Object messaging and intelligent objects (OMIO)

Abstract

An intelligent, self-aware system of objects is disclosed. An intelligent object may include structured data, comprising workflow parameters, and unstructured data, comprising messages, emails, multimedia, and the like. A stakeholder module may be configured to establish communication channels for stakeholder natural language dialog associated with the object. A generative artificial intelligence (AI) module may be embedded within the object. The AI module may be configured to process each object module's activity. It may facilitate natural language communication between the object, the stakeholders of the object, related objects, and related stakeholders associated with the related objects, in a designated communications channel based on stakeholder class. The AI module may transmit messages in the communications channels, update the structured data, and operate a deterministic code module to execute at least one action related to the object or related objects, thereby updating a state of a workflow in which the object operates.

Description

[0001] TITLE

[0002] OBJECT MESSAGING AND INTELLIGENT OBJECTS (OMIOJ

[0003] RELATED APPLICATIONS

[0004] The Applicant claims the benefit of U.S. Provisional Application No. 63 / 509,107 filed on June 20, 2023, which is incorporated herein by reference.

[0005] The Applicant claims the benefit of U.S. Provisional Application No. 63 / 553,704 filed on February 15, 2024, which is incorporated herein by reference.

[0006] The Applicant hereby incorporates by references PCT / US2023 / 70516 filed in the name of the Applicant, assigned to the assignee of the present application, is hereby incorporated by reference, in its entirety.

[0007] It is intended that each of the referenced applications may be applicable to the concepts and embodiments disclosed herein, even if such concepts and embodiments are disclosed in the referenced applications with different limitations and configurations and described using different examples and terminology.

[0008] FIELD OF DISCLOSURE

[0009] The present disclosure generally relates to the integration of artificial intelligence in operating system architecture.

[0010] BACKGROUND

[0011] The recent advancements in Al, particularly Large Language Models (LLMs) and their applications in textual, graphical, and audio processing, have generated considerable excitement. These innovations have shown great promise in creating more interactive, engaging, and intelligent systems. However, despite these remarkable achievements, a fundamental gap remains in the application of Al within the business context. Businesses predominantly rely on software applications for automation and process management, but these traditional applications often operate in a structured, rigid manner that does not seamlessly incorporate the fluid, intuitive capabilities of modern AL This disconnect presents a significant challenge: How can Al be meaningfully integrated into business applications to optimize communications between stakeholders, the objects their tracking, and the workflow they are a part of?

[0012] Another problem addressed by the present disclosure is that of intercommunication between desperate software systems. How can one software module interact with the data and functionality of another? By way of non-limiting example, one software application may not have an ace to another app, an API may not be provided, and the two applications may not be configured to interact with one another without additional development. The present disclosure presents a solution to this problem embodied within the OMIO architecture.

[0013] Yet another problem addressed by the present disclosure is that of intercommunication between desperate software systems. A user may require the use of a plurality of mobile apps, each serving a separate function and use, in order to execute an initiative. For example, a user may often need to search for a product on one app, check a financial account balance on another app, while making a purchase yet on another app).

[0014] BRIEF OVERVIEW

[0015] This brief overview is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This brief overview is not intended to identify key features or essential features of the claimed subject matter. Nor is this brief overview intended to be used to limit the claimed subject matter’s scope.

[0016] An intelligent, self-aware system of objects is disclosed. An intelligent object may include structured data, comprising workflow parameters, and unstructured data, comprising messages, emails, multimedia, and the like. A stakeholder module may be configured to establish communication channels for stakeholder natural language dialog associated with the object. A generative artificial intelligence (Al ) module may be embedded within the object. The Al module may be configured to process each object module’s activity. It may facilitate natural language communication between the object, the stakeholders of the object, related objects, and related stakeholders associated with the related objects, in a designated communications channel based on stakeholder class. The Al module may transmit messages in the communications channels, update the structured data, and operate a deterministic code module to execute at least one action related to the object or related objects, thereby updating a state of a workflow in which the object operates.

[0017] The object messaging and intelligent object (0MI0) system may include an intelligent object. The object may comprise structured data including attributes and parameters relating to the object. Unstructured data may comprise at least one of the following: messages, documents, emails, and multimedia content relating to the object. A computer-readable code module may comprise computer-readable instructions for executing an action relating to the object. A stakeholder module may be configured to establish communication channels for stakeholders associated with the object. A generative artificial intelligence (Al ] module may be embedded within the object. The Al module may be configured to train on each object module’s activity. The Al module may facilitate natural language communication between at least one of the following: the object, the stakeholders of the object, related objects, and related stakeholders associated with the related objects. The Al module may update the structured data of the object. The Al module may operate the computer-readable code module to execute at least one action.

[0018] The object messaging and intelligent object (OMIO) system may comprise an intelligent object. The intelligent object may include structured data comprising attributes and parameters relating to the object. It may also include unstructured data comprising at least one of the following: messages, documents, emails, and multimedia content relating to the object. Additionally, a computer-readable code module comprising computer-readable instructions for executing an action relating to the object may be included. A stakeholder module may be configured to establish communication channels for stakeholders associated with the object. Furthermore, a generative artificial intelligence (Al) module embedded within the object may be configured to train on each object module’s activity, facilitate natural language communication between the object, the stakeholders of the object, related objects, and related stakeholders associated with the related objects. The Al module may also update the structured data of the object and operate the computer-readable code module to execute at least one action.

[0019] The structured data may comprise parameters defining the object's state within the workflow. The Al module may comprise a large language and multi-modal model configured to analyze the unstructured data to update the object's state within the workflow. The Al module may be further configured to transmit messages, using natural language,within the communications channels. Additionally, the Al module may be further configured to autonomously update its state based on interactions within the communications channels of the objects and related communications channels within related objects. The object may further comprise at least one encoder and decoder configured to convert natural language inputs into computer-executable instructions for the computer-readable code module and vice versa. The encoder and decoder may enable the object to convert stakeholder inputs into modifications to the structured data via the computer-readable code module. The modifications to the structured data may cause changes to the object’s state within a workflow. The modifications to the structured data may cause changes to the related object’s structured data. The change to the related object's structured data may cause a change to the related object’s state within the workflow. The related stakeholder of the related object may be notified upon modifications to the structured data of the object and the unstructured data of the object. The related stakeholder may be notified via the related object’s Al model transmission of a message via the communications module of the related object into a relevant communications channel linked to the related stakeholder.

[0020] The stakeholder module maybe configured to identify stakeholders related to the object and manage communications with the identified stakeholders through a plurality of communication channels. Each communications channel may be designed by a stakeholder class and a privacy setting. The Al module may be configured to monitor activity data within its object and receive natural language updates associated with the related objects’ monitored activity data. The Al module may also transmit natural language activity data to the related objects. The Al module may be configured to update its attributes and parameters based on the activity associated with the object and the natural language updates with the related objects’ monitored activity data. The Al module may be further configured to manage interactions between the object and external applications. The Al module may be further configured to dynamically adapt operations within the computer-readable code module based on real-time, natural language data received from external applications. The Al module may be configured to employ machine learning techniques to continuously train on each object module’s activity. The Al module maybe further configured to execute predictive analytics based on historical data related to the object and generate predictive notificationsto the users about future states of the object. The stakeholder module may be configured to provide notifications related to object activity and related object activity relevant to each stakeholder. The notification may comprise a delivery of a user-editable interface of the underlying object itself.

[0021] An arbiter module may be operatively connected to the intelligent object. The arbiter module may be configured to manage communication and data flow between the intelligent object and the related objects. The arbiter module may further be configured to determine the optimal communication pathways for data exchanges between the object and the related objects. The arbiter module may utilize a large language model to facilitate data exchanges between the object and the related object in natural language. The arbiter module may be further configured to prioritize communication based on predefined criteria including relevance of information to the related objects. The arbiter module may comprise an encoder and decoder configured to convert natural language inputs into computerexecutable instructions for operating with the computer-readable code modules of the object and the related objects.

[0022] A super object may comprise a plurality of said intelligent objects. The super object may comprise a super group generative Al module configured to interface with the Al module associated with the plurality of intelligent objects within the super object. The super object may further comprise super groups structured data including aggregated attributes and parameters from the included intelligent objects. The super object may further comprise super objects functionality configured to execute combined actions based on the aggregated attributes and parameters of the included intelligent objects. The super object may further comprise super object’s unstructured data including aggregated unstructured data from the included intelligent objects. The super object may further comprise super objects stakeholders and relative objects configured to manage communication and interactions with stakeholders associated with the super object. The super group generative Al module may be configured to interface with the Al module of each of the intelligent objects within the super object. The Al module may be further configured to employ machine learning techniques to optimize operations and interactions within the super object. The super object may be configured to dynamically adapt its operations based on real-time data received fromthe included intelligent objects. The Al module may be configured to train on each object modules’ activity. The Al module may facilitate natural language communication between at least one of the following: each object within the super object and the stakeholders of each object. The Al module may update the structured data of the object. The Al module may operate the computer-readable code module to execute at least one action.

[0023] The super object may be configured to exchange natural language data between the included intelligent objects and external applications. The super object may comprise at least one arbiter module. The arbiter module may be configured to manage communication and data flow between the intelligent object and the related objects within the super object. The arbiter module may be further configured to determine the optimal communication pathways for data exchanges between the intelligent objects within the super object. The Al module may utilize a large language model to facilitate data exchanges between the intelligent object and the related object in natural language.

[0024] Each intelligent object may comprise an encoder and decoder configured to convert natural language inputs into computer-executable instructions for operating with the computer-readable code modules of the intelligent object and the related objects. The encoder and decoder may enable the intelligent object to autonomously update its state based on natural language instructions derived from natural language interactions within the communication channels established by the stakeholder module or supergroup stakeholder module. The Al module may be further configured to analyze interactions between the intelligent object and related objects to dynamically adapt communications and computer-executable operations within the intelligent object.

[0025] The arbiter module may be configured to interface with external applications to extend the functionality of the intelligent object based on received natural language inputs. An OMIO master object (OMIO OS) may be configured to facilitate user interaction with a plurality of super objects through a master Al module embedded within the OMIO OS. The master Al module may be configured to process user inputs in natural language and execute actions across the super object based on the processed inputs. The master Al model may be further configured to dynamically manage interactions between the super object and external applications or devices by translating natural language inputs from a user intoexecutable commands for the super object and the external applications or devices. The external applications may comprise database systems, cloud services, or other intelligent object systems. The master Al model may be configured to synchronize data between the intelligent object, super object, and the external applications to maintain data integrity and up-to-date information. The master Al model may employ machine learning techniques to continuously improve its data synchronization and communication strategies between the intelligent object and external applications.

[0026] The master LLM module may comprise an encoder and decoder configured to convert natural language inputs into computer-executable instructions for operating with the computer-readable code modules of the intelligent object and the related objects. The encoder and decoder may enable the OMIO OS to autonomously update the states of the various objects and super objects based on natural language instructions derived from natural language interactions within the communication channels established with at least one of the following: a user interfacing with master LLM module, the stakeholder module, and the supergroup stakeholder module.

[0027] The master Al module may be further configured to receive natural language inputs from the user. The inputs may comprise a request for information or action related to at least one object within the OMIO OS. The master Al module may process the user's natural language inputs to interpret user requirements and generate commands to retrieve information from at least one object within the OMIO OS related to the user's request. The commands generated by the master Al model may comprise instructions to fulfill the user’s request. The instructions may comprise instructions to structured data and unstructured data related to any intelligent object within the OMIO OS. The instructions may comprise instructions to perform operations by the computer-readable instructions module related to any intelligent object within the OMIO OS. The master Al module may utilize a database system to retrieve the information. The database system may comprise stored structured data related to multiple objects within the OMIO OS. The master Al module may further format the retrieved information into a natural language response and communicate the response to the user. The master Al module may be further configured to manage interactions between the object and external applications. The master Al module maydynamically adapt operations within the computer-readable code module based on realtime, natural language data received from the user and any object or super object within the 0M10 OS. The master Al module may coordinate with external applications to implement computer executable instructions based on a user's instructions. The external applications may comprise at least one of database systems, cloud services, or other intelligent object systems.

[0028] A method for processing natural language inputs to perform tasks across a plurality of applications within an OMIO OS may involve receiving a user input in natural language. The method may involve interpreting the user input to determine an intended action. The method may involve identifying at least one application relevant to the intended action. The method may involve transmitting instructions to the at least one application based on the interpreted user input. The method may involve receiving data from the at least one application. The method may involve generating a response to the user based on the data received from the at least one application. The method may involve providing the generated response to the user in natural language. The user input may be received via an audio interface. The user input may comprise spoken language.

[0029] Both the foregoing brief overview and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing brief overview and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.

[0030] BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure. The drawings contain representations of various trademarks and copyrights owned by the Applicant. In addition, the drawings may contain other marks owned by third parties and are being used for illustrative purposes only. All rights to various trademarks and copyrights represented herein, except those belonging to their respective owners, are vested in and the property of the Applicant. The Applicant retains and reserves all rights in its trademarks and copyrightsincluded herein, and grants permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.

[0032] Furthermore, the drawings may contain text or captions that may explain certain embodiments of the present disclosure. This text is included for illustrative, non-limiting, explanatory purposes of certain embodiments detailed in the present disclosure. In the drawings: The drawings may contain illustrations that depict various embodiments of the disclosed technology. These illustrations are provided to facilitate understanding of the technology and are not intended to be limiting. The following descriptions of the figures provide an overview of possible embodiments.

[0033] FIG. 1 illustrates a self-aware, intelligent object with embedded Al capabilities.

[0034] FIG. 2 illustrates multiple intelligent objects operating in relation to one another.

[0035] FIG. 3 illustrates a super object comprised of multiple interconnected intelligent objects.

[0036] FIG. 4 illustrate one embodiment of a computing architecture incorporating various aspects of the present disclosure.

[0037] FIG. 5 illustrates one embodiment of a mobile computing environment incorporating various aspects of the present disclosure.

[0038] DETAILED DESCRIPTION

[0039] As a preliminary matter, it will readily be understood by one having ordinary skill in the relevant art that the present disclosure has broad utility and application. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the disclosure and may further incorporate only one or a plurality of the abovedisclosed features. Furthermore, any embodiment discussed and identified as being “preferred” is considered to be part of a best mode contemplated for carrying out the embodiments of the present disclosure. Other embodiments also may be discussed for additional illustrative purposes in providing a full and enabling disclosure. Moreover, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, will be implicitly disclosed by the embodiments described herein and fall within the scope of the present disclosure.

[0040] Accordingly, while embodiments are described herein in detail in relation to one or more embodiments, it is to be understood that this disclosure is illustrative and exemplary of the present disclosure and are made merely to provide a full and enabling disclosure. The detailed disclosure herein of one or more embodiments is not intended, nor is to be construed, to limit the scope of patent protection afforded in any claim of a patent issuing here from, which scope is to be defined by the claims and the equivalents thereof. It is not intended that the scope of patent protection be defined by reading into any claim a limitation found herein that does not explicitly appear in the claim itself.

[0041] Thus, for example, any sequence(s) and / or temporal order of steps of various processes or methods that are described herein are illustrative and not restrictive. Accordingly, it should be understood that, although steps of various processes or methods may be shown and described as being in a sequence or temporal order, the steps of any such processes or methods are not limited to being carried out in any particular sequence or order, absent an indication otherwise. Indeed, the steps in such processes or methods generally may be carried out in various different sequences and orders while still falling within the scope of the present invention. Accordingly, it is intended that the scope of patent protection is to be defined by the issued claim(s) rather than the description set forth herein.

[0042] Additionally, it is important to note that each term used herein refers to that which an ordinary artisan would understand such a term to mean based on the contextual use of the term herein. To the extent that the meaning of a term used herein — as understood by the ordinary artisan based on the contextual use of such term — differs in any way from any particular dictionary definition of such term, it is intended that the meaning of the term as understood by the ordinary artisan should prevail.

[0043] Regarding applicability of 35 U.S.C. §112, ][6, no claim element is intended to be read in accordance with this statutory provision unless the explicit phrase "means for” or "step for” is actually used in such claim element, whereupon this statutory provision is intended to apply in the interpretation of such claim element.

[0044] Furthermore, it is important to note that, as used herein, “a” and “an” each generally denotes "at least one,” but does not exclude a plurality unless the contextual use dictates otherwise. When used herein to join a list of items, "or” denotes “at least one of theitems,” but does not exclude a plurality of items of the list. Finally, when used herein to join a list of items, “and” denotes “all of the items of the list.”

[0045] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While many embodiments of the disclosure may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the appended claims. The present disclosure contains headers. It should be understood that these headers are used as references and are not to be construed as limiting upon the subject matter disclosed under the header.

[0046] The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in a certain context, such as natural language communication between generative Al modules, embodiments of the present disclosure are not limited to use only in specified contexts. For example, the Al modules may interface in any language, including coding language, between each other - even though the present disclosure may reference only natural language communication.

[0047] This overview is provided to introduce a selection of concepts in a simplified form that are further described below. This overview is not intended to identify key features or essential features of the claimed subject matter. Nor is this overview intended to be used to limit the claimed subject matter’s scope.

[0048] The term “modules” or “component” may be used. Details with regards to each module is provided below. Although modules are disclosed with specific functionality, it should be understood that functionality may be shared between modules, with some functions split between modules, while other functions duplicated by the modules. Furthermore, the name of the module should not be construed as limiting upon the functionality of the module. Moreover, each component disclosed within each module can be considered independently without the context of the other components within the samemodule or different modules. Each component may contain language defined in other portions of this specifications. Each component disclosed for one module may be mixed with the functionality of another module. In the present disclosure, each component can be claimed on its own and / or interchangeably with other components of other modules.

[0049] The present disclosure provides examples of methods that may be performed by at least one of the aforementioned modules, or components thereof. Various hardware components may be used at the various stages of operations disclosed with reference to each module. For example, although methods may be described to be performed by a single computing device, it should be understood that, in some embodiments, different operations may be performed by different networked elements in operative communication with the computing device. For example, at least one computing device may be employed in the performance of some or all of the stages disclosed with regard to the methods. Similarly, an apparatus may be employed in the performance of some or all of the stages of the methods. As such, the apparatus may comprise at least those architectural components as found in various computing devices.

[0050] Furthermore, although methods are disclosed in a particular order, it should be understood that the order is disclosed for illustrative purposes only. Stages may be combined, separated, reordered, and various intermediary stages may exist. Accordingly, it should be understood that the various stages, in various embodiments, maybe performed in arrangements that differ from the ones claimed below. Moreover, various stages may be added or removed without altering or deterring from the fundamental scope of the depicted methods and systems disclosed herein.

[0051] Consistent with embodiments of the present disclosure, any method may be performed by at least one of the modules disclosed herein. The method may be embodied as, for example, but not limited to, computer instructions, which when executed, perform the method.I. Overview

[0052] The recent advancements in Al, particularly Large Language Models (LLMs) and their applications in textual, graphical, and audio processing, have generated considerableexcitement. These innovations have shown great promise in creating more interactive, engaging, and intelligent systems.

[0053] Technical ProblemsA. Interaction Between People and Objects in Workflow

[0054] However, despite these remarkable achievements, a fundamental gap remains in the application of Al within the business context. Businesses predominantly rely on software applications for automation and process management, but these traditional applications often operate in a structured, rigid manner that does not seamlessly incorporate the fluid, intuitive capabilities of modern Al. This disconnect presents a significant challenge: How can Al be meaningfully integrated into business applications to optimize communications between stakeholders, the objects their tracking, and the workflow they are a part of?

[0055] B. Interaction Within and Between Software Objects

[0056] Another problem addressed by the present disclosure is that of intercommunication between desperate software systems. How can one software module interact with the data and functionality of another? By way of non-limiting example, one software application may not have an ace to another app, an API may not be provided, and the two applications may not be configured to interact with one another without additional development. The present disclosure presents a solution to this problem embodied within the 0M10 architecture.

[0057] C. Interaction Between People and Multiple Objects (and Apps)

[0058] Yet another problem addressed by the present disclosure is that of intercommunication between desperate software systems. A user may require the use of a plurality of mobile apps, each serving a separate function and use, in order to execute an initiative. For example, a user may often need to search for a product on one app, check a financial account balance on another app, while making a purchase yet on another app). Adopting 0M10 into the apps and on operating system enables apps to communicate information through a central LLM, which can read, process, report, and take actions on the user's behalf across any application built on an OMIO-architecture operating system.

[0059] Technical Solutions

[0060] The Object Messaging and Intelligent Objects (OMIO) model, as presented in this disclosure, solves the aforementioned problems and more. OM1O provides a new paradigm integrating Al with conventional software, along with new way of thinking about Al and applications of LLM. OMIO ushers in a new era of intelligent software and give rise to collective intelligence.

[0061] The solution can be described in the context of various implementations. The present disclosure will detail four of those implementations.

[0062] The first implementation considers the transformation of legacy workflow based systems (use definition from prior filings) into an 0M10 adapted system. This may be formed on the basis of identifying Pivot Points and Critical entities, as described in (A) below.

[0063] The second implementation considers the creation of a self-aware, Intelligent Object as will be described in (B) below. The self-ware object changes the paradigm of communications with stakeholders and other intelligent objects within a system of intelligent objects. The second implementation further provides a user interface that replaces the traditional communications model involving email, messaging applications, and commentary on objects.

[0064] The third implementation considers the intelligent objects progression through various states (e.g., a workflow or state machine), by the update of its attributes and parameters, based on its interactions with its stakeholders, its system, and its related intelligent objects. This third fourth is further detailed in (C) below.

[0065] The third implementation considers the building of an 0M10 complaint operating system from the ground-up, forming super-objects with LLM arbiters between the user, the operating system, and its apps. This OMIO OS will be described in (D) below.A. Work Flow Integration

[0066] Business processes are fundamentally deterministic state machines, characterized by a series of predefined steps that lead to specific outcomes based on certain inputs. These processes also involve understanding the intricate relationships between stakeholders and the critical entities within the workflow.

[0067] LLMs excel in processing and generating natural language, making them incredibly powerful tools for tasks that involve human language comprehension and production. However, business workflows require more than just language understanding; they necessitate a deep comprehension of the specific logic and rules that govern the workflow's progression. Deterministic state machines operate on the basis of clear, logical pathways that lead from one state to another, often involving conditional logic that LLMs are not inherently designed to manage.

[0068] Moreover, the complexity of business workflows extends beyond the mere sequence of actions. It encompasses an understanding of the roles and relationships between different stakeholders, as well as their interactions with various entities within the workflow. Each stakeholder has specific rights, responsibilities, and needs that must be accurately understood and managed throughout the workflow process. This level of understanding requires more than natural language processing; it demands contextual awareness and a dynamic adaptability to the evolving nature of the business environment. Current automation tools, designed specifically to handle such deterministic processes and relational dynamics, already effectively manage these aspects. They are built with the explicit purpose of capturing and enforcing the logic, rules, and roles that define business workflows.

[0069] This is where the OMIO model offers a novel approach to integrating Al into workflow automation. Unlike LLMs, which primarily deal with language understanding and generation, the OMIO model embeds Al into every relevant object within the workflow. This means that instead of trying to manage the workflow through external analysis and intervention, the intelligence is built directly into the workflow's components. Each intelligent object is aware of its role, state, and the relevant stakeholders, allowing for more nuanced and effective management of the workflow. This object-centric approach enables a dynamic and context-aware system that can adapt to changes within the workflow, understand the complex inter-dependencies between different entities, and make informed decisions based on the embedded logic and data. In essence, the OMIO model recognizes that the solution to automating complex business workflows lies not in overarching language models but in imbuing the workflow itself with the intelligence necessary to navigate its intricacies.

[0070] The specific requirements of these workflows — logical progression, stakeholder relationships, and entity inter-dependencies — are better served by models designed to operate within these constraints. The OM1O model represents a compelling alternative, offering a way to integrate Al directly into the fabric of the workflow, thus providing a more effective and context-aware approach to automation. i. Pivot Point and Critical Entities

[0071] Embodiments of the present disclosure provide a method that may begin with an in-depth semantic analysis. The semantic analysis may be used to identify key entities involved in the function, process, workflow, or the like - akin to components moving along an assembly line. These entities, representing both tangible and intangible objects, may be identified based on their subject to discussions among humans.

[0072] In various embodiments, other indicators may be used to identify key entities. For example, one indicator may be that an object is, for example, the smallest divisible component in a system, to which parameters, if adjusted, make an impact on the system. As another example, the analysis may consider each object’s interdependence with other entities and stakeholders, as well be discussed more in detailed below. FIG. 1 illustrates one embodiment of an object 100. These objects may be, in turn, selected as critical entities serving as pivot points in a process. Accordingly, the terms objects, entities, critical entities, and pivot points, may be used interchangeably throughout the present disclosure.

[0073] The entities may be part of a workflow or state machine. The entities may comprise parameters defining their state within a workflow or state machine.

[0074] In some embodiments, the entities may be grouped into larger entities. For instance, a task may be grouped into a project, which may be grouped into a release. This may be referred to as 'objects’ and related 'super objects', as will be explored further below.

[0075] Each of the objects and super object may be part of their own workflow, with their own stakeholders. Each of the stakeholders may provide commentary and discussion on a channel of communication allocated to the object or super object, in either public or private channel types. Channels may be allocated based on type (e.g., public or private) and associated with a stakeholder class. The object or super object itself may join in on thediscussion in its channel. This relates to the object’s self-awareness, as well as any related object networked thereto.

[0076] Additional information with regard to pivot points is provided in PCT / US2023 / 70516.

[0077] B. Intelligent Objects and Stakeholder Communications Channels i. LLM

[0078] Built into the framework of a software architecture, OMIO provides self-aware objects comprising an LLM integrated into the object, as an arbitrator of all data and activities relating thereto.

[0079] Objects or entities are not only the pivot of connection for the relevant stakeholders but are always aware of their own states and are responsible to maintain all the information about themselves. The LLM integration enables this awareness.

[0080] In the 0M10 model, software designers identify key entities or objects and establish one or more dedicated communication channels for each. This approach integrates human communication as an inherent feature of the object. Through these channels, objects can store diverse formats of information, including videos, audio, and file attachments. As new data is added to these channels, it is simultaneously fed into the object's LLM, ensuring the object remains up-to-date with its evolving status. The LLM utilizes the object's structured data to better understand the context of stakeholder conversations within its chat channels and interpret any additional data or media files, like video call transcripts.

[0081] This model forms the backbone of the entire application, with each crucial object equipped with its own chat channel and structured data. The LLM is connected to the object at a singular point within the application, enabling deep integration. The LLM thus becomes an active participant in every chat room for all the objects. Applications built on the OMIO framework consist of a network of intelligent objects or 'super components' (assembled objects), allowing the LLM to engage at every user interaction point. This represents what we term deep integration or Al fusion. Essentially, each object becomes a miniature LLM, trained on its specific data set that includes structured data, stakeholder dialogue, attachments, and a record of events and state changes across various channels. It analyzesY1channel conversations in real time and serves as a knowledgeable advisor to stakeholders in natural language.

[0082] When people interact with an object and affect any of its attributes or when the object changes state moving within the workflow, the LLM converts these events into messages and records them in their appropriate chat channels on behalf of the person or process responsible for the event. Messages or other types of information such as video / audio files, links or documents can be inserted into the object’s channel for informing stakeholders and the object itself. The object then utilizes Al to read, understand and remember the information from these files as well as keeping them organized for later access by stakeholders. There is no need for users to come up with their own elaborate ways of creating folders for organizing files. That’s the job of the object as the information belongs to the object and so it is processed, understood, remembered, and organized by the object itself. Any message insertion in the object’s chat channel triggers the routing of the object to all the stakeholders’ Inboxes associated with this object. In this way, the stakeholders are informed in real time of important events affecting their objects of interest and can react and intervene, if necessary, by posting their reactions, thoughts, or concerns in the same channel. One can imagine that the object is calling for a virtual asynchronous meeting amongst the stakeholders for each significant event affecting the object.

[0083] The OMIO model provisions for an encoder and a decoder within the LLM configuration. These components may convert natural language inputs into computerexecutable instructions, which in turn, may dictate the functionality of various applications and devices. This conversion facilitates the dynamic modification and updating of attributes and parameters associated with objects and super objects within the workflow. As updates occur, these changes may be processed by the object's LLM, ensuring the object maintains an accurate representation of its state.

[0084] Furthermore, these updates may trigger deterministic coding functions within the object or super object, enabling them to adapt their behavior in real time to meet the demands of the workflow. The encoder and decoder serve as critical intermediaries, translating user commands or object communications into actionable data that can influence the operational logic of the system.

[0085] In scenarios where an object's attributes are modified — whether through stakeholder interaction or automated processes — the encoder may translate these modifications into specific commands. These commands may then be executed by the object's deterministic functions, potentially altering the object's behavior or state in the workflow. The decoder may act in reverse, interpreting the object's state changes into understandable updates for stakeholders or other connected systems.

[0086] This bidirectional communication facilitated by the encoder and decoder ensures that all components within the OMIO model remain synchronized and responsive to changes. As a result, the system may dynamically adapt to evolving conditions, maintaining efficient operations and accurate information flow among stakeholders. This has great importance in the OMIO OS models explored in FIGS. 4 and 5.

[0087] This detailed management of attributes and operational logic underscores the adaptability and responsiveness of the OMIO model, positioning it as a valuable tool for enhancing workflow automation and stakeholder communication in complex business environments. By embedding Al directly into the workflow components, the OMIO model provides a robust framework for managing the intricate interdependencies and dynamic conditions typical of modern business processes.

[0088] ii. Stakeholder Based Communication Channels

[0089] Stakeholders are linked to these objects based on their relevance or involvement in the workflow, forming myriad micro-networks centered around these objects. This new central point optimizes communication among stakeholders connected to a specific object. When different objects merge to form a super component, the stakeholder associations from the original objects are transferred to the new entity. Consequently, stakeholders are kept informed about the super component's status through these inherited connections.

[0090] An object containing people’s chatter and any other information in any format about itself within its own appropriate channels can provide detailed context to an LLM for processing the conversation about the object. The structured data associated with the object is utilized as constraints for the processing of human chatter and other information about the object. The object’s structured data may also be used in ‘Prompt Engineering’ fashion to create the context for the LLM and a certain awareness as to its fundamental features beforefeeding it the information or channel chatters about the object. The object’s chat channels include stakeholder’s dialogs as well as the object’s critical events and changes of state in the form of natural language. The OM1O provides the identification of all critical objects and extends one or more dialog channels as an innate attribute of the object. This streamlines a deep Al integration since there exists only a few localized points for this purpose within the application and those points of connections are the actual objects. And once the Al is connected to the object, it has access to the structured data and natural language in its chat channels as well as any attachments such as documents, media files, and hyperlinks.

[0091] Furthermore, human dialog or additional information in various formats about the data fields do not exist within the application. Such dialogs are somewhat chaotically scattered all over emails and Slack or Teams’ various channels with arbitrary chosen names for channels, or email subjects. The 0M10 model deals with these shortcomings in an elegant and effective way to create a system composed of lots of tiny bespoke LLMs representing critical entities in the system wired together using business logic, aware of workflow and its stakeholders.

[0092] Each entity may comprise stakeholders that have an underlying interest or responsibility to an attribute, parameter, feature, function, discussion, etc. related to the key entity. The stakeholder may be a person, or it may be another entity that relies on an attribute or parameter of the other. Stakeholders may be identified based on their related entities.

[0093] The system may be configured to determine stakeholders during the semantic analysis. The system may be configured to determine stakeholders during the semantic analysis by evaluating the relationships and interactions outlined in textual data or metadata associated with the objects. This process utilizes semantic parsing to assess the relevance and role of various potential stakeholders in relation to specific critical entities or pivot points within the system. Through this analysis, the system may identify individuals, groups, or software entities that have a significant impact or dependency on the object’s attributes or parameters.

[0094] Following the identification of stakeholders, the system may be adapted to further refine the stakeholder categorization based on the intensity of interactions or dependenciesobserved during the semantic analysis. Each stakeholder may be associated with one or more categories, reflecting their involvement in the workflow or their impact on the object's lifecycle. These categories are dynamically generated and updated to accurately represent the current state of the workflow and the evolving roles of each stakeholder.

[0095] Additionally, the system may be structured to automatically assign communication and information channels to each identified stakeholder or stakeholder category. These channels serve as conduits for targeted information dissemination, ensuring that each stakeholder receives pertinent information related to the key entities or pivot points they are associated with. This assignment of channels is based on predefined rules or algorithms that consider the nature of the stakeholder's interaction with the object.

[0096] Furthermore, the semantic analysis may also contribute to the configuration of the channels’ privacy settings, determining whether a channel should be public or private based on the sensitivity of the information and the stakeholder’s role. This ensures that confidential information remains protected while still facilitating efficient communication among stakeholders.

[0097] In summary, the semantic analysis within the system plays a crucial role in identifying and categorizing stakeholders, assigning appropriate communication channels, and setting privacy levels, thereby enhancing the workflow management and object interactions within the 0M10 model.

[0098] Embodiments of the present disclosure may be further configured to designate distinct, unique communication and information channels to each entity, corresponding to different stakeholder categories within the organization. Consequently, all communication and information pertaining to an object are sorted by stakeholder class, each maintained within its dedicated channel and overseen by the entity itself.

[0099] This process is akin to an individual managing her interactions based on the nature of her relationships, such as differentiating between interactions with her mother and her boss. Channels may be private or may be public.

[0100] Importantly, these channels are inherent to the entity and not user generated. They are either pre-configured by software designers or autonomously established by the entity as necessary. Thus, these communication channels become intrinsic properties of theentities, much like the structured data elements that define and characterize them. The Object Messaging Model, coupled with Intelligent Objects, adeptly integrates structured data and natural language communication, incorporating additional resources like videos, audio, internet links, and identifying information about relevant stakeholders and human resources within the object. Utilizing its LLM, the object continually updates itself with this plethora of information, thereby relieving humans from the tasks of organizing and directing information flow within the system. Understanding its stakeholders, their roles, and their connection to the object, it efficiently manages the distribution of pertinent events and information promptly. This task, critical in timely informing the right stakeholders, has traditionally seen human involvement as the least reliable factor. OM1O revolutionizes this aspect.

[0101] Example One

[0102] Consistent with embodiments of the present disclosure, and by way of illustrative example, an object maybe a task in a project worked on by multiple stakeholders. The project can be considered a 'super object' consisting of multiple such tasks, each being a critical entity within the larger framework of the project. Each task, or object, may comprise various attributes or parameters which define its state within the workflow of the project. These parameters may be adjusted or updated by stakeholders, influencing the progression of the task through various states in the workflow.

[0103] As tasks progress and their attributes or parameters are updated by stakeholders, these changes may impact related tasks within the project. For instance, completion of a preliminary task may trigger a subsequent task to commence, effectively moving it to a new state within the project's workflow. This interdependent relationship between tasks ensures a coordinated progression through the project workflow, optimizing efficiency and resource allocation. Moreover, stakeholders’ dialog in the communications channels related to task may cause an LLM to read the dialog and decide to trigger an autonomous update to a corresponding attribute or parameter of the task or related task.

[0104] To facilitate communication and updates among stakeholders, each task may be connected to distinct, unique communication channels. These channels, specific to each task and / or stakeholder class, may be configured to notify relevant stakeholders about updatesor changes in the task's status. For example, when a stakeholder updates a parameter of a task, this update is communicated through the task’s dedicated channel, informing all stakeholders who are part of that channel about the change. This mechanism ensures that stakeholders receive timely and relevant notifications pertinent to their role within the project.

[0105] The notification process may employ an object messaging model, where each task within the project dynamically updates its state based on interactions with stakeholders and other related tasks. This model leverages intelligent objects, embedded with capabilities to process and communicate information autonomously. Each object, or task, continually assesses its status and broadcasts relevant updates through its communication channels, seamlessly integrating these updates into the workflow without requiring manual intervention.

[0106] Furthermore, the interlinking of tasks within the project allows for a cascading effect of updates. An adjustment in one task may necessitate modifications in another, linked task. This is managed autonomously by the intelligent objects, which analyze the dependencies and adjust the workflow accordingly. Stakeholders connected to the impacted tasks are notified through the respective communication channels, ensuring that all relevant parties are aware of the changes and can react if necessary.

[0107] This detailed interaction and autonomous management of tasks and notifications within a project framework exemplify the application of the OMIO model. This model not only enhances the efficiency of project management through intelligent automation but also ensures precise and targeted communication among stakeholders, reducing the noise and inefficiency associated with conventional project management tools. Through the integration of intelligent objects and dedicated communication channels, the 0M10 model provides a sophisticated, responsive, and adaptive system for managing complex projects with multiple stakeholders.

[0108] In embodiments of the present disclosure, an object may be represented as a task within a project, managed by multiple stakeholders. Each task may embody a set of parameters, attributes, and functionalities that define its state within a workflow. Such a taskmight be grouped under a project that is conceptualized as a 'super object', comprising multiple interrelated tasks.

[0109] Each task or object within this super object may have stakeholders or entities associated with it. These stakeholders may be notified of the task's progress and updates through communication channels that are integral to the object itself. When a stakeholder updates an attribute or parameter of a task, this action may trigger a progression of the task from one state to another in the workflow. This update mechanism is facilitated by the intelligent objects which are embedded with LLM capabilities, enabling them to process and react to changes autonomously.

[0110] Furthermore, the progression of a task may influence the status of another related task within the same project or super object. For instance, the completion of a preliminary task may enable the commencement of a subsequent task, thereby affecting its state and notifying its respective stakeholders. These updates are communicated through their dedicated channels, ensuring that all relevant parties are informed in real time.

[0111] This dynamic interaction between tasks within a super object ensures a seamless flow of information and operational efficiency. The updates are automatically propagated across the relevant tasks and stakeholders without requiring manual intervention, thus enhancing the overall responsiveness of the workflow system.

[0112] Moreover, the intelligent objects maintain a record of all communications and events related to them, stored within their respective channels. This repository of information is continually analyzed by the embedded LLM, which assists in maintaining an up-to-date context for each task and its progress within the workflow. Consequently, stakeholders receive notifications that are not only timely but also contextually relevant, allowing them to make informed decisions rapidly.

[0113] This structured yet flexible communication framework within the 0M10 model significantly reduces the complexity typically associated with managing large-scale projects involving multiple tasks and stakeholders. By automating the routing and timing of notifications based on the intelligent analysis of task states and stakeholder roles, the system ensures that all communications are pertinent and timely.

[0114] The described architecture not only streamlines project management but also leverages advanced Al capabilities to enhance the interactivity and adaptability of traditional workflow systems, making it a robust solution for modern business environments.

[0115] The OMIO-based user interface, notably, harnesses the potential of Al to dynamically manage and optimize workflow interactions. This interface acts as a sophisticated nexus, integrating tasks (objects) within a project (super object). Each task, treated as an object, may be associated with distinct attributes or parameters, which are essential for its progression through various states in the workflow. These states may reflect stages such as initiation, execution, review, and completion.

[0116] Example Two

[0117] One example may be an Electronic Medical Record which has lots of text within each encounter. Perhaps you can summarize the text but that is of very limited value. And there are better ways which may sound more like science fiction. Imagine an intelligent Electronic Medical Record where at every point of user interaction, the care provider can call up the Al for assistance and the LLM is fully aware of the situation and provides meaningful information about some aspect of the patient such as an x-ray or some lab result or an illness. And all of that interaction is using natural language and recorded within the Xray's private channel reserved just for care providers’ discussions. We call that deep integration because the Al is passive and awaits patiently until it is called upon to review the records and then communicate its opinion to the provider. Additional details disclosed with reference to [PCT / US23 / 70516],

[0118] iii. OMIO Inbox Based User Interface

[0119] Moreover, the 0M10 based user interface may provide updates to the users in realtime regarding the status of tasks or events, thereby facilitating prompt responses or adjustments necessary within their specific roles or responsibilities within a project.

[0120] Stakeholders, categorized according to their relevance to specific tasks, receive notifications (which are actual U1 representation of the underlying object itself) via their OMIO inbox about updates or modifications pertinent to their tasks. Opening such a notification takes the user directly to the object and enables them to interact with all the objects parameters, attributes, and communications channels.

[0121] For example, if a task parameter is updated — say, the completion percentage of a task is revised — the relevant stakeholders get notified. The notification consists of the actual task object being displayed in the appropriate tier of the stakeholder’s inbox. Rather than providing a traditional notification of the event, the actual object appears in the user’s inbox. The object appears in its appropriate tier (e.g., task, project, spring, deployment, and the like). See PCT / US23 / 70516 for additional disclosure.

[0122] This update may also trigger an evaluation of dependent or subsequent tasks within the project, potentially modifying their state or parameters based on the revised information from the prior task.

[0123] The project, conceptualized as a super object, comprises multiple interconnected tasks. Each task's progression influences the overall project trajectory. An update in a single task may adjust the workflow pathway of related tasks, emphasizing the interconnected nature of tasks within the super object. These relationships are governed by pre-set rules defined within the 0M10 architecture, which dictate how changes in one object's state or attributes affect others.

[0124] The notification system (e.g., the delivery of objects directly to the stakeholder) within the 0M10 inbox is tailored to ensure that stakeholders are made aware of changes that directly affect their roles or responsibilities. This system prioritizes efficiency by disseminating relevant information to appropriate parties, thus avoiding the clutter of irrelevant data. This targeted communication fosters a streamlined workflow environment where stakeholders can effectively collaborate and respond to the dynamic needs of the project.

[0125] When a stakeholder modifies a task attribute, this action is recorded and communicated across the 0M10 system. The integration of the LLM within each object allows for intelligent processing and dissemination of this information, ensuring that all related tasks within the super object are updated accordingly. The system then automatically notifies other stakeholders of the related tasks about the updates, maintaining synchronization across the project's workflow.

[0126] This methodical and automated communication mechanism within OMIO not only enhances operational efficiency but also reduces the likelihood of errors and delays typicallyassociated with manual updates and notifications. By embedding Al within each task and the overarching project, the OMIO model provides a robust framework for managing complex workflows, ensuring that each stakeholder is informed, engaged, and equipped to contribute effectively to the collective objectives of the project.

[0127] The stakeholder based communications approach in the present disclosure provides a new way to interface with the traditional email inbox, chat apps, and workflow tracking solutions, and the like. This new interface will eliminates the need to manual create channels, organize them, track communication with the channels, and manually integrate them with a workflow management software.

[0128] Providing a new OMIO-based interface will eliminate a user's getting messages and notifications that are not relevant to them, when they are not relevant, and reduce the steps a user is required to get the entire context of a communication with regard to an obj ect they are tracking in a workflow.

[0129] Referring back to the X-Ray example, one can also imagine yet another scenario where a patient’s x-ray has just arrived in the provider’s inbox and the Al within the x-ray itself has already examined it, read through the patient’s history and relevant lab results, alerted the specialist, researched the latest on the issue on authorized pre-designated sections of the Internet, sent a follow up appointment reminder email to the patient, inserted its comments into the appropriate private channel and is ready to have a discussion with the care provider and the specialist in the private channel all using natural language. We call that Al fusion. And that is what OMIO makes possible today.

[0130] C. Event Based Attribute Updates in Intelligent Objects

[0131] In this fashion, it’s the object’s responsibility to communicate the event information to the stakeholders as soon as the event occurs. This also solves the synchronization problem with the workflow. All the stakeholders are thus tuned to the heartbeat of the organization which is the tempo of the workflow. The events are usually changes in status or one of its attributes or they can be messages inserted into one of its channels by stakeholders. All discussions and occurrences of events are stored in the appropriate channel of the object and are maintained by the obj ect itself. And since the object is responsible for communicating any changes to its state in the form of natural languagemessage insertion into its channels, the communication happens in real time and only to the appropriate stakeholders.

[0132] This is a significant feature of this model as it maintains a totally optimized communication in the form of relevant recipients only and precise timing for the transmission of these objects to human nodes within the organization. The precise timing is in synchronicity with the resonating pulse or tempo of the workflow as the heartbeat of the organization. The significance of this timing is usually overlooked in the current automated forms of communication in the workplace as the routing and timing of information transfer is dependent on the least reliable agents within the network, namely humans!

[0133] Object Messaging relegates the routing and the timing of all significant events about the object to the object itself. As a result, the emerging automated world is composed of a collection of intelligent objects, or super objects as they get assembled, while they maintain all the conversations and event messages about themselves within appropriate channels as well as the list of all relevant stakeholders of various types. Even the insertion of a message is considered an event that causes the object to deliver a digital representation of itself along with its entire entourage (media, files, links, etc.) to the appropriate stakeholder’s inbox.

[0134] D. OMIO OS

[0135] Built into the framework of a software architecture, OMIO provides self-aware objects comprising an LLM integrated into the object, as an arbitrator of all data and activities relating to that object. As will be shown below, the object may be associated with stakeholders (and channels of communication associated therewith), structured data (attributes and parameters), unstructured data (images, audio, chatters), and a process / function (commands) by which it operates. The stakeholders may be other self- aware objects in the OMIO architecture, comprising their own LLM. Envisioning a graph, a plurality of networked OMIO self-aware, intelligent objects may form a 'super object' with its own LLM governing the channels of communication with stakeholders, structured data, unstructured data, and its own master process / function by which it operates. Similarly, entire applications may comprises an LLM for analyzing, reading, reporting, and acting on behalf of the application and the user. Said LLM may be in communication, via the masterLLM as part of its super object network, with other LLMs of related objects. The Master LLM passes down an instruction across the different apps to accomplish a desired function as may be, for example, dictated by a user. In this way, the Master LLM may enable a user to interface with the OMIO OS to perform any tasks as dictated by the user in natural language. In turn, the Master LLM may communicate, using natural language, down to the networked objects, as those networked objects (which form a part of the Master LLM's governed super object), have LLMs configured to communicate within themselves (self-ware, intelligent), as well as other stakeholders (e.g., other networked objects), all in natural language.

[0136] Collectively, the end product of this innovation is an ensemble of these self-aware intelligent objects or 'super objects', each embedded with an LLM that has thoroughly processed the object's structured data, associated attachments, and relevant discussions. These objects are primed to respond to any queries from human stakeholders as soon as they appear in their inboxes. They are adept at navigating through the workflow, autonomously determining their pace and timing at each stage, and conveying crucial information to their stakeholders in real-time. Encapsulated within a sophisticated messaging framework, these objects possess an advanced understanding of the application's nature and context, ensuring they are routed to the correct 'inbox', named appropriately according to the application's type and class. This overarching framework serves as the central 'brain' of the application, orchestrating and interacting with these intelligent entities or super components."

[0137] Encoders and decoders within the system may convert natural language into computer-readable instructions and commands. This functionality enables the execution of various actions such as attribute modifications and updates. These updates may be essential for adjusting the deterministic coding aspects integral to the objects' and super objects' functionality within the workflow.

[0138] The system may utilize encoders to interpret natural language inputs from users or stakeholders, converting these inputs into a format that can be executed by software applications or devices. Decoders may then translate the instructions back into natural language, enabling communication with other components of the system or feedback to theuser. Furthermore, the natural language outputs of the LLM itself may be fed into the encoders to cause certain commands to be executed.

[0139] Through this process, the system may dynamically update attributes and parameters of objects and super objects based on the interactions and commands received. Each object, being embedded with an LLM, can process these updates and adjust its behavior within the workflow accordingly.

[0140] Such updates may trigger a sequence of operations that align with the predetermined rules and logic defined in the system's code. This allows for a seamless flow of operations and enhances the adaptability of the system to changing conditions or requirements in the workflow.

[0141] This method of using encoders and decoders to integrate natural language processing capabilities into the system's operations ensures that the system remains flexible and responsive to user interactions and needs. It also supports a more intuitive and natural interaction between the system and its users, thereby improving user experience and system efficiency.

[0142] A user may interface with the Master LLM in order to interface with multiple applications seamlessly to execute a function. This interaction is facilitated by a central Large Language Model (LLM), known as the Master LLM, which is capable of interpreting user commands in natural language and translating these into actionable tasks across various applications.

[0143] The Master LLM may be configured to receive input from a user in the form of spoken or written language. This input may describe a desired action or query, such as requesting a financial report or scheduling a meeting. Upon receiving the user input, the Master LLM may analyze the content to understand the intent and the context of the request.

[0144] Once the intent is understood, the Master LLM may determine which applications are relevant to the request. For instance, if the request involves scheduling a meeting, the Master LLM may interact with calendar applications, email clients, and messaging apps. The Master LLM may then orchestrate the flow of information between these apps, ensuring that each app receives the necessary data to perform its part of the task.

[0145] The Master LLM may also be responsible for aggregating the responses from various applications. For example, after initiating interactions with the relevant apps to schedule a meeting, the Master LLM may collect confirmation from the calendar app, send out invitations via the email client, and notify the user through a messaging app. Throughout this process, the Master LLM maintains a central role, acting as an intermediary that translates and routes information between the user and the applications.

[0146] This orchestration by the Master LLM may remove the need for the user to manually interact with multiple apps to accomplish a single task, thereby streamlining the user experience. Additionally, by using natural language as the medium of communication, the Master LLM may facilitate a more intuitive and accessible way for users to interact with technology.

[0147] In this manner, the Master LLM enhances the functionality of the OMIO architecture by enabling efficient, context-aware interactions between the user and multiple applications through a single interface. This capability may be crucial in environments where quick and effective communication across different platforms is necessary.

[0148] Moreover, the Master LLM may be configured to operate hardware devices such as, by way of non-limiting example, printers, scanners, lights, and HVAC systems, autonomous vehicles, mobile computing devices (e.g., phones, wearables, AR / VR glasses, and the like), enhancing their functionality by enabling them to communicate directly with the OMIO architecture. The Master LLM may also manage the interaction between these hardware devices and software applications, ensuring seamless operation across different platforms and environments. This integration allows the Master LLM to act as a central hub, coordinating and optimizing device operations based on user commands or predefined workflows.

[0149] Additionally, the Master LLM may be configured to access and control security systems, such as surveillance cameras and alarm systems, by processing inputs from these devices and making decisions based on the context of the situation. For instance, the LLM may analyze video feeds to detect unauthorized access and automatically initiate appropriate security protocols.

[0150] The Master LLM's capability extends to managing networked devices in smart home or office setups. It may adjust lighting based on the time of day or occupancy, control temperature settings for optimal comfort and energy efficiency, and manage multimedia systems for presentations or entertainment purposes.

[0151] In a manufacturing context, the Master LLM may interface with industrial machinery, facilitating real-time adj ustments to production lines based on workflow analysis or immediate operational data. This interaction helps in optimizing manufacturing processes, reducing downtime, and enhancing product quality.

[0152] Furthermore, the Master LLM may be involved in managing transportation systems within a business context, such as scheduling and routing of delivery vehicles based on real-time traffic data and delivery priorities. This capability ensures efficient logistics operations, reducing delays and improving service delivery.

[0153] Each interaction or control decision made by the Master LLM involves processing a vast amount of data from various sources, analyzing it, and executing actions that are communicated back to the devices in a language or protocol they understand. This process exemplifies the integration of Al capabilities within practical applications, demonstrating the versatility and adaptability of the 0M10 model in enhancing device interoperability and operational efficiency across diverse sectors.

[0154] II. The Intelligent Object

[0155] Biological Systems Analogy

[0156] Before exploring the solution and the features of the Object Messaging Model, let's first conceptualize the problem abstractly, using the metaphor of a biological cell, which in many ways mirrors actual organizational structures more accurately than one might think. Contemporary organizations consist of human elements and workflows, typically structured in a hierarchical, pyramid-like formation. These workflows are modern abstractions of the early 20th-century assembly lines. Within these structures, human participants are grouped by specialized functions such as Production, Support, Sales, Marketing, and Administration, akin to organelles in a cell, all orbiting around the central workflow - the heart of the organization. These groups convene in a collective entity, or a 'superorganism,' with theunified goal of producing specific goods and services. Here, the hierarchical structure serves as the organization's skeleton, while the workflow is its pulsating heart.

[0157] Each group's capacity, much like organelles in a cell, hinges on the expertise and skill of its individual members. Crucially, though, the overall effectiveness of the organization relies on the timely sharing of pertinent information and the harmonization of these human elements with the workflow, or the objects moving within this process. It's important to note that not every member is directly involved with every aspect of the workflow. Products and services, being the culmination of various components assembled within this workflow, suggest a web of intricate connections among these parts and the respective group members. The obvious conclusion is that the real glue that binds everyone in the organization are the components, parts or the final product or products. And the conversations that matter in this setting are the ones related to the objects or virtual objects that are moving around within the workflows. And these conversations are innate attribute of the object itself. As described in the 3rd wave of corporate communication, these objects represent the new pivots of human communication in the workplace. The objects also have the responsibility of managing the variety of information about themselves as well as informing the stakeholders of significant events in a timely fashion.

[0158] Understanding the Object in Software Terms

[0159] III. Object Architecture

[0160] The Object Model

[0161] Figure 1

[0162] Figure 1 depicts an illustrative embodiment of an intelligent object containing structure and not structure data along with pointers to stakeholders. Each object is a small model trained on all its available data to give it self-awareness. This training continues as the object moves about the workflow and interacts with stakeholders and other objects. This information is transcribed into text and inserted into a predesignated channel according to the application’s rules. The text is also used to update the object itself of its new state as part of its continuous awareness. The logic then dictates the transmission of the object to the appropriate stakeholders in real time in order to keep them informed and seek feedback.

[0163] 100 - Object

[0164] An individual object 100, as described, may be an abstraction of software functionalities or representations of ideas or physical entities. This object may store data, execute code, perform functions, and adapt its parameters based on interactions within its environment. These modifications may occur as the object passes through various stages within a structured workflow or state-machine, ensuring that the object's attributes are consistently updated to reflect its current state. The object may be the smallest indivisible component in a domain.

[0165] Accordingly, an object refers to an abstraction of a set of software functionalities or representations of ideas or physical objects. The object may comprise data, code, a function, and modifiable parameters. The object may be operated on by its stakeholders (e.g., parameter modifications) as it moves through a workflow. Object, Pivot Points, and Entities may be used as interchangeable terms throughout this disclosure.

[0166] One fundamental premise of the 0M10 architecture is that objects can report on themselves, and can communicate with other stakeholders, as well as other objects. This communication between objects enables a new paradigm in software organizations.

[0167] The self-aware object may monitor and manage its own state, parameters, and attributes by continuously updating itself based on interactions and inputs received from its environment and stakeholders. Each interaction or significant event affecting the obj ect may be automatically logged and communicated to relevant stakeholders through the object’s associated channels.

[0168] Furthermore, the object can be part of a larger network of interconnected objects within the OMIO framework. Each object in this network may share data and interact with other objects to facilitate a collective intelligence across the system. This interconnectedness allows for a dynamic and responsive workflow where objects collectively manage and adapt to changes in real time.

[0169] As will be detailed, the object may employ an integrated, multi-modal LLM to analyze and interpret both structured and unstructured data, and train on each module of the object and its related objects. The LLM integration enables the object to understand complex data sets and generate actionable insights that can be communicated tostakeholders in natural language. The LLM integrated within the object may further assist in decision-making processes by providing recommendations based on data analysis.

[0170] 105 - Structured Data

[0171] Structured data may comprise parameters, attributes, state-machine / workflow parameters. The structured data may be used by the deterministic code of the object for the execution of functions. For example, structured data may be utilized by the deterministic code of an object to handle the execution of functions by associating the data with specific states or actions within a workflow or state machine. For instance, structured data such as parameters and attributes may be crucial in determining the next steps in a process flow or in making decisions about state transitions within the system.

[0172] Additionally, structured data may be dynamically updated based on interactions within the workflow or external inputs that affect the state or behavior of the object. These updates may trigger specific functions in the deterministic code, enabling the object to respond dynamically to changes and maintain its operation within the prescribed parameters of the workflow.

[0173] In some embodiments, structured data may also include metadata that describes the data or the object itself, such as data types, permissible values, or relationships with other data elements or objects. This metadata may be used by the deterministic code to validate inputs, enforce business rules, or ensure data integrity throughout the workflow.

[0174] Furthermore, the deterministic code may leverage structured data to interface with other software systems or components. For example, structured data can be formatted or packaged in a manner that is compatible with external systems, allowing for seamless data exchange and integration across different platforms or environments.

[0175] In the broader scope of the workflow or state machine, structured metrics may serve as checkpoints or milestones, guiding the progress of the object through various stages and ensuring that all necessary conditions are satisfied before proceeding to subsequent stages. This structured approach helps in maintaining the order and predictability of processes, which is essential in environments where precision and reliability are critical.

[0176] Overall, the interaction between structured data and deterministic code is fundamental in managing complex systems and processes, providing a framework for automation, control, and optimization in various applications.

[0177] 110 - Object Functionality

[0178] Object functionality may comprise, for example, computer readable code, functions, commands, and automated responses. In a specific embodiment, the system may utilize software code to dynamically adjust the parameters of an object based on the interactions with its stakeholders and other related objects within a workflow. This adjustment may be triggered by changes in the attributes of the object, which are monitored and analyzed by the integrated LLM.

[0179] The software code may further enable the object to execute specific functions within its operational environment. Moreover, the software code may include features that allow the object to participate in decision-making processes. This may involve analyzing incoming data, generating insights based on this data, and proposing actions based on these insights. The LLM integrated into the object may assist in this process by providing natural language processing capabilities, which can interpret and generate human-like responses to facilitate more intuitive interactions, to that object. The object may interact with these stakeholders through designated communication channels established within the OMIO architecture.

[0180] Additionally, the object may be equipped with an encoder and decoder mechanism. This mechanism may translate human-readable instructions into machineexecutable commands and vice versa. The encoder may take natural language inputs from the LLM and convert them into code or commands that can be executed by the object’s operational software. Conversely, the decoder may translate the outputs from the object’s operations into natural language, allowing for seamless communication between the object and its stakeholders.

[0181] By integrating these features into the OMIO architecture, the object becomes capable of performing autonomous operations, making informed decisions, and communicating effectively with its stakeholders, all while maintaining a high level of self- awareness and adaptability within its operational environment. This integration not onlyenhances the efficiency and responsiveness of business workflows but also bridges the gap between human interactions and machine operations, fostering a more intuitive and effective communication system within organizations.

[0182] Additionally, the object may be equipped with functionalities that enable it to autonomously manage its state within a workflow or state machine. This may involve updating its attributes in response to external events or internal conditions, thereby maintaining its relevance and effectiveness within the workflow. The software code responsible for these functionalities may be designed to ensure that all changes are logged and communicated to relevant stakeholders in real-time, enhancing transparency and trust within the system.

[0183] Accordingly, the system may leverage a combination of software code, LLM integration, and dynamic parameter adjustment to enhance the functionality of objects within a business workflow. This approach not only increases the efficiency and adaptability of the system but also fosters a more collaborative and responsive environment for all stakeholders involved.

[0184] 115 - Unstructured Data

[0185] The object may be associated with unstructured data, such as, by way of nonlimiting example, photos, videos, emails, messages, and other media forms which may not follow a predefined format or structure. This incorporation of unstructured data into the operational framework of an object enhances its ability to process and respond to a more diverse array of inputs and interactions.

[0186] In the context of the 0M10 architecture, unstructured data is handled through intelligent objects that utilize large language models (LLMs) to interpret and organize this data. Each object in the 0M10 system may be associated with various forms of unstructured data, which it can autonomously process using its embedded LLM capabilities. This processing may involve the extraction of relevant information, the interpretation of content within documents and messages, and the contextual understanding of multimedia inputs.

[0187] Furthermore, the objects may be enabled to perform semantic analysis on the unstructured data to identify and react to significant events or changes in the data that are relevant to the object’s functionality within a workflow or process. For instance, anintelligent object might analyze an email to determine its importance based on the content and context, then update its state or notify stakeholders as necessary.

[0188] The integration of unstructured data within the 0M10 model allows for a more comprehensive and holistic approach to data handling and object interaction. Objects may be configured to manage both structured and unstructured data, ensuring a seamless flow of information and maintaining the continuity of processes without the need for manual intervention.

[0189] By virtue of this model, objects become more than passive entities; they turn into active participants in the workflow, capable of making decisions and taking actions based on a complex mixture of structured directives and unstructured data insights. This capability significantly enhances the flexibility and adaptability of business workflows, making them more responsive to real-time changes and more aligned with the dynamic nature of modern business environments.

[0190] Objects in the 0M10 architecture may also interact with each other through the unstructured data they process. This interaction can lead to the formation of a networked intelligence where insights and data are shared seamlessly across objects, enhancing collective decision-making and efficiency.

[0191] Additionally, the architecture’s handling of untriggered data such as spontaneous stakeholder inputs and incidental multimedia content can provide a rich substrate for enhancing object intelligence. By continuously learning from a diverse set of unstructured data, each intelligent object may refine its understanding and improve its operational efficacy over time.

[0192] The versatility in handling unstructured data within the OMIO framework underscores its utility in diverse applications, ranging from business process automation to complex system management and user interaction enhancement, thereby providing a robust solution to the integration of Al in traditional and contemporary digital ecosystems.

[0193] 120 - Stakeholders and Relative Objects

[0194] The object may also be associated with specific stakeholders, each stakeholder potentially acting as a reference to other objects within the workflow or system. Private and public communication channels and sub-channels may be configured for these stakeholdersto facilitate tailored and secure exchanges of information. These channels serve to streamline interactions and ensure that each stakeholder receives only the most relevant data, enhancing the efficiency of communications. These features collectively contribute to the robustness of the system by ensuring that all interactions are both context-aware and appropriately directed, thereby minimizing information redundancy and enhancing the relevance of communications.

[0195] In some embodiments, a stakeholder may be a reference to another object within the system, serving as a dynamic link in the network of communications and operations managed by the OMIO architecture. This configuration facilitates a unified level of interaction where entities are interconnected in intricate, yet clearly defined, patterns of relationships.

[0196] Private and public channels or sub-channels maybe established for stakeholders, allowing for tailored communication pathways that align with the specific needs and privacy requirements of each stakeholder. Public channels may be accessible to a broader audience within the organization, facilitating transparency and collective input, whereas private channels may restrict access to specific stakeholders, ensuring confidentiality and focused discussions. These channels are fundamental to maintaining organized and efficient exchange of information, ensuring that each stakeholder receives relevant data pertinent to their role or interest in the workflow.

[0197] The relationships with relatives are defined, in part, by the connections established through these channels. Relatives, in this context, refer to other entities or objects within the system that have a defined relationship with the main object, whether through shared attributes, common stakeholders, or participation in the same process or workflow. By treating objects as stakeholders, the system may enhance the capability of objects to act autonomously, making decisions and initiating communications based on the system's overarching rules and logic. These relationships are crucial for the coherent operation of the system, as they help in maintaining the integrity and accuracy of information flow across the network of intelligent objects.

[0198] Each of these elements contributes to a sophisticated and dynamic architecture where intelligent objects are not only aware of their state and functionality but are alsocapable of engaging with their environment in a manner that mimics intelligent, context- aware behavior. This system architecture facilitates a seamless and effective communication process that enhances the operational efficiency and responsiveness of business workflows.

[0199] A stakeholder may be a reference to another object within the system. This linkage may facilitate the dynamic interaction between different objects or entities, enabling a more integrated and responsive workflow environment. The relationships with relatives, or related objects, may be defined, in part, by the connections established through the system's architecture. These relationships may be instrumental in forming a coherent network of interactions and dependencies, which may be critical for the efficient functioning of the workflow. The system may utilize these relationships to optimize the flow of information and ensure that all relevant stakeholders are timely and appropriately informed about the developments within the workflow.

[0200] These features may collectively contribute to a robust framework where the flow of information is meticulously managed and tailored according to the specific needs and roles of each stakeholder involved in the workflow. This may lead to enhanced operational efficiency, reduced redundancy, and improved decision-making capabilities within the organization.

[0201] The OMIO model may provide a system that integrates artificial intelligence (Al ) into business workflows through intelligent objects which are capable of self-awareness and autonomous communication. These objects maybe embedded with a Large Language Model (LLM) that facilitates their interactions within a network of similar objects and stakeholders.

[0202] The intelligent objects may be designed to participate actively in the workflows by processing and responding to the information dynamically. Each object may possess structured data that defines its attributes and state, as well as unstructured data such as messages, documents, and multimedia content. This data may be continuously updated and managed through interactions with stakeholders and other intelligent objects.

[0203] Stakeholders, categorized into different classes based on their roles and interests, may interact with the intelligent objects via dedicated communication channels. These channels may be configured automatically by the object itself or predefined by system designers. The communication within these channels may be facilitated by the object's LLM,which can interpret and generate natural language responses. In this way, users no longer have to manually create channels or manually join channels.

[0204] 125 - Al Model

[0205] The system may be capable of identifying and responding to events that affect the state or attributes of an object. These events may trigger updates to the object's data and notifications to relevant stakeholders through the communication channels. This mechanism ensures that all stakeholders are promptly informed about changes that are relevant to their interests or responsibilities. The system may further include a variety of models such as LLM, GPT, CLAUDE, or Multi-Model Model configurations, which may be integrated into each object to enhance its functionality. These models may process natural language inputs, generate outputs, and interact with both structured and unstructured data associated with the object. This integration may enable the object to perform complex data analysis and decision-making processes autonomously.

[0206] Each objectin the system maybe equipped with an encoder / decoder module. This module may translate natural language inputs into computer-executable instructions, which may be used to control other applications or devices within the system. Similarly, outputs from applications or devices may be converted into natural language, facilitating seamless communication between the object and its stakeholders.

[0207] Furthermore, the system may use these Al models to manage and update the object's attributes and parameters based on interactions within its environment. For instance, changes in data received from interconnected devices or updates in a linked database may trigger the Al model to adjust the object's attributes accordingly. This dynamic update mechanism ensures that the object remains relevant and accurate in real-time.

[0208] In addition to managing data and executing commands, the Al models within each object may also handle communications. This may involve interpreting and organizing incoming messages, documents, emails, and multimedia files, storing them in a structured manner that aligns with the object's current state and data parameters.

[0209] The objects, through their integrated Al models, may also engage in proactive communication with stakeholders. This may involve sending alerts, updates, or recommendations based on the analysis of accumulated data and ongoing interactions.These communications may be routed through predefined channels that are associated with specific stakeholders or stakeholder groups, ensuring that information reaches the appropriate parties efficiently.

[0210] Each object may thus act as a central node in a network of interactions, where it not only responds to external inputs but also anticipates needs and coordinates actions across various elements of the system. This proactive capability may be critical in environments where timely information exchange and decision-making are crucial.

[0211] Accordingly, the system may provide a comprehensive framework where each object is enhanced with Al capabilities, allowing it to process and interact with a wide range of data types, execute complex functions, and communicate effectively with its environment. This may lead to improved efficiency, accuracy, and responsiveness in systems that require dynamic data handling and sophisticated interaction capabilities.

[0212] Detailed Description of the Obj ect

[0213] Referring first to FIG. 1, an individual object, as described, may be an abstraction of software functionalities or representations of ideas or physical entities. This object may store data, execute code, perform functions, and adapt its parameters based on interactions within its environment. These modifications may occur as the object passes through various stages within a structured workflow or state-machine, ensuring that the object's attributes are consistently updated to reflect its current state.

[0214] Such an object may communicate with stakeholders through designated channels that are either predefined by system architects or autonomously established by the object itself, based on the needs identified during its operation. These channels may facilitate the exchange of messages, documents, or multimedia content, which are essential for keeping all relevant stakeholders informed about the object's status and any changes occurring therein.

[0215] Each object within the system may comprise a set of structured data elements. These structured data elements may include, but are not limited to, parameters that define the state of the object within a workflow or a state machine. These parameters may be subject to adjustments based on interactions with stakeholders or other objects within the system.

[0216] Additionally, each object may be equipped with functionality that enables the processing of commands and the execution of determinist code. This functionality may allow the object to perform specific tasks or operations within a predefined workflow, thereby contributing to the overall process efficiency.

[0217] The architecture may also include unstructured data associated with each object. This unstructured data may consist of messages, documents, emails, and multimedia content. The integration of unstructured data allows for a richer informational context, enabling stakeholders to gain a comprehensive understanding of the object’s status and activities.

[0218] Stakeholders and their relationships with the objects are another vital component of the system. Each object may be linked to one or more stakeholders, who may have an underlying interest or responsibility related to the object. Stakeholders may interact with the object by modifying its attributes or responding to updates communicated through the object's designated communication channels.

[0219] Communication channels established for each object may be either public or private, allowing for controlled access to information. These channels serve as conduits for the flow of information, ensuring that all relevant stakeholders are kept informed about the object's status and any significant events or changes. In embodiments of the present disclosure, a plurality of classes of stakeholders may be identified, each with differing levels of access and interaction requirements with respect to the object or super object. Each stakeholder class may be provisioned with distinct communication and information channels that are unique, adapted, or tailored to the specific needs and responsibilities of that class.

[0220] Some stakeholder classes may have access to both private and public channels. These dual-access channels allow for versatile communication strategies where sensitive information may be discussed within a private context, while more general information may be shared in a public forum. This dual-channel access facilitates robust interaction dynamics and ensures that all relevant information is accessible to the right stakeholders at the right times.

[0221] In contrast, other stakeholders may only have access to public channels. This configuration may be suitable for stakeholders whose interaction with the object requires less sensitive or confidential communication. Public channels can serve as a platform for broader dissemination of information, updates, and general communications that do not require restricted access.

[0222] Through the implementation of tailored communication channels, the system may ensure effective and efficient dissemination of information. This architecture not only supports the dynamic nature of business workflows but also enhances the responsiveness and adaptability of the system to the evolving needs of its users.

[0223] Furthermore, the 0M10 model integrates these communication channels directly into the framework of each object or super object. This integration enables objects to autonomously manage and direct communications based on their current state and the needs of their associated stakeholders. As objects transition through various states within a workflow or state machine, they may update their attributes and parameters, which in turn may trigger specific communications to relevant stakeholders via the appropriate channels. Such communications may be inserted into the communications channel itself, and designated as a message sent on the behalf of the object itself. This reflects the object’s intelligence, self-awareness, and awareness of its related objects. Moreover, each communication's channel may receive a communication tailored to the interests of its stakeholders. In this way, stakeholders only receive messages in their communications channel when the content of the message or underlying activity triggering the message is relevant to them.

[0224] The communication channels are inherently designed to support the flow of structured and unstructured data, including but not limited to text, images, audio, and video content. This flexibility ensures that all pertinent information regarding the object’s status, environmental changes, or stakeholder actions is captured and communicated effectively. The flexibility of the communication channels is pivotal in enabling the objects within the OMIO architecture to interact effectively with one another. These channels may be configured to support a variety of data formats, including structured and unstructured data,thus facilitating comprehensive communication capabilities across different software applications.

[0225] Each object may be equipped with the ability to process and interpret the data received through these channels, enabling them to respond dynamically to changes in their environment or to actions taken by stakeholders. Within the OMIO framework, stakeholders may engage in discussions within designated communication channels linked to specific objects. As conversations unfold within these channels, the dialogue may trigger the OMIO system to update various attributes or parameters of the object based on the content of the discussions. Such updates may not only reflect changes in the object's status but may also influence its progression through the associated workflow.

[0226] For instance, stakeholder interactions within a channel may cause the system to modify an object’s parameters, which in turn may prompt the object to transition to a new state within the workflow. This dynamic interaction ensures that the object remains aligned with the evolving requirements and context as dictated by stakeholder inputs. To elaborate further on the dynamics within the workflow, consider the situation where alterations in the communication channels or parameter updates in one object could directly influence the attributes and operational states of another interconnected object. This inter-object influence forms a core component of the OMIO model, which integrates self-aware objects with robust communication links. Each object, equipped with an LLM, as will be detailed below, may respond to changes in its environment by adjusting its parameters autonomically.

[0227] For example, when an object undergoes a state transformation due to a stakeholder's input or other external factors, this transition may trigger a cascade of updates across related objects within the network. These updates may involve adjustments to the attributes that define the object's current state, or they may involve the initiation of new communication threads in designated channels.

[0228] Moreover, the objects may possess the capability to monitor and analyze communications and data flow within their channels, thereby enabling them to respond more effectively to the needs and inputs of their stakeholders. This capability is facilitatedby the integration of a Large Language Model that processes natural language inputs and transforms them into actionable insights and responses.

[0229] Each object or super object within the 0M10 architecture may be designed to function within a specific workflow or state machine, where the progression through various states is governed by the attributes and parameters of the object. These parameters may be influenced by stakeholder interactions, which are managed through dedicated communication channels associated with the object.

[0230] In this context, the communication channels play a crucial role. They are not merely pathways for message exchange but are integral components of the object's architecture, influencing its behavior and state transitions. Where channels are public, they may allow broad stakeholder participation. Where channels are private, they may be restricted to specific stakeholders with pertinent roles or interests in the object's functionality, thereby excluded from triggering any actions in related objects or related communications channels that are not privy to the private dialog.

[0231] This interplay between objects, enhanced by Al capabilities, ensures a dynamic and responsive system where objects are not only reactive but also proactive in addressing changes and requirements in their operational environment. The result is a more fluid, adaptable workflow that mirrors the natural interactions and dependencies found in complex systems, thereby enhancing overall efficiency and effectiveness.

[0232] Moreover, these interactions and subsequent parameter updates can lead to the aggregation of individual objects into larger, more complex entities, referred to as super objects within the 0M10 architecture, which will be detailed with respect to FIG. 3. This aggregation process is facilitated by the system recognizing common attributes or related states among multiple objects, thus allowing them to merge and form a cohesive super object. Such super objects inherit the communication channels and stakeholder interactions of their constituent objects, maintaining a continuous and seamless flow of information and updates.

[0233] This system provides a robust mechanism for ensuring that objects within a workflow are not only self-aware but also dynamically responsive to stakeholder interactions and capable of evolving into more complex structures when necessary. Thisflexibility and responsiveness are critical in maintaining the efficiency and effectiveness of the workflow, adapting to changes in real-time, and ensuring that all stakeholder needs are met promptly and accurately.

[0234] Figure 2

[0235] Figure 2 depicts an illustrative embodiments of the relationship between intelligent objects. In OMIO, objects can be associated with other objects based on some common attribute or state. In this fashion, they can form an intelligent super object. This can be akin to biology where a collection of cells can form an organ. The new super object is aware of its constituents and can communicate with them using LLM in a broadcast fashion or one at a time. The super object can have its own stakeholders and business logic independent of its constituents. This also helps eliminate any duplicate messages thus reducing noise in communication by aggregating similar messages and delivering it to the appropriate stakeholders only once. Again an example from biology may make this clear. Cells can form organs by aggregating their efforts and communicate with each other within a framework or system. And organs form systems by connecting with each other using arteries, veins and nervous system. OMIO is inspired by this biological model and uses LLMs as only one element to make all the constituents in a system intelligent, self-aware and capable of communicating using natural language.

[0236] 100 - Object

[0237] Reference to object 100 above, and all of its related components and modules.

[0238] 205 - Arbiter

[0239] An arbiter may comprise a node in a network of intelligent objects. For example, an arbiter may be utilized to facilitate interactions within a network of intelligent objects. For instance, the arbiter may serve as a node that not only processes data but also ensures the effective communication among various interconnected entities within the system. This function may be particularly crucial in scenarios where objects within a network need to share or process information in a synchronized manner.

[0240] The arbiter might function by receiving inputs from various nodes or entities within the network, processing these inputs based on predefined rules or algorithms, and then disseminating outputs to appropriate nodes. This process may involve the use ofadvanced Al models that enable the arbiter to make informed decisions about data routing, priority management, and communication timing.

[0241] Moreover, the arbiter may be responsible for maintaining the integrity and security of the data as it flows between nodes. This may include implementing encryption protocols, verifying data authenticity, and ensuring compliance with data protection regulations. By performing these roles, the arbiter ensures that the network operates efficiently and securely, facilitating seamless interactions between intelligent objects.

[0242] Additionally, the arbiter may be equipped with functionalities that allow it to adapt to changes within the network dynamically. This could involve reconfiguring its operation parameters in response to new nodes being added to the network, changes in data flow patterns, or emergent security threats. Such adaptability would be critical in maintaining the robustness and resilience of the network.

[0243] In some embodiments, the arbiter may comprise its own LLM for interfacing with the LLMs of other objects. For example, in some embodiments, the arbiter may comprise its own LLM for interfacing with the LLMs of other objects. For example, the arbiter may utilize its LLM to decode and interpret communications from other LLMs within the network. This capability allows for efficient and accurate data exchange, enhancing the overall system's ability to respond to dynamic conditions and stakeholder inputs.

[0244] In various embodiments, an arbiter may help one object’s LLM determine which other object it must communicate with. For example, In various embodiments, an arbiter may help one object’s LLM determine which other object it must communicate with. For example, the arbiter may be configured to assess the relationship between objects based on their involvement in a shared workflow. This may involve analyzing the sequence and dependencies of tasks within the workflow to determine the optimal communication pathways between objects. Additionally, the arbiter may access a database to retrieve information regarding the relationships between objects, such as shared attributes or historical interactions, to facilitate effective communication.

[0245] Furthermore, the arbiter may consider the stakeholders associated with each object. By identifying stakeholders with interests in specific objects, the arbiter can ensure that communications are routed to relevant parties, enhancing the efficiency and relevanceof information exchange. This may involve analyzing stakeholder roles and responsibilities in relation to the objects to tailor the communication process appropriately.

[0246] By dynamically linking objects based on workflow connections, database relationships, and stakeholder interests, the arbiter ensures a coherent and contextually aware interaction among objects within a system. This approach may be particularly beneficial in complex environments where multiple objects and stakeholders interact in intricate workflows, requiring precise and timely communication to maintain operational efficiency.

[0247] Furthermore, the arbiter may act as a coordinator among the interconnected objects, facilitating the flow of information and commands across the system. By managing these interactions, the arbiter ensures that data integrity is maintained and that actions are executed based on the most current and relevant information available.

[0248] Additionally, the arbiter may be configured to prioritize communications based on predefined criteria, such as urgency, relevance to ongoing processes, or stakeholder importance. This prioritization helps in optimizing the system's performance by focusing resources on critical tasks and reducing response times for vital operations.

[0249] The arbiter's integration with an LLM also enables advanced decision-making capabilities. By analyzing the data received from various objects and stakeholders, the arbiter can make informed decisions that align with the system's goals and constraints. This decision-making process may involve complex algorithms that consider multiple factors, including historical data, current system status, and projected outcomes.

[0250] In summary, the arbiter, equipped with its own LLM, plays a multifacial role within the system, acting as an interface, coordinator, prioritizer, decision-maker, and security agent. Through these functions, the arbiter significantly contributes to the system's efficiency, effectiveness, and security, ultimately enhancing the overall performance and reliability of the network.

[0251] Detailed Description of the Related Object Model

[0252] Referring now to FIG. 2, objects within this system maybe interconnected in such a way that the activities and interactions of one object can influence the parameters and attributes of related objects. For instance, an object may adjust its behavior based on theinput it receives from other objects in the network, which may include stakeholders' communications observed through dedicated channels. This inter-object communication enables a cohesive and adaptive system where changes in one part of the system can propagate necessary adjustments throughout the related objects.

[0253] Furthermore, each object may be designed to maintain visibility over the components and activities of other related objects. This visibility ensures that each object can make informed decisions based on the comprehensive data available from its network. The parameters and attributes of an object can therefore dynamically change, not only based on its direct interactions but also from indirect influences observed through the network of connected objects and stakeholders.

[0254] Through the integration of a Large Language Model (LLM), each object in the 0M10 architecture gains the capability to understand and process natural language, thereby enhancing its ability to communicate with stakeholders in a more human-like manner. The LLM can analyze conversations, interpret stakeholder inputs, and provide responses that are contextually relevant to the ongoing discussions within its channels. This capability allows the object to act as a mediator or facilitator in communications, ensuring that all relevant information is accurately conveyed and understood by all parties involved.

[0255] In essence, the OMIO architecture promotes a system where objects are not only self-aware but also highly interdependent. By enabling objects to monitor, react to, and communicate with each other through intelligently managed communication channels, the system ensures that information flows seamlessly across the network, enhancing the overall efficiency and responsiveness of the process. This architecture not only supports the dynamic exchange of information but also fosters an environment where continuous learning and adaptation are integral to the system’s operations.

[0256] In this way, the OMIO platform provides a sophisticated framework for managing interactions and communications within complex workflows, significantly enhancing the capability of business systems to adapt to and integrate advanced Al functionalities. This not only improves operational efficiency but also leverages the full potential of Al to create more connected, intelligent, and responsive business environments.

[0257] The integration of an LLM within each object forms a core part of the system’s functionality. The LLM may act as an arbitrator of all data and activities associated with the object, processing both structured and unstructured data to maintain an updated state of the object. By leveraging the capabilities of the LLM, the object may autonomously manage communications, update its parameters, and interact with other objects and stakeholders effectively.

[0258] In terms of interaction with other objects, the system may facilitate the establishment of relationships between different objects. These relationships may be defined based on common attributes, shared stakeholders, or their roles within the workflow. By networking objects together, the system can create a more integrated and coherent environment, enhancing the collective intelligence of the system.

[0259] Overall, the system provides a dynamic and adaptive framework that harnesses the power of Al to optimize the functionality and communication capabilities of objects within a workflow. This framework not only enhances the efficiency of processes but also improves the accuracy and timeliness of information distribution among stakeholders.

[0260] The Super Object Model

[0261] Figure 3

[0262] FIG 3 depicts an illustrative embodiment an intelligent super object containing a group of related intelligent objects. In 0M10, objects can be associated with other objects based on some common attribute or state. In this fashion, they can form an intelligent super object. This can be akin to biology where a collection of cells can form an organ. The new super object is aware of its constituents and can communicate with them using LLM in a broadcast fashion or one at a time. The super object can have its own stakeholders and business logic independent of its constituents. This also helps eliminate any duplicate messages thus reducing noise in communication by aggregating similar messages and delivering it to the appropriate stakeholders only once. Referring back to an example from biology may make this clear. Cells can form organs by aggregating their efforts and communicate with each other within a framework or system. And organs form systems by connecting with each other using arteries, veins and nervous system. 0M10 is inspired bythis biological model and uses LLMs as only one element to make all the constituents in a system intelligent, self-aware and capable of communicating using natural language.

[0263] 300 - Super Object

[0264] A super object may be an aggregation of related objects. A super object may be an aggregation of related objects. Each object within the super object retains its individual attributes and functionalities while contributing to the broader capabilities of the super object. Super object may be structured to include multiple objects, each representing a distinct segment of a workflow or business process. The aggregation into a super object allows for enhanced communication and data processing capabilities, leveraging the attributes of the included objects.

[0265] Attributes, parameters, and functionalities of individual objects are integrated within super object, facilitating a unified approach to inter-object operation. Each object may bring specific data, functionality, and communication channels, which are then shared or distributed within the super object. This integration may enable super object to perform complex tasks that are beyond the capability of any single object.

[0266] The communication channels established for each object may also be combined or interconnected within super object. This may allow stakeholders to interact with super object through a centralized communication interface, potentially simplifying the flow of information and making the interactions more efficient. The channels through which objects communicate may include both private and public sub-channels, ensuring that information is shared appropriately based on stakeholder roles and privacy requirements.

[0267] Super object may function under a master Al model, which coordinates the activities and data flow between different objects through their corresponding Al models. This Al model may be responsible for processing commands, directing workflows, and ensuring that the super object operates according to predetermined parameters and goals. The Al model may utilize advanced algorithms to optimize the performance of the super object, adapting dynamically to changes in the workflow or external conditions.

[0268] In terms of implementation, super object may be configured to operate within various systems, including but not limited to business management systems, automated manufacturing processes, or customer service platforms. The flexibility in design allowssuper object to be tailored to specific industry needs, providing a scalable solution that can grow with the business or application requirements.

[0269] Overall, the concept of a super object, as described in this disclosure, provides a robust framework for enhancing the capabilities of individual objects by combining them into a cohesive unit. This unit, governed by a central Al model, may offer significant improvements in efficiency and effectiveness across different applications and industries. The integration of multiple objects into a super object ensures that the strengths and functionalities of the individual components are leveraged to their fullest, creating a system that is greater than the sum of its parts.

[0270] 305 - Super Groups Structured Data

[0271] Reference back to Structured Data 105

[0272] 310 - Super Objects Functionality

[0273] Reference back to Object Functionality 110

[0274] 315 - Super Objects Unstructured Data

[0275] Reference back to Object Unstructered Data 115

[0276] 320 - Super Objects Stakeholders and Relative Objects

[0277] Reference back to Object Stakeholder and Relative Objects 120

[0278] 325 - Super Al Model

[0279] Reference back to Object Al Model 125

[0280] Detailed Description of the Super Object

[0281] Transitioning from individual objects to super objects, as illustrated in FIG. 3, the system may allow for the aggregation of multiple related objects into a single, more complex entity known as a super object. This grouping may be based on shared attributes, common stakeholders, or their collective role within a larger process. A super cobblestone may inherit or amalgamate the functionalities and data channels of its constituent objects, thereby enhancing the efficiency of communication and operation within the system.

[0282] The super object, being a higher-order entity, may coordinate the activities of its constituent objects, making it possible to manage complex interactions and dependencies more effectively. For instance, a super object may oversee the synchronization of updates across its components to ensure that all stakeholders are uniformly informed about thestatus across the system. This capability enables the super object to act as a central hub for information dissemination and decision-making within the workflow.

[0283] Furthermore, the super object may be equipped with an advanced Al model, such as an LLM, which could provide deep insights by analyzing the aggregate data from all linked objects. This Al model may facilitate complex decision-making processes, offering predictions or recommendations based on comprehensive data analysis. The Al’s capabilities may extend to interpreting natural language inputs and generating outputs that are contextually relevant to the stakeholders’ needs, thereby enhancing the interaction between human users and the system.

[0284] The super object, as described, may be an entire application that encompasses all the functionalities, attributes, and parameters of a single object. Within this framework, the super object may be self-aware, continually aware of its own state and the states of its related objects. Each super object may transition within a workflow, aggregating into larger super objects, thereby forming a complex network of intelligent entities capable of effective communication and operation.

[0285] In the context of a workflow or a state machine, a super object may progress through various states by updating its attributes and parameters based on its interactions with other objects and stakeholders within the system. Each super object may host a multitude of smaller objects or entities, each contributing to the overarching functionality and intelligence of the super object. This structure allows for scalable and dynamic interactions within the business workflow, enhancing the efficacy and responsiveness of the system.

[0286] The super object, as detailed in the disclosure, may be configured as an entire application within an operating system, harnessing the potential of the 0M10 model. This application, or super object, comprises multiple child objects each embedded with their own localized LLMs. The super LLM, assigned to the super object, manages communications and interactions among these child objects by transmitting instructions and information seamlessly across the network.

[0287] Each child object within the super object may be designed to perform specific functions, contributing to the broader utility of the super object. The super LLM facilitatesthis by interpreting and routing commands or data to the appropriate child objects within the super object, ensuring efficient operation and synchronization.

[0288] The communication between the super LLM and the individual child object LLMs is integral to maintaining the flow of data and commands within the application. This architecture allows the super LLM to effectively orchestrate the actions of its constituent child objects, leveraging their individual capabilities to fulfill complex tasks.

[0289] By embedding intelligence directly into the application structure, the OMIO model enables the super object to act autonomously, managing its internal operations and interactions with other systems or applications. This autonomous capability is crucial for reducing the need for manual oversight and increases the efficiency of operations within the system.

[0290] Furthermore, the super object architecture ensures that all interactions and data exchanges within the application are conducted through predefined communication channels. These channels are crucial for maintaining the integrity and security of the data being processed and shared within the super object.

[0291] In summary, the super object functions as a comprehensive application within an operating system, capable of interacting with and managing multiple child objects. This structure not only enhances the functionality and efficiency of the application but also simplifies the management and operational processes involved in running complex systems.

[0292] The super object may also manage communications with stakeholders through designated channels. These channels may be configured to support both public and private interactions, ensuring that sensitive information is safeguarded while still promoting transparent and efficient communication. The integration of an LLM within the super object itself allows it to act as an arbitrator of all data and activities relating to the object, including communication management.

[0293] Furthermore, the super object may continually update itself with new information through its LLM, which can process and understand natural language inputs from various sources. This capability ensures that the super object remains current with all relevant data, allowing it to make informed decisions and provide timely updates to stakeholders.

[0294] In addition to managing its own states and communications, a super object may interact with other super objects or external systems. This interaction may be facilitated by a master LLM that coordinates activities across multiple super objects, ensuring cohesive operation within the larger system. The master LLM may relay instructions and information between super objects, leveraging their collective intelligence to optimize overall system performance.

[0295] Overall, the super object model in the OMIO architecture represents a significant advancement in the integration of Al and workflow management. By embedding intelligence directly into the workflow components, the system achieves a higher level of automation and efficiency, capable of adapting to the complex and dynamic nature of modern business environments. This model not only enhances operational capabilities but also simplifies interactions for users, ultimately contributing to a more effective and intuitive workflow system.

[0296] By integrating individual objects into a super object framework, the system leverages both the detailed precision of small-scale operations and the broad oversight capabilities necessary for managing extensive workflows. This integration not only improves operational efficiency but also ensures that the system remains adaptable and responsive to changes in its environment or operational requirements.

[0297] IV. OMIO OS

[0298] Figure 4

[0299] 400 - OMIO Os

[0300] OMIO OS is designed around the core principle of user interaction through natural language, eliminating traditional interfaces' complexity. By incorporating a master Large Language Model (LLM), OMIO OS understands user commands, intentions, and contexts, crafting strategies to fulfill requests by coordinating with applications built on the OMIO framework. These applications, inherently capable of natural language interaction, allow OMIO OS to seamlessly integrate various services, providing a cohesive and intuitive user experience. In addition to managing its own data and communications, an intelligent object in the OMIO model may also interact with other objects and external systems. This may be achieved through a network of relationships defined within the system architecture, whereeach object can serve as a node that connects to multiple other nodes, including stakeholders, other intelligent objects, and external data sources.

[0301] The 0M10 model may also include a sophisticated operating system (0M10 OS] that utilizes a master LLM to oversee the interactions between intelligent objects and external applications. This OS may be designed to process user inputs in natural language, interpret their intentions, and execute appropriate actions by coordinating with the relevant objects and applications.

[0302] The 0MI0 OS may enhance user experience by simplifying interactions with technology through a natural language interface. This interface may allow users to communicate with the system in a conversational manner, making it easier to perform tasks and access information.

[0303] At the heart of 0M10 OS is a sophisticated LLM that processes natural language inputs from users, interprets their needs, and devises strategies to address those needs using available applications and resources. Figure 1C depicts the master LLM along with all the 0M10 based apps and their dedicated LLMs which they use to interface with the master LLM using natural language.

[0304] Applications developed using the 0M10 model can understand and process natural language requests. They are designed to work in harmony within the ecosystem of 0M10 OS, facilitating a broad range of tasks from productivity to entertainment.

[0305] Augmenting traditional GUIs, the OS features a natural language interface that allows users to communicate with their devices as they would with a human assistant. The device uses its display as well as natural language to communicate.

[0306] 300 - Super Object

[0307] Reference to Super Object 300.

[0308] 405 - Super Object Arbiter

[0309] Reference to Arbiter 205.

[0310] 450 - User

[0311] A user may be a person or Al model that interacts with the 0M10 OS Al Model, which may be embedded within various applications and devices. The user, through natural language interactions, may communicate directly with the OMIO OS Al Model to executetasks or retrieve information. The Al Model may interpret these natural language inputs to understand the user's intent and then process the request accordingly.

[0312] The user may initiate interactions with the OMIO OS by simply speaking or typing natural language commands. The Al Model, equipped with natural language processing capabilities, may analyze these inputs to determine the necessary actions. These actions could include querying databases, activating device functionalities, or manipulating software applications that are integrated within the 0M10 OS.

[0313] Once the Al Model has processed the user's request, it may execute the appropriate commands across the connected applications and devices. This interaction may be facilitated by the Al Model's ability to communicate with other embedded LLMs within the system, forming a cohesive network that can efficiently handle complex requests.

[0314] The OMIO OS Al Model may also proactively provide information to the user based on the context of previous interactions or predefined user preferences. This may include sending notifications or suggestions relevant to the user's interests or daily activities, thus enhancing the overall user experience with personalized content.

[0315] Furthermore, the Al Model may maintain a continuous dialogue with the user to refine the understanding of user needs and to optimize the responses. This ongoing interaction allows the OMIO OS to adapt to the user's preferences over time, making the system more intuitive and responsive to individual requirements.

[0316] In addition to direct user interaction, the OMIO OS Al Model may also handle background tasks that support system functionality. This includes managing data synchronization across devices, updating software components, and ensuring the security of user data. By handling these operations, the Al Model supports a seamless and secure user experience across the OMIO-enabled ecosystem.

[0317] 460 - External Devices

[0318] OMIO may interface with external devices. For example, external devices may include, but not be limited to, smart homes, autonomous vehicles, smartphones, tablets, PCs, smart watches, virtual reality (VRJ headsets, and various types of sensors such as temperature, motion, and location sensors. The interface with these devices may be facilitated through various communication protocols including, but not limited to, Bluetooth,Wi-Fi, NFC, and USB. Furthermore, OMIO may utilize APIs to connect with these external devices, allowing for the exchange of data and control commands in a seamless manner. Continuing from the Incomplete Text:

[0319] OMIO may also be configured to operate external devices through its interface capabilities. For instance, an autonomous vehicle may be operated by OMIO. The operation of such a vehicle may involve receiving data from the vehicle's onboard sensors, processing this data to understand the vehicle's environment, and sending control commands back to the vehicle to navigate or perform specific tasks. In another example, OMIO may interact with smart home devices such as thermostats, lighting systems, or security cameras. Through a Wi-Fi connection, OMIO may receive temperature readings from a thermostat, analyze these readings in the context of user preferences and external weather conditions, and adjust the thermostat settings accordingly. Similarly, lighting systems may be controlled based on user behavior patterns recognized by OMIO, and security cameras may be monitored with OMIO analyzing the video feed to detect unusual activities.

[0320] Furthermore, OMIO's may be enabled to interface with smartphones and tablets. For instance, if OMIO detects a security issue through a connected camera, it may immediately send an alert to the user's smartphone. This interaction involves OMIO processing the camera's video feed, detecting the presence of an intruder, and using the smartphone's communication protocols to alert the user.

[0321] Additionally, OMIO may be utilized to enhance the functionality of wearable devices such as smart watches. By interfacing with a smart watch via Bluetooth, OMIO may access health-related data collected by the watch, such as heart rate or activity levels. In this way, through the use of APIs, OMIO's interaction with these external devices not only involves direct control but also the aggregation and analysis of data from multiple sources. This capability allows OMIO to offer a seamless and integrated user experience across various devices and platforms, adapting to user needs and environmental conditions in realtime.

[0322] Each of these examples illustrates how OMIO may be configured to interface with, control, and enhance the functionality of various external devices, utilizing a range of communication protocols and APIs to facilitate these interactions. Through thesecapabilities, OMIO provides a versatile and adaptive system that enhances user interaction with technology in multiple domains.

[0323] In yet further embodiments, OMIO may send notifications or alerts to a smartphone or smart watch when a significant event occurs within the system it governs. Similarly, OMIO may receive data from sensors that monitor various parameters, which can then be processed to adjust operations within the workflow automatically.

[0324] 455 - External Data and Apps

[0325] Similarly, OMIO’s Master LLM may access a variety of data sources, including external applications and databases. This capability allows the Master LLM to retrieve, analyze, and utilize data from these sources to enhance the decision-making processes within the OMIO OS environment.

[0326] Furthermore, the Master LLM facilitates a bidirectional interaction where not only does it pull data from external sources, but it can also push updates or commands back to these sources. This interaction may be facilitated through APIs or direct database access, depending on the configuration of the external application or data source. This capability ensures that the OMIO OS can maintain up-to-date information across all connected systems and applications, thereby enhancing the overall efficiency and responsiveness of the system.

[0327] In the context of external applications, the Master LLM may send commands to initiate specific actions within these applications. For instance, upon analyzing user input and external data, the Master LLM might instruct a financial app to execute a transaction or update a budget forecast. This proactive functionality of the Master LLM ensures that all actions taken are in alignment with the user's objectives and the current data context, thereby optimizing operational efficiency and user satisfaction.

[0328] The interaction with databases involves not only querying for information but also updating records to reflect the latest states or decisions made within the OMIO OS. This ensures that all stakeholders, whether human users or other software systems, are working with the most current and relevant information. The ability to write back to the database also supports the functionality of learning and adapting over time, as the Master LLM can refine its decision-making processes based on the outcomes of previous interactions.

[0329] In some embodiments, an arbiter may serve to manage each interaction between the Master LLM and external applications or databases. Accordingly, the arbiter may ensure each interaction logged and managed through a secure communication protocol. This protocol may include authentication measures, encryption, and compliance with data privacy standards, ensuring that all data exchanges are secure and authorized.

[0330] The architecture of the OMIO OS, with its Master LLM at the core, thus serves as a dynamic and intelligent hub that not only processes and responds to internal commands but also actively engages with and manipulates external data and applications to achieve comprehensive and informed outcomes. This integration of internal and external functionalities through the Master LLM exemplifies the advanced capabilities of the OMIO OS in facilitating complex, data-driven interactions in a seamless and efficient manner.

[0331] 425 - Master Al Model

[0332] Reference back to Super Al Model 325.

[0333] 435 - OMIO OS Arbiter

[0334] Reference back to Arbiter 205.

[0335] Figure 5

[0336] Figure 5 depicts an illustrative embodiment OMIO OS with all OMIO style Apps interacting using natural language. The master LLM is the one in the center under the mic symbol. It is responsible to interact with the user. As shown in FIG. 5, the OMIO based apps are installed on the device and being controlled by the master LLMs. The master LLM becomes aware of all the app during installation and the app informs the master LLM of its capabilities, its authentication requirements, etc. The master LLM then remembers this new capability and includes it in its future strategy to fulfill the user’s requirements.

[0337] 500 - Mobile OMIO OS

[0338] Provides an example of OMIO in the common mobile OS environment.

[0339] 300 - Super Object

[0340] In this embodiment, the super object (which may be comprised of a plurality of other super objects, down to the smallest divisible object) is an App within the Mobile OMIO 500.

[0341] 425 - OMIO OS Al Model

[0342] Reference to Master Al Model 425.

[0343] Example One

[0344] Below is the illustrative execution of a planned strategy by the master LLM as a response to "OMI, 1 am hungry”.

[0345] The following illustrates an example of the OMIO OS operation with a user. "Hey OMI, I am hungry." Upon receiving the user's statement of hunger, OMIO OS's master LLM interprets it as a request for meal options. In response to the user's expression of hunger, the Master LLM may initiate a series of interactions with various Al models associated with different applications. The Master LLM may send a query to a diet application's Al model to retrieve suggestions for meal options based on the user's dietary preferences and history. This query may be processed by the diet application's Al model, which may then return suggestions such as specific food items or meal plans that align with the user's nutritional requirements.

[0346] Simultaneously, the Master LLM may communicate with an OMIO-enabled budget application's Al model to ascertain the available budget for the meal. The budget application's Al model may analyze the user's financial limitations and spending habits to determine an appropriate amount for the meal, which may be communicated back to the Master LLM.

[0347] Upon gathering meal options and budget constraints, the Master LLM may then engage with an OMIO-enabled map application's Al model. This model may use the information provided to identify nearby restaurants that offer the suggested meals within the specified budget. The restaurant application's Al model may consider factors such as location, user ratings, and availability to propose suitable dining establishments.

[0348] Additionally, the Master LLM may interact with an OMIO-enabled map application's Al model to calculate the distance to the selected restaurants from the user's current location. This interaction may involve sending location data and receiving back estimated travel times and distances, which can be crucial for the user's decision-making process.

[0349] If the user opts for meal delivery, the Master LMM may further interface with OMIO-enabled delivery application's Al model. This model may handle the logistics of ordering the chosen meal from the selected restaurant and scheduling delivery to the user's address. The delivery application's Al model may coordinate with the restaurant and delivery services to ensure timely and accurate order fulfillment.

[0350] Throughout this process, the Master LLM acts as a central coordinator, interfacing seamlessly with the Al models of various applications. Each interaction may be conducted using natural language, facilitated by the LLM's ability to understand and generate humanlike text. This capability allows the Master LLM to manage complex tasks by breaking them down into simpler, executable actions that align with the user's needs and preferences.

[0351] These interactions demonstrate the OMIO OS's ability to integrate diverse functionalities within a unified system, enhancing user experience through efficient and intelligent automation. The system's design enables it to adapt to the user's immediate context and requirements, offering personalized service that simplifies daily tasks and decision-making processes. In some embodiments, arbiters may be employed through the process.

[0352] Example Two

[0353] In another example, a user may inquire as to issues or bugs being experienced in a computing environment. To address the query regarding issues or bugs in a computing environment, the user may interact directly with the 0M10 OS's Master LLM. This interface, through natural language processing capabilities, interprets the user's request and strategically coordinates across various linked applications to troubleshoot or gather status updates.

[0354] For instance, consider a scenario where a user suspects a bug in a software module within an OMIO-compliant system. The user might phrase their query as, "OMIO, check for any current issues in the payment processing module." Upon receiving this request, the Master LLM decodes the user's language into executable commands.

[0355] The Master LLM may then interact with the intelligent object representing the payment processing module. This object, equipped with its own LLM, performs a selfassessment by reviewing its logs and operational parameters. Concurrently, it might consultother related objects or software modules that interact with it, to ascertain if the issue is isolated or part of a broader systemic problem.

[0356] After collecting and analyzing the necessary data, the object updates its status, which includes flagging any anomalies or confirming all operations are running smoothly. This update is communicated back to the Master LLM, which compiles the findings into a coherent response to the user.

[0357] The user receives this information directly through a user-friendly interface that might mimic a conversation, effectively reducing the complexities typically associated with debugging software. The dialogue could be, "1 have checked the payment processing system. There is a minor delay due to a syncing issue with the database, which is being resolved now. No major bugs were detected."

[0358] This example demonstrates how OMIO OS facilitates an intuitive, efficient, and direct interaction between the user and the system's intelligent objects, leveraging the Master LLM's capability to orchestrate complex operations across a network of interconnected software modules and objects. Through this intelligent interaction, users can effortlessly manage, troubleshoot, and interact with various components of their computing environment, effectively enhancing productivity and system reliability.

[0359] Strategic Application Interaction:1. Diet App Consultation:

[0360] OMIO OS queries the diet application, asking, "What is planned for lunch today?" The diet app, analyzing the user's meal history and nutritional plan, suggests "Fish for lunch," considering variety and health.

[0361] 2. Budget App Inquiry:

[0362] Concurrently, OMIO OS checks with the budget app about the day's meal allocation, receiving a response of "$15" for lunch.

[0363] 3. Restaurant App Coordination:

[0364] Armed with dietary recommendations and budget constraints, OMIO OS communicates with the restaurant app: "Find me the closest fish restaurants with a budget of $15." The restaurant app processes this request, identifying suitable options within the specified parameters.

[0365] 4. User Notification:

[0366] 0MI0 OS consolidates the information and informs the user, "According to our budget and diet plan for today, here are a few fish restaurants nearby," displaying the options.

[0367] Advantages of OMIO OS

[0368] Simplification of User Interaction:

[0369] By leveraging natural language, OMIO OS drastically reduces the learning curve associated with new technology, making digital tools more accessible to a broader audience.

[0370] Seamless Integration:

[0371] The OMIO model ensures that all applications can communicate effectively, breaking down silos between different services and functionalities.

[0372] Personalization:

[0373] The system adjusts responses and suggestions to the user's unique preferences, history, and needs, enhancing the overall user experience.

[0374] Efficiency and Productivity:

[0375] By handling coordination between different applications, OMIO OS streamlines tasks that would typically require multiple steps, saving users time and effort.

Claims

The Following Is Claimed:

1. An object messaging and intelligent object (0M10) system, the system comprising: an intelligent object (100), the object comprising: structured data (105) comprising attributes and parameters relating to the object- unstructured data (115) comprising at least one of the following: messages, documents, emails, and multimedia content relating to the object; a computer-readable code module (110) comprising computer readable instructions for executing an action relating to the object; a stakeholder module (120) configured to establish communication channels for stakeholders associated with the object; and a generative artificial intelligence (Al) module (125) embedded within the object, wherein the Al module is configured to: train on each object module’s activity, facilitate natural language communication between at least one of the following: the object, the stakeholders of the object, related objects, and related stakeholders associated with the related objects; and update the structured data of the object, and operate the computer-readable code module to execute at least one action.

2. The system of claim 1, wherein the structured data (105) comprises parameters defining the object's state within the workflow.

3. The system of claim 2, wherein the Al module (125) comprises a large language and multi-modal model configured to analyze the unstructured data (115) to update the object's state within the workflow.

4. The system of claim 3, wherein the Al module (125) is further configured to transmit messages, using natural language, within the communications channels.

5. The system of claim 1, wherein the Al Module (125) is further configured to autonomously update its state based on interactions within at least one of the following: the communications channels of the objects, and related communications channels within related objects.

6. The system of claim 2, wherein the object (100) further comprises at least one encoder and decoder configured to: convert natural language inputs into computer-executable instructions for the computer readable code module (110) and vice versa.

7. The system of claim 6, wherein the encoder and decoder enable the object (100) to convert stakeholder inputs into modifications to the structured data (105) via the computer readable code module (110).

8. The system of claim 7, wherein the modifications to the structured data (105) cause changes to the object’s state within a workflow.

9. The system of claim 7, wherein the modifications to the structured data (105) causes changes to the related object's structured data (105).

10. The system of claim 9, wherein the related object’s change to the related object’s structured data causes a change to the related object’s state within the workflow.

11. The system of claim 10, wherein the related stakeholder of the related object is notified upon modifications to: the structured data (105) of the object (100) and the unstructured data (115) of the object (100).

12. The system of claim 11, wherein the related stakeholder is notified via the related object’s Al model transmission of a message via the communications module of the related object into a relevant communications channel linked to the related stakeholder.

13. The system of claim 1, wherein the stakeholder module (120) configured to: identify stakeholders related to the object, and manage communications with the identified stakeholders through a plurality of communication channels, wherein each communications channel is designed by the following:a stakeholder class, and a privacy setting.

14. The system of claim 13, wherein the Al module (125) is configured to: monitor activity data within its object; and receive natural language updates associated with the related objects’ monitored activity data, and transmit natural language activity data to the related objects.

15. The system of claim 14, wherein the Al module (125) is configured to update its attributes and parameters based on at least one of the following: the activity associated with the object (100), and the natural language updates with the related objects’ monitored activity data.

16. The system of claim 1, wherein the Al module (125) is further configured to manage interactions between the object (100) and external applications.

17. The system of claim 16, wherein the Al module (125) is further configured to dynamically adapt operations within the computer readable code module (110) based on real-time, natural language data received from external applications.

18. The system of claim 1, wherein the Al module (125) is configured to employ machine learning techniques to continuously train on each object module’s activity.

19. The system of claim 1, wherein the Al module (125) is further configured to execute predictive analytics based on historical data related to the object and generate predictive notifications to the users about future states of the object.

20. The system of claim 19, wherein the stakeholder module (120) is configured to provide notifications related to object activity and related object activity relevant to each stakeholder, wherein the notification comprises a delivery of a user-editable interface of the underlying object itself.

21. An object messaging and intelligent object (OM1O) system, the system comprising: an intelligent object (100), the object comprising: structured data (105) comprising attributes and parameters relating to the object; unstructured data (115) comprising at least one of the following: messages, documents, emails, and multimedia content relating to the object;a computer-readable code module (110) comprising computer readable instructions for executing an action relating to the object; a stakeholder module (120) configured to establish communication channels for stakeholders associated with the object; a generative artificial intelligence (Al) module (125) embedded within the object, wherein the Al module is configured to: train on each object module’s activity, facilitate natural language communication between at least one of the following: the object, the stakeholders of the object, related objects, and related stakeholders associated with the related objects; and update the structured data of the object, and operate the computer- readable code module to execute at least one action; and an arbiter module (205) operatively connected to the intelligent object, wherein the arbiter module is configured to manage communication and data flow between the intelligent object and the related objects.

22. The system of claim 21, wherein the arbiter module (205) is further configured to determine the optimal communication pathways for data exchanges between the object and the related objects.

23. The system of claim 21, wherein the arbiter module (205) utilizes a large language model to facilitate data exchanges between the object and the related object in natural language.

24. The system of claim 23, wherein the arbiter module (205) is further configured to prioritize communication based on predefined criteria including relevance of information to the related objects.

25. The system of claim 24, wherein the arbiter module (205) comprises an encoder and decoder configured to convert natural language inputs into computer-executable instructions for operating with the computer readable code modules of the object and the related objects.

26. The system of claim 21, wherein the Al module (125) further comprises an encoder and decoder to enable the intelligent object (100) to autonomously update its state based onnatural language instructions derived from natural language interactions within the communication channels established by the stakeholder module (120).

27. The system of claim 26, wherein the Al module (125) is further configured to analyze interactions between the object (100) and related objects to dynamically adapt communications and operations within the intelligent object.

28. The system of claim 27, wherein the arbiter module (205) is configured to interface with external applications to extend the functionality of the intelligent object (100) based on received natural language inputs.

29. The system of claim 28, wherein the external applications comprise database systems, cloud services, or other intelligent object systems.

30. The system of claim 29, wherein the arbiter module (205) is further configured to synchronize data between the intelligent object (100) and the external applications to maintain data integrity and up-to-date information.

31. The system of claim 30, wherein the arbiter module (205) employs machine learning techniques to continuously improve its data synchronization and communication strategies.

32. The system of claim 31, wherein the arbiter module (205) is further configured to manage security protocols for communications between the intelligent object (100) and external systems, ensuring encrypted and secured data exchanges.

33. The system of claim 32, wherein the arbiter module (205) monitors data inputs from the communication channels to detect anomalies and potential security threats.

34. The system of claim 33, wherein the arbiter module (205) responds to detected security threats by modifying communication routes or restricting data exchanges to protect the intelligent object (100).

35. The system of claim 21, wherein the network of intelligent objects comprises objects within the same organizational entity or across multiple entities.

36. An object messaging and intelligent object (OM1O) system, the system comprising: an intelligent object (100), the object comprising: structured data (105) comprising attributes and parameters relating to the object; unstructured data (115) comprising at least one of the following: messages, documents, emails, and multimedia content relating to the object;a computer-readable code module (110) comprising computer-readable instructions for executing an action relating to the object; a stakeholder module (120) configured to establish communication channels for stakeholders associated with the object; a generative artificial intelligence (Al) module (125) embedded within the object; and a super object (300) comprising a plurality of said intelligent objects (100), wherein the super object comprises an super group generative Al module (325) configured to interface with the Al module 125 associated the plurality of intelligent objects (100) within the super object.

37. The system of claim 36, wherein the super object (300) further comprises: super groups structured data (305) comprising aggregated attributes and parameters from the included intelligent objects (100).

38. The system of claim 37, wherein the super object (300) further comprises: super objects functionality (310) configured to execute combined actions based on the aggregated attributes and parameters of the included intelligent objects (100).

39. The system of claim 38, wherein the super object (300) further comprises: super objects unstructured data (315) comprising aggregated unstructured data from the included intelligent objects (100).

40. The system of claim 39, wherein the super object (300) further comprises: super objects stakeholders and relative objects (320) configured to manage communication and interactions with stakeholders associated with the super object (300).

41. The system of claim 40, wherein the super object (300) further comprises: a super group generative Al module (325) configured to interface with the Al module of each of the intelligent objects (100) within the super object.

42. The system of claim 41, wherein the Al model (325) is further configured to employ machine learning techniques to optimize operations and interactions within the super object (300).

43. The system of claim 42, wherein the super object (300) is configured to dynamically adapt its operations based on real-time data received from the included intelligent objects(100).

44. The system of claim 43, wherein the Al module (325) is configured to: train on each object modules' (100) activity, facilitate natural language communication between at least one of the following: each object (100) within the super object and the stakeholders of each object; and update the structured data of the object, and operate the computer-readable code module to execute at least one action.

45. The system of claim 44, wherein the super object (300) is configured to exchange natural language data between the included intelligent objects (100) and external applications.

46. The system of claim 45, wherein the super object (300) comprises at least one arbiter module (205), wherein the arbiter module is configured to manage communication and data flow between the intelligent object and the related objects within the super object.

47. The system of claim 46, wherein the arbiter module (205) is further configured to determine the optimal communication pathways for data exchanges between the intelligent objects within the super object.

48. The system of claim 36, wherein the Al module (325) utilizes a large language model to facilitate data exchanges between the intelligent object and the related object in natural language.

49. The system of claim 48, wherein the arbiter module (205) comprises an encoder and decoder configured to convert natural language inputs into computer-executable instructions for operating with the computer-readable code modules of the intelligent object and the related objects.

50. The system of claim 49, wherein the encoder and decoder enable the intelligent object (100) to autonomously update its state based on natural language instructions derived from natural language interactions within the communication channels established by the stakeholder module (120) or supergroup stakeholder module (320).

51. The system of claim 50, wherein the Al module (125) is further configured to analyze interactions between the intelligent object (100) and related objects to dynamically adapt communications and computer-executable operations within the intelligent object.

52. The system of claim 51, wherein the arbiter module (205) is configured to interface with external applications to extend the functionality of the intelligent object (100) based on received natural language inputs.

53. An object messaging and intelligent object (OMIO) system, the system comprising: an intelligent object (100), the object comprising: structured data (105) comprising attributes and parameters relating to the object; unstructured data (115) comprising at least one of the following: messages, documents, emails, and multimedia content relating to the object; a computer-readable code module (110) comprising computer-readable instructions for executing an action relating to the object; a stakeholder module (120) configured to establish communication channels for stakeholders associated with the object; a generative artificial intelligence (Al) module (125) embedded within the object; and a super object (300) comprising a plurality of said intelligent objects (100), wherein the super object comprises a super group generative Al module (325) configured to interface with the Al module (125) associated with the plurality of intelligent objects (100) within the super object, and an OMIO master object (OMIO OS) (400) configured to facilitate user interaction with a plurality of super objects (300) through a master Al module (425) embedded within the OMIO OS, wherein the master Al module is configured to process user inputs in natural language and execute actions across the super object based on the processed inputs.

54. The system of claim 53, wherein the master Al model (425) is further configured to dynamically manage interactions between the super object (300) and external applications or devices by translating natural language inputs from a user into executable commands for the super object and the external applications or devices.

55. The system of claim 54, wherein the external applications comprise database systems, cloud services, or other intelligent object systems, and wherein the master Al model (425) is configured to synchronize data between the intelligent object (100), super object (300), and the external applications to maintain data integrity and up-to-date information.

56. The system of claim 55, wherein the master Al model (425) employs machine learning techniques to continuously improve its data synchronization and communication strategies between the intelligent object (100) and external applications.

57. The system of claim 53, wherein master LLM module (425) comprises an encoder and decoder configured to convert natural language inputs into computer-executable instructions for operating with the computer-readable code modules of the intelligent object and the related objects.

58. The system of claim 57, wherein the encoder and decoder enable the OM1O OS to autonomously update the states of the various objects and super objects based on natural language instructions derived from natural language interactions within the communication channels established with at least one of the following: a user (450) interfacing with master LLM module (425), the stakeholder module (120), and the supergroup stakeholder module (320).

59. The system of claim 58, wherein the master Al module (425) is further configured to receive natural language inputs from the user, the inputs comprising a request for information or action related to at least one object within the OM1O OS.

60. The system of claim 59, wherein the master Al module (425) processes the user's natural language inputs to interpret user requirements and generates commands to retrieve information from at least one object within the OM1O OS related to the user's request.

61. The system of claim 60, wherein the commands generated by the master Al model (425) comprise instructions to fulfill the user’s request, the instructions comprising instructions to: structured data (105, 305, 405) and unstructured data (115, 315, 415) related to any intelligent object within the OM1O OS, andperform operations by the computer readable instructions module (110, 310, 410 related to any intelligent object within the OMIO OS.

62. The system of claim 61, wherein the master Al module [425] utilizes a database system to retrieve the information, the database system comprising stored structured data (105) related to multiple objects within the OMIO OS.

63. The system of claim 62, wherein the master Al module (425) further formats the retrieved information into a natural language response and communicates the response to the user.

64. The system of claim 61, wherein the master Al module (425) is further configured to manage interactions between the object (100) and external applications, dynamically adapting operations within the computer-readable code module (110) based on real-time, natural language data received from the userand any object or super object within the OMIO OS.

65. The system of claim 64, wherein the master Al module (425) coordinates with external applications to implement computer executable instructions based on a user's instructions, the external applications comprising at least one of database systems, cloud services, or other intelligent object systems.

66. A method for processing natural language inputs to perform tasks across a plurality of applications within an OMIO OS as claimed in claims 53 through 65, the method comprising: receiving a user input in natural language; interpreting the user input to determine an intended action; identifying at least one application relevant to the intended action; transmitting instructions to the at least one application based on the interpreted user input; receiving data from the at least one application; generating a response to the user based on the data received from the at least one application; and providing the generated response to the user in natural language.

67. The method of claim 66, wherein the user input is received via an audio interface, and the user input comprises spoken language.

68. The method of claim 66, wherein the intended action involves scheduling an event, and the at least one application comprises a calendar application.

69. The method of claim 68, wherein the transmitting instructions further comprise checking availability for a requested time slot in the calendar application.

70. The method of claim 66, wherein the at least one application relevant to the intended action includes a restaurant recommendation application, and the intended action includes identifying a meal option.

71. The method of claim 70, wherein the transmitting instructions further comprise querying the restaurant recommendation application based on dietary preferences specified in the user input.

72. The method of claim 66, further comprising: ascertaining budget constraints from a budget management application; and incorporating the budget constraints into the instructions transmitted to the at least one application.

73. The method of claim 72, wherein the budget constraints relate to financial limitations for purchasing a meal, and the at least one application includes a food ordering application.

74. The method of claim 66, wherein the at least one application relevant to the intended action includes a mapping application, and the intended action includes navigating to a location.

75. The method of claim 74, wherein the transmitting instructions to the mapping application further comprise obtaining directions to the location specified in the user input.

76. The method of claim 66, wherein the data received from the at least one application includes availability of a product or service, and the generating a response to the user further comprises confirming the availability of the product or service to the user.

77. The method of claim 76, wherein the product or service includes a meal from a restaurant, and the availability is confirmed based on real-time inventory data received from the restaurant.

78. The method of claim 66, wherein the at least one application includes a delivery service application, and the intended action includes arranging delivery of a product specified in the user input.

79. The method of claim 78, wherein the generating a response to the user further comprises providing an estimated time of arrival for the delivery.

80. The method of claim 66, wherein the at least one application includes a payment processing application, and the intended action includes processing a payment for a transaction specified in the user input.

81. The method of claim 80, wherein the transmitting instructions further comprise sending payment details to the payment processing application and receiving confirmation of the payment.

82. The method of claim 66, wherein the at least one application includes a customer service application, and the intended action includes resolving a customer service issue specified in the user input.

83. The method of claim 66, further comprising: identifying an error in the data received from the at least one application; and initiating an error resolution process with the at least one application.

84. The method of claim 66, wherein the user input is related to a coding or software debugging request, and the at least one application includes a code analysis tool.

85. The method of claim 66, wherein the user input includes a query regarding the status of a workflow, and the at least one application comprises a project management tool configured to provide status updates.

86. The method of claim 85, wherein the transmitting instructions further comprise requesting a detailed report on the workflow progress from the project management tool.

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