Patent Application
20250217116
Many industries are turning to artificial intelligence tools to automate tasks that previously required significant human labor or were infeasible or impossible for humans to perform. However, despite advancement of these tools, integrating them into some types of environments has proven challenging. Existing tools, for example, lack the inherent capacity to autonomously comprehend and navigate structured software environments without extensive manual guidance. These limitations hamper the ability of artificial intelligence tools to perform tasks seamlessly and efficiently within these environments.
Reference will now be made, by way of example, to the accompanying drawings which show example embodiments of the present application, and in which:
The technologies described herein will become more apparent to those skilled in the art by studying the Detailed Description in conjunction with the drawings. Embodiments or implementations describing aspects of the invention are illustrated by way of example, and the same references can indicate similar elements. While the drawings depict various implementations for the purpose of illustration, those skilled in the art will recognize that alternative implementations can be employed without departing from the principles of the present technologies. Accordingly, while specific implementations are shown in the drawings, the technology is amenable to various modifications.
Artificial intelligence tools provide many beneficial features, from automating routine tasks to performing complex analyses of large datasets. However, integrating these tools into an environment can prove challenging because the tools cannot autonomously navigate these environments. The present technology provides a structured workflow by which an artificial intelligence tool can interact with an environment to perform tasks in the environment. In this workflow, a computer system implements an artificial intelligence tool that performs tasks in the environment in response to natural language instructions received from a user. The tool leverages a large language model to generate computer-readable instruction sets that can perform tasks autonomously in any environment. The system also stores a state of interactions between a user and the artificial intelligence tool such that past interactions can be readily resumed or modified, without losing the context on which these interactions are dependent.
In some aspects, the techniques described herein relate to a computer system that inputs a series of instructions to a large language model (LLM) to generate computer-readable code for performing a plurality of tasks within a digital environment. At least one of the instructions includes a context of the digital environment, and the computer-readable code is executable by the data processor of the computer system to perform the plurality of tasks. The system generates a transcript including the instructions in the series of instructions and corresponding computer-readable code generated by the LLM in response to each instruction. The system stores a representation of each of a plurality of states of the transcript, where each state includes a portion of the transcript that corresponds to a task of the plurality of tasks. The system accesses a stored representation of a first state of the plurality of states and executes computer-readable code associated with the first state, using a context of the environment corresponding to the first state, to reperform a corresponding task in the digital environment.
The description and associated drawings are illustrative examples and are not to be construed as limiting. This disclosure provides certain details for a thorough understanding and enabling description of these examples. One skilled in the relevant technology will understand, however, that the invention can be practiced without many of these details. Likewise, one skilled in the relevant technology will understand that the invention can include well-known structures or features that are not shown or described in detail, to avoid unnecessarily obscuring the descriptions of examples.
The controlled environment 110 is a physical or virtual environment that is operated by one or more computing systems. In an example, the controlled environment 110 is a virtual environment that is accessed via user devices, such as a website, a web application, or a native application. Other example controlled environments 110 include physical systems that are operated by controllers coupled to computing systems, such as manufacturing or testing facilities that employ robotic systems to perform tasks. An example controlled environment 110, in the form of a data and project management platform, is described with respect to
A user can interact with the controlled environment 110 via a user device. When the controlled environment 110 is a virtual environment, for example, the user device can access and display pages or content from the virtual environment to a user. A user can read or edit the environment's content from the user device. Users can also interact with the assistant 120 via the user devices to automate tasks in the environment 110.
The LLM 130 includes one or more language models that are configured to generate text-based outputs in response to prompts. The LLM 130 can include any commercially available or custom models, or a set or ensemble of two or more models. Example features of LLMs are described with respect to
The LLM 130 can be trained to manipulate computer-readable instructions and operate application programming interfaces (APIs) in order to perform tasks in the controlled environment 110. During training, the LLM 130 can be provided with example transcripts that include sample instructions from a user and corresponding code to implement a task in the controlled environment. Training the LLM 130 can include, for example, preference model training that trains the LLM to predict a human preference based on ranked or contrastive pairs, supervised learning that uses human feedback data or synthetic data to train the LLM to predict a next token in a sequence, or reinforcement learning that penalizes the LLM for outputs that do not satisfy specified preference requirements.
The assistant 120 is a computer system or software application that communicates with the controlled environment 110 and a user to perform tasks in the environment 110 based on natural language inputs received from the user. In various implementations, the assistant 120 can communicate with the computer systems that implement or control the environment 110 via a network such as a local network or the Internet, or can be integrated into a device or system that controls the environment. The assistant 120 can interface with the controlled environment 110 to perform observations of the environment and to effect actions within the environment.
An example interface for a user to interact with the assistant 120 is illustrated in
A user of the data management platform can interact with the assistant 120 to perform tasks associated with the page 205. Any of a variety of mechanisms for accessing the functionality of the assistant 120 can be provided within, or associated with, the environment 110. In the example in
In one example, illustrated in
A user can also interact with the assistant 120 within the context of the page 205 to modify the page's content. In
Users can interact with the assistant 120 in ways other than those illustrated in
As will be described further below, the assistant 120 generates computer program code in order to perform observations of the controlled environment 110 and to effects actions within the environment. During a user's interactions with the assistant 120, the assistant 120 generates a transcript that represents a sequence of inputs, outputs, or observations and that maintains a persistent state of this sequence. The transcript includes computer-readable inputs and computer program code that is executed by the assistant 120 to perform tasks related to the controlled environment 110. For example, the transcript includes a set of extensible markup language (XML), JavaScript, or a combination of XML and JavaScript. Provided below is a portion of an example transcript generated during the chat interaction illustrated in
The transcript can include a series of steps, where each step includes computer program instructions associated with user steps or assistant steps. User steps in the transcript can include computer-readable inputs that are generated based on natural language instructions received from a user. The computer-readable inputs can include translating a user's natural language input into a computer-readable form. For example, a user enters, at a text entry box associated with the assistant 120, the natural language instruction, “Please update this page to include this information: {information}.” When generating the transcript, the assistant 120 translates this instruction into the following XML:
At least some user steps can also include a context of the controlled environment 110 at the time a user input was received. The context can include, for example, a state of the environment 110 (e.g., a page the user is viewing or a thread in a collaboration application with which a user is interacting), a date and time of the user input, previous interactions between the user and the assistant 120, or other information that enables the assistant 120 to perform tasks. In some implementations, context is included in any user step that initiates a new interaction with the assistant 120. Context may not be included in user steps of the transcript that continue a prior interaction with the assistant 120, such as the user continuing a chat conversation with the assistant or the user asking the assistant to revise content that the assistant previously generated.
Assistant steps in the transcript include observation steps and action steps. Observations of the controlled environment 110 can include computer program code that, when executed, causes the assistant to observe a value or state of the controlled environment 110. For example, an observation step can return the name of an element on a page or a block of text that matches a query, retrieve a value from a table, identify a relevant document in a document repository, observe properties of elements on a page, determine a last edit date of a document, and so forth.
Action steps in the transcript can include computer program code that, when executed, modifies properties or content within the controlled environment 110. For example, when the controlled environment 110 is a virtual environment such as a data management platform, action steps can include commands to load a page, insert content before or after a specified point on a page, insert content inside of another content block, move content on a page, delete content, or set or modify properties or attributes of items on a page.
Some tasks requested by users are deterministic tasks for which the task result is expected to be a certain, predictable output. For example, reading a value from the environment 110, writing a certain value to the environment, or performing a mathematical operation are deterministic tasks. Other tasks are non-deterministic tasks, such as if a user requests a summary of a document or a recipe that includes items from a shopping list. To perform such deterministic or non-deterministic tasks, at least some implementations of the assistant 120 can cause the LLM to generate XML or some other code, like JavaScript.
In an example, a user inputs a natural language instruction to add content to a structured digital environment, such as a data management platform, where the instruction specifies a location within the environment at which the content should be added. The assistant 120 processes the user's instruction and adds a computer-readable input for the instruction to a transcript. The assistant 120 then generates a prompt to the LLM 130 to generate computer-readable code to perform the requested task. In response, the LLM 130 generates code that is configured to use context of the environment, provided with the computer-readable input or referenced earlier in the transcript, to identify the location within the structured digital environment at which the content is to be inserted. The code generated by the LLM 130 also is executable to cause the content to be written to the structured digital environment at the identified location. The code generated by the LLM 130 is added to the transcript and executed by the assistant 120 to complete the task. In another example, the task requested to be performed by the assistant 120 can be either deterministic or non-deterministic.
In another example, a user inputs a natural language instruction at a data management platform that instructs the assistant 120 to generate content for output via a chat thread (e.g., on a third-party messaging or collaboration platform). To write content to a chat thread, the assistant 120 can collaborate with the LLM 130 on a transcript in a manner like that described in the example above. However, when generating a computer-readable input based on the natural language instruction, the assistant 120 can include a context of the chat thread in the input to enable the assistant to write content to the specified chat thread based on the code generated by the LLM 130. Similarly, if a user inputs a natural language instruction within a chat thread that instructs the assistant 120 to perform a task in a data management platform, the computer-readable input generated by the assistant 120 can include a context of the data management platform.
The assistant 120 can also validate outputs from the LLM 130 to ensure that tasks are performed correctly. The LLM 130 may at times output incorrect code, for example by hallucinating APIs or libraries that do not exist in the language in which the code is written or that are not accessible to the assistant 120. The LLM 130 may also employ improper syntax, generate incorrect data types, fail to fully implement algorithms, or otherwise generate code with bugs, logic errors, or other problems. Errors in the code can also arise based on incorrect inputs by a human user or based on changes to the controlled environment 110. For example, an error may arise when a user requests an update to a table entitled “Quarterly Finances” but the controlled environment 110 has two tables with the same name.
To validate the computer program code output by the LLM 130, the assistant 120 executes instructions and observes results of these executions. Generally, if an observed result matches an expected task response, the assistant 120 determines that the instructions output by the LLM 130 are valid. If the assistant 120 detects an error in an observed result, the assistant 120 causes the LLM 130 to produce new code to correct the error before a task response is finalized. A process for the assistant 120 to validate code written by the LLM 130 is described further with respect to
Some implementations of the controlled environment 110 are structured according to a block data model (“block model”). According to these implementations, the blocks are dynamic units of information that can be transformed into other block types and move across workspaces in response to either user inputs or based on automated tasks performed by a computing system (such as the assistant 120). The block model allows users or the computer system to customize how information is moved, organized, and shared. Hence, blocks contain information but are not siloed.
Blocks are singular pieces that represent all units of information inside an editor. In one example, text, images, lists, a row in a database, etc., are all blocks in a workspace. The attributes of a block determine how that information is rendered and organized. Every block can have attributes including an identifier (ID), properties, and type. Each block is uniquely identifiable by its ID. The properties can include a data structure containing custom attributes about a specific block. An example of a property is “title,” which stores text content of block types such as paragraphs, lists, and the title of a page. More elaborate block types require additional or different properties, such as a page block in a database with user-defined properties. Every block can have a type, which defines how a block is displayed and how the block's properties are interpreted.
A block has attributes that define its relationship with other blocks. For example, the attribute “content” is an array (or ordered set) of block IDs representing the content inside a block, such as nested bullet items in a bulleted list or the text inside a toggle. The attribute “parent” is the block ID of a block's parent, which can be used for permissions. Blocks can be combined with other blocks to track progress and hold all project information in one place.
A block type is what specifies how the block is rendered in a user interface (UI), and the block's properties and content are interpreted differently depending on that type. Changing the type of a block does not change the block's properties or content—it only changes the type attribute. The information is thus rendered differently or even ignored if the property is not used by that block type. Decoupling property storage from block type allows for efficient transformation and changes to rendering logic and is useful for collaboration.
Blocks can be nested inside of other blocks (e.g., infinitely nested sub-pages inside of pages). The content attribute of a block stores the array of block IDs (or pointers) referencing those nested blocks. Each block defines the position and order in which its content blocks are rendered. This hierarchical relationship between blocks and their render children are referred to herein as a “render tree.” In one example, page blocks display their content in a new page, instead of rendering it indented in the current page. To see this content, a user would need to click into the new page.
In the block model, indentation is structural (e.g., reflects the structure of the render tree). In other words, when a user indents something, the user is manipulating relationships between blocks and their content, not just adding a style. For example, pressing Indent in a content block can add that block to the content of the nearest sibling block in the content tree.
Blocks can inherit permissions of blocks in which they are located (which are above them in the tree). Consider a page: to read its contents, a user must be able to read the blocks within that page. However, there are two reasons one cannot use the content array to build the permissions system. First, blocks are allowed to be referenced by multiple content arrays to simplify collaboration and a concurrency model. But because a block can be referenced in multiple places, it is ambiguous which block it would inherit permissions from. The second reason is mechanical. To implement permission checks for a block, one needs to look up the tree, getting that block's ancestors all the way up to the root of the tree (which is the workspace). Trying to find this ancestor path by searching through all blocks' content arrays is inefficient, especially on the client. Instead, the model uses an “upward pointer”—the parent attribute—for the permission system. The upward parent pointers and the downward content pointers mirror each other.
A block's life starts on the client. When a user takes an action in the interface—typing in the editor, dragging blocks around a page—these changes are expressed as operations that create or update a single record. The “records” refer to persisted data, such as blocks, users, workspaces, etc. Because many actions usually change more than one record, operations are batched into transactions that are committed (or rejected) by the server as a group.
Creating and updating blocks can be performed by, for example, pressing Enter on a keyboard. First, the client defines all the initial attributes of the block, generating a new unique ID, setting the appropriate block type (to_do), and filling in the block's properties (an empty title, and checked: [[“No”]]). The client builds operations to represent the creation of a new block with those attributes. New blocks are not created in isolation: blocks are also added to their parent's content array, so they are in the correct position in the content tree. As such, the client also generates an operation to do so. All these individual change operations are grouped into a transaction. Then, the client applies the operations in the transaction to its local state. New block objects are created in memory and existing blocks are modified. In native apps, the model caches all records that are accessed locally in an LRU (least recently used) cache on top of SQLite or IndexedDB, referred to as RecordCache. When records are changed on a native app, the model also updates the local copies in RecordCache. The editor re-renders to draw the newly created block onto the display. At the same time, the transaction is saved into TransactionQueue, the part of the client responsible for sending all transactions to the model's servers so that the data is persisted and shared with collaborators. TransactionQueue stores transactions safely in IndexedDB or SQLite (depending on the platform) until they are persisted by the server or rejected.
A block can be saved on a server to be shared with others. Usually, TransactionQueue sits empty, so the transaction to create the block is sent to the server in an application programming interface (API) request. In one example, the transaction data is serialized to JSON and posted to the /saveTransactions API endpoint. Save Transactions gets the data into source-of-truth databases, which store all block data as well as other kinds of persisted records. Once the request reaches the API server, all the blocks and parents involved in the transaction are loaded. This gives a “before” picture in memory. The block model duplicates the “before” data that had just been loaded in memory. Next, the block model applies the operations in the transaction to the new copy to create the “after” data. Then the model uses both “before” and “after” data to validate the changes for permissions and data coherency. If everything checks out, all created or changed records are committed to the database-meaning the block has now officially been created. At this point, a “success” HTTP response to the original API request is sent by the client. This confirms that the client knows the transaction was saved successfully and that it can move on to saving the next transaction in the TransactionQueue. In the background, the block model schedules additional work depending on the kind of change made for the transaction. For example, the block model can schedule version history snapshots and indexing block text for a Quick Find function. The block model also notifies MessageStore, which is a real-time updates service, about the changes that were made.
The block model provides real-time updates to, for example, almost instantaneously show new blocks to members of a teamspace. Every client can have a long-lived WebSocket connection to the MessageStore. When the client renders a block (or page, or any other kind of record), the client subscribes to changes of that record from MessageStore using the WebSocket connection. When a team member opens the same page, the member is subscribed to changes of all those blocks. After changes have been made through the saveTransactions process, the API notifies MessageStore of new recorded versions. MessageStore finds client connections subscribed to those changing records and passes on the new version through their WebSocket connection. When a team member's client receives version update notifications from MessageStore, it verifies that version of the block in its local cache. Because the versions from the notification and the local block are different, the client sends a syncRecordValues API request to the server with the list of outdated client records. The server responds with the new record data. The client uses this response data to update the local cache with the new version of the records, then re-renders the user interface to display the latest block data.
Blocks can be shared instantaneously with collaborators. In one example, a page is loaded using only local data. On the web, block data is pulled from being in memory. On native apps, loading blocks that are not in memory are loaded from the RecordCache persisted storage. However, if missing block data is needed, the data is requested from an API. The API method for loading the data for a page is referred to herein as loadPageChunk; it descends from a starting point (likely the block ID of a page block) down the content tree and returns the blocks in the content tree plus any dependent records needed to properly render those blocks. Several layers of caching for loadPageChunk are used, but in the worst case, this API might need to make multiple trips to the database as it recursively crawls down the tree to find blocks and their record dependencies. All data loaded by loadPageChunk is put into memory (and saved in the RecordCache if using the app). Once the data is in memory, the page is laid out and rendered using React.
In some implementations, the user application 302 is a cross-platform software application configured to work on several computing platforms and web browsers. The user application 302 can include a variety of templates. A template refers to a prebuilt page that a user can add to a workspace within the user application 302. The templates can be directed to a variety of functions. Exemplary templates include a docs template 308, a wikis template 310, a projects template 312, and a meeting and calendar template 314. In some implementations, a user can generate, save, and share customized templates with other users.
The user application 302 templates can be based on content “blocks.” For example, the templates of the user application 302 include a predefined and/or pre-organized set of blocks that can be customized by the user. Blocks are content containers within a template that can include text, images, objects, tables, maps, and/or other pages (e.g., nested pages or sub-pages). Blocks can be assigned to certain properties. The blocks are defined by boundaries having dimensions. The boundaries can be visible or non-visible for users. For example, a block can be assigned as a text block (e.g., a block including text content), a heading block (e.g., a block including a heading) or a sub-heading block having a specific location and style to assist in organizing a page. A block can be assigned as a list block to include content in a list format. A block can be assigned as an AI prompt block (also referred to as a “prompt block”) that enables a user to provide instructions (e.g., prompts) to the AI tool 304 to perform functions. A block can also be assigned to include audio, video, or image content.
A user can add, edit, and remove content from the blocks. The user can also organize the content within a page by moving the blocks around. In some implementations, the blocks are shared (e.g., by copying and pasting) between the different templates within a workspace. For example, a block embedded within multiple templates can be configured to show edits synchronously.
The docs template 308 is a document generation and organization tool that can be used for generating a variety of documents. For example, the docs template 308 can be used to generate pages that are easy to organize, navigate, and format. The wikis template 310 is a knowledge management application having features similar to the pages generated by the docs template 308 but that can additionally be used as a database. The wikis template 310 can include, for example, tags configured to categorize pages by topic and/or include an indication of whether the provided information is verified to indicate its accuracy and reliability. The projects template 312 is a project management and note-taking software tool. The projects template 312 can allow the users, either as individuals or as teams, to plan, manage, and execute projects in a single forum. The meeting and calendar template 314 is a tool for managing tasks and timelines. In addition to traditional calendar features, the meeting and calendar template 314 can include blocks for categorizing and prioritizing scheduled tasks, generating to-do and action item lists, tracking productivity, etc. The various templates of the user application 302 can be included under a single workspace and include synchronized blocks. For example, a user can update a project deadline on the projects template 312, which can be automatically synchronized to the meeting and calendar template 314. The various templates of the user application 302 can be shared within a team, allowing multiple users to modify and update the workspace concurrently.
The AI tool 304 is an integrated AI assistant that enables AI-based functions for the user application 302. In one example, the AI tool 304 is based on a neural network architecture, such as the transformer 512 described in
The writing assistant tool 316 can operate as a generative AI tool for creating content for the blocks in accordance with instructions received from a user. Creating the content can include, for example, summarizing, generating new text, or brainstorming ideas. For example, in response to a prompt received as a user input that instructs the AI to describe what the climate is like in New York, the writing assistant tool 316 can generate a block including a text that describes the climate in New York. As another example, in response to a prompt that requests ideas on how to name a pet, the writing assistant tool 316 can generate a block including a list of creative pet names. The writing assistant tool 316 can also operate to modify existing text. For example, the writing assistant can shorten, lengthen, or translate existing text, correct grammar and typographical errors, or modify the style of the text (e.g., a social media style versus a formal style).
The knowledge management tool 318 can use AI to categorize, organize, and share knowledge included in the workspace. In some implementations, the knowledge management tool 318 can operate as a question-and-answer assistant. For example, a user can provide instructions on a prompt block to ask a question. In response to receiving the question, the knowledge management tool 318 can provide an answer to the question, for example, based on information included in the wikis template 310. The project management tool 320 can provide AI support for the projects template 312. The AI support can include auto filling information based on changes within the workspace or automatically track project development. For example, the project management tool 320 can use AI for task automation, data analysis, real-time monitoring of project development, allocation of resources, and/or risk mitigation. The meeting and scheduling tool 322 can use AI to organize meeting notes, unify meeting records, list key information from meeting minutes, and/or connect meeting notes with deliverable deadlines.
The server 306 can include various units (e.g., including compute and storage units) that enable the operations of the AI tool 304 and workspaces of the user application 302. The server 306 can include an integrations unit 324, an application programming interface (API) 328, databases 326, and an administration (admin) unit 330. The databases 326 are configured to store data associated with the blocks. The data associated with the blocks can include information about the content included in the blocks, the function associated with the blocks, and/or any other information related to the blocks. The API 328 can be configured to communicate the block data between the user application 302, the AI tool 304, and the databases 326. The API 328 can also be configured to communicate with remote server systems, such as AI systems. For example, when a user performs a transaction within a block of a template of the user application 302 (e.g., in a docs template 308), the API 328 processes the transaction and saves the changes associated with the transaction to the database 326. The integrations unit 324 is a tool connecting the platform 200 with external systems and software platforms. Such external systems and platforms can include other databases (e.g., cloud storage spaces), messaging software applications, or audio or video conference applications. The administration unit 330 is configured to manage and maintain the operations and tasks of the server 306. For example, the administration unit 330 can manage user accounts, data storage, security, performance monitoring, etc.
A teamspace can refer to a collaborative space associated with a team or an organization that is hierarchically below a workspace. For example, a workspace can include a teamspace accessible by all users of an organization and multiple teamspaces that are accessible by users of different teams. Accessibility generally refers to creating, editing, and/or viewing content (e.g., pages) included in the workspace or the one or more teamspaces.
In the hierarchical organization illustrated in
The relationships and organization of the content can be modified by changing the location of the pages. For example, when a child page is moved to be under a different parent, the child page's access permission modifies to correspond to the access permission of the new parent. Also, when the access permission of “Parent Page” is modified, the access permission of “Page 1,” “Page 2,” and “Page 3” can be automatically modified to correspond to the access permission of “Parent Page” based on the inheritance character of access permissions.
In contrast, however, a user can modify the access permission of the children independently of their parents. For example, the user can modify the access permission of “Page 2 Child” in
To assist in understanding the present disclosure, some concepts relevant to neural networks and machine learning (ML) are discussed herein. Generally, a neural network comprises a number of computation units (sometimes referred to as “neurons”). Each neuron receives an input value and applies a function to the input to generate an output value. The function typically includes a parameter (also referred to as a “weight”) whose value is learned through the process of training. A plurality of neurons may be organized into a neural network layer (or simply “layer”) and there may be multiple such layers in a neural network. The output of one layer may be provided as input to a subsequent layer. Thus, input to a neural network may be processed through a succession of layers until an output of the neural network is generated by a final layer. This is a simplistic discussion of neural networks and there may be more complex neural network designs that include feedback connections, skip connections, and/or other such possible connections between neurons and/or layers, which are not discussed in detail here.
A deep neural network (DNN) is a type of neural network having multiple layers and/or a large number of neurons. The term DNN can encompass any neural network having multiple layers, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), multilayer perceptrons (MLPs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Auto-regressive Models, among others.
DNNs are often used as ML-based models for modeling complex behaviors (e.g., human language, image recognition, object classification, etc.) in order to improve the accuracy of outputs (e.g., more accurate predictions) such as, for example, as compared with models with fewer layers. In the present disclosure, the term “ML-based model” or more simply “ML model” may be understood to refer to a DNN. Training an ML model refers to a process of learning the values of the parameters (or weights) of the neurons in the layers such that the ML model is able to model the target behavior to a desired degree of accuracy. Training typically requires the use of a training dataset, which is a set of data that is relevant to the target behavior of the ML model.
As an example, to train an ML model that is intended to model human language (also referred to as a “language model”), the training dataset may be a collection of text documents, referred to as a “text corpus” (or simply referred to as a “corpus”). The corpus may represent a language domain (e.g., a single language), a subject domain (e.g., scientific papers), and/or may encompass another domain or domains, be they larger or smaller than a single language or subject domain. For example, a relatively large, multilingual, and non-subject-specific corpus can be created by extracting text from online webpages and/or publicly available social media posts. Training data can be annotated with ground truth labels (e.g., each data entry in the training dataset can be paired with a label) or may be unlabeled.
Training an ML model generally involves inputting into an ML model (e.g., an untrained ML model) training data to be processed by the ML model, processing the training data using the ML model, collecting the output generated by the ML model (e.g., based on the inputted training data), and comparing the output to a desired set of target values. If the training data is labeled, the desired target values may be, e.g., the ground truth labels of the training data. If the training data is unlabeled, the desired target value may be a reconstructed (or otherwise processed) version of the corresponding ML model input (e.g., in the case of an autoencoder), or can be a measure of some target observable effect on the environment (e.g., in the case of a reinforcement learning agent). The parameters of the ML model are updated based on a difference between the generated output value and the desired target value. For example, if the value outputted by the ML model is excessively high, the parameters may be adjusted so as to lower the output value in future training iterations. An objective function is a way to quantitatively represent how close the output value is to the target value. An objective function represents a quantity (or one or more quantities) to be optimized (e.g., minimize a loss or maximize a reward) in order to bring the output value as close to the target value as possible. The goal of training the ML model typically is to minimize a loss function or maximize a reward function.
The training data can be a subset of a larger data set. For example, a data set may be split into three mutually exclusive subsets: a training set, a validation (or cross-validation) set, and a testing set. The three subsets of data may be used sequentially during ML model training. For example, the training set may be first used to train one or more ML models, each ML model, e.g., having a particular architecture, having a particular training procedure, being describable by a set of model hyperparameters, and/or otherwise being varied from the other of the one or more ML models. The validation (or cross-validation) set may then be used as input data into the trained ML models to, e.g., measure the performance of the trained ML models and/or compare performance between them. Where hyperparameters are used, a new set of hyperparameters can be determined based on the measured performance of one or more of the trained ML models, and the first step of training (e.g., with the training set) may begin again on a different ML model described by the new set of determined hyperparameters. In this way, these steps can be repeated to produce a more performant trained ML model. Once such a trained ML model is obtained (e.g., after the hyperparameters have been adjusted to achieve a desired level of performance), a third step of collecting the output generated by the trained ML model applied to the third subset (the testing set) may begin. The output generated from the testing set may be compared with the corresponding desired target values to give a final assessment of the trained ML model's accuracy. Other segmentations of the larger data set and/or schemes for using the segments for training one or more ML models are possible.
Backpropagation is an algorithm for training an ML model. Backpropagation is used to adjust (e.g., update) the value of the parameters in the ML model, with the goal of optimizing the objective function. For example, a defined loss function is calculated by forward propagation of an input to obtain an output of the ML model and a comparison of the output value with the target value. Backpropagation calculates a gradient of the loss function with respect to the parameters of the ML model, and a gradient algorithm (e.g., gradient descent) is used to update (e.g., “learn”) the parameters to reduce the loss function. Backpropagation is performed iteratively so that the loss function is converged or minimized. Other techniques for learning the parameters of the ML model can be used. The process of updating (or learning) the parameters over many iterations is referred to as training. Training may be carried out iteratively until a convergence condition is met (e.g., a predefined maximum number of iterations has been performed, or the value outputted by the ML model is sufficiently converged with the desired target value), after which the ML model is considered to be sufficiently trained. The values of the learned parameters can then be fixed and the ML model may be deployed to generate output in real-world applications (also referred to as “inference”).
In some examples, a trained ML model may be fine-tuned, meaning that the values of the learned parameters may be adjusted slightly in order for the ML model to better model a specific task. Fine-tuning of an ML model typically involves further training the ML model on a number of data samples (which may be smaller in number/cardinality than those used to train the model initially) that closely target the specific task. For example, an ML model for generating natural language that has been trained generically on publicly available text corpora may be, e.g., fine-tuned by further training using specific training samples. The specific training samples can be used to generate language in a certain style or in a certain format. For example, the ML model can be trained to generate a blog post having a particular style and structure with a given topic.
Some concepts in ML-based language models are now discussed. It may be noted that, while the term “language model” has been commonly used to refer to an ML-based language model, there could exist non-ML language models. In the present disclosure, the term “language model” can refer to an ML-based language model (e.g., a language model that is implemented using a neural network or other ML architecture), unless stated otherwise. For example, unless stated otherwise, the “language model” encompasses LLMs.
A language model can use a neural network (typically a DNN) to perform natural language processing (NLP) tasks. A language model can be trained to model how words relate to each other in a textual sequence, based on probabilities. A language model may contain hundreds of thousands of learned parameters or, in the case of an LLM, can contain millions or billions of learned parameters or more. As non-limiting examples, a language model can generate text, translate text, summarize text, answer questions, write code (e.g., Python, JavaScript, or other programming languages), classify text (e.g., to identify spam emails), create content for various purposes (e.g., social media content, factual content, or marketing content), or create personalized content for a particular individual or group of individuals. Language models can also be used for chatbots (e.g., virtual assistance).
A type of neural network architecture, referred to as a “transformer,” can be used for language models. For example, the Bidirectional Encoder Representations from Transformers (BERT) model, the Transformer-XL model, and the Generative Pre-trained Transformer (GPT) models are types of transformers. A transformer is a type of neural network architecture that uses self-attention mechanisms in order to generate predicted output based on input data that has some sequential meaning (i.e., the order of the input data is meaningful, which is the case for most text input). Although transformer-based language models are described herein, it should be understood that the present disclosure may be applicable to any ML-based language model, including language models based on other neural network architectures such as recurrent neural network (RNN)-based language models.
The transformer 512 includes an encoder 508 (which can include one or more encoder layers/blocks connected in series) and a decoder 510 (which can include one or more decoder layers/blocks connected in series). Generally, the encoder 508 and the decoder 510 each include multiple neural network layers, at least one of which can be a self-attention layer. The parameters of the neural network layers can be referred to as the parameters of the language model.
The transformer 512 can be trained to perform certain functions on a natural language input. Examples of the functions include summarizing existing content, brainstorming ideas, writing a rough draft, fixing spelling and grammar, and translating content. Summarizing can include extracting key points or themes from an existing content in a high-level summary. Brainstorming ideas can include generating a list of ideas based on provided input. For example, the ML model can generate a list of names for a startup or costumes for an upcoming party. Writing a rough draft can include generating writing in a particular style that could be useful as a starting point for the user's writing. The style can be identified as, e.g., an email, a blog post, a social media post, or a poem. Fixing spelling and grammar can include correcting errors in an existing input text. Translating can include converting an existing input text into a variety of different languages. In some implementations, the transformer 512 is trained to perform certain functions on other input formats than natural language input. For example, the input can include objects, images, audio content, or video content, or a combination thereof.
The transformer 512 can be trained on a text corpus that is labeled (e.g., annotated to indicate verbs, nouns) or unlabeled. LLMs can be trained on a large unlabeled corpus. The term “language model,” as used herein, can include an ML-based language model (e.g., a language model that is implemented using a neural network or other ML architecture), unless stated otherwise. Some LLMs can be trained on a large multi-language, multi-domain corpus to enable the model to be versatile at a variety of language-based tasks such as generative tasks (e.g., generating human-like natural language responses to natural language input).
For example, the word “greater” can be represented by a token for [great] and a second token for [er]. In another example, the text sequence “write a summary” can be parsed into the segments [write], [a], and [summary], each of which can be represented by a respective numerical token. In addition to tokens that are parsed from the textual sequence (e.g., tokens that correspond to words and punctuation), there can also be special tokens to encode non-textual information. For example, a [CLASS] token can be a special token that corresponds to a classification of the textual sequence (e.g., can classify the textual sequence as a list, a paragraph), an [EOT] token can be another special token that indicates the end of the textual sequence, other tokens can provide formatting information, etc.
In
An embedding 506 is a learned numerical representation (such as, for example, a vector) of a token that captures some semantic meaning of the text segment represented by the token 502. The embedding 506 represents the text segment corresponding to the token 502 in a way such that embeddings corresponding to semantically related text are closer to each other in a vector space than embeddings corresponding to semantically unrelated text. For example, assuming that the words “write,” “a,” and “summary” each correspond to, respectively, a “write” token, an “a” token, and a “summary” token when tokenized, the embedding 506 corresponding to the “write” token will be closer to another embedding corresponding to the “jot down” token in the vector space as compared to the distance between the embedding 506 corresponding to the “write” token and another embedding corresponding to the “summary” token.
The vector space can be defined by the dimensions and values of the embedding vectors. Various techniques can be used to convert a token 502 to an embedding 506. For example, another trained ML model can be used to convert the token 502 into an embedding 506. In particular, another trained ML model can be used to convert the token 502 into an embedding 506 in a way that encodes additional information into the embedding 506 (e.g., a trained ML model can encode positional information about the position of the token 502 in the text sequence into the embedding 506). In some implementations, the numerical value of the token 502 can be used to look up the corresponding embedding in an embedding matrix 504, which can be learned during training of the transformer 512.
The generated embeddings 506 are input into the encoder 508. The encoder 508 serves to encode the embeddings 506 into feature vectors 514 that represent the latent features of the embeddings 506. The encoder 508 can encode positional information (i.e., information about the sequence of the input) in the feature vectors 514. The feature vectors 514 can have very high dimensionality (e.g., on the order of thousands or tens of thousands), with each element in a feature vector 514 corresponding to a respective feature. The numerical weight of each element in a feature vector 514 represents the importance of the corresponding feature. The space of all possible feature vectors 514 that can be generated by the encoder 508 can be referred to as a latent space or feature space.
Conceptually, the decoder 510 is designed to map the features represented by the feature vectors 514 into meaningful output, which can depend on the task that was assigned to the transformer 512. For example, if the transformer 512 is used for a translation task, the decoder 510 can map the feature vectors 514 into text output in a target language different from the language of the original tokens 502. Generally, in a generative language model, the decoder 510 serves to decode the feature vectors 514 into a sequence of tokens. The decoder 510 can generate output tokens 516 one by one. Each output token 516 can be fed back as input to the decoder 510 in order to generate the next output token 516. By feeding back the generated output and applying self-attention, the decoder 510 can generate a sequence of output tokens 516 that has sequential meaning (e.g., the resulting output text sequence is understandable as a sentence and obeys grammatical rules). The decoder 510 can generate output tokens 516 until a special [EOT] token (indicating the end of the text) is generated. The resulting sequence of output tokens 516 can then be converted to a text sequence in post-processing. For example, each output token 516 can be an integer number that corresponds to a vocabulary index. By looking up the text segment using the vocabulary index, the text segment corresponding to each output token 516 can be retrieved, the text segments can be concatenated together, and the final output text sequence can be obtained.
In some implementations, the input provided to the transformer 512 includes instructions to perform a function on an existing text. The output can include, for example, a modified version of the input text and instructions to modify the text. The modification can include summarizing, translating, correcting grammar or spelling, changing the style of the input text, lengthening or shortening the text, or changing the format of the text (e.g., adding bullet points or checkboxes). As an example, the input text can include meeting notes prepared by a user and the output can include a high-level summary of the meeting notes. In other examples, the input provided to the transformer includes a question or a request to generate text. The output can include a response to the question, text associated with the request, or a list of ideas associated with the request. For example, the input can include the question “What is the weather like in San Francisco?” and the output can include a description of the weather in San Francisco. As another example, the input can include a request to brainstorm names for a flower shop and the output can include a list of relevant names.
Although a general transformer architecture for a language model and its theory of operation have been described above, this is not intended to be limiting. Existing language models include language models that are based only on the encoder of the transformer or only on the decoder of the transformer. An encoder-only language model encodes the input text sequence into feature vectors that can then be further processed by a task-specific layer (e.g., a classification layer). BERT is an example of a language model that can be considered to be an encoder-only language model. A decoder-only language model accepts embeddings as input and can use auto-regression to generate an output text sequence. Transformer-XL and GPT-type models can be language models that are considered to be decoder-only language models.
Because GPT-type language models tend to have a large number of parameters, these language models can be considered LLMs. An example of a GPT-type LLM is GPT-3. GPT-3 is a type of GPT language model that has been trained (in an unsupervised manner) on a large corpus derived from documents available online to the public. GPT-3 has a very large number of learned parameters (on the order of hundreds of billions), can accept a large number of tokens as input (e.g., up to 2,048 input tokens), and is able to generate a large number of tokens as output (e.g., up to 2,048 tokens). GPT-3 has been trained as a generative model, meaning that it can process input text sequences to predictively generate a meaningful output text sequence. ChatGPT is built on top of a GPT-type LLM and has been fine-tuned with training datasets based on text-based chats (e.g., chatbot conversations). ChatGPT is designed for processing natural language, receiving chat-like inputs, and generating chat-like outputs.
A computer system can access a remote language model (e.g., a cloud-based language model), such as ChatGPT or GPT-3, via a software interface (e.g., an API). Additionally or alternatively, such a remote language model can be accessed via a network such as the Internet. In some implementations, such as, for example, potentially in the case of a cloud-based language model, a remote language model can be hosted by a computer system that can include a plurality of cooperating (e.g., cooperating via a network) computer systems that can be in, for example, a distributed arrangement. Notably, a remote language model can employ multiple processors (e.g., hardware processors such as, for example, processors of cooperating computer systems). Indeed, processing of inputs by an LLM can be computationally expensive/can involve a large number of operations (e.g., many instructions can be executed/large data structures can be accessed from memory), and providing output in a required timeframe (e.g., real time or near real time) can require the use of a plurality of processors/cooperating computing devices as discussed above.
Inputs to an LLM can be referred to as a prompt, which is a natural language input that includes instructions to the LLM to generate a desired output. A computer system can generate a prompt that is provided as input to the LLM via an API (e.g., the API 328 in
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At 604, the computer system generates a computer-readable input based on the received instructions. The computer-readable input can include a context of the environment, as well as a computer-readable form of the user's natural language instructions.
At 606, the computer system sends the computer-readable input to a large language model (LLM) to cause the LLM to generate a set of computer program code, such as XML or JavaScript, to perform the task requested by the user. The LLM can be trained using pairs of user instructions and code to perform the instructions, such that the LLM is configured to process the computer-readable input and to generate computer program code in response. Depending on the task that is to be performed or the structure of the environment, the computer system can cause the LLM to generate different types of computer program code. The computer system can validate the output of the LLM to ensure that the code is correct, such as verifying that it employs real APIs, functions, and syntax within the appropriate coding language.
At 608, after the LLM has returned the generated computer program code, the computer system executes the code to perform the task in the environment.
Using the process illustrated in
As discussed with respect to
At 702, the computer system uses an LLM to generate a first set of XML instructions associated with performing a first task in an environment coupled to the computing system. Like implementations described above, the environment in which the task is performed can be the data management platform, a collaboration platform, or any other physical or virtual environment whose state or content can be remotely read and/or modified by a computing system. The first task can include any action to retrieve a current state of the environment or to modify a current state of the environment, such as observing a value stored in the data management platform or writing content to a chat thread within the collaboration platform. In some cases, the computer system causes the LLM to generate the first set of XML instructions in response to a natural language input from a user to perform the first task or to perform a series of tasks that includes the first task. Alternatively, the computer system can trigger generation of the first set of XML in response to another task performed by the computer system.
After receiving a first set of XML instructions from the LLM, the computer system executes the first set of instructions (at 704) and observes a result of the execution (at 706). The computer system can evaluate, for example, whether the instructions are executable or whether the instructions cannot be executed due to syntax errors, hallucinated API calls, or the like. If the instructions cannot be executed, the computer system can further process the first set of instructions to identify a cause of the error. Some implementations of the system can perform some automated evaluation of the first set of instructions, such as verifying that each tag is closed. Alternatively, the system can send a non-executable line of XML instructions to an LLM, optionally a different LLM than the LLM used to initially produce the XML, to ask the LLM to identify any errors in the non-executable line. Identifications of lines with errors can also be output to a user of the computer system for review by the user.
If the first set of XML instructions can be executed, the computer system can evaluate whether execution of the instructions yields an expected result at 708. For example, if the instruction is to write content to a specified location within a structured digital environment, the computer system evaluates whether the specified location can be found in the environment, whether the content can be generated, and—after performing the write operation—observing whether the correct content was written to the correct location. Similarly, if the instruction is to read a value from the environment, the computer system can evaluate whether execution of the instructions returns a read value or whether the read operation returns a null value.
If execution of the first set of XML instructions does not return an expected result, the computer system can use the LLM to modify the first set of XML instructions, at 710. Depending on the error detected, the system may provide the original set of instructions back to the LLM, with a request to change an aspect of the XML that is produced. For other types of errors, the system can the LLM to generate new instructions or can ask a different LLM to generate the instructions.
The computer system can repeat the operations at 704-710 until a set of instructions that correctly perform the first task has been generated.
Once the first set of XML instructions have been determined to be executable to produce an expected result, the computer system executes the first set of XML, at 712, to perform the first task in the environment. For example, the computer system can write content to the environment or delete content from the environment according to the instructions for the first task.
Finally, at 714, the computer system uses the LLM to generate a second set of XML instructions to perform a second task in the environment. The second set of XML instructions can be generated concurrently with the first set of XML instructions or after the first set of instructions have been determined to produce an expected result, for example. For the second set of instructions, the computer system can use a similar process as that described above to validate the second set of instructions.
According to some implementations, the assistant 120 described herein maintains a data model that allows the assistant 120 to resume past interactions between a user and the assistant. At times, users may want to restore a transcript from a prior point in order to fix an error from earlier in an interaction or because the user was dissatisfied with an earlier task result output by the assistant. Alternatively, a user may desire to access an earlier state of a transcript in order to repeat a task. Since actions performed by the assistant 120 are context-dependent, the assistant 120 stores states of the transcript in a manner that enables past interactions to be resumed in accordance with the context of the environment that existed at the time of the interaction.
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As a transcript is generated, the computer system stores representations of progressive states of the transcript. Each state includes a portion of the transcript that corresponds to a task of the plurality of tasks. The computer system can capture a state of the transcript after selected steps, such as after each assistant step is added. Each captured state can be stored in a repository associated with the controlled environment 110. The stored components can include:
In some implementations, the assistant 120 stores each state within the transcript in a corresponding data structure, such as a row of a table or an entry of a database. Each of these data structures contains a full copy of the data needed to reconstruct an interaction between a user and the assistant back to the corresponding state. By storing each state as it is produced, the state can be restored at any point to, for example, re-execute or modify any step in the interaction. However, in other implementations, the computer system does not store every state. For example, a transcript can be collapsed or compressed into a representation of a current state and a representation of one or more previous states. In this collapsed or compressed representation, the computer system can store data sufficient to reconstruct the current state and the one or more previous states, potentially without storing an entirety of the transcript at this state. For example, the collapsed or compressed representation can include context of the environment and a user input, but not the computer-readable code generated by the LLM in response to the user input. In another example, the collapsed or compressed representation includes LLM-generated code, but not the user inputs that were used to generate the LLM-generated code. The assistant can use the collapsed or compressed transcript to reconstruct any state that is represented in the transcript, which may be less than all existing states.
At 808, the computer system accesses a stored representation of a first state of the plurality of states and executes, at 810, the computer-readable code associated with the first state to reperform a corresponding task in the environment.
The computer system can execute the computer-readable code using a context of the environment that is included within the stored representation of the first state. For example, the first state can identify a particular page within a data management platform on which the computer-readable code is to be executed, and the computer system can perform corresponding actions on the identified page. In other cases, the context of the environment may not be included in the stored representation of the first state, but can instead be included in a stored representation of a second state that is associated with the first state. For example, if a transcript included a series of interactions between a user and the assistant 120, the context may be included in an earlier user step in the transcript but not included in later steps. Accordingly, to execute the computer-readable code for the first state, the computer system may access the context from a stored representation of a second state.
When accessing the stored representation of the first state, the computer system can enable a user to modify the first state. Users can be enabled to change the natural language input corresponding to the state, computer-readable code generated by the LLM, or the environment itself in order to change the task that is performed or to improve the performance of the task within the environment. If a user modifies the input or the environment itself, the computer system can cause the LLM to regenerate the computer-readable code for performing a corresponding task and subsequently execute the regenerated code.
In some implementations, the computer system provides suggested edits in a transcript history, such as recommending a modification to a user's natural language input or to recommend an additional natural language input that can perform a task that is predicted to be relevant to the environment. When a user accesses a previous state of a transcript, for example to modify the task performed by the assistant at a corresponding step, the computer system can suggest that the user modify a natural language instruction for the task to better perform the task, modify the environment to make the task easier to perform, manually modify the code generated by the LLM 130, or otherwise change the contents of the stored transcript states. The computer system can generate the suggestions by, for example, analyzing transcripts generated as other users interact with the assistant to determine transcript elements that produce successful task results. Other suggestions can be generated by analyzing transcripts generated as users interact with similar environments, in order to determine tasks that are commonly performed in these environments and thus predict a task that the user may desire to perform.
The computer system can enforce permissions for a transcript. A transcript associated with an environment can be visible, in some cases, to any user who has write permissions for the environment. Alternatively, the computer system can store a transcript in association with a portion of an environment (e.g., a page within a data management platform) and make the transcript available to any user who has permission to view the associated portion of the environment. In other cases, only a subset of users with read access to the environment or a portion of the environment are permitted to access the stored transcript and to make changes to the states stored within the transcript.
The computer system 900 can take any suitable physical form. For example, the computer system 900 can share a similar architecture as that of a server computer, personal computer (PC), tablet computer, mobile telephone, wearable electronic device, network-connected (“smart”) device (e.g., a television or home assistant device), AR/VR system (e.g., head-mounted display), or any electronic device capable of executing a set of instructions that specify action(s) to be taken by the computer system 900. In some implementations, the computer system 900 can be an embedded computer system, a system-on-chip (SOC), a single-board computer (SBC) system, or a distributed system such as a mesh of computer systems or include one or more cloud components in one or more networks. Where appropriate, one or more computer systems 900 can perform operations in real time, near real time, or in batch mode.
The network interface device 912 enables the computer system 900 to mediate data in a network 914 with an entity that is external to the computer system 900 through any communication protocol supported by the computer system 900 and the external entity. Examples of the network interface device 912 include a network adapter card, a wireless network interface card, a router, an access point, a wireless router, a switch, a multilayer switch, a protocol converter, a gateway, a bridge, bridge router, a hub, a digital media receiver, and/or a repeater, as well as all wireless elements noted herein.
The memory (e.g., main memory 906, non-volatile memory 910, machine-readable medium 926) can be local, remote, or distributed. Although shown as a single medium, the machine-readable medium 926 can include multiple media (e.g., a centralized/distributed database and/or associated caches and servers) that store one or more sets of instructions 928. The machine-readable medium 926 can include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the computer system 900. The machine-readable medium 926 can be non-transitory or comprise a non-transitory device. In this context, a non-transitory storage medium can include a device that is tangible, meaning that the device has a concrete physical form, although the device can change its physical state. Thus, for example, non-transitory refers to a device remaining tangible despite this change in state.
Although implementations have been described in the context of fully functioning computing devices, the various examples are capable of being distributed as a program product in a variety of forms. Examples of machine-readable storage media, machine-readable media, or computer-readable media include recordable-type media such as volatile and non-volatile memory devices 910, removable flash memory, hard disk drives, optical disks, and transmission-type media such as digital and analog communication links.
In general, the routines executed to implement examples herein can be implemented as part of an operating system or a specific application, component, program, object, module, or sequence of instructions (collectively referred to as “computer programs”). The computer programs typically comprise one or more instructions (e.g., instructions 904, 908, 928) set at various times in various memory and storage devices in computing device(s). When read and executed by the processor 902, the instruction(s) cause the computer system 900 to perform operations to execute elements involving the various aspects of the disclosure.
The terms “example,” “embodiment,” and “implementation” are used interchangeably. For example, references to “one example” or “an example” in the disclosure can be, but not necessarily are, references to the same implementation; and such references mean at least one of the implementations. The appearances of the phrase “in one example” are not necessarily all referring to the same example, nor are separate or alternative examples mutually exclusive of other examples. A feature, structure, or characteristic described in connection with an example can be included in another example of the disclosure. Moreover, various features are described that can be exhibited by some examples and not by others. Similarly, various requirements are described that can be requirements for some examples but not other examples.
The terminology used herein should be interpreted in its broadest reasonable manner, even though it is being used in conjunction with certain specific examples of the invention. The terms used in the disclosure generally have their ordinary meanings in the relevant technical art, within the context of the disclosure, and in the specific context where each term is used. A recital of alternative language or synonyms does not exclude the use of other synonyms. Special significance should not be placed upon whether or not a term is elaborated or discussed herein. The use of highlighting has no influence on the scope and meaning of a term. Further, it will be appreciated that the same thing can be said in more than one way.
Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense, as opposed to an exclusive or exhaustive sense; that is to say, in the sense of “including, but not limited to.” As used herein, the terms “connected,” “coupled,” or any variant thereof means any connection or coupling, either direct or indirect, between two or more elements; the coupling or connection between the elements can be physical, logical, or a combination thereof. Additionally, the words “herein,” “above,” “below,” and words of similar import can refer to this application as a whole and not to any particular portions of this application. Where context permits, words in the Detailed Description above using the singular or plural number may also include the plural or singular number respectively. The word “or” in reference to a list of two or more items covers all of the following interpretations of the word: any of the items in the list, all of the items in the list, and any combination of the items in the list. The term “module” refers broadly to software components, firmware components, and/or hardware components.
While specific examples of technology are described above for illustrative purposes, various equivalent modifications are possible within the scope of the invention, as those skilled in the relevant art will recognize. For example, while processes or blocks are presented in a given order, alternative implementations can perform routines having steps, or employ systems having blocks, in a different order, and some processes or blocks may be deleted, moved, added, subdivided, combined, and/or modified to provide alternative or sub-combinations. Each of these processes or blocks can be implemented in a variety of different ways. Also, while processes or blocks are at times shown as being performed in series, these processes or blocks can instead be performed or implemented in parallel, or can be performed at different times. Further, any specific numbers noted herein are only examples such that alternative implementations can employ differing values or ranges.
Details of the disclosed implementations can vary considerably in specific implementations while still being encompassed by the disclosed teachings. As noted above, particular terminology used when describing features or aspects of the invention should not be taken to imply that the terminology is being redefined herein to be restricted to any specific characteristics, features, or aspects of the invention with which that terminology is associated. In general, the terms used in the following claims should not be construed to limit the invention to the specific examples disclosed herein, unless the Detailed Description above explicitly defines such terms. Accordingly, the actual scope of the invention encompasses not only the disclosed examples but also all equivalent ways of practicing or implementing the invention under the claims. Some alternative implementations can include additional elements to those implementations described above or include fewer elements.
Any patents and applications and other references noted above, and any that may be listed in accompanying filing papers, are incorporated herein by reference in their entireties, except for any subject matter disclaimers or disavowals, and except to the extent that the incorporated material is inconsistent with the express disclosure herein, in which case the language in this disclosure controls. Aspects of the invention can be modified to employ the systems, functions, and concepts of the various references described above to provide yet further implementations of the invention.
To reduce the number of claims, certain implementations are presented below in certain claim forms, but the applicant contemplates various aspects of an invention in other forms. For example, aspects of a claim can be recited in a means-plus-function form or in other forms, such as being embodied in a computer-readable medium. A claim intended to be interpreted as a mean-plus-function claim will use the words “means for.” However, the use of the term “for” in any other context is not intended to invoke a similar interpretation. The applicant reserves the right to pursue such additional claim forms in either this application or in a continuing application.
This application claims the benefit of U.S. Provisional Patent Application No. 63/616,450, filed Dec. 29, 2023, which is incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63616450 | Dec 2023 | US |
