Voice command execution

Abstract

The present invention discloses a computer system, a computer program product and a method for voice command execution. The method includes: analyzing a received voice command by identifying multiple contextual factors associated with at least one user among multiple users using a natural language processing algorithm; dynamically identifying at least one user among multiple users based on the analysis of the identified contextual factors associated with the received voice command; transmitting the received voice command to another computing device among multiple computing devices associated with another user among the multiple users; and generating a communication line between the multiple computing devices based on a correlation between a sum of multiple security factors and a predetermined risk threshold, the predetermined risk threshold being associated with authenticating the identity of each user among the multiple users.

Description

Technical Field

[0001] The present invention generally relates to the field of voice command system technology, and more specifically to artificial intelligence voice assistance system technology. Background Art

[0002] A microphone is a device that converts sound into an electrical signal. Microphones are used in many applications, such as telephones, hearing aids, sound reinforcement systems for concert halls and public events, film production, live and recorded audio engineering, sound recording, two-way radios, megaphones, radio and television broadcasting. They are also used in computers for recording speech, speech recognition, and for non-acoustic purposes, such as ultrasonic sensors or knock sensors. Several types of microphones are used today, which employ different methods to convert the air pressure changes of sound waves into electrical signals. The most common are dynamic microphones, which use a coil of wire suspended in a magnetic field; condenser microphones, which use a vibrating diaphragm as a capacitor plate; and contact microphones, which use a crystal of piezoelectric material. Before the signal can be recorded or reproduced, the microphone usually needs to be connected to a preamplifier.

[0003] A loudspeaker is an electroacoustic transducer; a device that converts an electrical audio signal into a corresponding sound. The most widely used type of loudspeaker is the dynamic loudspeaker. Before the signal is sent to the loudspeaker, the sound source must be amplified or enhanced (e.g., a sound record or microphone) using an audio power amplifier. A dynamic loudspeaker works on the same basic principle as a dynamic microphone, but in reverse, to produce sound from an electrical signal. When an alternating current audio signal is applied to its voice coil (a coil suspended in a circular gap between the poles of a permanent magnet), the coil is forced to move back and forth rapidly due to Faraday's law of induction, which causes the diaphragm (usually conical) attached to the coil to move back and forth, pushing the air to create sound waves. In addition to this most common method, there are several alternative technologies that can be used to convert electrical signals into sound. Loudspeakers are usually enclosed in a speaker housing, and the material and design of the housing play an important role in the quality of the sound. The housing must usually be as hard and non-resonant as practical. Smaller loudspeakers are found in devices such as radios, televisions, portable audio players, computers, and electronic music instruments. Larger speaker systems are used for music, sound reinforcement, and sound reinforcement in theaters and concert halls. Summary of the invention

[0004] Embodiments of the present invention provide a computer system, a computer program product, and a method, including: analyzing a received voice command by identifying multiple contextual factors associated with at least one user among multiple users using a natural language processing algorithm; dynamically identifying at least one user among multiple users based on the analysis of the identified contextual factors associated with the received voice command; transmitting the received voice command to another computing device within a plurality of computing devices associated with another user among the multiple users; and generating a communication line between the multiple computing devices based on a correlation between a sum of multiple security factors and a predetermined risk threshold, the predetermined risk threshold being associated with authenticating the identity of each user within the multiple users. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 is a functional block diagram depicting an environment having a computing device connected to or in communication with another computing device in accordance with at least one embodiment of the present invention;

[0006] Figure 2 is a flow chart illustrating operational steps for dynamically authenticating voice commands from multiple users according to at least one embodiment of the present invention;

[0007] Figure 3 is a flow chart illustrating operational steps for establishing communications between multiple artificial intelligence devices according to at least one embodiment of the present invention; and

[0008] Figure 4 Describes an embodiment of the present invention Figure 1 A block diagram of the components of a computing system within a computing display environment. DETAILED DESCRIPTION

[0009] Embodiments of the present invention recognize the need to improve the current voice recognition technology system initiated by a wake-up command and authenticated by a single user associated with a voice recognition device. Currently, the voice recognition technology system requires a user known to the device to submit an initiation command, followed by a voice command, where the voice command can be a task execution or search request. Typically, the voice recognition technology system does not communicate with the voice recognition technology systems associated with other unknown users. In addition, the current voice recognition technology system authenticates the identity of a single user based on voice identification at a given time. Embodiments of the present invention improve these voice recognition technology systems by allowing users to transmit voice commands without the need for a wake-up command, by storing known users' voice identification in a voice database of possible users and querying unknown voice users to dynamically authenticate multiple users' identities based on known and unknown users' voice identification, and by transmitting information between multiple voice recognition devices associated with multiple users without the need for multiple authentication steps that allow multiple artificial intelligence devices to communicate with each other without user input. Embodiments of the present invention provide these improvements to a voice identification technology system that allows multiple artificial intelligence devices to communicate with each other without user input by analyzing voice commands and limiting individual security based on the context of the voice commands, time-based or event-based permission times of the voice commands, and location-based permission of the voice commands. Embodiments of the present invention increase the efficiency of communication between multiple artificial intelligence devices by receiving data, where the data is a voice command, analyzing the received voice command for a requested factor using an artificial intelligence voice assistance algorithm, dynamically identifying a suitable user associated with the requested factor within a database of multiple users, transmitting the requested factor to the identified suitable user, and allowing communication between multiple users associated with the requested factor.

[0010] Figure 1 is a functional block diagram of a computing environment 100 according to an embodiment of the present invention. Computing environment 100 includes computing device 102 and server computing device 108. Computing device 102 and server computing device 108 may be desktop computers, laptop computers, dedicated computer servers, smart phones, wearable technology, or any other computing devices known in the art. In some embodiments, computing device 102 and server computing device 108 may represent computing devices that utilize multiple computers or components to act as a single seamless resource pool when accessed through network 106. In general, computing device 102 and server computing device 108 may represent any electronic device or combination of electronic devices capable of executing machine-readable program instructions, such as with respect to Figure 3 Described in more detail.

[0011] The computing device may include a program 104. The program 104 may be a stand-alone program 104 running on the computing device. In another embodiment, the program 104 may be stored on a server computing device 108. In this embodiment, the program 104 establishes communication between multiple artificial intelligence devices without user input; in response to receiving a request for permission from a user associated with the particular artificial intelligence device, provides information to the user; and in response to authenticating the artificial intelligence device, determines the amount of access given to different artificial intelligence devices. In this embodiment, the program 104 receives data from at least one user, and the received data is a voice command. In this embodiment, the program 104 uses an artificial intelligence voice assistance algorithm to analyze the received data for factors associated with the request within the voice command. In this embodiment, the program 104 dynamically identifies the user associated with the requested contextual factor by comparing the received data with multiple entries of a database associated with data of multiple users. In this embodiment, the program 104 transmits the received data to another computing device associated with a different user based on the analysis of the received voice command and the identified request factor. In another embodiment, the program 104 opens a communication line between multiple computing devices 102 associated with multiple users associated with the identified request factors. For example, in response to user A executing a voice command to schedule a vacation to coincide with user B, the program 104 generates communication between an artificial intelligence associated with user A and an artificial intelligence device associated with user B by accessing user B's vacation schedule, identifying user B's contact information from a directory, and in response to user B providing permission to the artificial intelligence device associated with user B to initiate communication between multiple artificial intelligence devices for user A to receive the required information from user B.

[0012] Network 106 may be a local area network ("LAN"), a wide area network ("WAN") such as the Internet, or a combination of both; and it may include wired, wireless, or fiber optic connections. In general, network 106 may be any combination of connections and protocols that will support communications between computing device 102 and server computing device 108, and in particular, program 104 according to a desired embodiment of the present invention.

[0013] Server computing device 108 may include program 104 and may communicate with computing device 102 via network 106 .

[0014] Figure 2 is a flow chart 200 illustrating operational steps for transmitting a received voice command associated with at least one user to another computing device associated with another user.

[0015] In step 202, program 104 receives data from at least one user. In this embodiment, program 104 receives data from at least one user of a plurality of users, wherein the received data includes voice commands. In this embodiment, program 104 defines data as information representing commands from users. In this embodiment, program 104 receives voice data, video data, image data, and text data. In this embodiment, program 104 receives voice commands associated with the execution of future actions. In this embodiment, program 104 defines voice commands as instructions transmitted to an artificial intelligence device, and the instructions are associated with the execution of future actions. In another embodiment, program 104 receives a wake-up command before receiving any data from the user. For example, program 104 receives a voice command to arrange a vacation for a user's family of 4, and coordinates the vacation with another user's vacation plan. In another example, program 104 receives a voice command to make an appointment at a restaurant for user A and user B.

[0016] In step 204, the program 104 analyzes the received data. In this embodiment, the program 104 analyzes the received data for contextual factors associated with the user by identifying the identity associated with the user based on the received voice command. In this embodiment, the program 104 uses a natural language programming algorithm to identify the identity associated with the user. In this embodiment, the program 104 defines the contextual factors as factors that provide additional information to the received voice command. In this embodiment, the program 104 analyzes the received data for the request factor by using an artificial intelligence voice assistance algorithm. In this embodiment, when the user is not near the artificial intelligence device, the program 104 can receive the voice command by pairing the mobile phone associated with the user with the artificial intelligence device to receive the voice command at any location. In this embodiment, the program 104 can transmit a transcription of the voice command to the paired mobile phone in response to receiving the voice command from the artificial intelligence device. For example, the program 104 analyzes the received voice command to identify other users required to complete future execution, such as other users associated with future vacation plans.

[0017] In step 206, the program 104 dynamically identifies at least one user. In this embodiment, the program 104 dynamically identifies at least one user among the multiple users based on the analysis of the received voice command by analyzing the received voice command associated with the at least one user, identifying multiple indicator tags within the analysis of the received voice command, and using a natural language programming algorithm and an artificial intelligence algorithm to match multiple indicator tags based on the identified contextual factors associated with the received voice command of the user with indicator tags of a storage database associated with the identification of additional users. In this embodiment, the program 104 defines the indicator tag as a symbol or indication of the identity associated with the user. For example, the program 104 identifies speech patterns, bass, vibrato, and stress as indicator tags. In this embodiment and in the case where the multiple indicator tags do not match the storage database of indicator tags, the program 104 generates a notification requesting permission to transmit the received voice command for the user associated with the artificial intelligence device. In this embodiment, the program 104 uses a neuro-linguistic programming algorithm to compare the indicator tag associated with the identity of the user and the requested contextual factor based on at least one voice feature unique to the identified user. In another embodiment and in response to identifying the user, the program 104 identifies any references associated with the user, such as a phone number, address, and security preferences. In this embodiment, the program 104 accesses a directory associated with the user and identifies additional users based on the mobile number or identified voice recognition. For example, the program 104 dynamically identifies a received voice command from user A and includes information from user B and user C to complete future execution.

[0018] In step 208, the program 104 transmits the received data to another computing device 102. In this embodiment, the program 104 transmits the received voice command associated with the first user to another computing device 102 associated with the second user, where the second user is required to complete a future execution associated with the received voice command. In this embodiment, the program 104 transmits the received voice command to the second computing device 102 in response to authenticating the identity of the first user. In another embodiment, the program 104 transmits the received voice command to the computing device 102 associated with the second user without authenticating the identity of the first user based on contextual factors associated with the received voice command. For example, due to the nature of the vacation information request that includes vacation schedules for user B and user C, the program 104 transmits vacation information for user A to the computing device 102 associated with user B and another computing device 102 associated with user C.

[0019] In step 210, the program 104 generates a communication line between the computing devices 102 of the plurality of identified users. In this embodiment and in response to transmitting the received voice command associated with the first user to the computing device 102 of the second user, the program 104 establishes communication between the plurality of artificial intelligence devices without authenticating the identity of each user by receiving security factor input from the user, identifying the type of communication occurring between the plurality of artificial intelligence devices, and aggregating the security factor input received from the plurality of users and the identified communication type. This step will be in Figure 3 As further explained in , in this embodiment, program 104 generates a communication line between multiple computing devices based on a sum of multiple security factors that meet or exceed a predetermined risk threshold associated with authenticating the identity of each user within the multiple users. In this embodiment, the open line allows multiple users to determine the future execution of actions. In this embodiment, program 104 determines the amount of information to be shared between multiple users based on the received user input. For example, program 104 generates a communication line between multiple computing devices associated with multiple users and coordinates user A's vacation plans with user B and user C's vacation plans, but does not provide shopping information.

[0020] Figure 3 is a flow chart 300 illustrating operational steps for generating a communication line between a plurality of artificial intelligence devices without authenticating the identity of a user associated with an artificial intelligence device within the plurality of artificial intelligence devices in accordance with at least one embodiment of the present invention.

[0021] In step 302, the program 104 receives a plurality of security factors associated with the user. In this embodiment and in response to executing a voice command on the artificial intelligence device, the program 104 prioritizes each of the plurality of security factors associated with the user associated with the artificial intelligence device by assigning a weight value to each of the plurality of security factors, and ranks each of the plurality of security factors based on the assigned weight values, wherein the security factor with the highest weight value is placed at the highest priority in order, and the security factor with the lowest weight value is placed at a lower priority in order. In this embodiment, the program 104 assigns a weight value to each security factor based on the received user preferences. For example, the program 104 defines information associated with finance as a security factor; information associated with time-sensitive information as a security factor; and information associated with private user data as a security factor. In this embodiment, the program 104 defines a security factor as an indicator associated with a voice command that requires permission from the user before distributing the information. In this embodiment, the program 104 identifies multiple security factors for each user by receiving security preferences for each user, compiling the received security preferences or multiple users, and determining similarities and differences between the compiled security preferences using machine learning algorithms and artificial intelligence algorithms, wherein the multiple security factors are based on the context of the information, time-based permission information, event-based permission information, and location-based permission information. In this embodiment, the program 104 attaches a time component to the multiple security factors, wherein the program 104 terminates the communication when the attached time component expires. For example, user B enters that only food information is shared for the next 20 minutes or until user A communicates with the appointment information.

[0022] In step 304, program 104 identifies the type of communication associated with multiple security factors. In this embodiment, program 104 determines which of the multiple security factors is required based on the identified communication type. In this embodiment, program 104 identifies the type of communication by analyzing the executed voice command for the context language indicating the type of communication using a natural language programming algorithm. In this embodiment, program 104 processes a large amount of natural language data and provides speech recognition, natural language understanding, and natural language generation using natural language programming algorithms. For example, user A and user B are discussing food reservations, and program 104 identifies food choices and food preferences within the communication between multiple artificial intelligence devices.

[0023] In step 306, the program 104 aggregates a plurality of security factors associated with the plurality of artificial intelligence devices based on the identified communication type. In this embodiment, the program 104 identifies the priority order of the security factors associated with each artificial intelligence device by assigning a weighted value (i.e., a range of 0-3, where the highest security preference received from the user is 3) to the highest ranked security factor, applies the priority order of each security factor associated with each artificial intelligence device, the artificial intelligence device compiles the plurality of security factors into a population of prioritized security factors using a machine learning algorithm and an artificial intelligence algorithm, and synchronizes the population of prioritized security factors based on the received user preferences associated with the plurality of users, to aggregate the values ​​of each of the plurality of security factors, wherein the synchronization requires permission from the user when the identified communication type meets or exceeds a predetermined risk threshold. In this embodiment and in response to the plurality of security factors meeting or exceeding the predetermined risk threshold, the program 104 defines the relationship between the plurality of security factors and the predetermined risk threshold as a correlation. In this embodiment, the program 104 defines the predetermined risk threshold associated with the security factors as the aggregated assigned weight value of 5. In this embodiment, program 104 requires authentication from a user associated with an artificial intelligence device that receives the voice command in response to an identified communication type meeting or exceeding a predetermined risk threshold.

[0024] For example, program 104 identifies that user A prioritizes time-sensitive security factors over financial security factors, user B prioritizes geographic security factors over both financial security factors and time-sensitive security factors, and user C prioritizes financial security factors over both time-sensitive security factors and geographic security factors, and applies the priority order of the received user preferences associated with multiple users. In this example and in response to applying the priority order, the program 104 compiles a prioritized population list with the financial security factor as the top security factor receiving an aggregate assigned weight value of 7, based on user A assigning a value of 2 to the security factor, user B assigning a value of 3 to the security factor, and user C assigning a value of 3 to the security factor, which aggregate totals 7; the time-sensitive security factor receiving an aggregate assigned weight value of 6, based on user A assigning a value of 3 to the security factor, user B assigning a value of 1 to the security factor, and user C assigning a value of 2 to the security factor, which aggregate totals 6; and the geographic security factor receiving an aggregate assigned weight value of 4, based on user A assigning a value of 0 to the security factor, user B assigning a value of 3 to the security factor, and user C assigning a value of 1 to the security factor, which aggregate totals 4. In this example and in response to aggregating the values ​​for each of the plurality of security factors, the program 104 synchronizes the prioritized population of security factors by generating a notification for the plurality of users detailing the received presence of the financial security factor and the time-sensitive security factor within the received voice command. In this example, program 104 generates a notification to multiple users requesting authentication for communication types having aggregated security values ​​that meet or exceed a predetermined risk threshold, where the predetermined risk threshold is 5; thereby requesting authentication from multiple users regarding communication types associated with financial security factors and time-sensitive security factors.

[0025] In this embodiment and in response to the user failing to place a security factor within their preferences, the program 104 assigns a weight value of 0 to the missing security factor. In this embodiment, the program 104 does not increase the assigned weight value attached to the security factor in response to the user failing to prioritize any other security factors within the received user preferences. In this embodiment, in response to the security factor associated with the communication type for the received voice command meeting or exceeding a predetermined risk threshold, the program 104 transmits a request for authentication (i.e., permission) to the user associated with the artificial intelligence device receiving the voice command. In another embodiment, where authentication is not required, the program 104 generates a communication line that allows other forms of data to be transmitted, where these other forms of data include image data, video data, and audio data.

[0026] In this embodiment, the program 104 aggregates multiple security factors by synchronizing each corresponding user preference associated with each security factor. In this embodiment and in response to applying the priority order of each security factor associated with each artificial intelligence and synchronizing the compilation of multiple security factors, the program 104 calculates an aggregate security score for each security factor within the multiple security factors based on the assigned weight value for each security factor, and adheres to the priority order of placing higher weighted security factors at a higher position on each artificial intelligence device associated with each user. For example, user A and user B ranked finance as their respective highest weighted security factor, so the program 104 aggregates each voice command and multiple security factors associated with each user to reflect their respective security factors.

[0027] Figure 4 Describes an embodiment of the present invention Figure 1 100 is a block diagram of components of a computing system within the computing environment 100. It should be understood that Figure 4 This merely provides an illustration of one implementation and does not imply any limitation with regard to the environments in which different embodiments may be implemented.Many modifications to the depicted environment may be made.

[0028] The procedures described herein are identified based on the applications used to implement them in specific embodiments of the invention. However, it should be understood that any specific procedure terminology herein is used only for convenience, and thus the present invention should not be limited to use only in any specific application identified and / or implied by such terminology.

[0029] Computer system 400 includes a communications fabric 402 that provides communications between cache 416, memory 406, persistent storage 408, communications unit 412, and input / output (I / O) interface(s) 414. Communications fabric 402 may be implemented using any architecture designed to pass data and / or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within the system. For example, communications fabric 402 may be implemented using one or more buses or crossbar switches.

[0030] Memory 406 and persistent storage 408 are computer-readable storage media. In this embodiment, memory 406 includes random access memory (RAM). In general, memory 406 may include any suitable volatile or non-volatile computer-readable storage media. Cache 416 is a fast memory that enhances the performance of computer processor(s) 404 by saving recently accessed data and closely accessed data from memory 406.

[0031] Program 104 may be stored in persistent storage 408 and memory 406 for execution by one or more corresponding computer processors 404 via cache 416. In one embodiment, persistent storage 408 comprises a magnetic hard drive. Alternatively, or in addition to a magnetic hard drive, persistent storage 408 may comprise a solid-state hard drive, a semiconductor memory device, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), flash memory, or any other computer-readable storage medium capable of storing program instructions or digital information.

[0032] The media used by persistent storage 408 also may be removable. For example, a removable hard drive may be used for persistent storage 408. Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer to another computer-readable storage medium that is also part of persistent storage 408.

[0033] In these examples, communication unit 412 provides for communication with other data processing systems or devices. In these examples, communication unit 412 includes one or more network interface cards. Communication unit 412 can provide communication using either or both of physical and wireless communication links. Program 104 can be downloaded to persistent storage device 408 through communication unit 412.

[0034] The I / O interface(s) 414 allow input and output of data with other devices that may be connected to the mobile device, approval device, and / or server computing device 108. For example, the I / O interface(s) 414 may provide a connection to an external device 418, such as a keyboard, a keypad, a touch screen, and / or some other suitable input device. The external device 418 may also include a portable computer-readable storage medium such as, for example, a thumb drive, a portable optical or magnetic disk, and a memory card. Software and data (e.g., program 104) for practicing embodiments of the present invention may be stored on such a portable computer-readable storage medium and may be loaded onto the persistent storage device 408 via the I / O interface(s) 414. The I / O interface(s) 414 are also connected to a display 422.

[0035] Display 422 provides a mechanism for displaying data to a user and may be, for example, a computer monitor.

[0036] The present invention may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium (or multiple media) having computer-readable program instructions thereon, the computer-readable program instructions being used to cause a processor to perform various aspects of the present invention.

[0037] A computer-readable storage medium may be any tangible device capable of holding and storing instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following items: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device such as a punch card or a raised structure in a groove on which instructions are recorded, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (e.g., a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0038] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a corresponding computing / processing device, or downloaded to an external computer or external storage device via a network (e.g., the Internet, a local area network, a wide area network, and / or a wireless network). The network can include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network, and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the corresponding computing / processing device.

[0039] The computer-readable program instructions for performing the operation of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Smalltalk, C++, etc.) and conventional procedural programming languages ​​(such as "C" programming languages ​​or similar programming languages). The computer-readable program instructions may be executed completely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider via the Internet). In some embodiments, in order to perform various aspects of the present invention, an electronic circuit including, for example, a programmable logic circuit, a field programmable gate array (FPGA) or a programmable logic array (PLA) may execute a computer-readable program instruction to personalize the electronic circuit by utilizing the state information of the computer-readable program instructions.

[0040] Various aspects of the present invention are described herein with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to embodiments of the present invention. It will be understood that each frame of the flowchart and / or block diagram and the combination of frames in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0041] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device create components for implementing the functions / actions specified in one or more boxes of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can guide the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable storage medium in which the instructions are stored includes an article of manufacture, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0042] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are executed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable apparatus, or other device implement the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0043] The flow chart and block diagram in the figure illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention.In this regard, each frame in the flow chart or block diagram can represent a module, segment or part of an instruction, which includes one or more executable instructions for realizing the specified (multiple) logical functions.In some alternative implementations, the functions noted in the frame may not occur in the order noted in the figure.For example, the two frames shown in succession can actually be executed substantially at the same time, or the frame can sometimes be executed in the opposite order, depending on the functions involved.It will also be noted that each frame of the block diagram and / or flow chart illustration and the combination of the frames in the block diagram and / or flow chart illustration can be realized by a dedicated hardware-based system that performs the specified function or action or realizes the combination of special hardware and computer instructions.

[0044] The description of various embodiments of the present invention has been presented for illustrative purposes, but is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the invention. The terms used herein are selected to best explain the principles of the embodiments, practical applications, or technical improvements found on the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A computer-implemented method comprising: analyzing the received voice command by identifying a plurality of contextual factors associated with at least one user of a plurality of users using a natural language processing algorithm, wherein the at least one user is associated with a computing device; dynamically identifying the at least one user from the plurality of users based on an analysis of the identified contextual factors associated with the received voice command; transmitting the received voice command to another computing device within the plurality of computing devices that is associated with another user from the plurality of users; generating a communication line between the plurality of computing devices based on a correlation between a sum of a plurality of security factors and a predetermined risk threshold associated with authenticating the identity of each user within the plurality of users; Wherein generating the communication line between the plurality of computing devices based on a correlation between a sum of a plurality of security factors and a predetermined risk threshold associated with authenticating the identity of each user within the plurality of users comprises: receiving security factor preference input from each of the plurality of users; identifying, based on the received security factor preference input, a type of communication associated with the received voice command occurring between a plurality of artificial intelligence devices; summing the security factor preference inputs received from the plurality of users based on the identified communication type; and A communication line between the plurality of computing devices is generated based on the sum of the received security factor preference inputs from the plurality of users.

2. The computer-implemented method of claim 1 , wherein: Dynamically identifying the at least one user among the plurality of users comprises: identifying a plurality of indicator marks based on analysis of the received voice command; and Using a natural language programming algorithm and an artificial intelligence algorithm, each indicator within the plurality of indicator markers is matched to a corresponding stored indicator marker within a plurality of stored indicator markers in a stored database of indicator markers associated with identities of additional users based on the identified contextual factors associated with the user's received voice command.

3. The computer-implemented method of claim 1 , further comprising: assigning a weight value to each of the plurality of security factors based on the security factor preference input received for each of the plurality of users; as well as Each security factor within the plurality of security factors associated with each user associated with each artificial intelligence device within the plurality of artificial intelligence devices is prioritized by ranking each of the plurality of security factors based on the assigned weight value.

4. The computer-implemented method of claim 3, wherein: Prioritizing each safety factor within the plurality of safety factors includes: placing the safety factor having the highest weight value at the highest priority in the ranking; and The security factor having the lowest weight value is placed at the lowest priority in the ranking.

5. The computer-implemented method of claim 1 , wherein summing the received security factor preference inputs from the plurality of users based on the identified communication type comprises: identifying a priority order of the plurality of security factors associated with each artificial intelligence device by assigning a weighted value to each security factor within the plurality of security factors; Based on the identified priority order of the plurality of security factors, compiling the plurality of security factors into a population of prioritized security factors using a machine learning algorithm and an artificial intelligence algorithm; as well as The population of prioritized security factors is re-ranked based on the compiled, received security factor preference inputs of each user within the plurality of users.

6. The computer-implemented method of claim 1 , further comprising: Generate a notification requesting authentication of the artificial intelligence device for a voice command that meets or exceeds a predetermined risk threshold, the predetermined risk threshold being associated with the security factor of the plurality of security factors associated with a communication type associated with the received voice command.

7. A computer program product comprising: One or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions comprising: program instructions for analyzing a received voice command by identifying a plurality of contextual factors associated with at least one user of a plurality of users using a natural language processing algorithm, wherein the at least one user is associated with a computing device; program instructions for dynamically identifying the at least one user from the plurality of users based on an analysis of the identified contextual factors associated with the received voice command; program instructions for transmitting the received voice command to another computing device within a plurality of computing devices associated with another user of the plurality of users; program instructions for generating a communication line between the plurality of computing devices based on a correlation between a sum of a plurality of security factors and a predetermined risk threshold associated with authenticating the identity of each of the plurality of users; Wherein the program instructions for generating a communication line between the plurality of computing devices based on a correlation between a sum of a plurality of security factors and a predetermined risk threshold associated with authenticating the identity of each user within the plurality of users include: program instructions for receiving security factor preference input from each of the plurality of users; program instructions for identifying a type of communication associated with said received voice command occurring between a plurality of artificial intelligence devices based on said received security factor preference input; program instructions for summing the security factor preference inputs received from the plurality of users based on the identified communication type; and Program instructions for generating a communication link between the plurality of computing devices based on the sum of the security factor preference inputs received from the plurality of users.

8. The computer program product according to claim 7, wherein: The program instructions for dynamically identifying the at least one user among the plurality of users include: program instructions for identifying a plurality of indicator marks based on said analysis of said received voice commands; and Program instructions for matching each indicator within the plurality of indicator markers to a corresponding stored indicator marker within a plurality of stored indicator markers in a stored database of indicator markers associated with identities of additional users based on the identified contextual factors associated with the user's received voice command using a natural language programming algorithm and an artificial intelligence algorithm.

9. The computer program product according to claim 7, wherein: The program instructions stored on the one or more computer-readable storage media also include: program instructions for assigning a weight value to each of the plurality of security factors based on the security factor preference input received for each of the plurality of users; and Program instructions for prioritizing each security factor within the plurality of security factors by ranking each of the plurality of security factors based on the assigned weight values, the plurality of security factors associated with each user associated with each artificial intelligence device within the plurality of artificial intelligence devices.

10. The computer program product according to claim 9, wherein: The program instructions for prioritizing each of the plurality of safety factors include: program instructions for placing the security factor having the highest weight value at the highest priority in the ranking; and Program instructions for placing a security factor having a lowest weight value at a lowest priority in the ranking.

11. The computer program product of claim 7, the program instructions for summing the security factor preference inputs received from the plurality of users based on the identified communication type comprising: program instructions for identifying a priority of the plurality of security factors associated with each artificial intelligence device by assigning a weighted value to each security factor within the plurality of security factors; program instructions for compiling said plurality of security factors into a population of prioritized security factors using a machine learning algorithm and an artificial intelligence algorithm based on said identified priorities of said plurality of security factors; as well as Program instructions for re-ranking the prioritized population of security factors based on the compiled, received security factor preference inputs of each user within the plurality of users.

12. The computer program product of claim 7, wherein: The program instructions stored on the one or more computer-readable storage media also include: Program instructions for generating a notification for requesting authentication of an artificial intelligence device for a voice command that meets or exceeds a predetermined risk threshold, wherein the predetermined risk threshold is associated with the security factor of the plurality of security factors, wherein the plurality of security factors are associated with a communication type associated with the received voice command.

13. A computer system comprising: one or more computer processors; one or more computer-readable storage media; as well as Program instructions stored on the one or more computer-readable storage media, the program instructions being configured to be executed by at least one of the one or more processors, the program instructions comprising: program instructions for analyzing a received voice command by identifying a plurality of contextual factors associated with at least one user of a plurality of users using a natural language processing algorithm, wherein the at least one user is associated with a computing device; program instructions for dynamically identifying the at least one user from the plurality of users based on an analysis of the identified contextual factors associated with the received voice command; program instructions for transmitting the received voice command to another computing device within a plurality of computing devices associated with another user of the plurality of users; and program instructions for generating a communication line between the plurality of computing devices based on a correlation between a sum of a plurality of security factors and a predetermined risk threshold associated with authenticating the identity of each of the plurality of users; Wherein the program instructions for generating a communication line between the plurality of computing devices based on a correlation between a sum of a plurality of security factors and a predetermined risk threshold associated with authenticating the identity of each user within the plurality of users include: program instructions for receiving security factor preference input from each of the plurality of users; program instructions for identifying a type of communication associated with the received voice command occurring between a plurality of artificial intelligence devices based on the received security factor preference input; program instructions for summing the security factor preference inputs received from the plurality of users based on the identified communication type; and Program instructions for generating a communication link between the plurality of computing devices based on a summation of the security factor preference inputs received from the plurality of users.

14. The computer system of claim 13, wherein: The program instructions for dynamically identifying the at least one user among the plurality of users include: program instructions for identifying a plurality of indicator marks based on said analysis of said received voice commands; and Program instructions for matching each indicator within the plurality of indicator markers to a corresponding stored indicator marker within a plurality of stored indicator markers in a stored database of indicator markers associated with identities of additional users based on the identified contextual factors associated with the user's received voice command using a natural language programming algorithm and an artificial intelligence algorithm.

15. The computer system according to claim 13, wherein: The program instructions stored on the one or more computer-readable storage media also include: program instructions for assigning a weight value to each of the plurality of security factors based on the security factor preference input received for each of the plurality of users; and Program instructions for prioritizing each security factor within the plurality of security factors by ranking each of the plurality of security factors based on the assigned weight values, the plurality of security factors associated with each user associated with each artificial intelligence device within the plurality of artificial intelligence devices.

16. The computer system of claim 15, wherein: The program instructions for prioritizing each safety factor within the plurality of safety factors include: program instructions for placing the security factor having the highest weight value at the highest priority in the ranking; and Program instructions for placing a security factor having a lowest weight value at a lowest priority in the ranking.

17. The computer system of claim 13, the program instructions for summing the received security factor preference inputs from the plurality of users based on the identified communication type comprising: program instructions for identifying a priority of the plurality of security factors associated with each artificial intelligence device by assigning a weighted value to each security factor within the plurality of security factors; program instructions for compiling said plurality of security factors into a population of prioritized security factors using a machine learning algorithm and an artificial intelligence algorithm based on said identified priorities of said plurality of security factors; as well as Program instructions for re-ranking the prioritized population of security factors based on the compiled, received security factor preference inputs of each user within the plurality of users.

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