Patent Publication Number: US-2021174241-A1

Title: Trait-modeled chatbots

Description:
BACKGROUND 
     The field of embodiments of the invention generally relate to chatbots. 
     A chatbot is software configured to conduct a conversation via auditory and/or textual methods (i.e., an automated conversational agent). 
     Conventional chatbots include an all-in-one chatbot (i.e., a single universal chatbot) that provides all advice from all perspectives. Such an all-in-one chatbot can be characterized as a “Jack of all trades, master of none” as this chatbot is not optimized for any particular trait (e.g., not optimized for a personality trait, an expertise trait, or any other trait that a subject matter expert or a domain expert has or exhibits). Further, a user will experience interacting with a conventional chatbot as an interaction with a hyper-specialized agent with one type of personality only. 
     For different topics of interest, individuals like to seek advice from others with different traits. For example, an individual desiring to make an investment (e.g., in mutual funds) may want to seek advice from other individuals with different perspectives, such as friends who invest regularly, family members whose opinions matter to the individual, financial experts who the individual trusts, lifestyle experts who can opine on how much the investment may negatively affect the individual&#39;s lifestyle today and the potential benefits the individual may reap from the investment in the future, etc. However, it may be impractical and costly (in terms of time and effort) for the individual to reach out to and schedule appointments/consultations with so many different individuals. Further, a conventional all-in-one chatbot is unable to provide advice from different perspectives (i.e., unable to model the other individuals with different perspectives). 
     SUMMARY 
     Embodiments of the invention generally relate to chatbots, and more specifically, to trait-modeled chatbots. 
     One embodiment of the invention provides a method of training a chatbot. The method comprises identifying one or more chat logs that exhibit a trait. The method further comprises identifying one or more labels associated with the trait based on the one or more chat logs. The method further comprises training the chatbot to generate a response that models the trait based on the one or more chat logs. The method further comprises labeling the chatbot with the one or more labels. The chatbot is invoked to join a virtual chatroom in response to a chat session involving a user in the virtual chatroom who requires one or more responses that models the trait. Each label comprises a keyword or a concept associated with the trait. The trait comprises a personality trait or an expertise trait that a subject matter expert/domain expert has. Other embodiments include a system of training a chatbot. These features contribute to the advantage of training a chatbot to specialize in generating responses, in a particular area/domain/topic, that exhibit/model/show a single trait that a subject matter expert/domain expert of the particular area/domain/topic has or exhibits. 
     One or more of the following features may be included. In some embodiments, the chatbot is trained to generate a response that jointly models a set of traits based on a set of chat logs. These optional features contribute to the advantage of training a chatbot to specialize in generating responses, in a particular area/domain/topic, that jointly exhibit/model/show a mix of traits that a subject matter expert/domain expert of the particular area/domain/topic has or exhibits. 
     Another embodiment of the invention provides a method of controlling invocation of at least one of a plurality of available chatbots in a virtual chatroom. The method comprises initiating a chat session with a user in the virtual chatroom. The method further comprises determining an intent of the user based on a chat log of the chat session. The method further comprises extracting a set of traits from the chat session based on the intent of the user, where the set of traits extracted is indicative of at least one trait requirement that a response to the user must satisfy. The method further comprises selecting a chatbot from the plurality of available chatbots based on the set of traits extracted, where the chatbot selected is configured to generate a response that satisfies the at least one trait requirement. The method further comprises invoking the chatbot selected in the virtual chatroom to interact with the user. The chatbot selected is either a first chatbot configured to generate a response that models the set of traits extracted or a second chatbot configured to generate a response that models a set of different traits similar to the set of traits extracted. Each trait comprises a personality trait or an expertise trait that a subject matter expert/domain expert has. The set of traits extracted comprises one of a single trait or multiple traits. These features contribute to the advantage of selectively invoking, based on the intent of the user, a chatbot from the plurality of available chatbots in the chatroom to interact with the user, thereby allowing the user to interact with an agent with one or more traits that the invoked chatbot models. Other advantages include providing a multi-agent chatroom that allows the user to experience interacting with multiple, different chat agents modeling different traits (i.e., providing advice from different perspectives) via the available chatbots. 
     One or more of the following features may be included. In some embodiments, a change in the intent of the user is detected based on the chat log. In some embodiments, an additional set of traits is extracted from the chat session based on the change, where the additional set of traits extracted is indicative of at least one different trait requirement that a different response to the user must satisfy. In some embodiments, a different chatbot from the plurality of available chatbots is selected based on the additional set of traits extracted, where the different chatbot selected is configured to generate a response that satisfies the at least one different trait requirement. In some embodiments, the different chatbot selected is invoked in the virtual chatroom to interact with the user. These optional features contribute to the advantage of selectively invoking, based on the change in the intent of the user, a different chatbot from the plurality of available chatbots in the chatroom to interact with the user, thereby allowing the user to interact with a different agent with one or more traits that the invoked different chatbot models. 
     These and other aspects, features and advantages of embodiments of the invention will be understood with reference to the drawing figures, and detailed description herein, and will be realized by means of the various elements and combinations particularly pointed out in the appended claims. It is to be understood that both the foregoing general description and the following brief description of the drawings and detailed description of embodiments of the invention are exemplary and explanatory of preferred embodiments of the invention, and are not restrictive of embodiments of the invention, as claimed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The subject matter which is regarded as embodiments of the invention are particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other features and advantages of embodiments of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which: 
         FIG. 1  depicts a cloud computing environment according to an embodiment of the present invention; 
         FIG. 2  depicts abstraction model layers according to an embodiment of the present invention; 
         FIG. 3  illustrates an example computing architecture for training a specialized chatbot, in accordance with an embodiment of the invention; 
         FIG. 4  illustrates an example computing architecture for deploying a multi-agent chatroom and controlling invocation of a specialized chatbot in the chatroom, in accordance with an embodiment of the invention; 
         FIG. 5  illustrates an example process involving a training system and a deployment system, in accordance with an embodiment of the invention; 
         FIG. 6  is a flowchart of an example process for training a chatbot, in accordance with an embodiment of the invention; 
         FIG. 7  is a flowchart of an example process for controlling invocation of at least one of a plurality of available chatbots in a virtual chatroom; and 
         FIG. 8  is a high level block diagram showing an information processing system useful for implementing an embodiment of the invention. 
     
    
    
     The detailed description explains the preferred embodiments of the invention, together with advantages and features, by way of example with reference to the drawings. 
     DETAILED DESCRIPTION 
     Embodiments of the invention generally relate to chatbots, and more specifically, to trait-modeled chatbots. One embodiment of the invention provides a method of training a chatbot. The method comprises identifying one or more chat logs that exhibit a trait. The method further comprises identifying one or more labels associated with the trait based on the one or more chat logs. The method further comprises training the chatbot to generate a response that models the trait based on the one or more chat logs. The method further comprises labeling the chatbot with the one or more labels. The chatbot is invoked to join a virtual chatroom in response to a chat session involving a user in the virtual chatroom who requires one or more responses that models the trait. Each label comprises a keyword or a concept associated with the trait. The trait comprises a personality trait or an expertise trait that a subject matter expert/domain expert has. The method trains the chatbot to specialize in generating responses, in a particular area/domain/topic, that exhibit/model/show a single trait or a mix of traits that a subject matter expert/domain expert of the particular area/domain/topic has or exhibits. 
     Another embodiment of the invention provides a system of training a chatbot. The system comprises at least one processor. The system further comprises a non-transitory processor-readable memory device storing instructions that when executed by the at least one processor causes the at least one processor to perform operations. The operations include identifying one or more chat logs that exhibit a trait. The operations further include identifying one or more labels associated with the trait based on the one or more chat logs. The operations further include training the chatbot to generate a response that models the trait based on the one or more chat logs. The operations further include labeling the chatbot with the one or more labels. Each label comprises a keyword or a concept associated with the trait. The trait comprises a personality trait or an expertise trait that a subject matter expert/domain expert has. The system trains the chatbot to specialize in generating responses, in a particular area/domain/topic, that exhibit/model/show a single trait or a mix of traits that a subject matter expert/domain expert of the particular area/domain/topic has or exhibits. 
     Another embodiment of the invention provides a method of controlling invocation of at least one of a plurality of available chatbots in a virtual chatroom. The method comprises initiating a chat session with a user in the virtual chatroom. The method further comprises determining an intent of the user based on a chat log of the chat session. The method further comprises extracting a set of traits from the chat session based on the intent of the user, where the set of traits extracted is indicative of at least one trait requirement that a response to the user must satisfy. The method further comprises selecting a chatbot from the plurality of available chatbots based on the set of traits extracted, where the chatbot selected is configured to generate a response that satisfies the at least one trait requirement. The method further comprises invoking the chatbot selected in the virtual chatroom to interact with the user. The chatbot selected is either a first chatbot configured to generate a response that models the set of traits extracted or a second chatbot configured to generate a response that models a set of different traits similar to the set of traits extracted. Each trait comprises a personality trait or an expertise trait that a subject matter expert/domain expert has. The set of traits extracted comprises one of a single trait or multiple traits. The method provides a multi-agent chatroom that allows the user to experience interacting with multiple, different chat agents modeling different traits (i.e., providing advice from different perspectives) via the available chatbots. 
     For expository purposes, the terms “chatbot” and “chat agent” are used interchangeably in this specification. 
     For expository purposes, the term “trait” as used herein in generally refers to a distinctive or distinguishing aspect, attribute, characteristic, feature, or property that a subject matter expert (SME), a domain expert, or any other person has or exhibits. There are different types of traits such as, but not limited to, personality traits (i.e., traits indicative of different types of personalities that different SMEs/domain experts have or exhibit, such as mild, pushy, story-telling, patient, fast talking, etc.), expertise traits (i.e., traits indicative of different types of expertise, such as different areas, domains, or topics that different SMEs/domain experts are authorities of or are specialized in, such as an aggressive stock trader, a defensive stock trader, a balanced/mixed-mode stock trader, etc.), etc. 
     For expository purposes, the term “specialized chatbot” as used herein generally refers to a chatbot specialized in generating/providing responses, in a particular area/domain/topic, that exhibit/model/show one or more particular traits (e.g., a single trait or a mix of traits that a SME/domain expert of the particular area/domain/topic has or exhibits). Each specialized chatbot is designed to model (e.g., mimic or imitate) a SME/domain expert with one or more particular traits. The terms “specialized chatbot” and “trait-modeled chatbot” are used interchangeably in this specification. 
     For expository purposes, the term “multi-entity chatroom” generally refers to a virtual chatroom (or chat room) in which multiple participants can interact with one another. Examples of participants include, but are not limited to, a user (i.e., a human participant, such as a customer or client user), a chatbot, a live agent (e.g., a human participant, such as a customer service or client service representative), etc. The terms “multi-entity chatroom”, “multi-agent chatroom”, and “multi-person chatroom” are used interchangeably in the specification. 
     Embodiments of the invention provide a method and system for training a specialized chatbot to model one or more traits based on training data. Embodiments of the invention further provide a method and system for deploying (i.e., operating) a multi-agent chatroom and controlling invocation of at least one of a plurality of available specialized chatbots in the chatroom. The chatroom allows a user to experience interacting with multiple, different chat agents modeling different traits (i.e., providing advice from different perspectives) via the specialized chatbots. Specifically, the chatroom is controlled by a main chatbot configured to: (1) interact with a user who joins the chatroom, and (2) selectively invoke at least one of the specialized chatbots in the chatroom to interact with the user based on an interaction with the user in the chat room (e.g., an interaction between the user and the main chatbot or a previously invoked specialized chatbot in the chatroom). Therefore, at any given point in time that the user is in the chatroom, the chatroom has multiple participants including the user (i.e., a human participant), the main chatbot (i.e., a machine participant), and, if invoked by the main chatbot, at least one specialized chatbot (i.e., at least one other machine participant). The specialized chatbots represent multiple, different chat agents available to join the chatroom to provide the user with wider and deeper information covering more perspectives than what a conventional chatbot covers. 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. In one embodiment, this cloud model includes at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and personal digital assistants). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. In one embodiment, there is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but is able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. In one embodiment, it is managed by the organization or a third party and exists on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). In one embodiment, it is managed by the organizations or a third party and exists on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
       FIG. 1  depicts a cloud computing environment  50  according to an embodiment of the present invention. As shown, in one embodiment, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N communicate. In one embodiment, nodes  10  communicate with one another. In one embodiment, they are grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N shown in  FIG. 1  are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
       FIG. 2  depicts a set of functional abstraction layers provided by cloud computing environment  50  according to an embodiment of the present invention. It should be understood in advance that the components, layers, and functions shown in  FIG. 2  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     In one embodiment, virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities are provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one embodiment, management layer  80  provides the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one embodiment, these resources include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     In one embodiment, workloads layer  90  provides examples of functionality for which the cloud computing environment is utilized. In one embodiment, examples of workloads and functions which are provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and a multi-agent chatroom system  96  that trains and/or invokes multiple, different trait-modeled chatbots (e.g., a training system  330  that trains the chatbots and/or a deployment system  430  that controls invocation of the chatbots in a multi-agent chatroom, as described in detail later herein). 
       FIG. 3  illustrates an example computing architecture  300  for training a specialized chatbot  340 , in accordance with an embodiment of the invention. In one embodiment, the computing architecture  300  is a centralized computing architecture. In another embodiment, the computing architecture  300  is a distributed computing architecture. 
     In one embodiment, the computing architecture  300  comprises computation resources such as, but not limited to, one or more processor units  310  and one or more storage units  320 . One or more applications may execute/operate on the computing architecture  300  utilizing the computation resources of the computing architecture  300 . In one embodiment, the applications on the computing architecture  300  include, but are not limited to, a training system  330  configured to train a specialized chatbot  340  to model one or more traits based on training data. A specialized chatbot  340  comprises a machine learning model trained to generate a conversation including one or more responses (i.e., chatbot responses) that exhibit/show one or more traits (e.g., a single trait, or a mix of traits). As described in detail later herein, in one embodiment, a specialized chatbot  340  is invoked in a virtual multi-agent chatroom to interact with a user in the chatroom who has expressed an intent to interact with an agent with a trait that the specialized chatbot  340  models. 
     In one embodiment, the training system  330  is configured to exchange data with one or more electronic devices  350  and/or one or more remote server devices  360  over a connection (e.g., a wireless connection such as a Wi-Fi connection or a cellular data connection, a wired connection, or a combination of the two). 
     In one embodiment, an electronic device  350  comprises one or more computation resources such as, but not limited to, one or more processor units  351  and one or more storage units  352 . One or more applications may execute/operate on an electronic device  350  utilizing the one or more computation resources of the electronic device  350  such as, but not limited to, one or more software applications  354  loaded onto or downloaded to the electronic device  350 . Examples of software applications  354  include, but are not limited to, artificial intelligence (AI) applications, natural language processing (NLP) applications, chat applications, messaging applications, etc. For example, in one embodiment, a specialized chatbot  340  trained by the training system  300  is invoked within a chat application on an electronic device  350 . 
     Examples of an electronic device  350  include, but are not limited to, a desktop computer, a mobile electronic device (e.g., a tablet, a smart phone, a laptop, etc.), a wearable device (e.g., a smart watch, etc.), an Internet of Things (IoT) device, a smart appliance such as a smart television, etc. 
     In one embodiment, an electronic device  350  comprises one or more input/output (I/O) units  353  integrated in or coupled to the electronic device  350 , such as a keyboard, a keypad, a touch interface, a display screen, etc. A user may utilize an I/O module  353  of an electronic device  350  to configure one or more user preferences, configure one or more parameters (e.g., thresholds, etc.), provide user input (e.g., explicitly provide/select one or more traits that the training system  300  should train one or more specialized chatbots  340  to model), etc. 
     In one embodiment, an electronic device  350  and/or a remote server device  360  may be a source of training data. 
     In one embodiment, the training system  330  may be accessed or utilized by one or more online services (e.g., AI services, NLP services, chat services, messaging services, etc.) hosted on a remote server device  360  and/or one or more software applications  354  (e.g., AI applications, NLP applications, chat applications, messaging applications) operating on an electronic device  350 . For example, in one embodiment, a virtual assistant, a search engine, or another type of software application  354  operating on an electronic device  350  invokes a specialized chatbot  340  trained by the training system  300  to interact with a user who has expressed an intent to interact with an agent with a trait that the specialized chatbot  340  models. 
     These features contribute to the advantage of training a specialized chatbot  340  to specialize in generating responses, in a particular area/domain/topic, that exhibit/model/show a single trait or a mix of traits that a subject matter expert/domain expert of the particular area/domain/topic has or exhibits. 
       FIG. 4  illustrates an example computing architecture  400  for deploying a multi-agent chatroom  440  and controlling invocation of at least one of a plurality of available specialized chatbots  340  in the chatroom  440 , in accordance with an embodiment of the invention. In one embodiment, the computing architecture  400  is a centralized computing architecture. In another embodiment, the computing architecture  400  is a distributed computing architecture. 
     In one embodiment, the computing architecture  400  comprises computation resources such as, but not limited to, one or more processor units  410  and one or more storage units  420 . One or more applications may execute/operate on the computing architecture  400  utilizing the computation resources of the computing architecture  400 . In one embodiment, the applications on the computing architecture  400  include, but are not limited to, a deployment system  430  configured to execute/operate a multi-agent chatroom  440  that allows multiple participants to interact with one another, and selectively invoke at least one of a plurality of available specialized chatbots  340  in the chatroom  440  via a main chatbot  450 . The specialized chatbots  340  represent multiple, different chat agents. In one embodiment, each specialized chatbot  340  utilized by the deployment system  430  is either trained online or offline (e.g., by the training system  330 ). 
     In one embodiment, the deployment system  430  maintains a plurality of different specialized chatbots  340 . For example, in one embodiment, the deployment system  430  includes specialized chatbots  340  modeling traits associated with domains of equity based stock markets, foreign exchange markets, derivatives markets, and debt funds. For example, for the domain of equity based stock markets, the deployment system  430  includes, but is not limited to, a specialized chatbot  340  modeling traits that an aggressive stock trader has (e.g., short term investing), a specialized chatbot  340  modeling traits that a defensive stock trader has (e.g., long term investing), a specialized chatbot  340  modeling traits that a balanced/mixed-mode stock trader (e.g., a mixture of traits that an aggressive stock trader has and traits that a defensive stock trader has), etc. 
     As described in detail later herein, the main chatbot  450  is configured to initiate a chat session with a user in response to the user joining the chatroom  440 , monitor the chat session to detect an intent of the user, and, based on the intent of the user, selectively invoke a specialized chatbot  340  in the chatroom  440  to interact with the user. For example, in one embodiment, the main chatbot  450  invokes a specialized chatbot  340  to join the chatroom  440  to interact with a user in the chatroom  440  in response to the user expressing an intent to interact with an agent with a trait that the specialized chatbot  340  models. 
     In one embodiment, the deployment system  430  is configured to exchange data with one or more electronic devices  470  and/or one or more remote server devices  480  over a connection (e.g., a wireless connection such as a Wi-Fi connection or a cellular data connection, a wired connection, or a combination of the two). 
     In one embodiment, an electronic device  470  comprises one or more computation resources such as, but not limited to, one or more processor units  471  and one or more storage units  472 . One or more applications may execute/operate on an electronic device  470  utilizing the one or more computation resources of the electronic device  470  such as, but not limited to, one or more software applications  474  loaded onto or downloaded to the electronic device  470 . Examples of software applications  474  include, but are not limited to, AI applications, NLP applications, chat applications, messaging applications, etc. 
     Examples of an electronic device  470  include, but are not limited to, a desktop computer, a mobile electronic device (e.g., a tablet, a smart phone, a laptop, etc.), a wearable device (e.g., a smart watch, etc.), an IoT device, a smart appliance such as a smart television, etc. 
     In one embodiment, an electronic device  470  comprises one or more I/O units  473  integrated in or coupled to the electronic device  470 , such as a keyboard, a keypad, a touch interface, a display screen, etc. A user may utilize an I/O module  473  of an electronic device  470  to configure one or more user preferences, configure one or more parameters (e.g., thresholds, etc.), provide user input (e.g., explicitly provide/select one or more traits that an agent the user wants to interact with must have), etc. 
     In one embodiment, an electronic device  470  and/or a remote server device  480  may be a source of one or more policies relating to one or more traits. 
     In one embodiment, the deployment system  430  may be accessed or utilized by one or more online services (e.g., AI services, NLP services, chat services, messaging services, etc.) hosted on a remote server device  480  and/or one or more software applications  474  (e.g., AI applications, NLP applications, chat applications, messaging applications) operating on an electronic device  470 . For example, in one embodiment, a virtual assistant, a search engine, or another type of software application  474  operating on an electronic device  470  utilizes the deployment system  430  to provide a multi-agent chatroom that allows a user to experience interacting with different specialized chatbots  340  modeling different traits. 
     These features contribute to the advantage of selectively invoking, based on an intent of a user in a virtual chatroom  440 , a specialized chatbot  340  from a plurality of available specialized chatbots  340  in the chatroom  440  to interact with the user, thereby allowing the user to interact with an agent with one or more traits that the invoked specialized chatbot  340  models. Other advantages include providing a multi-agent virtual chatroom  440  that allows a user in the chatroom  440  to experience interacting with multiple, different chat agents modeling different traits (i.e., providing advice from different perspectives) via a plurality of available specialized chatbots  340 . 
       FIG. 5  illustrates an example process  500  involving the training system  330  and the deployment system  430 , in accordance with an embodiment of the invention. In one embodiment, the process  500  comprises two separate phases: (1) a training phase during which one or more specialized chatbots are trained via the training system  330 , and (2) a deployment (i.e., operations) phase during which a multi-agent chatroom  440  is deployed and at least one of a plurality of available specialized chatbots  340  are invoked in the chatroom  440 , all via the deployment system  430 . The plurality of available specialized chatbots  340  are received from the training system  330  and/or maintained in the deployment system  430 . 
     In one embodiment, the training system  330  comprises a training data extraction unit  510 . In the training phase, the training data extraction unit  510  is configured to receive, as input, at least one of the following: (1) a database  501  of chat logs (“chat log database”), and (2) a database  502  of policies relating to traits (“trait policy database”). 
     In one embodiment, the chat log database  501  is obtained from an electronic device  350  or a remote server device  360 . The chat log database  501  comprises chat logs for a large volume of chat sessions, wherein each chat session is a conversation between two or more participants (e.g., human participants, machine participants such as chatbots, etc.). 
     For expository purposes, let the term “text unit” generally denote a block of text included in a chat log. Examples of text units include, but are not limited to, a chat message, a sentence, a paragraph, etc. 
     In one embodiment, the trait policy database  502  is obtained from an electronic device  350  or a remote server device  360 . In one embodiment, the trait policy database  502  comprises one or more policies relating to one or more traits. For example, in one embodiment, the one or more policies define at least one of the following: (1) a set of traits, and (2) a set of subdomains, wherein each subdomain is defined by at least one trait. In the training phase, for each trait and/or each subdomain, the training data extraction unit  510  is configured to extract training data relevant to the trait and/or the subdomain from the chat log database  501 . 
     Specifically, for each trait and/or each subdomain, the training data extraction unit  510  is configured to: (1) parse each chat log of the chat log database  501  into one or more text units, (2) determine a degree of match (i.e., similarity) between each text unit of each chat log of the chat log database  501  and the trait and/or the subdomain based on keywords included in the chat logs of the chat log database  501 , (3) generate a mapping (“text unit mapping”) between each text unit of each chat log of the chat log database  501  and the trait and/or the subdomain, wherein the text unit mapping has a weight that is based on a degree of match between the text unit and the trait and/or the subdomain, and (4) identify (or filter), from the chat log database  501 , one or more chat logs that exhibit/show the trait and/or the subdomain based on at least one text unit mapping between at least one text unit and the trait and/or the subdomain, wherein the one or more chat logs identified (or filtered) are provided as training data relevant to the trait and/or the subdomain. 
     In one embodiment, if a text unit mapping between a text unit and a trait and/or a subdomain has a weight that satisfies a pre-defined weight threshold, the training data extraction unit  510  identifies (or filters) a chat log containing the text unit as exhibiting/showing the trait and/or the subdomain. For example, in one embodiment, the training data extraction unit  510  identifies (or filters) the chat log as exhibiting/showing the trait and/or the subdomain by annotating/flagging/marking up the chat log and/or the text unit. 
     For example, in one embodiment, chatlogs exhibiting/showing traits associated with the domain of equity based stock markets are identified by detecting equity related text units in the chatlogs, and associating actions such as trading, long term investing, and balancing in the text units. 
     In one embodiment, the training system  330  comprises a specific trait model training unit  515  configured to train, in the training phase, a specialized chatbot  340  to model a single trait based on training data relevant to the trait. Specifically, for each trait, the specific trait model training unit  515  is configured to: (1) receive training data relevant to the trait (e.g., from the training data extraction unit  510 ), and (2) train a machine learning model to model the trait based on the training data, wherein the resulting trained machine learning model is a specialized chatbot  340  configured to generate a conversation including one or more responses that exhibit/show the trait. 
     In one embodiment, the specific trait model training unit  515  is configured to apply one or more machine learning techniques such as, but not limited to, density-based techniques (e.g., k-nearest neighbor, local outlier factor, etc.), subspace and correlation-based outlier detection for high-dimensional data techniques, support-vector machines, artificial neural networks. 
     In one embodiment, the training system  330  comprises a keyword/concept extractor unit  520  configured to associate, in the training phase, one or more keywords and/or concepts with a trait. Specifically, the keyword/concept extractor unit  520  is configured to: (1) receive the chat log database  501 , and (2) for each trait, identify and record one or more keywords and/or concepts associated with the trait based on one or more chat logs of the chat log database  501  that are identified as exhibiting/showing the trait (e.g., identified by the training data extraction unit  510 ). For example, in one embodiment, a keyword/concept is identified and recorded as associated with the trait if the keyword/concept frequently occurs in the one or more chat logs identified as exhibiting/showing the trait (i.e., the keyword/concept is commonly/highly associated with the trait). 
     In one embodiment, the training system  330  comprises a keyword/concept model mapper unit  525  configured to label/tag, in the training phase, a specialized chatbot that models a trait with one or more keywords and/or concepts associated with the trait. Specifically, for each trait, the keyword/concept model mapper unit  525  is configured to: (1) receive a specialized chatbot that models the trait (e.g., from the specific trait model training unit  515 ), (2) receive one or more keywords and/or concepts associated with the trait (e.g., from the keyword/concept extractor unit  520 ), (3) label/tag the specialized chatbot with the one or more keywords and/or concepts associated with the trait, and (4) generate a mapping (“keyword/concept mapping”) between each keyword/concept associated with the trait. 
     In one embodiment, for each trait, the training system  330  maintains at least one of the following: (a) at least one text unit mapping (e.g., generated by the training data extraction unit  510 ) between at least one text unit and the trait, and (b) at least one keyword/concept mapping (e.g., generated by the keyword/concept model mapper unit  525 ) between at least one keyword/concept and the trait. A keyword/concept mapping associates a particular keyword/concept with a particular trait that in turn is associated with a particular chat log based on a text unit mapping between a text unit of the particular chat log and the particular trait. 
     In one embodiment, the training system  330  comprises a multiple traits model training unit  530  configured to train, in the training phase, a specialized chatbot to jointly model multiple traits based on training data relevant to the multiple traits. Specifically, the multiple traits model training unit  530  is configured to: (1) receive multiple traits, wherein the multiple traits are a subset of the set of traits included in the trait policy database  502 , (2) receive training data relevant to the multiple traits, wherein the training data is a union of all chat logs of the chat log database  501  that are identified as exhibiting/showing at least one of the multiple traits (e.g., identified by the training data extraction unit  510 ), and (3) train a machine learning model to jointly model the multiple traits based on the training data, wherein the resulting trained machine learning model is a specialized chatbot  340  configured to generate a conversation including one or more responses that jointly exhibit/show the multiple traits. In one embodiment, the multiple traits are explicitly selected/provided (e.g., via user input). In one embodiment, the specialized chatbot  340  is labeled/tagged with each keyword/concept associated with each of the multiple traits. 
     For example, in one embodiment, a specialized chatbot  340  trained to model traits of an aggressive stock trader is labeled/tagged with labels/tags such as “equity based stock markets”, “short term investing”, “aggressive”, etc. As another example, in one embodiment, a specialized chatbot  340  trained to model traits of a defensive stock trader is labeled/tagged with labels/tags such as “equity based stock markets”, “long term investing”, “defensive”, etc. As yet another example, in one embodiment, a specialized chatbot  340  trained to model traits of a balanced/mixed-mode stock trader is labeled/tagged with labels/tags such as “equity based stock markets”, “mixed-mode”, “balanced”, etc. 
     In one embodiment, the multiple traits model training unit  530  is configured to apply one or more machine learning techniques such as, but not limited to, density-based techniques (e.g., k-nearest neighbor, local outlier factor, etc.), subspace and correlation-based outlier detection for high-dimensional data techniques, support-vector machines, artificial neural networks. 
     In one embodiment, the deployment system  330  comprises a multi-agent chatroom interface  540  configured to provide, in the deployment phase, a chatroom interface for a multi-agent chatroom  440 , wherein the chatroom interface allows multiple participants to interact with one another in the chatroom  440 . 
     In one embodiment, the deployment system  330  comprises a main chatbot  450  configured to control, in the deployment phase, invocation of a specialized chatbot  340  in the chatroom  440 . Specifically, in the deployment phase, the main chatbot  450  is configured to: (1) in response to a user joining the chatroom  440 , interact with the user by initiating a chat session with the user via the multi-agent chatroom interface  540 , (2) monitor the chat session to determine an intent of the user, and (3) based on the intent of the user, selectively invoke on demand a specialized chatbot  340  (from the plurality of available specialized chatbots  340 ) in the chatroom  440  to interact with the user via the multi-agent chatroom interface  540 . For example, in one embodiment, the main chatbot  450  invokes a specialized chatbot  340  to join the chatroom  440  to interact with a user in the chatroom  440  in response to the user indicating an intent to interact with an agent with a trait that the specialized chatbot  340  models. The main chatbot  450  is configured to invoke on demand different specialized chatbots  340  with different traits in the chatroom  440  to interact with the user via the multi-agent chatroom interface  540 . 
     In one embodiment, the deployment system  330  comprises a chat session trait extractor  550  configured to identify, in the deployment phase, one or more traits that an agent interacting with a user in the chatroom  440  must have. Specifically, the chat session trait extractor  550  is configured to: (1) receive, as input, the trait policy database  502  and/or keyword/concept mappings (e.g., from the keyword/concept model mapper  525 ), and (2) based on the input and a chat log of a current/ongoing chat session involving a user in the chatroom  440  (e.g., from the main chatbot  450 ), determine an intent of the user and extract one or more traits from the chat session. Each trait extracted from the chat session represents a trait requirement that a response (i.e., chatbot response) to the user must satisfy (e.g., traits that a chatbot interacting with the user must model). For example, if during the chat session the user provides a query (e.g., question) that includes one or more keywords and/or concepts that map to one or more traits that an aggressive stock trader has, the chat session trait extractor  550  determines an intent of the user is to seek investing advice from the perspective of an aggressive stock trader, and extracts from the chat session the one or more traits that an aggressive stock trader has. As another example, if during the chat session the user provides a query (e.g., question) that includes one or more keywords and/or concepts that map to one or more traits that a beginner/novice investor has, the chat session trait extractor  550  determines an intent of the user is to learn about investing step-by-step, and extracts from the chat session one or more traits that an agent interacting with the user must have (e.g., patient, a teacher of investing, etc.). 
     In one embodiment, each trait that the chat session trait extractor  550  extracts from a current/ongoing chat session has a corresponding weight that is based on when a text unit (of a chat log of the chat session) from which the trait is extracted occurs. For example, in one embodiment, if a first point of time is closer to a current point of time than a second point of time, a trait extracted from a text unit of the chat log that occurs at the first point of time is weighted higher than another trait extracted from another text unit of the chat log that occurs at the second point of time. 
     In one embodiment, the deployment system  330  comprises a specialized chatbot invocation unit  555 . In the deployment phase, the main chatbot  450  is configured to trigger the specialized chatbot invocation unit  555  to invoke a specialized chatbot  340  (from the plurality of available specialized chatbots  340 ) in the chatroom  440  to interact with a user in the chatroom  440 . Specifically, the specialized chatbot invocation unit  555  is configured to: (1) for each available specialized chatbot  340 , receive a set of labels/tags comprising one or more keywords and/or concepts that the specialized chatbot  340  is labeled/tagged with (e.g., from the keyword/concept model mapper  525 ), (2) receive one or more traits extracted from a current/ongoing chat session involving a user in the chatroom  440  (e.g., from the chat session trait extractor  550 ), and (3) determine whether a specialized chatbot  340  that models the one or more traits extracted is available (i.e., included in the plurality of available specialized chatbots  340 ) based each set of labels/tags received. 
     If a specialized chatbot  340  that models the one or more traits extracted is available (i.e., the specialized chatbot  340  is labeled/tagged with one or more keywords and/or concepts associated with the one or more traits extracted), the specialized chatbot invocation unit  555  is configured to invoke the specialized chatbot  340  in the chatroom  440  to interact with the user via the multi-agent chatroom interface  540 . During an interaction between the specialized chatbot  340  and the user, the specialized chatbot  340  outputs, via the multi-agent chatroom interface  540 , one or more responses modeling the one or more traits extracted. 
     If a specialized chatbot  340  that models the one or more traits extracted is not available (i.e., the plurality of available specialized chatbots  340  does not include any specialized chatbots  340  labeled/tagged with one or more keywords and/or concepts associated with the one or more traits extracted), the specialized chatbot invocation unit  550  is configured to trigger identification of a specialized chatbot  340  from the plurality of available specialized chatbots  340  that models one or more traits similar to the one or more traits extracted (“alternative specialized chatbot”). 
     In one embodiment, the deployment system  430  comprises an alternative specialized chatbot identification unit  560 . In the deployment phase, the specialized chatbot invocation unit  555  is configured to trigger the alternative specialized chatbot identification unit  560  to identify an alternative specialized chatbot  340  from the plurality of available specialized chatbots  340 . Specifically, the alternative specialized chatbot identification unit  560  is configured to: (1) receive one or more traits extracted from a current/ongoing chat session involving a user in the chatroom  440  (e.g., from the chat session trait extractor  550 ), (2) identify an alternative specialized chatbot  340  from the plurality of available specialized chatbots  340  based on the one or more traits extracted, and (3) trigger invocation of the alternative specialized chatbot  340  in the chatroom  440 , if identified. 
     For example, in one embodiment, for each available specialized chatbot  340 , the alternative specialized chatbot identification unit  560  is configured to compute a distance mapping (e.g., vector distance mapping, cosine similarity between embeddings) between one or more keywords and/or concepts associated with the one or more traits extracted and one or more keywords and/or concepts that the specialized chatbot  340  is labeled/tagged with. If the distance mapping satisfies a pre-determined similarity threshold, the alternative specialized chatbot identification unit  560  identifies the specialized chatbot  340  as an alternative specialized chatbot  340 , and triggers invocation of the alternative specialized chatbot  340  in the chatroom  440  to interact with the user. 
     In one embodiment, the deployment system  430  comprises an alternative specialized chatbot invocation unit  565 . In the deployment phase, the alternative specialized chatbot identification unit  560  is configured to trigger the alternative specialized chatbot invocation unit  565  to invoke an alternative specialized chatbot  340  in the chatroom  440  to interact with a user in the chatroom  440  via the multi-agent chatroom interface  540 . During an interaction between the alternative specialized chatbot  340  and the user, the alternative specialized chatbot  340  outputs, via the multi-agent chatroom interface  540 , one or more responses modeling the one or more traits extracted. 
     For example, in one embodiment, if a specialized chatbot  340  that models a first set of traits extracted from a current/ongoing chat session is not available (i.e., not included in the plurality of available specialized chatbots  340 ), the alternative specialized chatbot identification unit  560  identifies an alternative specialized chatbots  340  from the plurality of available specialized chatbots  340  that models a second set of traits similar to the first set of traits (e.g., satisfies a pre-determined similarity threshold), and invokes the alternative specialized chatbot  340  in the chatroom  440 . For example, the first set of traits may comprise one or more traits that restaurant experts for Italian restaurants in New York have, and the second set of traits may comprise one or more traits that restaurant experts for French restaurants in New York have. 
     In one embodiment, the chat session trait extractor  550  continuously analyzes a current/ongoing chat session involving a user in the chatroom  440  to determine a new intent (i.e., a change in intent) of the user and extract one or more new traits from the chat session (e.g., extracted from one or more text units (of a chat log of the chat session) that occur when the new intent is detected), thereby enabling the main chatbot  450  to invoke on demand a new specialized chatbot  340  that models the one or more new traits (i.e., a specialized chatbot  340  that has not yet been invoked during the chat session). To the user, the new specialized chatbot  340  represents a SME/domain expert with a different perspective (i.e., the one or more new traits) that joins the chatroom  440  to assist the user. These features contribute to the advantage of selectively invoking, based on the change in the intent of the user, a different specialized chatbot  340  from the plurality of available chatbots  340  in the chatroom  440  to interact with the user, thereby allowing the user to interact with a different agent with one or more traits that the invoked different specialized chatbot  340  models. 
     For example, assume a current/ongoing chat session involves a user in the chatroom  440  interacting with a specialized chatbot  340  that models traits that an aggressive stock trader has (“chatbot A”). The number of participants in the chatroom  440  is 3—the user, the main chatbot  450 , and chatbot A. If the chat session trait extractor  550  detects, during the chat session, that an intent of the user has changed/shifted from seeking investing advice from the perspective of an aggressive stock trader to seeking investing advice from the perspective of a defensive stock trader instead, the main chatbot  450  invokes a new specialized chatbot  340  that models traits that a defensive stock trader has (“chatbot B”) to join the chatroom  440  and interact with the user. The number of participants in the chatroom  440  is now 4—the user, the main chatbot  450 , chatbot A, and chatbot B. After a pre-determined amount of time, if the user continues to express an intent to seek investing advice from the perspective of a defensive stock trader, the main chatbot  450  may place chatbot A in sleep mode or even remove chatbot A from the chatroom  440  (which reduces the number of participants in the chatroom  440  to 3—the user, the main chatbot  450 , and chatbot B). If the chat session trait extractor  550  detects, during the chat session, that an intent of the user has changed/shifted again from seeking investing advice from the perspective of a defensive stock trader to seeking investing advice from the perspective of a balanced/mixed-mode stock trader instead, the main chatbot  450  invokes a new specialized chatbot  340  that models traits that a balanced/mixed-mode stock trader has (“chatbot C”) to join the chatroom  440  and interact with the user. 
     As another example, assume a current/ongoing chat session involves a player of a sport who formulates a strategy. Multiple, different specialized chatbots  340  are available that can advise the player from multiple perspectives. For example, if the sport is tennis and the player inquires whether attacking an opponent in a match by making a shot from his/her forehand to his/her high backhand is a good strategy, a first specialized chatbot  340  can provide a response that accounts for the physical fitness of the player (e.g., a medical perspective), a second specialized chatbot  340  can provide a different response that accounts for strengths and weaknesses of both the player and the opponent (e.g., a coaching perspective), and a third specialized chatbot  340  can provide another different response to the inquiry that accounts for training and warm-up the player will go through prior to the match to prepare for making the shot on a consistent basis (e.g., a physical conditioning perspective). 
     As another example, assume a current/ongoing chat session involves a patient who can receive advice from multiple, different specialized chatbots  340  representing different medical professionals/specialists based on the patient&#39;s medical reports. For example, the available chatbots  340  can include a cardiologist chatbot, a neurology chatbot, a chest-and-lung chatbot, a General Practitioner chatbot, etc. 
       FIG. 6  is a flowchart of an example process  700  for training a chatbot, in accordance with an embodiment of the invention. Process block  701  includes identifying one or more chat logs that exhibit a trait. Process block  702  includes identifying one or more labels associated with the trait based on the one or more chat logs. Process block  703  includes training the chatbot to generate a response that models the trait based on the one or more chat logs. Process block  704  includes labeling the chatbot with the one or more labels. 
     In one embodiment, process blocks  701 - 704  are performed by one or more components of the training system  330 . 
       FIG. 7  is a flowchart of an example process  710  for controlling invocation of at least one of a plurality of available chatbots in a virtual chatroom. Process block  711  includes initiating a chat session with a user in the virtual chatroom. Process block  712  includes determining an intent of the user based on a chat log of the chat session. Process block  713  includes extracting a set of traits from the chat session based on the intent of the user, where the set of traits extracted is indicative of at least one trait requirement that a response to the user must satisfy (e.g., the response must model either the set of traits extracted or a set of different traits similar to the set of traits extracted based on distance mapping and a pre-determined similarity threshold). Process block  714  includes selecting a chatbot from the plurality of available chatbots based on the set of traits extracted, where the chatbot selected is configured to generate a response that satisfies the at least one trait requirement (e.g., the chatbot selected models either the set of traits extracted or a set of different traits similar to the set of traits extracted based on distance mapping and a pre-determined similarity threshold). Process block  715  includes invoking the chatbot selected in the virtual chatroom to interact with the user. 
     In one embodiment, process blocks  711 - 715  are performed by one or more components of the deployment system  430 . 
       FIG. 8  is a high level block diagram showing an information processing system  600  useful for implementing one embodiment of the invention. The computer system includes one or more processors, such as processor  602 . The processor  602  is connected to a communication infrastructure  604  (e.g., a communications bus, cross-over bar, or network). 
     The computer system can include a display interface  606  that forwards graphics, text, and other data from the voice communication infrastructure  604  (or from a frame buffer not shown) for display on a display unit  608 . In one embodiment, the computer system also includes a main memory  610 , preferably random access memory (RAM), and also includes a secondary memory  612 . In one embodiment, the secondary memory  612  includes, for example, a hard disk drive  614  and/or a removable storage drive  616 , representing, for example, a floppy disk drive, a magnetic tape drive, or an optical disk drive. The removable storage drive  616  reads from and/or writes to a removable storage unit  618  in a manner well known to those having ordinary skill in the art. Removable storage unit  618  represents, for example, a floppy disk, a compact disc, a magnetic tape, or an optical disk, etc. which is read by and written to by removable storage drive  616 . As will be appreciated, the removable storage unit  618  includes a computer readable medium having stored therein computer software and/or data. 
     In alternative embodiments, the secondary memory  612  includes other similar means for allowing computer programs or other instructions to be loaded into the computer system. Such means include, for example, a removable storage unit  620  and an interface  622 . Examples of such means include a program package and package interface (such as that found in video game devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and other removable storage units  620  and interfaces  622 , which allows software and data to be transferred from the removable storage unit  620  to the computer system. 
     In one embodiment, the computer system also includes a communication interface  624 . Communication interface  624  allows software and data to be transferred between the computer system and external devices. In one embodiment, examples of communication interface  624  include a modem, a network interface (such as an Ethernet card), a communication port, or a PCMCIA slot and card, etc. In one embodiment, software and data transferred via communication interface  624  are in the form of signals which are, for example, electronic, electromagnetic, optical, or other signals capable of being received by communication interface  624 . These signals are provided to communication interface  624  via a communication path (i.e., channel)  626 . In one embodiment, this communication path  626  carries signals and is implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, and/or other communication channels. 
     Embodiments of the invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of embodiments of the invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is 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 the computer readable storage medium includes the following: a portable computer diskette, 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 mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of embodiments of the invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of embodiments of the invention. 
     Aspects of embodiments of the invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     From the above description, it can be seen that embodiments of the invention provide a system, computer program product, and method for implementing the embodiments of the invention. Embodiments of the invention further provide a non-transitory computer-useable storage medium for implementing the embodiments of the invention. The non-transitory computer-useable storage medium has a computer-readable program, wherein the program upon being processed on a computer causes the computer to implement the steps of embodiments of the invention described herein. References in the claims to an element in the singular is not intended to mean “one and only” unless explicitly so stated, but rather “one or more.” All structural and functional equivalents to the elements of the above-described exemplary embodiment that are currently known or later come to be known to those of ordinary skill in the art are intended to be encompassed by the present claims. No claim element herein is to be construed under the provisions of 35 U.S.C. section 112, sixth paragraph, unless the element is expressly recited using the phrase “means for” or “step for.” 
     The terminology used herein is for the purpose of describing particular embodiments of the invention only and is not intended to be limiting. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. 
     The descriptions of the various embodiments of the invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.