DETAILED ACTION
The instant application having Application No. 18/675682 filed on 05/28/2024 is presented for examination by the examiner.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Information Disclosure Statement
As required by M.P.E.P. 609, the applicant’s submissions of the Information Disclosure Statement dated 06/25/2024 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 2-21 are rejected under 35 U.S.C. 101 as directed to non-statutory subject matter of abstract ideas.
Step 1: Claim 2 recites “a system”, therefore is a machine. Claim 10 recites “A method for…”; the claim recites a series of steps and therefore is a process. Claim 18 recites “medium” therefore the claim is an article of manufacture.
Step 2A Prong One:
Claim 2 recites the limitations "determining a conversational workflow ", “mapping the conversational workflow to a workflow diagram of a chatbot workflow …”, and “injecting, to the chatbot workflow at one or more nodes of the workflow diagram" These limitations are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a "memory", and “hardware processors”, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, "determining a conversational workflow ", “mapping the conversational workflow to a workflow diagram of a chatbot workflow …”, and “injecting, to the chatbot workflow at one or more nodes of the workflow diagram" in the context of this claim encompasses a user mentally, and with the aid of pen and paper writing the changes down on a sheet of paper and examine the list to identify the relevant ones (rationale).
Claims 10 and 18 recite the limitations "determining that the request ", and “determining a workflow executable" These limitations are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a "machine", nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, "determining that the request ", and “determining a workflow executable" in the context of this claim encompasses a user mentally, and with the aid of pen and paper writing the changes down on a sheet of paper and examine the list to identify the relevant ones (rationale).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. The claim recites “deploying”, this limitation amounts to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g). The claim recites the additional elements "receiving"; this limitation amounts to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g); and "responding"; this limitation is a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements "receiving” and “responding" The limitations amount to a data gathering step and a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)).
Further, the claims recite the following additional element “executing the workflow …” fails to meaningfully limit the claim because it does not require any particular application of the recited “executing” and is at best the equivalent of merely adding the words “apply it” to the judicial exception. The “machine” in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)). The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations “the memory”, “the hardware processors”, “the machine”, “deploying”, "receiving” and “responding" are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv) Storing and retrieving information in memory, Versata Dev. Group Inc....
Regarding claim 2, the limitation “determine the workflow data for the device under test based on the configuration data” is an additional metal process under prong 1. Under prong 2, the “transmit” and “receive” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 3, the limitation “wherein the determining the conversational workflow is based on the workflow generation data” is an additional metal process under prong 1. Under prong 2, the “receiving workflow generation data” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 4, under prong 2, the “wherein the workflow generation data further comprises request and response parameter data for one or more application program interface (API) requests and one or more API responses that enable an execution with one or more endpoints corresponding to the conversational AI skill with the AI platform and the computing resource, and wherein the injecting the data is performed at one or more data load points associated with the request and response parameter data” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 5, the limitation “parsing the diagram”, “determining the AI platform corresponding to the workflow generation data”, “generating program code for connecting with the Al platform”, and generating the chatbot workflow “ are an additional metal process under prong 1. Under prong 2, the “retrieving, utilizing the one or more identifiers, the data corresponding to the conversational AI skill” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 6, the limitation “wherein the determining the conversational workflow comprises generating the conversational workflow based on the parsing” is an additional metal process under prong 1.
Regarding claim 7, the limitation “generating” is an additional metal process under prong 1. Under prong 2, the “wherein the workflow diagram is associated with the dialog tree, and wherein the chatbot traverses the dialog tree when providing the conversational AI skill during the automated self-service assistance based on API calls made in association with the injected data” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 8, the limitation “wherein the injecting comprises embedding data for one or more endpoints at one or more data load points corresponding to the one or more nodes that enables the conversational AI skill to be executed via the AI platform when the chatbot workflow reaches the one or more data load points of the chatbot workflow” is an additional metal process under prong 1.
Regarding claim 9, under prong 2, the “wherein the chatbot provides the automated self-service assistance across a plurality of channels comprising one or more of an email channel, a digital alert channel, a text message channel, a push notification channel, or an instant message channel” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claims 11 and 19, the limitation “generating” is an additional metal process under prong 1. Under prong 2, the “receiving” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 12, under prong 2, the “wherein the workflow generation data further comprises request and response parameter data for one or more application program interface (API) requests and one or more API responses that enable an execution with one or more endpoints corresponding to the skill with the AI system and the computing resource” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 13, the limitation “wherein the generating the workflow comprises injecting data corresponding to the one or more identifiers at one or more data load points associated with the request and response parameter data” is an additional metal process under prong 1.
Regarding claim 14, the limitation “wherein the injecting comprises embedding data for the one or more endpoints at the one or more data load points” is an additional metal process under prong 1.
Regarding claim 15, the limitation “in response to the receiving the workflow generation data, parsing the diagram”, “determining the AI system corresponding to the workflow generation data”, “generating program code for connecting with the AI system”, and “generating the workflow based at least on the data and the program code” is an additional metal process under prong 1. Under prong 2, the “retrieving, utilizing the one or more identifiers, data corresponding to the skill” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 16, the limitation “generating the dialog tree for the workflow” is an additional metal process under prong 1.
Regarding claim 17, under prong 2, the “wherein the chatbot provides the skill for an automated self-service assistance across a plurality of channels comprising one or more of an email channel, a digital alert channel, a text message channel, a push notification channel, or an instant message channel” limitations are additional elements that recite insignificant extra
Regarding claim 20, the limitation “generating the dialog tree” is an additional metal process under prong 1.
Regarding claim 21, the limitation “wherein the generating the workflow comprises injecting data corresponding to the skill based on the one or more identifiers” is an additional metal process under prong 1.
Allowable Subject Matter
Claims 3-7, 11-15, and 19-21 would be allowable if rewritten to overcome the rejection(s) under 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following prior art made of record and not relied upon is cited to establish the level of skill in the applicant’s art and those arts considered reasonably pertinent to applicant’s disclosure. See MPEP 707.05(c).
Prior arts:
US 2022/0230462 to Xu
Creating a dialog flow for the skill bot—A dialog flow specified for a skill bot describes how the skill bot reacts as different intents for the skill bot are resolved responsive to received user input. The dialog flow defines operations or actions that a skill bot will take, e.g., how the skill bot responds to user utterances, how the skill bot prompts users for input, how the skill bot returns data.
US 2022/0059097 to Lavi
The server computer 102 clusters agent responses 110, via the ARCM 112 in block 208, into different response types 404 (shown, for example, in FIG. 4), with possibly several escalated conversations 402 belonging to each response type
US 2021/0083994 to Pan
the master bot 114 may have access to metadata that identifies the various available skill bots 116, and for each skill bot 116, the capabilities of the skill bot 116 including the tasks that can be performed by the skill bot 116. Upon receiving a user request in the form of an input utterance, the master bot 114 is configured to, from the multiple available skill bots 116, identify or predict a specific skill bot 116 that can best serve or handle the user request or, in the alternative, determine that the input utterance is unrelated to any of the skill bots 116. If it is determined that a skill bot 116 can handle the input utterance, the master bot 114 may route the input utterance, or at least a portion of the input utterance, to that skill bot 116 for further handling. Control thus flows from the master bot 114 to the skill bots 116.
US 2020/0242199 to Ploennigs
generate one or more dialog elements from the one or more conversation patterns, wherein a dialog element includes a query, a response, and a condition that indicates entities to be detected by a conversational classifier associated with the conversational agent, generate one or more conversational dialog workflows from context dependencies in the knowledge graph, and/or instantiate and match one or more conversational dialogs to the knowledge graph to select a potential conversation dialog and identify one or more search results from the knowledge graph according to the one or more queries.
US 2019/0268288 to Chandra
A chatbot may be developed for each function or for each function of a subset of the functions of a system. For each system function, a conversational flow for the chatbot may be developed in the form of a map or tree structure with multiple nodes and branches for responding to a variety of input from a user. It can be appreciated that a conversational flow may be very complex based on what function the chatbot is covering.
US 2019/0132264 to Jafar
In chatbot interface APIs a workspace may be provided as a container for artifacts that define the conversation flow. A dialog is a flow of conversation in a logic tree with each node of the tree has a condition that triggers it based on user input.
The prior art of record does not disclose and/or fairly suggest at least claimed limitations recited in such manners in dependent claims 3-7, 11-15 and 19-21.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2 and 8 are rejected under 35 U.S.C. 103(a) as being unpatentable over US 2020/0344185 to Singaraju et al. (hereafter “Singaraju”) in view of US 2019/0311036 to Shanmugam et al. (hereafter “Shanmugam”), US 2022/0103684 to Chavez et al. (hereafter “Chavez”) and US 2019/0356700 to Suhail et al. (hereafter “Suhail”)
As per claim 2, Singaraju discloses a system comprising:
a non-transitory memory; and
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory (FIGs. 2 and 10) to cause the system to perform operations comprising:
determining a conversational workflow executable by a chatbot (FIGs. 1-2; paragraphs 0027-0028, 0031, 003, 0035-0036, 0038, 0041, 0043 and 0047: “ A bot intent may be associated with one or more dialog flows for starting a conversation with the user and in a certain state. For example, the first message for the order pizza intent could be the question “What kind of pizza would you like?” In addition to associated utterances, a bot intent may further comprise named entities that relate to the intent. For example, the order pizza intent could include variables or parameters used to perform the task of ordering pizza, e.g., topping 1, topping 2, pizza type, pizza size, pizza quantity, and the like. The value of an entity is typically obtained through conversing with the user. A conversation may take different paths based on the end user input, which may impact the decision the bot makes for the flow. For example, at each state, based on the end user input or utterances, the bot may determine the end user's intent in order to determine the appropriate next action to take. As used herein and in the context of an utterance, the term “intent” refers to an intent of the user who provided the utterance. For example, the user may intend to engage a bot in conversation for ordering pizza, so that the user's intent could be represented through the utterance “Order pizza.”[Wingdings font/0xE0] A user intent can be directed to a particular task that the user wishes a chatbot to perform on behalf of the user.”) that utilizes a conversational artificial intelligence (AI) skill on an AI platform (FIGs. 1-2 and 10; paragraphs 0047, 0050, 0065-0066: “Each skill associated with a digital assistant helps a user of the digital assistant complete a task through a conversation with the user, where the conversation can include a combination of text or audio inputs provided by the user and responses provided by the skill bots.”);
representing a dialog by the chatbot with one or more users (FIGs. 1-2; paragraphs 0036, 0043 and 0050-0052: “A bot intent may be associated with one or more dialog flows for starting a conversation with the user and in a certain state. For example, the first message for the order pizza intent could be the question “What kind of pizza would you like?” In addition to associated utterances, a bot intent may further comprise named entities that relate to the intent. For example, the order pizza intent could include variables or parameters used to perform the task of ordering pizza, e.g., topping 1, topping 2, pizza type, pizza size, pizza quantity, and the like. The value of an entity is typically obtained through conversing with the user.”) when providing an automated self-service assistance using the conversational AI skill via the Al platform (paragraphs 0050-0052 and 0065-0066: skill chatbots providing services to users without direct human intervention);
data corresponding to the conversational AI skill (FIGs. 1-2; paragraphs 0036, 0043 and 0050-0052: “A bot intent may be associated with one or more dialog flows for starting a conversation with the user and in a certain state. For example, the first message for the order pizza intent could be the question “What kind of pizza would you like?” In addition to associated utterances, a bot intent may further comprise named entities that relate to the intent. For example, the order pizza intent could include variables or parameters used to perform the task of ordering pizza, e.g., topping 1, topping 2, pizza type, pizza size, pizza quantity, and the like. The value of an entity is typically obtained through conversing with the user.”)”), wherein the data is usable to provide the conversational AI skill from the AI platform via the dialog (FIGs. 1-2; paragraphs 0036, 0043 and 0050-0052: “A bot intent may be associated with one or more dialog flows for starting a conversation with the user and in a certain state. For example, the first message for the order pizza intent could be the question “What kind of pizza would you like?” In addition to associated utterances, a bot intent may further comprise named entities that relate to the intent. For example, the order pizza intent could include variables or parameters used to perform the task of ordering pizza, e.g., topping 1, topping 2, pizza type, pizza size, pizza quantity, and the like. The value of an entity is typically obtained through conversing with the user.”)”); and
Singaraju discloses data corresponding to the conversational AI skill (FIGs. 1-2; paragraphs 0036, 0043 and 0050-0052: “A bot intent may be associated with one or more dialog flows for starting a conversation with the user and in a certain state. For example, the first message for the order pizza intent could be the question “What kind of pizza would you like?” In addition to associated utterances, a bot intent may further comprise named entities that relate to the intent. For example, the order pizza intent could include variables or parameters used to perform the task of ordering pizza, e.g., topping 1, topping 2, pizza type, pizza size, pizza quantity, and the like. The value of an entity is typically obtained through conversing with the user.”)”), however, Singaraju does not explicitly disclose mapping the conversational workflow to a workflow diagram of a chatbot workflow; injecting, to the chatbot workflow at one or more nodes of the workflow diagram; deploying the chatbot workflow with the chatbot, the data; wherein the chatbot provides the automated self-service assistance to the one or more users using the workflow diagram of the chatbot workflow.
Shanmugam further discloses mapping the conversational workflow to a workflow diagram of a chatbot workflow (FIGs. 10C-D; paragraphs 0089-0094: searching for match [Wingdings font/0xE0] clicking SEND for either or both IT-1 and IT2);
injecting, to the chatbot workflow at one or more nodes of the workflow diagram, the data (FIG. 5; paragraphs 0028, 0035, 0041-0042 and 0068: “Operations can be applied at 508 to add or insert metadata required for decision points in the tree. To illustrate, in the description above of conversation controllers of a type to control flow based on a chatbot end user's (e.g., customer's) role, a presented example is a hypothetical control of a flow dependent, at least in part, on whether the user is an account owner or an account member. Operations applied 508 can include insertion of metadata for such types of conversation controllers.”).
It would have been obvious to a person having ordinary skill in the art at the time before the effective filling date of the claimed invention to combine a teaching of Shamugam into Singaraju’s teaching because it would provide for the purpose of providing a continuous self-improvement to reduce fallout and improve accuracy, and also provide efficient allocation and hand-off of tasking between chatbots and live agents (Shamugam, paragraph 0006).
Chavez further discloses wherein the chatbot provides the automated self-service assistance to the one or more users using the workflow diagram of the chatbot workflow (paragraph 0057: “When the customer 116 is interacting with the contact center 108 using a voice channel 176, the customer 116 may receive self-service via an IVR 164. The IVR 164 may present the customer 116 with various self-service options based on an IVR tree as is known in the contact center arts. On the other hand, when the customer 116 is interacting with the contact center using a non-voice channel 180, the customer 116 may receive self-service via the chatbot engine 148, other automated mechanisms, and/or a human agent 172.”).
It would have been obvious to a person having ordinary skill in the art at the time before the effective filling date of the claimed invention to combine a teaching of Chavez into Singaraju’s teaching and Shamugam’s teaching because it would provide for the purpose of providing the digital options menu may include a visual component (e.g., icons, buttons, touch-based user inputs, soft buttons, etc.) or multiple GUI elements that present one or more options to the customer for navigating the self-service framework of the digital domain (Chavez, paragraph 0070).
Suhail further discloses deploying the chatbot workflow with the chatbot (paragraph 0042: “the group-based communication platform may provide comprehensive third party developer support that grants appropriate access to the data and allows third parties to build applications and bots to integrate with customer's workflows.”)
It would have been obvious to a person having ordinary skill in the art at the time before the effective filling date of the claimed invention to combine a teaching of Suhail into Singaraju’s teaching, Shamugam’s teaching, and Chavez’s teaching because it would provide for the purpose of enabling substantive interactions between a group-based communication platform and one or more external systems, thereby enabling the group-based communication platform to perform various functions within those external systems (Suhail, paragraph 0004).
As per claim 8, Singaraju discloses the one or more nodes (paragraphs 0035-0036 and 0072-0074: “Each state node within a dialog flow definition names a component that provides the functionality needed at that point in the dialog.”) that enables the conversational AI skill to be executed via the AI platform when the chatbot workflow reaches the one or more data load points of the chatbot workflow (FIGs. 1-2; paragraphs0035-0036 and 0072-0074: “A conversation may take different paths based on the end user input, which may impact the decision the bot makes for the flow. For example, at each state, based on the end user input or utterances, the bot may determine the end user's intent in order to determine the appropriate next action to take. As used herein and in the context of an utterance, the term “intent” refers to an intent of the user who provided the utterance. For example, the user may intend to engage a bot in conversation for ordering pizza, so that the user's intent could be represented through the utterance “Order pizza.” A user intent can be directed to a particular task that the user wishes a chatbot to perform on behalf of the user. Therefore, utterances can be phrased as questions, commands, requests, and the like, that reflect the user's intent. An intent may include a goal that the end user would like to accomplish.”).
Singaraju does not explicitly disclose wherein the injecting comprises embedding data for one or more endpoints at one or more data load points corresponding to the one or more nodes.
Shanmugam further discloses wherein the injecting comprises embedding data for one or more endpoints at one or more data load points corresponding to the one or more nodes (FIG. 5; paragraphs 0028, 0035, 0041-0042 and 0068: “Operations can be applied at 508 to add or insert metadata required for decision points in the tree. To illustrate, in the description above of conversation controllers of a type to control flow based on a chatbot end user's (e.g., customer's) role, a presented example is a hypothetical control of a flow dependent, at least in part, on whether the user is an account owner or an account member. Operations applied 508 can include insertion of metadata for such types of conversation controllers.”).
It would have been obvious to a person having ordinary skill in the art at the time before the effective filling date of the claimed invention to combine a teaching of Skiba into Singaraju’s teaching, Shamugam’s teaching, Chavez’s teaching, and Suhail’ because it would provide for the purpose of enabling substantive interactions between a group-based communication platform and one or more external systems, thereby enabling the group-based communication platform to perform various functions within those external systems (Suhail, paragraph 0004).
Claim 9 is rejected under 35 U.S.C. 103(a) as being unpatentable over Singaraju in view of Shanmugam, Chavez and to Suhail, as applied to claim 2, and further in view of US 2018/0115643 to Skiba et al. (hereafter “Skiba”)
As per claim 9, Singaraju does not explicitly disclose wherein the chatbot provides the automated self-service assistance across a plurality of channels comprising one or more of an email channel, a digital alert channel, a text message channel, a push notification channel, or an instant message channel.
Skiba further discloses wherein the chatbot provides the automated self-service assistance across a plurality of channels comprising one or more of an email channel, a digital alert channel, a text message channel (paragraph 0006), a push notification channel, or an instant message channel.
It would have been obvious to a person having ordinary skill in the art at the time before the effective filling date of the claimed invention to combine a teaching of Skiba into Singaraju’s teaching, Shamugam’s teaching, Chavez’s teaching, and Suhail’s teaching because it would provide for the purpose of micro-tasks often occur infrequently in a session and pausing the automated interaction so that an agent can intervene, while the remainder of the transaction can be handled by automation, helps maintain efficient utilization of resources (Skiba, paragraph 0021).
Claims 10, 16 and 18 are rejected under 35 U.S.C. 103(a) as being unpatentable over Singaraju and further in view of US 2021/0004885 to Abitbol et al. (hereafter “Abitbol”)
As per claim 10, Singaraju discloses a method comprising:
receiving, for a chatbot of a service provider, a request from a user during a dialog (paragraphs 0036, and 0065: “A bot intent may be associated with one or more dialog flows for starting a conversation with the user and in a certain state. For example, the first message for the order pizza intent could be the question “What kind of pizza would you like?” In addition to associated utterances, a bot intent may further comprise named entities that relate to the intent. For example, the order pizza intent could include variables or parameters used to perform the task of ordering pizza, e.g., topping 1, topping 2, pizza type, pizza size, pizza quantity, and the like. The value of an entity is typically obtained through conversing with the user.”);
determining that the request during the dialog utilizes a skill provided by an artificial intelligence (AI) system (paragraphs 0050-0051 and 0063-0065: “A digital assistant implemented according to the master-child bot architecture enables users of the digital assistant to interact with multiple skills through a unified user interface. When a user engages with a digital assistant 106, the user input is received by the master bot 114, which then processes the user input to identify a user request and based upon the processing determines whether the user request task can be handled by the master bot 114 itself, else the master bot 114 selects an appropriate skill bot 116-1,2, or 3 for handling the user request and routes the conversation to the selected skill bot 116-1,2, or 3.”);
determining a workflow executable by the chatbot for a use of the skill during the dialog (FIGs. 1-2; paragraphs 0027-0028, 0031, 003, 0035-0036, 0038, 0041, 0043 and 0047: “ A bot intent may be associated with one or more dialog flows for starting a conversation with the user and in a certain state. For example, the first message for the order pizza intent could be the question “What kind of pizza would you like?” In addition to associated utterances, a bot intent may further comprise named entities that relate to the intent. For example, the order pizza intent could include variables or parameters used to perform the task of ordering pizza, e.g., topping 1, topping 2, pizza type, pizza size, pizza quantity, and the like. The value of an entity is typically obtained through conversing with the user. A conversation may take different paths based on the end user input, which may impact the decision the bot makes for the flow. For example, at each state, based on the end user input or utterances, the bot may determine the end user's intent in order to determine the appropriate next action to take. As used herein and in the context of an utterance, the term “intent” refers to an intent of the user who provided the utterance. For example, the user may intend to engage a bot in conversation for ordering pizza, so that the user's intent could be represented through the utterance “Order pizza.”[Wingdings font/0xE0] A user intent can be directed to a particular task that the user wishes a chatbot to perform on behalf of the user.”), wherein the workflow includes a dialog tree (paragraphs 0033-0035, 0041-0043 and 0128: “The NLU processing performed by a digital assistant, such as digital assistant 106, can include various NLP related processing such as sentence parsing (e.g., tokenizing, lemmatizing, identifying part-of-speech tags for the sentence, identifying named entities in the sentence, generating dependency trees to represent the sentence structure, splitting a sentence into clauses, analyzing individual clauses, resolving anaphoras, performing chunking, and the like). A digital assistant 106 may use a NLP engine and/or a machine learning model (e.g., an intent classifier) to map end user utterances to specific intents (e.g., specific task/action or category of task/action that the chatbot can perform).” [Wingdings font/0xE0] workflow including sentences represented to dependency tree);
executing the workflow with the chatbot and the AI system during the dialog (paragraphs 0032-0036 and 0041-0043: “For example, the user may intend to engage a bot in conversation for ordering pizza, so that the user's intent could be represented through the utterance “Order pizza.” A user intent can be directed to a particular task that the user wishes a chatbot to perform on behalf of the user. Therefore, utterances can be phrased as questions, commands, requests, and the like, that reflect the user's intent. An intent may include a goal that the end user would like to accomplish.”); and
responding to the request based on the executing the workflow (paragraphs 0032-0036 and 0041-0043: “Once digital assistant 106 has the requisite information from the user, digital assistant 106 may then cause a pizza to be ordered. Digital assistant 106 may end the conversation with the user by outputting information indicating that the pizza has been ordered.”).
Singaraju does not explicitly disclose a dialog tree that includes one or more nodes for the use of the skill using a resource of the service provider and Al system.
Abitbol further discloses a dialog tree that includes one or more nodes for the use of the skill (FIGs. 3-4; paragraphs 0025-0028: “Dialogue nodes represent messages between the computer and the human, and the edges between them instruct the conversational computer program what message to send next, based on a preceding node that represents a human message.”) using a resource of the service provider (FIG. 1; paragraphs 0024-0028: “Dialogue nodes represent messages between the computer and the human, and the edges between them instruct the conversational computer program what message to send next, based on a preceding node that represents a human message. Fulfillment nodes represent certain actions outside of the conversational computer program that have been fulfilled by the conversational computer program.”) and Al system (paragraphs 0024-0029: chat bot/voice chat).
It would have been obvious to a person having ordinary skill in the art at the time before the effective filling date of the claimed invention to combine a teaching of Abitbol into Singaraju’s teaching because it would provide for the purpose of based on the one or more conversational flows and the desirability scores, automatically assigning a polarity score to each of at least some of the dialogue nodes (Abitbol, paragraph 0007).
As per claim 16, Singaraju does not explicitly disclose generating the dialog tree for the workflow when the chatbot utilizes the skill, wherein the chatbot traverses the dialog tree for the executing the workflow during the dialog.
Abitbol further discloses generating the dialog tree for the workflow when the chatbot utilizes the skill (FIGs. 3-4; paragraphs 0025-0028: “Dialogue nodes represent messages between the computer and the human, and the edges between them instruct the conversational computer program what message to send next, based on a preceding node that represents a human message.”), wherein the chatbot traverses the dialog tree for the executing the workflow during the dialog (paragraphs 0036-0037 0042 and 0044: the tree is traversed during the conversation).
It would have been obvious to a person having ordinary skill in the art at the time before the effective filling date of the claimed invention to combine a teaching of Abitbol into Singaraju’s teaching because it would provide for the purpose of based on the one or more conversational flows and the desirability scores, automatically assigning a polarity score to each of at least some of the dialogue nodes (Abitbol, paragraph 0007).
As per claim 18, Singaraju discloses a non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:
receiving a request from a user during a dialog between the user and a chatbot (paragraphs 0036, and 0065: “A bot intent may be associated with one or more dialog flows for starting a conversation with the user and in a certain state. For example, the first message for the order pizza intent could be the question “What kind of pizza would you like?” In addition to associated utterances, a bot intent may further comprise named entities that relate to the intent. For example, the order pizza intent could include variables or parameters used to perform the task of ordering pizza, e.g., topping 1, topping 2, pizza type, pizza size, pizza quantity, and the like. The value of an entity is typically obtained through conversing with the user.”);
identifying a skill provided by an artificial intelligence (AI) system for processing the request (paragraphs 0050-0051 and 0063-0065: “A digital assistant implemented according to the master-child bot architecture enables users of the digital assistant to interact with multiple skills through a unified user interface. When a user engages with a digital assistant 106, the user input is received by the master bot 114, which then processes the user input to identify a user request and based upon the processing determines whether the user request task can be handled by the master bot 114 itself, else the master bot 114 selects an appropriate skill bot 116-1,2, or 3 for handling the user request and routes the conversation to the selected skill bot 116-1,2, or 3.”);
determining a workflow for the skill (FIGs. 1-2; paragraphs 0027-0028, 0031, 003, 0035-0036, 0038, 0041, 0043 and 0047: “ A bot intent may be associated with one or more dialog flows for starting a conversation with the user and in a certain state. For example, the first message for the order pizza intent could be the question “What kind of pizza would you like?” In addition to associated utterances, a bot intent may further comprise named entities that relate to the intent. For example, the order pizza intent could include variables or parameters used to perform the task of ordering pizza, e.g., topping 1, topping 2, pizza type, pizza size, pizza quantity, and the like. The value of an entity is typically obtained through conversing with the user. A conversation may take different paths based on the end user input, which may impact the decision the bot makes for the flow. For example, at each state, based on the end user input or utterances, the bot may determine the end user's intent in order to determine the appropriate next action to take. As used herein and in the context of an utterance, the term “intent” refers to an intent of the user who provided the utterance. For example, the user may intend to engage a bot in conversation for ordering pizza, so that the user's intent could be represented through the utterance “Order pizza.”[Wingdings font/0xE0] A user intent can be directed to a particular task that the user wishes a chatbot to perform on behalf of the user.”), wherein the workflow includes a dialog tree (paragraphs 0033-0035, 0041-0043 and 0128: “The NLU processing performed by a digital assistant, such as digital assistant 106, can include various NLP related processing such as sentence parsing (e.g., tokenizing, lemmatizing, identifying part-of-speech tags for the sentence, identifying named entities in the sentence, generating dependency trees to represent the sentence structure, splitting a sentence into clauses, analyzing individual clauses, resolving anaphoras, performing chunking, and the like). A digital assistant 106 may use a NLP engine and/or a machine learning model (e.g., an intent classifier) to map end user utterances to specific intents (e.g., specific task/action or category of task/action that the chatbot can perform).” [Wingdings font/0xE0] workflow including sentences represented to dependency tree);
executing the workflow by the chatbot with the AI system during the dialog (paragraphs 0032-0036 and 0041-0043: “For example, the user may intend to engage a bot in conversation for ordering pizza, so that the user's intent could be represented through the utterance “Order pizza.” A user intent can be directed to a particular task that the user wishes a chatbot to perform on behalf of the user. Therefore, utterances can be phrased as questions, commands, requests, and the like, that reflect the user's intent. An intent may include a goal that the end user would like to accomplish.”); and
responding to the request based on the executing the workflow (paragraphs 0032-0036 and 0041-0043: “Once digital assistant 106 has the requisite information from the user, digital assistant 106 may then cause a pizza to be ordered. Digital assistant 106 may end the conversation with the user by outputting information indicating that the pizza has been ordered.”).
Singraju does not explicitly disclose a dialog tree that includes one or more nodes that provide the skill using a resource of Al system.
Abitbol further discloses a dialog tree that includes one or more nodes that provide the skill (FIGs. 3-4; paragraphs 0025-0028: “Dialogue nodes represent messages between the computer and the human, and the edges between them instruct the conversational computer program what message to send next, based on a preceding node that represents a human message.”) using a resource of Al system (paragraphs 0024-0029: chat bot/voice chat).
It would have been obvious to a person having ordinary skill in the art at the time before the effective filling date of the claimed invention to combine a teaching of Abitbol into Singaraju’s teaching because it would provide for the purpose of based on the one or more conversational flows and the desirability scores, automatically assigning a polarity score to each of at least some of the dialogue nodes (Abitbol, paragraph 0007).
Claim 17 is rejected under 35 U.S.C. 103(a) as being unpatentable over Singaraju in view of Abitbol, as applied to claim 10, and further in view of Skiba.
As per claim 17, Singaraju discloses wherein the chatbot provides the automated self-service assistance across a plurality of channels (FIG. 1; paragraphs 0050-0052 and 0065-0066: skill chatbots providing services to users without direct human intervention).
Singaraju does not explicitly disclose channels comprising one or more of an email channel, a digital alert channel, a text message channel, a push notification channel, or an instant message channel.
Skiba further discloses a plurality of channels comprising one or more of an email channel, a digital alert channel, a text message channel (paragraph 0006), a push notification channel, or an instant message channel.
It would have been obvious to a person having ordinary skill in the art at the time before the effective filling date of the claimed invention to combine a teaching of Skiba into Singaraju’s teaching, and Abitbol’s teaching because it would provide for the purpose of micro-tasks often occur infrequently in a session and pausing the automated interaction so that an agent can intervene, while the remainder of the transaction can be handled by automation, helps maintain efficient utilization of resources (Skiba, paragraph 0021).
Conclusion
Any inquiry concerning this communication should be directed to examiner Tuan Dao, whose telephone/fax numbers are (571) 270 3387 and (571) 270 4387, respectively. The examiner can normally be reached on every Monday-Thursday, and the second Friday of the bi-week from 7:30AM to 5:00PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Vital, can be reached at (571) 272 4215.
The fax phone number for the organization where this application or proceeding is assigned is (571) 273 8300.
Any inquiry of a general nature of relating to the status of this application or proceeding should be directed to the TC 2100 Group receptionist whose telephone number is (571) 272 2100.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/TUAN C DAO/ Primary Examiner, Art Unit 2198
Prosecution Insights