant limitation is the inherent imprecision in its measurements. Standard GPS systems, which rely on signals from a network of satellites, typically offer accuracy within 5 to 10 meters under ideal conditions. This layer can fall short in applications requiring pinpoint accuracy, such as military applications, autonomous vehicles, drone operations, or detailed 3D mapping. Environmental factors, such as tall buildings, dense foliage, or adverse weather conditions, further degrade signal quality, leading to increased positional errors and a lack of consistency in measurements.

[0006] Another critical challenge is the difficulty of achieving consistent precision within a three-dimensional (3D) environment. While GPS can provide latitude and longitude coordinates with relative accuracy, its ability to measure elevation or altitude often lags behind. This is particularly problematic in urban environments with multi-story buildings, mountainous regions, or any scenario where vertical positioning is as critical as horizontal positioning. The limitations arise because satellite signals must traverse the atmosphere and other obstructions, introducing errors and delays that are difficult to account for precisely. Furthermore, the lack of standardization in how elevation is measured — often relative to different reference points like sea layer or the Earth’s ellipsoid model — adds another layer of complexity to achieving reliable 3D positioning.

[0007] Further, the real-world application of GPS technologies exposes its susceptibility to interference and inconsistencies. Signal multipath, where GPS signals bounce off reflective surfaces like buildings or water, can confuse receivers and lead to erroneous location data.Similarly, reliance on satellite signals makes GPS vulnerable to intentional jamming or unintentional disruptions, such as solar flares or electromagnetic interference. These vulnerabilities underscore the need for complementary technologies, such as ground-based positioning systems, enhanced signal processing algorithms, or hybrid approaches combiningPATENT APPLICATION Attorney Docket No.: 34014-70817-PC GPS with other localization methods, to bridge the gap in precision and reliability for modem applications.

[0008] Thus, traditional locationing systems, such as GPS, can fall short because they can be imprecise and error-prone in locating objects in the real world, virtual space, or otherwise environment, such as an operating environment. Such traditional locationing systems can lack sufficient precision and fidelity and can cause difficulties in accurate measurements because a given environment may require different layers of precision

[0009] Further, such issues can arise in virtual environments, too. For example, locationing or positioning virtual objects within a 3D virtual space can require the development of accurate world view or model in order to sufficiently reference objects with the 3D virtual space. For example, applications that require precise measurements can suffer when using a limited world view or model that is insufficiently granular for track an object’s position within a 3D space, where measuring whether one object acted on other object may be critical to defining the objects’ respective interaction(s) with one another.

[0010] In view of this, there is a need for cube-based locationing systems and methods for updating and accessing cube-based data for positioning and locating objects, e.g., within the real-world, virtual space, or otherwise environment, which overcomes the aforementioned limitations in the prior art.SUMMARY

[0011] In various aspects, the cube-based locationing systems and methods described herein offers significant advancements in object positioning and data organization by introducing a hierarchical and recursive model of nested cubes. This system structures data into multiple layers of cubes, each varying in size and precision, allowing for seamless scalability and adaptability across applications ranging from millimeter-scale accuracy to large-scale environments like planetary mapping. Unlike traditional systems, the cube-based model employs a logical framework that reduces data redundancy by enabling lower-layer cubes to inherit spatial, temporal, and payload data from higher-layer cubes, resulting in more efficient memory utilization and computational processes.PATENT APPLICATION Attorney Docket No.: 34014-70817-PC

[0012] The cube-based locationing systems and methods implement a cube-based coordinate navigation algorithm or format, which facilitates highly accurate navigation and locationing within three-dimensional space. The system’s precision is achieved through a left-bottom-front (LBF) navigation format, which streamlines the selection and retrieval of specific cubes based on their coordinate values and precision indicators. This approach eliminates the need for complex calculations often associated with traditional systems like GPS, which rely on inconsistent reference models for elevation and are prone to errors from environmental interference.

[0013] The cube-based system improves computational efficiency by leveraging simplified geometric calculations for distance and positional determinations. Using a standardized nested structure, the system significantly reduces processing cycles compared to traditional methods, which often involve iterative algorithms for complex geospatial calculations. This enhancement enables faster and more precise data retrieval, making the system suitable for real-time applications, such as drone navigation, autonomous vehicles, and virtual environments.

[0014] Furthermore, the system’s capacity to standardize disparate data formats into a unified representation, such as the Time United Location System Address (TULSA), enhances its interoperability across various platforms and use cases. This transformation simplifies the integration of real-time data inputs from external devices, such as sensors or user interfaces, into the cube-based model, enabling accurate updates and robust state management of the modeled environment.

[0015] Further, the cube-based locationing systems and methods improve data organization and object tracking. By addressing the shortcomings of traditional systems in precision, scalability, and computational efficiency, the based locationing systems and methods allows for enhanced locationing and positioning capabilities and deployments for applications requiring highly accurate and adaptable locationing solutions in both physical and virtual domains.

[0016] With respect to a specific disclosure, in some aspects, the techniques described herein relate to a cube-based locationing system configured to update and access cube-based data for positioning and locating objects, the cube-based locationing system including: one or more processors; a memory communicatively coupled to the one or more processors; a database (104) communicatively coupled to the one or more processors and storing a cube-based data model (200) defining a plurality of cubes (202) each having cube-based dimensions (301) within aPATENT APPLICATION Attorney Docket No.: 34014-70817-PC cube-based coordinate space (204) mapped to a target environment (206), wherein the plurality of cubes includes multiple layers of nested cubes including at least: a first cube (21 Oct) having a first size (300), a second cube (210c2nl) nested within the first cube and having a second size (300nl) of a smaller measurement that the first size, and a third cube (210c3n2 or 210c4n2) nested within the second cube and having a third size (300n2) having a smaller measurement than the second size, and wherein the cube-based data model implements a nested cube navigation format (e.g., left-bottom-front (LBF) 208) for selecting a cube from the multiple layers of nested cubes; and a cube locationing application programming interface (API) (106) configured to access the cube-based model, wherein the memory stores computing instructions that when executed by the one or more processors, causes the one or more processors to: receive, from a computing device (e.g., computing device via website 108), a request for data corresponding to a target position in the target environment, the request including a precision indication (e.g., 300n2), invoke, based on the request, the cube locationing API to access the cube-based data model, wherein the cube locationing API accesses the cube-based data model with the nested cube navigation (e.g., reference) format and based on the precision indication to select an identified cube (e.g., third cube (210c4n2)) from the multiple layers of nested cubes, wherein the identified cube defines payload data , and wherein the identified cube further defines a cube-based coordinate value (e.g., 228 (555.888)) defining a position of the identified cube within the cube-based coordinate space, and return, to the computing device, the payload data of the identified cube.

[0017] In some aspects, the techniques described herein relate to a method of update and access cube-based data for positioning and locating objects, the method including: receiving, from a computing device (e.g., computing device via website 108), a request for data corresponding to a target position in a target environment of a cube-based data model, the request including a precision indication (e.g., 300n2), wherein: the cube-based data model (200) defines a plurality of cubes (202) each having cube-based dimensions (301) within a cube-based coordinate space (204) mapped to the target environment, the plurality of cubes include multiple layers of nested cubes including at least: a first cube (21 Oct ) having a first size (300nl), a second cube (210c2nl ) nested within the first cube and having a second size (300n2) of a smaller measurement that the first size, and a third cube (210c3n2 or 210c4n2) nested within the second cube and having a third size (300n3) having a smaller measurement than the second size, and thePATENT APPLICATION Attorney Docket No.: 34014-70817-PC cube-based data model implements a nested cube navigation format (e.g., LBF 208) for selecting a cube from the multiple layers of nested cubes; invoking, based on the request, a cube locationing API to access the cube-based data model; accessing, via the cube locationing API, the cube-based data model with the nested cube navigation format; selecting an identified cube (e.g., third cube (210c4n2)) from the multiple layers of nested cubes based on the precision indication, wherein the identified cube defines payload data and a cube-based coordinate value (e.g., 228 (555.888)) defining a position of the identified cube within the cube-based coordinate space; and returning, to the computing device, the payload data of the identified cube.

[0018] In some aspects, the techniques described herein relate to a tangible, non-transitory computer-readable medium storing instructions for positioning and locating objects, that when executed by one or more processors cause the one or more processors to: receive, from a computing device (e.g., computing device via website 108), a request for data corresponding to a target position in a target environment of a cube-based data model, the request including a precision indication (e.g., 300n2), wherein: the cube-based data model (200) defines a plurality of cubes (202) each having cube-based dimensions (301) within a cube-based coordinate space (204) mapped to the target environment, the plurality of cubes include multiple layers of nested cubes including at least: a first cube (210cl) having a first size (300nl), a second cube (210c2nl) nested within the first cube and having a second size (300n2) of a smaller measurement that the first size, and a third cube (210c3n2 or 210c4n2) nested within the second cube and having a third size (300n3) having a smaller measurement than the second size, and the cube-based data model implements a nested cube navigation format (e.g., LBF 208) for selecting a cube from the multiple layers of nested cubes; invoke, based on the request, a cube locationing API to access the cube-based data model; access, via the cube locationing API, the cube-based data model with the nested cube navigation format; select an identified cube (e.g., third cube (210c4n2)) from the multiple layers of nested cubes based on the precision indication, wherein the identified cube defines payload data and a cube-based coordinate value (e.g., 228 (555.888)) defining a position of the identified cube within the cube-based coordinate space; and return, to the computing device, the payload data of the identified cube.

[0019] In accordance with the above, and with the disclosure herein, the present disclosure includes improvements in computer functionality or in improvements to other technologies at least because the claims recite that, e.g., an underlying computing system implements a cube-PATENT APPLICATION Attorney Docket No.: 34014-70817-PC based data model. The cube-based model reduces memory storage by eliminating redundant storage of cube-based data. For example, data stored a lower layer cube of one size may be accessed by a higher layer cube of a larger size. The data need not be redundantly stored in a second, third, or otherwise further cube of the lower layer cube. As long as the lower layer cube includes its data, underlying computing system can fulfill requests for the data for the higher layer cube by accessing the data in the lower layer cube. In this way, the system can eliminate data storage for two cube locations commonly associated with the higher layer cube. That is, the present disclosure describes improvements in the functioning of the computer itself or “any other technology or technical field” because previous systems do not have this feature. This improves over the prior art at least because previous system would store redundant, and often times, imprecise data for a same object.

[0020] The present disclosure relates to improvement to other technologies or technical fields at least because it describes defining a plurality of cubes each having cube-based dimensions, which reduces the number of processing cycles or otherwise computations of the underlying computing device when calculating distances between or amount locations within the cubebased data model. That is, because the cube-based data model uses a defined cube-based coordinate space, computations performed among locations within the space can use simplified computational distance equations (e.g., Euclidian geometry), which require fewer compute cycles when performed by an underlying computing device. This improves over the prior art because prior art systems typically require complex computations regarding axis of the earth, GPS locations, calculus, and other multiple iteration computational complexity that greatly increases the number of computational cycles needed to arrive at a similar, albeit, less precise location. Furthermore, in some embodiments, the locations of each of the plurality of cubes within the cube-based coordinate space are defined only by positive value integers relative to a common origin, which also improves the processing efficiency of locating a cube within cubebased data model and accessing payload data associated therewith.

[0021] Additionally, the present disclosure relates to improvement to other technologies or technical fields at least because it defines an improved method of organizing and storing data. In particular, the improved method of organizing and storing data described herein may improve functioning of artificial intelligence (Al) and / or machine learning (ML) related systems and methods. For example, the cube-based data model described herein provides an absolute spatialPATENT APPLICATION Attorney Docket No.: 34014-70817-PC and temporal reference for any dataset mapped thereto, which when used as training data for an Al or ML type model improves the training process to produce acceptable training outputs with fewer training cycles. Additionally, the cube-based data model described herein also reduces hallucinations at the inference stage because structured format of the cube-based data model enables the trained Al or ML model to better detect similar or related elements by referencing the absolutely defined spatial and temporal values that are consistently defined in both the data used to train the Al or ML model and the data input at the inference stage of operation.

[0022] Still further, the present disclosure relates to improvement to other technologies or technical fields at least because the precision and accuracy of locationing, when using the cubebased data model, allows for accurately defining, updating, and locating objects within the cubebased coordinate space. Because all objects are located or defined based on the plurality of cubes, the cube-based locationing systems and methods can update and access cube-based data for positioning and locating objects at extremely precise layers or otherwise layers of granularity (at the millimeter scale or lower).

[0023] The present disclosure includes effecting a transformation or reduction of a particular article to a different state or thing, e.g., receiving data in a variety of different data formats and / or at a variety of different granularity, and transforming or reducing such disparate or otherwise different data into a unique format standardized format (e.g., a Time United Location System Address (TULSA) format) unique to a cube-based data model as described herein.

[0024] The present disclosure includes specific features other than what is well-understood, routine, conventional activity in the field, and / or otherwise adds unconventional steps that confine the disclosure to a particular useful application, e.g., cube-based locationing system configured to update and access cube-based data for positioning and locating objects, for example, as described herein.

[0025] Advantages will become more apparent to those of ordinary skill in the art from the following description of the preferred aspects that have been shown and described by way of illustration. As will be realized, the present embodiments may be capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.BRIEF DESCRIPTION OF THE DRAWINGSPATENT APPLICATION Attorney Docket No.: 34014-70817-PC

[0026] The Figures described below depict various aspects of the system and methods disclosed therein. It should be understood that each Figure depicts an embodiment of a particular aspect of the disclosed system and methods, and that each of the Figures is intended to accord with a possible embodiment thereof. Further, wherever possible, the following description refers to the reference numerals included in the following Figures, in which features depicted in multiple Figures are designated with consistent reference numerals.

[0027] There are shown in the drawings arrangements which are presently discussed, it being understood, however, that the present embodiments are not limited to the precise arrangements and instrumentalities shown, wherein:

[0028] FIG. 1 illustrates a cube-based locationing system for updating and accessing cubebased data for positioning and locating objects in accordance with various embodiments disclosed herein.

[0029] FIGS. 2A-2D illustrate an example visualizations of a cube-based model and related cube-based data in accordance with various embodiments disclosed herein.

[0030] FIG. 3 illustrates a table of locationing data associated with different cube sizes for respective layers of an example cube-based data model and for an example data element (e.g., payload data) stored and / or accessible within the cube-based model in accordance with various embodiments disclosed herein.

[0031] FIG. 4 illustrates an example visualization regarding an implementation for requesting an environmental distance between two real- world locations using the cube-based model in accordance with various embodiments disclosed herein.

[0032] FIG. 5 illustrates a schematic diagram of two positions within a cube-based model and a Euclidian distance between them in accordance with various embodiments disclosed herein.

[0033] FIG. 6 illustrates a flow chart of a cube-based locationing method for updating and accessing cube-based data for positioning and locating objects in accordance with various embodiments disclosed herein.

[0034] The Figures depict preferred embodiments for purposes of illustration only.Alternative embodiments of the systems and methods illustrated herein may be employed without departing from the principles of the invention described herein.PATENT APPLICATION Attorney Docket No.: 34014-70817-PC DESCRIPTION

[0035] In various aspects, cube-based locationing system(s) and / or method(s) described herein my comprise one or more controller(s) (e.g., processor(s) as executing on one or more servers or cloud platform(s)), and one or more computer memories, storing computing instructions for implementing algorithms or methods for positioning and locating objects within a cube-based data model. The cube-based locationing system(s) and / or method(s) may store the cube-based model in a database or, more generally, computer memory, and may access such data or information therefrom. The memory may comprise tangible, non-transitory computer-readable medium storing instructions for retrieving payload data from the cube-based model and / or for performing any other algorithms, methods, or functions as described herein.

[0036] FIG. 1 illustrates a computer-implemented cube-based locationing system 100 for storing payload data in hierarchical and recursive manner and retrieving payload data in response to a request. The request may indicate a location within a cube-based data model managed by cube-based locationing system 100. The cube-based locationing system 100 comprises a controller (e.g., a processor), a memory (not shown), and a database 104 communicatively coupled to the controller.

[0037] In some embodiments, the controller, memory and database 104 are part of a remote cloud-based server platform that is accessible over network type connection using cube locationing application programming interface (API) 106. For example, a computing device via website 108 may access the cloud-based server platform using the API 106 to request particular payload data stored at a specified location within the cube-based data model hosted by the database 104.

[0038] The payload data stored in database 104 may be received from an external source such as a user computing device. As shown in FIG. 1 , the received data may include real-time data 110 (e.g., data from internet connected sensors, user inputs, etc.). The cube-based locationing system 100 uploads, at block 112, the real-time data 110 into database 104. When uploading the real-time data 110, the cube-based locationing system 100 may map or otherwise store the realtime data 110 to a new cube of the cube-based model and / or update an existing cube to reflect new or changed values of a previously mapped or stored cube. When updating a previously mapped or stored cube, the cube-based locationing system 100 may define or update a timestampPATENT APPLICATION Attorney Docket No.: 34014-70817-PC associated with new payload data that corresponds to the real-time data 110. In some embodiments, the timestamp indicates a time at which the new payload data was generated. In other embodiments, the timestamp may indicate a time at which the new payload data was saved into database 104. In some embodiments, prior to uploading the real-time data 110 at block 112, the cube-based locationing system 100 may, at block 114, categorize the real-time data 110 according to a Time United Location System Address (TULSA) standard and save the categorized real-time data 110 in a local storage cache 116.

[0039] The payload data stored in database 104 may also include pay load data representing user provided data 118 (e.g., user provided spreadsheets, arrays, databases, tables, csvs, etc). As shown in FIG. 1 , the cube-based locationing system 100 may be configured to aceept a plurality of different data formats 119. The plurality of different data fromats 119 may inlcdue, but are not limited to, CSV, JSON, XLS, XML, PDF, PPT, SQL, RDF, Neo4j, DynamoDB, HBase, InfluxDB, ORC, HDF5, NetCDF, KML, KMZ, Parquet, Avro, BSON, Redis, Apache, AranjoDB, and Timescale DB.

[0040] As shown in FIG. 1 at blocks 120 the cube-based locationing system 100 may receive the user provided data 118 from interaction of a user computing device with a homepage 122 of the website 108. Then, at block 124, the cube-based locationing system 100 uploads the user provided data 118 for further processing. In particular, the uploading at block 124 may include storing the user provided data 118 within the local storage cache 116. Once uploaded, the cubebased locationing system 100, at block 126, processes of prepares the user provided data 118 for categorization and conversion according to the TULSA standard. For example, at block 128, the cube-based locationing system 100 may divide the user provided data 118 into grains and, at block 130 assign values of the user provided data 118 a TULSA code, an identification value or number, and descriptor text to generate processed payload data 132 that can be stored and mapped into the database 104. As described in more detail below, a “grain” may include a four dimensional (e.g., three dimensions of space and one time dimension) location of each distinct element of the user provided data 118 within the database 104.

[0041] The payload data stored within the database 104 may take multiple different forms as described herein. For example, as shown in FIG. 1 , these forms may include asynchronous payload data 134 Assured Positioning Navigation and Timing (APNT) file data 136 among otherPATENT APPLICATION Attorney Docket No.: 34014-70817-PC types. Tn some embodiments, the APNT file data 136 may be displayed on the homepage 122 when requested. It should be appreciated that any of the data stored within the database 104 may likewise be displayed on the homepage 122 or other page of the website 108 when requested.

[0042] As shown in FIG. 1, in some embodiments, the cube-based locationing system 100 may also interface or host a generative pretrained transformer (GPT) model 138 for analyzing the payload data stored in the database 104. For example, the cube-based locationing system 100 may receive a prompt 140 via the API 106. In response to receiving the prompt 140, the cubebased locationing system 100 may retrieve relevant pay load data from the database 104 and transmit the relevant data and the prompt for processing by the GPT model 138 to generate an output 142.

[0043] More generally, GPT model 138 represents a generative artificial intelligence (Al) model or otherwise Al model (e.g., a machine learning model, neural network, or other learning model) configured to analyze payload data, TULSA codes, or other information or data described from database 104 and / or cube-based model 200. For example, GPT model 138 may comprise an artificial intelligence model, which may be trained using a supervised or unsupervised machine learning program or algorithm. The machine learning program or algorithm may employ a neural network, which may comprise a convolutional neural network, a vision transformer, a deep learning neural network, a large language model (LLM), a generative Al model, a multimodal model, and / or a combined learning algorithm or program that is trained or otherwise leams based on features or feature datasets, which may comprise, by way of nonlimiting example, payload data, spatial data, temporal data, TULSA codes, cube-based data such as positions or coordinates, locations, cube-based navigation schemes or algorithms, and / or any other data described herein, including any data that can be stored or accessed from cube-based data model 200. The machine learning programs or algorithms may also include natural language processing, semantic analysis, automatic reasoning, regression analysis, support vector machine (SVM) analysis, decision tree analysis, random forest analysis, K-Nearest neighbor analysis, naive Bayes analysis, clustering, reinforcement learning, and / or other machine learning algorithms and / or techniques. In some embodiments, the artificial intelligence and / or machine learning based algorithms may be included as a library or package executed on cube-based locationing system 100. For example, libraries may include the TENSORFLOW based library, the PYTORCH library, and / or the SCIKIT-LEARN Python library.PATENT APPLICATION Attorney Docket No.: 34014-70817-PC

[0044] Machine learning may involve identifying and recognizing patterns in existing data (such as identifying features of cube-based data model 200, which may comprise, by way of nonlimiting example, payload data, spatial data, temporal data, TULSA codes, cube-based data such as positions or coordinates, locations, cube-based navigation schemes or algorithms, and / or any other data described herein, including any data that can be stored or accessed from cube-based data model 200) in order to facilitate making predictions, classifications, or identification for subsequent data (such as using the Al model on new data input into the Al model in order to generate predictions, classifications, or otherwise output based on the relationship, such as spatial and / or temporal relationship of the features within the cube-based data model 200).

[0045] Al model(s), such as described herein (e.g. GPT model 138), may be created and trained based upon example data (e.g., training data and related pixel data) inputs or data (which may be termed “features” and “labels”) in order to make valid and reliable predictions for new inputs, such as testing level or production level data or inputs. In supervised machine learning, a machine learning program operating on a server, computing device, or otherwise processor(s), may be provided with example inputs (e.g., “features”) and their associated, or observed, outputs (e.g., “labels”) in order for the machine learning program or algorithm to determine or discover rules, relationships, patterns, or otherwise machine learning “models” that map such inputs (e.g., “features”) to the outputs (e.g., labels), for example, by determining, assigning, and / or mapping weights or other metrics to the model across its various feature categories. Such rules, relationships, or otherwise models may then be provided subsequent inputs in order for the model, executing on the server, computing device, or otherwise processor(s), to predict, based on the discovered rules, relationships, or model, an expected output.

[0046] In unsupervised machine learning, the server, computing device, or otherwise processor(s), may be required to find its own structure in unlabeled example inputs, where, for example multiple training iterations are executed by the server, computing device, or otherwise processor(s) to train multiple generations of models until a satisfactory model, e.g., a model that provides sufficient prediction accuracy when given test level or production level data or inputs, is generated.

[0047] Supervised learning and / or unsupervised machine learning may also comprise retraining, relearning, or otherwise updating models with new, or different, information, whichPATENT APPLICATION Attorney Docket No.: 34014-70817-PC may include information received, ingested, generated, or otherwise used over time. The disclosures herein may use one or both of such supervised or unsupervised machine learning techniques.

[0048] The use of Al models can be applied for storage, access, prediction, classification, or otherwise update and output of cube-based data from cube-based data model 200. For example, as described for various aspects, an artificial intelligence (Al) model (e.g., GPT model 138) or otherwise an Al model, is accessible by a processor of cube-based locationing system 100, and trained with cube based data, such as multiple layers of nested cubes and related data, e.g., payload data, spatial data, temporal data, TULSA codes, cube-based data such as positions or coordinates, locations, cube-based navigation schemes or algorithms, and / or any other data described herein, including any data that can be stored or accessed from cube-based data model 200). The Al model can then be provided with new data, such as new data selected from a same or similar set of data as used to train the Al model (e.g., payload data, spatial data, temporal data, TULSA codes, cube-based data such as positions or coordinates, locations, cube-based navigation schemes or algorithms, and / or any other data described herein, including any data that can be stored or accessed from cube-based data model 200) in order to output predictions, classifications, or identification for the new data. Such output may comprise, for example, a prediction of a future location or position of an object (e.g., an electronic signal and / or aircraft or other moving object) within a target environment within a cube-based coordinate space (e.g., cube-based coordinate space 204) of cube-based data model 200. As a further example, such output may comprise a classification or identification of an object (e.g., an aircraft, satellite, vehicle; an electronic signal; and / or a resource (e.g., liquid or oil)) within a cube-based coordinate space (e.g., cube-based coordinate space 204) of cube-based data model 200.

[0049] In another example, an Al model (e.g., GPT model 138) may be implemented using generative Al, such as an LLM based generative Al model. In one aspect, a generative Al model may comprise a generative multimodal LLM model that can combines a visual and contextual implementation. More generally, examples of multimodal large language models include, by way of non-limiting example, GPT-4 (e.g., GPT-4o) by OPENAI, GEMINI by GOOGLE, DALL-E, IMAGEBIND (from META), LLaVA, and UNIFIED-IO 2; each of which can process and generate information across various modalities like text, images, audio, and video, allowing them to understand and respond to complex prompts combining different data types. In aspects wherePATENT APPLICATION Attorney Docket No.: 34014-70817-PC an Al model (e.g., GPT model 138) comprises a generative Al model, such generative Al model can be trained with cube-based data via fine tuning, retrieval augmented generation (RAG), and / or other generative Al training techniques to configure or otherwise update the generative Al model (e.g., GPT model 138) to recognize cube-based data (e.g., including by not limited to payload data, spatial data, temporal data, TULSA codes, cube-based data such as positions or coordinates, locations, cube-based navigation schemes or algorithms, and / or any other data described herein, including any data that can be stored or accessed from cube-based data model 200). Such generative Al training techniques can also be used to further train or otherwise update the Al model (e.g., GPT model 138) with such cube-based data.

[0050] For example, a generative Al based model can be adapted to predict or otherwise detect or identifying locations of objects through RAG, where the generative Al model is updated by providing object locations having temporal values within cube-based coordinate space 204 of cube-based data model 200 to enhance the generative Al model’s contextual understanding of time-and-space of cube-based data, including how quickly a given object may travel between locations within the cube-based coordinate space 204. The RAG training process updates the generative model to produce more accurate assessments by incorporating domainspecific knowledge dynamically, e.g., by training the generative Al model to recognize and predict specific features (e.g., objects and their movement within cube-based coordinate space 204), for example, as described herein. Fine-tuning an Al model (e.g., GPT model 138) can involve retraining the model on a cube-based data to train the model on various types of payload data, at various temporal values and spatial data or otherwise sizes of cubes, in order to map the relationships between objects in cube-based coordinate space 204 and their interaction in fourdimensional space. By optimizing weights specific to this task, the model improves its ability to analyze cube-based data and generate accurate predictions with respect to such objects within cube-based coordinate space 204 of cube-based data model 200.

[0051] In one example, a generative Al-based model (e.g., GPT model 138) may comprise an instance of a multimodal LLM model that has been trained via RAG and / or fine-tuning. In such examples, a generative Al model (e.g., such as the GEMINI multimodal LLM model or another LLM model) could be leveraged to synthesize data, improve generalization, or provide explainable insights into the cube-based data and / or generate predictions related thereto. In one example, a user may submit a request that the generative Al model provide a prediction for anPATENT APPLICATION Attorney Docket No.: 34014-70817-PC object within the cube-based model 200. The request may comprise a prompt engineered to access cube-based data, e.g., “determine the future position of the aircraft at current position 555.000 position after 1 minute.” The generative Al model may access cube-based data model 200 then output a prediction, based on analysis of cube-based coordinate space 204 including analysis of cube 120c3n2, that the aircraft is predicted to be at point 555.888 as defined by cube 210c4n2 within 1 minute. This can be, for example, as described herein for FIG. 2C, or elsewhere herein.

[0052] Still further, with reference to FIG 1., data (e.g., payload data) or the output 142 can be visualized or otherwise rendered by a visualization application (app) 144 of cube-based locationing system 100. Such rendering applies to Visualization app 144 may comprise computing instructions executable by one or more processors of cube-based locationing system 100. In various aspects, visualizations of the cube-based model and / or cube-based data, such as those described herein for FIGs. 2A-2D, may be rendered via a user-interface (e.g., a graphic user interface (GUI)) of a display device having a display screen. In some embodiments, the Visualization app 144 may access an external mapping service 146 to render or visualize the payload data. Furthermore, rendering by the visualization app 144 may include the cube-based locationing system 100 determining, at block 148, a requested or default perspective for viewing the payload data or output 142. Then, at block 150, the cube-based locationing system 100 may annotate the the final view scene 152 (e.g., the visualizations of the cube-based model and / or cube-based data, such as those described herein for FIGs. 2A-2D).

[0053] FIGS. 2A-2D illustrate an example visualizations of a cube-based model and related cube-based data in accordance with various embodiments disclosed herein. In various aspects, the example visualizations of FIGS. 2A-2D illustrate a logical arrangement of payload data or otherwise data elements within a cube-based data model, for example, as described herein. The logical arrangement may comprise a nested arrangement of cubes (e.g., comprising payload data) within a cube-based coordinate space, for example, as described herein. Additionally, or alternatively, the FIGS. 2A-2D represent visualizations on a user-interface display, such as renderings of a cube-based model and its related cube-based data, via a graphical user interface (GUI) on a display screen of a display device. Visualizations may be rendered, for example, by a visualization software application (app) 114 of cube-based locationing system 100.PATENT APPLICATION Attorney Docket No.: 34014-70817-PC

[0054] With further reference to FIGS. 2A and 2B, these figures illustrate example visualizations of a cube-based model 200 that is managed by the cube-based locationing system 100 of FIG. 1 and stored in the database 104. In particular, FIG. 2A depicts a visualization that maps a three-dimensional representation of the cube-based model 200 in three dimensions (3D). In various aspects, the cube-based model 200 is logically defined in 3D dimensional space and can be used to map or otherwise locate or position objects in 3D space including, but not limited to, a real-world environment, a virtual environment, or otherwise an environment for locationing objects and / or related object data, including electronic signals occurring or may have occurred within a given environment. FIG. 2B shows a visualization (e.g., which may be rendered on user interface display) of a side view of the cube-based model 200. The visualization of FIG. 2B is depicted in two dimensions (2D) as the cube-based model 200 is rotated from a side view perspective. As shown in FIGS. 2 A and 2B, the cube-based model 200 includes a center 201 and defines a plurality of cubes 202.

[0055] Each of the cubes 202 have cube-based dimensions 301 within a cube-based coordinate space 204. In various aspects, the cube-based coordinate space 204 can be mapped to a target environment 206. In some embodiments, the center 201 of the cube-based coordinate space 204 is mapped to a representative real-world location of the target environment such as the geographic center of a planet (E.G. the earth, mars, Jupiter, etc.). In these embodiments, each of the plurality of cubes 202 represent a distinct location within the target environment.Furthermore, navigation within the cube-based model 200 may be done based on left-bottomfront (LBF) location 208 of a highest layer hyper cube (e.g., a first cube 21 Ocl ) .

[0056] The plurality of cubes 202 may further include multiple layers of nested cubes. For example, the first cube 21 Ocl, a second cube 210c2nl nested within the first cube 21 Ocl, and a third cube 210c3n2 nested within the second cube 210c2nl. The first, second, and third cubes 210cl, 210c2nl, 210c3n2, have sizes noted by the cube-based dimensions 301. In the example of FIGs. 2A-2D, the first, second, and third cubes 210cl, 210c2nl, 210c3n2, have sizes 300, 300nl, and 300n2, respectively. It is to be understood, however, that additional and / or fewer layers and / or sizes of cubes may form a part or a whole of a given cube-based data model (e.g., cubebased model 200). In general, each layer of the nested cubes is associated with a corresponding size noted by the cube-based dimensions 301. In particular, the sizes 300nl, 300n2, 300n3, 300n3, 300n4, 300n5, 300n6, 300n7, 300n8, 300n9, 300nl0 shown in FIG. 2A correspond to thePATENT APPLICATION Attorney Docket No.: 34014-70817-PC cube sizes for the first, second, third, fourth, fifth, sixth, seventh, eighth ninth, and tenth layers, or otherwise sizes, of the plurality of cubes 202 within the hypercube 210cl, respectively. It should be appreciated that the cube-based model 200 may include additional layers beyond those shown in FIG. 2A. Additional details on the plurality of cubes 202, the cube-based coordinate space 204, and other aspect of the cube-based model 200 are shown and described in International Patent Application No. WO 2024 / 030396, all of which is incorporated by reference herein in its entirety.

[0057] As shown in FIG. 2A, the corresponding size of a layer is an order of magnitude larger or smaller than the corresponding sizes associated with the next highest and lowest layers of the nested cubes, respectively. For example, the size 300nl of the second cube 210c2nl is ten times smaller than the first size 300, and the size 300n2 of the third cube 210c3n2 is ten times smaller than the size 300n2. In particular, as shown in FIG. 2A, the size 300nl is 2000 kilometers (km) and the size 300n2 is 200 km. In these embodiments the size 300 of the hypercube 210cl would be 20,000 km. It should be appreciated that other dimension change values besides an order of magnitude between each cube layer may be used. It should also be appreciated that, at least with respect to some aspects herein, a size of a cube as referenced herein refers to the length of a single side of a cube, where the volume of each cube is the size value cubed.

[0058] The plurality of cubes 202 may be used to define and store different data elements. For example, a cube may define and store a combination of payload data (e.g. sensor data, text, numbers, etc.), spatial data such as a spatial position indicating a real-world location linked to the payload data (e.g., a real- world location where the payload data was captured by a sensor), and temporal data linked to the payload data (e.g., a time at witch the payload data was captured, created, etc.). This linked combination of a spatially defined cube, temporal data, and payload data may constitute a “grain” of the cube-based model 200, which may be addressable by a single unique TULSA code as described herein. In some embodiments, the cubes may also includes a spatial error. In these embodiments, the payload data is linked to the cube that defines the payload data such that boundaries of the cube within the target environment contain a spatial region that is defined by the spatial position and the spatial error. Furthermore, a current state of the cube-based model 200 is defined by respective states of each of the plurality of cubes 202 and updating the cube-based model 200 with the new data updates the current state of the cubebased model 200.PATENT APPLICATION Attorney Docket No.: 34014-70817-PC

[0059] The nested and recursive relationship of each of the plurality of cubes 202 enables lower layer cubes to inherit data attributes from a higher layer cube. In various aspects, various cubes are defined by a nested relationship, where such cubes have nested, inherited, hierarchical, or otherwise different levels or layers with respect to one another. This nested relationship may be used to define data relationships, and may be used to eliminate or reduce otherwise redundant data storage, which improves the operating of the underlying computing device (e.g., database 104 and / or cube-based locationing system 100) by reducing the required amount of storage and / or reducing the number of compute cycles necessary for identifying or processing cubes within the cube-based model 200. For example, the third cube 210c3n2 may inherit at least one of (1) spatial data, (2) temporal data, or (3) payload data from at least one higher layer cube (e.g., the first 210cl, the second cube 210c2nl, etc.). Similarly, the second cube 210c2nl may inherit spatial data, temporal data, or payload data form the first cube 21 Oct.

[0060] FIG. 2C illustrates a zoomed-in example user interface displays of the cube-based model 200 shown in FIG. 2A. As shown in FIG. 2C, the multiple layers of nested cubes may include a fourth cube 210c4n2 that is nested within the second cube 210c2nl at the same layer as the third cube 210c3n2 (e.g., both cubes have the same size 300n2). In some embodiments, storing data in the third cube 210c3n2 prevents redundant storage of the data in the fourth cube 210c4n2 because they are located within the same layer.

[0061] The cube-based data model 200 implements a nested cube navigation format originating from the LBF location 208 of the hypercube 210cl. Accessing cube-based model 200 via the LBF navigation format allows the underlying computing system to implement rapid lookout that results from one-way access of the cube-based model 200, which results in reduced computational cycles for one or more processors of the underlying system (e.g., cube-based locationing system 100). Details of the cube navigation format are further described with reference to FIG. 1 and FIG. 2C. In particular, the nested cube navigation format selects and identifies a cube from the multiple layers of the nested cubes based on a precision indication received from a computing device such as via the website 108. The cube-based locationing system 100 may invoke the API 106 to access the cube-based model 200 within the database 104. The cube-based locationing system 100 uses the API 106 to select an identified cube such as the third cube 210c3n2 or a fourth cube 210c4n2 from the multiple layers of nested cubes. Once selected, the cube-based locationing system 100 returns the payload data of the identifiedPATENT APPLICATION Attorney Docket No.: 34014-70817-PC cube (e.g., the third cube 210c3n2 or the fourth cube 210c4n2) to the computing device that initiated the request.

[0062] Each of the plurality of cubes 202 are defined by a cube-based coordinate value (e.g., a cube-based coordinate value or point) within the cube-based coordinate space 204. A given cubebased coordinate cube-based coordinate value identifies both the location of the cube within the coordinate space 204 and the layer at which the cube resides. For example, the third cube 210c3n2 is defined by a cube-based coordinate value 222 and the fourth cube 210c4n2 is defined by a cube-based coordinate value 228. In general, the cube-based coordinate value assigned to a cube indicates both the position of the LBF corner of the cube within the cube-based coordinate space 204 and the particular layer of the cube relative to the hypercube 210cl and the LBF location 208 thereof. For example, the second cube 210c2nl may be assigned a cube-based coordinate value of 555, which indicates that second cube 210c2nl is located in a first layer inside or otherwise within the hypercube 210cl and that the LBF comer of the second cube 210c2nl, from the perspective of second cube 210c2nl, is located 5 cube size units to the left, 5 cube size units to the right, and 5 cube size units up in sequence from the LBF location 208 (e.g., a starting origin for the cube-based model 200). The size of each cube size unit can be equivalent to the size dimension of the cubes at that layer (e.g. 2000 km for the second cube 210c2nl). Additionally, or alternatively, the digits of the cube-based coordinate value may correspond to a percentage of the total size distance of the higher layer cube along the particular dimension. For example, a cube-based coordinate value of 585 would indicate a LBF corner location of a cube in the first layer down from the hypercube (e.g., first cube 210cl) that is 50% of the x-dimension distance, 80% of the y-dimension distance, and 50% of the z-dimension distance of the hyper cube.

[0063] As shown in FIG. 2C, the cube-based coordinate value 222 of the third cube 210c3n2 may be 555.000 and the cube-based coordinate value 228 of the fourth cube 210c4n2 may be 555.888. The two part form (e.g., xyz.xyz) of the cube-based coordinate values 222 and 228 indicate that the third cube 210c3n2 and the fourth cube 210c4n2 are in the second layer down from the hypercube 210cl and are nested within the cube with the LBF corner location of 555 relative to the LBF location 208 (e.g., the second cube 210c2nl). The second portion of the cubebased coordinate values 222 and 228 indicates the location of each cube relative to the LBF comer of the second cube 210c2nl. For example the “000” of the cube-based coordinate valuePATENT APPLICATION Attorney Docket No.: 34014-70817-PC 222 indicates that the third cube 210c3n2 has a LBF corner that is coterminous with the LBF corner of the second cube 210c2nl. Similarly, the “888” of the cube-based coordinate value 222 indicates that the third cube 210c3n2 has a LBF comer that is 8 cube size units right, 8 cube size units up, and 8 cube size units back from the LBF comer of the second cube 210c2nl. This indicates that cube-based coordinate value of 888 is the LBF corner location of fourth cube 210c4n2, which is located at 80% of the x-dimension distance, 80% of the y-dimension distance, and 80% of the z-dimension distance of within the dimensions of its higher layer cube (e.g., second cube 210c2nl as shown for FIG. 2C).

[0064] The right-to-up-to-back direction from left-bottom-front points is shown in FIG. 2C by (1 ) right arrow 223 from the LBF corner of the second cube 210c2nl to the point 224 (e.g., cubebased coordinate value 555.800), (2) up arrow 225 from the point 224 to the point 226 (e.g., cube-based coordinate value 555.880), and (3) back arrow 227 from the point 226 to the point represented by the cube-based coordinate value 228 (e.g., 555.888). The right-to-up-to-back direction may comprise a locationing algorithm that the cube-based locationing system 100 uses to select and identify a cube based on the precision indication received from the computing device. Although shown with reference to second cube 210c2nl, it should be appreciated that the same process may be used to locate any of the plurality of cubes 202 within the cube-based model 200. Furthermore, the LBF location 208 may be an absolute position of the cube-based coordinate space 204 from which the locationing algorithm is always executed. However, in some cases, operation of the locationing algorithm may be implemented from the absolute LBF location of a different hypercube of the cube-based model 200 than the hypercube 210cl.

[0065] FIG.3 illustrates a table of locationing data associated with different cube sizes for respective layers of an example cube-based data model and for an example data element (e.g., payload data) stored and / or accessible within the cube-based model in accordance with various embodiments disclosed herein. Said another way, the table in FIG. 3 comprises an example of location data and other features at different cube-based dimensions 301 for a given example data element (e.g., payload data) as stored and / or as accessible within the cube-based model 200. As shown in FIG. 3, the data element may be assigned a single Time United Location TULSA code 302. In some embodiments, TULSA code 302 may include the precision indication used to recall the associated cube for the data element from the cube-based model 200. Furthermore, the table shows different features for the data element at each of the cube sizes 300nl, 300n2, 300n3,PATENT APPLICATION Attorney Docket No.: 34014-70817-PC 300n3, 300n4, 300n5, 300n6, 300n7, 300n8, 300n9, 300nl0. In particular, the different features include travel time between cubes 305, an APNT file size 306, a transmission time (TX) in milliseconds for different frequencies (e.g., 64 kbs, 512 kbs, and 1 Mbps), a certainty percentage 310, and an uncertainty percentage 312.

[0066] As shown in FIG. 3, assigning the single example data element to different sized cubes corresponds to different certainty and uncertainty percentages noted by the certainty percentage 310 and uncertainty percentage 312. For example, there would 100% certainty that the data element would be contained within the cubes having sizes 300nl, 300n2, and 300n3 (e.g., sizes of 2,000km, 200km, and 20km. The cubes having sizes 300n4, 300n5, and 300n6 (e.g., sizes of 2km, 200m, and 20m) would have a certainty of containing the data element of 99.9%, 99.8%, and 99.5% respectively. The cubes having sizes 300n7, 300n8, 300n9, and 300nl0 e.g., sizes of 2m, 20cm, 2cm, and 2mm) have comparably lower certainties of 51%, 40%, 30%, and 10%, respectively, of containing the data element. The cube-based locationing system 100 may assign the data element to a particular sized cube based on user preferences for the acceptable amount of error. In particular, the acceptable amount of error may be dependent on the relevant application. As can be seen for FIG. 3, the cube-based locationing systems and methods, as described herein, may be implemented to reduce error (e.g., as shown for certainty percentage 310 and uncertain percentage 312), which results in highlight accurate locationing and positioning even down to positions at the millimeter scale.

[0067] With reference now to FIG. 2D, operation of the cube-based locationing system 100 to return an environmental distance between two data elements will be described in detail. In particular, returning and calculating an environmental distance 205 between a data element linked to the third cube 210c3n3 and a data element linked to a fifth cube 210c5n3, which is nested within a sixth cube 210c6n2. To return the distance, the cube-based locationing system 100 (FIG. 1) determines the positions of the third cube 210c3n3 and fifth cube 210c5n3 within the cube-based coordinate space 204 using the locationing algorithm described herein. Next, the cube-based locationing system 100 determines a cube-based distance between the positions of the third cube 210c3n3 and fifth cube 210c5n3 within the cube-based coordinate space 204, generates an environmental distance based on the cube-based distance, and return the environmental distance to the computing device that sent in the request. In some embodiments, the environmental distance defines a distance in the target environment 206.PATENT APPLICATION Attorney Docket No.: 34014-70817-PC

[0068] FIG. 4 illustrates an example visualization regarding an implementation for requesting an environmental distance between two real-world locations using the cube-based model in accordance with various embodiments disclosed herein. In various aspects, the example visualization of FIG. 4 may rendered, for example, by a visualization software app 114 of cubebased locationing system 100. For example, in one aspect, FIG. 4 depicts an example user interface display 400 for providing details on and requesting the environmental distance 405 between two real- world locations using the cube based model 200. In particular, the user interface display 400 accepts a TULSA code 402 as an input and returns a location 403 and associated details 404 using the locationing algorithm described herein. The location 403 may include a GPS puck location saved in the cube-based model 200 (such as at the third cube 210c3n3 of FIG. 2D) and the associated details 404 may include lagitude, longitude, altitude, and National Marine Electronics Association (NMEA) sentence values that represent the location 403 of the GPS puck.

[0069] The user interface display 400 may then accept user input defining a second point 406 representing some other point a real-world distance 405 away from the location 403 corresponding to the TULSA code 402. In response to the user input defining the second point 406, the cube-based locationing system 100 may identify a cube within the cube-based model 200 that contains the second point 406 (e.g., the fifth cube 210c5n3), calculate the distance in the cube-based coordinate space 204 between that cube and the cube that contains the location 403 to generate the environmental distance 405. Once generated, the cube-based locationing system 100 may display the value of the environmental distance 405 within the user interface display 400 as a value 408 e.g., 21.16m). As shown in FIG. 4, the cube-based locationing system 100 may also cause the user interface display 400 to display accuracy values for the location 403 within the cube-based model 200 (e.g., the certainty percentages 310).

[0070] Additionally or alternatively, the cube-based locationing system 100 may generate and display within the user interface display 400 and accuracy value for the environmental distance 405. The accuracy value may define a percentage accuracy of the environmental distance 405 as generated compared to a reference value.

[0071] Calculation of real world distances (e.g., the environmental distance 205, the environmental distance 405, etc.) between points of the cube-based model 200 will be discussedPATENT APPLICATION Attorney Docket No.: 34014-70817-PC with reference to FIG. 5. In particular, FIG. 5 illustrates a schematic diagram 500 of positions 502, 504, 508 within the cube-based model 200 (e.g., locations of cubes 000, 555, and 555.888 as described herein). The schematic diagram 500 also shows a first Euclidian distance 506 between the positions 502 and 504 and a second Euclidian distance 510 between the positions 504 and 508, which when calculated correspond to the real world distance between the points within the target environment 206. To calculate the first Euclidian distance 506 and the second Euclidian distance 510, the cube-based locationing system 100 identifies the X,Y,Z coordinates that correspond to the positions 502, 504, 508 within the cube-based model 200 and calculates the Euclidian distances using equation 1 below.Euclidean = Sqrt((X2-Xl)A2+(Y2-Yl)A2+(Z2-Zl)A2)) (1)When identifying the X, Y,Z coordinates to use within equation 1 , the cube-based locationing system 100 may convert the cube-based coordinate values that identify the cube locations (e.g., 000, 555, 555.888, etc.) into unified coordinates that reflect the full spatial distance from the LBF location 208 as a function of the cube-based coordinate values and cubes sizes. For example, the cube-based coordinate value 555 may correspond to X,Y,Z coordinates of 2000,2000,2000. Additionally, or alternatively, the cube-based locationing system 100 may account for the cube sizes to after calculating the Euclidian distance by using the cube-based coordinate values (e.g., 555) directly as the X,Y,Z coordinates.

[0072] Fig. 6 is a flowchart of an example cube-based locationing method 600 for updating and accessing cube-based data for positioning and locating objects. In various aspects, cubebased locationing method 600 may comprise an algorithm comprising computing instructions executable on one or more processors of cube-based locationing system 100.

[0073] At block 610, cube-based locationing method 600 includes receiving, from a computing device (e.g., computing device via website 108), a request for data corresponding to a target position in a target environment of a cube-based data model, the request comprising a precision indication (e.g., e.g., precision indication 300n2). The cube-based data model (e.g., cube-based data model 200) defines a plurality of cubes (e.g., cubes 202) each having cubebased dimensions (301) within a cube-based coordinate space (e.g., cube-based coordinate space 204) mapped to the target environment, the plurality of cubes comprise multiple layers of nestedPATENT APPLICATION Attorney Docket No.: 34014-70817-PC cubes comprising at least: a first cube e.g., first cube 21 Ocl ) having a first size (e.g., first size 3OOnl), a second cube (210c2nl) nested within the first cube and having a second size (e.g., second size 300n2) of a smaller measurement that the first size, and a third cube (e.g., cube 210c3n2 or cube210c4n2) nested within the second cube and having a third size (e.g., third size 300n3) having a smaller measurement than the second size, and the cube-based data model implements a nested cube navigation format (e.g., LBF 208) for selecting a cube from the multiple layers of nested cubes.

[0074] Mapping the cube-based coordinate space to the target environment may comprise mapping a center (201 e.g., center “555”) of the cube-based coordinate space to a geographic center of a planet. The request may comprises a Time United Location System Address (TULSA) code (e.g., 302 or 402), and the TULSA code may include the precision indication (e.g., 300n2). The first size of the first cube may be 20,000 kilometers (km) (300), the second size of the second cube may be 2000 km (300nl), and the third size of the third cube may be 200 km (300n2). Each layer of the nested cubes may be associated with a corresponding size. The corresponding size of a layer is an order of magnitude larger or smaller than the corresponding sizes associated with a next highest and lowest layers of the nested cubes. The second size may be ten times smaller than the first size, and the third may be ten times smaller than the second size. The payload data may comprise a spatial position and a spatial error; and the payload data may linked to the identified cube that defines the payload data such that boundaries of the identified cube within the target environment contains a spatial region that is defined by the spatial position and the spatial error. In some embodiments, each nested cube (e.g., 210c3n2) of the multiple layers of nested cubes inherits at least one of (1) spatial data, (2) temporal data, or (3) payload data from at least one higher layer cube (e.g., 210cl ). The multiple layers of nested cubes may include a nested layer having a two nested cubes comprising a first nested cube (e.g., 210c3n2) and a second nested cube (e.g., 210c4n2), and wherein storing data in the first nested cube prevents redundant storage of the data in the second nested cube. In some cases, at least one of: the second cube (e.g., 210c2nl) inherits spatial data, temporal data, or payload data form the first cube (e.g., 21 Ocl), or the third cube (e.g., 210c3n2) may inherit spatial data, temporal data, or payload data form the second cube (e.g., 210c2nl ) or the first cube (e.g., 21 Ocl ).PATENT APPLICATION Attorney Docket No.: 34014-70817-PC

[0075] At block 620, cube-based locationing method 600 includes invoking, based on the request, a cube locationing API to access the cube-based data model (e.g., cube-based model 200).

[0076] At block 630, cube-based locationing method 600 includes accessing, via the cube locationing API, the cube-based data model with the nested cube navigation format.

[0077] At block 640, cube-based locationing method 600 includes selecting an identified cube (e.g., third cube (210c4n2)) from the multiple layers of nested cubes based on the precision indication, wherein the identified cube defines payload data and a cube-based coordinate value (e.g., 228 (555.888)) defining a position of the identified cube within the cube-based coordinate space. The identified cube may comprise a temporal value defining when the payload data was generated.

[0078] At block 650, cube-based locationing method 600 includes returning, to the computing device, the payload data of the identified cube.

[0079] In some embodiments, the nested cube navigation format comprises a left-bottom-front (LBF) format (108), and wherein the cube locationing API accesses the cube-based data model by implementing a locationing algorithm that uses the LBF format by navigating (e.g., referencing) the cube-based coordinate system in a right-to-up-to-back direction from respective left-bottom-front points (e.g., point 222 at e.g., 222 at “555.000”) of one or more cubes (e.g., second cube 210c2nl, respectively) within the plurality of cubes to select the identified cube (e.g., 210c4n2 at “555.888”). The nested cube navigation format may begin navigation (e.g., referencing) from an absolute position (e.g., LBF position 208, position “000”) of the cube-based coordinate space.

[0080] The method 600 may also include receiving, from a computing device, new payload data (e.g., 110) for the identified cube and updating (e.g., 112) the cube-based data model by updating the payload data with the new payload data. The method 600 may also include defining or updating a timestamp associated with the new payload data indicating a time at which the new payload data was generated. A current state of the cube-based locationing model may be defined by respective states of each of the cubes in the at least a subset of the plurality of cubes, and wherein updating the cube-based data model with the new payload data updates the current state of the cube-based data model.PATENT APPLICATION Attorney Docket No.: 34014-70817-PC

[0081] In some embodiments, the request further includes a request for distance between the target position and a second target position. In these embodiments, the method 600 may include invoking, based on the request, the cube locationing API to access the cube-based data model. The cube locationing API accesses the cube-based data model with the nested cube navigation format and based on the second target position to select a second identified cube (e.g., 210c5n3) from the multiple layers of nested cubes. The second identified cube defines second payload data, and the second identified cube further defines a second cube-based coordinate value defining a second position of the second identified cube within the cube-based coordinate space. The method 600 may also include determining a cube-based distance between the first position and the second position within the cube-based coordinate space; generating an environmental distance (e.g., 205, 305, 405) based on the cube-based distance, and returning, to the computing device, the environmental distance. The environmental distance defines a distance in the target environment. The method 600 may also include generating an accuracy value (410) for the environmental distance and returning, to the computing device, the accuracy value. The accuracy value defines a percentage accuracy of the environmental distance as generated compared to reference value.

[0082] The method 600 may also include determining a Euclidean distance between the second cube-based coordinate value and the first cube-based coordinate value. The first cubebased coordinate value may include a first set of x, y, z coordinates within the cube-based coordinate space and the second cube-based coordinate value may include a second set of x, y, z coordinates within the cube-based coordinate space. The first set of x, y, z coordinates may correspond to a LBF corner of the first cube and the second set of x, y, z coordinates nay correspond to a LBF corner of the second cube

[0083] It should be appreciated that the blocks of cube-based locationing method 600 may be performed in any suitable order including simultaneously.

[0084] Example disclosure describing use cases for the cube-based locationing systems and methods are described further below herein. It is to be understood, however, that such disclosures are exemplary in nature and are not intended to be limiting.

[0085] Example Use Case (Locationing regarding an Object in Real World Space Above the Earth).PATENT APPLICATION Attorney Docket No.: 34014-70817-PC

[0086] In one example use case, a real world object is tracked in real world space above the earth. For example, the real-world object may comprise an aircraft or a satellite above planet Earth. A cube-based model (e. ., cube-based model 200) may be mapped to a cube-based coordinate space (e.g., cube-based coordinate space 204) that includes and surrounds Earth, including above Earth extending into space.

[0087] In such example embodiments, and applying the cube-based locationing method 600 of Figure 6, at block 610, cube-based locationing method 600 includes receiving, from a computing device (e.g., computing device via website 108), a request for data corresponding to a target position in a target environment of a cube-based data model. The request may comprise a precision indication (e.g., e.g., precision indication 300n2), which may be a location of an aircraft or satellite.

[0088] In the example, the cube-based data model (e.g., cube-based data model 200) can define a plurality of cubes (e.g., cubes 202) each having cube-based dimensions (301) within a cube-based coordinate space (e.g., cube-based coordinate space 204) mapped to the target environment. The target environment may comprise cubes associated with positions above the Earth, on the Earth, or within the Earth.

[0089] The plurality of cubes may comprise multiple layers of nested cubes comprising at least: a first cube (e.g., first cube 210cl) having a first size (e.g., first size 300nl), a second cube (210c2nl) nested within the first cube and having a second size (e.g., second size 300n2) of a smaller measurement that the first size, and a third cube (e.g., cube 210c3n2 or cube210c4n2) nested within the second cube and having a third size (e.g., third size 300n3) having a smaller measurement than the second size. The cube-based data model may implement a nested cube navigation format (e.g., LBF 208) for selecting a cube from the multiple layers of nested cubes.

[0090] At block 620, cube-based locationing method 600 includes invoking, based on the request, a cube locationing API to access the cube-based data model (e.g., cube-based model 200). The request may comprise a request to locate the aircraft and / or satellite.

[0091] At block 630, cube-based locationing method 600 includes accessing, via the cube locationing API, the cube-based data model with the nested cube navigation format. That is, the request may cause the cube locationing API to access the cube-based data model using the nestedPATENT APPLICATION Attorney Docket No.: 34014-70817-PC cube navigation formation (e.g., the LBF) in order to identify or locate the aircraft and / or satellite based on its recorded position within cube-based model 200.

[0092] At block 640, cube-based locationing method 600 includes selecting an identified cube (e.g., third cube (210c4n2)) from the multiple layers of nested cubes based on the precision indication. The identified cube defines payload data and a cube-based coordinate value (e.g., 228 (555.888)) defining a position of the identified cube within the cube-based coordinate space. In the present use case, the payload data may define a location or position of the aircraft and / or satellite.

[0093] At block 650, cube-based locationing method 600 includes returning, to the computing device, the payload data (e.g., the location of the aircraft or satellite) of the identified cube. In this way, the requesting computing device would be able to track or locate the location of the aircraft or satellite by accessing or implementing the cube-based locationing systems and methods as described herein.

[0094] In various aspects, as the aircraft and / or satellite moves within real-world space, the cube-based locationing system 100 may be provided with updated pay load data so that the cubebased model 200 may be updated accordingly. Further requests to the cube locationing API can then track and / or report the updated payload data.

[0095] Example Use Case (Locationing regarding Cavity or Oil Reserve within the Earth)

[0096] In one example use case, real-world objects are tracked in the real world below the surface of earth. For example, these real- world objects may comprise an oil reserve, mineral deposits, or similar object located inside the earth. A cube-based model (e.g., cube-based model 200) may be mapped to a cube-based coordinate space (e.g., cube-based coordinate space 204) that includes and surrounds Earth, including below the Earth’s crust extending down toward the core.

[0097] In such example embodiments, and applying the cube-based locationing method 600 of Figure 6, at block 610, cube-based locationing method 600 includes receiving, from a computing device (e.g., computing device via website 108), a request for data corresponding to a target position in a target environment of a cube-based data model. The request may comprise aPATENT APPLICATION Attorney Docket No.: 34014-70817-PC precision indication (e.g., precision indication 300n2), which may be a location of an oil reserve deposit inside the earth.

[0098] In the example, the cube-based data model (e.g., cube-based data model 200) can define a plurality of cubes e.g., cubes 202) each having cube-based dimensions (301) within a cube-based coordinate space (e.g., cube-based coordinate space 204) mapped to the target environment. The target environment may comprise cubes associated with positions above the Earth, on the Earth, or within the Earth.

[0099] The plurality of cubes may comprise multiple layers of nested cubes comprising at least: a first cube (e.g., first cube 210cl) having a first size (e.g., first size 300nl), a second cube (210c2nl) nested within the first cube and having a second size (e.g., second size 300n2) of a smaller measurement that the first size, and a third cube (e.g., cube 210c3n2 or cube210c4n2) nested within the second cube and having a third size (e.g., third size 300n3) having a smaller measurement than the second size. The cube-based data model may implement a nested cube navigation format (e.g., LBF 208) for selecting a cube from the multiple layers of nested cubes.

[0100] At block 620, cube-based locationing method 600 includes invoking, based on the request, a cube locationing API to access the cube-based data model (e.g., cube-based model 200). The request may comprise a request to locate the oil reserve deposit.

[0101] At block 630, cube-based locationing method 600 includes accessing, via the cube locationing API, the cube-based data model with the nested cube navigation format. That is, the request may cause the cube locationing API to access the cube-based data model using the nested cube navigation formation (e.g., the LBF) in order to identify or locate the oil reserve deposit based on its recorded position within cube-based model 200.

[0102] At block 640, cube-based locationing method 600 includes selecting an identified cube (e.g., third cube (210c4n2)) from the multiple layers of nested cubes based on the precision indication. The identified cube defines payload data and a cube-based coordinate value (e.g., 228 (555.888)) defining a position of the identified cube within the cube-based coordinate space. In the present use case, the payload data may define a location or position of the oil reserve deposit.

[0103] At block 650, cube-based locationing method 600 includes returning, to the computing device, the payload data (e.g., the location of the aircraft or satellite) of the identifiedPATENT APPLICATION Attorney Docket No.: 34014-70817-PC cube. In this way, the requesting computing device would be able to track or locate the location of the oil reserve deposit by accessing or implementing the cube-based locationing systems and methods as described herein.

[0104] In various aspects, as the oil reserve deposit moves within real- world space, the cubebased locationing system 100 may be provided with updated payload data so that the cube-based model 200 may be updated accordingly. Further requests to the cube locationing API can then track and / or report the updated payload data.

[0105] Example Disclosure (Locationing regarding an Object within a Virtual Environment)

[0106] In one example use case, a virtual object is tracked in three dimensional virtual world environment. For example, the virtual object may comprise any element that is configured to represent a tangible object and adopt different positions within the virtual world environment. A cube-based model (e.g., cube-based model 200) may be mapped to a cube-based coordinate space (e.g., cube-based coordinate space 204) that includes the totality of the three dimensional virtual world environment.

[0107] In such example embodiments, and applying the cube-based locationing method 600 of Figure 6, at block 610, cube-based locationing method 600 includes receiving, from a computing device (e.g., computing device via website 108), a request for data corresponding to a target position in a target environment of a cube-based data model. The request may comprise a precision indication (e.g., e.g., precision indication 300n2), which may be a location of the virtual object.

[0108] In the example, the cube-based data model (e.g., cube-based data model 200) can define a plurality of cubes (e.g., cubes 202) each having cube-based dimensions (301) within a cube-based coordinate space (e.g., cube-based coordinate space 204) mapped to the target environment. The target environment may comprise cubes associated with positions within the three dimensional virtual world environment.

[0109] The plurality of cubes may comprise multiple layers of nested cubes comprising at least: a first cube (e.g., first cube 210cl) having a first size (e.g., first size 300nl), a second cube (210c2nl) nested within the first cube and having a second size (e.g., second size 300n2) of aPATENT APPLICATION Attorney Docket No.: 34014-70817-PC smaller measurement that the first size, and a third cube (e.g., cube 210c3n2 or cube210c4n2) nested within the second cube and having a third size (e.g., third size 300n3) having a smaller measurement than the second size. The cube-based data model may implement a nested cube navigation format (e.g., LBF 208) for selecting a cube from the multiple layers of nested cubes.

[0110] At block 620, cube-based locationing method 600 includes invoking, based on the request, a cube locationing API to access the cube-based data model (e.g., cube-based model 200). The request may comprise a request to locate the virtual object.

[0111] At block 630, cube-based locationing method 600 includes accessing, via the cube locationing API, the cube-based data model with the nested cube navigation format. That is, the request may cause the cube locationing API to access the cube-based data model using the nested cube navigation formation (e.g., the LBF) in order to identify or locate the virtual object based on its recorded position within cube-based model 200.

[0112] At block 640, cube-based locationing method 600 includes selecting an identified cube (e.g., third cube (210c4n2)) from the multiple layers of nested cubes based on the precision indication. The identified cube defines payload data and a cube-based coordinate value (e.g., 228 (555.888)) defining a position of the identified cube within the cube-based coordinate space. In the present use case, the payload data may define a location or position of the virtual object.

[0113] At block 650, cube-based locationing method 600 includes returning, to the computing device, the payload data (e.g., the location of the aircraft or satellite) of the identified cube. In this way, the requesting computing device would be able to track or locate the location of the virtual object by accessing or implementing the cube-based locationing systems and methods as described herein.

[0114] In various aspects, as the virtual object moves within the virtual environment, the cube-based locationing system 100 may be provided with updated pay load data so that the cubebased model 200 may be updated accordingly. Further requests to the cube locationing API can then track and / or report the updated payload data.

[0115] ADDITIONAL ASPECTS OF THE DISCLOSURE

[0116] The following aspects are provided as examples in accordance with the disclosure herein and are not intended to limit the scope of the disclosure. Furthermore, the below AspectsPATENT APPLICATION Attorney Docket No.: 34014-70817-PC 1 -9 regard systems and methods for updating and accessing cube-based data for positioning and locating objects.

[0117] Aspect 1. A cube-based locationing system configured to update and access cubebased data for positioning and locating objects, the cube-based locationing system comprising: one or more processors; a memory communicatively coupled to the one or more processors; a database (104) communicatively coupled to the one or more processors and storing a cube-based data model (200) defining a plurality of cubes (202) each having cube-based dimensions (301) within a cube-based coordinate space (204) mapped to a target environment (206), wherein the plurality of cubes comprises multiple layers of nested cubes comprising at least: a first cube (21 Ocl ) having a first size (300), a second cube (210c2nl ) nested within the first cube and having a second size (300nl) of a smaller measurement that the first size, and a third cube (210c3n2 or 210c4n2) nested within the second cube and having a third size (300n2) having a smaller measurement than the second size, and wherein the cube-based data model implements a nested cube navigation format (e.g., LBF 208) for selecting a cube from the multiple layers of nested cubes; and a cube locationing application programming interface (API) (106) configured to access the cube-based model, wherein the memory stores computing instructions that when executed by the one or more processors, causes the one or more processors to: receive, from a computing device (e.g., computing device via website 108), a request for data corresponding to a target position in the target environment, the request comprising a precision indication (e.g., 300n2), invoke, based on the request, the cube locationing API to access the cube-based data model, wherein the cube locationing API accesses the cube-based data model with the nested cube navigation (e.g., reference) format and based on the precision indication to select an identified cube (e.g., third cube (210c4n2)) from the multiple layers of nested cubes, wherein the identified cube defines payload data, and wherein the identified cube further defines a cubebased coordinate value (e.g., 228 (555.888)) defining a position of the identified cube within the cube-based coordinate space, and return, to the computing device, the payload data of the identified cube.

[0118] Aspect 2. The cube-based locationing system of Aspect 1, wherein the nested cube navigation format comprises a left-bottom-front (LBF) format (108), and wherein the cube locationing API accesses the cube-based data model by implementing a locationing algorithm that uses the LBF format by navigating (e.g., referencing) the cube-based coordinate system in aPATENT APPLICATION Attorney Docket No.: 34014-70817-PC right-to-up-to-back direction from respective left-bottom-front points (e.g., point 222 at e.g., 222 at "555.000") of one or more cubes (e.g., second cube 210c2nl, respectively) within the plurality of cubes to select the identified cube (e.g., 210c4n2 at "555.888").

[0119] Aspect 3. The cube-based locationing system of Aspects 1 or 2, wherein the nested cube navigation format begins navigation (e.g., referencing) from an absolute position (e.g., LBF position 208, position "000") of the cube-based coordinate space.

[0120] Aspect 4. The cube-based locationing system of any of Aspects 1-3, wherein each nested cube (e.g., 210c3n2) of the multiple layers of nested cubes inherits at least one of (1) spatial data, (2) temporal data, or (3) payload data from at least one higher layer cube (e.g., 210cl).

[0121] Aspect 5. The cube-based locationing system of any of Aspects 1-4, wherein the multiple layers of nested cubes includes a nested layer having a two nested cubes comprising a first nested cube (e.g., 210c3n2) and a second nested cube (e.g., 210c4n2), and wherein storing data in the first nested cube prevents redundant storage of the data in the second nested cube.

[0122] Aspect 6. The cube-based locationing system of any of Aspects 1-5, wherein at least one of: the second cube (e.g., 210c2nl) inherits spatial data, temporal data, or payload data form the first cube (e.g., 210cl), or the third cube (e.g., 210c3n2) inherits spatial data, temporal data, or payload data form the second cube (e.g., 210c2nl ) or the first cube (e.g., 21 Ocl ).

[0123] Aspect 7. The cube-based locationing system of any of Aspects 1-6, wherein mapping the cube-based coordinate space to the target environment comprises mapping a center (201 e.g., center "555") of the cube-based coordinate space to a geographic center of a planet.

[0124] Aspect 8. The cube-based locationing system of any of Aspects 1-7, wherein the request comprises a Time United Location System Address (TULSA) code (e.g., 302 or 402), and wherein the TULSA code includes the precision indication (e.g., 300n2).

[0125] Aspect 9. The cube-based locationing system of any of Aspects 1-8, wherein the first size of the first cube is 20,000 kilometers (km) (300), wherein the second size of the second cube is 2000 km (300nl), and wherein the third size of the third cube is 200 km (300n2).

[0126] Aspect 10. The cube-based locationing system of any of Aspects 1-9, wherein the computing instructions, when executed by the one or more processors, further cause the one orPATENT APPLICATION Attorney Docket No.: 34014-70817-PC more processors to: receive, from a computing device, new payload data (e.g., 110) for the identified cube; and update (e.g., 112) the cube-based data model by updating the payload data with the new payload data.

[0127] Aspect 11. The cube-based locationing system of any of Aspects 1-10, wherein the computing instructions, when executed by the one or more processors, further cause the one or more processors to: define or update a timestamp associated with the new payload data indicating a time at which the new payload data was generated.

[0128] Aspect 12. The cube-based locationing system of any of Aspects 1-11, wherein a current state of the cube-based locationing model is defined by respective states of each of the cubes in the at least a subset of the plurality of cubes, and wherein updating the cube-based data model with the new payload data updates the current state of the cube-based data model.

[0129] Aspect 13. The cube-based locationing system of any of Aspects 1-12, wherein the identified cube comprises a temporal value defining when the payload data was generated.

[0130] Aspect 14. The cube-based locationing system of any of Aspects 1-13, wherein the request further includes a request for distance between the target position and a second target position, and wherein the computing instructions, when executed by the one or more processors, further cause the one or more processors to: invoke, based on the request, the cube locationing API to access the cube-based data model, wherein the cube locationing API accesses the cubebased data model with the nested cube navigation format and based on the second target position to select a second identified cube e.g., 210c5n3) from the multiple layers of nested cubes, wherein the second identified cube defines second payload data, and wherein the second identified cube further defines a second cube-based coordinate value defining a second position of the second identified cube within the cube-based coordinate space, determine a cube-based distance between the first position and the second position within the cube-based coordinate space, generate an environmental distance (e.g., 205, 305, 405) based on the cube-based distance, wherein the environmental distance defines a distance in the target environment , and return, to the computing device, the environmental distance.

[0131] Aspect 15. The cube-based locationing system of any of Aspects 1-14, wherein the computing instructions, when executed by the one or more processors, further cause the one or more processors to: generate an accuracy value (410) for the environmental distance, thePATENT APPLICATION Attorney Docket No.: 34014-70817-PC accuracy value defining a percentage accuracy of the environmental distance as generated compared to reference value; and return, to the computing device, the accuracy value.

[0132] Aspect 16. The cube-based locationing system of any of Aspects 1-15, wherein determine the cube-based distance between the first position and the second position within the cube-based coordinate space, the computing instructions, when executed by the one or more processors, further cause the one or more processors to: determine a Euclidean distance between the second cube-based coordinate value and the first cube-based coordinate value.

[0133] Aspect 17. The cube-based locationing system of any of Aspects 1-16 wherein the first cube-based coordinate value includes a first set of x, y, z coordinates within the cube-based coordinate space and the second cube-based coordinate value includes a second set of x, y, z coordinates within the cube-based coordinate space.

[0134] Aspect 18. The cube-based locationing system of any of Aspects 1-17 wherein the first set of x, y, z coordinates correspond to a LBF corner of the first cube and the second set of x, y, z coordinates correspond to a LBF comer of the second cube.

[0135] Aspect 19. The cube-based locationing system of any of Aspects 1-18 wherein each layer of the nested cubes is associated with a corresponding size, the corresponding size of a layer being an order of magnitude larger or smaller than the corresponding sizes associated with a next highest and lowest layers of the nested cubes.

[0136] Aspect 20. The cube-based locationing system of any of Aspects 1-19 wherein the second size is ten times smaller than the first size, and the third is ten times smaller than the second size.

[0137] Aspect 21. The cube-based locationing system of any of Aspects 1-20 wherein: the payload data comprises a spatial position and a spatial error; and the payload data is linked to the identified cube that defines the payload data such that boundaries of the identified cube within the target environment contains a spatial region that is defined by the spatial position and the spatial error.

[0138] Aspect 22. A method to update and access cube-based data for positioning and locating objects, the method comprising: receiving, from a computing device (e. ., computing device via website 108), a request for data corresponding to a target position in a targetPATENT APPLICATION Attorney Docket No.: 34014-70817-PC environment of a cube-based data model, the request comprising a precision indication (e.g., 300n2), wherein: the cube-based data model (200) defines a plurality of cubes (202) each having cube-based dimensions (301) within a cube-based coordinate space (204) mapped to the target environment, the plurality of cubes comprise multiple layers of nested cubes comprising at least: a first cube (210cl) having a first size (300nl), a second cube (210c2nl) nested within the first cube and having a second size (300n2) of a smaller measurement that the first size, and a third cube (210c3n2 or 210c4n2) nested within the second cube and having a third size (300n3) having a smaller measurement than the second size, and the cube-based data model implements a nested cube navigation format (e.g., LBF 208) for selecting a cube from the multiple layers of nested cubes; invoking, based on the request, a cube locationing API to access the cube-based data model; accessing, via the cube locationing API, the cube-based data model with the nested cube navigation format; selecting an identified cube (e.g., third cube (210c4n2)) from the multiple layers of nested cubes based on the precision indication, wherein the identified cube defines payload data and a cube-based coordinate value (e.g., 228 (555.888)) defining a position of the identified cube within the cube-based coordinate space; and returning, to the computing device, the payload data of the identified cube.

[0139] Aspect 23. The method of Aspect 22, wherein the nested cube navigation format comprises a left-bottom-front (LBF) format (108), and wherein the cube locationing API accesses the cube-based data model by implementing a locationing algorithm that uses the LBF format by navigating (e.g., referencing) the cube-based coordinate system in a right-to-up-to-back direction from respective left-bottom-front points (e.g., point 222 at e.g., 222 at "555.000") of one or more cubes (e.g., second cube 210c2nl, respectively) within the plurality of cubes to select the identified cube (e.g., 210c4n2 at "555.888").

[0140] Aspect 24. The method of any of Aspects 22 or 23, wherein the nested cube navigation format begins navigation (e.g., referencing) from an absolute position (e.g., LBF position 208, position "000") of the cube-based coordinate space.

[0141] Aspect 25. The method of any of Aspects 22-24, wherein each nested cube (e.g., 210c3n2) of the multiple layers of nested cubes inherits at least one of (1) spatial data, (2) temporal data, or (3) payload data from at least one higher layer cube (e.g., 210cl).PATENT APPLICATION Attorney Docket No.: 34014-70817-PC

[0142] Aspect 26. The method of any of Aspects 22-26, wherein the multiple layers of nested 5cubes includes a nested layer having a two nested cubes comprising a first nested cube (e.g., 210c3n2) and a second nested cube (e.g., 210c4n2), and wherein storing data in the first nested cube prevents redundant storage of the data in the second nested cube.

[0143] Aspect 27. The method of any of Aspects 22-26, wherein at least one of: the second cube (e.g., 210c2nl) inherits spatial data, temporal data, or pay load data form the first cube (e.g., 210cl ), or the third cube (e.g., 210c3n2) inherits spatial data, temporal data, or payload data form the second cube (e.g., 210c2nl) or the first cube (e.g., 210cl).

[0144] Aspect 28. The method of any of Aspects 22-27, wherein mapping the cube-based coordinate space to the target environment comprises mapping a center (201 e.g., center "555") of the cube-based coordinate space to a geographic center of a planet.

[0145] Aspect 29. The method of any of Aspects 22-28, wherein the request comprises a Time United Location System Address (TULSA) code (e.g., 302 or 402), and wherein the TULSA code includes the precision indication (e.g., 300n2).

[0146] Aspect 30. The method of any of Aspects 22-29, wherein the first size of the first cube is 20,000 kilometers (km) (300), wherein the second size of the second cube is 2000 km (300nl), and wherein the third size of the third cube is 200 km (300n2).

[0147] Aspect 31. The method of any of Aspects 22-30, further comprising: receiving, from a computing device, new payload data (e.g., 110) for the identified cube; and updating (e.g., 112) the cube-based data model by updating the payload data with the new payload data.

[0148] Aspect 32. The method of any of Aspects 22-31, further comprising: defining or updating a timestamp associated with the new payload data indicating a time at which the new payload data was generated.

[0149] Aspect 33. The method of any of Aspects 22-32, wherein a current state of the cubebased locationing model is defined by respective states of each of the cubes in the at least a subset of the plurality of cubes, and wherein updating the cube-based data model with the new payload data updates the current state of the cube-based data model.

[0150] Aspect 34. The method of any of Aspects 22-33, wherein the identified cube comprises a temporal value defining when the payload data was generated.PATENT APPLICATION Attorney Docket No.: 34014-70817-PC

[0151] Aspect 35. The method of any of Aspects 22-34, wherein the request further includes a request for distance between the target position and a second target position, and further comprising: invoking, based on the request, the cube locationing API to access the cube-based data model, wherein the cube locationing API accesses the cube-based data model with the nested cube navigation format and based on the second target position to select a second identified cube (e.g., 210c5n3) from the multiple layers of nested cubes, wherein the second identified cube defines second payload data, and wherein the second identified cube further defines a second cube-based coordinate value defining a second position of the second identified cube within the cube-based coordinate space, determining a cube-based distance between the first position and the second position within the cube-based coordinate space, generating an environmental distance (e.g., 205, 305, 405) based on the cube-based distance, wherein the environmental distance defines a distance in the target environment, and returning, to the computing device, the environmental distance.

[0152] Aspect 36. The method of any of Aspects 22-35, further comprising: generating an accuracy value (410) for the environmental distance, the accuracy value defining a percentage accuracy of the environmental distance as generated compared to reference value; and returning, to the computing device, the accuracy value.

[0153] Aspect 37. The method of any of Aspects 22-36, wherein determining the cube-based distance between the first position and the second position within the cube-based coordinate space, includes: determining a Euclidean distance between the second cube-based coordinate value and the first cube-based coordinate value.

[0154] Aspect 38. The method of any of Aspects 22-37, wherein the first cube-based coordinate value includes a first set of x, y, z coordinates within the cube-based coordinate space and the second cube-based coordinate value includes a second set of x, y, z coordinates within the cube-based coordinate space.

[0155] Aspect 39. The method of any of Aspects 22-38, wherein the first set of x, y, z coordinates correspond to a LBF comer of the first cube and the second set of x, y, z coordinates correspond to a LBF corner of the second cube.

[0156] Aspect 40. The method of any of Aspects 22-39, wherein each layer of the nested cubes is associated with a corresponding size, the corresponding size of a layer being an order ofPATENT APPLICATION Attorney Docket No.: 34014-70817-PC magnitude larger or smaller than the corresponding sizes associated with a next highest and lowest layers of the nested cubes.

[0157] Aspect 41. The method of any of Aspects 22-40, wherein the second size is ten times smaller than the first size, and the third is ten times smaller than the second size.

[0158] Aspect 42. The method of any of Aspects 22-41, wherein: the payload data comprises a spatial position and a spatial error; and the payload data is linked to the identified cube that defines the payload data such that boundaries of the identified cube within the target environment contains a spatial region that is defined by the spatial position and the spatial error.

[0159] Aspect 43. A tangible, non-transitory computer- readable medium storing instructions for positioning and locating objects, that when executed by one or more processors cause the one or more processors to: receive, from a computing device (e.g., computing device via website 108), a request for data corresponding to a target position in a target environment of a cubebased data model, the request comprising a precision indication (e.g., 300n2), wherein: the cubebased data model (200) defines a plurality of cubes (202) each having cube-based dimensions (301) within a cube-based coordinate space (204) mapped to the target environment, the plurality of cubes comprise multiple layers of nested cubes comprising at least: a first cube (210cl) having a first size (300nl), a second cube (210c2nl) nested within the first cube and having a second size (300n2) of a smaller measurement that the first size, and a third cube (210c3n2 or 210c4n2) nested within the second cube and having a third size (300n3) having a smaller measurement than the second size, and the cube-based data model implements a nested cube navigation format (e.g., LBF 208) for selecting a cube from the multiple layers of nested cubes; invoke, based on the request, a cube locationing API to access the cube-based data model; access, via the cube locationing API, the cube-based data model with the nested cube navigation format; select an identified cube (e.g., third cube (210c4n2)) from the multiple layers of nested cubes based on the precision indication, wherein the identified cube defines payload data and a cube-based coordinate value (e.g., 228 (555.888)) defining a position of the identified cube within the cube-based coordinate space; and return, to the computing device, the payload data of the identified cube.

[0160] Aspect 44. The tangible, non-transitory computer-readable medium of Aspect 43, wherein the nested cube navigation format comprises a left-bottom-front (LBF) format (108),PATENT APPLICATION Attorney Docket No.: 34014-70817-PC and wherein the cube locationing API accesses the cube-based data model by implementing a locationing algorithm that uses the LBF format by navigating (e.g., referencing) the cube-based coordinate system in a right-to-up-to-back direction from respective left-bottom-front points (e.g., point 222 at e.g., 222 at "555.000") of one or more cubes (e.g., second cube 210c2nl, respectively) within the plurality of cubes to select the identified cube (e.g., 210c4n2 at "555.888").

[0161] Aspect 45. The tangible, non-transitory computer- readable medium of Aspect 43 or 44, wherein the nested cube navigation format begins navigation (e.g., referencing) from an absolute position (e.g., LBF position 208, position "000") of the cube-based coordinate space.

[0162] Aspect 46. The tangible, non-transitory computer-readable medium of any of Aspects 43-45, wherein each nested cube (e.g., 210c3n2) of the multiple layers of nested cubes inherits at least one of (1) spatial data, (2) temporal data, or (3) payload data from at least one higher layer cube (e.g., 210cl).

[0163] Aspect 47. The tangible, non-transitory computer- readable medium of any of Aspects 43-46, wherein the multiple layers of nested cubes includes a nested layer having a two nested cubes comprising a first nested cube (e.g., 210c3n2) and a second nested cube (e.g., 210c4n2), and wherein storing data in the first nested cube prevents redundant storage of the data in the second nested cube.

[0164] Aspect 48. The tangible, non-transitory computer-readable medium of any of Aspects 43-47, wherein at least one of: the second cube (e.g., 210c2nl) inherits spatial data, temporal data, or payload data form the first cube (e.g., 210cl), or the third cube (e.g., 210c3n2) inherits spatial data, temporal data, or payload data form the second cube (e.g., 210c2nl) or the first cube (e.g., 210cl).

[0165] Aspect 49. The tangible, non-transitory computer- readable medium of any of Aspects 43-48, wherein mapping the cube-based coordinate space to the target environment comprises mapping a center (201 e.g., center "555") of the cube-based coordinate space to a geographic center of a planet.PATENT APPLICATION Attorney Docket No.: 34014-70817-PC

[0166] Aspect 50. The tangible, non-transitory computer-readable medium of any of Aspects 43-49, wherein the request comprises a Time United Location System Address (TULSA) code (e.g., 302 or 402), and wherein the TULSA code includes the precision indication (e.g., 300n2).

[0167] Aspect 51. The tangible, non-transitory computer- readable medium of any of Aspects 43-50, wherein the first size of the first cube is 20,000 kilometers (km) (300), wherein the second size of the second cube is 2000 km (300nl), and wherein the third size of the third cube is 200 km (300n2).

[0168] Aspect 52. The tangible, non-transitory computer- readable medium of any of Aspects 43-51, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: receive, from a computing device, new payload data (e.g., 110) for the identified cube; and update (e.g., 112) the cube-based data model by updating the payload data with the new payload data.

[0169] Aspect 53. The tangible, non-transitory computer-readable medium of any of Aspects 43-52, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: define or update a timestamp associated with the new payload data indicating a time at which the new payload data was generated.

[0170] Aspect 54. The tangible, non-transitory computer-readable medium of any of Aspects 43-53, wherein a current state of the cube-based locationing model is defined by respective states of each of the cubes in the at least a subset of the plurality of cubes, and wherein updating the cube-based data model with the new payload data updates the current state of the cube-based data model.

[0171] Aspect 55. The tangible, non-transitory computer-readable medium of any of Aspects 43-54, wherein the identified cube comprises a temporal value defining when the payload data was generated.

[0172] Aspect 56. The tangible, non-transitory computer-readable medium of any of Aspects 43-55, wherein the request further includes a request for distance between the target position and a second target position, and wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: invoke, based on the request, the cube locationing API to access the cube-based data model, wherein the cube locationing API accessesPATENT APPLICATION Attorney Docket No.: 34014-70817-PC the cube-based data model with the nested cube navigation format and based on the second target position to select a second identified cube (e.g., 210c5n3) from the multiple layers of nested cubes, wherein the second identified cube defines second pay load data, and wherein the second identified cube further defines a second cube-based coordinate value defining a second position of the second identified cube within the cube-based coordinate space, determine a cube-based distance between the first position and the second position within the cube-based coordinate space, generate an environmental distance (e.g., 205, 305, 405) based on the cube-based distance, wherein the environmental distance defines a distance in the target environment , and return, to the computing device, the environmental distance.

[0173] Aspect 57. The tangible, non-transitory computer- readable medium of any of Aspects 43-56, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: generate an accuracy value (410) for the environmental distance, the accuracy value defining a percentage accuracy of the environmental distance as generated compared to reference value; and return, to the computing device, the accuracy value.

[0174] Aspect 58. The tangible, non-transitory computer- readable medium of any of Aspects 43-57, wherein determine the cube-based distance between the first position and the second position within the cube-based coordinate space, the instructions, when executed by the one or more processors, further cause the one or more processors to: determine a Euclidean distance between the second cube-based coordinate value and the first cube-based coordinate value.

[0175] Aspect 59. The tangible, non-transitory computer- readable medium of any of Aspects 43-58, wherein the first cube-based coordinate value includes a first set of x, y, z coordinates within the cube-based coordinate space and the second cube-based coordinate value includes a second set of x, y, z coordinates within the cube-based coordinate space.

[0176] Aspect 60. The tangible, non-transitory computer-readable medium of any of Aspects 43-59, wherein the first set of x, y, z coordinates correspond to a LBF corner of the first cube and the second set of x, y, z coordinates correspond to a LBF comer of the second cube.

[0177] Aspect 61. The tangible, non-transitory computer- readable medium of any of Aspects 43-60, wherein each layer of the nested cubes is associated with a corresponding size, the corresponding size of a layer being an order of magnitude larger or smaller than the corresponding sizes associated with a next highest and lowest layers of the nested cubes.PATENT APPLICATION Attorney Docket No.: 34014-70817-PC

[0178] Aspect 62. The tangible, non-transitory computer- readable medium of any of Aspects 43-61, wherein the second size is ten times smaller than the first size, and the third is ten times smaller than the second size.

[0179] Aspect 63. The tangible, non-transitory computer- readable medium of any of Aspects 43-62, wherein: the payload data comprises a spatial position and a spatial error; and the payload data is linked to the identified cube that defines the payload data such that boundaries of the identified cube within the target environment contains a spatial region that is defined by the spatial position and the spatial error.

[0180] ADDITIONAL CONSIDERATIONS

[0181] Although the disclosure herein sets forth a detailed description of numerous different embodiments, it should be understood that the legal scope of the description is defined by the words of the aspects set forth at the end of this patent and equivalents. The detailed description is to be construed as exemplary only and does not describe every possible embodiment since describing every possible embodiment would be impractical. Numerous alternative embodiments may be implemented, using either current technology or technology developed after the filing date of this patent, which would still fall within the scope of the aspects herein.

[0182] The following additional considerations apply to the foregoing discussion.Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.

[0183] Additionally, certain embodiments are described herein as including logic or a number of routines, subroutines, applications, or instructions. These may constitute either software (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware. In hardware, the routines, etc., are tangible units capable of performing certainPATENT APPLICATION Attorney Docket No.: 34014-70817-PC operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein.

[0184] In various embodiments, a hardware module may be implemented mechanically or electronically. For example, a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.

[0185] Accordingly, the term “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where the hardware modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware modules at different times.Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time.

[0186] Hardware modules may provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple of such hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits andPATENT APPLICATION Attorney Docket No.: 34014-70817-PC buses) that connect the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and may operate on a resource (e.g., a collection of information).

[0187] The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules.

[0188] Similarly, the methods or routines described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented hardware modules. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location, while in other embodiments the processors may be distributed across a number of locations.

[0189] The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the one or more processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other embodiments, the one or more processors or processor-implemented modules may be distributed across a number of geographic locations.

[0190] This detailed description is to be construed as exemplary only and does not describe every possible embodiment, as describing every possible embodiment would be impractical, ifPATENT APPLICATION Attorney Docket No.: 34014-70817-PC not impossible. A person of ordinary skill in the art may implement numerous alternate embodiments, using either current technology or technology developed after the filing date of this application.

[0191] Those of ordinary skill in the art will recognize that a wide variety of modifications, alterations, and combinations can be made with respect to the above described embodiments without departing from the scope of the invention, and that such modifications, alterations, and combinations are to be viewed as being within the ambit of the inventive concept.

[0192] The patent aspects at the end of this patent application are not intended to be construed under 35 U.S. C. § 812(f) unless traditional means-plus-function language is expressly recited, such as “means for” or “step for” language being explicitly recited in the aspects herein. The systems and methods described herein are directed to an improvement to computer functionality, and improve the functioning of conventional computers.

Claims

PATENT APPLICATION Attorney Docket No.: 34014-70817-PC What is claimed is:

1. A cube-based locationing system configured to update and access cube-based data for positioning and locating objects, the cube-based locationing system comprising:one or more processors;a memory communicatively coupled to the one or more processors;a database (104) communicatively coupled to the one or more processors and storing a cube-based data model (200) defining a plurality of cubes (202) each having cube-based dimensions (301) within a cube-based coordinate space (204) mapped to a target environment (206),wherein the plurality of cubes comprises multiple layers of nested cubes comprising at least: a first cube (210cl) having a first size (300), a second cube (210c2nl) nested within the first cube and having a second size (300nl) of a smaller measurement that the first size, and a third cube (210c3n2 or 210c4n2) nested within the second cube and having a third size (300n2) having a smaller measurement than the second size, andwherein the cube-based data model implements a nested cube navigation format (e. ., LBF 208) for selecting a cube from the multiple layers of nested cubes; anda cube locationing application programming interface (API) (106) configured to access the cube-based model,wherein the memory stores computing instructions that when executed by the one or more processors, causes the one or more processors to:receive, from a computing device (e.g., computing device via website 108), a request for data corresponding to a target position in the target environment, the request comprising a precision indication (e.g., 300n2),invoke, based on the request, the cube locationing API to access the cube-based data model, wherein the cube locationing API accesses the cube-based data model with the nested cube navigation (e.g., reference) format and based on the precision indication to select an identified cube (e.g., third cube (210c4n2)) from the multiple layers of nested cubes,wherein the identified cube defines payload data, and wherein the identified cube further defines a cube-based coordinate value (e.g., 228 (555.888)) defining a position of the identified cube within the cube-based coordinate space, andPATENT APPLICATION Attorney Docket No.: 34014-70817-PC return, to the computing device, the payload data of the identified cube.

2. The cube-based locationing system of claim 1 , wherein the nested cube navigation format comprises a left-bottom-front (LBF) format (108), and wherein the cube locationing API accesses the cube-based data model by implementing a locationing algorithm that uses the LBF format by navigating (e.g., referencing) the cube-based coordinate system in a right-to-up-to-back direction from respective left-bottom-front points (e.g., point 222 at e.g., 222 at “555.000”) of one or more cubes (e.g., second cube 210c2nl, respectively) within the plurality of cubes to select the identified cube (e.g., 210c4n2 at “555.888”).

3. The cube-based locationing system of claim 2, wherein the nested cube navigation format begins navigation (e.g., referencing) from an absolute position (e.g., LBF position 208, position “000”) of the cube-based coordinate space.

4. The cube-based locationing system of claim 1, wherein each nested cube (e.g., 210c3n2) of the multiple layers of nested cubes inherits at least one of (1) spatial data, (2) temporal data, or (3) payload data from at least one higher layer cube (e.g., 210cl).

5. The cube-based locationing system of claim 4, wherein the multiple layers of nested cubes includes a nested layer having a two nested cubes comprising a first nested cube (e.g., 210c3n2) and a second nested cube (e.g., 210c4n2), and wherein storing data in the first nested cube prevents redundant storage of the data in the second nested cube.

6. The cube-based locationing system of claim 4, wherein at least one of: the second cube (e.g., 210c2nl) inherits spatial data, temporal data, or payload data form the first cube (e.g., 210cl), or the third cube (e.g., 210c3n2) inherits spatial data, temporal data, or payload data form the second cube (e.g., 210c2nl) or the first cube (e.g., 210cl).

7. The cube-based locationing system of claim 1, wherein mapping the cube-based coordinate space to the target environment comprises mapping a center (201 e.g., center “555”) of the cube-based coordinate space to a geographic center of a planet.PATENT APPLICATION Attorney Docket No.: 34014-70817-PC8. The cube-based locationing system of claim 1, wherein the request comprises a Time United Location System Address (TULSA) code (e.g., 302 or 402), and wherein the TULSA code includes the precision indication (e.g., 300n2).

9. The cube-based locationing system of claim 1, wherein the first size of the first cube is 20,000 kilometers (km) (300), wherein the second size of the second cube is 2000 km (300nl), and wherein the third size of the third cube is 200 km (300n2).

10. The cube-based locationing system of claim 1, wherein the computing instructions, when executed by the one or more processors, further cause the one or more processors to:receive, from a computing device, new pay load data (e.g., 110) for the identified cube; andupdate (e.g., 112) the cube-based data model by updating the payload data with the new payload data.

11. The cube-based locationing system of claim 10, wherein the computing instructions, when executed by the one or more processors, further cause the one or more processors to:define or update a timestamp associated with the new payload data indicating a time at which the new payload data was generated.

12. The cube-based locationing system of claim 11, wherein a current state of the cube-based locationing model is defined by respective states of each of the cubes in the at least a subset of the plurality of cubes, and wherein updating the cube-based data model with the new payload data updates the current state of the cube-based data model.

13. The cube-based locationing system of claim 1, wherein the identified cube comprises a temporal value defining when the payload data was generated.PATENT APPLICATION Attorney Docket No.: 34014-70817-PC 14. The cube-based locationing system of claim 1 , wherein the request further includes a request for distance between the target position and a second target position, and wherein the computing instructions, when executed by the one or more processors, further cause the one or more processors to:invoke, based on the request, the cube locationing API to access the cube-based data model, wherein the cube locationing API accesses the cube-based data model with the nested cube navigation format and based on the second target position to select a second identified cube (e.g., 210c5n3) from the multiple layers of nested cubes, wherein the second identified cube defines second payload data, and wherein the second identified cube further defines a second cube-based coordinate value defining a second position of the second identified cube within the cube-based coordinate space, determine a cube-based distance between the first position and the second position within the cube-based coordinate space,generate an environmental distance (e.g., 205, 305, 405) based on the cube-based distance,wherein the environmental distance defines a distance in the target environment, andreturn, to the computing device, the environmental distance.

15. The cube-based locationing system of claim 14, wherein the computing instructions, when executed by the one or more processors, further cause the one or more processors to:generate an accuracy value (410) for the environmental distance, the accuracy value defining a percentage accuracy of the environmental distance as generated compared to reference value; andreturn, to the computing device, the accuracy value.

16. The cube-based locationing system of claim 14, wherein determine the cubebased distance between the first position and the second position within the cube-based coordinate space, the computing instructions, when executed by the one or more processors, further cause the one or more processors to:PATENT APPLICATION Attorney Docket No.: 34014-70817-PC determine a Euclidean distance between the second cube-based coordinate value and the first cube-based coordinate value.

17. The cube-based locationing system of claim 16 wherein the first cube-based coordinate value includes a first set of x, y, z coordinates within the cube-based coordinate space and the second cube-based coordinate value includes a second set of x, y, z coordinates within the cube-based coordinate space.

18. The cube-based locationing system of claim 17 wherein the first set of x, y, z coordinates correspond to a LBF corner of the first cube and the second set of x, y, z coordinates correspond to a LBF corner of the second cube.

19. The cube-based locationing system of claim 1 wherein each layer of the nested cubes is associated with a corresponding size, the corresponding size of a layer being an order of magnitude larger or smaller than the corresponding sizes associated with a next highest and lowest layers of the nested cubes.

20. The cube-based locationing system of claim 1 wherein the second size is ten times smaller than the first size, and the third is ten times smaller than the second size.

21. The cube-based locationing system of claim 1 wherein:the payload data comprises a spatial position and a spatial error; andthe payload data is linked to the identified cube that defines the payload data such that boundaries of the identified cube within the target environment contains a spatial region that is defined by the spatial position and the spatial error.

22. A method of update and access cube-based data for positioning and locating objects, the method comprising:receiving, from a computing device (e.g., computing device via website 108), a request for data corresponding to a target position in a target environment of a cube-based data model, the request comprising a precision indication (e.g., 300n2), wherein:PATENT APPLICATION Attorney Docket No.: 34014-70817-PC the cube-based data model (200) defines a plurality of cubes (202) each having cube-based dimensions (301) within a cube-based coordinate space (204) mapped to the target environment,the plurality of cubes comprise multiple layers of nested cubes comprising at least: a first cube (210cl) having a first size (300nl), a second cube (210c2nl) nested within the first cube and having a second size (300n2) of a smaller measurement that the first size, and a third cube (210c3n2 or 210c4n2) nested within the second cube and having a third size (300n3) having a smaller measurement than the second size, and the cube-based data model implements a nested cube navigation format (e.g., LBF 208) for selecting a cube from the multiple layers of nested cubes;invoking, based on the request, a cube locationing API to access the cube-based data model;accessing, via the cube locationing API, the cube-based data model with the nested cube navigation format;selecting an identified cube (e.g., third cube (210c4n2)) from the multiple layers of nested cubes based on the precision indication, wherein the identified cube defines payload data and a cube-based coordinate value (e.g., 228 (555.888)) defining a position of the identified cube within the cube-based coordinate space; andreturning, to the computing device, the payload data of the identified cube.

23. A tangible, non-transitory computer-readable medium storing instructions for positioning and locating objects, that when executed by one or more processors cause the one or more processors to:receive, from a computing device (e.g., computing device via website 108), a request for data corresponding to a target position in a target environment of a cube-based data model, the request comprising a precision indication (e.g., 300n2), wherein:the cube-based data model (200) defines a plurality of cubes (202) each having cube-based dimensions (301) within a cube-based coordinate space (204) mapped to the target environment,the plurality of cubes comprise multiple layers of nested cubes comprising at least: a first cube (210cl) having a first size (300nl), a second cube (210c2nl) nestedPATENT APPLICATION Attorney Docket No.: 34014-70817-PC within the first cube and having a second size (300n2) of a smaller measurement that the first size, and a third cube (210c3n2 or 210c4n2) nested within the second cube and having a third size (300n3) having a smaller measurement than the second size, and the cube-based data model implements a nested cube navigation format (e.g., LBF 208) for selecting a cube from the multiple layers of nested cubes;invoke, based on the request, a cube locationing API to access the cube-based data model;access, via the cube locationing API, the cube-based data model with the nested cube navigation format;select an identified cube (e.g., third cube (210c4n2)) from the multiple layers of nested cubes based on the precision indication, wherein the identified cube defines payload data and a cube-based coordinate value (e.g., 228 (555.888)) defining a position of the identified cube within the cube-based coordinate space; andreturn, to the computing device, the payload data of the identified cube.

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