Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The office action sent in response to Applicant’s communication received on 12/7/2023 for the application number 18532747. The office hereby acknowledges receipt of the following placed of record in the file: Specification, Abstract, Oath/Declaration and claims.
Status of the claims
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/24/2024 filed before the mailing date of first office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-8 and 13-20 are rejected under 101.
Claim 13 includes:
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 to cause the system to perform operations comprising: (a) determining that an event has been initiated by a user; (b) accessing a plurality of previous electronic chat transcripts associated with the user; (c)analyzing the event and the plurality of previous electronic chat transcripts; (d) predicting, based on the analyzing, an intent of the user corresponding to the event; (e) assigning, based on the predicted intent, a service agent of a plurality of service agents to the user, the service agent having a skill associated with the predicted intent; (f) determining a first set of Natural Language Map (NLM) models for the user and a second set of NLM models for the service agent; and (g) automatically generating, based on the first set of NLM models and the second set of NLM models, a simulated electronic conversation between the user and the service agent, the simulated electronic conversation comprising a plurality of sentences exchanged between the user and the service agent, wherein at least one of the sentences is associated with the predicted intent.
Step a can be performed mentally as a human can determine an event (set of word spoken, written etc.) by user ( someone – another user etc.)
Step b -e can be performed mentally as a human can access the conversation with another user and analyse that conversation and predict what the user is trying to ask based on the event which is what user has asked and a given context which is previous chats and assign a service agent ( another person to them )
Step f corresponds to additional element which is a set of natural language map models
Step g can be by human as two human can simulate ( talk it out ) a conversation based on the intent
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one machine, a system to simulate conversations. Thus, the claim is , recites a statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. As discussed above, the broadest reasonable interpretation of steps (a)-(e) and (g) that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Steps a-e (a) determining that an event has been initiated by a user; (b) accessing a plurality of previous electronic chat transcripts associated with the user; (c)analyzing the event and the plurality of previous electronic chat transcripts; (d) predicting, based on the analyzing, an intent of the user corresponding to the event; (e) assigning, based on the predicted intent, a service agent of a plurality of service agents to the user, the service agent having a skill associated with the predicted intent – these steps can be performed mentally as human can hear a words and decided to access the conversation with another user and analyse that conversation and predict what the user is trying to ask based on the event which is what user has asked and a given context which is previous chats and assign a service agent ( another person to them ). These steps can be performed by a human, using “observation, evaluation, judgment, [and] opinion,” because they involve making determinations and identifications, which are mental tasks humans routinely do,' ” and thus can practically be performed in the human mind, In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022). Similarly, step g recites (g) automatically generating, based on the first set of NLM models and the second set of NLM models, a simulated electronic conversation between the user and the service agent, the simulated electronic conversation comprising a plurality of sentences exchanged between the user and the service agent, wherein at least one of the sentences is associated with the predicted intent. Step g can be performed mentally as human can simulate conversation based on given intent using “observation, evaluation, judgment, [and] opinion,” because they involve making determinations and identifications, which are mental tasks humans routinely do,' ” and thus can practically be performed in the human mind, In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022). Therefore, these limitations are considered together as a abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). Claim requires (f) determining a first set of Natural Language Map (NLM) models for the user and a second set of NLM models for the service agent; and using the natural language map models. Determining step is a mere data gathering and at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). The limitations of “using a natural language map models”, “processor”, “memory” and non-transitory computer readable medium provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, there are four additional elements. The additional element of model are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f). Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, there are four additional elements. The additional element of “determining a natural language model and using natural language models ” in limitations (f) and (g) are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
Regarding claim 1 and 18, analysis applicable to claim 13, are applicable.
Regarding claim 2-8, 14-17 and 19-20 recites mental steps of using a previous scripts and determining further event and simulating conversation which all can be performed by human mind hence an abstract idea. Similar analysis analogous to claim 13 are applicable for step 2a, prong 2 and step 2b.
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 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.
Claims 1-3, 6-9 and 13-20 /are rejected under 35 U.S.C. 103 as being unpatentable over Friio ( US 20220070296) and further in view of Wang (US 20230237275 )
Regarding claim 1, Friio teaches a method, comprising: determining that a customer event has been initiated by a customer, the customer event being associated with a plurality of words ( input from a customer, Fig 3, Para 0074) ; parsing the plurality of words ( parsing the natural language input, Para 0075) ; based on a result of the parsing of the plurality of words, predicting an intent of the customer corresponding to the customer event (determining intent, Fig 7; interaction predictions, Para 0097-0098) ; associating, based on the predicted intent, a first simulated service agent of a plurality of simulated service agents with the customer event ( After the customer's intent is determined, the flowchart 350 proceeds to an operation 365 where the customer automation system 300 loads a script associated with the given intent, Para 0076-0077) ; accessing, based on the predicted intent, a models for the customer first simulated service agent ( determine the profile or other person profile, datasets etc.) , Para 0136, 0147 ) ; and generating, based on the models, conversation between the customer and the first service agent, the conversation involving the predicted intent ( based on the predictors, predict how the interaction would go, Para 0137-0142) generating conversation, Fig 9 and Fig 10, Para 0162)
Friio does not explicitly teach accessing, based on the predicted intent, a first set of Natural Language Map (NLM) models for the customer and a second set of NLM models for the first simulated service agent; and generating, based on the first set of NLM models and the second set of NLM models , a simulated conversation between the customer and the first simulated service agent, the simulated conversation involving the predicted intent
However, Wang teaches accessing, based on the predicted intent, a first set of Natural Language Map (NLM) models for the customer ( At step 820, the generator (e.g., generator 204 of FIG. 2) determines a plurality of natural language generation (NLG) templates based on the task-oriented dialog data. The NLG templates serve as the language generator for system 200. The templates may be maintained as a JSON file to map from dialog acts to delexicalized utterances, Para 0045-0046) and a second set of NLM models for the first simulated service agent (a generator (e.g., generator 204 in FIG. 2) determines a plurality of natural language understanding pairs including bot dialog acts and respective bot messages based on the task-oriented dialog data. The data may be in a form that is easily readable by humans, as compared to the source dialog data from the dialog agent. For example, the bot dialog act may take a form of “I had a problem with my order and I would like to know if there is an update”, and the respective bot message may take a form of “I'm unsure if there is an update on my order”, Para 0043) ; and generating, based on the first set of NLM models and the second set of NLM models, a simulated conversation between the customer and the first simulated service agent, the simulated conversation involving the predicted intent (simulated conversation, Fig 8, S830)
Friio has a base concept of generating the suggested insights based on predictions how user will react/respond. Friio different by the claimed invention on the concept of simulating conversation. The results of the combination would be predictable and it would have been obvious having the teachings of Friio to further include the concept of Wang before effective filing date because by doing so a more appropriate actionable item can be suggested (Para 0048, Wang)
Regarding claim 2, Friio as above in claim 1 teaches, accessing previous electronic chat transcripts involving the customer, wherein the determining is further based on the previous electronic chat transcripts ( historical interactions, Para 0075)
Regarding claim 3, Friio modified by Wang as above in claim 2, teaches , wherein the first NLM models are established at least in part based on performing one or more natural language processing (NLP) processes on the previous electronic chat transcripts ( load the script (bot ) and relevant information ( historical transcripts), Fig 8, Friio; previously stored information, Para 0036, Wang)
Regarding claim 6, Friio as above in claim 1, teaches predicting, based on the simulated conversation, that the customer will a request an action to be taken at a future point in time (That is, such communication may include any follow-up actions that the customer may need to complete to make that happen. When necessary, the personal bot assistant also may follow-up with the customer during the process to collect any additional information that, along the way, is determined to be necessary for a resolving the customer's request, Para 0156-0158; the resolution facilitator compiles a case related to the customer request with all the relevant details, including, for example, the workflow that is to follow. Once this is done, the resolution package is routed to an appropriate agent, Para 0161)
Regarding claim 7, Friio as above in claim 6, teaches performing the action before the future point in time; and informing the customer about the performing of the action ( follow up with the customer, Para 0158)
Regarding claim 8, Wang as above in claim 1, teaches wherein the generating the simulated conversation comprises: generating, for each of the customer and the first simulated service agent, a plurality of sentences; and sequencing the plurality of sentences ( simulated conversation, Para 0031)
Regarding claim 9, Friio modified by Wang as above in claim 1,teaches receiving additional input from the customer after the simulated conversation has been generated; altering, based on the received additional input, one or more NLM models in the first set of NLM models or in the second set of NLM models; and re-generating the simulated conversation based on the altered first set of NLM models or the altered second set of the NLM models ( updating the model, Fig 6-10, Friio; The plug-and play user response templates can be constantly updated to include more variations as encountered in real world use cases, Para 0027, Wang)
Regarding claim 13, Friio teaches 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 to cause the system to perform operations comprising: determining that an event has been initiated by a user (receive input from a customer, Para 0074) ; accessing a plurality of previous electronic chat transcripts associated with the user ( historical information from the user, Para 0075) ; analyzing the event and the plurality of previous electronic chat transcripts ( intents are mined, Para 0075) ; predicting, based on the analyzing, an intent of the user corresponding to the event ( intent of the user, Para 0075/ (interaction predictions, Para 0097-0098)) ; assigning, based on the predicted intent, a service agent of a plurality of service agents to the user (After the customer's intent is determined, the flowchart 350 proceeds to an operation 365 where the customer automation system 300 loads a script associated with the given intent, Para 0076-0077 ) , the service agent having a skill associated with the predicted intent ( script of a particular bot, Fig 8-9) ; determining models based on intent ( based on intent determine relevant information ( determine the profile or other person profile, datasets etc.) , Para 0136, 0147) ; and automatically generating, based on the models an electronic conversation between the user and the service agent, the electronic conversation comprising a plurality of sentences exchanged between the user and the service agent, wherein at least one of the sentences is associated with the predicted intent ( based on the predictors, predict how the interaction would go, Para 0137-0142) generating conversation, Fig 9 and Fig 10, Para 0162)
Friio does not explicitly teach determining a first set of Natural Language Map (NLM) models for the user and a second set of NLM models for the service agent ; and automatically generating, based on the first set of NLM models and the second set of NLM models, a simulated electronic conversation between the user and the service agent, the simulated electronic conversation comprising a plurality of sentences exchanged between the user and the service agent, wherein at least one of the sentences is associated with the predicted intent
However, Wang teaches determining a first set of Natural Language Map (NLM) models for the user( At step 820, the generator (e.g., generator 204 of FIG. 2) and a second set of NLM models for the service agent (a generator (e.g., generator 204 in FIG. 2) determines a plurality of natural language understanding pairs including bot dialog acts and respective bot messages based on the task-oriented dialog data. The data may be in a form that is easily readable by humans, as compared to the source dialog data from the dialog agent. For example, the bot dialog act may take a form of “I had a problem with my order and I would like to know if there is an update”, and the respective bot message may take a form of “I'm unsure if there is an update on my order”, Para 0043) ; and generating, based on the first set of NLM models and the second set of NLM models, and automatically generating, based on the first set of NLM models and the second set of NLM models, a simulated electronic conversation between the user and the service agent, the simulated electronic conversation comprising a plurality of sentences exchanged between the user and the service agent, wherein at least one of the sentences is associated with the predicted intent (simulated conversation, Fig 8, S830)
Friio has a base concept of generating the suggested insights based on predictions how user will react/respond. Friio different by the claimed invention on the concept of simulating conversation. The results of the combination would be predictable and it would have been obvious having the teachings of Friio to further include the concept of Wang before effective filing date because by doing so a more appropriate actionable item can be suggested (Para 0048, Wang)
Regarding claim 14, Wang as above in claim 13, teaches the first set of NLM models are unique to the user; and the second set of NLM models are unique to the service agent ( messages are unique to the generator and simulator, Fig 8)
Regarding claim 15, Friio as above in claim 13, teaches wherein the analyzing further comprises performing a natural language processing (NLP) process on a textual content of the event or the plurality of previous electronic chat transcripts ( parsing, Para 0075)
Regarding claim 16, Friio as above in claim 13, teaches wherein the operations further comprise: predicting a future point in time at which the user will submit a request associated with the intent, wherein the simulated electronic conversation is automatically generated before the predicted future point in time (That is, such communication may include any follow-up actions that the customer may need to complete to make that happen. When necessary, the personal bot assistant also may follow-up with the customer during the process to collect any additional information that, along the way, is determined to be necessary for a resolving the customer's request, Para 0156-0158; the resolution facilitator compiles a case related to the customer request with all the relevant details, including, for example, the workflow that is to follow. Once this is done, the resolution package is routed to an appropriate agent, Para 0161)
Regarding claim 17, Friio modified by Wang as above in claim 13 teaches wherein the operations further comprise: receiving additional information from the user regarding the event; and updating the automatically generated simulated electronic conversation based on the additional information (( updating the model, Fig 6-10, Para 0094, 0103-0104, 0118-0119) Friio; The plug-and play user response templates can be constantly updated to include more variations as encountered in real world use cases, Para 0027, Wang)
Regarding claim 18, Friio teaches a non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising: accessing textual content associated with an event initiated by a user of a platform and textual content of historical electronic chat records involving the user ( based on input and historical information determine the intent, Para 0074-0075) ; predicting, based on the textual content of the event and the historical electronic chat records, a concern of the user that has not been specifically raised in association with the event ( intent and predicted intent/behavior or a type of interaction , Para 0109) ; determining, based on the predicting, a service agent of a plurality of service agents for engaging in a potential interaction with the user, wherein the service agent is determined at least in part based on a match between a skill of the service agent and a skill for addressing the concern ( (After the customer's intent is determined, the flowchart 350 proceeds to an operation 365 where the customer automation system 300 loads a script associated with the given intent, Para 0076-0077, script of a particular bot, Fig 8-10) ) ; determining models based on intent ( based on intent determine relevant information ( determine the profile or other person profile, datasets etc., Fig 7-8) , an electronic conversation between the user and the service agent in which the concern of the user is addressed ( based on the predictors, predict how the interaction would go, Para 0137-0142) generating conversation, Fig 9 and Fig 10, Para 0162)
Friio does not explicitly teach generating a first set of Natural Language Map (NLM) models for the user and a second set of NLM models for the service agent; and simulating, based on the first set of NLM models and the second set of NLM models
However, Wang teaches generating a first set of Natural Language Map (NLM) models for the user(e.g., generator 204 of FIG. 2) and a second set of NLM models for the service agent (a generator (e.g., generator 204 in FIG. 2) determines a plurality of natural language understanding pairs including bot dialog acts and respective bot messages based on the task-oriented dialog data. The data may be in a form that is easily readable by humans, as compared to the source dialog data from the dialog agent. For example, the bot dialog act may take a form of “I had a problem with my order and I would like to know if there is an update”, and the respective bot message may take a form of “I'm unsure if there is an update on my order”, Para 0043) ; and generating, based on the first set of NLM models and the second set of NLM models, and simulating, based on the first set of NLM models and the second set of NLM models (simulated conversation, Fig 8, S830)
Friio has a base concept of generating the suggested insights based on predictions how user will react/respond. Friio different by the claimed invention on the concept of simulating conversation. The results of the combination would be predictable and it would have been obvious having the teachings of Friio to further include the concept of Wang before effective filing date because by doing so a more appropriate actionable item can be suggested (Para 0048, Wang)
Regarding claim 19, Friio modified by Wang as above in claim 18, teaches herein the operations further comprise customizing the first set of NLM models to the user and customizing the second set of NLM models to the service agent ( messages are unique to the generator and simulator, Fig 8, Wang; load and proceed a unique conversation, Fig 8-10, Friio)
Regarding claim 20, Friio modified by Wang as above in claim 18, teaches wherein the operations further comprise adjusting the simulating of the electronic conversation based on additional input received from the user, wherein the adjusting comprises changing an NLM model of the first set or the second set of the NLM models (( updating the model, Fig 6-10, Para 0094, 0103-0104, 0118-0119) Friio; The plug-and play user response templates can be constantly updated to include more variations as encountered in real world use cases, Para 0027, Wang)
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Friio ( US 20220070296) and further in view of Wang (US 20230237275 ) and further in view of Dorogusker (US 20220337898)
Regarding claim 4, Friio as above in claim 1, teaches wherein one or more of the determining, the parsing, the predicting, the associating, the accessing, or the generating is performed by one or more hardware processors of a platform on which the customer is a user ( fig 1-2) , Friio modified by Wang does not explicitly teach wherein the method further comprises: recommending an action for incentivizing the customer to stay with the platform
However Dorogusker teaches wherein the method further comprises: recommending an action for incentivizing the customer to stay with the platform (the multi-media platform 108 can recommend actions to incentivize a user or other users to engage more with the media/content based on an audience member 236 attending a virtual or in-person live experience(s), Para 0135)
It would have been obvious having the teachings of Friio and Wang to further include the concept of Dorogusker before the effective date to improve user experience
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Friio ( US 20220070296) and further in view of Wang (US 20230237275 ) and further in view of Kaveti ( US 20230031111)
Regarding claim 5, Friio modified by Wang as above in claim 1, does not explicitly teach calculating a propensity-to-contact score based at least in part on the parsing of the plurality of words, wherein the predicting the intent is performed in response to the propensity-to-contact score exceeding a predefined threshold.
However Kaveti teaches calculating a propensity-to-contact score based at least in part on the parsing of the plurality of words, wherein the predicting the intent is performed in response to the propensity-to-contact score exceeding a predefined threshold (the confidence score indicates a likelihood that recommending the predicted next action will result in the user listed in the row continuing to use the system 100., Para 0113, 0120)
It would have been obvious having the teachings of Friio and Wang to further include the concept of Kaveti before effective filing date to it is desirable to generate more effective user guidance that can better help users with discovering, accessing, and using various system features ( Para 0005, Kaveti )
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Friio ( US 20220070296) and further in view of Wang (US 20230237275 ) and further in view of Lingjiao ( FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance)
Regarding claim 10, Friio modified by Wang as above in claim 9, does not explicitly teaches before the altering, the first set of NLM models comprises a mother NLM model and a first child NLM model having a first affinity score with the mother NLM model that exceeds a specified threshold; and after the altering, the altered first set of NLM models comprises the mother NLM model and a second child NLM model having a second affinity score with the mother NLM model that exceeds the first affinity score
However Lingiao teaches before the altering, the first set of NLM models comprises a mother NLM model and a first child NLM model having a first affinity score with the mother NLM model that exceeds a specified threshold; and after the altering, the altered first set of NLM models comprises the mother NLM model and a second child NLM model having a second affinity score with the mother NLM model that exceeds the first affinity score ( cascading the model based on different input based on the cost function (scores), Fig 2, Under 3 How to Use LLMs Affordably and Accurately)
It would have been obvious having the teachings of Friio and Wang to further include the teachings of Lingiao before effective filing date to reduce the computational cost ( 3 How to Use LLMs Affordably and Accurately—conclusion)
Regarding claim 10, Friio as above in claim 10, teaches causing the simulated conversation to be displayed via a user interface of a mobile device ( mobile device, Para 0138, S780, Fig 7; )
Regarding claim 10, Friio modified by Wang as above in claim 10, teaches wherein the first simulated service agent is a computerized chatbot ( bot, Fig 6-7).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Richa Sonifrank whose telephone number is (571)272-5357. The examiner can normally be reached M-T 7AM - 5:30PM.
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/Richa Sonifrank/Primary Examiner, Art Unit 2654