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 .
Claim Rejections - 35 USC § 101
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
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.
Step “extracting, by the computing system and from the interaction, one or more instructions having a natural language structure, the one or more instructions associated with at least one action to resolve the situation” (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (e.g., computing system). That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about listening to a conversation (i.e., interaction) and identifying actions (e.g., the computer is slow, restart the computer) to resolve the problem. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment)
Step “generating, by the computing system and based on the one or more instructions and the natural language structure, a sentiment metric that indicates a characteristic of the interaction between the device and the computing system” (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (e.g., computing system). That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about reading the mood of the conversation and assigning a label or score to it. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment)
Step “selecting, by the computing system and based on the communication metric and the sentiment metric, a mode of operation of a generative artificial intelligence circuit of the computing system” (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (e.g., computing system, generative artificial intelligence circuit). That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the help desk operator evaluates the mood of the conversation between the operator and client and transfers the call to upper tier if the conversation is heated up to resolve the problem. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment)
“Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. However, if possible, the examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Here, the mentioned steps fall within the mental process grouping of abstract ideas and are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A – Prong Two: The claim recites the additional elements/limitations:
“identifying, by a computing system, a communication metric corresponding to an identifier of a communication channel of an interaction between a device and the computing system regarding a situation” => data gathering;
“generating, by the computing system and via the generative artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions” => data outputting; and
“computing system”, “generative artificial intelligence circuit” => the generic computer components.
a) MPEP § 2106.05(a) "Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field."
The generic computer components “computing system” and “generative artificial intelligence circuit” do not improve the functioning of a computer or any other technology. The claim provides no specific technical detail showing the computer itself runs faster, uses less memory, fewer cycles, or operates more efficiently.
b) MPEP § 2106.05(b) Particular Machine. The judicial exception does not apply to any particular machine.
The claim is silent regarding specific limitations directed to an improved computer system, processor, memory, network, database, or Internet, nor do applicant direct examiner’s attention to such specific limitations. "[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 573 U.S. at 223; see also Bascom Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1348 (Fed. Cir. 2016) ("An abstract idea on 'an Internet computer network' or on a generic computer is still an abstract idea."). Applying this reasoning here, the claim is not directed to a particular machine, but rather merely implement an abstract idea using generic computer components such as “computing system”, “generative artificial intelligence circuit” Thus, the claims fail to satisfy the "tied to a particular machine" prong of the Bilski machine-or-transformation test.
c) MPEP § 2106.05(c) Particular Transformation.
The claim operates on data only. The named generic computer components “computing system” and “generative artificial intelligence circuit” do not transform any article into a different state or thing. The steps are mere data manipulation. The steps are not a "transformation or reduction of an article into a different state or thing constituting patent-eligible subject matter[.]" See In re Bilski, 545 F.3d 943, 962 (Fed. Cir. 2008) (en bane), aff'd sub nom, Bilski v. Kappas, 561 U.S. 593 (2010); see also CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) ("The mere manipulation or reorganization of data ... does not satisfy the transformation prong."). Applying this guidance here, the claims fail to satisfy the transformation prong of the Bilski machine-or-transformation test.
d) MPEP § 2106.05(e) Other Meaningful Limitations.
This section of the MPEP guides: Diamond v. Diehr provides an example of a claim that recited meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. 450 U.S. 175, ... (1981). In Diehr, the claim was directed to the use of the Arrhenius equation ( an abstract idea or law of nature) in an automated process for operating a rubber-molding press. 450 U.S. at 177-78 .... The Court evaluated additional elements such as the steps of installing rubber in a press, closing the mold, constantly measuring the temperature in the mold, and automatically opening the press at the proper time, and found them to be meaningful because they sufficiently limited the use of the mathematical equation to the practical application of molding rubber products. 450 U.S. at 184... In contrast, the claims in Alice Corp. v. CLS Bank International did not meaningfully limit the abstract idea of mitigating settlement risk. 573 U.S._ .... In particular, the Court concluded that the additional elements such as the data processing system and communications controllers recited in the system claims did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers") or were well-understood, routine, conventional activity. MPEP § 2106.05(e). The generic computer components “computing system” and “generative artificial intelligence circuit” do not impose a meaningful limit on the abstract idea. The limitations are not meaningful limitations.
e) MPEP § 2106.05(g) Insignificant Extra-Solution Activity.
The limitation “identifying, by a computing system, a communication metric corresponding to an identifier of a communication channel of an interaction between a device and the computing system regarding a situation” is mere pre-solution data-gathering. It only sets up an input for the next abstract steps. The limitation “generating, by the computing system and via the generative artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions” is mere post-solution activity. It only displays or outputs the result of the abstract steps.
6) MPEP § 2106.05(h) Field of Use and Technological Environment.
[T]he Supreme Court has stated that, even if a claim does not wholly pre-empt an abstract idea, it still will not be limited meaningfully if it contains only insignificant or token pre- or post-solution activity-such as identifying a relevant audience, a category of use, field of use, or technological environment. Ultramercial, Inc. v. Hulu, LLC, 722 F.3d 1335, 1346 (Fed. Cir. 2013). Limitations “computing system” and “generative artificial intelligence circuit” are simply a field of use that attempts to limit the abstract idea to a particular technological environment.
Accordingly, the additional limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. 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 “identifying, by a computing system, a communication metric corresponding to an identifier of a communication channel of an interaction” → data gathering and “generating, by the computing system and via the generative artificial intelligence circuit operating according to the selected mode of operation, one or more responses” → data outputting do not recite any non-generic arrangement for gathering or outputting data. The recited “computing system” and “generative artificial intelligence circuit” are at a high level of generality. Taking the named tools as an ordered combination adds nothing beyond what each does alone in implementing the abstract idea. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 2 recites “wherein the mode of operation is a next best action”. The claim merely describes a field of use rather than a specific technical solution to a technical problem. The claim does not amount to significantly more than the abstract idea.
Claim 3 recites “generating the sentiment metric based on customer complaint data”. The claim merely describes a field of use rather than a specific technical solution to a technical problem. It likes a user just gives a score a value to the complaint data. The claim does not amount to significantly more than the abstract idea.
Claim 4 recites “simplifying the one or more responses according to natural language”. The claim merely recites a functional result. The claim does not amount to significantly more than the abstract idea.
Claim 5 recites “obtaining, by the computing system, one or more documents fitting a domain, the one or more documents having the natural language structure and describing one or more actions of a flow; and generating, by the computing system and via a first artificial intelligence engine receiving one or more of the characteristics as an input, a flow object having a structure according to the one or more actions of the flow” (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (e.g., computing system, first artificial intelligence engine). That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about reading documents that describe a sequence of steps/actions and drawing a flow that follows those steps. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind through observation, evaluation and judgment). The claim does not amount to significantly more than the abstract idea.
Claim 6 recites “generating, by the computing system and via a second artificial intelligence engine receiving the flow object as an input, a summary object including a text description of the flow” (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (e.g., computing system, second artificial intelligence engine). That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about looking at a flow and writing a short text description of it. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind through observation, evaluation and judgment). The claim does not amount to significantly more than the abstract idea.
Claim 7 recites “wherein the first artificial intelligence engine is configured to execute a machine learning model, and wherein the second artificial intelligence engine is configured to execute a large language model”. The claim merely describes a field of use rather than a specific technical solution to a technical problem. The claim does not amount to significantly more than the abstract idea.
Claim 8 recites “segmenting, by the computer system, the one or more documents into a subset of at least one of the one or more actions” (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (e.g., computer system). That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about reading the documents and breaking them into smaller chunks, one chunk per action. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind through observation, evaluation and judgment). The claim does not amount to significantly more than the abstract idea.
Claim 9 recites “wherein the flow object corresponds to the subset of at least one of the one or more actions”. The claim merely describes a field of use rather than a specific technical solution to a technical problem. The claim does not amount to significantly more than the abstract idea.
Claims 11-20 are similar to claims 1-3, and 5-18. The claims are rejected based on the same reasons.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding to claim 1 and 10
“… selecting, by the computing system and based on the communication metric and the sentiment metric, a mode of operation of a generative artificial intelligence circuit of the computing system; and
generating, by the computing system and via the generative artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions…”
The underlined terms are not supported by the specification.
[0004] At least one aspect is directed to a method. The method can include identifying, by a computing system, a communication metric corresponding to a type of a communication from a device. The method can include extracting, by the computing system and from the communication, one or more instructions having a natural language structure. The method can include generating, by the computing system and based on the one or more instructions and the natural language structure, a sentiment metric that indicates a characteristic of the interaction between the device and the computing system. The method can include selecting, by the computing system and based on the communication metric and the sentiment metric, a mode of operation of an artificial intelligence circuit of the computing system. The method can include generating, by the computing system and via the artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure based on the one or more instructions.
[0005] At least one aspect is directed to a computing system including one or more processors. The one or more processors may be configured to identify a communication metric corresponding to a type of a communication from a device interacting with the computing system; extract, from the communication, one or more instructions having a natural language structure; generate, based on the one or more instructions and the natural language structure, a sentiment metric that indicates a characteristic of the interaction between the device and the computing system; select, based on the communication metric and the sentiment metric, a mode of operation of an artificial intelligence circuit of the computing system; and generate, via the artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure based on the one or more instructions.
[0074] At process 910, the AI engine(s) 302 select a mode of the AI engine. More specifically, at process 912, the AI engine(s) 302 select the mode by the computing system. The “mode” may be based on the determined sentiment metric to match interactions with the determined sentiment. For example, the mode can correspond to a “collegial” mode in which the AI engine(s) 302 generate one or more responses according to a first natural language structure. The first natural language structure can correspond to one or more given natural languages (e.g., English, Spanish, Chinese). The mode can correspond to a “corrective” mode in which the AI engine(s) 302 generate one or more responses according to a second natural language structure. For example, a corrective mode is a mode in which the AI engine(s) 302 generate responses that are shorter or more directed to specific actions to take to resolve an issue. For example, a shorter response can be “Will a refund resolve this for you?” or “Will a replacement resolve this for you?” Outside of a corrective mode, the AI engine(s) 302 can provide broader or more open-ended responses, including “What kinds of features do you prefer for this product?” or “How may I help you find what you are looking for today?” At process 914, the AI engine(s) 302 select the mode based on the communication metric and the sentiment metric.
[0075] In some embodiments, the AI engine(s) 302 may be configured to switch between modes of operation (e.g., between the “collegial” mode and the “corrective” mode, for example) using at least one of a predefined profile or a machine learning control system. The predefined profile, for example, may relate a particular scenario to a particular mode of operation of the AI engine(s) 302 such that when the particular scenario is identified, the predefined profile provides the corresponding mode of operation of the AI engine(s) 302 to be used in such a scenario. The machine learning control system may be configured to switch the model of operation of the AI engine(s) 302 by analyzing large state spaces and non-linear correlations between variables, such as the determined sentiment of the interaction and the mode of operation of the AI engine(s) 302.
[0076] At process 920, the AI engine(s) 302 generate one or more responses having the structure. The AI engine(s) 302 can deliver the responses via one or more communication channels. For example, the AI engine(s) 302 generate a response and transmit the response to the API(s) 202, and the API(s) 202 transmit the response by text message to a text messaging application at the computing device 104. At process 922, the AI engine(s) 302 generate the responses by the computing system. At process 924, the AI engine(s) 302 generate the responses via the AI engine operating according to the mode. At process 926, the AI engine(s) 302 generate the responses via the AI engine receiving as input the one or more instructions.
[0083] For example, the method can include the artificial intelligence engine configured receive as input the one or more instructions having the structure. For example, the AI engine(s) 302 receive text input having a natural language structure and is configured to parse parts of speech tokens in the natural language text input. For example, the method can include a mode corresponding to the complexity and modifying operation of the artificial intelligence engine. For example, the AI engine(s) 302 select a mode that indicates a number of responses that are estimated to resolve a given complaint. For example, the number of responses is a complexity score.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Klemm (U.S. Pub 2017/0098282 A1), in view of Jungmeisteris (U.S. Pub 20220374956 A1)
Claim 1
Klemm discloses a method, comprising:
identifying, by a computing system, a communication metric corresponding to an identifier of a communication channel of an interaction between a device and the computing system regarding a situation ([0054], “…While customer communication devices 108A and 108B… enable customer 214 to access two different communication channels… wherein customer communication device 108B is a configuration to access a social media site… and customer communication device 108A is a configuration to access a different communication channel, such as a telephony voice channel…” [0068], “… Step 504 receives a call initiating a work item utilizing a channel different from the social media channel associated with step 502…” <examiner note: the system identifies different communication channels that the customer contacts the customer service. Fig. 4 shows a log of customer complaint [Wingdings font/0xF3] situation>);
However, Klemm does not explicitly disclose
extracting, by the computing system and from the interaction, one or more instructions having a natural language structure, the one or more instructions associated with at least one action to resolve the situation;
generating, by the computing system and based on the one or more instructions and the natural language structure, a sentiment metric that indicates a characteristic of the interaction between the device and the computing system;
selecting, by the computing system and based on the communication metric and the sentiment metric, a mode of operation of a generative artificial intelligence circuit of the computing system; and
generating, by the computing system and via the generative artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions
Jungmeisteris discloses
extracting, by the computing system and from the interaction, one or more instructions having a natural language structure, the one or more instructions associated with at least one action to resolve the situation; ([0057], line 9-10, “… system 110 may take a freeform text input string or query from a user…” [0053], line 24-25, “… For example, where the user is seeking support on a payment question or problem…” [0057, line 17-19, “… one or more semantic classification models may be applied to the textual input… through a keyword extraction method…” [0065], line 9-13, “… extract, from the input data, various information about the input text and /or other values including… the actual content of the query input string…” <examiner note: user contacts the system to make a query for seeking support on payment question or problem. The situation that the user seeks for support is “payment problem”. The user enters his request/instructions (i.e., seeking support/resolve payment problem/situation). The system extracts the content of user’s request to find relevant response(s)> [0068], line 14-23, “… for “payment”, the models might detect patterns such as currency symbols, numbers, related words such as credit/debit, expensive/cheap, refund, bank, worth, price, account, and/or combinations of words in particular relevant order and/or structure. The models would then label the corresponding text in the input string appropriately….” [0072], “… As an output of step 522, one or more topic classifiers may be assigned to the user input string. These classifiers may be used in step 540 to filter and select a set of potential textual responses to the user query or input…” <examiner note: the topic classification model analyzes patterns (e.g., currency symbol, numbers, credit/debit, and so on) in the user query and label the corresponding text in the user with topic classifier(s)/label(s) such as “payment”. The user query about the payment having structure/patterns/terms matches with topic/theme “payment”> [0067], line 4-9, “… In step 522, a topic classification is performed to determine the semantic meaning of the input text. In one embodiment, the user may, in a plain text sentence, phrase, or passage, reference a concept or description connecting the input to a particular scenario, circumstance, product, problem type, or the like…” <examiner note: in this example, instructions/user query are user seeking support on a payment problem/question>)
generating, by the computing system and based on the one or more instructions and the natural language structure, a sentiment metric that indicates a characteristic of the interaction between the device and the computing system; ([0066], line 1-4, “… in step 520, the trained ML model is applied to the input text… and vector representation(s) of the text input is generated…” [0073], line 4-10, “… step 524, a sentiment analysis is performed on… the generated same vector(s) used in step 520. Every time the user interacts with the interface… a sentiment analysis is performed to derive signals regarding user sentiment. This sentiment analysis is conducted via NLP methodology based on the user's freeform text entered into a chatbot. For each user response, a sentiment score is determined… [0075], line 1-2, “… sentiment score is performed…” <examiner note: a sentiment analysis is performed on the user query i.e., the user is seeking support on a payment question or problem…”
selecting, by the computing system and based on the communication metric and the sentiment metric, a mode of operation of a generative artificial intelligence circuit of the computing system ([0059], line 1-3, “… sentiment analysis process 500 performed by the sentiment analysis logic 124 and the autoencoder 118, sometimes in combination with workflow logic 220 or other components of customer support system 110…” [0043], line 5-9, “… one or more of sentiment analysis logic 124, workflow logic 220, or autoencoder 240 or any subset of any of those logics) may be implemented at least in part as one or more machine learning algorithms…” [0073], line 17-28, “… Lowered sentiment can be compared to a bottom threshold value or limit, and when that limit is exceeded (e.g., the value falls below the threshold), the system may understand the customer support efforts to be upsetting or unsatisfactory to the user. Accordingly, the system may take steps to modify the manner of interaction with the user, for example by changing the channel of the interaction, such as escalation of the issue to an actual person, or taking other action such as approving cancellations or returns, or other traits that would allow the issue to be resolve expediently prior to any argument or negative review or action by the user…” <examiner note: when the sentiment score does not fall withing acceptable range, the system modifies the manner of interaction, for instance, approving cancellations or returns>); and
generating, by the computing system and via the generative artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions ([0053], “… Thematic response data 234 may include data generated by the system 110 that can be used in response to data input by the user… Each theme may be identified by a unique theme classification ID. As thematic response data 234 may contain all possible response data for display to the user, any subset of data, sharing a common classification ID, can be understood to contain all possible response data relating to the theme or classification in which the user is seeking customer support. For example, where the user is seeking support on a payment question or problem, system 110 may obtain from thematic response data 234 any of all of the set of possible responses regarding “payment…” <examiner note: The response data has the theme/topic “payment” will have similar patterns/terms as user query>)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate machine learning algorithm to derive the meaning of the input text as well as to determine the user sentiment expressed therein as disclosed Jungmeisteris in to Klemm so that the expressed user sentiment is considered along with other historical or session-based user data to generate tailored questions and responses to be delivered in real-time to the user. The responses are displayed to the user along with information that routes the user to a workflow resolution.
Claim 2
Claim 1 is included, Jungmeisteris discloses wherein the mode of operation is a next best action ([0073], line 17-28, “… the system may understand the customer support efforts to be upsetting or unsatisfactory to the user. Accordingly, the system may take steps to modify the manner of interaction with the user, for example by changing the channel of the interaction, such as escalation of the issue to an actual person, or taking other action such as approving cancellations or returns, or other traits that would allow the issue to be resolve expediently prior to any argument or negative review or action by the user…” <examiner note: the actions such as escalation, approving cancellation or returns that would allow the issue to be resolve quickly are considered as next best actions>)
Claim 3
Claim 1 is included, Jungmeisteris discloses further comprising: generating the sentiment metric based on customer complaint data ([0061], line 1-2, “… a user interface is displayed to a user and a freeform text input (query) by the user is obtained …” [0053], line 21-23, “… For example, where the user is seeking support on a payment question or problem…” [0021], line 1-2, “… sentiment analysis is done based on the user's natural language text…” [0074], line 16-19, “… The output of the models is a probability distribution across different sentiment categories, then a sentiment score is generated based on the distribution…”)
Claim 4
Claim 1 is included, Jungmeisteris discloses further comprising: simplifying the one or more responses according to natural language ([0082],line 16-20, “… Based on the contextual topics or themes of the user input (determined in step 522) sentiment analysis logic 124 may filter this possible response data to a subset of data relating to the relevant topics on which the user is seeking customer support…”)
Claim 5
Claim 1 is included, Jungmeisteris discloses further comprising: obtaining, by the computing system, one or more documents fitting a domain, the one or more documents having the natural language structure and describing one or more actions of a flow; and generating, by the computing system and via a first artificial intelligence engine receiving one or more of the characteristics as an input, a flow object having a structure according to the one or more actions of the flow ([0090] FIG. 6 illustrates a similar process to that of FIG. 5, where the user's session workflow is considered in addition to the sentiment of the user's input. Process 600 may involve an evaluation of whether the user is currently attempting to accomplish a task, and if so, what they have tried and still need to do to accomplish that task (workflow). In the case of an intercept survey, messaging application, chatbot, or the like presented while the user is attempting to accomplish a task, the information generated and displayed to the user can be typically be directed to either completing a self-solve workflow, or directing the user to a third-party agent to complete an agent-based workflow…”)
Claim 6
Claim 5 is included, Jungmeisteris discloses further comprising: generating, by the computing system and via a second artificial intelligence engine receiving the flow object as an input, a summary object including a text description of the flow ([0090], “… With reference to FIG. 6, in order to optimize this metric, a personalized workflow can be triggered to optimize user satisfaction metrics. For example, the workflow logic 220 may be applied to suggest a tailored action, such as where to route the user, when to escalate to an agent-based solution rather than a self-solve solution, when to forward the interaction to a community expert, when to connect the user with another person of interest (e.g., seller or host, among others), when to trigger an automated workflow, and/or another customized response…”)
Claim 7
Claim 6 is included, Jungmeisteris discloses wherein the first artificial intelligence engine is configured to execute a machine learning model, and wherein the second artificial intelligence engine is configured to execute a large language model ([0091] The process begins at step 602 in which one or more machine learning models have been trained on a training set of freeform user text inputs. The process of step 602 may be generally understood to be similar to that of step 502 (from FIG. 5), though other embodiments may differ..”)
Claim 8
Claim 5 is included, Jungmeisteris discloses further comprising: segmenting, by the computer system, the one or more documents into a subset of at least one of the one or more actions ([0092], “… a post-activity survey may be presented to the user, for example when the user has ended a session or finished an action. However, in some embodiments, in step 604 (steps indicated in dotted lines are considered being optional), it may be determined whether a customer activity necessitating an intercept survey for support has been triggered. In some embodiments, it may be assumed that a “trigger point” has been reached where the user has intentionally called up a chatbot or other messaging application, for example by clicking a link, pop-up, button, widget, or other UI displayed on their device to initiate a customer support interaction…”)
Claim 9
Claim 8 is included, Jungmeisteris discloses wherein the flow object corresponds to the subset of at least one of the one or more actions ([0096], “… If the workflow has been resolved (Yin step 630), the system may simply request feedback (step 640) and store and/or aggregate the provided feedback data, in association with the user data, workflow data, and other relevant information (step 642). An exemplary set of screens illustrating this process is shown in FIG. 4A…”)
Claims 10-20 are similar to claim 1-9. The claims are rejected based on similar reasoning.
Response to Arguments
Section – Rejections Under 35 U.S.C. 102 – pg. 8-9
Applicant argument have been considered; however, examiner fails to find the support for the underlined limitation.
PNG
media_image1.png
204
638
media_image1.png
Greyscale
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAU HAI HOANG whose telephone number is (571)270-5894. The examiner can normally be reached 1st biwk: Mon-Thurs 7:00 AM-5:00 PM; 2nd biwk: Mon-Thurs: 7:00 am-5:00pm, Fri: 7:00 am - 4:00pm.
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) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at 571-270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
HAU HAI. HOANG
Primary Examiner
Art Unit 2154
/HAU H HOANG/Primary Examiner, Art Unit 2154
Prosecution Insights