Systems and methods for soliciting and processing intent responses - Patents.com

Abstract

The disclosed embodiments provide a framework for soliciting and evaluating responses from brands and other users to an intent communicated by a customer. In response to obtaining the intent, the intent messaging service provides the intent to an application implemented on the user's computing device to solicit a response to the intent. In response to obtaining the intent response, the intent messaging service inhibits the user from generating further intent responses and provides the obtained intent response to the customer. In response to another request corresponding to the intent, the intent messaging service establishes a communication channel between the customer and the user, thereby allowing additional responses to be provided to the customer by the user identified by the intent messaging service.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This patent application claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 033,534, filed June 2, 2020, the disclosure of which is incorporated herein by reference.

[0002]

[0002] The present disclosure relates generally to systems and methods for facilitating messaging between customers and brands. More particularly, techniques are provided for developing a framework for soliciting and evaluating responses from brands and other users to intents communicated by customers. Summary of the Invention

[0003]

[0003] The disclosed embodiments provide a framework for an intent processing system that facilitates messaging between customers and brands, whereby the system may solicit and evaluate responses from brands and other users to intents communicated by these customers. According to some embodiments, a computer-implemented method is provided. The computer-implemented method comprises obtaining an intent. The intent corresponds to a request and is associated with the customer. The computer-implemented method further comprises identifying an agent to receive the intent. The identified agent is associated with a commercial endeavor. Further, the agent is identified using a machine learning model that is updated using sample intents and sample outputs corresponding to characteristics of a set of agents. The computer-implemented method further comprises providing the intent. When the intent is received at an application implemented on a computing device associated with the identified agent, the intent is used to solicit an intent response from the identified agent. The computer-implemented method further comprises obtaining an intent response. The intent response is dynamically used to update the machine learning model. The computer-implemented method further comprises sending a first instruction to the application. When the first instruction is received at the application, the first instruction causes the application to prohibit obtaining additional intent responses. The computer-implemented method further comprises providing an intent response. When the intent response is received, the intent response is presented to a customer associated with the intent. The computer-implemented method further comprises obtaining a new request to facilitate a communication channel between an application implemented on a computing device of the identified agent and a computing device associated with the customer. The new request corresponds to the intent. The computer-implemented method further comprises sending a second instruction to the application. When the second instruction is received at the application, the second instruction causes the application to obtain additional responses to the intent.

[0004] In one example, a system includes one or more processors and a memory containing instructions that, when executed by the one or more processors, cause the system to perform a process described herein. In another example, a non-transitory computer-readable storage medium has executable instructions stored thereon that, when executed by one or more processors of a computer system, cause the computer system to perform a process described herein.

[0005]

[0005] This Summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in isolation to determine the scope of the claimed subject matter, which subject matter should be understood by reference to appropriate portions of the entire specification of this patent application, any or all drawings, and each claim.

[0006]

[0006] The above, together with other features and examples, are described in more detail below in the following specification, claims, and accompanying drawings.

[0007]

[0007] Various embodiments of the present disclosure are discussed in detail below. Although specific implementations are discussed, it should be understood that this is done for illustrative purposes only. Those skilled in the art will recognize that other components and configurations can be used without departing from the spirit and scope of the present disclosure. Therefore, the following description and drawings are illustrative and should not be construed as limiting. Numerous specific details are described to provide a thorough understanding of the present disclosure. However, in some instances, well-known or conventional details are not described to avoid obscuring the description. Reference to an embodiment or an embodiment in the present disclosure may be a reference to the same embodiment or any embodiment, and such reference means at least one of the embodiments.

[0008]

[0008] Reference to "one embodiment" or "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. The appearances of the phrase "one embodiment" in various places in this specification do not necessarily all refer to the same embodiment, or to separate or alternative embodiments that are mutually exclusive of other embodiments. Moreover, various features are described that may be exhibited by some embodiments but not by other embodiments.

[0009]

[0009] The terms used herein generally have their ordinary meanings in the art, within the context of this disclosure and in the specific context in which each term is used. Alternative wording and synonyms may be used for any one or more of the terms described herein, and no special importance should be placed on whether a term is detailed or explained herein. In some cases, synonyms for some terms are provided. The detailed explanation of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification, including examples of any term described herein, is illustrative only and is not intended to further limit the scope and meaning of the disclosure or any exemplary term. Similarly, the disclosure is not limited to the various embodiments provided herein.

[0010]

[0010] Without intending to limit the scope of the present disclosure, examples of devices, apparatus, methods and their related results according to the embodiments of the present disclosure are given below. Please note that titles or subtitles may be used in the examples for the convenience of the reader, and in any case, this should not limit the scope of the present disclosure. Unless otherwise defined, technical and scientific terms used herein have the meaning as commonly understood by those skilled in the art to which the present disclosure pertains. In case of conflict, the present document, including definitions, shall control.

[0011]

[0011] Additional features and advantages of the present disclosure will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the principles disclosed herein. The features and advantages of the present disclosure may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the present disclosure will become more fully apparent from the following description and the appended claims, or may be learned by practice of the principles described herein.

[0012]

[0012] The present disclosure is described in conjunction with the accompanying drawings, in which: [Brief description of the drawings]

[0013] [Figure 1]

[0013] FIG. 1 illustrates an illustrative example of an environment in which an intent messaging service obtains intents from customers and presents responses to the intents from brands and other users to the customers, according to at least one embodiment. [Diagram 2]

[0014] FIG. 1 illustrates an illustrative example of an environment in which an intent messaging service identifies brands and other users to solicit responses to intents submitted by customers, according to at least one embodiment. [Diagram 3]

[0015] FIG. 1 illustrates an illustrative example of an environment in which a customer generates an intent to solicit responses from brands and other users of an intent messaging service via a user interface, according to at least one embodiment. [Figure 4]

[0016] FIG. 1 illustrates an illustrative example of an environment in which a customer is provided with status regarding intents submitted by the customer via a user interface, according to at least one embodiment. [Diagram 5]

[0017] FIG. 1 illustrates an illustrative example of an environment in which a brand may be provided with new intents from a customer via a user interface of an intent messaging application utilized by the brand to access various customer intents, according to at least one embodiment. [Figure 6]

[0018] FIG. 1 illustrates an illustrative example of an environment in which a brand provides responses to customer intents via a user interface of an intent messaging application utilized by the brand, according to at least one embodiment. [Figure 7]

[0019] FIG. 1 illustrates an illustrative example of an environment in which a brand may be permitted to communicate directly with a customer as a result of a customer request for the establishment of a communication channel between the customer and the brand, according to at least one embodiment. [Figure 8]

[0020] FIG. 1 illustrates an illustrative example of an environment in which an intent messaging service establishes a communication channel between a brand and a customer to enable direct communication between the brand and the customer via an intent messaging application, according to at least one embodiment. [Figure 9]

[0021] FIG. 1 illustrates an illustrative example of a process for broadcasting an intent to one or more brands without providing an identity of the customer who submitted the intent, according to at least one embodiment. [Figure 10]

[0022] FIG. 1 illustrates an illustrative example of a process for obtaining intent responses from a brand and evaluating the intent responses to determine their relevance to an intent, according to at least one embodiment. [Figure 11]

[0023] FIG. 1 illustrates an illustrative example of a process for establishing a communication channel between a customer and a brand in response to a request from the customer to initiate a conversation with the brand, according to at least one embodiment. [Figure 12]

[0024] FIG. 1 illustrates an illustrative example of an environment in which various embodiments may be implemented. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014]

[0025] In the accompanying figures, similar components and / or features may have the same reference label. Furthermore, various components of the same type may be distinguished by following the reference label with a dash and a second label that distinguishes between the similar components. When only a first reference label is used in this specification, the description is applicable to any one of the similar components having the same first reference label, regardless of the second reference label.

[0015]

[0026] The following description merely provides preferred examples of embodiments and is not intended to limit the scope, applicability or configuration of the present disclosure. Instead, the following description of preferred examples of embodiments provides those skilled in the art with an enablement description for implementing the preferred examples of embodiments. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the appended claims.

[0016]

[0027] FIG. 1 illustrates an illustrative example of an environment 100 in which an intent messaging service 102 obtains intents from customers 108 and presents responses to the intents from brands 112 and other users 114 to the customers, according to at least one embodiment. In the environment 100, the customers 108, via an intent messaging application implemented on a computing device 110, send requests to an intent processing system 104 of the intent messaging service 102 to obtain one or more responses to the requests from one or more brands 112 and / or other users 114 associated with the intent messaging service 102. The one or more brands 112 may include entities involved in a particular commercial endeavor, such as providing goods and / or services to customers in a retail setting. The other users 114 may include members of the intent messaging service 102 community who may also interact with them to obtain responses to the brand's 112 intents and may have experience with various topics, goods, services, or other areas that may be associated with the intents. An intent messaging application implemented on a computing device 110 may be provided by the intent messaging service 102 to enable customers, such as customer 108, to interact with brands 112 associated with the intent messaging service 102 and other users 114 (e.g., a community of users that utilize the intent messaging service 102) and may offer products and services to the customers. The intent messaging service 102 may provide a platform for customers and brands to connect to provide recommendations and advice regarding intents submitted by these customers.

[0017]

[0028] In response to obtaining a request from a customer 108, the intent processing system 104 may evaluate the request to extract intents expressed by the customer 108 that may be used to identify brands 112 and other users 114 that may provide relevant responses to the customer's request. In one embodiment, the intent processing system 104 utilizes a machine learning model to process the request to identify and extract intents from the request. The machine learning model may be used to perform a semantic analysis of the request (e.g., by identifying keywords, sentence structure, repeated words, punctuation characters, and / or non-article words) to identify the intent expressed in the request. The machine learning model utilized by the intent processing system 104 may be dynamically trained using supervised learning techniques. For example, a dataset of input requests and known intents contained in the input requests may be selected for training the machine learning model. In some implementations, the known intents used to train the machine learning model may include characteristics of these intents. The machine learning model may be evaluated to determine whether the machine learning model is extracting an expected intent from each of the requests based on the input sample requests fed to the machine learning model. Based on this evaluation, the machine learning model may be modified to increase the likelihood that the machine learning model will generate the desired result. The machine learning model may be further dynamically trained by soliciting feedback from customers, including the customer 108, regarding the extracted intent obtained from the submitted request. For example, prior to submitting the extracted intent for identification of one or more brands 112 or other users 114 to solicit a response to the intent, the extracted intent may be presented to the customer 108 to determine whether the extracted intent corresponds to the request submitted by the customer 108. The response from the customer 108 may thus be utilized to train the machine learning model based on the accuracy of the machine learning model in identifying the intent from the request.

[0018]

[0029] An intent may correspond to a problem a customer desires to have solved. Examples of intents may include (for example) topic, sentiment, complexity, and urgency. Topics may include, but are not limited to, subjects, products, services, technical issues, usage questions, complaints, refund requests, or purchase requests, etc. Intent may be determined, for example, based on semantic analysis of the message (e.g., by identifying keywords, sentence structure, repeated words, punctuation characters, and / or non-article words), user input (e.g., selecting one or more categories), and / or message-related statistics (e.g., typing speed and / or response latency).

[0019]

[0030] The intent processing system 104 may provide the extracted intent to an intent matching system 106 of the intent messaging service 102. The intent matching system 106 may be implemented on the computing system of the intent messaging service 102 or other systems (e.g., a server, a virtual machine instance, etc.). Alternatively, the intent matching system 106 may be implemented as an application or other process running on the computing system of the intent messaging service 102. In one embodiment, in response to obtaining a new intent from the intent processing system 104, the intent matching system 106 utilizes the new intent, as well as information about the various brands 112 and other users associated with the intent messaging service 102, as input to a machine learning model to identify one or more brands 112 and / or other users 114 that may be likely to provide relevant responses to the intent. The information about the various brands 112 and other users 114 may include historical data corresponding to responses submitted by the various brands 112 and other users 114 to previously provided intents. The historical data may indicate the performance of the brand or other user in providing relevant responses to various intents submitted to the brand or other user, information about any prior interactions between the brand / other user and the customer 108, customer feedback (if any) related to the customer's interactions with the brand or other user, etc. Additionally, the information about the brand or other user may specify the types of intents to which the brand or other user may have expertise in responding. For example, the information may indicate what products and services are offered by the brand, what products and services the user utilizes, or any other information that may be useful in identifying whether resolution of a particular intent may involve these products and services.

[0020]

[0031] The machine learning model utilized by the intent matching system 106 may be dynamically trained using sample intents and sample outputs corresponding to characteristics of the brands 112 and other users 114 that may be used to identify the brands 112 and other users 114 to which the intent should be provided. Additionally, the machine learning model may be dynamically trained using feedback from the various brands 112 and other users 114 that received the intent. This feedback may be used to determine whether the machine learning model is selecting brands 112 and other users 114 that are capable of responding to the intent with a relevant response or that are otherwise associated with the characteristics of the intent. This feedback may be used to further train the machine learning model utilized by the intent matching system 106. In this manner, the machine learning model may be dynamically trained in real-time as feedback is obtained by the intent matching system 106 from the various brands 112 and other users 114 for the myriad of intents submitted to these various brands 112 and other users 114. Alternatively, the machine learning model may be trained periodically based on feedback obtained from the brands 112 and other users 114.

[0021]

[0032] The output of the machine learning model utilized by the intent matching system 106 may include an identifier corresponding to one or more brands 112 and / or other users 114 to which a new intent should be provided to solicit a response from each of the one or more brands 112 and / or other users 114. The new intent may be assigned a unique identifier such that responses obtained from the one or more brands 112 and / or other users 114 may be associated with the particular intent by reference to the unique identifier. In one embodiment, the intent matching system 106 updates a user interface of an intent messaging application utilized by each brand 112 or other user 114 to indicate the solicitation of a response to the particular intent. For example, through this user interface, the intent matching system 106 may present the new intent, as well as options for responding to the intent, to an agent associated with the brand 112 or other user 114. An agent associated with the brand 112 or other user 114 may, via this user interface, respond to the intent, reject / ignore the intent, or provide an indication to the intent matching system 106 that the intent is not relevant to the brand 112 or other user. Actions performed by agents associated with the brand 112 or other users 114 may be used to further dynamically train machine learning models utilized by the intent matching system 106 to select brands 112 and other users for responding to intents. For example, if an agent associated with a brand indicates that a provided intent is not relevant to the brand, the intent matching system may utilize this feedback to train machine learning models to prevent unrelevant intents from being provided to the brand.

[0022]

[0033] In one embodiment, a brand 112 (e.g., an agent associated with the brand assigned to interact with the intent messaging service 102) or other user 114 that receives an intent is limited to a single response to the intent. For example, when an agent associated with the brand 112 or other user 114 submits a response to the intent to the intent processing system 104, the intent messaging application utilized by the agent associated with the brand 112 or other user 114 may disable the ability to submit additional responses to the intent until the customer 108 indicates that they would like to further engage with the brand 112 or other user 114 regarding the intent. This may prevent the brand 112 or other user 114 from flooding the customer 108 with responses to the intent (e.g., "spamming"). Additionally, if the customer 108 declines to engage with the brand 112 or other user 114, the customer 108 may be spared from additional responses from the brand 112 or other user 114.

[0023]

[0034] When an agent associated with the brand 112 or other user 114 submits an intent response to the intent processing system 104 on behalf of the customer 108, the intent processing system 104 may evaluate the intent response to determine whether the intent response is relevant to the intent submitted by the customer 108. In one embodiment, the intent processing system 104 utilizes a classification algorithm or other machine learning model to classify the intent response as either intent-relevant or intent-irrelevant. The classification algorithm or other machine learning model utilized by the intent processing system 104 may be dynamically trained using supervised learning techniques. For example, a data set of input intents, known relevant responses, known irrelevant responses, and classifications may be selected for training the classification algorithm or other machine learning model. In some examples, the input intents may be obtained from an administrator of the intent messaging service, a customer of the intent messaging service, or other source associated with the intent messaging service. In some implementations, the known relevant responses and known irrelevant responses used to train the classification algorithm or other machine learning model utilized by the intent processing system include responses generated by the entity that generated the sample intent. Additionally, the classification algorithm or other machine learning model used to classify intent responses as relevant or irrelevant may be trained using feedback from customers, including the customer 108. For example, if a response classified as relevant to an intent is provided to the customer 108, the customer 108 may provide feedback indicating whether the response was indeed relevant to the intent. This feedback may be used to augment the model (e.g., a response classified as relevant was deemed relevant to the intent by the customer) or to update the model (e.g., a response classified as relevant was deemed irrelevant to the intent by the customer).As customers of the intent messaging service 102 provide this feedback to the intent messaging service 102, the classification algorithm or other machine learning model may be dynamically updated in real time.

[0024]

[0035] The intent processing system 104 may discard any responses classified as not relevant to the intent using the classification algorithms or machine learning models described above. In one embodiment, if the intent processing system 104 determines that the brand 112 or other user has provided an unrelated response to the intent, the intent processing system 104 updates the brand 112 or other user's profile to indicate that it has provided an unrelated response to the intent. This may reduce the likelihood that the brand 112 or other user 114 will be selected by the intent matching system 106 to respond to similar intents. In some instances, this feedback may also be used to dynamically train a machine learning model utilized by the intent matching system 106 to further reduce the likelihood that the brand 112 or other user 114 will be solicited to provide responses to similar intents. If the brand 112 or other user 114 is deemed to be consistently providing unrelated responses, other remedial actions may be implemented, such as disassociating or removing the brand 112 or other user from the intent messaging service 102.

[0025]

[0036] If the intent processing system 104 determines that a relevant response has been obtained for the intent, the intent processing system 104 may provide the response to the customer 108 via an intent messaging application running on the computing device 110. The intent response may then be displayed on a user interface of the computing device 110. Through this user interface, the customer 108 may evaluate the intent response and decide whether to further engage with the brand 112 or other user 114, thank or acknowledge the brand 112 or other user for the response, or ignore the intent response. Additionally, the customer 108 may provide feedback to the intent processing system 104 regarding the quality of the intent response. This feedback may be utilized to further dynamically train the machine learning models utilized by the intent matching system 106 used to identify brands 112 and other users 114 to which intents are provided in order to solicit responses to the intent.

[0026]

[0037] In one embodiment, if the customer 108 determines that they wish to communicate further with the brand 112 or other user 114 regarding the intent submitted by the customer 108, the intent messaging service 102 establishes a communication channel between the customer 108 and the brand 112 or other user 114, through which the customer 108 and the brand 112 or other user 114 may exchange messages and other content. As described above, when the brand 112 or other user 114 submits a response to an intent, the intent messaging application utilized by the brand 112 or other user may prohibit the brand 112 or other user 114 from submitting additional responses. However, if the customer 108 indicates that they wish to converse with the brand 112 or other user 114 regarding the intent, the intent messaging service 102 may send a command or other indication to the intent messaging application to enable the brand 112 or other user 114 to submit additional responses to the customer 108 via the intent messaging application. Additionally, the intent messaging service 102 may provide additional information about the customer 108 (e.g., the customer's name, the customer's address, a picture of the customer, the customer's contact information, etc.) to the brand 112 or other users.

[0027]

[0038] 2 illustrates an illustrative example of an environment 200 in which an intent messaging service 202 identifies brands 220 and other users 222 to solicit responses to intents submitted by a customer 218, according to at least one embodiment. In the environment 200, the intent messaging service 202 obtains a request from a customer 218 to solicit responses from one or more brands 220 and / or other users 222 for a particular intent. For example, the customer 218 may generate a request using an intent messaging application installed on a computing device utilized by the customer 218 or otherwise accessed by the customer 218 using the computing device (e.g., via a website, etc.) and indicate in the request an intent for which the customer 218 seeks an associated response. An intent may be (for example) a topic, emotion, complexity, and / or urgency level. A topic may include, but is not limited to, a subject, a product, a service, a technical issue, a usage question, a complaint, a refund request, or a purchase request, etc.

[0028]

[0039] A customer 218 may send its request and corresponding intent via an intent messaging application to a customer messaging system 206 of the intent processing system 204. The customer messaging system 206 may be implemented on a computing system of the intent messaging service 202 or as an application executed by a computing system of the intent processing system 204. The customer messaging system 206 may facilitate communication between the customer 218, the intent messaging service 202, and any brands 220 and / or other users 222 associated with the intent messaging service 202. For example, the customer messaging system 206 may establish a communication channel with any brands 220 or other users 222 that the customer 218 has selected to engage in a conversation via the intent messaging application regarding a particular intent. Additionally, the customer messaging system 206 may act to update the user interface of the intent messaging application based on the intent provided and responses obtained from the intent messaging service 202 that identify brands 220 and other users 222 to which intent responses may be solicited for a particular intent.

[0029]

[0040] In response to obtaining the request and the corresponding intent from the customer 218, the customer messaging system 206 may send the request and the corresponding intent to an intent extraction engine 208 of the intent processing system 204 to extract the intent from the request. The intent extraction engine 208 may be implemented as a computer system that utilizes machine learning models to process incoming requests from the customer 218 and identify the intent expressed in the request. For example, the machine learning models may be used to perform a semantic analysis of the request (e.g., by identifying keywords, sentence structure, repeated words, punctuation characters, and / or non-article words) to identify the intent expressed in the request. The intent may also be determined based on, for example, user input (e.g., selecting one or more categories) and / or message-related statistics (e.g., typing speed and / or response latency).

[0030]

[0041] The machine learning model utilized by the intent extraction engine 208 may be trained using supervised learning techniques. For example, a dataset of input requests and known intents contained in the input requests may be selected for training the machine learning model implemented by the intent extraction engine 208. In some examples, the input requests may be obtained from an administrator of the intent messaging service 202, a customer of the intent messaging service 202, or other sources related to the intent messaging service 202. In some implementations, the known intents used to train the machine learning model utilized by the intent extraction engine 208 may include characteristics of those intents provided by an entity that generated the sample requests. The machine learning model may be evaluated to determine whether the machine learning model is extracting an expected intent from each of the requests based on the input sample requests provided to the machine learning model. Based on this evaluation, the machine learning model may be modified (e.g., one or more parameters or variables may be updated) to increase the likelihood that the machine learning model will generate a desired result (e.g., an expected intent).

[0031]

[0042] In one embodiment, the intent extraction engine 208 evaluates the request from the customer 208 to determine whether additional information is needed to extract or otherwise supplement the intent from the request to enable identification of brands 220 and / or other users 222 that may provide relevant responses to the intent. For example, the intent extraction engine 208 may determine that the customer's geographic location and time frame are needed for resolution of the intent. The intent extraction engine 208 may send a request to the customer messaging system 206 to solicit this additional information from the customer 218. In one embodiment, the customer messaging system 206 utilizes natural language processing (NLP) or other artificial intelligence to solicit the customer 208 for the requested information. For example, using NLP or other artificial intelligence, the customer messaging system 206 may ask the customer 218 to provide its location, a time frame for resolution of the intent, what users or brands the customer 218 would like to solicit a response from, etc. The responses provided by the customer 218 may be provided to the intent extraction engine 208, which may use these responses and the supplied request from the customer 218 to extract intents and supplement the intents with additional information provided by the customer 218. This may be used by the intent matching system 210 to identify brands 220 and / or other users 222 that may be solicited to obtain responses to the intents.

[0032]

[0043] The intent extraction engine 208 may provide extracted intents from requests submitted by customers 218 to an intent machine learning modeling engine 212 of the intent matching system 210. The intent machine learning modeling engine 212 may be implemented as an application or process implemented on or otherwise executed on a computing system of the intent matching system 210. In one embodiment, the intent machine learning modeling engine 212 implements a machine learning model configured to identify brands 220 and / or other users 222 to which an intent may be provided to solicit a response to the intent. The machine learning model implemented by the intent machine learning modeling engine 212 may utilize the brand database 214 and user database 216 maintained by the intent matching system 210, as well as the customer's intent, as inputs to the machine learning model to identify brands 220 and / or users 222 that may provide relevant responses to the intent. The brand database 214 and user database 216 may include profiles for each of the brands 220 and users 222 that may be associated with the intent messaging service 202. Each profile may indicate the user's or brand's experience responding to a particular intent or category of intents, as well as the user's or brand's interest in the topics or taxonomies underlying the intents. Additionally, each profile may indicate feedback regarding the user's or brand's responses to previously provided intents. This feedback may specify whether the responses provided for a particular intent were relevant, useful, or otherwise praised by the corresponding customers.

[0033]

[0044] The user database 216 may further include profiles of customers 218. The profiles of customers 218 may indicate any customer's preferences for particular brands 220 or other users 222. For example, the profiles of customers 218 may specify which brands 220 or other users 222 the customer 218 has interacted with to address a previously provided intent. Additionally, for each of these interactions, the profile may include feedback from the customer 218. This feedback may indicate whether the interaction with the particular brand or other user was relevant to the corresponding intent, was helpful in addressing the corresponding intent, or otherwise led to a positive experience with the brand or other user. This information may also be utilized by the intent machine learning modeling engine 212 to identify which brands 220 and / or other users 222 to solicit to obtain a response to the intent.

[0034]

[0045] The machine learning model utilized by the intent machine learning modeling engine 212 may be trained using sample intents and sample outputs corresponding to characteristics of the brands 220 and other users 222 that may be used to identify the brands 220 and other users 222 to which the intent should be provided. Additionally, the machine learning model may be trained using feedback from the various brands 220 and other users 222 that received the intent. This feedback may be used to determine whether the machine learning model is selecting brands 220 and other users 222 that are capable of responding to the intent with a relevant response or that are otherwise associated with the characteristics of the intent. For example, if a brand that provides interior design services gets an intent that is not related to interior design, the brand may provide feedback indicating that the provided intent is not related to the brand. This feedback may be used to further train the machine learning model utilized by the intent machine learning modeling engine 212.

[0035]

[0046] An output generated by the machine learning model implemented by the intent machine learning modeling engine 212 may include an identifier corresponding to the brand 220 and / or other users 222 to which an intent should be provided to solicit a response from the brand 220 and / or other users 222. Based on this output, the intent machine learning modeling engine 212 may send an intent to the identified brand 220 and / or other users 222. For example, the intent machine learning modeling engine 212 may update a user interface of an intent messaging application of each of the identified brand 220 and / or other users 222 to present the intent. The intent may be assigned a unique identifier such that responses from the brand 220 and / or other users 222 to the intent may be associated with the intent by the unique identifier. A brand or other user receiving an intent via the intent messaging application may respond to the intent and submit a response to the intent processing system 204, which may evaluate the response to determine whether the response is relevant to the intent.

[0036]

[0047] In one embodiment, the intent is provided to the identified brand 220 and / or other users 222 anonymously. For example, the intent machine learning modeling engine 212 may remove any identifying information of the customer 218 (e.g., the customer's name, the customer's address, the customer's contact information, etc.) when serving the intent to the identified brand 220 and / or other users 222. However, the intent machine learning modeling engine 212 may provide other information in addition to the intent that may be useful to the brand or other users in preparing a response to the intent. For example, the intent may be provided with a general location of the customer 218 (e.g., city, state, etc.). This general location may be used to determine whether the brand or other users can provide a response to the intent, which may indicate an ability to assist the customer 218 at the general location (e.g., the brand may maintain a store in the general location, the user may offer goods and services at the general location, etc.).

[0037]

[0048] In one embodiment, once a brand or other user submits a response to an intent, the intent messaging service 202 may disable the brand or other user's ability to provide additional responses to the intent via the intent messaging application. This prevents the brand or other user from potentially flooding a customer 218 with responses to a particular intent. A conversation with a customer 218 may be established via the intent messaging application upon a request from the customer 218 to initiate a communication channel with a particular brand 220 or other user 222.

[0038]

[0049] In one embodiment, the intent processing system 204, in response to obtaining an intent response from the brand or other user, evaluates the intent response utilizing a classification algorithm or other machine learning model to classify the intent response as either relevant to the intent or not relevant to the intent. Any response classified as not relevant to the intent may be discarded by the intent processing system 204. Responses classified as relevant to the intent may be presented to the customer 218 by the customer messaging system 206 via an intent messaging application implemented on the customer's computing device. Additionally, the intent responses may be provided with options for the customer 218, such as inviting the brand or other user who submitted the intent response into a conversation about the intent, thanking the brand or other user for their intent response, ignoring the intent response from the brand or other user, ignoring future responses from the brand or other user (e.g., blocking the brand or other user), etc. If the customer 218 selects to converse with the brand or other user based on the provided intent response, the customer messaging system 206 may establish a communication channel between the customer 218 and the brand or other user to enable the customer 218 and the brand or other user to converse using their respectful intent messaging application. In one embodiment, upon establishing the communication channel, the customer messaging system 206 provides the brand or other user with customer information (e.g., name, picture, address, contact information, etc.). Additionally, the customer messaging system 206 again enables the brand or other user to send messages or responses to the customer 218 over the newly established communication channel.

[0039]

[0050] Responses to the intent from the brand 220 and / or other users 222 may be evaluated by the intent processing system 204 to determine their relevance to the customer's 218 intent.

[0040]

[0051] 3 illustrates an illustrative example of an environment 300 in which a customer 306 generates an intent via a user interface 310 to solicit responses from a brand 304 and other users 305 of the intent messaging service 302, according to at least one embodiment. In the environment 300, the customer 306 generates a new intent via an intent messaging application operating on a computing device 308 that can be submitted to the intent messaging service 302 to solicit one or more responses from the brand 304 and other users 305 associated with the intent messaging service 302. For example, using the intent messaging application via the user interface 310, the customer 306 can define a request or intent to which the customer 306 is seeking a response from the brand 304 and other users 305 associated with the intent messaging application 302.

[0041]

[0052] As shown in FIG. 3, the customer 306, via the user interface 310, may be presented with an option to provide a title to its request or intent, as well as an option to define the request or intent. For example, the customer 306 may provide as a title to its request or intent, "Interior Design Recommendations," a request or intent that serves as a solicitation from the customer 306 for recommendations of interior designers that may be useful to the customer 306. Additionally, via the user interface 310, the customer 306 may define parameters of the request or intent (e.g., "I am looking for a good interior designer"). This information may be utilized by the intent messaging service 302 to extract the intent of the customer 306 and identify one or more brands 304 and other users 305 to solicit responses to the intent submitted by the customer 306.

[0042]

[0053] In one embodiment, the intent messaging service 302 can utilize natural language processing or other artificial intelligence to query the customer 306 for additional information that can be used to supplement the intent and enable tailored identification of brands 304 and other users 305 that may be able to provide relevant responses to the intent. For example, the intent messaging service 302 can query the customer 306 to identify the customer's location, whether other users can help the customer with that intent, what the time frame is for resolution of the intent, etc. Responses provided by the customer 306 to the intent messaging service 302 can be used to further narrow the selection of brands 304 and other users 305 to which responses to the intent may be solicited.

[0043]

[0054] In one embodiment, the customer 306 may be presented by the intent messaging application via the user interface 310 with a listing of contacts or other participants who may be added to a conversation related to the submitted intent. For example, the intent messaging application may retrieve from the customer's computing device 308 a listing of contacts that may be presented via the user interface 310. Alternatively, the intent messaging application may retrieve from the intent messaging service 302 a listing of contacts associated with the customer 306 who also utilizes the intent messaging service 302. This listing of contacts from the intent messaging service 302 may include users that the customer 306 has designated as "friends" or who are otherwise available to communicate directly with the customer 306 via the intent messaging service 302.

[0044]

[0055] In one embodiment, via the user interface 310, the intent messaging service 302 may further provide recommendations of brands 304 and / or other users 305 from which the customer 306 may solicit responses to the submitted intent. For example, based on an evaluation of the intent by the intent messaging service 302, the intent messaging service 302 may recommend one or more brands 304 or other users 305 that may be likely to provide relevant responses to the submitted intent. The intent messaging service 302 may present these one or more brands 304 or other users 305 to allow the customer 306 to select which brands 304 or other users 305 should receive the intent to solicit a response to the intent. Based on the customer's selection, the intent messaging service 302 may solicit the selected brands and / or other users for a response to the intent.

[0045]

[0056] FIG. 4 illustrates an illustrative example of an environment 400 in which a customer 406 is provided with status regarding an intent submitted by the customer 406 via a user interface 410, according to at least one embodiment. In the environment 400, in response to submitting a new intent to the intent messaging service 402 to solicit one or more brands 404 and other users 405 to provide a response to the intent, the intent messaging service 402 may evaluate the intent and various brand and user profiles associated with the intent messaging service 402 to identify a subset of the brands 404 and other users 405 that may be solicited to provide a response to the intent. In one embodiment, the intent messaging service 402 uses a machine learning model implemented by the intent matching system to identify brands 404 and other users 405 that may provide a relevant response to the intent submitted by the customer 406. The machine learning model may utilize the brand and user databases maintained by the intent matching system and the customer's intent as input to the machine learning model to identify brands and / or users that may provide a relevant response to the intent. The brand database and user database may include profiles of each of the brands and users, respectively, that may be associated with the intent messaging service 402. Each profile may indicate the user's or brand's experience responding to a particular intent or category of intents, as well as the user's or brand's interest in the topics or taxonomy underlying the intent. Additionally, each profile may indicate feedback regarding the user's or brand's responses to previously provided intents. This feedback may specify whether the responses provided in response to the intent were relevant, useful, or otherwise praised by the corresponding customers.

[0046]

[0057] In response to obtaining an intent from the customer 406, the intent messaging service 402 may update the user interface 410 to provide a new conversation window for the provided intent. The name or title of the conversation window presented via the user interface 410 may correspond to the name or title of the request submitted by the customer 406 to the intent messaging service 402 via the user interface 410. Additionally, via the user interface 410 and within the conversation window for the intent, the intent messaging service 402 may provide a status regarding the processing of the customer's provided intent. For example, as shown in FIG. 4, the intent messaging service 402 may indicate that the intent has been shared with the intent messaging service's community (e.g., the brand 404 and other users 405 solicited to provide responses to the intent). Additionally, the intent messaging service 402 may indicate via the user interface 410 that any surveyed responses from the brand 404 and / or other users 405 may be presented to the customer 406 within a conversation window related to the intent submitted by the customer 406.

[0047]

[0058] In one embodiment, the customer 406 can introduce one or more other contacts to the conversation window to enable communication between the customer 406 and the one or more contacts within the conversation window. For example, the customer 406 can select one or more contacts from a listing of available contacts that the customer 406 may invite to join a conversation tied to the submitted intent. This listing of available contacts can be maintained by the intent messaging service 402 and provided to the customer 406 via an intent messaging application running on the computing device 408. Alternatively, the listing of available contacts can be maintained on the computing device 408. If the customer 406 selects one or more contacts from this listing, the intent messaging service 402 can retrieve contact information for each of the one or more contacts and send a notification to each of the one or more contacts to invite the one or more contacts to join a conversation with the customer 406 regarding the intent. If a listing of contacts is maintained on the computing device 408, the intent messaging application may retrieve the contact information from the computing device 408 and provide this contact information to the intent messaging service 402 to provide notifications to these contacts regarding the conversation. In one embodiment, the notifications may include a uniform resource identifier (URI) or other network address of the conversation. The contacts may access the conversation by utilizing the URI or other network address provided by the intent messaging service 402.

[0048]

[0059] As responses to the intent are obtained from the brands 404 and other users 405 to which the intent was provided to solicit the responses, the intent messaging service 402 may sort through these responses to determine whether the responses are relevant and responsive to the intent submitted by the customer 406. In one embodiment, the intent messaging service 402 utilizes a classification algorithm or other machine learning model to classify responses to the intent as either intent-related or intent-irrelevant. Responses classified as intent-irrelevant may be discarded by the intent messaging service 402 and not presented to the customer 406 via the user interface 410. However, any responses classified as relevant to the intent submitted by the customer 406 may be presented to the customer 406 via the user interface 410. For example, the intent messaging service 402 may send the relevant (e.g., surveyed) responses to an intent messaging application on the customer's computing device 408 to cause the intent messaging application to update a conversation window associated with the intent to present the obtained responses. The customer 406 may select any of the responses obtained for the intent to initiate a conversation with the brand 404 or other user 405 that provided the selected response. The conversation may be presented in a conversation window through which the customer 406 may communicate with the brand 404 or other user 405.

[0049]

[0060] FIG. 5 illustrates an illustrative example of an environment 500 in which a brand 504 is provided with a new intent from a customer 506 via a user interface 508 of an intent messaging application utilized by the brand 504 to access various customer intents, according to at least one embodiment. In the environment 500, a user associated with the brand 504 is presented with various intents provided by the intent messaging service 502 via a chat window via a user interface 508 provided by the intent messaging application. Each intent submitted to the intent messaging application may be presented in the user interface 508 using a unique intent panel, such as intent panel 510. When a new intent is retrieved by the intent messaging application, the intent may be presented in the intent panel without any identifying information of the customer 506 that submitted the intent. For example, as shown in FIG. 5, the intent presented in the intent panel 510 may be absent the customer's name, the customer's profile picture or image, the customer's contact information, and any other identifying information of the customer 506. Instead, the intent panel 510 may provide the topic or subject of the intent submitted by the customer 506 and a description of the intent.

[0050]

[0061] When a user associated with the brand 504 selects an intent panel 510 from the user interface 508, the intent messaging application may update the user interface 508 to present a conversation window corresponding to the selected intent. Through this conversation window, the user associated with the brand 504 may view intents and generate intent responses that may be provided to the intent messaging service 502 for rating and presentation to customers 506.

[0051]

[0062] FIG. 6 illustrates an illustrative example of an environment 600 in which a brand 604 provides a response to an intent of a customer 606 via a user interface 608 of an intent messaging application utilized by the brand 604, according to at least one embodiment. In the environment 600, a user associated with the brand 604 can access a conversation window via the user interface 608 provided by the intent messaging application in which the user may rate an intent submitted by the customer 606 via the intent messaging service 602. For example, from a chat window presented by the intent messaging application via the user interface 608, a user associated with the brand 604 may select an intent provided to the user by the intent messaging service 602. In response to the user selection of an intent, the intent messaging application may update the user interface 608 to present a conversation window specific to the intent. Through this conversation window, the intent messaging application may present the intent submitted by the customer 606 to the user via the user interface 608.

[0052]

[0063] As described above, the intent provided to the brand or other user is modified to remove any identifying information of the customer 606 that submitted the intent. For example, the intent messaging service 602 may remove from the intent the customer's name, the customer's contact information, the customer's address, an image or picture of the customer, etc. This may prevent the brand 604 or other user from sending unsolicited messages or responses to the customer 606 outside the context of the intent. The intent messaging service 602 may further supplement the intent with any of the information obtained by the intent messaging service from the customer 606 that may be useful to the brand 604. This additional information may include a time frame for resolution of the intent, the general geographic location (e.g., city and state) of the customer (as opposed to something specific, such as an address), a budget for resolution of the intent, etc.

[0053]

[0064] Through a conversation window presented via the user interface 608, the intent messaging application may provide the intent submitted by the customer 606 without the customer's identifying information. For example, rather than providing the customer's name and profile picture, the intent messaging application may provide in the conversation window the topic of the intent (e.g., "I'm looking for a baby car seat") and a general image that may be relevant to the intent (e.g., a picture of a car). Additionally, through the conversation window, the intent messaging application may provide other information that may be useful to a user associated with the brand 604 in responding to the intent. For example, as shown in FIG. 6, the intent is presented with the general geographic location of where the intent originated (e.g., "San Juan") and the time the intent was submitted (e.g., "10:34 AM").

[0054]

[0065] In one embodiment, the intent messaging application enables a user associated with a brand 604 to submit a single response to an intent, after which the intent messaging application disables the conversation window to prevent the user from submitting additional responses. For example, as shown in Figure 6, when a user submits a response to an intent (which may be presented in the conversation window), the intent messaging application removes any interface elements that would otherwise allow the user to enter a response and replaces those elements with an indication that the conversation between the user and the customer 606 is now closed.

[0055]

[0066] In response to obtaining a response to the intent from a user associated with the brand 604, the intent messaging application may send the response to the intent messaging service 602 for evaluation and delivery to the customer 606. The intent messaging service 602 may evaluate the response to determine whether the response from the user associated with the brand 604 is relevant and responsive to the intent submitted by the customer 606. In one embodiment, the intent messaging service 602 utilizes a classification algorithm or other machine learning model to classify the response to the intent submitted by the customer 606 as either intent-related or intent-irrelevant. If the response is classified as intent-irrelevant, the intent messaging service 602 may discard the intent. However, if the response is classified as intent-related, the intent messaging service 602 may send the response to an intent messaging application utilized by the customer 606 to present the response to the customer 606. For example, the intent messaging service 602 may send the relevant (e.g., surveyed) response to an intent messaging application on the customer's computing device to cause the intent messaging application utilized by the customer 606 to update a conversation window associated with the intent to present the obtained response.

[0056]

[0067] 7 illustrates an illustrative example of an environment 700 in which a brand 704 is permitted to communicate directly with a customer 706 as a result of a customer 706 request for the establishment of a communication channel between the customer 706 and the brand 704, according to at least one embodiment. In the environment 700, the intent messaging service 702 may provide an indication to a user associated with the brand 704, via a user interface 708 of an intent messaging application operating on a computing device utilized by the user associated with the brand 704, that the customer 706 has selected to establish a communication channel with the user associated with the brand 704. As discussed above, the intent messaging application utilized by the user associated with the brand 704, via the user interface 708, may present a chat window in which the user may interact with various intents submitted to the user by the intent messaging service 702 or engage in conversations with other customers of the intent messaging service 702.

[0057]

[0068] Each intent submitted to the intent messaging application may be presented in the user interface 708 using a unique intent panel, such as intent panel 710. When a new intent is retrieved by the intent messaging application, the intent may be presented in the intent panel without any identification of the customer 706 that submitted the intent. Selection of an intent panel may cause the intent messaging application to update the user interface 708 to present a conversation window corresponding to the selected intent. Through this conversation window, a user associated with the brand 704 may view the intents and generate intent responses that may be provided to the intent messaging service 702 for rating and presentation to the customer 706.

[0058]

[0069] In one embodiment, when a customer 706 submits a request to the intent messaging service 702 to establish a communication channel between the customer 706 and a user associated with the brand 704, the intent messaging service 702 sends instructions to an intent messaging application utilized by the user associated with the brand 704 to indicate that the user may now interact with the customer 706 regarding the intent. In addition to these instructions, the intent messaging service 702 may send identifying information for the customer 706, such as the customer's name, the customer's contact information (e.g., email address, physical address, phone number, etc.), any image or picture of the customer 706 (e.g., a customer profile picture, etc.). In response to receiving these instructions and the identifying information for the customer 706, the intent messaging application utilized by the user associated with the brand 704 may update the intent panel 710 corresponding to the intent submitted by the customer 706 to provide the identifying information for the customer 706. 7, the intent panel 710 is updated to replace the generic image associated with the intent and the title of the intent with the customer's name and profile picture. Additionally, the intent messaging application may update the intent panel 710 to indicate that a conversation was initiated by the customer 706 regarding the intent (e.g., "Chat started from intent"). A user associated with the brand 704 may select the intent panel 710 to access a conversation window associated with the intent and interact with the customer 706.

[0059]

[0070] FIG. 8 illustrates an illustrative example of an environment 800 in which a communication channel between a brand 804 and a customer 806 is established by an intent messaging service 802 to enable direct communication between the brand 804 and the customer 806 via an intent messaging application, according to at least one embodiment. In the environment 800, the intent messaging service 802 may be provided with the ability to interact with the customer 806 regarding a particular intent via a user interface 808 of an intent messaging application operating on a computing device utilized by a user associated with the brand 804. For example, if the customer 806 indicates a desire to communicate with this user based on an intent response submitted by a user associated with the brand 804, the intent messaging service 802 may send instructions to the intent messaging application to enable the user associated with the brand 804 to interact with the customer 806. This may include updating the user interface 808 to remove a previously provided indication stating that the conversation with the customer 806 has been closed. Additionally, the intent messaging application may update the user interface 808 to provide the user associated with the brand 804 with the ability to submit additional responses to the customer 806 via a communication channel established by the intent messaging service 802 between the user associated with the brand 804 and the customer 806.

[0060]

[0071] In one embodiment, when a customer 806 indicates a desire to communicate with a user associated with a brand 804 via the intent messaging service 802, the intent messaging service 802 provides additional information of the customer to an intent messaging application utilized by the user associated with the brand 804. For example, the intent messaging service 802 may send the customer's name, the customer's contact information (e.g., email address, physical address, phone number, etc.), some image or picture of the customer 806 (e.g., a customer profile picture, etc.), etc. to the intent messaging application utilized by the user associated with the brand 804. This may cause the intent messaging application to update the user interface 808 to present the customer's additional information to the user associated with the brand 804. For example, as shown in FIG. 8, the user interface 808 is updated to present the profile picture and name of the customer 806 at the top of the conversation window representing the title of the conversation window. Thus, the conversation title may change from the title of the intent provided to the user to the name and image of the customer 806. Additionally, any messages from the customer 806 presented via the conversation window may show the customer's name and profile picture as opposed to the title of the intent.

[0061]

[0072] FIG. 9 illustrates an illustrative example of a process 900 for broadcasting an intent to one or more brands without providing an identity of the customer that submitted the intent, according to at least one embodiment. The process 900 may be implemented by an intent matching system of an intent messaging service that may process incoming intents submitted by customers of the intent messaging service to identify brands and / or other users to which these intents may be provided to solicit responses to the intent. As described above, the intent matching system may utilize a machine learning model trained using sample intents and sample outputs corresponding to characteristics of brands and other users that may be used to identify brands and other users to which the intent should be provided. Additionally, the machine learning model may be trained using feedback from various brands and other users that received the intent. This feedback may be used to determine whether the machine learning model is selecting brands and other users that are capable of responding to the intent with a relevant response or that are otherwise associated with the characteristics of the intent. This feedback may be used to further train the machine learning model utilized by the intent matching system.

[0062]

[0073] In step 902, the intent matching system obtains an intent from a customer of the intent messaging service. For example, using an intent messaging application provided by the intent messaging service and installed on the customer's computing device, the customer may generate a request to solicit one or more responses from various brands or other users within the network of users of the intent messaging service. In the request, the customer may specify a name for the request, as well as provide its request to be associated with the provided name. The request may include an intent that may be used to identify brands and other users that may be able to provide a relevant response to the customer's request. Examples of intents may include (for example) topic, sentiment, complexity, and urgency. Topics may include, but are not limited to, a subject, a product, a service, a technical issue, a usage question, a complaint, a refund request, or a purchase request, etc. The intent may be determined based on, for example, semantic analysis of the message (e.g., by identifying keywords, sentence structure, repeated words, punctuation characters and / or non-article words), user input (e.g., selecting one or more categories), and / or message-related statistics (e.g., typing speed and / or response latency).

[0063]

[0074] The intent messaging service may extract intents from the request that may be used to identify brands and users that may respond to the request with relevant information or responses that may be useful to the customer. Additionally, the intent messaging service may associate the intent with a unique identifier that may be used to track the intent and any obtained responses to the intent. This may assist the intent messaging service in identifying responses to the intent from various brands or users for which the intent should be provided and in providing the customer with relevant responses from these brands or users. Additionally, the intent messaging service may solicit additional information from the customer associated with the intent. For example, the intent messaging service may utilize natural language processing (NLP) or other artificial intelligence algorithms via the intent messaging application to ask the customer various questions related to the customer's intent for which this additional information should be solicited. For example, if a customer submits a request for recommendations of interior design firms, the intent messaging service may ask the customer where the customer lives, what is the time frame for completion of a new interior design project, what is the budget for the new interior design project, aspects of the interior design project, and other questions tied to the customer's interior design query. In response to these additional questions from the intent messaging service, the customer may provide additional information that can be used to supplement the intent and identify brands and other users that may provide relevant responses to the customer's intent.

[0064]

[0075] In response to obtaining an intent from a customer via the intent processing system of the intent messaging service, the intent matching system evaluates the intent in step 904 to identify one or more brands and / or other users that may provide a relevant response to the intent. In one embodiment, the intent matching system utilizes the machine learning model described above to identify the one or more brands and / or other users to be provided with the intent in order to solicit responses from the one or more brands and / or other users. The intent matching system may utilize the brand database and user database maintained by the intent matching system and the customer's intent as inputs to the machine learning model to identify brands and / or users that may provide a relevant response to the intent. The brand database and user database may include profiles of each of the brands and users that may be associated with the intent messaging service. Each profile may indicate the user's or brand's experience responding to a particular intent or category of intent, as well as the user's or brand's interest in the topic or taxonomy underlying the intent (e.g., interior design, etc.). Additionally, each profile may indicate feedback regarding the user's or brand's responses to previously provided intents. This feedback may specify whether the response provided in response to the intent was relevant, useful, or otherwise praised by the corresponding customer.

[0065]

[0076] In step 906, the intent matching system modifies the intent to remove any customer identifying information and to include any additional information that may be useful to the identified brand and / or other users to respond to the intent. As described above, the intent may be provided to the brand or other users anonymously, such that the intent is presented without any identifying information of the customer (e.g., the customer's name, the customer's contact information, the customer's address, an image or picture of the customer, etc.). This may prevent the brand or other users from sending unsolicited messages or responses to the customer outside the context of the intent. Thus, the intent matching system may evaluate the intent and remove any information that may uniquely identify the customer. Additionally, the intent matching system may supplement the intent with any of the information obtained by the intent messaging service from the customer that may be useful to the brand and / or other users to retrieve the intent. This additional information may include a time frame for resolution of the intent, a general geographic location (e.g., city and state, etc.) of the customer (as opposed to something specific, such as an address), a budget for resolution of the intent, etc.

[0066]

[0077] In step 908, the intent matching system broadcasts the intent to the identified brands and / or other users to enable them to respond to the intent. For example, the intent matching system may send the intent to an intent messaging application of a user associated with the identified brand. This may cause the intent messaging application to update a chat window of the intent messaging application to present a new panel corresponding to the intent. This may enable the user to select an intent from this new panel and access a conversation window corresponding to the intent. Through this conversation window, the user may generate and submit a response to the intent, which may be evaluated by the intent processing system of the intent messaging service. The intent may be assigned a unique identifier that may be broadcast along with the intent to the identified brands and / or other users. Thus, responses to the intent may also be associated with this unique identifier. This may assist the intent processing system in providing intent responses for a particular intent to a customer for its consideration.

[0067]

[0078] FIG. 10 illustrates an illustrative example of a process 1000 for obtaining an intent response from a brand and evaluating the intent response to determine the relevance of the intent response to the intent, according to at least one embodiment. The process 1000 may be implemented by an intent processing system of an intent messaging service. The intent processing system of the intent messaging service may evaluate a response to an intent from a brand to which the intent was provided to determine whether the response is relevant to the intent and can be provided to the customer. In one embodiment, the intent processing system implements a machine learning model that is used to evaluate the response from the brand to determine whether the intent response can be provided to the customer in response to the customer's intent. Additionally, the intent processing system interacts with an intent messaging application utilized by a user associated with the brand to allow the user to provide a response to the intent, and thereafter restricts further submission of responses until the customer who submitted the intent indicates that a communication channel between the customer and the user should be established.

[0068]

[0079] In step 1002, the intent processing system detects, via an intent messaging application implemented on the user's computing device associated with the brand, a selection of a customer intent from a chat window presented to the user by the intent messaging application. As described above, when an intent submitted by a customer is obtained by the intent messaging service, the intent messaging service may identify, via the intent matching system, one or more brands and / or other users to which the intent may be provided in order to solicit an intent response from the one or more brands and / or other users. In one embodiment, the intent messaging service utilizes an intent machine learning modeling engine to identify one or more brands and / or other users to which the intent should be provided in order to solicit a response from the one or more brands and / or other users via the intent matching system. The intent machine learning modeling engine may utilize the brand database and user database maintained by the intent matching system, as well as the customer's intent, as inputs to a machine learning model to identify brands and / or users that may provide a relevant response to the intent. The machine learning model utilized by the intent machine learning modeling engine may be trained using sample intents and sample outputs corresponding to characteristics of brands and other users that may be used to identify brands and other users to which an intent should be provided. Additionally, the machine learning model may be trained using feedback from various brands and other users that received the intent. This feedback may be used to determine whether the machine learning model is selecting brands and other users that are capable of responding to the intent with a relevant response or that are otherwise associated with the characteristics of the intent.For example, if a brand that offers interior design services receives an intent that is not related to interior design, the brand may provide feedback indicating that the provided intent is not relevant to the brand, which may be used to further train the machine learning model utilized by the intent matching system.

[0069]

[0080] The output of the machine learning model described above may include an identifier corresponding to the brand, which may cause the intent messaging service to send an intent to the user associated with the brand to solicit an intent response. The intent may be presented to the user via a chat window of an intent messaging application implemented on the user's computing device. In this manner, through this chat window, the user associated with the brand may select an intent submitted by a customer for consideration and to generate a response to the intent. The intent processing system may detect this selection of a particular customer intent from a chat window presented to the user via a user interface of the intent messaging application via the intent messaging application implemented on the user's computing device.

[0070]

[0081] In step 1004, in response to detecting a selection of a customer intent from the chat window, the intent processing system may present a conversation window including the intent submitted by the customer via a user interface of an intent messaging application implemented on the user's computing device. Through the conversation window, the user may view the intent submitted by the customer, as well as options for responding to the intent and for dismissing or ignoring the intent. In one embodiment, the intent submitted by the customer is presented without any identifying information of the customer. For example, the intent may be presented without the customer's name, the customer's contact information, an image or picture of the customer, or any other information that is unique to the customer and may be usable to identify the customer. This ensures that the intent is presented anonymously to users associated with the brand to protect the customer's privacy.

[0071]

[0082] In step 1006, the intent processing system determines whether a response to the intent has been submitted by a user associated with the brand. The intent processing system may monitor communications from an intent messaging application implemented on the user's computing device to identify any communications that include a response to the customer intent. For example, if a user associated with the brand generates a response to the intent via a conversation window associated with the intent, the intent messaging application may send the response to the intent processing system. In one embodiment, the intent processing system waits for a response to the provided intent for a limited period of time, after which the intent processing system determines that the brand has chosen to refrain from providing a response to the intent. If this occurs, the intent processing system may send instructions to the intent messaging application implemented on the user's computing device to remove the customer intent from the chat window and / or to close the conversation window utilized to present the customer intent. This may prevent the user from responding to the customer intent after the limited period of time for the response has elapsed. In an alternative embodiment, the intent processing system may continue to wait for a response from the user to the intent until a response is submitted by the user or until the customer indicates that the intent has been resolved (e.g., by another brand or another user).

[0072]

[0083] If a response to the intent is provided by a user associated with the brand, the intent processing system may send an instruction to an intent messaging application implemented on the user's computing device in step 1008 to close the conversation option of the user associated with the brand via a conversation window. For example, an instruction from the intent processing system may cause the intent messaging application to remove from the conversation window any options available to the user associated with the brand to enter and submit a response to the presented intent. The intent messaging application may present an indication to the user that the conversation with the customer via the conversation window has been closed (e.g., as shown in FIG. 6, for example). The user may still be able to view the intents submitted by the customer and the intent responses submitted by the user to the intent processing system through the conversation window. Additionally, the particular customer intent may still be accessible via a chat window of the intent messaging application. It should be noted that, as an alternative to the intent processing system sending an instruction to the intent messaging application to close the user's conversation options, the intent messaging application may automatically close these conversation options in response to the user submitting a response to a customer intent via the conversation window.

[0073]

[0084] In step 1010, the intent processing system evaluates the responses submitted by the user to determine whether the responses are relevant to the intent submitted by the customer. For example, the intent processing system may utilize a classification algorithm or other machine learning model to evaluate the responses and classify the responses as either relevant to the intent or irrelevant to the intent. The classification algorithm or other machine learning model may be trained using supervised learning techniques. For example, a data set of input intents, known relevant responses, known irrelevant responses, and classifications may be selected for training the classification algorithm or other machine learning model. In some examples, the input intents may be obtained from an administrator of the intent messaging service, a customer of the intent messaging service, or other source related to the intent messaging service. In some implementations, the known relevant responses and known irrelevant responses used to train the classification algorithm or other machine learning model utilized by the intent processing system include responses generated by the entity that generated the sample intent.

[0074]

[0085] In some examples, the resulting classification of known relevant and known irrelevant responses to the sample intent is evaluated to determine a loss or error that can be used to train a classification algorithm or other machine learning model. For example, if the classification algorithm or other machine learning model classified a relevant response to the sample intent as being an irrelevant response or classified an irrelevant response to the sample intent as being a relevant response, the parameters of the classification algorithm or other machine learning model can be adjusted according to the loss resulting from the misclassification of the response to the sample intent. These parameters can include weights and biases of the classification algorithm or other machine learning model utilized by the intent processing system.

[0075]

[0086] In one embodiment, the classification algorithm or other machine learning model is also trained to identify characteristics or other features of the response as well as characteristics or other features of the intent for which the response was generated. These characteristics or features may be used to classify the response as either relevant or irrelevant to the intent. For example, if the intent includes one or more characteristics corresponding to an interior design request, the classification algorithm or other machine learning model may evaluate the response to determine whether the response includes one or more characteristics that also correspond to interior design. Based on the similarity between these characteristics, the classification algorithm or other machine learning model may determine a relevance score, which may correspond to a relevant response if it exceeds a relevance threshold. In this manner, using the classification algorithm or other machine learning model, the intent processing system may evaluate the responses and classify them as either relevant or irrelevant to a particular intent.

[0076]

[0087] In step 1012, the intent processing system determines whether the intent response is relevant to the intent submitted by the customer. For example, the intent processing system may use the output of the classification algorithm or other machine learning model described above to determine whether the intent response was classified as relevant to the intent or as irrelevant to the intent. If the intent processing system determines that the intent response is not relevant to the intent, the intent processing system discards the intent response in step 1014. In one embodiment, if a brand is identified as providing an irrelevant response, the intent processing system may provide the intent matching system with information about the brand, as well as the customer intent for which the brand was determined to have provided an irrelevant response. The intent matching system may use this information to further train its machine learning models utilized to identify brands and other users that may be selected for solicitation of responses to specific intents. As a result of this, brands that provided irrelevant responses may be less likely to be selected by the intent matching system to provide responses to intents similar to the customer intents to which the brand or other users provided an irrelevant response.

[0077]

[0088] If the intent processing system determines that the intent response provided by the user is relevant to the intent, the intent processing system provides the intent response to the customer in step 1016. For example, the intent processing system may send the relevant response to an intent messaging application utilized by the customer, which may then update a user interface to present the response to the customer via the customer's computing device. From this user interface, the customer may decide whether to initiate a conversation with a user associated with the brand that provided the relevant response. If the customer does not wish to initiate a conversation with a user that provided a response deemed relevant by the intent processing system, the customer may instead send a notification to the user via the intent messaging service thanking the user for its response. Additionally or alternatively, the customer may submit feedback regarding the obtained response. For example, the customer may indicate whether the provided response was relevant to the intent submitted by the customer. This feedback may be used by the intent processing system to further train a classification algorithm or other machine learning model utilized to identify relevant responses for a given intent. If the customer chooses to initiate a conversation with the user, the intent messaging service may open a communication channel between the customer and the user, which allows the user associated with the brand to interact with the customer over the communication channel.

[0078]

[0089] 11 illustrates an illustrative example of a process 1100 for establishing a communication channel between a customer and a brand in response to a request from a customer to initiate a conversation with the brand, according to at least one embodiment. The process 1100 may be implemented by an intent messaging service that may utilize an intent processing system to establish a communication channel between a customer's computing device and a computing device of a user associated with a particular brand in response to a request from a customer to initiate a conversation with the brand for a particular intent. For example, if a customer determines that an intent response from the brand is related to an intent submitted by the customer to the intent messaging service and further conversation regarding the intent is desired, the customer may submit a request to the intent messaging service to initiate a conversation with the brand regarding the intent.

[0079]

[0090] At step 1102, the intent messaging service receives a request to initiate a conversation with a particular brand. In response to obtaining an intent response from the brand, as described above, the customer may select to initiate a conversation with a user associated with the brand to further discuss the intent and the intent response submitted by the user associated with the brand via the intent messaging service. Through a user interface of an intent messaging application implemented on the customer's computing device, the customer may select an intent response and indicate that the customer wishes to initiate a conversation with the user who provided the intent response. This may cause the intent messaging application to generate a request to the intent messaging service to establish a communication channel between the customer's computing device and the computing device of the user who submitted the intent response to initiate a conversation with the user associated with the brand. The request may include a unique identifier corresponding to the customer intent, as well as other identifying information of the user associated with the brand and / or the intent response itself.

[0080]

[0091] In response to receiving the request from the intent messaging application utilized by the customer, in step 1104, the intent messaging service, via the intent processing system, updates a chat window of the intent messaging application utilized by the user associated with the brand to provide additional information about the customer and to indicate that a conversation has been opened with the customer. For example, the intent processing system may update the chat window to provide the customer's name, an image or picture of the customer, contact information for the customer (e.g., email address, physical address, phone number, etc.), and the like. Additionally, via the chat window, the intent processing system may modify a panel associated with an intent response submitted by the user associated with the brand to indicate that a conversation between the customer and the user has been initiated. The modifications may include, for example, a change in the color of the panel, a textual statement indicating that a conversation between the customer and the user has been opened, an audible indication that a conversation between the customer and the user has been opened (e.g., a chime, an audible statement, etc.), and the like. As a result of the modifications to the brand chat window of the intent messaging application utilized by the user associated with the brand, the user may select the panel corresponding to the conversation to access a conversation window for the conversation between the customer and the user associated with the brand.

[0081]

[0092] In step 1106, the intent processing system detects a selection of a conversation from the brand chat window by a user associated with the brand. For example, if a user selects a panel corresponding to an intent response previously submitted to a customer using a user interface of an intent messaging application implemented on a computing device of the user associated with the brand, the intent messaging application may determine that the user requests to access a conversation window of an intent submitted by the customer. In one embodiment, if the customer has not provided an indication that a conversation between the customer and a user associated with the brand should be initiated, the conversation window corresponding to the intent may be disabled, thereby prohibiting the user associated with the brand from submitting additional intent responses to the intent submitted by the customer. However, if the customer has indicated that a conversation between the customer and a user associated with the brand should be initiated and the intent messaging application detects a selection of a panel corresponding to an intent submitted by the customer, the intent messaging application may send a request to the intent processing system to retrieve any messages submitted by the customer in response to intent responses submitted by the user associated with the brand that may be presented in the conversation window.

[0082]

[0093] In step 1108, the intent processing system presents a conversation window through which the customer intent, customer information, and responses from the customer to the intent responses submitted by the user may be displayed via an intent messaging application implemented on the computing device of the user associated with the brand. For example, a conversation window may be presented that includes the original intent submitted by the customer and the corresponding intent responses submitted by the user to the intent messaging service via a user interface of the intent messaging application utilized by the user associated with the brand. Additionally, the conversation window may be updated to include the customer's name, an image or other picture of the customer, and responses submitted by the customer to the intent responses submitted by the user associated with the brand via a user interface of the intent messaging application utilized by the customer.

[0083]

[0094] In one embodiment, the intent processing system establishes a communication channel between the customer and the user associated with the brand in response to detecting the selection of the conversation panel corresponding to the intent and intent response. Thus, by presenting the conversation window, the intent processing system may enable the user associated with the brand to communicate with the customer via the conversation window and the newly established communication channel in step 1110. As described above, when the user associated with the brand submits an intent response for a particular intent, the intent processing system may prohibit or otherwise restrict the user from submitting additional intent responses or other communications to the customer. This prevents the user from submitting unsolicited messages that might otherwise inundate the customer with responses that are not relevant to the particular intent. By establishing a communication channel between the customer and the user, the intent processing system may remove this restriction and allow the user to engage with the customer via the conversation window established for the particular intent.

[0084]

[0095] FIG. 12 illustrates a computing system architecture 1200 including various components in electrical communication with each other using a connection 1206, such as a bus, according to some implementations. The exemplary system architecture 1200 includes a processing unit (CPU or processor) 1204 and system connections 1206 coupling various system components, including a system memory 1220, such as a ROM 1218 and a RAM 1216, to the processor 1204. The system architecture 1200 may include a cache 1202 of high-speed memory connected directly to the processor 1204, in close proximity to the processor 1204, or integrated as part of the processor 1204. The system architecture 1200 may copy data from the memory 1220 and / or storage device 1208 to the cache 1202 for quick access by the processor 1204. In this manner, the cache may provide performance improvements that avoid processor 1204 delays while waiting for data. These and other modules may control or be configured to control the processor 1204 to perform various actions.

[0085]

[0096] Other system memory 1220 may also be available for use. Memory 1220 may include multiple different types of memory with different performance characteristics. Processor 1204 may include any general purpose processor and hardware or software services, such as service 1 1210, service 2 1212, and service 3 1214 stored in storage device 1208, configured to control processor 1204 as well as special purpose processors, where software instructions are incorporated into the actual processor design. Processor 1204 may be a fully self-contained computing system, including multiple cores or processors, buses, memory controllers, caches, etc. Multi-core processors may be symmetric or asymmetric.

[0086]

[0097] To enable user interaction with the computing system architecture 1200, the input devices 1222 can represent any number of input mechanisms, such as a microphone for audio, a touch-sensitive screen for gesture or graphic input, a keyboard, a mouse, motion input, voice, etc. The output devices 1224 can also be one or more of several output mechanisms known to those skilled in the art. In some cases, a multimodal system can enable a user to provide multiple types of input to communicate with the computing system architecture 1200. The communication interface 1226 can generally govern and manage user input and system output. There is no restriction to operating on any particular hardware configuration, and thus the basic features herein can be easily substituted with improved hardware or firmware configurations as they are developed.

[0087]

[0098] The storage device 1208 is a non-volatile memory and may be a hard disk or other type of computer-readable medium capable of storing data that is accessible by a computer, such as a magnetic cassette, a flash memory card, a solid-state memory device, a digital versatile disk, a cartridge, RAM 1216, ROM 1218, and hybrids thereof.

[0088]

[0099] The storage device 1208 can include services 1210, 1212, 1214 for controlling the processor 1204. Other hardware or software modules are contemplated. The storage device 1208 can be connected to a system connection 1206. In one aspect, a hardware module that performs a particular function can include software components stored in a computer readable medium in relation to the necessary hardware components, such as the processor 1204, the connection 1206, the output device 1224, etc., to perform that function.

[0089]

[0100] The disclosed methods may be performed using a computing system. An exemplary computing system may include a processor (e.g., a central processing unit), a memory, a non-volatile memory, and an interface device. The memory may store data and / or one or more sets of code, software, scripts, etc. The components of the computer system may be coupled together via a bus or through some other known or convenient device. The processor may be configured, for example, to perform all or part of the methods described herein by executing code stored in the memory, for example. One or more of the user device or computer, the provider server or system, or the suspended database update system may include components of a computing system or variations of such systems.

[0090]

[0101] The present disclosure contemplates a computer system taking any suitable physical form, including, but not limited to, a point-of-sale system ("POS"). By way of example and not limitation, the computer system may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (e.g., a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile phone, a personal digital assistant (PDA), a server, or a combination of two or more of these. Where appropriate, the computer system may include one or more computer systems that are unitary or distributed, across multiple locations, across multiple machines, and / or reside in a cloud that may include one or more cloud components in one or more networks. Where appropriate, the one or more computer systems may perform one or more steps of one or more methods described or illustrated herein without substantial spatial or temporal limitations. By way of example and not limitation, the one or more computer systems may perform one or more steps of one or more methods described or illustrated herein in real time or in batch mode. One or more computer systems may perform one or more steps of one or more methods described or illustrated herein at different times or at different locations, where appropriate.

[0091]

[0102] The processor may be, for example, a conventional microprocessor such as an Intel Pentium microprocessor or a Motorola Power PC microprocessor. Those skilled in the relevant art will recognize that the terms "machine-readable (storage) medium" or "computer-readable (storage) medium" include any type of device that can be accessed by a processor.

[0092]

[0103] The memory may be coupled to the processor, for example, by a bus. The memory may include, by way of example and not limitation, random access memory (RAM), such as dynamic RAM (DRAM) and static RAM (SRAM). The memory may be local, remote, or distributed.

[0093]

[0104] The bus may also couple the processor to non-volatile memory and drive units. Non-volatile memory is often a magnetic floppy disk or hard disk, a magneto-optical disk, an optical disk, a CD-ROM, a read-only memory (ROM) such as an EPROM or EEPROM, a magnetic or optical card, or another form of storage for large amounts of data. Part of this data is often written to the memory during the execution of software in the computer by a direct memory access process. Non-volatile storage may be local, remote, or distributed. Non-volatile storage is optional since a system may be created with all applicable data available in the memory. A typical computer system usually includes at least a processor, a memory, and a device (e.g., a bus) that couples the memory to the processor.

[0094]

[0105] The software may be stored in a non-volatile memory and / or a drive unit. In fact, for large programs, it may not even be possible to store the entire program in memory. Nevertheless, it should be understood that in order for the software to run, it is moved, when necessary, to a computer-readable location suitable for processing, and for purposes of explanation, that location is referred to herein as memory. Even when the software is moved to memory for execution, the processor may utilize hardware registers to store values ​​related to the software, and ideally, a local cache that serves to speed up execution. As used herein, a software program is assumed to be stored in any known or convenient location (from non-volatile storage to hardware registers) when the software program is referred to as being "implemented in a computer-readable medium." A processor is considered to be "configured to execute a program" when at least one value related to the program is stored in a register readable by the processor.

[0095]

[0106] The bus may also couple the processor to a network interface device. The interface may include one or more of a modem or a network interface. It will be appreciated that the modem or network interface may be considered to be part of the computer system. The interface may include an analog modem, an integrated services digital network (ISDN0 modem, a cable modem, a token ring interface, a satellite transmission interface (e.g., "Direct PC"), or other interface for coupling the computer system to other computer systems. The interface may include one or more input and / or output (I / O) devices. The I / O devices may include, by way of example and not limitation, a keyboard, a mouse or other pointing device, a disk drive, a printer, a scanner, and other input and / or output devices including a display device. The display device may include, by way of example and not limitation, a cathode ray tube (CRT), a liquid crystal display (LCD), or any other applicable known or convenient display device.

[0096]

[0107] In operation, a computer system may be controlled by operating system software, including a file management system, such as a disk operating system. One example of operating system software with its associated file management system software is the family of operating systems known as Windows® from Microsoft Corporation of Redmond, Washington, and their associated file management systems. Another example of operating system software with its associated file management system software is the Linux® operating system and its associated file management system. The file management system may be stored in non-volatile memory and / or the drive unit, and may cause the processor to perform various actions required for the operating system to input and output data and store data in memory, including storing files on the non-volatile memory and / or the drive unit.

[0097]

[0108] Some portions of the detailed descriptions may be presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations require physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, primarily for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0098]

[0109] It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities.Unless otherwise indicated, and as will be apparent from the following description, it will be appreciated that throughout the description, descriptions utilizing terms such as "processing" or "calculating" or "computing" or "determining" or "displaying" or "generating" refer to the acts and processes of a computer system or similar electronic computing device that manipulates and converts data represented as physical (electronic) quantities in the computer system's registers and memory into other data that is similarly represented as physical quantities in the computer system's memory or registers or other such information storage, transmission or display device.

[0099]

[0110] The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform some example methods. The required structure for a variety of these systems will appear from the description below. In addition, the techniques are not described with reference to any particular programming language, and thus the various examples may be implemented using a variety of programming languages.

[0100]

[0111] In various implementations, the system may operate as a standalone device or may be connected (e.g., networked) to other systems. In a networked deployment, the system may operate in the capacity of a server or a client system in a client-server network environment, or as a peer system in a peer-to-peer (or distributed) network environment.

[0101]

[0112] The system may be a server computer, a client computer, a personal computer (PC), a tablet PC, a laptop computer, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, an iPhone, a Blackberry, a processor, a telephone, a web appliance, a network router, a switch or bridge, or any system capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by the system.

[0102]

[0113] Although a machine-readable medium or machine-readable storage medium is shown as being a single medium by way of example, the terms "machine-readable medium" and "machine-readable storage medium" should be interpreted to include a single medium or multiple media (e.g., a centralized or distributed database and / or associated caches and servers) that store one or more sets of instructions. The terms "machine-readable medium" and "machine-readable storage medium" are also intended to include any medium capable of storing, encoding, or carrying a set of instructions for a system to execute, causing the system to perform any one or more of the methods or modules disclosed herein.

[0103]

[0114] Generally, the routines executed to implement implementations of the present disclosure may be implemented as parts of an operating system or a specific application, component, program, object, module or set of instructions referred to as a "computer program." A computer program generally comprises one or more sets of instructions at different times in various memory and storage devices in the computer that, when read and executed by one or more processing units or processors in a computer, cause the computer to perform operations to implement elements involved in various aspects of the present disclosure.

[0104]

[0115] Furthermore, while the examples have been described in the context of fully functional computers and computer systems, those skilled in the art will appreciate that the various examples may be distributed as program objects in a variety of forms, and that the present disclosure applies equally regardless of the particular type of machine or computer-readable medium used to actually affect the distribution.

[0105]

[0116] Further examples of machine-readable storage media, machine-readable media, or computer-readable (storage) media include, but are not limited to, recordable-type media such as volatile and non-volatile memory devices, floppy disks and other removable disks, hard disk drives, optical disks (e.g., compact disks read-only (CD ROM), digital versatile disks (DVD), etc.), and transmission-type media such as digital and analog communications links, among others.

[0106]

[0117] In some circumstances, an operation of a memory device, such as a change of state from a binary 1 to a binary 0 or vice versa, may comprise a transformation, such as, for example, a physical transformation. In certain types of memory devices, such a physical transformation may comprise a physical transformation of an article to a different state or thing. For example, but not limited to, for some types of memory devices, a change of state may involve the accumulation and storage of an electric charge or the release of a stored electric charge. Similarly, in other memory devices, a change of state may comprise a physical change or transformation of a magnetic orientation or a physical change or transformation of a molecular structure from crystalline to amorphous or vice versa. The above is not intended to be an exhaustive list of all examples in which a change of state of a binary 1 to a binary 0 or vice versa in a memory device may comprise a transformation, such as a physical transformation. Rather, the above is intended as an illustrative example.

[0107]

[0118] Storage media may generally be non-transitory or comprise a non-transitory device. In this context, non-transitory storage media may include a device that is tangible, meaning that the device has a concrete physical form, but the device may change its physical state. Thus, for example, non-transitory refers to a device that remains tangible despite this change in state.

[0108]

[0119] The above description and drawings are illustrative and should not be construed as limiting the subject matter to the precise form disclosed. Those skilled in the art can appreciate that many modifications and variations are possible in light of the above disclosure. Numerous specific details have been described in order to provide a thorough understanding of the present disclosure. However, in some instances, well-known or conventional details have not been described in order to avoid obscuring the description.

[0109]

[0120] As used herein, the terms "connected," "coupled," or any variation thereof, when applied to modules of a system, refer to any direct or indirect connection or coupling between two or more elements, and the coupling of connections between elements may be physical, logical, or any combination thereof. Additionally, terms such as "herein," "above," "below," and terms of similar import, when used herein, shall refer to this specification as a whole and not to any particular portion of this specification. Where the context permits, terms in the above detailed description using the singular or plural may also include the plural or singular, respectively. The term "or" referring to a list of two or more items covers all of the interpretations of the term: any of the items in the list, all of the items in the list, or any combination of the items in the list.

[0110]

[0121] Those skilled in the art will appreciate that the disclosed subject matter may be embodied in other forms and manners not set forth below. It should be understood that the use of relationship-indicating terms such as first, second, top, and bottom, if any, are used only to distinguish one entity or act from another, without requiring or implying any such actual relationship or order between such entities or acts.

[0111]

[0122] Although processes or blocks are presented in a given order, alternative implementations may perform routines having steps or employ systems having blocks in a different order, and some processes or blocks may be deleted, moved, added, sub-divided, substituted, combined, and / or modified to provide alternative or sub-combinations. Each of these processes or blocks may be implemented in a variety of different ways. Also, while processes or blocks are sometimes shown as being performed in sequence, these processes or blocks may instead be performed in parallel or at different times. Additionally, any specific numbers referenced herein are examples only, and alternative implementations may employ different values ​​or ranges.

[0112]

[0123] The teachings of the disclosure provided herein may be applied to other systems, not necessarily limited to the systems described above. Elements and acts of the various examples described above may be combined to provide further examples.

[0113]

[0124] Any patents and applications and other references referred to above, including any that may be listed in accompanying application papers, are incorporated herein by reference. Aspects of the present disclosure may be modified, where necessary, to adopt the systems, functions, and concepts of the various references described above to provide yet further examples of the present disclosure.

[0114]

[0125] These and other changes may be made to the present disclosure in light of the above detailed description. Although the above description describes some examples and describes the best modes contemplated, no matter how detailed in text it may appear, the teachings may be implemented in many ways. The details of the system are still encompassed by the subject matter disclosed herein, but may vary widely in its implementation details. As stated above, a particular term used when describing some features or aspects of the present disclosure should not be taken to imply that the term is redefined herein to be limited to any particular characteristic, feature, or aspect of the disclosure with which the term is associated. In general, the terms used in the following claims should not be construed to limit the disclosure to the particular implementations disclosed herein, unless the above detailed description section explicitly defines such terms. Thus, the actual scope of the present disclosure encompasses not only the disclosed implementations, but also all equivalent ways of practicing or implementing the present disclosure under the claims.

[0115]

[0126] Although some aspects of the disclosure are presented below in some claim form, the inventors contemplate various aspects of the disclosure in any number of claim forms. Any claim intended to be treated under 35 U.S.C. 112(f) will begin with the phrase "means for." Accordingly, the applicants reserve the right to add additional claims after filing this application to pursue such additional claim forms for other aspects of the disclosure.

[0116]

[0127] The terms used in this specification generally have their ordinary meaning in the art in the specific context in which each term is used within the context of this disclosure. Some terms used to describe this disclosure are explained above or elsewhere in this specification to provide additional guidance to attorneys regarding the description of this disclosure. For convenience, some terms may be highlighted, for example, using capitalization, italics, and / or quotation marks. The use of highlighting has no effect on the scope and meaning of a term, and the scope and meaning of a term is the same in the same context whether it is highlighted or not. It will be appreciated that the same element may be described in more than one way.

[0117]

[0128] Thus, alternative wording and synonyms may be used for any one or more of the terms described herein, and no special importance is placed on whether a term is recited or explained herein. Synonyms for some terms are provided. The recitation of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification, including examples of any term described herein, is merely illustrative and is not intended to further limit the scope and meaning of the disclosure or any exemplified term. Similarly, the disclosure is not limited to the various examples provided herein.

[0118]

[0129] Without intending to further limit the scope of the present disclosure, examples of instruments, devices, methods and their related results according to the examples of the present disclosure are given below. Please note that titles or subtitles may be used in the examples for the convenience of the reader, and in any case, this should not limit the scope of the present disclosure. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure pertains. In case of conflict, this document, including definitions, shall prevail.

[0119]

[0130] Some portions of this specification describe examples in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to effectively convey the substance of their work to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Further, it has proven convenient at times to refer to these configurations of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combination thereof.

[0120]

[0131] Any of the steps, operations, or processes described herein may be performed or implemented using one or more hardware or software modules, alone or in combination with other devices. In some examples, a software module is implemented in a computer program object comprising a computer-readable medium containing computer program code, which may be executed by a computer processor to perform any or all of the steps, operations, or processes described.

[0121]

[0132] Examples may also relate to an apparatus for performing the operations herein. The apparatus may be specially constructed for the required purposes and / or may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory tangible computer-readable storage medium or any type of medium suitable for storing electronic instructions that may be coupled to a computer system bus. Furthermore, any computing system referred to herein may include a single processor or may be an architecture employing a multiple processor design to increase computing power.

[0122]

[0133] Examples may also relate to objects generated by the computing processes described herein. Such objects may comprise information resulting from the computing processes, which may be stored on a non-transitory, tangible, computer-readable storage medium, and may include any implementation of the computer program objects or other data combinations described herein.

[0123]

[0134] The language used herein has been selected primarily for ease of reading and instructional purposes, and may not have been selected to delineate or limit the subject matter. Accordingly, the scope of the disclosure is intended to be limited not by this detailed description, but by any claims issued on an application based hereon. Accordingly, the disclosure of the examples is intended to be illustrative, but not limiting, of the scope of the subject matter described in the following claims.

[0124]

[0135] Specific details have been provided in the preceding description to provide a thorough understanding of various implementations of systems and components for a context-connected system. However, those skilled in the art will appreciate that the implementations described above may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form so as not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail so as to avoid obscuring the embodiments.

[0125]

[0136] It should also be noted that the individual implementations may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe operations as a sequential process, many of the operations may be performed in parallel or simultaneously. Additionally, the order of operations may be rearranged. A process is terminated when its operations are completed, but may have additional steps not included in the diagram. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination may correspond to a return of the function to the calling function or to the main function.

[0126]

[0137] The client devices, network devices, and other devices may be computing systems that include, among other things, one or more integrated circuits, input devices, output devices, data storage devices, and / or network interfaces. The integrated circuits may include, among other things, one or more processors, volatile memory, and / or non-volatile memory. The input devices may include, among other things, keyboards, mice, keypads, touch interfaces, microphones, cameras, and / or other types of input devices. The output devices may include, among other things, display screens, speakers, haptic feedback systems, printers, and / or other types of output devices. Data storage devices, such as hard drives or flash memory, may enable the computing device to store data temporarily or permanently. Network interfaces, such as wireless or wired interfaces, may enable the computing device to communicate with a network. Examples of computing devices include desktop computers, laptop computers, server computers, handheld computers, tablets, smartphones, personal digital assistants, digital home assistants, and machines and equipment in which computing devices are embedded.

[0127]

[0138] The term "computer-readable medium" includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other media capable of storing, containing, or transporting instructions and / or data. Computer-readable media may include non-transitory media on which data may be stored, which does not include carrier waves and / or transitory electronic signals propagating wirelessly or over wired connections. Examples of non-transitory media may include, but are not limited to, magnetic disks or tapes, optical storage media such as compact disks (CDs) or digital versatile disks (DVDs), flash memory, memory or memory devices. A computer-readable medium may have code and / or machine-executable instructions stored thereon, which may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.

[0128]

[0139] The various examples described above may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware, or microcode, the program code or code segments (e.g., computer program product) for performing the necessary tasks may be stored in a computer-readable or machine-readable storage medium (e.g., a medium for storing program code or code segments). A processor implemented in an integrated circuit may perform the necessary tasks.

[0129]

[0140] When a component is described as being "configured to" perform a certain operation, such configuration may be achieved, for example, by designing electronic circuitry or other hardware to perform the operation, by programming a programmable electronic circuitry (e.g., a microprocessor or other suitable electronic circuitry) to perform the operation, or by any combination thereof.

[0130]

[0141] The various exemplary logic blocks, modules, circuits, and algorithm steps described in connection with the implementations disclosed herein may be implemented as electronic hardware, computer software, firmware, or a combination thereof. To clearly illustrate this interchangeability of hardware and software, the various exemplary components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in various ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0131]

[0142] The techniques described herein may also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in any of a variety of devices, such as a general purpose computer, a wireless communication device handset, or an integrated circuit device having multiple uses, including applications in wireless communication device handsets and other devices. Any features described as modules or components may be implemented together in an integrated logic device, or separately as separate but interoperable logic devices. When implemented in software, the techniques may be realized at least in part by a computer-readable data storage medium comprising program code including instructions that, when executed, perform one or more of the methods described above. The computer-readable data storage medium may form part of a computer program product, which may include packaging materials. The computer-readable medium may comprise a memory or data storage medium, such as a random access memory (RAM), such as a synchronous dynamic random access memory (SDRAM), a read-only memory (ROM), a non-volatile random access memory (NVRAM), an electrically erasable programmable read-only memory (EEPROM), a FLASH memory, a magnetic or optical data storage medium, or the like. The techniques may additionally or alternatively be realized at least in part by a computer-readable communications medium, such as a propagated signal or radio wave, that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and / or executed by a computer.

[0132]

[0143] The program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Such a processor may be configured to perform any of the techniques described in this disclosure. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Thus, the term "processor" as used herein may refer to any of the above structures, any combination of the above structures, or any other structure or apparatus suitable for implementing the techniques described herein. Furthermore, in some aspects, the functionality described herein may be provided within dedicated software or hardware modules configured to implement an interrupted database update system.

[0133]

[0144] The above detailed description of the present technology has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the present technology to the precise form disclosed. Many modifications and variations are possible in light of the above teachings. The described embodiments have been chosen to best explain the principles of the present technology and its practical application, and to enable others skilled in the art to best utilize the present technology in various embodiments with various modifications as may be suitable for the particular applications contemplated. It is intended that the scope of the present technology be defined by the claims. The invention as described in the claims of the original application is set forth below. [C1] 1. A computer-implemented method comprising: obtaining an intent, wherein the intent corresponds to a request, and wherein the intent is associated with a customer; identifying an agent to receive the intent, where the identified agent is associated with a commercial endeavor, where the agent is identified using a machine learning model, and where the machine learning model is updated using sample intents and sample outputs corresponding to characteristics of a set of agents; providing the intent, where, when the intent is received at an application implemented on a computing device associated with the identified agent, the intent is used to solicit an intent response from the identified agent; and obtaining the intent response, wherein the intent response is dynamically used to update the machine learning model; and sending a first instruction to the application, where the first instruction, when received at the application, causes the application to prohibit obtaining additional intent responses; and providing the intent response, wherein when the intent response is received, the intent response is presented to the customer associated with the intent; and obtaining a new request for facilitating a communication channel between the application implemented on the computing device of the identified agent and a computing device associated with the customer, wherein the new request corresponds to the intent; and sending a second instruction to the application, where the second instruction, when received at the application, causes the application to obtain an additional response to the intent; and A computer-implemented method comprising: [C2] The computer-implemented method of C1, wherein the first instructions are sent without identifying information of the customer. [C3] The computer-implemented method of C1, wherein the second instructions include an identification associated with the customer, and wherein the second instructions cause the application to present the identification associated with the customer. [C4] 3. The computer-implemented method of claim 1, further comprising: evaluating the response to the intent to determine that the response is relevant to the intent. [C5] The computer-implemented method of C1, wherein the intent is extracted from the request based on a semantic analysis of the request. [C6] The computer-implemented method of C1, wherein the new requests are used to update the machine learning model. [C7] The computer-implemented method of C1, wherein the agent is selected based on a set of characteristics of the intent, where the set of characteristics is obtained in response to a query for additional information associated with the intent. [C8] 1. A system comprising: one or more processors; A memory that stores instructions the instructions, when executed by the one or more processors, cause the system to: obtaining an intent, wherein the intent corresponds to a request, and wherein the intent is associated with a customer; identifying an agent to receive the intent, where the identified agent is associated with a commercial endeavor, where the agent is identified using a machine learning model, and where the machine learning model is updated using sample intents and sample outputs corresponding to characteristics of a set of agents; providing the intent, where, when the intent is received at an application implemented on a computing device associated with the identified agent, the intent is used to solicit an intent response from the identified agent; and obtaining the intent response, wherein the intent response is dynamically used to update the machine learning model; and sending a first instruction to the application, where the first instruction, when received at the application, causes the application to prohibit obtaining additional intent responses; and providing the intent response, wherein when the intent response is received, the intent response is presented to the customer associated with the intent; and obtaining a new request for facilitating a communication channel between the application implemented on the computing device of the identified agent and a computing device associated with the customer, wherein the new request corresponds to the intent; and sending a second instruction to the application, where the second instruction, when received at the application, causes the application to obtain an additional response to the intent; and A system that allows the user to: [C9] The instructions include: sending a query to the computing device of the customer to obtain additional information associated with the intent; identifying a set of characteristics associated with the intent, wherein the set of characteristics of the intent is identified using the additional information; and using the set of characteristics of the intent as input to the machine learning model to identify the agent; The system according to C8, further comprising: [C10] 9. The system of claim 8, wherein the instructions further cause the system to update the machine learning model based on the new request. [C11] 9. The system of claim 8, wherein the instructions further cause the system to remove the identifying information associated with the customer from the intent such that the intent is provided without the identifying information associated with the customer. [C12] 9. The system of claim 8, wherein the instructions further cause the system to evaluate the response to the intent to determine that the response is relevant to the intent. [C13] The system of C8, wherein the second instructions include an identification associated with the customer, and wherein the second instructions cause the application to present the identification associated with the customer in addition to the intent. [C14] The system of claim 8, wherein the first instructions have no identity associated with the customer, and wherein the first instructions cause the application to present the intent without the identity associated with the customer. [C15] A non-transitory computer-readable storage medium having executable instructions stored thereon, the executable instructions, when executed by one or more processors of a computer system, causing the computer system to: obtaining an intent, wherein the intent corresponds to a request, and wherein the intent is associated with a customer; identifying an agent to receive the intent, where the identified agent is associated with a commercial endeavor, where the agent is identified using a machine learning model, and where the machine learning model is updated using sample intents and sample outputs corresponding to characteristics of a set of agents; providing the intent, where, when the intent is received at an application implemented on a computing device associated with the identified agent, the intent is used to solicit an intent response from the identified agent; and obtaining the intent response, wherein the intent response is dynamically used to update the machine learning model; and sending a first instruction to the application, where the first instruction, when received at the application, causes the application to prohibit obtaining additional intent responses; and providing the intent response, wherein when the intent response is received, the intent response is presented to the customer associated with the intent; and obtaining a new request for facilitating a communication channel between the application implemented on the computing device of the identified agent and a computing device associated with the customer, wherein the new request corresponds to the intent; and sending a second instruction to the application, where the second instruction, when received at the application, causes the application to obtain an additional response to the intent; and A non-transitory computer-readable storage medium for causing [C16] The non-transitory computer-readable medium of C15, wherein the first instructions have no identification information associated with the customer, wherein the first instructions cause the application to present the intent without the identification information associated with the customer. [C17] The non-transitory computer-readable storage medium of C15, wherein the second instructions include an identification associated with the customer, and wherein the second instructions cause the application to present the identification associated with the customer in addition to the intent. [C18] The executable instructions further cause the computer system to remove the identifying information associated with the customer from the intent such that the intent is provided without the identifying information associated with the customer. [C19] 16. The non-transitory computer-readable storage medium of claim 15, wherein the executable instructions further cause the computer system to update the machine learning model based on the new request. [C20] The executable instructions further include for the system to: identifying a set of characteristics of the intent, wherein the set of characteristics of the intent is identified using additional information of the customer; and using the set of characteristics of the intent as input to the machine learning model to identify the agent; The non-transitory computer-readable storage medium according to C15,

Claims

1. 1. A computer-implemented method, comprising: obtaining an intent, the intent corresponding to a request and the intent being associated with a customer; identifying an agent to receive the intent, the identified agent being capable of providing goods and / or services to the customer, the agent being identified using a machine learning model, and the machine learning model being updated using sample intents and sample outputs corresponding to characteristics of a set of agents; providing the intent, wherein when the intent is received at an application implemented on a computing device associated with the identified agent, the intent is used to solicit an intent response from the identified agent; obtaining an intent response, the intent response being dynamically used to update the machine learning model; and sending to the application a first instruction that does not include identifying information associated with the customer, the first instruction, when received at the application, causing the application to prohibit from providing additional intent responses to the intent; providing the intent response, wherein when the intent response is received, the intent response is presented to the customer associated with the intent; obtaining a new request for establishing a communication channel between the application implemented on the computing device of the identified agent and a computing device associated with the customer, the new request corresponding to the intent; and sending to the application second instructions including the identification information associated with the customer, the second instructions, when received at the application, causing the application to provide an additional intent response to the intent; A computer-implemented method comprising:

2. The computer-implemented method of claim 1 , wherein the second instructions cause the application to submit the identification information associated with the customer.

3. The computer-implemented method of claim 1 , further comprising evaluating the intent response against the intent to determine that the intent response is relevant to the intent.

4. The computer-implemented method of claim 1 , wherein the intent is extracted from the request based on a semantic analysis of the request.

5. The computer-implemented method of claim 1 , wherein the new requests are used to update the machine learning model.

6. The computer-implemented method of claim 1 , wherein the agent is selected based on a set of characteristics of the intent, the set of characteristics being obtained in response to a query for additional information associated with the intent.

7. 1. A system comprising: one or more processors; A memory that stores instructions the instructions, when executed by the one or more processors, cause the system to: obtaining an intent, the intent corresponding to a request and the intent being associated with a customer; identifying an agent to receive the intent, the identified agent being capable of providing goods and / or services to the customer, the agent being identified using a machine learning model, and the machine learning model being updated using sample intents and sample outputs corresponding to characteristics of a set of agents; providing the intent, wherein when the intent is received at an application implemented on a computing device associated with the identified agent, the intent is used to solicit an intent response from the identified agent; obtaining an intent response, the intent response being dynamically used to update the machine learning model; and sending to the application a first instruction that does not include identifying information associated with the customer, the first instruction, when received at the application, causing the application to prohibit from providing additional intent responses to the intent; providing the intent response, wherein when the intent response is received, the intent response is presented to the customer associated with the intent; obtaining a new request for establishing a communication channel between the application implemented on the computing device of the identified agent and a computing device associated with the customer, the new request corresponding to the intent; and sending to the application second instructions including the identification information associated with the customer, the second instructions, when received at the application, causing the application to provide an additional intent response to the intent; A system that allows the user to:

8. The instructions include: sending a query to the computing device of the customer to obtain additional information associated with the intent; identifying a set of characteristics associated with the intent, the set of characteristics of the intent being identified using the additional information; using the set of characteristics of the intent as input to the machine learning model to identify the agent; The system of claim 7 , further comprising:

9. 8. The system of claim 7, wherein the instructions further cause the system to update the machine learning model based on the new request.

10. 8. The system of claim 7, wherein the instructions further cause the system to remove the identifying information associated with the customer from the intent such that the intent is provided without the identifying information associated with the customer.

11. The system of claim 7 , wherein the instructions further cause the system to evaluate the intent response against the intent to determine that the intent response is relevant to the intent.

12. The system of claim 7 , wherein the second instructions cause the application to present the identification information associated with the customer in addition to the intent.

13. The system of claim 7 , wherein the first instructions cause the application to present the intent without the identification information associated with the customer.

14. A non-transitory computer-readable storage medium having executable instructions stored thereon, the executable instructions, when executed by one or more processors of a computer system, causing the computer system to: obtaining an intent, the intent corresponding to a request and the intent being associated with a customer; identifying an agent to receive the intent, the identified agent being capable of providing goods and / or services to the customer, the agent being identified using a machine learning model, and the machine learning model being updated using sample intents and sample outputs corresponding to characteristics of a set of agents; providing the intent, wherein when the intent is received at an application implemented on a computing device associated with the identified agent, the intent is used to solicit an intent response from the identified agent; obtaining an intent response, the intent response being dynamically used to update the machine learning model; and sending to the application a first instruction that does not include identifying information associated with the customer, the first instruction, when received at the application, causing the application to prohibit from providing additional intent responses to the intent; providing the intent response, wherein when the intent response is received, the intent response is presented to the customer associated with the intent; obtaining a new request for establishing a communication channel between the application implemented on the computing device of the identified agent and a computing device associated with the customer, the new request corresponding to the intent; and sending to the application second instructions including the identification information associated with the customer, the second instructions, when received at the application, causing the application to provide an additional intent response to the intent; A non-transitory computer-readable storage medium for causing

15. 15. The non-transitory computer-readable medium of claim 14, wherein the first instructions cause the application to present the intent without the identification information associated with the customer.

16. The non-transitory computer-readable storage medium of claim 14 , wherein the second instructions cause the application to submit, in addition to the intent, the identification information associated with the customer.

17. 15. The non-transitory computer-readable storage medium of claim 14, wherein the executable instructions further cause the computer system to remove the identifying information associated with the customer from the intent such that the intent is provided without the identifying information associated with the customer.

18. 15. The non-transitory computer-readable storage medium of claim 14, wherein the executable instructions further cause the computer system to update the machine learning model based on the new request.

19. The executable instructions further include for the computer system: identifying a set of characteristics of the intent, the set of characteristics of the intent being identified using additional information of the customer; using the set of characteristics of the intent as input to the machine learning model to identify the agent; 15. The non-transitory computer-readable storage medium of claim 14,

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