Systems and methods for storing spatiotemporal data within a CUBE-based data model

Abstract

A 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 layers of nested cubes. The cube-based data model implements a nested cube navigation format for selecting a cube from the layers of nested cubes. The system may receive radio frequency (RF) signal data, determine first spatial coordinate data within the target environment and first time data that is associated with the RF signal data, and determine, based on the first spatial coordinate data, a first precision indication. The system may invoke the cube locationing API to access the cube-based data model to store the RF signal data as payload data of one or more first target cubes based on the first precision indication, and set a temporal value of the payload data based on the first time data.

Description

PATENT APPLICATION Attorney Docket No.: 34014-70815-PC SYSTEMS AND METHODS FOR STORING SPATIOTEMPORAL DATA 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 data systems and methods, and more particularly, to systems and methods for storing cube-based spatiotemporal data within a cubebased data model.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.: 34014-70815-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 level or the Earth’s ellipsoid model — adds another layer of complexity to achieving reliable 3D positioning. Further still, GPS uses multiple satellites and time information to produces latitude and longitude location indicator. However, during this process the timing of overlapping area determined from each satellite is never exact because of circular overlap, which results in horizontal error from the flashlight effect.

[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. ThesePATENT APPLICATION Attorney Docket No.: 34014-70815-PC vulnerabilities underscore the need for complementary technologies, such as ground-based positioning systems, enhanced signal processing algorithms, or hybrid approaches combining 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. For example, hyper precise “targeting” data at 2mm may not be applicable for military operations but truthful 2km level data without known error may be preferable. Essentially, in some use cases it is preferable to operate at a higher level coarse but exact position or location instead of a lower level more granular position or location that is indeterminate because of known errors in 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 or lower to large-scale environmentsPATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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.

[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. More particularly, the reference model for GPS systems is not transferable to other locations, which means the same accuracy in precision from one point in space and time cannot be recreated at any other point especially when curvature and altitude / elevation changes. For example, an exact location at a first 2cm point cannot be used to generate the same 2cm preci sion / accuracy when the distance is or altitude / elevation changes because the arc tangent of the new position is not consistent and a ray from the center of the earth must become a segment and a separate ray must become a segment. This produces derivative and high computational processing and is reenforced with trial and error producing the least square to the multiple data sets.

[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 modeledPATENT APPLICATION Attorney Docket No.: 34014-70815-PC environment. Tn particular, the system as described herein provides a unified representation of time and space to provide context and understanding to the interaction of all data stored inside the cubes.

[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 store cube-based spatiotemporal data, the cube-based locationing system including: one or more processors; a memory communicatively coupled to the one or more processors; a database communicatively coupled to the one or more processors and storing a cube-based data model defining a plurality of cubes each having cubebased dimensions within a cube-based coordinate space mapped to a target environment, wherein the plurality of cubes includes multiple layers of nested cubes including at least: a first cube having a first size, a second cube nested within the first cube and having a second size of a smaller measurement that the first size, and a third cube nested within the second cube and having a third size having a smaller measurement than the second size, and wherein the cubebased data model implements a nested cube navigation format for selecting a cube from the multiple layers of nested cubes; and a cube locationing application programming interface (API) 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, radio frequency (RF) signal data; determine first spatial coordinate data within the target environment that are associated with the RF signal data and first time data that is associated with the RF signal data; determine, based on the first spatial coordinate data, a first precision indication for one or more first target cubes of the plurality of cubes in which to store the RF signal data, wherein the one or more first target cubes defines a first cube-based coordinate value defining a first position of the one or more first target cubes within the cubebased coordinate space; and invoke the cube locationing API to access the cube-based data model with the nested cube navigation format to: store the RF signal data as pay load data of thePATENT APPLICATION Attorney Docket No.: 34014-70815-PC one or more first target cubes based on the first precision indication, and set a temporal value of the payload data the one or more first target cubes based on the first time data.

[0017] In some aspects, the techniques described herein relate to a method of storing cubebased spatiotemporal data, the method including: receiving, from a computing device, radio frequency (RF) signal data; determining first spatial coordinate data within a target environment of a cube-based data model that are associated with the RF signal data and first time data that is associated with the RF signal data, wherein: the cube-based data model defines a plurality of cubes each having cube-based dimensions within a cube-based coordinate space mapped to the target environment, the plurality of cubes include multiple layers of nested cubes including at least: a first cube having a first size, a second cube nested within the first cube and having a second size of a smaller measurement that the first size, and a third cube nested within the second cube and having a third size having a smaller measurement than the second size, and the cube-based data model implements a nested cube navigation format for selecting a cube from the multiple layers of nested cubes; determining, based on the first spatial coordinate data, a first precision indication for one or more first target cubes of the plurality of cubes in which to store the RF signal data, wherein the one or more first target cubes defines a first cube-based coordinate value defining a first position of the one or more first target cubes within the cubebased coordinate space; and invoking a cube locationing API to access the cube-based data model with the nested cube navigation format to: store the RF signal data as payload data of the one or more first target cubes based on the first precision indication, and set a temporal value of the payload data the one or more first target cubes based on the first time data.

[0018] In some aspects, the techniques described herein relate to a tangible, non-transitory computer-readable medium storing instructions for storing cube-based spatiotemporal data, that when executed by one or more processors cause the one or more processors to: receive, from a computing device, radio frequency (RF) signal data; determine first spatial coordinate data within a target environment of a cube-based data model that are associated with the RF signal data and first time data that is associated with the RF signal data, wherein: the cube-based data model defines a plurality of cubes each having cube-based dimensions within a cube-based coordinate space mapped to the target environment, the plurality of cubes include multiple layers of nested cubes including at least: a first cube having a first size, a second cube nested within the first cube and having a second size of a smaller measurement that the first size, and a third cubePATENT APPLICATION Attorney Docket No.: 34014-70815-PC nested within the second cube and having a third size having a smaller measurement than the second size, and the cube -based data model implements a nested cube navigation format for selecting a cube from the multiple layers of nested cubes; determine, based on the first spatial coordinate data, a first precision indication for one or more first target cubes of the plurality of cubes in which to store the RF signal data, wherein the one or more first target cubes defines a first cube-based coordinate value defining a first position of the one or more first target cubes within the cube-based coordinate space; and invoke a cube locationing API to access the cubebased data model with the nested cube navigation format to: store the RF signal data as payload data of the one or more first target cubes based on the first precision indication, and set a temporal value of the payload data the one or more first target cubes based on the first time data.

[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 cubebased 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] In particular, huge opportunities exist where data is not stored on different datum’s or levels but where a smallest common parent Cube which ensures accuracy matched to precision as a confidence level. For example, representing a person in their house, in a city, in a state, and in the USA does not require that data to be placed at all of the levels. Instead, data only needs to be placed for one level (e.g., the house) and the hierarchical and recursive nature of the data structure may account for each of the other levels. Further, the ratio of 1 / 1000 in volume and known location of child cubes is preserved due to the nested (hierarchical) recursivePATENT APPLICATION Attorney Docket No.: 34014-70815-PC determination each parent cube, which can only have 1000 children. This mathematical certain relationship creates immutability and block chain like agreement from the six surrounding cubes, which in turn are also determined by six surround cubes. As such, data is not stored twice in two independent children at the same time but two children can still be analyzed in time and space for interaction.

[0021] 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.

[0022] 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 spatial 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 thePATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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 DRAWINGS

[0027] 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 refersPATENT APPLICATION Attorney Docket No.: 34014-70815-PC to the reference numerals included in the following Figures, in which features depicted in multiple Figures are designated with consistent reference numerals.

[0028] 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:

[0029] 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.

[0030] FIGS. 2A-2D illustrate an example visualizations of a cube-based model and related cube-based data in accordance with various embodiments disclosed herein.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 7 illustrates a flow chart of a method of storing cube-based spatiotemporal data in accordance with various embodiments disclosed herein.

[0036] FIG. 8 illustrates a quantum optimized example of the cube-based locationing system of FIG. 1 in accordance with various embodiments disclosed herein.

[0037] FIGS. 9A and 9B illustrates cube-based navigation systems for objects or entities in accordance with various embodiments disclosed herein.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0038] FIG. 10 illustrates an artificial intelligence data analysis system for cube-based data in accordance with various embodiments disclosed herein.

[0039] 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.DESCRIPTION

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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 dataPATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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 timestamp 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.

[0044] The pay load 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.

[0045] 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 fourPATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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.

[0046] The pay load 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 other types. In 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.

[0047] 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.

[0048] 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 pay load 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 learns 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 vectorPATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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.

[0049] 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).

[0050] 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.

[0051] 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, forPATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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.

[0052] Supervised learning and / or unsupervised machine learning may also comprise retraining, relearning, or otherwise updating models with new, or different, information, which 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.

[0053] 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.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0054] 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 where 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.

[0055] 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 toPATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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.

[0056] 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 an 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.

[0057] 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 final view scene 152 (e.g., the visualizations of the cube-based model and / or cubebased data, such as those described herein for FIGs. 2A-2D).

[0058] 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,PATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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.

[0059] 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. 2A and 2B, the cube-based model 200 includes a center 201 and defines a plurality of cubes 202.

[0060] 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 210cl).PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0061] The plurality of cubes 202 may further include multiple layers of nested cubes. For example, the first cube 210cl, a second cube 210c2nl nested within the first cube 210cl, 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 the 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.

[0062] 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. 2 A, 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.

[0063] 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 pay load 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), andPATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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.

[0064] 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 pay load data form the first cube 210cl.

[0065] 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.

[0066] 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 200PATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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 pay load data of the identified cube (e.g., the third cube 210c3n2 or the fourth cube 210c4n2) to the computing device that initiated the request.

[0067] 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 cornerPATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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.

[0068] 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 corner of the second cube 210c2nl. For example the “000” of the cube-based coordinate value 222 indicates that the third cube 210c3n2 has a LBF comer that is coterminous with the LBF comer 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).

[0069] 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 2I0c2nl, 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, inPATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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.

[0070] 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, 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.

[0071] 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.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0072] 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.

[0073] 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.

[0074] 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 403PATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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).

[0075] 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.

[0076] 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 discussed 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-Y1 )A2+(Z2-Z1 )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.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0077] 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.

[0078] 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 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, 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.

[0079] 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 thePATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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., 210c 1), 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., 210cl).

[0080] 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).

[0081] 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.

[0082] 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.

[0083] At block 650, cube-based locationing method 600 includes returning, to the computing device, the payload data of the identified cube.

[0084] 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.,PATENT APPLICATION Attorney Docket No.: 34014-70815-PC referencing) from an absolute position (e.g., LBF position 208, position “000”) of the cube-based coordinate space.

[0085] 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.

[0086] 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.

[0087] 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, zPATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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

[0088] It should be appreciated that the blocks of cube-based locationing method 600 may be performed in any suitable order including simultaneously.

[0089] 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.

[0090] Example Use Case (Locationing regarding an Object in Real World Space Above the Earth).

[0091] 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.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 above Earth extending into space.

[0092] 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.

[0093] 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.

[0094] 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.: 34014-70815-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.

[0095] 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.

[0096] 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 aircraft and / or satellite based on its recorded position within cube-based model 200.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Example Use Case (Locationing regarding Cavity or Oil Reserve within the Earth)PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0101] 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.

[0102] 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., precision indication 300n2), which may be a location of an oil reserve deposit inside the earth.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.: 34014-70815-PC 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.

[0107] 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.

[0108] 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 oil reserve deposit by accessing or implementing the cube-based locationing systems and methods as described herein.

[0109] In various aspects, as the oil reserve deposit moves within real-world space, the cubebased locationing system 100 may be provided with updated pay load 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.

[0110] Example Disclosure (Locationing regarding an Object within a Virtual Environment)

[0111] 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.

[0112] 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.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0119] In various aspects, as the virtual object moves within the virtual environment, the cube-based locationing system 100 may be provided with updated payload 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.

[0120] Signal Fingerprinting and Geolocation Using Recursive Spatiotemporal Encoding

[0121] The cube-based locationing system 100 as described herein improves how spatiotemporal data is encoded, stored, and analyzed. The unique TULS coding system that represents both spatial (X, Y, Z coordinates) and temporal (UTC-based time) components provides a globally unique identifier for any point in space and time, allowing for highly precise tracking and analysis. The system works by recursively subdividing space into smaller cubes, starting from a Supercube centered on the Earth (e.g., the hypercube 210cl of FIG. 2A), and encoding time down to ultra-precise intervals such as yoctoseconds.

[0122] The TULSA coding of the cube-based locationing system 100 and the cube-based model 200 may be applied to a variety of applications as described herein including to signal fingerprinting and geolocation related tasks. In particular, the cube-based locationing system 100 and the cube-based model 200 may be used to encode the dynamic signal characteristics of radio frequency (RF) signals across both space and time. In particular, the signal characteristics may be combined with spatiotemporal data for use in signal identification, tracking, and analysis.

[0123] In particular, the cube-based locationing system 100 may generate and analyze signal fingerprints that specifically target RF navigation and geolocation. For example, the cube-based locationing system 100 may store different signal characteristics such as a Received Signal Strength Indicator (RS SI), Signal-to-Noise Ratio (SNR), and Carrier- to-Noise Ratio (CNR) within the cube-based model 200. Each signal characteristics may be assigned a corresponding TULSA code and one or more cubes and time values (e.g., may be assigned to a grain) within the cube-based model 200 that document real- world spatial location and time values for where and when the signal characteristics were captured by corresponding sensors. The TULSA codes and grains of the cube-based model 200 may enable precise signal geolocation and correlation of the different signal characteristics in time and space. The correlated in time and space signal characteristics may then be processed algorithmically or via machine learning systems (see, e.g.,PATENT APPLICATION Attorney Docket No.: 34014-70815-PC GPT model 138 of FIG. 1 ) to recognize and predict patterns in signal propagation, which enhances real-time navigation and geospatial awareness. In some embodiments, the machine learning systems may include models specifically trained on historical signal characteristics encoded within the cube-based model 200.

[0124] In operation, the cube-based locationing system 100 collects the signal characteristics across multiple frequencies and locations and generates a unique fingerprint for each signal. The signals are geotagged with a TULSA code and stored within the cube-based model 200 to ensure high precision in both time and space. In particular, the signal characteristics are stored as grains and encoded so each signal includes both spatial (X, Y, Z) and temporal (UTC timestamp) components. The recursive subdivision of space enabled by the cube-based model 200 ensures that each signal is localized to the smallest possible spatial unit, which maintains the hierarchical integrity of TULSA codes. In some embodiments, the cube-based locationing system 100 continuously updates the signal fingerprints stored in the cube-based model 200 based on realtime data feeds from various sensors (e.g., RF antennas configured to receive RF signals at various frequencies and protocols). The cube-based locationing system 100 may also adjust for temporal changes and environmental factors when making the real-time updates.

[0125] Once the signal characteristics are compiled and encoded within the cube-based model 200 they may be utilized for a variety of different purposes in connection with determining real-time object locations and / or performing real-time navigation tasks. For example, the encoded and stored signal characteristics may be compared with newly observed signals characteristics to identify the location of an RF signal source or emitter. The cube-based location Ing system 100 may also determine real-time location estimates by cross-referencing different signal characteristics as stored in the cube-based model 200, for example signal strength and spatiotemporal data.

[0126] Furthermore, the signal characteristics are compiled and encoded within the cubebased model 200 may be visualized (see, e.g., the visualization app 144) as a 3D profiles embedded within the cube-based model 200 framework. In particular, the signal characteristics may be recalled and viewed in any manner described herein, which includes, but is not limited to, the depictions of the cube-based model 200 shown in FIGs. 2A-2D.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0127] Multi-Dimensional Spatiotemporal Fingerprinting and Cross-Domain Data Correlation

[0128] In addition to single domain applications, like spatiotemporal signal fingerprinting described above, the cube-based locationing system 100 may be used to in connection with cross-domain data correlation applications. For example, the cube-based locationing system 100 may create a comprehensive environmental or object fingerprint using data from various domains, such as RF signals, GPS coordinates, and / or environmental metrics (temperature, humidity, etc.). These multi-dimensional fingerprints are encoded into the cube-based model 200 and assigned TULSA codes as described herein, which allows for precise spatiotemporal analysis and cross-domain data correlation. In this way the cube-based locationing system 100 integrates both spatial and temporal data in a hierarchical structure to detect, analyze, and predict environmental changes or object movement, which facilitates use for at least real-time navigation, tracking, and prediction. In particular, enhanced tracking, navigation, and environmental change detection may be achieved by cross-referencing data stored in the cubebased model 200 from RF signals, GPS coordinates, weather patterns, satellite data, environmental metrics, and external data sources such as weather patterns. Analysis of these cross-domain signals saved and encoded as grains within the cube-based model 200 may be performed using the algorithmic and machine learning processes as described herein.

[0129] In operation, the cube-based locationing system 100 may receive data (in real-time or otherwise) from multiple sensor sources. These sources may include RF signals, GPS coordinates, environmental metrics (e.g., temperature, humidity, pressure, etc.), and more. Once receive, the cube-based locationing system 100 creates a comprehensive fingerprint of an object or environment associated with those signal sources by saving and encoding the data as grains within the cube-based model 200. In this way, each data point is tied to a specific time and place with global precision.

[0130] When analyzing the data encoded in the cube-based model 200, the cross-domain values and shared spatiotemporal space of the cube-based model 200 allow for improved tracking and location dependent operations, especially in challenging environments where single-source data might be unreliable. For example, navigation may be done with increased accuracy by considering environmental factors such as atmospheric conditions. Additionally,PATENT APPLICATION Attorney Docket No.: 34014-70815-PC because the data is recursively encoded across multiple levels of spatial and temporal granularity, the cube-based locationing system 100 may capture changes in the environment or movement of the object with a high level of precision to enable real-time analysis as well as historical comparisons.

[0131] As with the single domain signal characteristics data, the multidomain data that is compiled and encoded within the cube-based model 200 may be visualized (see, e.g., the visualization app 144) as a 3D profiles embedded within the cube-based model 200 framework. In particular, the signal characteristics may be recalled and viewed in any manner described herein, which includes, but is not limited to, the depictions of the cube-based model 200 shown in FIGs. 2A-2D.

[0132] A Method for Spatiotemporal Fingerprinting for Single and Multiple Domains

[0133] FIG. 7 is a flowchart of an example method 700 for storing cube-based spatiotemporal data. In various aspects, method 700 may comprise an algorithm comprising computing instructions executable on one or more processors of cube-based locationing system 100.

[0134] At block 710, the method 700 includes receiving, from a computing device, radio frequency (RF) signal data.

[0135] At block 720, the method 700 includes determining first spatial coordinate data within a target environment of a cube-based data model that are associated with the RF signal data and first time data that is associated with the RF signal data. The cube-based data model defines a plurality of cubes each having cube-based dimensions within a cube-based coordinate space mapped to the target environment, the plurality of cubes comprise multiple layers of nested cubes comprising at least: a first cube having a first size, a second cube nested within the first cube and having a second size of a smaller measurement that the first size, and a third cube nested within the second cube and having a third size having a smaller measurement than the second size, and the cube-based data model implements a nested cube navigation format for selecting a cube from the multiple layers of nested cubes.

[0136] In some embodiments, the first spatial coordinate data corresponds to a location of the computing device within the target environment at a first time when computing device receivedPATENT APPLICATION Attorney Docket No.: 34014-70815-PC the RF signal data; and the first temporal data indicates the first time. Additionally or alternatively, the first spatial coordinate data may corresponds to a location of a source device for the RF signal data within the target environment at a first time when the source device initiated the RF signal data; and the first temporal data indicates the first time.

[0137] At block 730, the method 700 includes determining, based on the first spatial coordinate data, a first precision indication for one or more first target cubes of the plurality of cubes in which to store the RF signal data, wherein the one or more first target cubes defines a first cube-based coordinate value defining a first position of the one or more first target cubes within the cube-based coordinate space.

[0138] At block 740, the method 700 includes invoking a cube locationing API to access the cube-based data model with the nested cube navigation format to: store the RF signal data as payload data of the one or more first target cubes based on the first precision indication, and set a temporal value of the payload data the one or more first target cubes based on the first time data. In some embodiments, the nested cube navigation format comprises a left-bottom-front (LBF) format, 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-bottomfront 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 one or more first target cubes (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.

[0139] In some embodiments the method 700 may also include receiving, from a computing device, a request for data corresponding to a target position in the target environment, the request comprising a second precision indication. The second precision indication may points to the one or more first target cubes within the cube-based coordinate space. The method 700 may include invoking, based on the request, the cube locationing API to access the cube-based data model with the nested cube navigation format to select the one or more first target cubes from the multiple layers of nested cubes based on the second precision indication; and return, to the computing device, the RF signal data.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0140] In some embodiments the method 700 may also include receiving, from the computing device, GPS signal data and environmental data; determining second spatial coordinate data within the target environment that are associated with the GPS signal data and second time data that is associated with the GPS signal data; determining third spatial coordinate data within the target environment that are associated with the environmental data and third time data that is associated with the environmental data; and determining, based on the second spatial coordinate data, a second precision indication for one or more second target cubes of the plurality of cubes in which to store the GPS signal data. The one or more second target cubes defines a second cube-based coordinate value defining a second position of the one or more second target cubes within the cube-based coordinate space. The method 700 may also include determining, based on the second spatial coordinate data, a third precision indication for one or more third target cubes of the plurality of cubes in which to store the environmental data. The one or more third target cubes may defines a third cube-based coordinate value defining a third position of the one or more third target cubes within the cube-based coordinate space. The method 700 may also include invoking the cube locationing API to access the cube-based data model with the nested cube navigation format to: store the GPS signal data as pay load data of the one or second more target cubes based on the second precision indication, store the environmental data as payload data of the one or third more target cubes based on the third precision indication, set a temporal value of the payload data of the one or more second target cubes based on the second time data, and set a temporal value of the payload data of the one or more third target cubes based on the third time data.

[0141] In some embodiments, the second spatial coordinate data and the third spatial coordinate data may include overlapping portions that at least partially overlap with the first spatial coordinate data. In these embodiments, the method 700 may include determining the second precision indication and the third precision indication such that at least one of the one or more second more target cubes and the one or more third target cubes is a shared cube also present in the one or more first target cubes. The shared cube may have a location in the cubebased coordinate space that maps to the overlapping portions within the target environment. The method 700 may also include invoking the cube locationing API to access the cube-based data model with the nested cube navigation format to: store a portion of the GPS signal data associated with the overlapping portions as first additional payload data of the shared cube-basedPATENT APPLICATION Attorney Docket No.: 34014-70815-PC on the second precision indication, store a portion of the environmental data associated with the overlapping portions as second additional payload data of the shared cube-based on the third precision indication, set a temporal value of the first additional payload data of the shared cubebased on the second time data, and set a temporal value of the second additional payload data of the shared cube-based on the third time data.

[0142] The method 700 may also include receiving, from a computing device, a request for data corresponding to a target position in the target environment, the request comprising a fifth precision indication, wherein the firth precision indication points to the shared cube within the cube-based coordinate space. In these embodiments, the method 700 may also include invoking, based on the request, the cube locationing API to access the cube-based data model with the nested cube navigation format to select the shared cube from the multiple layers of nested cubes based on the fifth precision indication and returning, to the computing device, the RF signal data, the GPS signal data, and the environmental data.

[0143] In some embodiments, each nested cube of the multiple layers of nested cubes that is nested within the one or more first target cubes inherits at least one of (1) spatial data, (2) the payload data that includes the RF signal data, or (3) the temporal value of the payload data from the one or more first target cubes. The multiple layers of nested cubes may includes a nested layer having two nested cubes comprising a first nested cube and a second nested cube, and wherein storing data in the first nested cube prevents redundant storage of the data in the second nested cube.

[0144] In some embodiments the method 700 includes assigning a Time United Location System Address (TULSA) code to the payload data of the one or more first target cubes when storing the RF signal data, wherein the TULSA code includes the first precision indication.

[0145] In some embodiments, the first spatial coordinate data includes a spatial error; and the one or more first target cubes define the payload data that comprises the RF signal data such that boundaries of the one or more first target cubes within the target environment contains a spatial region that is defined by the first spatial coordinate data and the spatial error.

[0146] In some embodiments, the method 700 may include receiving, from a computing device, a request to determine a location of a source device for the RF signal data. The request may comprise a second precision indication that points to the one or more first target cubesPATENT APPLICATION Attorney Docket No.: 34014-70815-PC within the cube-based coordinate space. The method 700 may then include invoking, based on the request, the cube locationing API to access the RF signal data within the cube-based data model with the nested cube navigation format by selecting the one or more first target cubes from the multiple layers of nested cubes based on the second precision indication; identifying one or more source cubes of the plurality of cubes based on features of the RF signal data and the location of the one or more first target cubes within the cube-based model; and returning, to the computing device, a precise location marker for the location of a source device. The precise location marker may be based on a position of the one or more source cubes within the cubebased coordinate space. The method 700 may then include displaying an indication of the location of the source device within the target environment based on the precise location marker. In some embodiments, the features of the RF signal data include one or more of frequency, amplitude, direction of receipt, or signal strength. The source device may include at least one of a moving object, a vehicle, a satellite, an aircraft, a drone, an transceiver, a cellular tower, or a radio frequency broadcast tower.

[0147] It should be appreciate that similar methods may be used to identify the source of other signal data described herein (e.g., the environmental data, the GPS signal data, etc.). In general, these methods uses features of the signal or other data as stored as cube payload data or otherwise and the location of the associated cube within the cube-based coordinate space 204 to identify another one or multiple cubes in the cube-based coordinate space 204 that are associated with the source device and then may convert the location of the source device associated cube into a precise indication of the source device within the target environment itself. For example, the system may use the features of the signal or data stored or associated with the cube to determine a travel distance and direction of the signal or data and locate the source cube in relation to the target cube using the determined distance and direction.

[0148] Defining Cube Structure Using Gaussian Distributions

[0149] In some embodiments, the plurality of cubes that make up the cube-based model 200 are formed using gaussian distributions. In particular, a cube is defined by Gaussian-based boundaries where each side of the cube is modeled with 4 Gaussian curves (one per dimension of up, down, left, and right) and for each Gaussian, there are 6 standard deviations (3 positive, 3 negative), which extend symmetrically along the mean. The boundaries then define subdivisionsPATENT APPLICATION Attorney Docket No.: 34014-70815-PC within the cube, where each subunit (or child cube) inherits properties of the parent Gaussian. For these subdivisions at the “child’s child child” level (e.g., 10th level of division), the scale of the cuboid narrows to extremely fine precision (e.g., millimeter- or micrometer-scale).Furthermore, at the core of the cuboid is a spherical or volumetric representation approximating a “ball,” containing the center-most high-probability densities across the Gaussian fields.

[0150] To construct a cuboid at a Child’s Child Child Level, for example, a parent cube such as the hypercube 21 Oct that is 20,000 km in dimension and represents the Earth’s spatial bounds is created and then recursively subdivided. Each recursive level divides the cube into 10x10x10 child cubes, reducing scale by a factor of 10 at each level as described herein. For each child cube at any level, Gaussian curves are used to determine positional densities. For example, standard deviation bounds define regions with 68%, 95%, and 99.7% probabilities of spatial localization. This results in much improved positional accuracy as compared with other grid systems such a Euclidean grids. Specifically, the combination of the Gaussian curves and recursive subdivision minimize positional error through logarithmic smoothing of standard deviations. Furthermore, Bounding errors (e.g., SEP x PDOP) are inherently managed logarithmically, ensuring that positional inaccuracies are bounded and adjusted as they propagate down hierarchical levels. The Recursive cubes also eliminate redundancy by storing child-level data only where necessary as parent cubes inherit attributes dynamically. The system also scales from global to hyper-local (e.g., nanometer precision) without introducing data loss or computational overhead.

[0151] Quantum-Optimized Recursive Spatiotemporal Encoding

[0152] In some embodiments, the cube-based locationing system 100 as described herein may employ quantum computing optimization techniques for encoding data within the cubebased model 200. These quantum computing optimization techniques enable high-speed and high-precision data processing as compared with general classical computing processes for reading and writing to the cube-based model 200.

[0153] FIG. 8 illustrates a quantum optimized example of the cube-based locationing system 100 described herein. In particular, as shown in FIG. 8, in some embodiments, the cube-based locationing system 100 may include a quantum computing processor 800 communicatively coupled to the database 104 that contains the cube-based model 200. The quantum computingPATENT APPLICATION Attorney Docket No.: 34014-70815-PC processor 800 is configured to recursively divide the cube-based coordinate space 204 into the plurality of cubes 202 so as to comprises the multiple layers of nested cubes as described herein in connection with at least FIGS. 2A -2D.

[0154] Further, the quantum computing processor 800 may implement the nested cube navigation format for the cube-based data model 200 to select cubes from the multiple layers of nested cubes for storing and retrieving payload data therefrom. In these embodiments the cube locationing application programming interface (API) may be configured to access the cube-based model 200 via the quantum computing processor 800. In particular, a classical computing interface 802 of the cube-based locationing system 100 may execute the cube location API to store and retrieve input data in the cube-based model 200. In some embodiments, the quantum computing processor 800 may interface with the hierarchical grid structure of the cube-based model 200 to recursively subdivide both space and time into smaller units down to millimeter and yoctoseconds (10A-24 seconds) precision. As described herein each recursive subdivision is assigned a unique TULSA code, which serves as a global identifier for spatiotemporal data points. In general, the quantum computing processor 800 accelerates the recursive subdivision process as compared to recursive subdivision as performed by classical processing systems. This acceleration enables the cube-based locationing system 100 to handle larger datasets at high speeds, while ensuring that even the most complex datasets are subdivided and encoded accurately. In some embodiments, the depth of quantum optimization performed by the quantum computing processor 800 may be modified based on the complexity of the data or time constraints. This modification may enable balancing between speed and accuracy in processing.

[0155] For example, the combination of the classical computing interface 802 and the quantum computing processor 800 may operate to encode, store, and recall input data (e.g., realtime data 110, user provided data 118, the RF signal data, GPS data, environmental data, etc.) within the cube-based model 200. In particular, the classical computing interface 802 may receive the input data from a computing device (e.g. the computing device as described herein via website 108); determine first spatial coordinate data within the target environment 206 that are associated with the input data and first time data that is associated with the input data; and format the input data, the first spatial coordinate data, and the first time data for ingestion by the quantum computing processor 800. Formatting the data may include converting the classical organized data into quantum bits used by the quantum computing processor 800. Following thePATENT APPLICATION Attorney Docket No.: 34014-70815-PC formatting, the classical computing interface 802 may invoke the cube locationing API to pass the input data, the first spatial coordinate data, and the first time data as formatted to the quantum computing processor 800. Then, the quantum computing processor 800 access the cube-based data model 200 with the nested cube navigation format to encode, based on the first spatial coordinate data, the input data as payload data of one or more target cubes (e.g., any of cubes 210cl, 210c2nl, 210c3n2, 210c4n2, etc.) of the plurality of cubes and set a temporal value of the payload data based on the first time data. The one or more target cubes may define a first cubebased coordinate value defining a first position of the one or more target cubes within the cubebased coordinate space 204.

[0156] As shown in FIG. 8, the classical computing interface 802 may also be in electrical communication with an output device 804. The output device may include the computing device a portion of the computing device that supplied the input data, a display device of the cube-based locationing system 100, or any other similar device that may receive and process an output supplied by the classical computing interface 802. In general, the classical computing interface 802 may receive data from the quantum computing processor 800 that is encoded as payload data in the cube-based model 200 and / or that is related to the data encoded in the cube-based model 200 such as data indicating the position of particular cubes within the cube-based coordinate space 204.

[0157] For example, in some embodiments the input data may be associated with an entity or object located in the target environment 206 and the classical computing interface 802 may receive, from the quantum computer processor 800, a precise location marker for the entity or object in response to encoding the input data as the payload data within the cube-based model 200. The precise location marker may be based on the first position of the one or more target cubes within the cube-based coordinate space 204. The classical computing interface 802 may then send the precise location marker to the output device 804, which may display an indication of the location of the entity or object within the target environment 206 based on the precise location marker. The entity or object may include at least one of a moving object, a vehicle, a satellite, an aircraft, a drone, an transceiver, a cellular tower, a radio frequency broadcast tower, etc.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0158] The classical computing interface 802 may also recall the input data encoded into the cube-based model 200 by the quantum computing processor 800 in response to a request. In particular, the classical computing interface 802 may receive, from a computing device of the type described herein, a request for data corresponding to a target position in the target environment 206. This request may include a precise location marker. In response, the classical computing interface 802 may invoke the cube locationing API to trigger the quantum computing processor 800 to access the cube-based data model 200 with the nested cube navigation format to select the one or more target cubes from the multiple layers of nested cubes based on the precise location marker and return, to the computing device, the input data. The input data may then be sent to the output device 804 for display or other additional processing as described herein.

[0159] In some embodiments, the output device 804 may include an artificial intelligence (Al) model. The Al model may leverage historical spatiotemporal data stored in cube-based model 200 to predict future trends and movement patterns of an object or entity associated with the input data. In some embodiments, the Al model may be trained on recursively subdivided data of the type stored in the cube-based model 200 to enable forecasting of future events and improved decision-making capabilities in fields such as autonomous navigation, logistics, and predictive maintenance.

[0160] For example, when the input data is associated with an entity or object located in the target environment 206, the classical computing interface 802 may receive, from the quantum computer processor 800, historical position data encoded in the cube-based data model 200 and the precise location marker for the entity or object as described above. The classical computing interface 802 may then input the historical position data and the precise location marker into the Al model to generate a future location prediction for the entity or object and cause the output device 804 to display an indication of the future location prediction of the entity or object within the target environment 206 based on the precise location marker. Additionally or alternatively, the future location prediction may be used by the output device 804 to make navigation decisions such as avoiding collision by another object or entity with the first object or entity at the future location predicted by the Al model.

[0161] As shown in FIG. 8, in some embodiments, the cube-based locationing system 100 may also include an error interpolation module 806 that is communicatively coupled to thePATENT APPLICATION Attorney Docket No.: 34014-70815-PC database 104. In general, the error interpolation module 806 may enable the cube-based locationing system 100 to handle underlap zones (e.g., areas between grid cells where no overlap exists). This handling of underlapped zones ensures accurate localization of data within the cubebased model 200 even in scenarios where traditional grid-based systems would produce erroneous results due to missing overlap. To handle and correct for these underlapped zones the error interpolation module 806 is configured to identify underlap areas of the cube-based coordinate space 204 by locating a set of one or more blank cubes of the plurality of cubes 202 that are positioned between two or more filled cubes for which payload data has been encoded by the quantum computing processor 800.

[0162] After identifying the underlap areas, the error interpolation module 806 may determine a Euclidean midpoint between the two or more filled cubes; identify at least one of the set of one or more blank cubes based on the Euclidean midpoint; and generate an interpolated data entry for the at least one of the set of one or more blank cubes based on the respective payload data of the two or more filled cubes. The error interpolation module 806 may then encode the interpolated data entry as payload data of the at least one of the set of one or more blank cubes. It should be appreciated that the error interpolation module 806 may be utilized in versions of the cube-based locationing system 100 that do not employ the quantum computing processor 800 (e.g., embodiments where the classical computing interface 802 or other processor of the cube-based locationing system 100 encode data in the cube-based model 200).

[0163] In some embodiments, the error interpolation module 806 or other component of the cube-based locationing system 100 may also integrate error-correcting codes (ECC) into the cube-based model 200. The ECC may further minimize errors in scenarios where there is low signal-to-noise ratios (SNR) to improve performance in challenging environments such as urban canyons or dense forests.

[0164] As shown in FIG. 8, in some embodiments, the cube-based locationing system 100 may include a multi signal input processor 808 that is in electrical communication with the classical computing interface 802. In general, the multi signal input processor 808 may be configured to ingests a plurality of unprocessed signals or data from various sources, including GPS, Wi-Fi, Signals of Opportunity (SoOP), loT sensors, etc. and construct the input data from the plurality of unprocessed signals. For example, the multi signal input processor 808 mayPATENT APPLICATION Attorney Docket No.: 34014-70815-PC construct the input data from the plurality of unprocessed signals to select at least one of the plurality of unprocessed signals for use as the input data based on reliability metrics for the plurality of unprocessed signals. The reliability metrics may include one or more of signal strength, noise levels, and environmental conditions associated with the plurality of unprocessed signals. In this way the multi signal input processor 808 dynamically selects and prioritizes the most reliable signals based on the current environmental conditions to provide the system with continuous access to accurate, real-time data streams. This capability is especially useful in signal-degraded or GPS -denied environments. Additionally or Alternatively, the multi signal input processor 808 may construct the input data by compiling at least two of the plurality of unprocessed signals together for use as the input data.

[0165] The quantum computing embodiments of the cube-based locationing system 100 may be used to provide improved performance for many different applications such as autonomous navigation, Al-based decision-making, geospatial intelligence, predictive maintenance and industrial IOT, real-time analytics for logistics and supply chain management, satellite and space operations, and / or environmental monitoring and climate science.

[0166] For example, with respect to autonomous navigation, ability of the cube-based locationing system 100 to handle multi-signal inputs (GPS, Signals of Opportunity, loT, etc.) and provide high-precision real-time positioning makes the cube-based locationing system 100 ideal for autonomous vehicles (e.g., cars, drones, ships). The quantum-optimized recursive grid allows for ultra-fast data processing, ensuring that autonomous systems receive accurate, up-to-date location data, even in signal-degraded environments (e.g., urban canyons or tunnels).Additionally, the system’s predictive analytics feature can anticipate the vehicle's future position, further enhancing navigation accuracy and safety.

[0167] Further, with respect to Al-based decision-making, Al models in fields such as robotics and industrial automation can benefit from the system’s ability to handle large datasets efficiently and accurately. This scale enables the models to make informed decisions based on real-time spatiotemporal data encoded in the cube-based model 200.

[0168] Furthermore, with respect to geospatial intelligence, the ability of the cube-based locationing system 100 to provide real-time, high-precision data over vast spatial and temporal scales makes it invaluable for military, defense, and geospatial intelligence operations. InPATENT APPLICATION Attorney Docket No.: 34014-70815-PC particular, the recursive grid combined with Al-powered predictive analytics enables intelligence agencies to track movements, anticipate changes, and provide strategic insights with unparalleled accuracy.

[0169] Further still , with respect to predictive maintenance and industrial loT, the ability of the cube-based locationing system 100 to forecast future spatiotemporal positions makes it a key asset for predictive maintenance. For example, in industries such as manufacturing or aerospace, the cube-based locationing system 100 can be used to predict the future location of machines or components, enabling proactive maintenance, reducing downtime, and increasing operational efficiency.

[0170] Moreover, with respect to real-time analytics for logistics and supply chain management, the quantum-enhanced real-time processing and multi-signal integration aspect of the cube-based locationing system 100 allow for precise tracking of shipments and assets across the supply chain. For example, the ability to handle dynamic environmental conditions and provide accurate real-time positioning in multi-signal or GPS-denied environments gives logistic operators enhanced control over their supply chains.

[0171] Additionally, with respect to satellite and space operations, the recursive grid and predictive analytics features of the cube-based locationing system 100 allow for high-precision satellite positioning and orbital calculations, which ensure accurate tracking of space objects and optimizing satellite communications and operations. In particular, the error correction mechanisms ensure that terminal learning errors (TLE) and other positional inaccuracies are minimized, which makes the cube-based locationing system 100 ideal for space missions or satellite constellations.

[0172] Finally, with respect to environmental monitoring and climate science, the cube-based locationing system 100 can be used to monitor large-scale environmental changes (e.g., deforestation, pollution, sea-level rise) by providing real-time geospatial data at millimeter and yoctosecond precision. Additionally, the Al-driven predictive features may allows scientists to forecast future environmental changes based on historical trends, offering invaluable insights for climate research and disaster management.

[0173] Grains as Quantum-Compatible Data StructuresPATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0174] In addition to using the quantum computing processor 800 to write and read data to the cube-based model 200, quantum data itself may be represented within the cube-based model 200 using one or more of the embodiments of the cube-based locationing system 100 as described herein. In particular, grain of the cube-based model 200 can serve as a basis state in a quantum system. For example, a grain delineated by a specific TULSA code may represent a single quantum state, while a superposition represents a blend of multiple grains. Furthermore, Each grain is converted into a quantum state or part of a quantum register wherein k grains can be represented and stored in superposition across log2(k) qubits. Additionally or alternatively amplitude encoding (mapping each grain probability probability a derived from error bounds or other weighting factors) may be used to encode the grains into the quantum domain.

[0175] In general, superposition allows a quantum bit (qubit) to exist in multiple states simultaneously. Similarly, grains in the cube-based model 200 exist at different hierarchical levels (parent, child, and child’s child) such that they can be encoded as a quantum superposition of states. For example, a single grain may represent multiple potential positions or times until a measurement collapses the grain to one definitive state. Furthermore, each grain may have an associated Gaussian distribution (e.g., a probability density) for spatial and temporal errors. This distribution naturally maps to quantum amplitudes, where probabilities are encoded in the quantum state’s amplitude.

[0176] In operation, the cube-based locationing system 100 may use the grains of the cubebased model 200 to quantum system super positions. First, the grains of the cube-based model 200 are encoded a Qbits. In particular, each grain (defined by a TULSA code) is converted into a quantum state where each Qbit represents one spatiotemporal grain (e.g., a parent cube) and a different grain (e.g., a child cube). The amplitudes of these encoded Qbits then relate to the Gaussian probabilities of the grains. Operations may be applied to the encoded Qbits using one ore more of Hadamard gates, phase estimation, quantum Fourier transforms, etc.

[0177] Next, the cube-based locationing system 100 establishes a superposition of multiple grains. In particular, multiple grains are combined into a single quantum state that (1) represents a specific grain within the CubeNexus framework and (2) for which probability amplitudes are derived from the Gaussian error distributions of the grains. The cube-based locationing system 100 may then represent the recursive hierarchy using quantum entanglement such as by holdingPATENT APPLICATION Attorney Docket No.: 34014-70815-PC the nested cubes recursively in quantum registers. In particular, grains within nested cubes (parent, child, child’s child, etc.) are naturally hierarchical and can be represented as entangled states. Finally, the state of the quantum encoded and represented grains may be measured to collapse the superposition into a specific grain. For example, querying a TULSA code may collapses the quantum state to the grain most relevant to the query, which provides the precise spatiotemporal location or data.

[0178] The encoding of the grains of the cube-based model 200 into quantum system representations provides several advantages as compared with classical computing operations. In particular, the quantum system mya provide optimized search within the cube-based model 200 by using Grover’s algorithm to find the most applicable grain to given application faster than classical methods. Furthermore, these systems enable dynamic error correction because Gaussian-based grains inherently encode spatial and temporal errors and Quantum error correction protocols can leverage these Gaussian properties to dynamically reduce noise in computations. The recursive grains additionally enable compact representation of large datasets which can be further represented efficiently via quantum states, which can encode vast amounts of spatial and temporal data in superpositions to reduce storage requirements. In some embodiments, the value of each grain is mapped to a quantum amplitude using Gaussian-based probability distributions and spatial errors are modeled as quantum uncertainty states.

[0179] The cube-based locationing system 100 may also perform quantum-assisted predictions by combining grain superpositions with quantum machine learning (e.g., variational quantum circuits) to predict future movements, environmental changes, or signal patterns. The quantum encoded grains may also be used to simulate physical phenomena where spatiotemporal superpositions are required (e.g., wave-particle interactions, GPS signal modeling, etc.).

[0180] It should also be appreciated that the incorporation of the cube-based model 200 recursive and hierarchical structure with quantum processing systems offers improvements over current quantum systems. In particular, current quantum computing frameworks cannot natively integrate hierarchical spatiotemporal data, and are forced to flatten or oversimplify complex real-world information. For example, when data is mapped to quantum states, it is typically structured as linearized or vectorized information, which removes natural hierarchical and recursivePATENT APPLICATION Attorney Docket No.: 34014-70815-PC properties. The current quantum systems lack a built-in capacity to preserve critical nesting of location, time, and uncertainty, which often leads to oversights in sensor-driven or time-evolved quantum applications. More particularly, existing quantum computing systems convert classical data into quantum states using a flat and unstructured binary encoding (classical bit-to-qubit mapping). This results in a non-hierarchical models that lose data granularity and produces inefficient quantum superposition encoding, which limits the data processing abilities to simple mathematical structures. For example, the traditional quantum encoding processes of direct mapping of classical bits to qubits (e.g., basis encoding); using floating-point values to define quantum amplitudes; and compressing classical data into quantum states for superdense coding and quantum machine learning approaches all suffer from this defect. In particular, the absence of spatiotemporal data structure in quantum computing results in a loss of spatial and temporal accuracy when converting real-world data, inefficient data compression which increases quantum memory costs, and reduced predictive power in time-evolving quantum applications.

[0181] In contrast, the quantum system described herein employs the superstructured nested TULSA defined grids to embed Gaussian-modeled error within amplitude encodings, which enables a more faithful probabilistic representations of sensor-derived data. Further, this hierarchical ingestion method allows quantum operations, such as Grover’s search and phase estimation, to explore multiple location / time granularity levels in superposition — a profound improvement over purely flat encodings. Embedding the smallest common TULSA encoded grain scale prevents artificially inflating precision, ensuring quantum algorithms conduct parallel explorations of bounding boxes spanning sub millimeters to kilometers (or years to femtoseconds). This integrated error modeling reduces or eliminates “hallucinations” in AI-driven quantum predictions by replacing deterministic illusions with authentic uncertainty distributions. Furthermore, the quantum approach as described herein enables robust quantumbased navigation in GPS-denied scenarios by continuously updating multi-scale TULSA codes as new sensor data refines the error bounds. This approach also supports novel quantum cryptography paradigms that utilize spatiotemporal entanglement for location-based key distribution and dynamic threat detection. Iterative refinement of TULSA levels preserves data fidelity over time, recording the evolution of precision and anchoring quantum predictions to known degrees of accuracy. Overall, superstructured nested recursive TULSA addresses as described herein offer a transformative leap in quantum system’s ability to ingest, process, andPATENT APPLICATION Attorney Docket No.: 34014-70815-PC forecast real-world spatiotemporal data at multiple scale levels (in time and / or space, location, volume), which is a capability not attainable with current quantum encoding methods.

[0182] As described elsewhere herein, the benefits described above may be achieved by encoding space-time data recursively into TULSA defined grains of the from the cube-based model 200 for quantum computing, such as in a manner that maintains global-to-local granularity in quantum registers. This encoding translates the nested recursive cube structure into quantum states to enable efficient superposition-based queries of real-world multidomain data.Furthermore, error models are embedded as probabilistic quantum amplitudes, which enhance predictive analysis. Finally, sensor data, environmental data, signal intelligence, navigational inputs, etc may be integrated in a multi-domain manner into a unified quantum dataset using the systems and methods as described herein. In particular, the data encoded into the quantum registers may preserve historical, real-time, and predicted future timestamps.

[0183] These advantages offer improvements in many possible applications and industries including the military, cyber security, navigation, logistics, space exploration etc.

[0184] A Signals of Opportunity (SOOP) Navigation System

[0185] In some embodiments, features of the cube-based locationing system 100 and the cube-based model 200 as well as other systems and components described herein, may be used in connection with a cube-based navigation system 900 for an object or entity. For example, FIG.9 A illustrates such a cube-based navigation system 900 that includes the database 104 or similar that contains the cube-based model 200, a navigation module 902 in electronic communication with the database 104 and cube-based model 200, and one or more radio frequency (RF) receivers(s) 904 in electronic communication with the navigation module 902. It should be appreciated that the RF receivers(s) 904 may include various systems that receive electromagnetic signals and radio frequency communications wirelessly through the air including the necessary antenna hardware. For example, the RF receivers(s) 904 may be configured to receive RF signals from one or more broadcast sources 906 as shown in FIG. 9A. Additionally, the RF receivers(s) 904 may be a part of transceiver systems that can both send and receive messages. Additional details and examples of the receivers(s) 904 are shown and described below in connection with FIG. 9B.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0186] After the RF signals are received, the navigation module 902 may determine location identifying features of at least a portion of the RF signals. In particular, these features may include properties inherent to the RF signals or features related to how and when they were received by the RF receivers(s) 904 that the navigation module 902 may use to locate the entity or object in space. Example features may include, but are not limited to received signal strength, angle-of-arrival, time-of-arrival, time difference of arrival, or frequency difference of arrival, etc.

[0187] After, determining the location identifying features the navigation module 902 may access the cube-based data model 200 with the cube-based navigation format as described herein. When accessing the cube-based model 200 the navigation module 902 may be configured to identify a current location cube of the plurality of cubes based on one or more reference cubes of the plurality of cubes and the location identifying features. For example, the navigation module 902 may locate the one or more reference cubes within the cube-based coordinate space 204 of cube-based model 200 and use the location identifying features (or values, scalers, vectors, angles, or other modifiers derived therefrom) to precisely locate and select the current location cube. In general, the one or more reference cubes define one or more first cube-based coordinate values defining one or more first positions of the one or more reference cubes within the cubebased coordinate space 204.

[0188] After the current location cube is identified, the navigation module 902 may encode the location identifying features as payload data of the current location cube within the cubebased model 200. The current location cube, in general, defines a second cube-based coordinate value defining a second position of the current location cube within the cube-based coordinate space. The navigation module 902 may also set a temporal value of the payload data as a time of receipt for the RF signals, and then receive as a return output from the cube-based model 200 a precise location marker. The precise location marker is based on the second position of the current location cube within the cube-based coordinate space and indicates a current location of the object or entity within the target environment 206.

[0189] Finally, the navigation module 902, may generate a navigation control signal based on the precise location marker. The navigation control signal is configured to directs movement of the object or entity from the current location toward a destination location. In particular, the navigation control signal may be sent to an output device 908 and / or an operation control modulePATENT APPLICATION Attorney Docket No.: 34014-70815-PC 910 to effectuate navigation of the object or entity from the current location toward the destination location.

[0190] For example, in some embodiments, the navigation control signal includes instructions for how to perform a next step along a path from the current location to the destination location. In these embodiments, the output device 908 may receive the navigation control signal and present the instructions in a human comprehendible format. Furthermore, in embodiments, where the object or entity is a user device (e.g„ a mobile phone, tablet, computer, vehicle display system, etc.) the output device 908 may include a display and the human comprehendible format may includes text or images rendered on the display. Additionally, or alternatively, the output device 908 may include a speaker of the user device and the human comprehendible format may include audio output from the speaker.

[0191] It should be appreciated that, the cube-based navigation system 900 may also employ additional real-time visualization techniques as described herein, such as those in connection with the visualization app 144 of FIG. 1. For example, the navigational grid and signal environment may be dynamically displayed an modleed within a 2D or 3D rending of the cubebased model 200 to enable interactive monitoring of signal strengths and data sources.

[0192] Additionally or alternatively, in some embodiments, the navigation control signal may include instructions for how to control the object to follow a path from the current location to the destination location. In these embodiments, the operation control module 910 may receive the navigation control signal and modify operation of one or more components of the object or entity to cause the object or entity to follow the path. For example the one or more components of the object or entity may include one or more of an electric motor, a fuel controller, a steering actuator, an electronic rudder control, an electronic throttle control etc. and the operation control module 910 may modify operating parameter of these components to cause the object to follow the path. For example, the operation control module 910 may actuate a rudder or other steering components of an unmanned drone so that the drone’s heading and course are directed from the current location to the destination location. Similarly, the operation control module 910 may control the fuel or power supplied to various motors or engines of the drone or similar device such that the thrust of the drone or similar device is aligned to direct the device along the path.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0193] In some embodiments, the navigation module 902 generates and stores the path from the current location to the destination location in the cube-based coordinate space 204 by accessing the cube-based model 200 with the cube-based navigation format. In particular the navigation module 902 may identify cubes that from a path between the current location cube and a destination cube, the destination cube being located in the cube-based coordinate space at a location that is mapped to the destination location within the target environment. The level of cubes selected for the path may be the same level as that of the current location cube and the destination cube or alternatively higher or lower levels as appropriate to properly represent the path and any foreseeable navigational error (e.g„ error associated with controlling the object using the operation control module 910).

[0194] In general, the navigation module 902 may be configured to identify the current location cube from the identifying features using various different possible reference cubes. For example, in some embodiments, the one or more first positions of the one or more reference cubes within the cube-based coordinate space 204 may correspond to a known source location within the target environment 206 for a broadcast source of the RF signals (e.g., a stored known location of a broadcast radio tower, tv broadcast tower, satellite, etc.). In some embodiments, where the location of the broadcast source varies over time, the time dependent location may also be recorded within the cube-based model 200. In these embodiments, the navigation module 902 may match identify or match the related reference cube to a source device cube as stored in the cube-based model 200 and then generate a position vector (or other similar distance and angle marker) originating from the one or more reference cubes using the location identifying features of the RF signals. Then, the navigation module 902 may identify the current location cube as a cube of the plurality of cubes that contains a termination point of the position vector.

[0195] In some embodiments, the current location cube includes a smallest size cube of the plurality of cubes that contains a region of the cube-based coordinate space 204 that is defined by boundaries of the object or entity. In particular, these boundaries may be determined relative to the termination point and error values associated with the generation of the position vector and / or the location identifying features of the RF signals. For example, the navigation module 902 may assume that the termination point corresponds to a location of a particular RF antenna on or within the object or entity and then may select the location cube’s location and size based on anPATENT APPLICATION Attorney Docket No.: 34014-70815-PC estimated error in the accuracy of the position vector and a known configuration of the object or entity relative to the location of the particular RF antenna.

[0196] Additionally or alternatively, in some embodiments, the navigation module 902 may use a simultaneous localization and mapping (SLAM) algorithm or process. In these embodiments, the one or more first positions of the one or more reference cubes within the cubebased coordinate space may correspond to a previously determined location of the object or entity within the target environment (e.g., a GPS determined position or similar). Then, to identify the current location cube, the navigation module 902 may first determine a localized position of the object or entity using the location identifying features of the RF signals, additional location identifying features of previously received RF signals, and a movement profile of the object or entity from the previously determined location to the current location;. Next, the navigation module 902 may map the localized position to the cube-based coordinate space 204 using the position of the one or more reference cubes within the cube-based coordinate space 204 and identify the current location cube as a cube of the plurality of cubes that contains the localized position as mapped into the cube-based coordinate space 204.

[0197] It should be appreciated that the cube-based navigation system 900 may also include a version of the error interpolation module 806 as described herein. For example, the cube-based navigation system 900 may employ the error interpolation module 806 to remove underlapped zones and ensure full unified coverage of the reference cubes and / or current location cube within the cube-based model 200. The error interpolation module 806 may be especially useful ensuring accurate location data is written into the cube-based model 200 in fluctuating or low signal-to-noise ratio (SNR) environments.

[0198] With reference now to FIG. 9B, an additional embodiment of the cube-based navigation system 900 is shown. In particular, as shown in FIG. 9B, the cube-based navigation system 900 may additionally include a multi signal input module 916 (e.g., multi signal input processor 808). Furthermore, the RF receivers(s) 904 may include a GPS receiver 912 that receives GPS or similar signals from a GPS satellite 906B and SOOP RF receivers 914 that receive SOOP signals from SOOP sources 906 A. The SOOP sources 906 A may include any type of device that regularly and predicably broadcast RF or other electro-magnetic signals (e.g., light). In particular, SOOP signals may include signals, such as Wi-Fi, Bluetooth, 5G, and radioPATENT APPLICATION Attorney Docket No.: 34014-70815-PC frequencies that can provide high-precision positioning in GPS-degraded or GPS-denied environments (e.g. environments where the GPS receiver 912 receives only weak GPS signals or none at all). Examples of such GP degraded areas include, but are not limited to, urban canyons, underground facilities, military zones, etc.

[0199] In operation, the cube-based navigation system 900 of FIG. 9B receives the SOOP signals and GPS signals and the multi signal input module 916 generates respective weighting metrics for the GPS signals and the SOOP signals. The weighting metrics indicate a reliability of the GPS signals and the SOOP signals. Then, the multi signal input module 916 selects, based on the respective weighting metrics, one or more of the GPS signals and the SOOP signals as the at least a portion of the RF signals that are processed by the navigation module 902 in the manner described above. In some embodiments, multi signal input module 916 is configured to compare the GPS signals and the SOOP signals to one or more reliability thresholds to generate the respective weighting metrics. The one or more reliability thresholds may include one or more of minimum signal strength, a minimum noise level, or a minimum environmental condition.Additionally or alternatively, in some embodiments, the multi signal input module may include or access a machine learning model that is configured to receive historical SOOP and GPS signals, and analyze the historical SOOP and GPS signals and the SOOP signals currently received to generate the respective weighting metrics.

[0200] In some embodiments, the multi signal input module 916 may filter or select different ones of the SOOP signals used by the navigation module 902 based on the environment. For example, where environmental conditions are correlated with producing weak or unreliable signals on specific frequency spectrums, the multi signal input module 916 may be configured to down weight or filter out such signals when those associated environments conditions are present in the proximity of the object or entity. The multi signal input module 916 may also be configured to prioritize signals based environmental conditions.

[0201] Furthermore, in some embodiments, the cube-based navigation system 900 may be used in conjunction with one ore more of the Al-based predictive systems and methods described herein. In particular, these predictive systems and methods may leverage historical to anticipate and forecast signal reliability in specific environments or conditions, to predict or project a future location of the object or entity, and / or to generate and project the path to the destination location.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0202] It should also be appreciated that the techniques described above with respect to the operations of any embodiments of the cube-based navigation system 900 may be carried out by a suitable computer-implemented method or be formed as instructions, stored on a non-transitory computer-readable medium, that when executed by one or more processors cause the one or more processors to perform those operations.

[0203] AN ARTIFICIAL INTELLIGENCE DATA ANALYSIS SYSTEM FOR CUBEBASED DATA

[0204] FIG. 10 illustrates an artificial intelligence data analysis system 1000 for cube-based data in accordance with various embodiments disclosed herein. In particular, the artificial intelligence data analysis system 1000 may generate Al powered analytics for interactions between spatiotemporal grains stored within any embodiment of the cube-based model 200 as described herein. In some embodiments, these analytics may include temporal forecasting, pattern recognition, anomaly detection, predictive modeling of spatial and temporal interactions, etc.

[0205] As shown in FIG. 10, the artificial intelligence data analysis system 1000 includes the cube-based model 200 (e.g., the database 104 that houses the model 200), a machine learning (ML) model encoder 1002, a ML model 1004, a cube-based model encoder 1006, and an output interface 1008. In general, the model encoder 1002 receives spatiotemporal data from the cubebased model 200 and converts the received data into a format for input into the ML model 1004.

[0206] The ML model 1004 analyzes or processes the data according to the input and the trained parameter values of the ML model 1004 to generate an analysis output. This output may then be directly presented or shared on the output interface 1008 or encoded for storage in the cube-based model 200 by the cube-based model encoder 1006 for later use and / or display on the output interface 1008 within the cube-based coordinate space 204. As described in more detail below, the model encoder 1002, the ML model 1004, and the cube-based model encoder 1006, may be selected from a plurality of different potential options based on features of a spatiotemporal data analysis request 1010 received by the artificial intelligence data analysis system 1000 in relation to spatiotemporal data or grains that are encoded within the cube-based model 200.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0207] The spatiotemporal data analysis request 1010 may include (1 ) one or more precision indicators associated with payload data encoded within the cube-based data model 200 and (2) analysis instructions for how to process the payload data associated with the plurality of precision indicators. In particular, the analysis instructions may include instructions to generate a specific output from the payload data such as to classify the data into a particular category, generate or predict new data values (e.g., a next value in a time series, a next location of an object in space, etc.), interpolate missing data or blank entries, identify anomalous data values, etc. In some embodiments, the spatiotemporal data analysis request 1010 may also include process parameters for the analysis such as sensitivity and threshold values for anomaly detection, which enable tailored insights for the specific application (e.g., finance, environmental monitoring, etc.). For example, in financial systems, a user may desire more stringent criteria for detecting unusual transaction patterns, whereas in environmental monitoring, the user may prefer broader sensitivity ranges to capture a wide variety of environmental shifts. The artificial intelligence data analysis system 1000 may also enable real-time alerts that notify users of detected anomalies and the results of the spatiotemporal data analysis request 1010 through various communication channels such as email, SMS, or in-app notifications. These notifications may be configured to include detailed anomaly reports and suggested actions based on the detected issue.

[0208] In operation, the artificial intelligence data analysis system 1000, following receipt of the spatiotemporal data analysis request 1010, may use the cube-based navigation format as described herein to identify one or more cubes of the plurality of cubes within the cube-based data model 200 using the one or more precision indicators. Then the artificial intelligence data analysis system 1000 may retrieve payload data, associated spatial data, and associated temporal data from the identified one or more cubes. In some embodiments, the retrieved payload data, the associated spatial data, and, the associate temporal data, document a position of an entity or object over time. In these embodiments, an analysis output from the ML model 1004 may include a future projected path of the entity or object. The entity or object may include at least one of a moving object, a vehicle, a satellite, an aircraft, or a drone.

[0209] The one or more cubes that are identified may include a single cube having encoded with different payload data entries with different temporal data components (e.g., data that is gathered at a single common spatial location over time). Additionally, or alternatively, the one orPATENT APPLICATION Attorney Docket No.: 34014-70815-PC more cubes may include multiple cubes encoded with different payload data entries, temporal and spatial values. For example, the one or more cubes may include data that is tied to different locations in time and space (e.g., data for moving objects, data captured at different stationary locations, etc.). It should be appreciated that the payload data retrieved in response to the spatiotemporal data analysis request 1010 may include both single data points from multiple cubes within the cube-based coordinate space 204 and multiple data points related to a singe cube within the cube-based coordinate space 204.

[0210] After the artificial intelligence data analysis system 1000 identifies the one or more cubes, the artificial intelligence data analysis system 1000 may select, based on the spatiotemporal data analysis request 1010, the ML model 1004 and the model encoder 1002 used to interface with the ML model 1004. Then, the artificial intelligence data analysis system 1000 may convert, using the selected model encoder 1002, the retrieved pay load data into an input for the selected ML model 1004 based on the analysis instructions, the associated spatial data, and the associated temporal data. As described in more detail below, the conversion process performed by the model encoder 1002 is dependent on and specific to the ML model 1004. For example, different input formats and type may be used when the ML model 1004 is an external general model run and operated by a third party compared to a model directly trained or tuned using historical cube-based data.

[0211] After the data is converted by the model encoder 1002, the artificial intelligence data analysis system 1000 may send the input to the selected ML model 1004 for processing according to the analysis instructions contained in the 1010. Sending the input may include, for example, transmitting the input to a cloud server platform that hosts and executes the ML model 1004 over the internet. However, some interactions of the ML model 1004 may be managed and executed by the artificial intelligence data analysis system 1000 itself. In these embodiments, sending the input to the ML model 1004 may include one or more processors associated with the artificial intelligence data analysis system 1000 executing the ML model 1004 with respect to the converted input (e.g., passing the converted input through trained parameter values of the selected ML model 1004). It should be appreciated that the artificial intelligence data analysis system 1000 may include a cloud-based platform accessible over the internet. In these embodiments, the spatiotemporal data analysis request 1010 may be received from a client device (e.g., a local user device, smart phone, tablet, computer, etc.). Furthermore, the outputPATENT APPLICATION Attorney Docket No.: 34014-70815-PC interface 1008 may include a display of the client device and or an interface for controlling the display of the client device.

[0212] After the ML model 1004 process the converted input, the artificial intelligence data analysis system 1000 may receive an analysis output that was generated by the ML model 1004 as a result of processing the converted input according to the analysis instructions. In particular, the analysis output may include text or other data values that are responsive to the spatiotemporal data analysis request. For example, data value, text, indicators, etc. that provided the requested (1) classification of the data, (2) newly generated or predicted data values, (3) missing data or blank entries, and / or (4) identification of anomalous data values. The analysis output may also include customized reports that summarize detected patterns, correlations, and anomalies over a specific time intervals. In some embodiments, the artificial intelligence data analysis system 1000 may be triggered to generate these reports at user-defined intervals or upon detection of significant anomalies (e.g., the spatiotemporal data analysis request 1010 may be periodically and automatically sent to the artificial intelligence data analysis system 1000). In some embodiments, the analysis output may include the results of simulations that forecast potential outcomes based on identified patterns, which may enable predictive maintenance, financial forecasting, or environmental trend analysis.

[0213] After receiving the analysis output the artificial intelligence data analysis system 1000 may cause the analysis output itself or a visualization graphic or similar indicator based on the analysis output to be displayed on the output interface 1008. For example, the artificial intelligence data analysis system 1000 may display the (1) classification of the data, (2) newly generated or predicted data values, (3) missing data or blank entries, and / or (4) identification of anomalous data values on the output interface 1008. Additionally, or alternatively, the artificial intelligence data analysis system 1000 may encode the analyzing output into the cube-based model 200 using the cube-based model encoder 1006.

[0214] For example where the analysis output generated includes a prediction of a next payload data value and an associated next spatial value and associated next temporal value, the artificial intelligence data analysis system 1000 may encode, using the cube-based navigationPATENT APPLICATION Attorney Docket No.: 34014-70815-PC format and the cube-based model encoder 1006, the next payload data value within the cubebased data model 200 based on the associated next spatial value and associated next temporal value (e.g., by encoding the payload data to the cube mapped to the associated next spatial value).

[0215] In some embodiments, the analysis output generated by the selected machine learning model includes a probability distribution of next payload data values and associated next spatial values and associated next temporal values. These probability values may be generated by the ML model 1004 directly from processing through the parameters of the model or may be determined from multiple passes of the converted input through the ML model 1004. In these embodiments, the artificial intelligence data analysis system 1000, via the cube-based model encoder 1006 or other component thereof, may determine an error free region of the target environment 206 that encompasses all of the associated next spatial values. Then the artificial intelligence data analysis system 1000 may identify, using the cube-based navigation format, a cube of the plurality of cubes with boundaries that contain a region in the cube-based coordinate space 204 that is mapped to the error free region and encode the next payload data values as payload data of the identified cube.

[0216] In this way the size and level of the selected cube into which the payload data is encoded may represent a region of the target environment 206 that the ML model 1004 predicts , within a high degree of certainty (e.g., 80% to 100% certainty) to be associated with the payload data. For example, in case where the ML model 1004 is predicting a next location of a moving object or entity, the selected cube may indicate the summation of all the possible next location predictions. In some embodiments, lower level cubes may be used to individually define each predicted next spatial location individually.

[0217] Furthermore, in case where the analysis output is encoded into the cube-based model 200, the visualization graphic or other rendered indicator that the artificial intelligence data analysis system 1000 causes to appear on the output interface 1008 may include a rendered display of the analysis output within the cube-based coordinate space 204. For example, a 2D or 3D image of the cube-based coordinate space 204 showing relevant cubes for the analysis output and data associated therewith (see e.g., FIGS. 2A-2D). in some embodiments, the visualization of the analysis output may performed by or in conjunction with the visualization app 144PATENT APPLICATION Attorney Docket No.: 34014-70815-PC described herein in connection with FIG. 1. Additional graphic visualization may include heat maps, temporal graphs, and dynamic timelines, which enable users of the artificial intelligence data analysis system 1000 to better understand the insights derived from the data. In some embodiments, the visualization may include custom views by the user that enable real-time monitoring dashboards, and sharing of visual reports with collaborators or stakeholders. These dashboards may seamlessly integrate with the output interface 1008 to enhance user interaction and decision-making processes by making complex data more interpretable.

[0218] As discussed above, the artificial intelligence data analysis system 1000 may select the ML model 1004 and model encoder 1002 based on details within the spatiotemporal data analysis request 1010. In particular, the ML model 1004 and the associated encoder model encoder 1002 may be specifically selected by the user or automatically selected to carry out the particular analysis request identified within the spatiotemporal data analysis request 1010. For example, if the spatiotemporal data analysis request 1010 includes a request to classify or categorize data within the cube-based model 200, the artificial intelligence data analysis system 1000 will select the ML model 1004 as a model specifically trained to generate such a classification. Furthermore, if the spatiotemporal data analysis request 1010 includes a request to generate or predict a value (e.g., a next object location, a next value in a time series, etc.), the artificial intelligence data analysis system 1000 will select the ML model 1004 as a model that is trained and tuned to generate such predictive values (e.g., a transformer model, a recurrent neural network (RNN) model, or the like).

[0219] This selection-based approach enables the artificial intelligence data analysis system 1000 to operate and interface with multiple models and tailor the analysis to the specific nature of the spatiotemporal data analysis request 1010. Furthermore, in some embodiments, this tailoring can save and optimize the computing resources needed to execute the selected ML model 1004. For example, in some embodiments, the artificial intelligence data analysis system 1000 may first determine a size of the already or to be retrieved payload data, associated spatial data, and associated temporal data and, then, select the ML model 1004 as a model with a context window sufficient to accommodate the determined size. Additionally, the artificial intelligence data analysis system 1000 may select a model with a context window size that closely matches the size of the data (e.g., avoiding models with context windows greater than aPATENT APPLICATION Attorney Docket No.: 34014-70815-PC preset threshold large than the determined size) so that processing resources, GPUs, etc. are used efficiently to run the ML model 1004 and are not wasted.

[0220] In embodiments, where the ML model 1004 is specifically trained or tuned to interface with data encoded within the cube-based model 200, the ML model 1004 may be trained on historical payload data, historical spatial data, and historical temporal data stored within the cube-based data model 200 that is converted with the selected encoder 1002. In these embodiments, the elected encoder 1004 may be configured to convert the retrieved payload data (e.g., the training data or the data retrieved according to the spatiotemporal data analysis request 1010) into embedded spatiotemporal vectors based on the associated spatial data and associated temporal data. It should be appreciated that various training process may be employed. These may include, but are not limited to, supervised, unsupervised, and reinforcement learning techniques.

[0221] In embodiments where the ML model 1004 includes a transformer or similar model configured to predict a next value, the selected encoder 1002 is configured to convert the retrieved payload data into the embedded spatiotemporal vectors by first converting the retrieved payload data into initial vectors using a preconfigured data to vector mapping schema. For example, where the data is text data, the preconfigured data to vector mapping schema may include Word2vec or a similar vector encoding algorithm. Additional schemas may be specifically specified by user input to match the nature of the data encoded within the cube-based model 200. After the initial vectors are created, the model encoder 1002 may generate the embedded spatiotemporal vectors by modifying the initial vectors to encode space and time inter-relationships indicated by the associated spatial data and associated temporal data.

[0222] Additionally, in some aspects, the ML model 1004 may include a classifier and the analysis output generated thereby is a predicted classification of the payload data, the selected ML model 1004 is further trained using historical ground truth classifications of the historical payload data. The classifier model may also employ the embedded spatiotemporal vectors as described above.

[0223] In some embodiments, the ML model 1004 may include a large language model (LLM). The LLM may be a third party hosted private or public model or a local instance LLM managed and operated by the artificial intelligence data analysis system 1000. In thesePATENT APPLICATION Attorney Docket No.: 34014-70815-PC embodiments, the model encoder 1002 is configured to convert the retrieved payload data, the associated spatial data, associated temporal data, and at least a portion of the spatiotemporal data analysis request into a text-based prompt for the LLM. In particular, this prompt may include text reiterating the analysis instruction and an organized presentation of the payload data that mirrors the hierarchical and recursive nature of the cube-based model 200.

[0224] In some embodiments, the model encoder 1002 or another component of the artificial intelligence data analysis system 1000 (e.g„ an advanced data preprocessing module) may clean, normalize, transform, and / or augment the spatiotemporal grain data before it is converted and fed into the ML model 1004. In particular, this preprocessing of the data may include detecting and removing outlier values of the retrieved payload data, detecting and interpolating missing data values of the retrieved payload data, smoothing of the of the retrieved payload data, and / or alignment of the retrieved payload data based on the associated temporal data. In some embodiments, the artificial intelligence data analysis system 1000 may be configured to assign weights to data points stored within the cube-based model 200 based on recent trends, environmental conditions, or forecast objectives, which refines the accuracy of predictions made by the ML model 1004 by prioritizing relevant data. Similarly, the artificial intelligence data analysis system 1000 may be configured to identify and filter out anomalous or low-confidence data points, reducing the influence of outliers within the ML model 1004. Further filtering may also be implemented based on current environmental factors, by adjusting the parameters of the ML model 1004 (or selecting a different ML model 1004) for analysis related to specific environments (e.g., urban, maritime, aerial). These adjustment and selections may be based on historical performance data, enabling refined prediction accuracy across diverse operational settings.

[0225] In some embodiments, the artificial intelligence data analysis system 1000 may employ self-learning algorithms that update or tune the ML model 1004 after being used to generate the analysis output. This process may continuously improve the ML model 1004 as more spatiotemporal grain data is processed and would enable the artificial intelligence data analysis system 1000 to adapt to evolving data trends without manual intervention. The selflearning models could also provide feedback to users about how their accuracy improves as they process new data, further building trust in the system’s recommendations.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0226] In some embodiments, the artificial intelligence data analysis system 1000 may be configured to integrate with external data sources (e.g., to fill in or missing data and / or to supplement data stored within the cube-based model 200). These external data sources may include loT devices, financial transaction databases, weather monitoring stations, satellite imagery databases, etc. By incorporating these external data streams into the spatiotemporal grain analysis, the system can generate even more comprehensive insights. For example, financial institutions could combine market data with economic indicators for more robust anomaly detection, while environmental monitoring systems could leverage satellite data to correlate grain-based insights with observed geographic or meteorological events.

[0227] In some embodiments, the artificial intelligence data analysis system 1000 employs a modular microservices architecture to enable the individual components described herein to function independently, ensuring scalability and fault tolerance. Communication between modules is facilitated through representational state transfer (RESTful) APIs and message queues, ensuring that data flows seamlessly between ingestion, preprocessing, and Al analysis components. Furthermore, the artificial intelligence data analysis system 1000 may utilize standard frameworks such as TensorFlow for Al models, Kafka for data streaming, and Kubernetes for containerized deployment, ensuring both flexibility and scalability.

[0228] In some embodiments, the artificial intelligence data analysis system 1000 may include a real-time data processing component that enables continuous processing of incoming spatiotemporal grain data to the cube-based model 200. This feature may employ parallel processing and distributed computing techniques to handle large data volumes and deliver immediate feedback. Such a module may be particularly valuable in industries that rely on up-to-the-minute information, such as financial trading, disaster response, or dynamic supply chain management.

[0229] In some embodiments, the artificial intelligence data analysis system 1000 may employ data security measures such as encryption of data both in transit and at rest, multi-factor authentication for user access, and role-based access control to ensure only authorized users can view or manipulate certain data sets. For industries dealing with sensitive data, such as healthcare, finance, or defense, these additional layers of security would be indispensable.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC Furthermore, this feature may include compliance tools that ensure data processing and storage conform to industry standards such as GDPR, HIPAA, or PCI DSS.

[0230] It should also be appreciated that the techniques described above with respect to the operations of any embodiments of the artificial intelligence data analysis system 1000 may be carried out by a suitable computer-implemented method or be formed as instructions, stored on a non-transitory computer-readable medium, that when executed by one or more processors cause the one or more processors to perform those operations.

[0231] ADDITIONAL ASPECTS OF THE DISCLOSURE

[0232] The following aspects are provided as examples in accordance with the disclosure herein and are not intended to limit the scope of the disclosure.

[0233] Aspect 1. A cube-based locationing system configured to store cube-based spatiotemporal data, the cube-based locationing system comprising: one or more processors; a memory communicatively coupled to the one or more processors; a database communicatively coupled to the one or more processors and storing a cube-based data model defining a plurality of cubes each having cube-based dimensions within a cube-based coordinate space mapped to a target environment, wherein the plurality of cubes comprises multiple layers of nested cubes comprising at least: a first cube having a first size, a second cube nested within the first cube and having a second size of a smaller measurement that the first size, and a third cube nested within the second cube and having a third size having a smaller measurement than the second size, and wherein the cube-based data model implements a nested cube navigation format for selecting a cube from the multiple layers of nested cubes; and a cube locationing application programming interface (API) 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, radio frequency (RF) signal data; determine first spatial coordinate data within the target environment that are associated with the RF signal data and first time data that is associated with the RF signal data; determine, based on the first spatial coordinate data, a first precision indication for one or more first target cubes of the plurality of cubes in which to store the RF signal data, wherein the one or more first target cubes defines a first cube-based coordinate value defining a first position of the one or more first target cubes within the cube-based coordinate space; and invoke the cube locationing API toPATENT APPLICATION Attorney Docket No.: 34014-70815-PC access the cube-based data model with the nested cube navigation format to: store the RF signal data as payload data of the one or more first target cubes based on the first precision indication, and set a temporal value of the payload data the one or more first target cubes based on the first time data.

[0234] Aspect 2. The cube-based locationing system of Aspect 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: receive, from a computing device, a request for data corresponding to a target position in the target environment, the request comprising a second precision indication, wherein the second precision indication points to the one or more first target cubes within the cube based coordinate space; invoke, based on the request, the cube locationing API to access the cube-based data model with the nested cube navigation format to select the one or more first target cubes from the multiple layers of nested cubes based on the second precision indication; and return, to the computing device, the RF signal data.

[0235] Aspect 3. The cube-based locationing system of any of Aspects 1 or 2, wherein the computing instructions, when executed by the one or more processors, further cause the one or more processors to: receive, from the computing device, GPS signal data and environmental data; determine second spatial coordinate data within the target environment that are associated with the GPS signal data and second time data that is associated with the GPS signal data; determine third spatial coordinate data within the target environment that are associated with the environmental data and third time data that is associated with the environmental data; determine, based on the second spatial coordinate data, a second precision indication for one or more second target cubes of the plurality of cubes in which to store the GPS signal data, wherein the one or more second target cubes defines a second cube-based coordinate value defining a second position of the one or more second target cubes within the cube-based coordinate space; determine, based on the second spatial coordinate data, a third precision indication for one or more third target cubes of the plurality of cubes in which to store the environmental data, wherein the one or more third target cubes defines a third cube-based coordinate value defining a third position of the one or more third target cubes within the cube-based coordinate space; invoke the cube locationing API to access the cube-based data model with the nested cube navigation format to: store the GPS signal data as payload data of the one or second more target cubes based on the second precision indication, store the environmental data as payload data ofPATENT APPLICATION Attorney Docket No.: 34014-70815-PC the one or third more target cubes based on the third precision indication, set a temporal value of the payload data of the one or more second target cubes based on the second time data, and set a temporal value of the payload data of the one or more third target cubes based on the third time data.

[0236] Aspect 4. The cube-based locationing system of any of Aspects 1-3, wherein when the second spatial coordinate data and the third spatial coordinate data include overlapping portions that at least partially overlap with the first spatial coordinate data, the instructions, when executed by the one or more processors, further cause the one or more processors to: determine the second precision indication and the third precision indication such that at least one of the one or more second more target cubes and the one or more third target cubes is a shared cube also present in the one or more first target cubes, the shared cube having a location in the cube based coordinate space that maps to the overlapping portions within the target environment; and invoke the cube locationing API to access the cube-based data model with the nested cube navigation format to: store a portion of the GPS signal data associated with the overlapping portions as first additional payload data of the shared cube based on the second precision indication, store a portion of the environmental data associated with the overlapping portions as second additional payload data of the shared cube based on the third precision indication, set a temporal value of the first additional payload data of the shared cube based on the second time data, and set a temporal value of the second additional payload data of the shared cube based on the third time data.

[0237] Aspect 5. The cube-based locationing system of any of Aspects 1-4, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: receive, from a computing device, a request for data corresponding to a target position in the target environment, the request comprising a fifth precision indication, wherein the firth precision indication points to the shared cube within the cube based coordinate space; invoke, based on the request, the cube locationing API to access the cube-based data model with the nested cube navigation format to select the shared cube from the multiple layers of nested cubes based on the fifth precision indication; and return, to the computing device, the RF signal data, the GPS signal data, and the environmental data.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0238] Aspect 6. The cube-based locationing system of any of Aspects 1 -5 wherein: the first spatial coordinate data corresponds to a location of the computing device within the target environment at a first time when computing device received the RF signal data; and the first temporal data indicates the first time.

[0239] Aspect 7. The cube-based locationing system of any of Aspects 1-6 wherein: the first spatial coordinate data corresponds to a location of a source device for the RF signal data within the target environment at a first time when the source device initiated the RF signal data; and the first temporal data indicates the first time.

[0240] Aspect 8. The cube-based locationing system of any of Aspects 1-7, wherein the nested cube navigation format comprises a left-bottom-front (LBF) format, 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 one or more first target cubes (e.g., 210c4n2 at "555.888").

[0241] Aspect 9. The cube-based locationing system of any of Aspects 1-8, 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.

[0242] Aspect 10. The cube-based locationing system of any of Aspects 1-9, wherein each nested cube of the multiple layers of nested cubes that is nested within the one or more first target cubes inherits at least one of (1) spatial data, (2) the payload data that includes the RF signal data, or (3) the temporal value of the payload data from the one or more first target cubes.

[0243] Aspect 11. The cube-based locationing system of any of Aspect 1-10, wherein the multiple layers of nested cubes includes a nested layer having two nested cubes comprising a first nested cube and a second nested cube, and wherein storing data in the first nested cube prevents redundant storage of the data in the second nested cube.

[0244] Aspect 12. The cube-based locationing system of any of Aspects 1-11, wherein mapping the cube-based coordinate space to the target environment comprises mapping a center of the cube-based coordinate space to a geographic center of a planet.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC

[0245] Aspect 13. The cube-based locationing system of any of Aspects 1-12 wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: assign a Time United Location System Address (TULSA) code to the payload data of the one or more first target cubes when storing the RF signal data, wherein the TULSA code includes the first precision indication.

[0246] Aspect 14. The cube-based locationing system of any of Aspects 1-13 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.

[0247] Aspect 15. The cube-based locationing system of any of Aspects 1-14 wherein the second size is ten times smaller than the first size, and the third is ten times smaller than the second size.

[0248] Aspect 16. The cube-based locationing system of any of Aspects 1-15 wherein: the first spatial coordinate data includes a spatial error; and the one or more first target cubes define the payload data that comprises the RF signal data such that boundaries of the one or more first target cubes within the target environment contains a spatial region that is defined by the first spatial coordinate data and the spatial error.

[0249] Aspect 17. A method of storing cube-based spatiotemporal data, the method comprising: receiving, from a computing device, radio frequency (RF) signal data; determining first spatial coordinate data within a target environment of a cube-based data model that are associated with the RF signal data and first time data that is associated with the RF signal data, wherein: the cube-based data model defines a plurality of cubes each having cube-based dimensions within a cube-based coordinate space mapped to the target environment, the plurality of cubes comprise multiple layers of nested cubes comprising at least: a first cube having a first size, a second cube nested within the first cube and having a second size of a smaller measurement that the first size, and a third cube nested within the second cube and having a third size having a smaller measurement than the second size, and the cube-based data model implements a nested cube navigation format for selecting a cube from the multiple layers of nested cubes; determining, based on the first spatial coordinate data, a first precision indication for one or more first target cubes of the plurality of cubes in which to store the RF signal data,PATENT APPLICATION Attorney Docket No.: 34014-70815-PC wherein the one or more first target cubes defines a first cube-based coordinate value defining a first position of the one or more first target cubes within the cube-based coordinate space; and invoking a cube locationing API to access the cube-based data model with the nested cube navigation format to: store the RF signal data as payload data of the one or more first target cubes based on the first precision indication, and set a temporal value of the payload data the one or more first target cubes based on the first time data.

[0250] Aspect 18. The method of Aspect 17, further comprising: receiving, from the computing device, GPS signal data and environmental data; determining second spatial coordinate data within the target environment that are associated with the GPS signal data and second time data that is associated with the GPS signal data; determining third spatial coordinate data within the target environment that are associated with the environmental data and third time data that is associated with the environmental data; determining, based on the second spatial coordinate data, a second precision indication for one or more second target cubes of the plurality of cubes in which to store the GPS signal data, wherein the one or more second target cubes defines a second cube-based coordinate value defining a second position of the one or more second target cubes within the cube-based coordinate space; determining, based on the second spatial coordinate data, a third precision indication for one or more third target cubes of the plurality of cubes in which to store the environmental data, wherein the one or more third target cubes defines a third cube-based coordinate value defining a third position of the one or more third target cubes within the cube-based coordinate space; invoking the cube locationing API to access the cube-based data model with the nested cube navigation format to: store the GPS signal data as payload data of the one or second more target cubes based on the second precision indication, store the environmental data as payload data of the one or third more target cubes based on the third precision indication, set a temporal value of the payload data of the one or more second target cubes based on the second time data, and set a temporal value of the payload data of the one or more third target cubes based on the third time data.

[0251] Aspect 19. A tangible, non-transitory computer-readable medium storing instructions for storing cube-based spatiotemporal data, that when executed by one or more processors cause the one or more processors to: receive, from a computing device, radio frequency (RF) signal data; determine first spatial coordinate data within a target environment of a cube-based data model that are associated with the RF signal data and first time data that is associated with thePATENT APPLICATION Attorney Docket No.: 34014-70815-PC RF signal data, wherein: the cube-based data model defines a plurality of cubes each having cube-based dimensions within a cube-based coordinate space mapped to the target environment, the plurality of cubes comprise multiple layers of nested cubes comprising at least: a first cube having a first size, a second cube nested within the first cube and having a second size of a smaller measurement that the first size, and a third cube nested within the second cube and having a third size having a smaller measurement than the second size, and the cube-based data model implements a nested cube navigation format for selecting a cube from the multiple layers of nested cubes; determine, based on the first spatial coordinate data, a first precision indication for one or more first target cubes of the plurality of cubes in which to store the RF signal data, wherein the one or more first target cubes defines a first cube-based coordinate value defining a first position of the one or more first target cubes within the cube-based coordinate space; and invoke a cube locationing API to access the cube-based data model with the nested cube navigation format to: store the RF signal data as payload data of the one or more first target cubes based on the first precision indication, and set a temporal value of the payload data the one or more first target cubes based on the first time data.

[0252] Aspect 20. The tangible, non-transitory computer-readable medium of Aspect 19, wherein the instructions when executed by one or more processors cause the one or more processors to: receive, from the computing device, GPS signal data and environmental data; determine second spatial coordinate data within the target environment that are associated with the GPS signal data and second time data that is associated with the GPS signal data; determine third spatial coordinate data within the target environment that are associated with the environmental data and third time data that is associated with the environmental data; determine, based on the second spatial coordinate data, a second precision indication for one or more second target cubes of the plurality of cubes in which to store the GPS signal data, wherein the one or more second target cubes defines a second cube-based coordinate value defining a second position of the one or more second target cubes within the cube-based coordinate space; determine, based on the second spatial coordinate data, a third precision indication for one or more third target cubes of the plurality of cubes in which to store the environmental data, wherein the one or more third target cubes defines a third cube-based coordinate value defining a third position of the one or more third target cubes within the cube-based coordinate space; invoke the cube locationing API to access the cube-based data model with the nested cubePATENT APPLICATION Attorney Docket No.: 34014-70815-PC navigation format to: store the GPS signal data as payload data of the one or second more target cubes based on the second precision indication, store the environmental data as payload data of the one or third more target cubes based on the third precision indication, set a temporal value of the payload data of the one or more second target cubes based on the second time data, and set a temporal value of the payload data of the one or more third target cubes based on the third time data.

[0253] Aspect 21. The cube-based locationing system of any of aspects 1-16 wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: receive, from a computing device, a request to determine a location of a source device for the RF signal data, the request comprising a second precision indication, wherein the second precision indication points to the one or more first target cubes within the cube based coordinate space; invoke, based on the request, the cube locationing API to access the RF signal data within the cube-based data model with the nested cube navigation format by selecting the one or more first target cubes from the multiple layers of nested cubes based on the second precision indication; identify one or more source cubes of the plurality of cubes based on features of the RF signal data and the location of the one or more first target cubes within the cube based model; return, to the computing device, a precise location marker for the location of a source device, wherein the precise location marker is based on a position of the one or more source cubes within the cube-based coordinate space; and display an indication of the location of the source device within the target environment based on the precise location marker.

[0254] Aspect 22. The cube-based locationing system of aspect 21, wherein the features of the RF signal data include one or more of frequency, amplitude, direction of receipt, or signal strength.

[0255] Aspect 23. The cube-based locationing system of any of aspects 21 or 22, wherein the source device is at least one of a moving object, a vehicle, a satellite, an aircraft, a drone, an transceiver, a cellular tower, or a radio frequency broadcast tower.

[0256] Aspect 24. The method of any of Aspects 17 and 18, further comprising: receiving, from a computing device, a request for data corresponding to a target position in the target environment, the request comprising a second precision indication, wherein the second precision indication points to the one or more first target cubes within the cube based coordinate space;PATENT APPLICATION Attorney Docket No.: 34014-70815-PC invoking, based on the request, the cube locationing APT to access the cube-based data model with the nested cube navigation format to select the one or more first target cubes from the multiple layers of nested cubes based on the second precision indication; and returning, to the computing device, the RF signal data.

[0257] Aspect 25. The method of any of Aspects 17, 18, and 24, wherein when the second spatial coordinate data and the third spatial coordinate data include overlapping portions that at least partially overlap with the first spatial coordinate data, further comprising: determining the second precision indication and the third precision indication such that at least one of the one or more second more target cubes and the one or more third target cubes is a shared cube also present in the one or more first target cubes, the shared cube having a location in the cube based coordinate space that maps to the overlapping portions within the target environment; and invoking the cube locationing API to access the cube-based data model with the nested cube navigation format to: store a portion of the GPS signal data associated with the overlapping portions as first additional payload data of the shared cube based on the second precision indication, store a portion of the environmental data associated with the overlapping portions as second additional payload data of the shared cube based on the third precision indication, set a temporal value of the first additional payload data of the shared cube based on the second time data, and set a temporal value of the second additional payload data of the shared cube based on the third time data.

[0258] Aspect 26. The method of any of Aspects 17, 18, 24, and 25, further comprising: receiving, from a computing device, a request for data corresponding to a target position in the target environment, the request comprising a fifth precision indication, wherein the firth precision indication points to the shared cube within the cube based coordinate space; invoking, based on the request, the cube locationing API to access the cube-based data model with the nested cube navigation format to select the shared cube from the multiple layers of nested cubes based on the fifth precision indication; and returning, to the computing device, the RF signal data, the GPS signal data, and the environmental data.

[0259] Aspect 27. The method of any of Aspects 17, 18, and 24-26 wherein: the first spatial coordinate data corresponds to a location of the computing device within the target environmentPATENT APPLICATION Attorney Docket No.: 34014-70815-PC at a first time when computing device received the RF signal data; and the first temporal data indicates the first time.

[0260] Aspect 28. The method of any of Aspects 17, 18, and 24-27 wherein: the first spatial coordinate data corresponds to a location of a source device for the RF signal data within the target environment at a first time when the source device initiated the RF signal data; and the first temporal data indicates the first time.

[0261] Aspect 29. The method of any of Aspects 17, 18, and 24-28, wherein the nested cube navigation format comprises a left-bottom-front (LBF) format, 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 one or more first target cubes (e.g., 210c4n2 at "555.888").

[0262] Aspect 30. The method of any of Aspects 17, 18, and 24-29, 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.

[0263] Aspect 31. The method of any of Aspects 17, 18, and 24-30, wherein each nested cube of the multiple layers of nested cubes that is nested within the one or more first target cubes inherits at least one of (1) spatial data, (2) the payload data that includes the RF signal data, or (3) the temporal value of the payload data from the one or more first target cubes.

[0264] Aspect 32. The method of any of Aspects 17, 18, and 24-31, wherein the multiple layers of nested cubes includes a nested layer having two nested cubes comprising a first nested cube and a second nested cube, and wherein storing data in the first nested cube prevents redundant storage of the data in the second nested cube.

[0265] Aspect 33. The method of any of Aspects 17, 18, and 24-32, wherein mapping the cube-based coordinate space to the target environment comprises mapping a center of the cubebased coordinate space to a geographic center of a planet.

[0266] Aspect 34. The method of any of Aspects 17, 18, and 24-33, further comprising: assigning a Time United Location System Address (TULSA) code to the payload data of the onePATENT APPLICATION Attorney Docket No.: 34014-70815-PC or more first target cubes when storing the RF signal data, wherein the TULSA code includes the first precision indication.

[0267] Aspect 35. The method of any of Aspects 17, 18, and 24-24, 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.

[0268] Aspect 36. The method of any of Aspects 17, 18, and 24-35, wherein the second size is ten times smaller than the first size, and the third is ten times smaller than the second size.

[0269] Aspect 37. The method of any of Aspects 17, 18, and 24-36, wherein: the first spatial coordinate data includes a spatial error; and the one or more first target cubes define the payload data that comprises the RF signal data such that boundaries of the one or more first target cubes within the target environment contains a spatial region that is defined by the first spatial coordinate data and the spatial error.

[0270] Aspect 38. The tangible, non-transitory computer-readable medium of any of Aspects 19 or 20, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: receive, from a computing device, a request for data corresponding to a target position in the target environment, the request comprising a second precision indication, wherein the second precision indication points to the one or more first target cubes within the cube based coordinate space; invoke, based on the request, the cube locationing API to access the cube-based data model with the nested cube navigation format to select the one or more first target cubes from the multiple layers of nested cubes based on the second precision indication; and return, to the computing device, the RF signal data.

[0271] Aspect 39. The tangible, non-transitory computer-readable medium of any of Aspects 19, 20, or 38, wherein when the second spatial coordinate data and the third spatial coordinate data include overlapping portions that at least partially overlap with the first spatial coordinate data, the instructions, when executed by the one or more processors, further cause the one or more processors to: determine the second precision indication and the third precision indication such that at least one of the one or more second more target cubes and the one or more third target cubes is a shared cube also present in the one or more first target cubes, the shared cube having a location in the cube based coordinate space that maps to the overlapping portions withinPATENT APPLICATION Attorney Docket No.: 34014-70815-PC the target environment; and invoke the cube locationing API to access the cube-based data model with the nested cube navigation format to: store a portion of the GPS signal data associated with the overlapping portions as first additional payload data of the shared cube based on the second precision indication, store a portion of the environmental data associated with the overlapping portions as second additional payload data of the shared cube based on the third precision indication, set a temporal value of the first additional payload data of the shared cube based on the second time data, and set a temporal value of the second additional payload data of the shared cube based on the third time data.

[0272] Aspect 40. The tangible, non-transitory computer-readable medium of any of Aspects 19, 20, 38, or 39, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: receive, from a computing device, a request for data corresponding to a target position in the target environment, the request comprising a fifth precision indication, wherein the firth precision indication points to the shared cube within the cube based coordinate space; invoke, based on the request, the cube locationing API to access the cube-based data model with the nested cube navigation format to select the shared cube from the multiple layers of nested cubes based on the fifth precision indication; and return, to the computing device, the RF signal data, the GPS signal data, and the environmental data.

[0273] Aspect 41. The tangible, non-transitory computer- readable medium of any of Aspects 19, 20, or 38-40 wherein: the first spatial coordinate data corresponds to a location of the computing device within the target environment at a first time when computing device received the RF signal data; and the first temporal data indicates the first time.

[0274] Aspect 42. The tangible, non-transitory computer-readable medium of any of Aspects 19, 20, or 38-41, wherein: the first spatial coordinate data corresponds to a location of a source device for the RF signal data within the target environment at a first time when the source device initiated the RF signal data; and the first temporal data indicates the first time.

[0275] Aspect 43. The tangible, non-transitory computer-readable medium of any of Aspects 19, 20, or 38-42, wherein the nested cube navigation format comprises a left-bottom-front (LBF) format, 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-PATENT APPLICATION Attorney Docket No.: 34014-70815-PC 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 one or more first target cubes (e.g., 210c4n2 at "555.888").

[0276] Aspect 44. The tangible, non-transitory computer-readable medium of any of Aspects 19, 20, or 38-43, 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.

[0277] Aspect 45. The tangible, non-transitory computer- readable medium of any of Aspects 19, 20, or 38-44, wherein each nested cube of the multiple layers of nested cubes that is nested within the one or more first target cubes inherits at least one of (1) spatial data, (2) the payload data that includes the RF signal data, or (3) the temporal value of the payload data from the one or more first target cubes.

[0278] Aspect 46. The tangible, non-transitory computer-readable medium of any of Aspects 19, 20, or 38-45, wherein the multiple layers of nested cubes includes a nested layer having two nested cubes comprising a first nested cube and a second nested cube, and wherein storing data in the first nested cube prevents redundant storage of the data in the second nested cube.

[0279] Aspect 47. The tangible, non-transitory computer-readable medium of any of Aspects 19, 20, or 38-46, wherein mapping the cube-based coordinate space to the target environment comprises mapping a center of the cube-based coordinate space to a geographic center of a planet.

[0280] Aspect 48. The tangible, non-transitory computer-readable medium of any of Aspects 19, 20, or 38-47, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: assign a Time United Location System Address (TULSA) code to the payload data of the one or more first target cubes when storing the RF signal data, wherein the TULSA code includes the first precision indication.

[0281] Aspect 49. The tangible, non-transitory computer-readable medium of any of Aspects 19, 20, or 38-48, 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.: 34014-70815-PC

[0282] Aspect 50. The tangible, non-transitory computer-readable medium of any of Aspects 19, 20, or 38-49, wherein the second size is ten times smaller than the first size, and the third is ten times smaller than the second size.

[0283] Aspect 51. The tangible, non-transitory computer- readable medium of any of Aspects 19, 20, or 38-50, wherein: the first spatial coordinate data includes a spatial error; and the one or more first target cubes define the payload data that comprises the RF signal data such that boundaries of the one or more first target cubes within the target environment contains a spatial region that is defined by the first spatial coordinate data and the spatial error.

[0284] ADDITIONAL CONSIDERATIONS

[0285] 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.

[0286] 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.

[0287] 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.: 34014-70815-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.

[0288] 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.

[0289] 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.

[0290] 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.: 34014-70815-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).

[0291] 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.

[0292] 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.

[0293] 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.

[0294] 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.: 34014-70815-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.

[0295] 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.

[0296] 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

1. PATENT APPLICATION Attorney Docket No.: 34014-70815-PC What is claimed is:

1. A cube-based locationing system configured to store cube-based spatiotemporal data, the cube-based locationing system comprising:one or more processors;a memory communicatively coupled to the one or more processors;a database communicatively coupled to the one or more processors and storing a cubebased data model defining a plurality of cubes each having cube-based dimensions within a cube-based coordinate space mapped to a target environment,wherein the plurality of cubes comprises multiple layers of nested cubes comprising at least: a first cube having a first size, a second cube nested within the first cube and having a second size of a smaller measurement that the first size, and a third cube nested within the second cube and having a third size having a smaller measurement than the second size, and wherein the cube-based data model implements a nested cube navigation format for selecting a cube from the multiple layers of nested cubes; anda cube locationing application programming interface (API) 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, radio frequency (RF) signal data; determine first spatial coordinate data within the target environment that are associated with the RF signal data and first time data that is associated with the RF signal data;determine, based on the first spatial coordinate data, a first precision indication for one or more first target cubes of the plurality of cubes in which to store the RF signal data, wherein the one or more first target cubes defines a first cube-based coordinate value defining a first position of the one or more first target cubes within the cube-based coordinate space; andinvoke the cube locationing API to access the cube-based data model with the nested cube navigation format to:store the RF signal data as payload data of the one or more first target cubes based on the first precision indication, andPATENT APPLICATION Attorney Docket No.: 34014-70815-PC set a temporal value of the payload data the one or more first target cubes based on the first time data.

2. The cube-based locationing system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:receive, from a computing device, a request for data corresponding to a target position in the target environment, the request comprising a second precision indication, wherein the second precision indication points to the one or more first target cubes within the cube based coordinate space;invoke, based on the request, the cube locationing API to access the cube-based data model with the nested cube navigation format to select the one or more first target cubes from the multiple layers of nested cubes based on the second precision indication; andreturn, to the computing device, the RF signal data.

3. 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 the computing device, GPS signal data and environmental data; determine second spatial coordinate data within the target environment that are associated with the GPS signal data and second time data that is associated with the GPS signal data;determine third spatial coordinate data within the target environment that are associated with the environmental data and third time data that is associated with the environmental data;determine, based on the second spatial coordinate data, a second precision indication for one or more second target cubes of the plurality of cubes in which to store the GPS signal data, wherein the one or more second target cubes defines a second cube-based coordinate value defining a second position of the one or more second target cubes within the cube-based coordinate space;determine, based on the second spatial coordinate data, a third precision indication for one or more third target cubes of the plurality of cubes in which to store the environmental data, wherein the one or more third target cubes defines a third cube-based coordinate value defining a third position of the one or more third target cubes within the cube-based coordinate space;PATENT APPLICATION Attorney Docket No.: 34014-70815-PC invoke the cube locationing API to access the cube-based data model with the nested cube navigation format to:store the GPS signal data as payload data of the one or second more target cubes based on the second precision indication,store the environmental data as payload data of the one or third more target cubes based on the third precision indication,set a temporal value of the payload data of the one or more second target cubes based on the second time data, andset a temporal value of the payload data of the one or more third target cubes based on the third time data.

4. The cube-based locationing system of claim 3, wherein when the second spatial coordinate data and the third spatial coordinate data include overlapping portions that at least partially overlap with the first spatial coordinate data, the instructions, when executed by the one or more processors, further cause the one or more processors to:determine the second precision indication and the third precision indication such that at least one of the one or more second more target cubes and the one or more third target cubes is a shared cube also present in the one or more first target cubes, the shared cube having a location in the cube based coordinate space that maps to the overlapping portions within the target environment; andinvoke the cube locationing API to access the cube-based data model with the nested cube navigation format to:store a portion of the GPS signal data associated with the overlapping portions as first additional payload data of the shared cube based on the second precision indication, store a portion of the environmental data associated with the overlapping portions as second additional payload data of the shared cube based on the third precision indication,set a temporal value of the first additional payload data of the shared cube based on the second time data, andset a temporal value of the second additional payload data of the shared cube based on the third time data.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC5. The cube-based locationing system of claim 4, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:receive, from a computing device, a request for data corresponding to a target position in the target environment, the request comprising a fifth precision indication, wherein the firth precision indication points to the shared cube within the cube based coordinate space;invoke, based on the request, the cube locationing API to access the cube-based data model with the nested cube navigation format to select the shared cube from the multiple layers of nested cubes based on the fifth precision indication; andreturn, to the computing device, the RF signal data, the GPS signal data, and the environmental data.

6. The cube-based locationing system of claim 1 wherein:the first spatial coordinate data corresponds to a location of the computing device within the target environment at a first time when computing device received the RF signal data; and the first temporal data indicates the first time.

7. The cube-based locationing system of claim 6 wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:receive, from a computing device, a request to determine a location of a source device for the RF signal data, the request comprising a second precision indication, wherein the second precision indication points to the one or more first target cubes within the cube based coordinate space;invoke, based on the request, the cube locationing API to access the RF signal data within the cube-based data model with the nested cube navigation format by selecting the one or more first target cubes from the multiple layers of nested cubes based on the second precision indication;identify one or more source cubes of the plurality of cubes based on features of the RF signal data and the location of the one or more first target cubes within the cube based model;PATENT APPLICATION Attorney Docket No.: 34014-70815-PC return, to the computing device, a precise location marker for the location of a source device, wherein the precise location marker is based on a position of the one or more source cubes within the cube-based coordinate space: anddisplay an indication of the location of the source device within the target environment based on the precise location marker.

8. The cube-based locationing system of claim 7, wherein the features of the RF signal data include one or more of frequency, amplitude, direction of receipt, or signal strength.

23. The cube-based locationing system of claim 7, wherein the source device is at least one of a moving object, a vehicle, a satellite, an aircraft, a drone, an transceiver, a cellular tower, or a radio frequency broadcast tower.

9. The cube-based locationing system of claim 1 wherein:the first spatial coordinate data corresponds to a location of a source device for the RF signal data within the target environment at a first time when the source device initiated the RF signal data; andthe first temporal data indicates the first time.

10. The cube-based locationing system of claim 1, wherein the nested cube navigation format comprises a left-bottom-front (LBF) format, 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 one or more first target cubes (e.g., 210c4n2 at “555.888”).

11. The cube-based locationing system of claim 10, 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.PATENT APPLICATION Attorney Docket No.: 34014-70815-PC 12. The cube-based locationing system of claim 1 , wherein each nested cube of the multiple layers of nested cubes that is nested within the one or more first target cubes inherits at least one of (1) spatial data, (2) the payload data that includes the RF signal data, or (3) the temporal value of the payload data from the one or more first target cubes.

13. The cube-based locationing system of claim 12, wherein the multiple layers of nested cubes includes a nested layer having two nested cubes comprising a first nested cube and a second nested cube, and wherein storing data in the first nested cube prevents redundant storage of the data in the second nested cube.

14. The cube-based locationing system of claim 1, wherein mapping the cube-based coordinate space to the target environment comprises mapping a center of the cube-based coordinate space to a geographic center of a planet.

15. The cube-based locationing system of claim 1 wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:assign a Time United Location System Address (TULSA) code to the payload data of the one or more first target cubes when storing the RF signal data, wherein the TULSA code includes the first precision indication.

16. 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.

17. 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.

18. The cube-based locationing system of claim 1 wherein:the first spatial coordinate data includes a spatial error; andPATENT APPLICATION Attorney Docket No.: 34014-70815-PC the one or more first target cubes define the payload data that comprises the RF signal data such that boundaries of the one or more first target cubes within the target environment contains a spatial region that is defined by the first spatial coordinate data and the spatial error.

19. A method of storing cube-based spatiotemporal data, the method comprising: receiving, from a computing device, radio frequency (RF) signal data;determining first spatial coordinate data within a target environment of a cube-based data model that are associated with the RF signal data and first time data that is associated with the RF signal data, wherein:the cube-based data model defines a plurality of cubes each having cube-based dimensions within a cube-based coordinate space mapped to the target environment, the plurality of cubes comprise multiple layers of nested cubes comprising at least: a first cube having a first size, a second cube nested within the first cube and having a second size of a smaller measurement that the first size, and a third cube nested within the second cube and having a third size having a smaller measurement than the second size, andthe cube-based data model implements a nested cube navigation format for selecting a cube from the multiple layers of nested cubes;determining, based on the first spatial coordinate data, a first precision indication for one or more first target cubes of the plurality of cubes in which to store the RF signal data, wherein the one or more first target cubes defines a first cube-based coordinate value defining a first position of the one or more first target cubes within the cube-based coordinate space; and invoking a cube locationing API to access the cube-based data model with the nested cube navigation format to:store the RF signal data as payload data of the one or more first target cubes based on the first precision indication, andset a temporal value of the payload data the one or more first target cubes based on the first time data.

20. The method of claim 19, further comprising:receiving, from the computing device, GPS signal data and environmental data;PATENT APPLICATION Attorney Docket No.: 34014-70815-PC determining second spatial coordinate data within the target environment that are associated with the GPS signal data and second time data that is associated with the GPS signal data;determining third spatial coordinate data within the target environment that are associated with the environmental data and third time data that is associated with the environmental data;determining, based on the second spatial coordinate data, a second precision indication for one or more second target cubes of the plurality of cubes in which to store the GPS signal data, wherein the one or more second target cubes defines a second cube-based coordinate value defining a second position of the one or more second target cubes within the cube-based coordinate space;determining, based on the second spatial coordinate data, a third precision indication for one or more third target cubes of the plurality of cubes in which to store the environmental data, wherein the one or more third target cubes defines a third cube-based coordinate value defining a third position of the one or more third target cubes within the cube-based coordinate space; invoking the cube locationing API to access the cube-based data model with the nested cube navigation format to:store the GPS signal data as payload data of the one or second more target cubes based on the second precision indication,store the environmental data as payload data of the one or third more target cubes based on the third precision indication,set a temporal value of the payload data of the one or more second target cubes based on the second time data, andset a temporal value of the payload data of the one or more third target cubes based on the third time data.

21. A tangible, non-transitory computer-readable medium storing instructions for storing cube-based spatiotemporal data, that when executed by one or more processors cause the one or more processors to:receive, from a computing device, radio frequency (RF) signal data;PATENT APPLICATION Attorney Docket No.: 34014-70815-PC determine first spatial coordinate data within a target environment of a cube-based data model that are associated with the RF signal data and first time data that is associated with the RF signal data, wherein:the cube-based data model defines a plurality of cubes each having cube -based dimensions within a cube-based coordinate space mapped to the target environment, the plurality of cubes comprise multiple layers of nested cubes comprising at least: a first cube having a first size, a second cube nested within the first cube and having a second size of a smaller measurement that the first size, and a third cube nested within the second cube and having a third size having a smaller measurement than the second size, andthe cube-based data model implements a nested cube navigation format for selecting a cube from the multiple layers of nested cubes;determine, based on the first spatial coordinate data, a first precision indication for one or more first target cubes of the plurality of cubes in which to store the RF signal data, wherein the one or more first target cubes defines a first cube-based coordinate value defining a first position of the one or more first target cubes within the cube-based coordinate space; andinvoke a cube locationing API to access the cube-based data model with the nested cube navigation format to:store the RF signal data as payload data of the one or more first target cubes based on the first precision indication, andset a temporal value of the payload data the one or more first target cubes based on the first time data.

22. The tangible, non-transitory computer- readable medium of claim 21, wherein the instructions when executed by one or more processors cause the one or more processors to: receive, from the computing device, GPS signal data and environmental data; determine second spatial coordinate data within the target environment that are associated with the GPS signal data and second time data that is associated with the GPS signal data;determine third spatial coordinate data within the target environment that are associated with the environmental data and third time data that is associated with the environmental data;PATENT APPLICATION Attorney Docket No.: 34014-70815-PC determine, based on the second spatial coordinate data, a second precision indication for one or more second target cubes of the plurality of cubes in which to store the GPS signal data, wherein the one or more second target cubes defines a second cube-based coordinate value defining a second position of the one or more second target cubes within the cube-based coordinate space;determine, based on the second spatial coordinate data, a third precision indication for one or more third target cubes of the plurality of cubes in which to store the environmental data, wherein the one or more third target cubes defines a third cube-based coordinate value defining a third position of the one or more third target cubes within the cube-based coordinate space; invoke the cube locationing API to access the cube-based data model with the nested cube navigation format to:store the GPS signal data as payload data of the one or second more target cubes based on the second precision indication,store the environmental data as payload data of the one or third more target cubes based on the third precision indication,set a temporal value of the payload data of the one or more second target cubes based on the second time data, andset a temporal value of the payload data of the one or more third target cubes based on the third time data.

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